Author response:

The following is the authors’ response to the original reviews.

Public reviews:

Reviewer #1:

(1) Which allele is alr1, the one upstream of mazEF or the one in the lysine biosynthetic operon?

Alr1 is encoded by SAUSA300_2027 and is the gene upstream to mazEF. We have now incorporated this information in the manuscript (Line# 127).

(2) Figure 3B. Where does the C3N2 species come from in the WT and why is it absent in the mutants? It is about 25% of the total dipeptide pool.

In Figure 3B, C3N2 species results from the combination of C3N1 (from Alr1) and C0N1 (from Dat). The reason this species is completely absent in either of the two mutants is because it requires one D-Ala from both Alr1 and Dat proteins to generate C3N2 D-Ala-D-Ala.

(3) Figure 3D could perhaps be omitted. I understand that the authors attained statistical significance in the fitness defect, but biologically this difference is very minor. One would have to look at the isotopomer distribution in the Dat overexpressing strain to make sure that increased flux actually occurred since there are other means of affecting activity (e.g. allosteric modulators).

Thank you for the suggestion. We agree with the reviewer that the fitness defect observed after increased dat expression is relatively minor and have moved this figure to the supplementary section as Figure 3-figure supplement 1.

Although we attempted to amplify the fitness defect of dat expression by cloning dat on to a multicopy vector, we couldn't maintain its stable expression in S. aureus. This instability may be due to the depletion of D-Ala when dat is overexpressed. As a result, we switched to expressing dat from a single additional copy integrated into the SaPI locus, which was sufficient to cause the expected fitness defect, albeit a minor one.

(4) In Figure 4A, why is the complete subunit UDP-NAM-AEKAA increasing in each strain upon acetate challenge if there was such a stark reduction in D-Ala-D-Ala, particularly in the ∆alr1 mutant? For that matter, why are the levels of UDP-NAM-AEKAA in the ∆alr1 mutant identical to that of WT with/out acetate?

Thank you for raising this important point. We have addressed this in line# 299-302 and 451-455 of the revised manuscript. In short, we believe that the inhibition of Ddl by acetate significantly increases the intracellular pool of the tripeptide UDP-NAM-AEK, which then outcompetes the substrate (pentapeptide; UDP-NAM-AEKAA) of MraY. As a result, the intracellular concentration of the pentapeptide increases since it is no longer efficiently consumed by MraY. This explanation is also supported by a kinetic study conducted in Ref (1), where the competition between UDP-NAM-AEKAA and UDP-NAM-AEK as substrates for MraY is demonstrated.

(5) Figure 4B. Is there no significant difference between ddl and murF transcripts between WT and ∆alr1 under acetate stress? This comparison was not labeled if the tests were done.

Thank you for suggesting this comparison. The ddl and murF transcripts between WT and alr1 under acetate stress were significantly different. We have added this comparison to Figure 4B.

(6) Although tricky, it is possible to measure intracellular acetate. It might be of interest to know where in the Ddl inhibition curve the cells actually are.

Thank you for the suggestion. We agree this would have been an excellent addition to the manuscript. However, accurately measuring intracellular acetate would require the use of radiolabeled acetate (2), and we currently lack the expertise to do this experiment. However, since our study clearly shows that acetate-mediated growth impairment is due to Ddl inhibition, and the IC50 of acetate for Ddl is around 400 mM, we predict that the intracellular concentration must be close to or above this IC50 to observe the growth phenotypes we report.

Reviewer #2:

Although the authors have conclusively shown that Ddl is the target of acetic acid, it appears that the acetic acid concentration used in the experiments may not truly reflect the concentration range S. aureus would experience in its environment. Moreover, Ddl is only significantly inhibited at a very high acetate concentration (>400 mM). Thus, additional experiments showing growth phenotypes at lower organic acid concentrations may be beneficial.

Thank you for the suggestion. In response to the reviewer, we have measured growth at various acetate concentrations and demonstrate a concentration-dependent effect (Figure 1C).

We use 20 mM acetic acid in our study. In the gut, where S. aureus colonizes, acetate levels can reach up to 100 mM, so we believe our concentrations are physiologically relevant. When S. aureus encounters 20 mM acetate, the intracellular concentration can rise to 600 mM if the transmembrane pH gradient is 1.5 units, which is well above the ~400 mM IC50 we report for Ddl.

Another aspect not adequately discussed is the presence of D-ala in the gut environment, which may be protective against acetate toxicity based on the model provided.

Thank you for pointing this out. We agree that D-Ala from the gut microbiota could protect against acetate toxicity, and we’ve included this in the discussion. However, our study clearly indicates that S. aureus itself maintains high intracellular D-Ala levels through Alr1 activity which is sufficient to counter acetate anion intoxication.

Recommendation for the authors:

Reviewer #2:

Major Comments:

(1) In Line 85, authors indicate S. aureus may encounter a high concentration of ~100 mM acetic acid (extracellular?). Could the authors cite more (and recent) references indicating S. aureus encounters >100 mM acetic acid in the environment?

To the best of our knowledge, no studies have specifically examined whether S. aureus encounters high mM concentration of acetate in the gut. Line 85 was surmised from multiple studies: recent findings that S. aureus colonizes the gut (3, 4) and that the gut environment has high acetate levels (~100 mM) (5). In response to the reviewers request, more recent references supporting high acetate concentrations in the gut (6, 7) have been added in Line# 86.

(2) In Line 117, it is mentioned that S. aureus when grown in vitro at 20 mM acetic acid can accumulate ~600 mM acetic acid in the cytoplasm.

a. Does the intracellular concentration go up proportionally if grown in 100 mM acetic acid? Given the IC50 of acetic acid-mediated inhibition of Ddl is ~400 mM, I wonder how physiologically relevant this finding presented here is.

Thank you for the opportunity to explain this further. If S. aureus encounters a concentration of 100 mM acetate and its transmembrane pH gradient (pHin-pHout) is held at 1.5, the intracellular concentration of acetate could theoretically increase up to 3 M based on Ref (8). However, previous studies have shown that bacteria can lower the magnitude of transmembrane pH gradient by decreasing their intracellular pH to limit accumulation of anions within cells (9, 10).

Although our study shows that the IC50 of Ddl inhibition by acetate is relatively high (~400 mM), we believe it’s still relevant because just 20 mM of environmental acetate at a pH of 6.0 can raise the intracellular concentration of acetate to over 600 mM, which is well above the IC50 we report for Ddl. Moreover, since S. aureus may encounter high concentrations of acetate during gut colonization, we believe our findings are physiologically relevant.

b. Could the authors show concentration-dependent growth inhibition in alr::tn by titrating a range of acetic acid concentrations (for example 0, 0.5, 1, 5, 10, 20 mM)? Measuring intracellular acetate concentration may be beneficial as well.

Thank you for this question. We now provide data to support that acetate-mediated inhibition of the alr1 mutant is concentration-dependent (see Figure 1C).

c. It appears that there may be excess D-ala in the gut environment (PMIDs: 30559391; 35816159), which could counter the high acetate based on the model presented here. Could the authors clarify and/or include this information in the manuscript?

This is an excellent point, and we have now included it in the discussion (Line# 470-475). It is indeed possible that D-Ala produced by the gut microbiome may further enhance S. aureus resistance to organic acid anions, in addition to the inherent contribution of Alr1 activity.

(3) The following is not needed; however, it would be interesting if the authors could show that S. aureus cells grown in the presence of acetate are highly sensitive to cycloserine (which targets Alr and Ddl) compared to cells grown in the absence of acetate.

Thank you for the suggestion. We are currently studying D-cycloserine (DCS) resistance in S. aureus. Although we provide the data below for clarification, it is not included in the current manuscript as it is part of a separate study.

As the reviewer speculated, S. aureus is more susceptible to DCS when grown in the presence of acetate (see figure below). Normally, complete growth inhibition occurs at 32 µg/ml of DCS. However, with 20 mM acetic acid present, complete inhibition is achieved at just 8 µg/ml of DCS. Furthermore, the growth inhibition is completely rescued when externally supplemented with 5 mM D-Ala. We believe that DCS works synergistically with acetate to inhibit Ddl activity, and we are conducting additional studies to explore this further.

Minor Comments:

(1) Many commas are missing.

Missing commas are now incorporated.

(2) Line 77: disassociate --> dissociate

Corrected.

(3) Line 103: that --> which

Corrected.

(4) Lines 199-203: authors could have used gfp/luciferase reporter to test their hypotheses.

Thank you for the suggestion. Initially, we created GFP translational fusions for all the mutants mentioned in Line# 199-203. However, the fluorescence intensity was too low to test the hypothesis, as these were single-copy fusions inserted at the SaPI site of the S. aureus genome. Because of this limitation, we took advantage of the essentiality of D-Ala-D-Ala in S. aureus to report on various mutants instead of a fluorescent reporter. In hindsight, a LacZ reporter assay might have been equally effective.

(5) Line 339: It would be beneficial to introduce that Ddl has two independent ATP and D-ala binding sites.

We have now added that information (Line# 338-339).

(6) Is ddl an essential gene? If so, explicitly mention that.

Yes, ddl is an essential gene and we have now incorporated this information in Line 103.

(7) Line 354: shows a difference in density?

The use of the term “difference density” is a technical crystallographic term commonly used to connote density observed for ligands in X-ray crystal structures. In this case, it simply refers to the observed density that corresponds to the two acetate ions bound within the Ddl active site.

(8) Line 498: "Thus." Typo, change period to comma.

We have corrected as suggested in Line 496.

(9) Figure 1 legend says "was screen" instead of screened.

This is now corrected.

(10) Figure 1- Figure Supplement 1B: including data for alr2::tn dat::tn may ensure no redundancy (Lines 171-172). It is currently missing.

Thank you for the suggestion. We now include both alr2dat double mutant and the alr1alr2dat triple mutant in Figure 1 - Figure Supplement 1B. In addition we also show that the alr1alr2dat mutant is resuced by the addition of D-Ala in Figure 1 - Figure Supplement 1C. The mutant information is also added to Table S5.

(11) Figure 7: pentaglycine coming off of NAM is misleading. Remove untethered pentaglycine bridges.

We thank you for pointing this out. We have modified the figure in the manuscript as suggested by the reviewer.

(12) Are alr1/ddl cells (with limited 4-3 PG crosslink) less sensitive to vancomycin?

On the contrary, the alr1 mutant is slightly more sensitive to vancomycin compared to the wild-type strain (see Figure below). We believe this happens because the alr1 mutant incorporates less D-Ala-D-Ala into the peptidoglycan, reducing the number of targets for vancomycin. As a result, vancomycin may be able to saturate the available D-Ala-D-Ala targets on the cell wall at a lower concentration in the alr1 mutant than in the wild type strain, leading to increased sensitivity. We haven’t included this data in the manuscript as it is part of a separate study.

(13) Based on the structural studies, could the authors mutate the residues of Ddl involved in acetic acid binding, thereby making it resistant to acetic acid stress?

The residues that the acetate anion interacts with are located within the ATP-binding and D-Ala-binding sites of Ddl. Since these residues are essential for Ddl function, we are unable to mutate them.

(14) Microscopy to show the cell morphologies of wild-type and mutants exposed to acetic acid (and with D-ala supplementation) could be potentially interesting.

Thank you for the suggestion. We did perform microscopy, expecting changes in cell shape or size, but the results were unremarkable and not included in the manuscript.

References:

(1) Hammes WP & Neuhaus FC (1974) On the specificity of phospho-N-acetylmuramyl-pentapeptide translocase. The peptide subunit of uridine diphosphate-N-actylmuramyl-pentapeptide. J Biol Chem 249(10):3140-3150.

(2) Roe AJ, McLaggan D, Davidson I, O'Byrne C, & Booth IR (1998) Perturbation of anion balance during inhibition of growth of Escherichia coli by weak acids. J Bacteriol 180(4):767-772.

(3) Acton DS, Plat-Sinnige MJ, van Wamel W, de Groot N, & van Belkum A (2009) Intestinal carriage of Staphylococcus aureus: how does its frequency compare with that of nasal carriage and what is its clinical impact? Eur J Clin Microbiol Infect Dis 28(2):115-127.

(4) Piewngam P_, et al. (2023) Probiotic for pathogen-specific _Staphylococcus aureus decolonisation in Thailand: a phase 2, double-blind, randomised, placebo-controlled trial. Lancet Microbe 4(2):e75-e83.

(5) Cummings JH, Pomare EW, Branch WJ, Naylor CP, & Macfarlane GT (1987) Short chain fatty acids in human large intestine, portal, hepatic and venous blood. Gut 28(10):1221-1227.

(6) Correa-Oliveira R, Fachi JL, Vieira A, Sato FT, & Vinolo MA (2016) Regulation of immune cell function by short-chain fatty acids. Clin Transl Immunology 5(4):e73.

(7) Hosmer J, McEwan AG, & Kappler U (2024) Bacterial acetate metabolism and its influence on human epithelia. Emerg Top Life Sci 8(1):1-13.

(8) Carpenter CE & Broadbent JR (2009) External concentration of organic acid anions and pH: key independent variables for studying how organic acids inhibit growth of bacteria in mildly acidic foods. J Food Sci 74(1):R12-15.

(9) Russell JB (1992) Another explanation for the toxicity of fermentation acids at low pH: anion accumulation versus uncoupling. Journal of Applied Bacteriology 73(5):363-370.

(10) Russell JB & Diez-Gonzalez F (1998) The effects of fermentation acids on bacterial growth. Adv Microb Physiol 39:205-234.

Author response:

The following is the authors’ response to the original reviews.

Reviewer 1: 

Limitations are that only the cytosolic fragments of the channel were studied, and the current manuscript does not do a good job of placing the results in the context of what is already known about CNBDs from other methods that yield similar information.

In the revision, we have now added a paragraph in the discussion that addresses why the cytosolic fragment was used and a paragraph putting our results into the context of previous work on CNBD channels where possible. 

(1) Why do the authors not apply their approach to the full-length channel? A discussion of any limitations that make this difficult would be worthwhile.” Full-length ion channel protein expression is more challenging, and it was important to start with a simpler system. This is now stated in the discussion.

(2) …nonetheless a comparison of the conformational heterogeneity and energetics obtained from these different approaches would help to place this work in a larger context.

We have now added a paragraph in the discussion putting our work in a larger context and addressing the challenges of comparing our results to previous studies. 

(3) Page 5 - 3:1 unlabeled:labeled subunits in mix => 42% of molecules have 3:1 stoichiometry as desired and 21% of molecules have 2:2 stoichiometry!!! (binomial distribution p=0.25, n=4). So 1/3 of molecules with labels have two labeled subunits. This does not seem like it is at all avoiding the problem of intersubunit FRET…

From the experimental perspective, the 3:1 molar ratio stated is certainly a low estimate of the actual subunit ratios given our FSEC data in Figure 2D and the higher expression of the WT protein compared to labeled protein. Furthermore, even without the addition of any WT protein, the calculated contribution of intersubunit FRET is negligible given that the FRET efficiency is heavily dominated by the closest donor-acceptor distances (Figure 4). 

(4) Figure 2E - Some monomers appear to still be present in the collected fraction. The authors should discuss any effect this might have on their results.

We now describe in the text that, at the low concentrations (~10nM) used for mass photometry, a second small peak was observed of ~30kDa, which is below the analytical range for this method. This would not affect our results since all tmFRET experiments used higher protein concentrations to ensure tetramerization.

(5) page 4 - "Time-resolved tmFRET, therefore, resolves the structure and relative abundance of multiple conformational states in a protein sample." - structure is not resolved, only a single distance.

We have reworded this sentence.  

Reviewer #2:

Regarding cyclic nucleotide-binding domain (CNBD)-containing ion channels, I disagree with the authors when they state that "the precise allosteric mechanism governing channel activation upon ligand binding, particularly the energetic changes within domains, remains poorly understood". On the contrary, I would say that the literature on this subject is rather vast and based on a significantly large variety of methodologies…

Despite this vast literature on the energetics of CNBD channels there is no consensus about the energetics and coupling of domains that underlies the allosteric mechanism in any CNBD channel. We have added a separate paragraph in the discussion to clarify our meaning.

In light of the above, I suggest the authors better clarify the contribution/novelty that the present work provides to the state-of-the-art methodology employed (steady-state and time-resolved tmFRET) and of CNBD-containing ion channels…

…In light of the above, what is the contribution/novelty that the present work provides to the SthK biophysics?

This work is the first use of the time-resolved tmFRET method to obtain intrinsic G (of an apo conformation) and G values for different ligands. It is also the first application of this approach to SthK or, indeed, to any protein other than MBP. This is mentioned in the introduction.  

…On the basis of the above-cited work (Evans et al., PNAS, 2020) the authors should clarify why they have decided to work on the isolated Clinker/CNBD fragment and not on the full-length protein…

We chose to start on the C-terminal fragment to provide a technically more tractable system for validating our approach using time-resolved tmFRET before moving to the more challenging full-length membrane protein. This is now addressed in a new paragraph in the discussion. 

What is the advantage of using the Clinker/CNBD fragment of a bacterial protein and not one of HCN channels, as already successfully employed by the authors (see above citations)?

We have chosen to perform these studies in SthK rather than a mammalian CNBD channel as SthK presents a useful model system that allows us to later express fulllength channels in bacteria. In addition, the efficiency of noncanonical amino acid incorporation is much higher in bacteria than in mammalian cells.

Reviewer #3: 

While the use of a truncated construct of SthK is justified, it also comes with certain limitations…

We agree that the truncated channel comes with limitations, but we still think that there is relevant energetic information from studies of the isolated CNBD. This is now addressed in the discussion. 

I recommend the authors carefully assess their statements on allostery. …The authors also should consider discussing the discrepancies between their truncated construct and full-length channels in more detail.

We added a paragraph in the introduction that now puts the conformational change of the CNBD in the context of the allosteric mechanism of the full-length channel. We also added a paragraph discussing in more detail the relationship between the energetics of the C-terminal fragment and the full-length channel.  

Regarding the in silico predictions, it is unclear to me why the authors chose the closed state of SthK Y26F and the 'open' state of the isolated C-linker CNBD construct…

The active cAMP bound structure (4d7t) was a high resolution X-ray crystallography structure chosen as the only model with a fully resolved C-helix. The resting state structure (7rsh) was selected as a the only resting state to resolve the acceptor residue studied here (V417).     

Previously it has been shown that SthK (and CNG) goes through multiple states during gating. This may be discussed in more detail, especially when it comes to the simplified four-state model…

As stated above, we added paragraphs to the introduction and discussion placing the conformational change of the CNBD in the context of the full-length channel.  

It would be interesting to see how the conformational distribution of the C-helix position integrates with available structural data on SthK. In general, putting the results more into the context of what is known for SthK and CNG channels, could increase the impact.

We now discuss the relationship between existing structures and energetics in the introduction.  

This may be semantics, but when working with a truncated construct that is missing the transmembrane domains using 'open' and 'closed' state is questionable. I recommend the authors consider a different nomenclature.

We refer to the conformational states of the CNBD as ‘resting’ and ‘active’ and used ‘closed’ and ‘open’ only for the conformational states of the pore.

Author response:

The following is the authors’ response to the original reviews.

Reviewer #1 (Public Review):

(1) The sample size of the in-house dataset used for training the model was relatively small (34 patients), which might limit the generalizability of the findings.

(2) The authors did not perform functional experiments to directly validate the roles of the identified key genes in radiotherapy sensitivity, relying instead on associations with immune features and signaling pathways.

(3) The study did not discuss the potential limitations of using machine learning algorithms, such as the risk of overfitting and the need for larger, diverse datasets for more robust model development and validation.

(1) Currently, we are actively expanding the dataset by incorporating additional patient samples to enhance the model's robustness and generalizability. Furthermore, we implement advanced statistical techniques, including cross-validation, during model development to mitigate the potential limitations associated with the small sample size on our results. This limitation has been comprehensively addressed in the discussion section of our manuscript.

(2) Given the current resource limitations, our study predominantly employed bioinformatics analyses. We acknowledge the critical importance of experimental validation and are actively pursuing additional funding and collaborative opportunities to facilitate future experimental studies. Concurrently, we have enhanced the discussion section to comprehensively address the limitations of our approach and emphasize the necessity for future experimental validation.

(3) We appreciate the reviewers' insightful comments regarding the potential limitations of machine learning algorithms, particularly the risk of overfitting. In response, we have incorporated a comprehensive discussion of these concerns, detailing the measures implemented to mitigate such risks, including the application of regularization techniques and the adoption of more rigorous cross-validation methodologies. We further acknowledge the necessity for larger and more diverse datasets to enhance model validity and generalizability, a concern we intend to address in our future research endeavors. The revised manuscript includes an expanded discussion on these critical points.

Here is the limitation section in the revised Manuscript:

“This study primarily focuses on specific subtypes of nasopharyngeal carcinoma (NPC), potentially limiting its direct generalizability to other NPC subtypes or related head and neck malignancies. Furthermore, the limited sample size of our dataset may impact the model's generalizability and extrapolation capabilities. To mitigate the potential limitations associated with the small sample size, we employed advanced statistical methodologies, including cross-validation, to enhance the robustness and reliability of our findings. Nevertheless, we acknowledge the necessity for larger datasets and are actively collaborating with other research institutions to expand our sample size, thereby enhancing the robustness and broader applicability of our findings. Additionally, while our study utilizes bioinformatics approaches to identify and analyze key genes, we recognize that the absence of direct experimental functional validation represents a significant limitation. To address this limitation, we are actively pursuing additional funding and establishing collaborations with specialized laboratories to conduct crucial functional validation experiments, which will further elucidate the specific roles of these genes in radiotherapy response. Moreover, we acknowledge the potential risk of overfitting inherent in the application of machine learning algorithms to biomedical data analysis. To mitigate this risk, we implemented regularization techniques during model development and adopted a rigorous cross-validation strategy for model validation. These methodological approaches aim to ensure that our models maintain robust predictive performance on unseen data. Notwithstanding these limitations, our study offers novel insights into the molecular mechanisms underlying radiotherapy sensitivity in NPC and indicates promising avenues for future investigation. Future research endeavors will prioritize expanding the dataset, conducting comprehensive experimental validation, and refining our predictive model to enhance its accuracy and clinical applicability.”

Reviewer #2 (Public Review):

(1) The study focuses on a specific type of nasopharyngeal carcinoma (NPC) and may not be generalizable to other subtypes or related head and neck cancers. The applicability of NPC-RSS to a broader range of patients and tumor types remains to be determined.

(2) The study does not account for potential differences in radiotherapy protocols, doses, and techniques between the training and validation cohorts, which could influence the performance of the predictive model. Standardization of treatment parameters would be important for future validation studies.

(3) The binary classification of patients into radiotherapy-sensitive and resistant groups may oversimplify the complex spectrum of treatment responses. A more granular stratification system that captures intermediate responses could provide more nuanced predictions and better guide personalized treatment decisions.

(4) The study does not address the potential impact of other relevant factors, such as tumor stage, histological subtype, and concurrent chemotherapy, on the predictive performance of NPC-RSS. Incorporating these clinical variables into the model could enhance its accuracy and clinical utility.

(1) We appreciate the reviewers' interest in the applicability of our study. This study specifically focuses on a particular subtype of nasopharyngeal carcinoma (NPC), which may limit its direct generalizability to other NPC subtypes or related head and neck malignancies. We have incorporated a detailed discussion of this limitation in the Discussion section and intend to investigate the applicability of NPC-RSS across a broader spectrum of tumor types and subtypes in subsequent studies.

(2) We acknowledge the reviewers' emphasis on the significance of potential variations in radiotherapy regimens, doses, and techniques. In the current study, we did not sufficiently account for these factors, potentially impacting the model's generalizability and accuracy. We aim to improve data consistency and strengthen model validation by standardizing treatment parameters in future investigations.

(3) We concur with the reviewers' assessment that binary categorization may oversimplify the intricate nature of treatment responses. Indeed, radiotherapy responses likely exist on a continuous spectrum. Consequently, we intend to develop more refined stratification systems to capture intermediate responses, thereby enhancing the accuracy of treatment outcome predictions and facilitating personalized treatment decisions.

(4) We appreciate the reviewers' recommendation to incorporate clinical variables, including tumor stage, histological subtype, and concurrent chemotherapy, into the model. We acknowledge that these factors are crucial for enhancing the accuracy and clinical applicability of predictive models. We are presently compiling these additional data and intend to integrate these variables into subsequent model iterations.

Reviewer #1 (Recommendations For The Authors):

(1) The manuscript would benefit from a more comprehensive comparison of the NPC-RSS with existing prognostic models or biomarkers for nasopharyngeal carcinoma. This would help highlight the unique value and potential superiority of the NPC-RSS in predicting radiotherapy sensitivity.

2) The authors should consider expanding their discussion on the potential molecular mechanisms underlying the association between the key NPC-RSS genes and radiotherapy response. They could explore whether these genes have been previously implicated in radiotherapy resistance in other cancer types and discuss the potential functional roles of these genes in the context of nasopharyngeal carcinoma.

(1) We appreciate your thorough review and valuable suggestions concerning our study. In response to the suggestion of comparing the Nasopharyngeal Carcinoma Radiotherapy Sensitivity Score (NPC-RSS) with existing prognostic models or biomarkers, we have carefully considered this proposal and determined that such a comparison is beyond the scope of our current study. The primary focus of our research is on the development and internal validation of the NPC-RSS model's accuracy and reliability. At present, we do not have access to the necessary external data to conduct a valid comparison, and the integration of such data extends beyond the parameters of this study. We intend to incorporate this comparative analysis in future studies to further validate the efficacy and explore the clinical application potential of the NPC-RSS model. We appreciate your understanding and continued support for our research endeavors.(2) In the revised manuscript, we have incorporated a comprehensive review of the functions of these key genes in various cancer types and explored their potential mechanisms of action in nasopharyngeal carcinoma (NPC). Through the citation of pertinent studies, we have elucidated the impact of these genes on radiotherapy sensitivity and resistance. Furthermore, we have proposed future research directions to elucidate the specific roles of these genes in the radiotherapy response of NPC.

The following are new additions to the revised draft：

“Previous studies have demonstrated that SMARCA2 significantly influences the radiotherapy response in non-small cell lung cancer (NSCLC). Depletion of SMARCA2 has been shown to enhance radiosensitivity, suggesting its potential as a therapeutic target for radiosensitization [30478150]. Additionally, the DMC1 gene has been incorporated into the radiosensitivity index (RSI) to evaluate radiotherapy sensitivity and prognosis, particularly in endometrial cancers. This inclusion provides valuable insights into the DNA damage repair process [38628740]. Studies on CD9 in glioblastoma multiforme (GBM) have revealed that post-radiotherapy increases in CD9 and CD81 levels in extracellular vesicles (EVs) are strongly correlated with the cytotoxic response to treatment. This finding suggests the potential of CD9 as a novel biomarker for monitoring radiotherapy efficacy [36203458]. In contrast, the association of PSG4 and KNG1 with radiotherapy resistance remains unexplored in the current literature.

Future research should focus on analyzing the expression patterns of SMARCA2 in NPC patients and its correlation with radiotherapy efficacy using clinical samples. This analysis could elucidate its potential as a target for radiosensitization therapy. Investigating the correlation between DMC1 expression levels and radiotherapy sensitivity in NPC could potentially aid in predicting treatment efficacy and optimizing therapeutic regimens. Furthermore, analysis of extracellular vesicles, particularly those containing CD9, in post-radiotherapy NPC patients could assess their feasibility as biomarkers for monitoring treatment response. These proposed studies would not only contribute to a deeper understanding of the mechanisms underlying the role of these genes in NPC radiotherapy but could also potentially lead to the development of novel strategies for enhancing radiotherapy efficacy.”

Minor Recommendations:

(1) It is recommended that the author share the code for the article on Github or a similar open source platform.

(2) The manuscript would benefit from a thorough review of the punctuation and sentence structure to improve readability and clarity.

(1) You suggest sharing the code utilized in this study on GitHub or a comparable open-source platform to enhance the transparency and reproducibility of the research. I fully recognize the significance of this suggestion. However, due to the sensitivity of the data involved and the existing intellectual property agreement with my research team, we are unable to make the code publicly available at this time. We are actively seeking a method to safeguard the intellectual property of the project while also planning to share our tools and methodologies in the future. At this stage, we are open to collaborating with other researchers under appropriate frameworks and conditions to validate and replicate our findings by providing essential code execution snippets or assisting with data analysis.

(2) Your suggestions are vital for enhancing the quality of the manuscript. I will perform a comprehensive linguistic and structural review of the manuscript to ensure that statements flow coherently and punctuation is employed correctly. We also intend to engage a professional scientific and technical writing editor to ensure that the manuscript adheres to the high standards required for academic publishing.

Reviewer #2 (Recommendations For The Authors):

(1) The manuscript would benefit from a more in-depth discussion of the potential clinical implications of the NPC-RSS. The authors should elaborate on how this score could be integrated into clinical decision-making and patient management.

(2) The authors should consider including a section discussing the limitations of their study and potential areas for future research. This could include the need for prospective validation of the NPC-RSS in larger patient cohorts and the exploration of additional biological mechanisms.

(1) We concur that a more comprehensive discussion regarding the application of the NPC-RSS in clinical decision-making would significantly enhance the practical value of this study. In the revised draft, we will include a section that elaborates on the integration of the NPC-RSS scoring system into daily clinical practice, detailing how it can assist physicians in developing individualized treatment plans and optimize patient management by predicting treatment responses.

The following are new additions to the revised draft:

“The incorporation of the NPC-RSS scoring system into clinical decision-making and patient management involves several key steps: first, establishing genetic testing as a standard component of nasopharyngeal cancer diagnosis and ensuring that physicians have prompt access to scoring results to guide treatment planning. Second, physicians should utilize the scoring results to tailor individualized treatment plans and engage in multidisciplinary discussions to optimize decision-making. Concurrently, physicians should elucidate the clinical significance of the scores and effectively communicate with patients to facilitate shared decision-making. Furthermore, continuous monitoring of the relationship between scoring and treatment outcomes, optimizing the scoring model based on empirical data, and ensuring the integration of technological platforms along with regulatory compliance are essential for safeguarding the effective operation of the scoring system and the protection of patient information.

(2) In light of the reviewers' valuable suggestions, we acknowledge the significance of prospective validation of the NPC-RSS scoring system in a broader patient population and the necessity for thorough exploration of the underlying biological mechanisms. Accordingly, we are incorporating a new section in the revised manuscript that elaborates on the limitations of the current study and outlines potential directions for future research. This encompasses plans to increase the sample size for validation and further investigations into the biological basis of the scoring system to enhance its predictive validity and clinical applicability. We believe that these additions will significantly enrich the depth and breadth of the study, thereby serving the scientific community and clinical practice more effectively.”

Minor Recommendations:

(1) The authors should ensure that all abbreviations are defined at their first mention in the text.

(2) The figure legends should be more descriptive and self-explanatory, allowing readers to understand the main findings without referring back to the main text.

(1) You pointed out the need to define all acronyms at the first mention in the text and suggested that a comprehensive list of acronyms be included in the revised draft. We fully concur and have included a comprehensive list of acronyms in the revised text. Additionally, to enhance clarity, we have included the full name and definition of each acronym alongside its first occurrence in the text. This will assist readers in comprehending the study without the need to repeatedly refer to the glossary.

(2) You recommended enhancing the descriptive quality of the figure legends to enable readers to discern the key findings from the figures without consulting the text. We have redesigned and refined all charts and legends to ensure they provide adequate information and are more descriptive. Each legend now outlines the experimental conditions, the variables employed, and the primary conclusions, ensuring that the charts themselves sufficiently convey the key findings of the study.

Author response:

The following is the authors’ response to the previous reviews.

Public Reviews:

Reviewer #1 (Public Review):

In their paper, Kang et al. investigate rigidity sensing in amoeboid cells, showing that, despite their lack of proper focal adhesions, amoeboid migration of single cells is impacted by substrate rigidity. In fact, many different amoeboid cell types can durotax, meaning that they preferentially move towards the stiffer side of a rigidity gradient. 

The authors observed that NMIIA is required for durotaxis and, buiding on this observation, they generated a model to explain how durotaxis could be achieved in the absence of strong adhesions. According to the model, substrate stiffness alters the diffusion rate of NMAII, with softer substrates allowing for faster diffusion. This allows for NMAII accumulation at the back, which, in turn, results in durotaxis. 

The authors responded to all my comments and I have nothing to add. The evidence provided for durotaxis of non adherent (or low-adhering) cells is strong. I am particularly impressed by the fact that amoeboid cells can durotax even when not confined. I wish to congratulate the authors for the excellent work, which will fuel discussion in the field of cell adhesion and migration.

We thank the reviewer for critically evaluating our work and giving kind suggestions. We are glad that the reviewer found our work to be of potential interest to the broad scientific community.

Reviewer #2 (Public Review):

Summary:

The authors developed an imaging-based device that provides both spatialconfinement and stiffness gradient to investigate if and how amoeboid cells, including T cells, neutrophils, and Dictyostelium, can durotax. Furthermore, the authors showed that the mechanism for the directional migration of T cells and neutrophils depends on non-muscle myosin IIA (NMIIA) polarized towards the soft-matrix-side. Finally, they developed a mathematical model of an active gel that captures the behavior of the cells described in vitro.

Strengths:

The topic is intriguing as durotaxis is essentially thought to be a direct consequence of mechanosensing at focal adhesions. To the best of my knowledge, this is the first report on amoeboid cells that do not depend on FAs to exert durotaxis. The authors developed an imaging-based durotaxis device that provides both spatial confinement and stiffness gradient and they also utilized several techniques such as quantitative fluorescent speckle microscopy and expansion microscopy. The results of this study have well-designed control experiments and are therefore convincing.

Weaknesses:

Overall this study is well performed but there are still some minor issues I recommend the authors address:

(1) When using NMIIA/NMIIB knockdown cell lines to distinguish the role of NMIIA and NMIIB in amoeboid durotaxis, it would be better if the authors took compensatory effects into account.

We thank the reviewer for this suggestion. We have investigated the compensation of myosin in NMIIA and NMIIB KD HL-60 cells using Western blot and added this result in our updated manuscript (Fig. S4B, C). The results showed that the level of NMIIB protein in NMIIA KD cells doubled while there was no compensatory upregulation of NMIIA in NMIIB KD cells. This is consistent with our conclusion that NMIIA rather than NMIIB is responsible for amoeboid durotaxis since in NMIIA KD cells, compensatory upregulation of NMIIB did not rescue the durotaxis-deficient phenotype. 

(2) The expansion microscopy assay is not clearly described and some details are missed such as how the assay is performed on cells under confinement.

We thank the reviewer for this comment. We have updated details of the expansion microscopy assay in our revised manuscript in line 481-485 including how the assay is performed on cells under confinement:

Briefly, CD4+ Naïve T cells were seeded on a gradient PA gel with another upper gel providing confinement. 4% PFA was used to fix cells for 15 min at room temperature. After fixation, the upper gradient PA gel is carefully removed and the bottom gradient PA gel with seeded cells were immersed in an anchoring solution containing 1% acrylamide and 0.7% formaldehyde (Sigma, F8775) for 5 h at 37 °C.

(3) In this study, an active gel model was employed to capture experimental observations. Previously, some active nematic models were also considered to describe cell migration, which is controlled by filament contraction. I suggest the authors provide a short discussion on the comparison between the present theory and those prior models.

We thank the reviewer for this suggestion. Active nematic models have been employed to recapitulate many phenomena during cell migration (Nat Commun., 2018, doi: 10.1038/s41467-018-05666-8.). The active nematic model describes the motion of cells using the orientation field, Q, and the velocity field, u. The director field n with (n = −n) is employed to represent the nematic state, which has head-tail symmetry. However, in our experiments, actin filaments are obviously polarized, which polymerize and flow towards the direction of cell migration. Therefore, we choose active gel model which describes polarized actin field during cell migration. In the discussion part, we have provided the comparison between active gel model and motor-clutch model. We have also supplemented a short discussion between the present model and active nematic model in the main text of line 345-347:

The active nematic model employs active extensile or contractile agents to push or pull the fluid along their elongation axis to simulate cells flowing (61). 

(4) In the present model, actin flow contributes to cell migration while myosin distribution determines cell polarity. How does this model couple actin and myosin together?

We thank the reviewer for this question. In our model, the polarization field is employed to couple actin and myosin together. It is obvious that actin accumulate at the front while myosin diffuses in the opposite direction. Therefore, we propose that actin and myosin flow towards the opposite direction, which is captured in the convection term of actin ) and myosin () density field.

Author response:

We want to thank the reviewers for their positive and constructive comments on the manuscript. We already addressed some of their concerns and are planning the following revisions to both BEHAV3D-TP and the corresponding manuscript to address the reviewers’ comments. Below, we provide a response to the most significant comments, followed by a detailed, point-by-point response:

(1) We acknowledge the reviewer's suggestion to incorporate open-source segmentation and tracking functionalities, increasing its accessibility to a wider user base; however, these additions fall outside the primary scope of our current work and represent a substantial undertaking in their own right. This topic has been comprehensively explored in other studies (e.g. https://doi.org/10.4049/jimmunol.2100811 ; https://doi.org/10.7554/eLife.60547 ; https://doi.org/10.1016/j.media.2022.102358 ; https://doi.org/10.1038/s41592-024-02295-6), which we will cite in our revised manuscript as indicated in our responses to the reviewers’ comments. Instead, the goal of our manuscript is to provide an analytical framework for processing data generated by existing segmentation and tracking pipelines. In our analyses, we used data processed with Imaris, a commercial software that, despite its limitations, is widely used by the intravital microscopy community due to its user-friendly platform for 3D image visualization and analysis. Nevertheless, to enhance compatibility with tracking data from various pipelines, we have modified our tool to accept data formats, such as those generated by open-source Fiji plugins like TrackMate (https://github.com/imAIgene-Dream3D/BEHAV3D_Tumor_Profiler?tab=readme-ov-file#data-input ). These updates are available in our GitHub repository, and we will describe this feature in the revised manuscript to emphasize compatibility with segmented and tracked data from diverse open-source platforms.

(2) We appreciate the reviewer’s suggestion to incorporate additional features into our analytical pipeline. In response, we have already updated the GitHub repository to allow users to input and select which features (dynamic, morphological, or spatial) they wish to include in the analysis (https://github.com/imAIgene-Dream3D/BEHAV3D_Tumor_Profiler?tab=readme-ov-file#feature-selection ) . In the revised manuscript, we will highlight this new functionality and provide examples using alternative datasets to demonstrate the application of these features.

(3) We appreciate the constructive feedback of reviewers #1 and #2 regarding the statistical analysis and interpretation of the data presented in Figures 3 and 4. We understand the importance of clarity and rigor in data analysis and presentation, and we are committed to addressing the concerns raised in the revised version of the manuscript.

(4) We appreciate Reviewer #1's suggestion regarding the inclusion of demo data, as we believe it would greatly enhance the usability of our pipeline. We acknowledge that this was an oversight on our part. To address this, we have now added demo data to our GitHub repository (https://github.com/imAIgene-Dream3D/BEHAV3D_Tumor_Profiler/tree/BEHAV3D_TP-v2.0/demo_datasets). In the upcoming revised manuscript, we will also ensure to reference this addition. Additionally, we will  provide both original and processed IVM movie samples to support users in navigating the complete pipeline effectively.

(5) Finally, we agree with the reviewers to make some small changes to the manuscript based on their feedback.

Below we provide a point-by-point response to the reviewers’ comments, along with proposed revisions.

Reviewer #1:

Comment: A key limitation of the pipeline is that it does not overcome the main challenges and bottlenecks associated with processing and extracting quantitative cellular data from timelapse and longitudinal intravital images. This includes correcting breathing-induced movement artifacts, automated registration of longitudinal images taken over days/weeks, and accurate, automated segmentation and tracking of individual cells over time. Indeed, there are currently no standardised computational methods available for IVM data processing and analysis, with most laboratories relying on custom-built solutions or manual methods. This isn't made explicit in the manuscript early on (described below), and the researchers rely on expensive software packages such as IMARIS for image processing and data extraction to feed the required parameters into their pipeline. This limitation unfortunately reduces the likely impact of BEHAV3D-TP on the IVM field.

As highlighted above, the tool does not facilitate the extraction of quantitative kinetic cellular parameters (e.g. speed, directionality, persistence, and displacement) from intravital images. Indeed, to use the tool researchers must first extract dynamic cellular parameters from their IVM datasets, requiring access to expensive software (e.g. IMARIS as used here) and/or above-average computational expertise to develop and use custom-made open-source solutions. This limitation is not made explicit or discussed in the text.

As mentioned previously, we agree with the reviewer that image processing steps, such as segmentation, tracking, and motion correction, present significant challenges in intravital microscopy (IVM) data processing. While these aspects are being addressed by other researchers, our publication centers on the analysis of acquired data rather than on the image processing itself. Our motivation, as outlined in the manuscript, arises from our own experience: despite the substantial effort invested in image processing, researchers often rely on simplistic analytical approaches, such as averaging single parameters and comparing them across conditions. These approaches tend to overlook potential tumor heterogeneity.

Our work aimed to develop an analytical tool that provides a comprehensive framework for extracting more insights from processed IVM data, with a focus on two key aspects: capturing the heterogeneity of tumor behavior and examining the spatial distribution of these behaviors within the tumor microenvironment. In the revised manuscript, we will clarify the scope of our study, emphasizing its limitations as an analytical tool rather than an image-processing solution. Additionally, we will provide references to relevant literature on available (open-source) software options for image processing (e.g. Diego Ulisse Pizzagalli et al J Immunol (2022); Aby Joseph et al eLife (2020) ;Molina-Moreno M et al Medical Image Analysis (2022); Hidalgo-Cenalmor, I et al, Nat Methods  (2024); Ershov. D et al Nat Methods  (2022)).

Regarding the reviewer’s comment on our use of Imaris, we acknowledge that Imaris is a costly commercial software. However, based on our experience, it is widely used by the intravital microscopy community due to its user-friendly interface for 3D image visualization and analysis. Despite its limitations in accuracy and the fact that it is not open-source, we believe that including data processed with Imaris will be valuable to the IVM community.

However, to improve compatibility with data from other segmentation and tracking pipelines, we have already updated our tool to support formats generated by open-source Fiji plugins like TrackMate. These updates are available in our GitHub repository, and we will describe this functionality in detail in the revised manuscript to ensure compatibility with segmented and tracked data from various open-source platforms.

Comment: The number of cells (e.g. per behavioural cluster), and the number of independent mice, represented in each result figure, is not included in the figure legends and are difficult to ascertain from the methods.

We appreciate the reviewer's constructive feedback regarding the clarity of the number and type of replicates used in our analyses. In the revised manuscript, we will include detailed information in the figure legends regarding the number of cells (e.g., per behavioral cluster) and the number of independent mice represented in each result figure to ensure transparency.

Comment: The data used to test the pipeline in this manuscript is currently not available, making it difficult to assess its usability. It would be important to include this for researchers to use as a 'training dataset'.

As stated above we acknowledge that this was an oversight on our part and thank the reviewer for pointing this out. To address this, we have now added demo data to our GitHub repository (https://github.com/imAIgene-Dream3D/BEHAV3D_Tumor_Profiler/tree/BEHAV3D_TP-v2.0/demo_datasets). In the upcoming revised manuscript, we will also make sure to reference this addition. Additionally, we intend to provide both original and processed IVM movie samples to support users in navigating the complete pipeline effectively.

Comment: Precisely how the BEHAV3D-TP large-scale phenotyping module can map large-scale spatial phenotyping data generated using LSR-3D imaging data and Cytomap to 3D intravital imaging movies is unclear. Further details in the text and methods would be beneficial to aid understanding.

We appreciate the reviewer’s comment and will provide additional details in the text and methods of the revised manuscript to clarify how the BEHAV3D-TP module maps LSR-3D and Cytomap data to 3D intravital imaging movies.

Comment: The analysis provides only preliminary evidence in support of the authors' conclusions on DMG cell migratory behaviours and their relationship with components of the tumour microenvironment. Conclusions should therefore be tempered in the absence of additional experiments and controls.

We appreciate the reviewer’s comment and acknowledge that our conclusions should be tempered due to the preliminary nature of our evidence. To be able to directly analyze the impact of the brain tumor microenvironment on cancer cell behavior, we will include a new set of analyses in the revised manuscript. Specifically, we will utilize BEHAV3D-TP to analyze existing IVM data from adult gliomas with and without macrophage depletion (Alieva et al, Scientific Reports, 2017; https://doi.org/10.1038/s41598-017-07660-4 ) to evaluate the differences in heterogeneous cell populations under these conditions. Since this analysis pertains to a different tumor type, we will revise our conclusions accordingly and emphasize the necessity for additional experiments and controls to further validate our findings on DMG cell migratory behaviors and their relationship with the tumor microenvironment.

Reviewer #2:

Comment: The strength of democratizing this kind of analysis is undercut by the reliance upon Imaris for segmentation, so it would be nice if this was changed to an open-source option for track generation.

As noted in our previous response to Reviewer #1, we would like to point out that although Imaris is a commercial software, it is widely used in the intravital microscopy (IVM) community due to its user-friendly interface. One of its key advantages, which we also utilized, is semi-automated data tracking that allows for manual corrections in 3D—a process that can be more challenging in other open-source software with less effective data visualization.

However, we recognize that enhancing our pipeline's compatibility with open-source options is important. To this end, we have already updated our tool to support data formats generated by open-source Fiji plugins like TrackMate, improving compatibility with various segmentation and tracking pipelines (https://github.com/imAIgene-Dream3D/BEHAV3D_Tumor_Profiler?tab=readme-ov-file#data-input ). We will describe these updates in the revised manuscript to clarify our study's scope and the available image processing options.

Comment: The main issue is with the interpretation of the biological data in Figure 3 where ANOVA was used to analyse the proportional distribution of different clusters. Firstly the n is not listed so it is unclear if this represents an n of 3 where each mouse is an individual or whether each track is being treated as a test unit. If the latter this is seriously flawed as these tracks can't be treated as independent. Also, a more appropriate test would be something like a Chi-squared test or Fisher's exact test. Also, no error bars are included on the stacked bar graphs making interpretation impossible. Ultimately this is severely flawed and also appears to show very small differences which may be statistically different but may not represent biologically important findings. This would need further study.

We appreciate the reviewer’s insightful comments regarding the interpretation of the biological data in Figure 3. To clarify, each mouse serves as an independent unit in this analysis. We believe that ANOVA is the appropriate test for comparing the proportions of different behavioral signatures across the tumor microenvironment (TME) regions identified by large-scale phenotyping. However, we acknowledge that using a stacked bar plot may have been misleading. While a Chi-squared test could show differences in the distribution of behavioral signatures, it would not indicate which specific signatures are responsible for those differences. Therefore, in the revised manuscript, we will retain the ANOVA analysis but will represent the proportions using a bar chart that clearly illustrates multiple conditions for each behavioral cluster. We also appreciate the reviewer’s concern regarding the transparency of our data. In the revised manuscript, we will include the number of replicates for all figures to enhance clarity and understanding.

Comment:  Figure 4 has similar statistical issues in that the n is not listed and, again, it is unclear whether they are treating each cell track as independent which, again, would be inappropriate. The best practice for this type of data would be the use of super plots as outlined in Lord et al. (2020) JCI - SuperPlots: Communicating reproducibility and variability in cell biology.

We appreciate the reviewer’s comments and suggestions regarding Figure 4. In the revised manuscript, we will clarify the number of replicates used and our approach to treating cell tracks as independent units. We will implement super-plots where appropriate, to enhance the communication of reproducibility and variability in our data.

Comment: The main issue that this raises is that the large-scale phenotyping module and the heterogeneity module appear designed to produce these statistical analyses that are used in these figures and, if they are based on the assumption that each track is independent, then this will produce inappropriate analyses as a default.

We appreciate the reviewer’s comment, though we find ourselves unsure about the specific concern being raised. To clarify, each mouse is treated as an independent unit in our analyses. For each large-scale phenotyping region, we measure the proportion of tumor cells displaying a specific behavioral phenotype independently for each mouse. These proportions are then used for statistical analysis. We hope this explanation provides clarity, and we will adjust the manuscript to better convey this methodology.

Reviewer #3:

Comment: The most challenging task of analyzing 3D time-lapse imaging data is to accurately segment and track the individual cells in 3D over a long time duration. BEHAV3D Tumor Profiler did not provide any new advancement in this regard, and instead relies on commercial software, Imaris, for this critical step. Imaris is known to have a very high error rate when used for analyzing 3D time-lapse data. In the Methods section, the authors themselves stated that "Tumor cell tracks were manually corrected to ensure accurate tracking". Based on our own experience of using Imaris, such manual correction is tedious and often required for every time step of the movie. Therefore, Imaris is not a satisfactory tool for analyzing 3D time-lapse data. Moreover, Imaris is expensive and many research labs probably can't afford to buy it. The fact that BEHAV3D Tumor Profiler critically depends on the faulty ImarisTrack module makes it unclear whether the BEHAV3D tool or the results are reliable.

If the authors want to "democratize the analysis of heterogeneous cancer cell behaviors", they should perform image segmentation and tracking using open-source codes (e.g., Cellpose, Stardisk & 3DCellTracker) and not rely on the expensive and inaccurate ImarisTrack Module for the image analysis step of BEHAV3D.

We appreciate the reviewer’s comments on the challenges of segmenting and tracking individual cells in 3D time-lapse imaging data. As mentioned previously, our primary focus is to develop an analytical tool for comprehensive data analysis rather than developing tools for image processing. To enhance accessibility, we have updated our tool to support data formats from open-source Fiji plugins, such as TrackMate, which will benefit users without access to commercial software (https://github.com/imAIgene-Dream3D/BEHAV3D_Tumor_Profiler?tab=readme-ov-file#data-input ).

While we recognize the limitations of Imaris, it remains widely used in the intravital microscopy community due to its user-friendly interface for 3D visualization and semi-automated segmentation capabilities. Since no perfect tracking method currently exist, we utilized Imaris for its ability to allow manual corrections of faulty tracks, ensuring the reliability of our results. This approach was the best available option when we began our analysis, allowing us to obtain accurate results efficiently.

In the revised manuscript, we will clarify our methodology and provide information on both Imaris and alternative processing options to strengthen the reliability of our findings.

Comment: The authors developed a "Heterogeneity module" to extract distinctive tumor migratory phenotypes from the cell tracks quantified by Imaris. The cell tracks of the individual tumor cells are all quite short, indicating relatively low motility of the tumor cells. It's unclear whether such short migratory tracks are sufficient to warrant the PCA analysis to identify the 7 distinctive migratory phenotypes shown in Figure 2d. It's also unclear whether these 7 migratory phenotypes correspond to unique functional phenotypes.

For the 7 distinctive motility clusters, the authors should provide a more detailed analysis of the differences between them. It's unclear whether the difference in retreating, slow retreating, erratic, static, slow, slow invading, and invading correspond to functional difference of the tumor cells.

While some tumor cells exhibit limited motility, indicated by short tracks, others demonstrate significant migratory capabilities. This variability in tumor cell behavior is a central focus of our analysis, and our tool is specifically designed to identify and distinguish these differences. Our PCA analysis effectively captures this variability, as illustrated in Figure 2 d-f. It differentiates between cells exhibiting varying degrees of migratory behavior, including both highly migratory and less migratory phenotypes, as well as their directionality relative to the tumor core and the persistence of their movements. Thus, we believe that our approach provides valuable insights into the distinct migratory phenotypes within the tumor microenvironment. We will clarify these aspects further in the revised manuscript to enhance the reader's understanding of our findings.

While our current manuscript does not provide explicit evidence linking each motility cluster to functional differences among the tumor cells, it is important to note that the state of the field supports the idea that cell dynamics can predict cell states and phenotypes. Research conducted by ourselves (Dekkers, Alieva et al., Nat Biotech, 2023) and others, such as Craiciuc et al. (Nature, 2022) and Freckmann et al. (Nat Comm, 2022) has shown that variations in cell motility patterns are indicative of underlying functional characteristics. For instance, cell morphodynamic features have been shown to reflect differences in cell types, T cell targeting states, tumor metastatic potential, and drug resistance states. In the revised manuscript, we will reference relevant studies to underscore the biological significance of these behaviors. By doing so, we hope to clarify the potential implications of our findings and strengthen the overall narrative of our research.

Comment: Using only motility to classify tumor cell behaviours in the tumor microenvironment (TME) is probably not sufficient to capture the tumor cell difference. There are also other non-tumor cell types in the TME. If the authors aim to develop a computational tool that can elucidate tumor cell behaviors in the TME, they should consider other tumor cell features, e.g., morphology, proliferation state, and tumor cell interaction with other cell types, e.g., fibroblasts and distinct immune cells.

The authors should expand the scale of tumor behavior features to classify the tumor phenotype clusters, e.g., to include tumor morphology, proliferation state, and tumor cell interaction with other TME cell types.

We believe that using dynamic features alone is sufficient to capture differences in tumor behavior, as demonstrated by our results in Figure 2. However, we appreciate the reviewer’s suggestion to consider additional features, such as cell morphology and interactions with other cell types, to finetune our analyses. To this end, we have adapted our pipeline to be compatible with various features present in the data (https://github.com/imAIgene-Dream3D/BEHAV3D_Tumor_Profiler/tree/BEHAV3D_TP-v2.0?tab=readme-ov-file#feature-selection ). We will emphasize this in the revised manuscript. However, we would like to point out that not all features may provide informative insights and that a wide range of features can instead introduce biologically irrelevant noise, making interpretation more challenging. For instance, in 3D microscopy, the z-axis resolution is typically lower, which can lead to artifacts like elongation in that direction. Adding morphological features that capture this may skew the analysis. Therefore, we believe that incorporating additional features should be approached with caution. We will clarify these considerations in the revised manuscript to better guide users in utilizing our computational tool effectively. We will also reference the use of unbiased feature selection techniques, such as bootstrapping methods, to identify biologically relevant features based on the conditions provided (D.G. Aragones et al, Computers in Biology and Medicine (2024)).

Comment: The authors have already published two papers on BEHAV3D [Alieva M et al. Nat Protoc. 2024 Jul;19(7): 2052-2084; Dekkers JF, et al. Nat Biotechnol. 2023 Jan;41(1):60-69]. Although the previous two papers used BEHAV3D to analyze T cells, the basic pipeline and computational steps are similar, in particular regarding cell segmentation and tracking. The addition of a "Heterogeneity module" based on PCA analysis does not make a significant advancement in terms of image analysis and quantification.

We want to emphasize that we have no intention of duplicating our previous publications. In this manuscript, we have consistently cited our foundational papers, where BEHAV3D was first developed for T cell migratory analysis in in vitro settings. In the introduction, we clearly state that our earlier work inspired us to adopt a similar approach for analyzing cell behavior in intravital microscopy (IVM) data, addressing the specific needs and complexities of analyzing tumor cell behaviors in the tumor microenvironment.

Importantly, our new work provides several key advancements: 1) a pipeline specifically adapted for intravital microscopy (IVM) data; 2) integration of spatial characteristics from both large-scale and small-scale phenotyping; and 3) a zero-code approach designed to empower researchers without coding skills to effectively utilize the tool. We believe that these enhancements represent meaningful progress in the analysis of cell behaviors within the tumor microenvironment which will be valuable for the IVM community. We will ensure that these points are clearly articulated in the revised manuscript.

Author response:

The following is the authors’ response to the original reviews.

Reviewer 1:

Comment 1: IPA analysis was performed after scRNA-seq. Although it is knowledge-based software with convenient graphic utilities, it is questionable whether an unbiased genome-level analysis was performed. Therefore, it is not convincing if WNT is the only and best signal for the branching-off marker. Perhaps independent approaches, such as GO, pathway, or module analyses, should be performed to validate the finding.

Thanks for your comment. We agree with the reviewer that IPA is a knowledge-based and a hypothesis-driven method. Our hypothesis was that WNT/BMP pathways, among others, are heavily involved in the development of mesenchymal tissues in general and differentiation of tendons specifically. Therefore, we have looked at differentially expressed genes between clusters from a broad array of pathways featured in IPA that could point us towards molecular function that could make a difference. We further corroborated this hypothesis by using WNT inhibitors in subsequent experiments. To address this point, we have supplemented the discussion section with the following remark:

“This study is not without limitations. The IPA network analysis is a knowledge-based and hypothesis driven platform. We have specifically targeted known pathways to be involved in syndetome differentiation. However, WNT signaling stood out with very specific affinity to the off-target populations and we have verified our findings with experiments proving this hypothesis.”

Per the reviewer’s suggestion, we also performed a non-biased GO analysis (Supp. Fig. 6). Multiple pathways were detected in the three clusters of interest (Supp. Fig. 6A-C), including integrin-related and TGFβ-related pathways. However, in these three clusters of interest, WNT signaling was also detected as a prominent pathway. Therefore, we could conclude that it plays a pivotal role in the differentiation process. This hypothesis was later corroborated with WNT inhibitor experiments.

Comment 2: According to the method section, two iPSC lines were used for the study. However, throughout the manuscript, it is not clearly described which line was used for which experiment. Did they show similar efficiency in differentiation and in responses to WNTi? It is also worrisome if using only two lines is the norm in the stem cell field. Please provide a rationale for using only two lines, which will restrict the observation of individual-specific differential responses throughout the study.

Thanks for your comment. This proof-of-concept study is the first investigation that compares data of an in vitro tenogenic induction protocol that has been tested in more than one human iPSC lines. We agree that line-specific phenomena are difficult to interpret and reproduce. Therefore, it is critical to provide data supporting that the findings can be reproduced in more than one line. Some early studies used one line as proof of concept, however now we realize the need to show that the protocol works in at least one additional line.

Here we used the GMP-ready iPSC line CS0007iCTR-n5 for all optimization experiments. This newer low passage feeder-free line was generated from PBMCs and was designated as GMP-ready in the manuscript because it has been derived and cultured using cGMP xeno-free components (mTESR plus medium and rhLaminin-521 matrix substrate instead of Matrigel). We then wanted to confirm the application of the optimized protocol using the reference control line CS83iCTR-22n1 which has already been more widely used by our group1-5 and others.6 This line has been derived from fibroblasts and has been grown and expanded using MatrigelTM and mTESR1, followed by mTESR plus media. 

The question of number of lines needed is stage-dependent. In our opinion at the proof-of-concept level, two lines, one of which has been generated in GMP-like conditions is sufficient. Confirmation with multiple lines becomes more pertinent as we move towards scale-up/manufacturing, where considerations regarding robustness and consistency are raised. However, at this stage, it is crucial to understand the developmental processes that are involved in cell differentiation to ensure a more robust protocol can be modified and adapted later. In future studies, as we move towards clinical translation, it is warranted that the approach presented in this work will be further optimized and subsequently evaluated using at least 3 different cell lines that have been generated from various sources.

Comment 3: How similar are syndetome cells with or without WNTi? It would be interesting to check if there are major DEGs that differentiate these two groups of cells.

Thanks for your comment. Single cell RNAseq analysis revealed that treatment with WNTi upregulated tenogenic markers. In SYNWNTi, the expression levels of stage-specific markers COL1A1, COL3A1, SCX, MKX, DCN, BGN, FN1, and TNMD were higher compared to the untreated SYN group, as shown in Figure 5C. Density plots depicted an increase in the number of cells expressing COL1A1, COL3A1, SCX and TNMD in SYNWNTi compared to the SYN group, as illustrated in Figure 5D. Trajectory analysis of the WNTi-treated group revealed the absence of bifurcations observed in the untreated group (Fig. 5E). Therefore, it can be conjured that syndetome cells with and without WNTi are different.

Comment 4: Please discuss the improvement of the current study compared to previous ones (e.g., PMID 36203346 my study, 35083031- Tsutsumi, 35372337- Yoshimoto).

Thanks for your comment. In Papalamprou et al (2023)3, we differentiated iPSCs to mesenchymal stromal-like cells (iMSCs), which were then cultured into a 2D dynamic bioreactor for 7 days. In that study, we examined the impact of simultaneous overexpression of the tendon transcription factor Scleraxis (SCX) using a lentiviral vector and mechanical stimulation on the process of tenogenic differentiation. Following 7 days of uniaxial cyclic loading, we observed notable modifications in the morphology and cytoskeleton organization of iPSC-derived MSCs (iMSCs) overexpressing SCX. Additionally, there was an increase in extracellular matrix (ECM) deposition and alignment, along with upregulation of early and late tendon markers. This proof-of-concept study showed that iPSC-derived MSCs could be a viable cell candidate for cell therapy applications and that mechanical stimulation is contributing to the differentiation of iMSCs towards the tenogenic lineage.

Similarly, Tsutsumi et al7 overexpressed the tendon transcription factor Mohawk (MKX) stably in iPSC-derived MSCs using lentiviral vectors. These cells were then used to seed collagen hydrogels which were mechanically stimulated in a cyclic stretch 3D culture bioreactor for 15 days to create artificial tendon-like tissues, which the authors termed “bio-tendons”. Bio-tendons were then decellularized to remove cellular remnants from the xenogeneic human iPSC-derived cells and were subsequently transplanted in an in vivo Achilles tendon rupture mouse model. The authors reported improved histological and biomechanical properties in the Mkx-bio-tendon mice vs. the GFP-bio-tendon controls, providing another proof-of-concept study in favor of the utilization of iPSC-derived MSCs for tendon cell therapies, while also addressing the immunogenicity of cells of allogeneic/xenogeneic origin. Therefore, the above two studies used tendon transcription factor overexpression and mechanical loading either in 2D or 3D to differentiate MSCs towards the tendon/ligament lineage.

Yoshimoto et al8 optimized a stepwise iPSC to tenocyte induction protocol using a SCX-GFP transgenic mouse iPSC line, by monitoring GFP expression over time. The group performed scRNA-seq to characterize the induction of mesodermal progenitors towards the tenogenic lineage and to shed light into their developmental trajectory. That study unveiled that Retinoic Acid (RA) signaling activation enhanced chondrogenic differentiation, which was in contrast to the study of Kaji et al (2021), which also used a SCX-GFP mouse iPSC line. Kaji et al inhibited TGF and BMP signaling during the process of mesodermal induction and reported that RA signaling eliminated SCX induction entirely and promoted a switch to neural fate. Yoshimoto et al suggested that variations in mesodermal cell identity could be due to the different methods used for mesodermal differentiation. In contrast to the Kaji et al study, Yoshimoto et al opted to stimulate WNT and block the Hedgehog pathway during mesoderm induction. Loh et al (2016) identified the branchpoint from the primitive streak to either the paraxial mesoderm (PSM) or the lateral plate mesoderm (LPM) as the result of two mutually exclusive signaling conditions. Specifically, they reported that induction of PSM was achieved through BMP suppression and WNT stimulation, while the specification of lateral mesoderm was accomplished by BMP stimulation and WNT suppression, all with concurrent TGFβ suppression/FGF stimulation. Lastly, a similar approach towards PSM induction from primitive streak (TGF off/BMP off/ WNT on/FGF on) has been used by many subsequent studies Matsuda et al (2020),9 Wu et al (2021)10 and Nakajima et al (2021).11 The diversity of the above-mentioned approaches points to the plasticity of mesodermal progenitors and the need for additional studies to better understand mesodermal specification and subsequent induction towards sclerotome and syndetome.   

In the current study we optimized a stepwise differentiation protocol using xeno-free cGMP ready media and two different cell lines, one of which was cGMP-ready. We used scRNA-seq to characterize the differentiation, which led us to identify off-target cells that were closer to a neural phenotype. We performed pathway analyses and hypothesized that WNT signaling activity might have contributed to the emergence of the off-target cells. To test this, we used a WNT inhibitor (PORCN) to block WNT activity at the SCL stage and at the SYN stage. We found that blockade of WNT signaling at the end of the SM stage and during SCL and SYN induction resulted in a more homogeneous population, while eliminating the neural-like cell cluster. This is the first study that utilized scRNA-seq to shed light into the developmental trajectory of stepwise iPSC to tendon differentiation of human iPSCs and provided a proof-of-concept for the generation of a more homogeneous syndetome population. Further studies are needed to further fine-tune both the process and the final product, as well as elucidate the functionality of iPSC-derived syndetome cells in vitro and in vivo.

Reviewer 2:

General concerns: The authors demonstrated the efficiency of syndetome induction solely by scRNA-seq data analysis before and after pathway inhibition, without using e.g. FACS analysis or immunofluorescence (IF)-staining based assessment. A functional assessment and validation of the induced cells is also completely missing.

We appreciate and agree with the reviewer’s critique regarding further analyses of differentiated iPSC-derived syndetome-like cells, including functional assessment of the differentiated cells. Immunofluorescence was used at all timepoints of induction for phenotype confirmation (Fig. 2,4). Flow cytometry for DLL1 was utilized to benchmark efficient differentiation to PSM (Loh et al,12 Nakajima et al11. Specifically, DLL1 expression was assessed with flow cytometry after 4 days of induction, and was used to optimize the parameter of initial iPSC aggregate seeding density, which has been previously found to be crucial for in vitro differentiation protocols (Loh et al12). Unfortunately, this parameter is usually not reported although it could be critical to establish protocol replication between different lines.

The function of tendon progenitors is usually reported as response to mechanical cues and the ability to regenerate tendon injuries. In future studies we intend to assess the functionality of the generated syndetome and tendon progenitors and their response to in vitro biomechanical stimulation as previously reported to iMSCSCX+ cells3, 13 and in vivo in a critical tendon defect  similarly to what has been previously reported.2 

Comment 1: Notably, in Figure 1D, certain PSM markers (TBXT, MSGN1, WNT3A) show higher expression on day 3. If the authors initiate SM induction on day 3 instead of day 4, could this potentially enhance the efficiency of syndetome-like cell induction?

Thanks for your comment. In the current work, we initially optimized differentiation to PSM via expression of DLL1, whose gene expression peaked at d4. We found that this was influenced by the initial iPSC aggregate seeding density. We wanted to generate a homogeneous DLL1+ population which we assessed via gene expression, flow cytometry, IF and scRNA-seq (Fig. 1D, 2C, 3C and Suppl. Fig.1). Given the fact that different lines might display a diverse developmental timeline, we also confirmed reproducibility of the protocol with a second cell line. We appreciate the reviewer’s suggestion to investigate additional protocol iterations, such as the proposed one at the PSM stage, as we move towards a better understanding of key developmental events during in vitro induction.

Comment 2:  In the third paragraph of the result section the authors note, "Interestingly, SCX, a prominent tenogenic transcription factor, was significantly downregulated at the SCL stage compared to iPSC, but upregulated during the differentiation from SCL to SYN." Despite this increase, the expression level of SCX in SYN remains lower than that in iPSCs in Fig.1G and Fig.3C. Can the authors provide an explanation for this? Can the authors provide IF data using iPSCs and compare it with in vitro-induced SYN cells? Can the authors provide e.g. additional scRNA-seq data which could support this statement?

Thank you for your comment. In Fig. 1G, SCX expression in SYN was upregulated compared to SCL, however, it was shown to be similar to iPSCs. This suggests a baseline stochastic expression of SCX possibly stemming from spontaneous differentiation of iPSCs in culture (Fig. 3C). Previous research has shown that tenogenic marker gene expression tends to reduce during postnatal tendon maturation (Yin et al., 2016b14 Grinstein et al., 2019.15 Yoshimoto et al (2022) utilized a transgenic mouse iPSC-SCX-GFP line  to track SCX expression. It was shown that SCX expression peaked after 7d of tenogenic induction and was then decreased at day 14, which marked the end of tenogenic induction. The authors postulated that this pattern of gene expression could either indicate further maturation of tenocytes at subsequent time points, or that the number of non-tenogenic cells increased from T7 to T14.

In the present work, we showed SCX gene expression upregulation in SYN compared to SCL, as well as significant upregulation of TNMD, EGR1, COL1A1 and COL3A1 (Fig.1G). Supp. Fig.8 has been added to show feature plots of SCX and TNMD expression from SCL, SYN and SYNWNTi.  The significant upregulation of later markers of tenogenic differentiation suggests that the 21 days of tenogenic induction might have matured the cells. Since gene expression analysis only conveys a snapshot of the transcriptional profile of a cell population, it is likely that we might have missed the peak of SCX upregulation (Supp. Fig. 5). Following treatment with the WNT inhibitor, the SYNWNTi group displayed increased SCX expression (% cells expressing SCX) compared to SYN, which might also be due to a more homogeneous population of syndetome-like cells following treatment with WNTi. In the SYNWNTi group, TNMD was shown to be expressed in the SYN cluster, whereas SCX was mostly found in the cluster that was labelled as fibrocartilage (FC) cluster based on the expression of COL2A1/SOX9/FN1/BGN/COL1A1 markers. Due to the fact that SCX+/SOX9+ progenitor cells are able to give rise to both tendon and cartilage (Sugimoto 2013)16, it could be postulated that this cluster contains tendon progenitors. Interestingly, the FC cluster was not observed in the second iPSC line that we tested, which resulted in a more homogeneous induction to syndetome (78.5% vs. 66.9% SYN cells, Supp. Table 1 & Supp. Fig.3). This slight discrepancy between the two lines and more specifically the presence of the FC cluster only in the 007i line, warrants further investigation. Taken together, these data indicate that the tenogenic induction duration could likely be shortened. Further work to assess the time course of SCX expression over the entire tenogenic induction could be used to further optimize the in vitro induction. For instance, a human edited iPSCSCX-GFP+ line could be generated and used to track SCX expression during the entire induction.

Comment 3: In the fourth paragraph of the result section the authors state, "SM markers (MEOX1, PAX3) and SCL markers (PAX1, PAX9, NKX3.2, SOX9) were upregulated in a stepwise manner." However, the data for MEOX1 and NKX3.2 seems to be missing from Figure 3B-C. The authors should provide this data and/or additional support for their claim.

Thanks for your comment. Feature plots for MEOX1 and NKX3.2 have been added to the Supplemental information (Supp. Fig. 9).

Comment 4: In Figures 2B and 2E, the background of the red channel seems extremely high. Are there better images available, particularly for MEOX1? Given the expected high expression of MEOX1 in SM cells, the authors should observe a strong signal in the nucleus of the stained somitic mesoderm-like cells, but that is not the case in the shown figure. The authors should provide separate channel images instead of merged ones for clarity. The antibody which the authors used might not be specific. Can the authors provide images using an antibody which has been shown to work previously e.g. antibody by ATLAS (Cat#: HPA045214)?

As requested by the reviewer, we have provided separate channels for those images in the Supplement (Supp. Fig. 7). The images show relatively high expression of these markers in SM cells.

Comment 5: In Fig. 2C and Supplementary Fig. 1, the authors present data from immunofluorescence (IF) staining and FACS analysis using a DLL1 antibody. While FACS analysis indicates an efficiency of 96.2% for DLL1+ cells, this was not clearly observed in their IF data. How can the authors explain this discrepancy? Could the authors quantify their IF data and compare it with the corresponding FACS data?

Thanks for your comment. We performed flow cytometric analysis of DLL1 expression to optimize cell seeding density using the 007i line. In the present study, we used IF only in a qualitative manner, that is to confirm protein expression of selected markers. It could be noted that the use of poly-lysine coated coverslips, which are needed for IF, might have slightly altered the density of the cells on the coverslip vs. the plate. Lastly, it cannot be ruled out that the different substrate could have influenced their phenotype differentially through matrix interactions and signaling. On the other hand, flow cytometry by nature is a quantitative and single cell approach, whereas IF staining is qualitative. Therefore, for the purpose of this proof-of-concept work, we tend to trust the quantitative data from the flow cytometry results more than semi-quantitative confirmation achieved through IF staining using coverslips. 

Comment 6: In Fig. 2G, PAX9 is expected to be expressed in the nucleus, but the shown IF staining does not appear to be localized to the nucleus. Could the authors provide improved or alternative images to clarify this? The authors should use antibodies shown to work with high specificity as already reported by other groups.

Thanks for your comment. Indeed, the staining seems to be mostly cytoplasmic. We have used antibodies that were previously reported3 and repeated the staining, however, the same results were replicated. We can speculate that this transcription factor has additional role in the iPSC-derived cells and might be traveling to the cytoplasm. Unfortunately, we have no evidence to this phenomenon.  

Comment 7: Why did the authors choose to display day 10 data for SYN induction in Fig. 4A? Could they provide information about the endpoint of their culture at day 21?

Thank you for your comment. In Fig. 1G we provided gene expression analyses results for several selected early and later tendon markers for the endpoint of our culture, that is day 21. Following scRNA-seq at each stage of the differentiation (iPSC at d0, PSM at d4, SM at d8, SCL at d11 and the endpoint day 32 for SYN), we performed DEG analysis using the IPA platform. We identified activation of genes associated with the WNT signaling pathway in the off-target clusters. We hypothesized that WNT pathway inhibition might block the formation of unwanted fates and induce a more homogeneous differentiation outcome. We thus tested a WNT inhibitor and compared the inhibitor-treated group with a non-treated group. We then assessed selected neural markers during the course of the inhibitor application. In Fig. 4A we presented gene expression of key selected markers at day 21 using qPCR, which was approximately in the middle of the syndetome induction. Since we observed that the inhibitor downregulated the selected neural markers, we then applied the inhibitor until the endpoint of the initial induction and proceeded to analyze the results using scRNA-seq (Fig. 5). Lastly, it should be acknowledged that this was a proof-of-concept study, and additional optimizations are needed regarding the application of the inhibitor (timing, duration, concentration, etc).

Comment 8: In Supplementary Fig. 5, the authors depicted the expression level of SCX, a SYN marker, which peaked at day 14 and then decreased. By day 21, it reached a level comparable to that of iPSCs. Given this observation, could the authors provide a characterization of the cells at day 21 during SYN induction using IF? What was the rationale behind selecting 21 days for SYN induction? The authors also need to show 'n numbers'; how many times were the experiments repeated independently (independent experiments)?

Thanks for your comment. During the optimization process, we initially used RT-qPCR to track gene expression of selected tenogenic markers using the 007i line. We found that after 21 days of tenogenic induction there was upregulation of the few established tendon markers, that is COL1A1, COL3A1, EGR1 and quite importantly, the more definitive later tendon marker, TNMD. Thus, we decided to proceed with this protocol prior to testing other compounds including the WNT inhibitor WNT-C59. However, as has been discussed in the manuscript, this extended tenogenic induction resulted in cell attrition without the application of the WNT inhibitor. This phenomenon was ameliorated following WNT inhibition. Thus, it could be postulated that the protocol could be further optimized by shortening tenogenic induction to less than 21 days.

The experiments that were conducted to optimize the differentiation process were repeated independently at least n=3 times using qPCR and IF using two lines, that is the 007i and the 83i line as described in the manuscript. The scRNAseq analysis represents a population of cells from in vitro differentiation that originated from the same donor line, therefore it was performed on n=1 sample at each stage. However, the effects of inhibitor application (sample SYNWNTi) were also confirmed using a second cell line (83i), thus a total of n=2 independent samples were analyzed.  

Comment 9: Overall the shown immunofluorescence (IF) data does not appear convincing. Could the authors please provide clearer images, including separate channel images, a bright field image, and magnified views of each staining?

Thanks for your comment. The separate channels images were added to the supplemental data (Supp. Fig. 7). We agree with the reviewer regarding the limitations of IF staining, especially with the added confounding factor of using poly-lysine coated coverslips. We would like to point out, that in the current work IF staining is not the main finding or the primary outcome measure, and that it is only used to further support the differentiation by providing a qualitative assessment of protein presence and localization. We describe in this paper our thesis regarding the limitations of IF and the need for more high-throughput unbiased approaches to quantification when using IF staining. For instance, spatial transcriptomics combined with mass cytometry or flow cytometry could be used for a more unbiased approach. Thus, in the present manuscript we based our conclusion on the quantitative gene expression, single cell sequencing and flow cytometry.

Comment 10: As stated by the authors in the manuscript, another research group performed FACS analysis to assess the efficiency of syndetome induction using SCX antibody, and/or quantification of immunofluorescence (IF) with SCX, MKX, COL1A1, or COL2A1 antibodies. Could the authors conduct a comparative analysis of syndetome induction efficiency both before and after protocol optimization, utilizing FACS analysis in conjunction with an SCX reporter line or antibody staining, e.g. quantifying induction efficiency via immunofluorescence (IF) staining with syndetome-specific marker genes?

Thank you for your comment. As discussed in a previous comment, we agree with the reviewer that the generation of a human iPSC-SCX-GFP line would shed light into SCX expression over the entire course of induction. In the current work we used IF as qualitative confirmation of specific marker expression and we showed the presence of SCX, MKX, COL1 and COL3 in SYNWNTi as well as the absence of neuronal markers. As we also pointed it out in the present manuscript, IF can only be considered as a semi-quantitative assessment burdened with several technical limitations as well as operator bias and lower sensitivity and accuracy compared to flow cytometry or scRNA-seq, unless performed in a more unbiased manner. To further clarify this point, firstly, using poly-lysine coated coverslips for IF staining, results in a different substrate environment compared to the Geltrex-coated plates that were used for the induction. Additionally, we noticed that cells grew overconfluent at the edges of the coverslips. This is an important point, since as we have observed in this work, seeding density is critical for the reproducibility of the protocol. It could further be postulated that a different cell substrate stiffness might also have an effect on this process. In our opinion, in this context IF should rather be used qualitatively and a combination of flow cytometry with scRNAseq should be utilized to draw quantitative conclusions such as induction efficiencies of a certain cell type. Since we also observed inconsistencies with the SCX antibodies we tested, the generation of edited human iPSC lines (such as SCX-GFP, MKX-GFP and TNMD-GFP) would be the preferred approach to further explore the efficiency of differentiation.

Comment 11: To enhance the paper's significance, the authors should conduct functional validation experiments and proper assessment of their induced syndetome-like cells. They could perform e.g. xeno-transplantation experiments with syndetome cells into SCID-mice or injury models. They could also assess whether the in vitro induced cells could be applied for in vitro tendon/ligament formation.

Thanks for your comment. For the purpose of this proof-of-concept in vitro study, our primary goal was to initially evaluate a stepwise tenogenic induction protocol using GMP-ready cell lines and chemically defined media. Then, we wanted to utilize the analytical power of scRNA-seq in order to characterize and optimize the protocol, thus focusing on one developmental stage that is not well understood, that of syndetome specification from sclerotome, and hypothesized that by fine-tuning the WNT pathway we would be able to generate a more homogeneous syndetome cell population. We fully agree with the reviewer that the warranted next steps should be to conduct several functional validation experiments, such as in vitro 2D/3D tendon/ligament formation and in vivo transplantation in allogeneic or xenogeneic injury models.

Comment 12: The authors should also compare their scRNA-seq data with actual human embryo data sets, something which could be done given the recent increase in available human embryo scRNA-seq data sets.

This is a great idea and intriguing study. Unfortunately, not all data sets are available at the moment and specifically embryonic and MSK scRNA-seq data is very scarce, although growing. We have no access to data sets from human tendon development, and thus will have to leave this comparison for future studies.

Reviewer 3:

Comment 1: The data outlining the differences between the differentiation outcome of the two tested iPSCs is intriguing, but the authors fail to comment on potential differences between the two iPSC lines that could result in drastically different cell outputs from the same differentiation protocol. This is a critically important point, as the majority of the SCX+ cells generated from the 007i cells using their WNTi protocol were found in the FC subpopulation that failed to form from the 83i line under the same protocol. From the analysis of only these 2 cell lines in vitro, it is difficult to assess whether this WNTi protocol can be broadly used to generate tenogenic cells.

Thanks for your comment. This proof-of-concept study is the first investigation that compares data of an in vitro tenogenic induction protocol that has been tested into more than one cell lines. Using unsupervised clustering we identified 11 clusters, which were classified into 6 cell subpopulations. The only observed difference between the two lines was a small subset that was labeled as fibrocartilage (FC), which displayed expression of both tenogenic and chondrogenic markers. This subpopulation was observed in 007i line but not in the 83i line at the end of the SYN induction. Importantly, DEG analysis also showed that it was enriched for SCX. It has been shown that SCX+/SOX9+ progenitors are a distinct multipotent cell group, responsible for the development of SCX−/SOX9+ chondrocytes and SCX+/SOX9− tenocytes/ligamentocytes (Sugimoto 2013)16. As noted in a previous comment (Comment 2 from Reviewer 1), we might have missed SCX upregulation during the 21-day syndetome induction. This can be further supported by Fig. 5E trajectory analysis which shows that this subpopulation (FC) precedes the SYN cell subpopulation. The fact that this subpopulation was present in one line but not the other, might indicate that 83i line resulted in a more mature tendon population. Therefore, we would rather posit that in the case of 83i line, it might not be that the FC subpopulation failed to form, but rather that it was missed in our scRNAseq endpoint analysis which showed that a more homogeneous SYN population was formed (8.7 % in 007i vs. 0.26 % in 83i, Supp. Table 1 & Supp. Fig. 3B). Future studies are warranted to characterize the SYN induction timeline as it pertains to SCX expression followed up by maturation from tenogenic progenitor to tenocytes.

Comment 2: The authors make claims to changes in protein expression but fail to quantify either fluorescence intensity or percent cell expression from their immunofluorescence analyses to substantiate these claims. These claims are not fully supported by the data as presented as it is unclear whether there is increased expression of tendon markers at the protein level or more cells surviving the protocol. Additionally, in images where 3 channels are merged, it would be helpful to show individual channels where genes are shown in similar spectra (ie. Fig 2I SCX/MKX). Furthermore, the current layout and labelling scheme of Figure 4 makes it very difficult to compare conditions between SYN and SYNWNTi protocols.

Thanks for your comment. Protein expression at each stage was verified with immunofluorescence cytochemistry whereby cells were cultured onto poly-lysine coated coverslips, which were then fixed, stained and imaged (Fig. 2). However, prior to WNT inhibitor application, we noticed gradual cell attrition in the cultures at the end of differentiation (Fig. 1B, 2I). The images show qualitative differences with and without the WNT inhibitor. This could be attributed to the heterogeneity of the cell population at SCL stage, which was confirmed by scRNA-seq (Fig. 3A). As it has been discussed previously (Reviewer 2 comments 5 & 9), in the current paper we didn’t provide any IF quantitative analysis because of the qualitative nature of the staining technique. In future work another high-resolution imaging modality will be considered like single cell proteomics and flow cytometry or mass cytometry in order to perform a more unbiased quantitative single cell analysis across different stages and samples. Furthermore, we have added single channel images in the supplemental information.

Comment 3: Individual data points should also be presented for all qPCR experiments (ie. Fig 4A). Biological replicate information is missing from several experiments, particularly the immunofluorescence data, and it is unclear whether the qPCR data was generated from technical or biological replicates.

Thanks for your comment. We have added additional information regarding replicates in each figure legend. We have also changed Fig. 4A.

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Author response:

The following is the authors’ response to the original reviews.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The authors want to elucidate which are the mechanisms that regulate the immune response in physiological conditions in cortical development. To achieve this goal, authors used a wide range of mutant mice to analyse the consequences of immune activation in the formation of cortical ectopia in mice.

Strengths:

The authors demonstrated that Abeta monomers are anti-inflammatory and inhibit microglial activation. This is a novel result that demonstrates the physiological role of APP in cortical development.

Weaknesses:

-On the other hand, cortical ectopia has been already described in mouse models in which the amyloid signalling has been disrupted (Herms et al., 2004; Guenette et al., 2006), making the current study less novel.

We agree these previous studies have implicated amyloid precursor protein in cortical ectopia. However, since these studies use whole-body knockouts, they have not implicated the functional roles of specific cell types.  Nor have they identified the specific mechanisms underlying the formation of this unique class of cortical ectopia. In contrast, our studies show that the disruption of a novel Abeta-regulated signaling pathway in microglia is the primary cause of ectopia formation in this class of ectopia mutants. This is the first time that microglia have been specifically implicated in the development of cortical ectopia. We further show that elevated MMP activity and resulting cortical basement membrane degradation is the underlying mechanism leading to ectopia formation.  This is also the first time that MMP activity and basement membrane degradation (instead of maintenance) have been implicated in cortical ectopia development. As such, our results have provided novel insights into the diverse mechanisms underlying cortical ectopia formation in developmental brain disorders.

One of the molecules analysed is Ric8a, a GTPase activator involved in neuronal development. Authors used the conditional mutant mice Emx1-Ric8a to delete Ric8a from early progenitors and glutamatergic neurons in the pallium. Emx1-Ric8a mutant mice present cortical ectopias and authors attributed this malformation to the increase in inflammatory response due to Ric8a deletion in microglia. Several discordances do not fit this interpretation:

- The role of Ric8a in cortical development and function has been already described in several papers, but none of them has been cited in the current manuscript (Kask et al., 2015, 2018; Ruisu et al., 2013; Tonissoo et al., 2006).

We have included reference to the published works on ric8a in cortical development in revision.

- Ectopia formation in the cortex has been already described in Nestin-Ric8a cKO mice (Kask et al., 2015). In the current manuscript, authors analyzed the same mutant mice (Nestin-Ric8a), but they did not detect any ectopia. Authors should discuss this discordance.

The expression pattern of nestin-cre is known to vary dependent on factors including transgene insertion site, genetic background, and sex. Early studies show, for example, that the nestin gene promoter drives cre expression in many non-neural tissues in another transgenic line in the FVB/N genetic background (Dubois et al Genesis. 2006 Aug;44(8):355-60. doi: 10.1002/dvg.20226).  The specific nestin-cre line used in Kask et al 2015 has also been shown to be active in brain microglia and lead to increased microglia pro-inflammatory activity upon breeding to a conditional allele of a cholesterol transporter gene (Karasinska et al., Neurobiol Dis. 2013 Jun:54:445-55; Karasinska et al.,  J Neurosci. 2009 Mar 18; 29(11): 3579–3589; Takampri et al., Brain Res. 2009 May 13:1270:10-8). These factors may in part underlie the apparent discrepancy.  We have now incorporated this discussion into the revision.

- Authors claim that microglia express Emx1, and therefore, Ric8a is deleted in microglia cells. However, the arguments for this assumption are very weak and the evidence suggests that this is not the case. This is an important point considering that authors want to emphasise the role of Ric8a in microglia activation, and therefore, additional experiments should demonstrate that Ric8a is deleted in microglia in Emx1-Ric8a mutant mice.

We have observed altered mRNA expression of several genes in purified microglia cultured from the emx1-cre mutants (Supplemental Fig. 8), which indicates that ric8a is deleted from microglia and suggests a role of microglial ric8a deficiency in ectopia formation.  This interpretation is further strengthened by the observation that deletion of ric8a from microglia using a microglia-specific cx3cr1-cre results in similar ectopia (Fig. 2). We also have other data supporting this interpretation, including data showing induction of the expression of a cre reporter in brain microglia by emx1-cre and loss of ric8a gene expression in microglia cells isolated from emx1-cre mutants. These data have now been incorporated into the text and in revised Supplemental Fig. 8 (new panels c-c” & d).

Reviewer #2 (Public Review):

Kwon et al. used several conditional KO mice for the deletion of ric8a or app in different cell types. Some of them exhibited pial basement membrane breaches leading to neuronal ectopia in the neocortex.

They first investigated ric8a, a Guanine Nucleotide Exchange Factor for Heterotrimeric G Proteins. They observed the above-mentioned phenotype when ric8a is deleted from microglia and neural cells (ric8a-emx1-cre or dual deletion with cre combination cx3cr1 (in microglia) and nestin (in neural cells)) but not in microglia alone or neural cells alone (whether it is in CR cells (ric8a-Wnt3a-cre), post-mitotic neurons (nex-cre or dlx5/6-cre), or in progenitors and their progeny (nestin-cre or foxg1-cre). They also show that ric8a KO mutant microglia cells stimulated in vitro by LPS exhibit an increased TNFa, IL6 and IL1b secretion compared to controls (Fig 2). They therefore injected LPS in vivo and observed the neuronal ectopia phenotype in the ric8a-cx3cr1-cre (microglial deletion) cortices at P0 (Fig 2). They suggest that ric8a KO in neuronal cells mimics immune stimulation (but we have no clue how ric8a KO in neural cells would induce immune stimulation).

We agree we do not currently know the precise mechanisms by which mutant microglia are activated in the mutant brain.  However, this does not affect the conclusion that deficiency in the Abeta monomer-regulated APP/Ric8a pathway in microglia is the primary cause of cortical ectopia in these mutants, since we have shown that genetic disruption of this pathway in microglia alone by targeting different pathway components, using cell type specific cre, in several different approaches, all results in similar cortical ectopia phenotypes.  Regarding the source of the immunogens, there are several possibilities which we plan to investigate in future studies. For example, the clearance of apoptotic cells and associated cellular debris is an important physiological process and deficits in this process have been linked to inflammatory diseases throughout life (Doran et al., Nat Rev Immunol. 2020 Apr;20(4):254-267; Boada-Romero et al., Nat Rev Mol Cell Biol. 2020 Jul;21(7):398-414.).  In the embryonic cortex, studies have shown that large numbers of cell death take place starting as early as E12 (Blaschke et al., Development. 1996 Apr;122(4):1165-74; Blaschke et al., J Comp Neurol. 1998 Jun 22;396(1):39-50).  Studies have also shown that radial glia and neuronal progenitors play critical roles in the clearance of apoptotic cells and associated cellular debris in the brain (Lu et al., Nat Cell Biol. 2011 Jul 31;13(9):1076-83; Ginisty et al., Stem Cells. 2015 Feb;33(2):515-25; Amaya et al., J Comp Neurol. 2015 Feb 1;523(2):183-96). Moreover, Ric8a-dependent heterotrimeric G proteins have been found to specifically promote the phagocytic activity of both professional and non-professional phagocytic cells (Billings et al., Sci Signal. 2016 Feb 2;9(413):ra14; Preissler et al., Glia. 2015 Feb;63(2):206-15; Pan et al. Dev Cell. 2016 Feb 22;36(4):428-39; Flak et al. J Clin Invest. 2020 Jan 2;130(1):359-373; Zhang et al., Nat Commun. 2023 Sep 14;14(1):5706).  Thus, it is probable that the failure to promptly clear up apoptotic cells and debris by mutant radial glia may play a role in triggering mutant microglial activation in ric8a-emx1-cre mutants. We have now included these possibilities in the text of the revised manuscript. However, the precise mechanisms remain to be determined in future studies, which, however, do not affect the conclusion of the current study.

The authors then turned their attention on APP. They observed neuronal ectopia into the marginal zone when APP is deleted in microglia (app-cxcr3-cre) + intraperitoneal LPS injection (they did not show it, but we have to assume there would not be a phenotype without the injection of LPS) (Fig 3). (The phenotype is similar but not identical to ric8a-cx3cr1-cre + LPS. They suggest that the reason is because they had to inject 3 times less LPS due to enhanced immune sensitivity in this genetic background but it is only a hypothesis). After in vitro stimulation by LPS, app mutant microglia show a reduced secretion of TNFa and IL6 but not IL1b (this is the opposite to ric8a-cx3cr1-cre microglia cells) while peritoneal macrophages in culture show increased secretion of TNFa, IL1, IL6 and IL23 (fig 3 and Suppl. Fig 9).

We have data showing that that app-cxcr3-cre mutants without LPS injection do not show ectopia, which has now been included in the revised supplemental Fig. 9 (new panels c-d).  The reason we employ LPS injection is, in the first place, that we do not see a phenotype without the injection. We agree, and have also stated in the text, that the phenotype of the app mutants is not as severe as that of the ric8a mutant.  Besides the low LPS dosage used, we also suggest that other app family members may compensate since the ectopia in the app family gene mutants reported previously were only observed in app/aplp1/2 triple knockouts, not even in any of the double knockouts (Herms et al., 2004). We have further clarified this point in the text. These possibilities are also not mutually exclusive. Nonetheless, the results clearly show that microglia specific app mutation causes cortical ectopia upon embryonic immune stimulation. They have thus implicated a specifical role of microglial APP in cortical ectopia formation.

The different response of ric8a and app mutant microglia to LPS results from in vitro culturing of microglia. We have shown that, when acutely isolated macrophages are used, these mutants show changes in the same direction (both increased cytokine secretion) (Fig. 4).  This demonstrates without culturing app mutant microglial lineage cells indeed behave in the same way as ric8a mutant cells.

The microglia used for analysis in in vitro assays in this study have all been cultured for two weeks before assay. They have thus been under chronic stimulation exposed to dead cells and debris in the culture dish through this period.  Previous studies have shown that dependent on the degree of perturbation to the inflammation-regulating pathways, such exposures can differentially affect microglial cytokine expression, sometimes in an opposite direction from expected.  For example, under chronic immune stimulation, while the trem2+/- microglia, which are heterozygous mutant for the anti-inflammatory Trem2, show elevated pro-inflammatory cytokine expression (as is expected), trem2-/- (null) microglia under the same conditions instead not only do not show increases but for some pro-inflammatory cytokines, actually show decreases in expression (Sayed et al.,, Proc Natl Acad Sci U S A. 2018 Oct 2;115(40):10172-10177).  In several systems, Ric8a-dependent heterotrimeric G proteins have been shown to act downstream of APP and mediate one of the branches of the signaling activated by APP (Milosch et al., Cell Death Dis. 2014 Aug 28;5(8):e1391; Fogel et al,, Cell Rep. 2014 Jun 12;7(5):1560-1576; Ramaker et al., J Neurosci. 2013 Jun 12;33(24):10165-81; Nishimoto et al., Nature. 1993 Mar 4;362(6415):75-9).  Indeed, APP cytoplasmic domain is known to also bind to and signalig through several other proteins including FE65, Mena, and TIP60 (Cao & Sudhof, Science 2001. 293:115-120).  It is likely that in microglia Ric8a-dependent heterotrimeric G proteins may also mediate only a subset of the signaling downstream of APP.  As such, app knockout in microglia may have more severe effects on microglial anti-inflammatory regulation than ric8a knockout.  As a result, upon chronic immune activation, app knockout may lead to a microglial phenotype similar to the trem2 null mutation phenotype as discussed above, while ric8a knockout leads to a phenotype similar to trem2+/- phenotype). This may explain the subdued TNF and IL6 secretion by cultured app (but not ric8a) mutant microglia.

Amyloid beta (Ab) being one of the molecules binding to APP, the authors showed that Ab40 monomers (they did not test Ab40 oligomers) partially inhibit cytokines (TNFa, IL6, IL1b, MCP-1, IL23a, IL10) secretion in vitro by microglia stimulated by LPS but does not affect secretion by microglia from app-cx3cr1-cre (tested for TNFa, IL6, IL1b, IL23a, IL10) (Fig 4, Suppl fig 10) (but still does it in aplp2-cx3cr1-cre) and does not affect secretion by ric8a-cx3cr1-cre microglia (tested for TNFa and IL6 but still suppress IL1b) (Therefore here is another difference between app and ric8a KO microglia).

We have tested the effects of Abeta40 oligomers, which induce instead of suppressing microglial cytokine secretion, and have included the data (new panel j in supplemental Fig. 10).  As mentioned above, in several systems, Ric8a-dependent heterotrimeric G proteins have been shown to act downstream of APP and mediate one of the branches of the signaling activated by APP (Milosch et al., Cell Death Dis. 2014 Aug 28;5(8):e1391; Fogel et al,, Cell Rep. 2014 Jun 12;7(5):1560-1576; Ramaker et al., J Neurosci. 2013 Jun 12;33(24):10165-81; Nishimoto et al., Nature. 1993 Mar 4;362(6415):75-9).  We assume that this is likely also true in microglia and that Ric8a-dependent heterotrimeric G proteins may mediate a subset and only a subset of the signaling downstream of APP.  This may explain the difference in the effects of app and ric8a knockout mutation in abolishing the anti-inflammatory effects of Abeta monomers on IL-1b vs TNF/IL-6.  This difference also suggests that TNF/IL-6 and IL-1b secretion must be regulated by different mechanisms in microglia. Indeed, it is well established in immunology that the secretion of IL1b, but not of TNF or IL6, is regulated by inflammasome-dependent mechanisms (see, for example, Proz & Dixit. Nat Rev Immunol. 2016 Jul;16(7):407-20. doi: 10.1038/nri.2016.58).

The authors injected inhibitors of Akt or Stat3 in the ric8a-emx1-cre cortex and found it suppressed neuronal ectopia (Fig 5, Suppl fig 11). It is not clear whether it suppresses immune stimulation from neuronal cells or immune reaction from microglia cells.

We agree at present the pharmacological approaches we have taken are not able to distinguish these possibilities.  However, no matter which is the case, our results still implicate a role of excessive microglial activation in the formation of cortical ectopia and support the conclusion of the study.  Thus, while worthwhile of further investigation, this question does not impact the conclusion of the current study. Furthermore, as mentioned, we plan to determine the mechanisms of how ric8a mutation in neural cells induces immune activation in future studies. These results will likely enable us to more specifically address this question.

Finally, the authors examined the activities of MMP2 and MMP9 in the developing cortex using gelatin gel zymography. The activity and protein levels of MMP9 but not MMP2 in the ric8a-emx1-cre cortex were claimed significantly increased (Fig 5, Suppl fig 12). Unfortunately, they did not show it in the app-cx3cr1-cre +LPS mouse. They make a connection between ric8a deletion and MMP9 but unfortunately do not make the connection between app deletion and MMP9, which is at the center of the pathway claimed to be important here). Then they injected BB94, a broad-spectrum inhibitor of MMPs or an inhibitor specific for MMP9 and 13. They both significantly suppress the number and the size of the ectopia in ric8a mutants (Fig5).

For all the gelatin gel zymography analysis, we quantify protein concentrations in the cortical lysates using the Bio-Rad Bradford assay kit and load the same amounts of proteins per lane. The results across lanes are all directly comparable. From the quantification, our results clearly show that MMP9 activity levels are increased in the mutants (we have now included whole gel images and quantification in a new supplemental Figure 13).  The similar levels of MMP2 in all lanes also provide an internal control further supporting the observation of a specific change in MMP9.  For this analysis, we focus on the ric8a-emx1-cre mutants since the app-cx3cr1-cre +LPS animals show ectopia only in only subsets of mutants and in most cases only in one of the hemispheres.  Experiments examining potential changes in MMP9 are therefore unlikely to yield meaningful results.  On the other hand, we have clearly shown that the administration of different classes of MMP inhibitors significantly eliminate ectopia in ric8a-emx1-cre mutants. This has strongly implicated a functional contribution of MMPs.

After reading the manuscript, I still do not know how ric8a in neural cells is involved in the immune inhibition. Is it through the control of Ab monomers? In addition, the authors did not show in vivo data supporting that Ab monomers are the key players here. As the authors said, this is not the only APP interactor. Finally, I still do not know how ric8a is linked to APP in microglia in the model.

As detailed above, there are several possibilities including potential deficits in the clearance of apoptotic cells and associated debris that may trigger microglial activation in ri8ca-emx1-cre mutants. We will investigate these possibilities in future studies.  We have now incorporated these possibilities in the revised text.  As for the role of Abeta monomers, we have indicated that we currently do not have evidence that in the developing cortex Abeta monomers play a role in inhibiting microglia.  We have also indicated in the manuscript that our conclusion is that a microglial signaling pathway that is activated by Abeta monomers in vitro regulates normal brain development in vivo, not that Abeta monomers themselves regulate brain development.  Regarding the link between Ric8a and APP, the reviewer has missed several major lines of supporting evidence. For example, we have shown that Abeta monomers activate a pathway in microglia that inhibits the secretion of several proinflammatory cytokines including TNF, IL-6, IL-10, and IL-23 (Figure 4 and Supplemental Figures 8-10).  This inhibition is abolished when either app or ric8a gene is deleted from microglia.  This clearly indicates that app and ric8a act in the same genetic pathway (the pathway activated by Abeta monomers) in microglia. We also show that this Abeta monomer-activated pathway also inhibits the transcription of several cytokines in microglia.  This inhibition is also abolished when either app or ric8a gene is deleted from microglia.  This reinforces the conclusion that app and ric8a act in the same pathway in microglia.  Furthermore, cell type specific deletion of app or ric8a from microglia in vivo also results in similar phenotypes of cortical ectopia. Together, these results strongly support the conclusion that app and ric8a act in the same pathway that is activated by Abeta monomers in vitro in microglia. This conclusion is also consistent with published findings that Ric8a dependent heterotrimeric G proteins bind to APP and mediate subsets of APP signaling across different species (Milosch et al., Cell Death Dis. 2014 Aug 28;5(8):e1391; Fogel et al,, Cell Rep. 2014 Jun 12;7(5):1560-1576; Ramaker et al., J Neurosci. 2013 Jun 12;33(24):10165-81; Nishimoto et al., Nature. 1993 Mar 4;362(6415):75-9).         

While several of the findings presented in this manuscript are of potential interest, there are a number of shortcomings. Here are some suggestions that could improve the manuscript and help substantiate the conclusions:

(1) As the title suggests it, the focus is on Ab and APP functions in microglia. However, the analysis is more focused on ric8a. The connection between ric8a and APP in this study is not investigated, besides the fact that their deletion induces somewhat similar but not identical phenotypes. Showing a similar phenotype is not enough to conclude that they are working on the same pathway. The authors should find a way to make that connection between ric8a and app in the cells investigated here.

As discussed above, the reviewer misses several major lines of evidence showing that APP and Ric8a acts in the same pathway in microglia.  Besides the similarity of the ectopia phenotypes, for example, we have shown that Abeta monomers activates a pathway in microglia that inhibits the secretion of several proinflammatory cytokines including TNF, IL-6, IL-10, and IL-23 (Figure 4 and Supplemental Figures 8-11).  These inhibitory effects are abolished when either app or ric8a gene is deleted from microglia.  This clearly indicates that app and ric8a act in the same genetic pathway, a pathway that is activated by Abeta monomers in vitro, in microglia. We also show that this Abeta monomer-activated pathway inhibits the transcription of several cytokine genes in microglia.  These effects are again abolished when either app or ric8_a gene is deleted from microglia.  This further reinforces the conclusion that _app and ric8a act in the same pathway in microglia.  Not only so we also show that the same results are true in macrophages.  Thus, these results strongly support the conclusion that app and ric8a act in the same genetic pathway in microglia. This conclusion is also consistent with published findings that Ric8a dependent heterotrimeric G proteins biochemically bind to APP and mediate subsets of APP signaling across different species (Milosch et al., Cell Death Dis. 2014 Aug 28;5(8):e1391; Fogel et al,, Cell Rep. 2014 Jun 12;7(5):1560-1576; Ramaker et al., J Neurosci. 2013 Jun 12;33(24):10165-81; Nishimoto et al., Nature. 1993 Mar 4;362(6415):75-9).  

(2) This would help to show the appearance of breaches in the pial basement membrane leading to neuronal ectopia; to investigate laminin debris, cell identity, Wnt pathway for app-cxcr3-cre + LPS injection as you did for ric8a-emx1-cre.

We have now provided further data on pial basement membrane breaches in the app-cxcr3-cre + LPS animals (new panels e-f” in supplemental Fig 9).  We have not observed any changes in cell identity or Wnt pathway activity in ric8a-emx1-cre mutants.  It is thus of limited value to examine potential changes in these areas in the app-cxcr3-cre + LPS animals.   

(3) As a control, this would help to show that app-cxcr3-cre without the LPS injection does not display the phenotype.

We have the data on app-cx3cr1-cre mutants without LPS injection, which show no ectopia.  We have now included the data in the revised supplemental Fig. 9 (new panels c-d).

(4) This would help to show the activity and protein levels of MMP9 and MMP2 and perform the rescue experiments with the inhibitors in the app-cx3cr1-cre cortex +LPS.

As discussed above, we focus analysis on the ric8a-emx1-cre mutants since app-cx3cr1-cre +LPS animals show ectopia in only a subset of mutants and in most cases only in one of the hemispheres.  Determining potential changes in MMP9 levels and effects of MMP inhibitors are therefore not likely to yield meaningful data.  On the other hand, we have shown that MMP9 levels are increased and administration of different classes of MMP inhibitors eliminate cortical ectopia in ric8a-emx1-cre mutants.  We have also shown a similar break in the basement membrane in app-cx3cr1-cre +LPS animals (new panels e-f” in supplemental Fig 9). These results together strongly implicates a role played by MMPs.

(5) Is MMP9 secreted by microglia cells or neural cells?

Our in situ hybridization data show MMP9 is most highly expressed in a sparse microglia-like cell population in the embryonic cortex, suggesting that microglia may be a major source of MMP9. We have incorporated these data in a new supplemental Fig. 12 (panel a). The precise identity of these cells, however, requires further validation.

(6) The in vitro evidence indicates that one of the multiple APP interactors, ie Ab40 monomers, is less effective in suppressing the expression of some cytokines by microglia cells mutants for ric8a (TNFa and IL6 but still suppress IL1b) or APP (TNFa, IL6, IL1b, IL23a, IL10) when compared to WT. But there are other interactors for APP. In order to support the claim, it seems crucial to have in vivo data to show that Ab40 monomers are the molecules involved in preventing the breach in the pial basement membrane.

As addressed in detail above, we have indicated that our conclusion is that a microglial signaling pathway that is activated by Abeta monomers in vitro regulates normal brain development in vivo, not that Abeta monomers themselves regulate brain development in vivo.  We currently do not have evidence that the Abeta monomers play a role in inhibiting microglia during cortical development.  There are candidate ligands for the pathway in the developing cortex, the functional study of which, however, is a major undertaking beyond the scope of the current study.

(7) In order to claim that this is specific to Ab40 monomers and not oligomers, it is necessary to show that the Ab40 oligomers do not have the same effect in vitro and in vivo. Also, an assay should be done to show that your Ab preparations are pure monomers or oligomers.

We have tested the effects of Abeta40 oligomers, which induce instead of suppressing microglial cytokine secretion, and have included these data in revision in a new panel j in supplemental Fig. 10. The protocols we use in preparing the monomers and oligomers are standard protocols employed in the field of Alzheimer’s disease research. They have been repeatedly optimized and validated over the past decades.  

(8) Most of the cytokine secretion assays used microglia cells in culture. Two results draw my attention. Ric8a deletion increases TNFa and IL6 secretion after LPS stimulation in vitro on microglia cells while app deletion decreases their secretion. Then later, papers show that the decrease in IL1b induced by Ab on microglia cells is prevented by APP deletion but not ric8a deletion. Those two pieces of data suggest that ric8a and APP might not be in the same pathway. In addition, the phenotype from app-cxcr3-cre + LPS injection and ric8a-cxcr3-cre + LPS injection are not exactly the same. It could be due to the level of LPS as the author suggests or it might not be. More experiments are needed to prove they are in the same pathway.

As discussed above, the reviewer misses several major lines of evidence, which strongly support the conclusion that APP and Ric8a act in the same pathway activated by Abeta monomers in microglia (see detailed discussion in point 1 above).  The differential response of TNFa/IL-6 of app and ric8a mutant microglia likely results from chronic immune stimulation during in vitro culturing, which is known to alter microglial cytokine response (see detailed discussion in point 9 below). We have demonstrated that this is indeed the case by showing that, without culturing, acutely isolated app and ric8a mutant macrophages both display elevated TNFa/IL-6 secretion (Figure 4). 

Regarding the different regulation of TNF/IL-6 vs IL-1b by APP and Ric8a, as discussed above, in several systems, Ric8a-dependent heterotrimeric G proteins (which are degraded in ric8a mutant cortices, see new supplemental Fig. 9) have been shown to act downstream of APP and mediate one of the branches of the signaling activated by APP (Milosch et al., Cell Death Dis. 2014 Aug 28;5(8):e1391; Fogel et al,, Cell Rep. 2014 Jun 12;7(5):1560-1576; Ramaker et al., J Neurosci. 2013 Jun 12;33(24):10165-81; Nishimoto et al., Nature. 1993 Mar 4;362(6415):75-9).  This is likely also the case in microglia and Ric8a-dependent heterotrimeric G proteins may mediate only a subset of the anti-inflammatory signaling activated by APP.  As such, app, mutation may abolish all the inhibitory effects of Abeta monomers (both those on TNF/IL-6 and those on IL-1b), but ric8a mutation may abolish only a subset only those on TNF/IL-6 but not those on IL-1b).  This also suggests that the secretion of TNF/IL-6 and IL-1b must be regulated by different mechanisms in microglia.  Indeed, it is well established in immunology that the secretion of IL1b, but not that of TNF or IL6, is regulated by inflammasome-dependent mechanisms (see, for example, Proz & Dixit. Nat Rev Immunol. 2016 Jul;16(7):407-20. doi: 10.1038/nri.2016.58).

(9) How do the authors reconcile the reduced TNFa and IL6 secretion upon stimulation of app mutant microglia with the model where app is attenuating immune response in vivo? Line 213 says that microglia exhibit attenuated immune response following chronic stimulation but I don't know if 3 hours of LPS in vitro is a chronic stimulation.

The reviewer has misunderstood.  The microglia used in this study have all been cultured in vitro for approximately two weeks before assay. They have thus been under chronic stimulation exposed to dead cells and debris in the culture dish.  Dependent on the degree of perturbation to the inflammation-regulating pathways, such exposures are known to change microglial cytokine expression, sometimes in an opposite direction than expected.  For example, under chronic immune stimulation, while the trem2+/- microglia, which are heterozygous mutant for the anti-inflammatory Trem2, show elevated pro-inflammatory cytokine expression, trem2-/- (null) microglia under the same conditions instead not only do not show increases but for some pro-inflammatory cytokines, actually show decreases in expression (Sayed et al.,, Proc Natl Acad Sci U S A. 2018 Oct 2;115(40):10172-10177).  As mentioned, in several systems, Ric8a-dependent heterotrimeric G proteins have also been shown to bind to APP and mediate one of the branches of the signaling activated by APP (Milosch et al., Cell Death Dis. 2014 Aug 28;5(8):e1391; Fogel et al,, Cell Rep. 2014 Jun 12;7(5):1560-1576; Ramaker et al., J Neurosci. 2013 Jun 12;33(24):10165-81; Nishimoto et al., Nature. 1993 Mar 4;362(6415):75-9). Thus, it is likely that in microglia, Ric8a-dependent heterotrimeric G proteins also mediate only a subset of the anti-inflammatory signaling activated by APP.  As such, app knockout in microglia may have more severe effects than ric8a knockout on microglial immune activation, resembling the relationship between trem2 null vs heterozygous mutation discussed above. As such, it is predicted that chronic immune stimulation such as in vitro culturing will result in attenuated pro-inflammatory cytokine expression in app mutant microglia but elevated cytokine expression in ric8a mutant microglia. This may explain why TNF and IL6 secretion by cultured app mutant microglia is subdued, but acutely isolated _a_pp mutant macrophages instead show increased cytokine secretion. The latter may be more representative of the response of app mutant microglia in the absence of chronic stimulation.

(10) Line 119: In their model, the authors suggest that there is a breach in pial basement membrane but that the phenotype is different from the retraction of the radial fibers due to reduced adhesion. So, could the author discuss to what substrate the radial fibers are attached to, in their model where the pial surface is destroyed?

Radial glial endfeet normally bind to the basement membrane via cell surface receptors including the integrin and the dystroglycan protein complexes. We observe free radial glial endfeet at the breach sites, apparently without attachment to any basement membrane.  However, we cannot exclude the possibility that there may be residual, broken-off basement membrane components bound to the endfeet that are not detected by the methodology employed. 

(11) The authors should show that the increased cytokine secretion observed in vitro is also happening in vivo in ric8a-emx1-cre compared to WT mice and compared to ric8a-nestin-cre mice. Or when app is deleted in microglia (app-cxcr3-cre) + LPS injection compared to WT mice +LPS.

Unfortunately, this is not technically feasible since it is not possible to extract the extracellular (secreted) fractions of cytokines from an embryonic brain without causing cell lysis and the release of the intracellular pool.  This, however, does not affect our conclusion that the Abeta monomer-regulated microglia pathway plays a key role in regulates normal brain development since its genetic disruption, by different approaches, clearly results in brain malformation.

(12) The authors injected inhibitors of Akt or Stat3 in the ric8a-emx1-cre cortex and found that it suppressed neuronal ectopia (Fig 5, Suppl fig 11). Does it suppress immune stimulation from neuronal cells or immune reaction from microglia cells?

As discussed above, we agree at present the pharmacological approaches we have taken are not able to distinguish these two possibilities.  However, whichever is true, it does not affect our conclusion.  Also, we plan to determine the mechanisms of how ric8a mutation in neural cells induce immune activation in future studies. These results will likely enable us to adopt specific approaches to address this question.

(13) Fig 5 and Supplementary fig 12: Please show a tubulin loading control in Fig 5i as you did in suppl fig 12 d (gel zymography). Please provide a gel zymography showing side by side Control, mutant and mutant +DM/S3I treatment. The same request for the MMP9 staining. Please provide statistics for control vs mutant for suppl fig 12c and d..

We have now included whole gel zymography images with four control and four mutant individual samples as well as quantification in a new supplemental Fig.13 (panels b-c). This clearly shows increases in MMP9, while the MMP2 levels appear similar between controls and mutants. For all of the experiments of gelatin gel zymography, we quantify protein concentrations in the cortical lysates using the Bio-Rad Bradford assay kit and load the same amounts of proteins per lane. The results across lanes are thus all comparable.  The MMP9 staining images for the controls and mutants have also all been taken with the same parameters on the microscope and can be directly compared.  The statistics have now been provided as suggested.

(14) Please provide the name and the source of the MMP9/13 inhibitor used in this study.

This inhibitor is MMP-9/MMP-13 inhibitor I (CAS 204140-01-2), from Santa Cruz Biotechnology. This information has been included in revision.

(15) The results show that deletion of ric8a in microglia and neural cells induced pia membrane breaches but no phenotype is apparent in ric8a deletion in microglia or neural cells alone. Then, the results showed that intraperitoneal injection of LPS induced the phenotype in ric8a-cxcr3-cre mutants. It would be beneficial as a control supporting the model to show that the insult induced by LPS injection does not induce the phenotype in the ric8a-foxg1-cre mice.

We agree it may potentially be useful to show that LPS injection does not induce ectopia in ric8a-foxg1-cre mice.  Unfortunately, since the ric8a-foxg1-cre mutation shows no phenotype, we are no longer in possession of this line.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

- The information in the abstract and the introduction is only related to app. So, it is very abrupt how authors start the manuscript studying the role of Ric8a, with no information at all about this protein and why the authors want to investigate this role in microglial activation. Later in the manuscript, the authors tried to link Ric8a with app to study the role of app in the inflammatory response and ectopia formation. This link is quite weak as well.

In the last paragraph of the Introduction, we explain the use of the ric8a mutant and how it leads to discovery of the Abeta monomer-regulated pathway. We have now improved the writing in revision to make these points especially the link between APP and Ric8a-regulated G proteins more clear.  In the Results section, we have also improved the writing on the potential link of Ric8a to APP by highlighting, among others, the fact that ric8a and app pathway mutants are among a unique group of a few mouse mutants (ric8a, app/aplp1/2, and apbb1/2) that show cortical ectopia exclusively in the lateral cortex, while all other cortical ectopia mutants also show severe ectopia are at the cortical midline.  This suggests that similar mechanisms may underlie the ectopia formation in this small group of mutants.

-In order to validate the mouse model, double immunofluorescence or immunofluorescence+in situ hybridization should be performed to show that microglia express ric8a and that is eliminated in the Emx1-Ric8a mutant mice.

As mentioned above, we have additional lines of evidence showing that ric8a is deleted from microglia in emx1-cre mutants. This includes data showing induction of the expression of a cre reporter in brain microglia by emx1-cre and loss of ric8a mRNA expression in microglia cells isolated from emx1-cre mutants.  These data have now been included in revised supplemental Fig. 8.

-In Supplemental Fig. 6, the authors claimed that cell proliferation is normal in Ric8a mutant mice without doing any quantification. They also quantified the angle of mitotic division of progenitors in the ventricular zone, but there are no images for the spindle orientation quantification, and no description of how they did it. In addition, this data is contrary to what has already been published in conditional Ric8a mutant mice (Kask et al., 2015). The Vimentin staining should be improved.

We have provided quantification of cell proliferation (phospho-histone 3 staining at the ventricular surface) in revised supplemental Fig. 6g, which shows no significant differences in the number of positive cells. We have also provided details on the definition of the angle of cleavage plane orientation in revised supplemental Fig. 6h and in the Methods section.  We are not sure why the results are different from the other study. We were indeed anticipating deficits in mitotic spindle orientation and spent major efforts in the analysis of this potential deficit.  However, based on the data, we could not draw the conclusion.     

-Analysis of the MMP9 expression should be done by western blot and not by immunofluorescence. In fact, the MMP9 expression shown in Figure 5g,h, does not correspond with RNA expression shown in gene expression atlas like genepaint or the allen atlas, doubting the specificity of the antibody. The expression of Mmp9 is quite low or absent in the cortex at E13.5-E14.5, making this protein very unlikely to be responsible for laminin degradation during development.

We have performed gelatin gel zymography on MMP2/9, which shows increased MMP9 activity levels in the mutant cortex. This is similar to Western blot analysis (all lanes are loaded with the same amounts of cortical lysates).  We have now included whole gel zymography images with four control and four mutant individual samples as well as quantification in a new supplemental Fig.13 (panels b-c).  The immunofluorescence staining of MMP9, a different type of analysis, was designed as a complementary approach, the results of which also support the interpretation of increases in MMP9 protein.  Regarding MMP9 RNA expression, please also note that MMP9 is secreted, and the protein expression pattern is expected to be different from that of RNA. We have performed wholemount in situ using dissected E13.5 mouse forebrains.  Our data (in new supplemental Fig.13a) show that MMP9 mRNA is strongly expressed in a sparse population of cells many of which appear to align along blood vessels. We suspect these are microglial lineage cells populating the embryonic cortex at this stage (see, for example, Squarzoni et al., Cell Rep. 2014 Sep 11;8(5):1271-9. doi: 10.1016/j.celrep.2014.07.042.).  Our control in situ using a Tnc5 probe also shows that the MMP9 signal is not a result of nonspecific probe binding.  Since the MMP9 expressing cells are very sparse even in the wholemount specimens while most database RNA in situ expression data are obtained using thin sections, we suspect this may be why the signal may have been missed in the databases.  As for functional contributions, we agree that we cannot rule roles played by other MMPs.  However, based on the ectopia suppression data, our results clearly indicate a critical contribution by MMP9/13.

For MMP9 activity, authors should show the whole membrane with a minimum of three control and three mutant individual samples and with the quantification.<br /> - The graphs should be improved, including individual values and titles of the Y axes.

We have included whole membrane zymography images with four control and four mutant individual samples as well as quantification in a new supplemental Fig.13b-c.  The graphs have also been improved as suggested.

Author response:

The following is the authors’ response to the current reviews.

We are grateful to the reviewers for their positive assessment of the revised version of the article.

Please find below our answers to the last, minor comments of the reviewers.

We thank the reviewer for this important comment. In our live imaging experiments, we actually tracked the dorsal and ventral borders of the omp:yfp positive clusters in control and sly mutant embryos. These measurements showed that the omp:yfp positive clusters are more elongated along the DV axis in mutants as compared with control siblings, as seen on fixed samples (data not shown), suggesting that this difference in tissue shape is not due to fixation.

Reviewer #4 (Public review):

Summary:

In this elegant study XX and colleagues use a combination of fixed tissue analyses and live imaging to characterise the role of Laminin in olfactory placode development and neuronal pathfinding in the zebrafish embryo. They describe Laminin dynamics in the developing olfactory placode and adjacent brain structures and identify potential roles for Laminin in facilitating neuronal pathfinding from the olfactory placode to the brain. To test whether Laminin is required for olfactory placode neuronal pathfinding they analyse olfactory system development in a well-established laminin-gamma-1 mutant, in which the laminin-rich basement membrane is disrupted. They show that while the OP still coalesces in the absence of Laminin, Laminin is required to contain OP cells during forebrain flexure during development and maintain separation of the OP and adjacent brain region. They further demonstrate that Laminin is required for growth of OP neurons from the OP-brain interface towards the olfactory bulb. The authors also present data describing that while the Laminin mutant has partial defects in neural crest cell migration towards the developing OP, these NCC defects are unlikely to be the cause of the neuronal pathfinding defects upon loss of Laminin. Altogether the study is extremely well carried out, with careful analysis of high-quality data. Their findings are likely to be of interest to those working on olfactory system development, or with an interest in extracellular matrix in organ morphogenesis, cell migration, and axonal pathfinding.

Strengths:

The authors describe for the first time Laminin dynamics during the early development of the olfactory placode and olfactory axon extension. They use an appropriate model to perturb the system (lamc1 zebrafish mutant), and demonstrate novel requirements for Laminin in pathfinding of OP neurons towards the olfactory bulb.

The study utilises careful and impressive live imaging to draw most of its conclusions, really drawing upon the strengths of the zebrafish model to investigate the role of laminin in OP pathfinding. This imaging is combined with deep learning methodology to characterise and describe phenotypes in their Laminin-perturbed models, along with detailed quantifications of cell behaviours, together providing a relatively complete picture of the impact of loss of Laminin on OP development.

Weaknesses:

Some of the statistical tests are performed on experiments where n=2 for each condition (for example the measurements in Figure S2) - in places the data is non-significant, but clear trends are observed, and one wonders whether some experiments are under-powered.

We initially planned the electron microscopy experiments in order to analyse 3 embryos per genotype per stage. However, because of technical issues we could not perform the measurements in all the cases, explaining why we have n = 2 in some of the graphs. The trends were quite clear, so we chose to keep these data in the article. We believe they nicely complement the immunostaining data assessing basement membrane integrity in control and mutant embryos.

The following is the authors’ response to the original reviews.

Public Reviews: 

Reviewer #1 (Public Review): 

Summary: 

The authors describe the dynamic distribution of laminin in the olfactory system and forebrain. Using immunohistochemistry and transgenic lines, they found that the olfactory system and adjacent brain tissues are enveloped by BMs from the earliest stages of olfactory system assembly. They also found that laminin deposits follow the axonal trajectory of axons. They performed a functional analysis of the sly mutant to analyse the function of laminin γ1 in the development of the zebrafish olfactory system. Their study revealed that laminin enables the shape and position of placodes to be maintained late in the face of major morphogenetic movements in the brain, and its absence promotes the local entry of sensory axons into the brain and their navigation towards the olfactory bulb. 

Strengths: 

- They showed that in the sly mutants, no BM staining of laminin and Nidogen could be detected around the OP and the brain. The authors then elegantly used electron microscopy to analyse the ultrastructure of the border between the OP and the brain in control and sly mutant conditions. 

- To analyse the role of laminin γ1-dependent BMs in OP coalescence, the authors used the cluster size of Tg(neurog1:GFP)+ OP cells at 22 hpf as a marker. They found that the mediolateral dimension increased specifically in the mutants. However, proliferation did not seem to be affected, although apoptosis appeared to increase slightly at a later stage. This increase could therefore be due to a dispersal of cells in the OP. To test this hypothesis, the authors then analysed the cell trajectories and extracted 3D mean square displacements (MSD), a measure of the volume explored by a cell in a given period of time. Their conclusion indicates that although brain cell movements are increased in the absence of BM during coalescence phases, overall OP cell movements occur within normal parameters and allow OPs to condense into compact neuronal clusters in sly mutants. The authors also analysed the dimensions of the clusters composed of OMP+ neurons. Their results show an increase in cluster size along the dorso-ventral axis. These results were to be expected since, compared with BM, early neurog1+ neurons should compact along the medio-lateral axis, and those that are OMP+ essentially along the dorso-ventral axis. In addition to the DV elongation of OP tissue, the authors show the existence of isolated and ectopic (misplaced) YFP+ cells in sly mutants. 

- To understand the origin of these phenotypes, the authors analysed the dynamic behaviour of brain cells and OPs during forebrain flexion. The authors then quantitatively measured brain versus OPs in the sly mutant and found that the OP-brain boundary was poorly defined in the sly mutant compared with the control. Once again, the methods (cell tracks, brain size, and proliferation/apoptosis, and the shape of the brain/OP boundary) are elegant but the results were expected. 

- They then analysed the dynamic behaviour of the axon using live imaging. Thus, olfactory axon migration is drastically impaired in sly mutants, demonstrating that Laminin γ1dependent BMs are essential for the growth and navigation of axons from the OP to the olfactory bulb. 

- The authors therefore performed a quantitative analysis of the loss of function of Laminin γ1. They propose that the BM of the OP prevents its deformation in response to mechanical forces generated by morphogenetic movements of the neighbouring brain. 

Weaknesses: 

- The authors did not analyse neurog1 + axonal migration at the level of the single cell and instead made a global analysis. An analysis at the cell level would strengthen their hypotheses.  

- Rescue experiments by locally inducing Laminin expression would have strengthened the paper. 

- The paper lacks clarity between the two neuronal populations described (early EONs and late OSNs).  

- The authors quantitatively measured brain versus OPs in the sly mutant and found that the OP-brain boundary was poorly defined in the sly mutant compared with the control. Once again, the methods (cell tracks, brain size, proliferation/apoptosis, and the shape of the brain/OP boundary) are elegant but the results were expected. 

- A missing point in the paper is the effect of Laminin γ1 on the migration of cranial NCCs that interact with OP cells. The authors could have analysed the dynamic distribution of neural crest cells in the sly mutant. 

We thank the reviewer for the overall positive assessment of our work, and we carefully responded to all her/his insightful comments below. Live imaging experiments to (1) visualise exit and entry point formation with only a few axons labelled, (2) characterise the behaviour of single neurog1:GFP-positive neurons/axons during OP coalescence and to (3) analyse the migration of cranial NCC are now included in the revised manuscript to address the reviewer’s questions, and reinforce our initial conclusions.

Reviewer #2 (Public Review): 

Summary: 

This manuscript addresses the role of the extracellular matrix in olfactory development. Despite the importance of these extracellular structures, the specific roles and activities of matrix molecules are still poorly understood. Here, the authors combine live imaging and genetics to examine the role of laminin gamma 1 in multiple steps of olfactory development. The work comprises a descriptive but carefully executed, quantitative assessment of the olfactory phenotypes resulting from loss of laminin gamma. Overall, this is a constructive advance in our understanding of extracellular matrix contributions to olfactory development, with a well-written Discussion with relevance to many other systems. 

Strengths: 

The strengths of the manuscript are in the approaches: the authors have combined live imaging, careful quantitative analyses, and molecular genetics. The work presented takes advantage of many zebrafish tools including mutants and transgenics to directly visualize the laminin extracellular matrix in living embryos during the developmental process. 

Weaknesses: 

The weaknesses are primarily in the presentation of some of the imaging data. In certain cases, it was not straightforward to evaluate the authors' interpretations and conclusions based on the single confocal sections included in the manuscript. For example, it was difficult to assess the authors' interpretation of when and how laminin openings arise around the olfactory placode and brain during olfactory axon guidance. 

We thank the reviewer for the overall positive assessment of our work, and we carefully responded to all her/his insightful comments below. To address these comments, live imaging data to visualise exit and entry point formation with a sparse labelling of axons, and z-stacks showing how exit and entry points are organised in 3D, have been added to the revised manuscript.

Reviewer #3 (Public Review): 

This is a beautifully presented paper combining live imaging and analysis of mutant phenotypes to elucidate the role of laminin γ1-dependent basement membranes in the development of the zebrafish olfactory placode. The work is clearly illustrated and carefully quantified throughout. There are some very interesting observations based on the analysis of wild-type, laminin γ1, and foxd3 mutant embryos. The authors demonstrate the importance of a Laminin γ1-dependent basement membrane in olfactory placode morphogenesis, and in establishing and maintaining both boundaries and neuronal connections between the brain and the olfactory system. There are some very interesting observations, including the identification of different mechanisms for axons to cross basement membranes, either by taking advantage of incompletely formed membranes at early stages, or by actively perforating the membrane at later ones. 

This is a valuable and important study but remains quite descriptive. In some cases, hypotheses for mechanisms are stated but are not tested further. For example, the authors propose that olfactory axons must actively disrupt a basement membrane to enter the brain and suggest alternative putative mechanisms for this, but these are not tested experimentally. In addition, the authors propose that the basement membrane of the olfactory placode acts to resist mechanical forces generated by the morphogenetic movement of the developing brain, and thus to prevent passive deformation of the placode, but this is not tested anywhere, for example by preventing or altering the brain movements in the laminin γ1 mutant. 

We thank the reviewer for the overall positive assessment of our work and for suggesting interesting experiments to attempt in the future, and we carefully responded to all her/his constructive comments below.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

In general, it would be easier to draw conclusions and compare data if the authors used similar stages throughout the article. 

Throughout the article we tried to focus on a series of stages that cover both the coalescence of the OP (up to 24 hpf) and later stages of olfactory system development spanning the brain flexure process (28, 32, 36 hpf). However, for technical reasons it was not always possible to stick to these precise stages in some of our experiments. Also, in Fig. 1E-J, we picked in the movies some images illustrating specific cell or axonal behaviours, and thus the corresponding stages could not match exactly the stage series used in Fig. 1A-D and elsewhere in the article. Nevertheless, this stage heterogeneity does not affect our main conclusions.

It would be useful to schematise the olfactory placode and the brain in an insert to clearly visualise the system in each figure. 

We hope that the schematic which was initially presented in Fig. 1K already helps the reader to understand how the system is organised. Although we have not added more schematic views to represent the system in each figure (we think this would make the figures overcrowded), we have added additional legends to point to the OP and the brain in the pictures in order to clarify the localisation of each tissue.

In the Summary, the authors refer to the integrity of the basement membrane. I don't think there is any attempt to affect basement membrane integrity in the article. It would be important to do so to look at the effect on CNS-PNS separation and axonal elongation. 

In the Summary, we use the term « integrity of the basement membrane » to mention that we have analysed this integrity in the sly mutant. Given the results of our immunostainings against three main components of the basement membrane (Laminin, Collagen IV and Nidogen), as well as our EM observations, we see the sly mutant as a condition in which the integrity of the basement membrane is strongly affected.

Rescue experiments by locally inducing Laminin expression would have strengthened the paper. 

We have attempted to rescue the sly mutant phenotypes by introducing the mutation in the transgenic TgBAC(lamC1:lamC1-sfGFP) background, in which Laminin γ1 tagged with sfGFP is expressed under the control of its own regulatory sequences (Yamaguchi et al., 2022). To do so, we crossed sly+/-;Tg(omp:yfp) fish with sly+/-; Tg(lamC1:LamC1-sfGFP) fish. Surprisingly, while a rescue of the global embryo morphology was observed, no clear rescue of the olfactory system defects could be detected at 36 hpf. This could be due to the fact that the expression level of LamC1-sfGFP obtained with one copy of the transgene is not sufficient to rescue the olfactory system phenotypes, or that the sfGFP tag specifically affects the function of the Laminin 𝛾1 chain during the development of the olfactory system, making it unable to rescue the defects. Given the results of our first attemps, we decided not to continue in this direction.

(1) Developing OP & brain are surrounded by laminin-containing BM (already described by Torrez-Pas & Whitlock in 2014). 

"we first noticed the appearance of a continuous Laminin-rich BM surrounding the brain from 14-18 hpf, while around the OP, only discrete Laminin spots were detected at this stage (Fig. 1A, A'). " 

Around 8ss for Torrez-Pas & Whitlock (before 14 hpf). Can you modify the text, or show an 8ss stage embryo? As far as I know, the authors do not show images at 14hpf. Please correct this sentence or show a 14 hpf picture. 

The reviewer is right, we do not show any 14 hpf stage in the images and thus have removed this stage in the text and replaced it by 17 hpf.

In Figure 1A, the labelling of laminin 111 does not appear to be homogeneous along the brain.

Is this true? 

At this stage the brain’s BM revealed by the Laminin immunostaining appears fairly continuous (while the OP’s one is clearly dotty and less defined), but indeed very tiny/local interruptions of the signal can been seen along the structure as detected by the reviewer. We thus modified the text to mention these tiny interruptions.

How is the Laminin antibody used by the authors specific to laminin 111?  

We thank the reviewer for raising this important point. The immunogen used to produce this rabbit polyclonal antibody is the Laminin protein isolated from the basement membrane of a mouse Engelbreth Holm-Swarm sarcoma (EHS). It is thus likely to recognise several Laminin isoforms and not only Laminin 111. We thus replaced Laminin 111 by Laminin when mentioning this antibody in the text and Figures.

Please schematise in Figure 1K the stages you have tested and shown here in the article i.e. stages 18 - 22 - 28 -36 hpf using immunohistochemistry and 17-26-27-29-33 and 38 hpf using transgenics for laminin 111 and LamC1 respectively.  

As suggested by the reviewer, we changed the stages in the schematics for stages we have presented in Figure 1 (analysed either with immunostaining or in live imaging experiments). We chose to represent 17 - 22 - 26 - 33 hpf (and thus adapted some of the schematics for them to match these stages).  

Please specify in the Figure 1 legend for panels A to D whether this is a 3D projection or a zsection.

We indicated in the Figure 1 legend that all these images are single z-sections (as well as for panels E-J).

Furthermore, the schematisation in Fig. 1K does not reflect what the authors show: at 22 hpf laminin 111 labelling appears to be present only near the brain, and no labelling lateral to the olfactory placode and anteriorly and posteriorly. Thus, the schematisation in Figure 1K needs to be modified to reflect what the authors show.

We agree with the reviewer that the Laminin staining at this stage is observed around the medial region of the OP, but not more laterally. We modified the schematic view accordingly in Figure 1K. Anterior and posterior sides of the OP are not represented in this schematic because we chose to represent a frontal view rather than a dorsal view.

The authors suggest that" the laminin-rich BM of OP assembles between 18 and 22 hpf, during the late phase of OP coalescence". However, their data indicate that this BM assembles around 28hpf (Figure 1C). Can they clarify this point?

What we meant with this sentence is that we cleary see two distinct BMs from 22 hpf. However, as noticed by the reviewer, the OP’s BM is only present around the medial/basal regions of the OP and does not surround the whole OP tissue at this stage. We modified the text to clarify this point (in particular by mentioning that the OP’s BM starts to assemble between 18 and 22 hpf), and replaced the image shown in Figure 1B, B’ with a more representative picture (the previous z-section was taken in very dorsal regions of the OP).

It would be useful to disrupt these cells that have a cytoplasmic expression of Laminin-sfGFP, to analyse their contribution to BM and OP coalescence.

Indeed it will be interesting in the future to test specifically the role of the cells expressing cytoplasmic Laminin-sfGFP around and within the OP, as proposed by the reviewer. Laser ablation of these cells could be attempted, but due to their very superficial localisation, close to the skin, we believe these ablations (with the protocol/set-up we currently use in the lab) would impair the skin integrity, preventing us to conclude. We consider that the optimisation of this experiment is out of the scope of the present work.

Tg(-2.0ompb:gapYFP)rw032 marks ciliated olfactory sensory neurons (OSNs) (Sato et al., 2005). The authors should mention this. 

Please see our detailed response to the next point below.

Points to be clarified: 

-Tg(-2.0ompb:gapYFP)rw032 marks ciliated olfactory sensory neurons (OSNs) (Sato et al., 2005). The authors should mention this here. Moreover, the authors refer to "OP neurons" throughout the article. In the development of the olfactory organ, two types of neurons have been described in the literature: early EONs (12hpf-26hpf) and later OSNs. Each could have a specific role in the establishment and maintenance of the BM described by the authors. The authors need to clarify this point as, in Figure 1 for example, they use a marker for Tg(neurog1:GFP) EONs and a marker for ciliated OSNs without distinction. The distinction between EONs and OSNs comes a little late in the text and should be placed higher up. 

As mentioned by the reviewer, according to the initial view of neurogenesis in the OP, OP neurons are born in two waves. A transient population of unipolar, dendrite-less pioneer neurons would differentiate first, in the ventro-medial region of the OP and elongate their axons dorsally out of the placode, along the brain wall. These pioneer axons would then be used as a scaffold by later born OSNs located in the dorso-lateral rosette to outgrow their axons towards the olfactory bulb (Whitlock and Westerfield, 1998). 

Another study further characterised OP neurogenesis and showed that the first neurons to differentiate in the OP (the early olfactory neurons or EONs) express the Tg(neurog1:GFP) transgene (Madelaine et al., 2011). As mentioned by the authors in the discussion of this article, neurog1:GFP+ neurons appear much more numerous than the previously described pioneer neurons, and may thus include pioneers but also other neuronal subtypes.

We would like here to share additional, unpublished observations from our lab that further suggest that the situation is more complex than the pioneer/OSN and EON/OSN nomenclatures. First, in many of our live imaging experiments, we can clearly visualise some neurog1:GFP+ unipolar neurons, initially located in a medial position in the OP, which intercalate and contribute to the dorsolateral rosette (where OSNs are proposed to be located) at the end of OP coalescence, from 22-24 hpf. Second, in fixed tissues, we observed that most neurog1:GFP+ neurons located in the rosette at 32 hpf co-express the Tg(omp:meRFP) transgene (Sato et al., 2005). These observations suggest that at least a subpopulation of neurog1:GFP+ neurons could incorporate in the dorsolateral rosette and become ciliated OSNs during development. We can share these results with the reviewer upon request. Further studies are thus needed to clarify and describe the neuronal subpopulations and lineage relationships in the OP, but this detailed investigation is out of the scope and focus of the present study. 

An additional complication comes from the fact that, as shown and acknowledged by the authors in Miyasaka et al., 2005, the Tg(omp:meYFP) line (6kb promoter) labels ciliated OSNs in the rosette but also some unipolar, ventral neurons (around 10 neurons at 1 dpf, Miyasaka et al. 2005, Figure 3A, white arrowheads). This was also observed using the 2 kb promoter Tg(omp:meYFP) line (see for instance Miyasaka et al., 2007) and in our study, we can indeed detect these ventro-medial neurons labelled in the Tg(omp:meYFP) line (2 kb promoter), see for instance Figure 1C’, D’ or Movie 6. It is unclear whether these unipolar omp:meYFPpositive cells are pioneer neurons or EONs expressing the omp:meYFP transgene, or OSN progenitors that would be located basally/ventrally in the OP at these stages.

For all these reasons, we decided to present in the text the current view of neurogenesis in the OP but instead of attributing a definitive identity to the neurons we visualise with the transgenic lines, we prefer to mention them in the manuscript (and in the rest of the response to the reviewers) as neurons expressing neurog1:GFP or omp:meYFP transgenes (or cells/axons/neurons expressing RFP in the Tg(cldnb:Gal4; UAS:RFP) background).

What we also changed in the text to be more clear on this point:

- we moved higher up in the text, as suggested by reviewer 1, the description of the current model of neurogenesis in the OP,

- we mentioned that neurog1:GFP+ neurons are more numerous than the initially described pioneer neurons, as discussed in Madelaine et al., 2011,

- we wrote more clearly that the Tg(omp:meYFP) line labels ciliated OSNs but also a subset of unipolar, ventral neurons (Miyasaka et al., 2005), and pointed to these ventral neurons in Figure 1C’, D’,

- in the initial presentation of the current view of OP neurogenesis we renamed neurog1:GFP+ into EONs to be coherent with Madelaine et al., 2011.

- To visualise pioneer axons, the authors should use an EONS marker such as neurog1 because, to my knowledge, OMP only marks OSN axons and not pioneer axons.  

To visualise neurog1:GFP+ axons during OP coalescence, we performed live imaging upon injection of the neurog1:GFP plasmid (Blader et al., 2003) in the Tg(cldnb:Gal4; UAS:RFP) background (n = 4 mutants and n = 4 controls from 2 independent experiments). We observed some GFP+ placodal neurons exhibiting retrograde axon extension in both controls and sly mutants. In such experiments it is very difficult to quantify and compare the number of neurons/axons showing specific behaviours between different experimental conditions/genetic background. Indeed, due to the cytoplasmic localisation of GFP, the axons can only be seen in neurons expressing high levels of GFP, and due to the injection the number of such neurons varies a lot in between embryos, even in a given condition. Nevertheless, our qualitative observations reinforce the idea that the basement membrane is not absolutely required for mediolateral movements and retrograde axon extension of neurog1:GFP+ neurons in the OP. We added examples of images extracted from these new live imaging experiments in the revised Fig. S5A, B.

- The authors should analyse the presence of laminin in the OP and forebrain in conjunction with neural crest cell dynamics (using a Sox10 transgenic line for example) to refine their entry and exit point hypotheses. 

As described in the answer to the next point, we performed new experiments in which we visualised NCC migration in the Tg(neurog1:GFP) background, which allowed us to analyse the localisation of NCC at the forebrain/OP boundary, in ventral and dorsal positions, both in sly mutant embryos and control siblings.

- A dynamic analysis of the distribution of neural crest cells in the sly mutant over time and during OP coalescence would be important. 

The dynamics of zebrafish cranial NCC migration in the vicinity of the OP has been previously analysed using sox10 reporter lines (Harden et al., 2012, Torres-Paz and Whitlock, 2014, Bryan et al., 2020). To address the point raised by the reviewer, we performed live imaging from 16 to 32 hpf on sly mutants and control siblings carrying the Tg(neurog1:GFP) and Tg(UAS:RFP) transgenes and injected with a sox10(7.2):KalTA4 plasmid (Almeida et al., 2015). This allows the mosaic labelling of cells that express or have expressed sox10 during their development which, in the head region at these stages, represents mostly NCC and their derivatives. 3 independent experiments were carried out (n = 4 mutant embryos in which 8 placodes could be analysed; n = 6 control siblings in which 10 placodes could be analysed). A new movie (Movie 9) has been added to the revised article to show representative examples of control and mutant embryos.

From these new data, we could make the following observations:

- As expected from previous studies (Harden et al., 2012, Torres-Paz and Whitlock, 2014, Bryan et al., 2020), in control embryos a lot of NCC had already migrated to reach the vicinity of the OP when the movies begin at 16 hpf, and were then seen invading mainly the interface between the eye and the OP (10/10 placodes). Surprisingly, in sly mutants, a lot of motile NCC had also reached the OP region at 16 hpf in all the analysed placodes (8/8), and populated the eye/OP interface in 7/8 placodes (10/10 in controls). Counting NCC or tracking individual NCC during the whole duration of the movies was unfortunately too difficult to achieve in these movies, because of the low level of mosaicism (a high number of cells were labelled) and of the high speed of NCC movements (as compared with the 10 min delta t we chose for the movies). 

- in some of the control placodes we could detect a few NCC that populated the forebrain/OP interface, either ventrally, close to the exit point of the axons (4/10 placodes), or more dorsally (8/10 placodes). By contrast, in sly mutants, NCC were observed in the dorsal region of the brain/OP boundary in only 2/8 placodes, and in the ventral brain/OP frontier in only 2/8 placodes as well. Interestingly, in these 2 last samples, NCC that had initially populated the ventral region of the brain/OP interface were then expelled from the boundary at later stages.

We reported these observations in a new Table that is presented in revised Fig. S6B. In addition, instances of NCC migrating at the eye/OP or forebain/OP interfaces are indicated with arrowheads on Movie 9. Previous Figure S6 was splitted into two parts presenting NCC defects in sly mutants (revised Figure S6) and in foxd3 mutants (revised Figure S7).

Altogether, these new data suggest that the first postero-anterior phase of NCC migration towards the OP, as well as their migration in between the eye and OP tissues, is not fully perturbed in sly mutants. The subset of NCC that populate the OP/forebrain seem to be more specifically affected, as these NCC show defects in their migration to the interface or the maintenance of their position at the interface. Since the crestin marker labels mostly NCC at the OP/forebrain interface at 32 hpf (revised Fig. S6A), this could explain why the crestin ISH signal is almost lost in sly mutants at this stage.

(2) Laminin distribution suggests a role in olfactory axon development 

"Laminin 111 immunostaining revealed local disruptions in the membrane enveloping the OP and brain, precisely where YFP+ axons exit the OP (exit point) and enter the brain (entry point) (Fig. 1C-D')." Can the authors quantify this situation? It would be important to analyse this behaviour on the scale of a neuron and thus axonal migration to strengthen the hypotheses. 

As suggested by the reviewer, to better visualise individual axons at the exit and entry point, we used mosaic red labelling of OP axons. To achieve this sparse labelling, we took advantage of the mosaic expression of a red fluorescent membrane protein observed in the Tg(cldnb:Gal4; UAS:lyn-TagRFP) background. The unpublished Tg(UAS:lyn-TagRFP) line was kindly provided by Marion Rosello and Shahad Albadri from the lab of Filippo Del Bene. We crossed the Tg(cldnb:Gal4; UAS:lyn-TagRFP) line with the TgBAC(lamC1:lamC1-sfGFP) reporter and performed live imaging on 2 embryos/4 placodes, in a frontal view. A new movie (Movie 3 in the revised article) shows examples of exit and entry point formation in this context.This allowed us to visualise the formation of the exit and entry points in more samples (6 embryos and 12 placodes in total when we pool the two strategies for labelling OP axons) and through the visualisation of a small number of axons, and reinforce our initial conclusions. 

(3) The integrity of BMs around the brain and the OP is affected in the sly mutant 

Why do the authors analyse the distribution of collagen IV and Nidogen and not proteoglycans and heparan sulphate? 

We attempted to label more ECM components such as proteoglycans and heparan sulfate, but whole-mount immunostainings did not work in our hands.

A dynamic analysis of the distribution of neural crest cells in the sly mutant over time and during OP coalescence would be important. 

See our detailed response to this point above.  

(4) Role of Laminin γ1-dependent BMs in OP coalescence 

The authors use the size of the Tg(neurog1:GFP)+ OP cell cluster at 22 hpf as a marker.  The authors should count the number of cells in the OP at the indicated time using a nuclear dye to check that in the sly mutant the number of cells is the same over time. Two time points as analysed in Figure S2 may not be sufficient to quantify proliferation which at these stages should be almost zero according to Whitlock & Westerfield and Madelaine et al.

Counting the neurog1:GFP+ cell numbers in our existing data was unfortunately impossible, due to the poor quality of the DAPI staining. We are nevertheless confident that the number of cells within neurog1:GFP+ clusters is fairly similar between controls and sly mutants at 22 hpf, since the OP dimensions are the same for AP and DV dimensions, and only slightly different for the ML dimension. In addition, we analysed proliferation and apoptosis within the neurog1:GFP+ cluster at 16 and 21 hpf and observed no difference between controls and mutants.

(5) Role of Laminin γ1-dependent BMs during the forebrain flexure 

In Figure 4F at 32hpf, the presence of 77% ectopic OMP+ cells medially should result in an increase in dimensions along the M-L? This is not the case in the article. The authors should clarify this point. 

As we explained in the Material and Methods, ectopic fluorescent cells (cells that are physically separated from the main cluster) were not taken into account for the measurement of the OP dimensions. This is now also also mentioned in the legends of the Figures (4 and S3) showing the quantifications of OP dimensions.

Cell distribution also seems to be affected within the OMP+ cluster at 36hpf, with fewer cells laterally and more medially. The authors should analyse the distribution of OMP+ cells in the clusters. in sly mutants and controls to understand whether the modification corresponds to the absence of BM function. 

On the pictures shown in Figure 4F,G, we agree that omp:meYFP+ cells appear to be more medially distributed in the mutant, however this is not the case in other sections or samples, and is rather specific to the z-section chosen for the Figure. We found that the ML dimension is unchanged in mutants as compared with controls, except for the 28 hpf stage where it is smaller, but this appears to be a transient phenomenon, since no change is detected at earlier or later stages (Figure 4A-D and Figure S3A-L). The difference we observe at 28 hpf is now mentioned in the revised manuscript.

The conclusions of Figures 4 and S3 would rather be that laminin allows OMP+ cells to be oriented along the medio-lateral axis whereas it would control their position along the dorsoventral axis. The authors should modify the text. It would be useful to map the distribution of OMP+ cells along the dorsoventral and mediolateral axes. The same applies to Neurog1+ cells. An analysis of skin cell movements, for example, would be useful to determine whether the effects are specific.  

We are confident that the measurements of OP dimensions in AP, DV and ML are sufficient to describe the OP shape defects observed in the sly mutants. Analysing cell distribution along the 3 axes as well as skin cell movements will be interesting to perform in the future but we consider these quantifications as being out of the scope of the present work.

(6) Laminin γ1-dependent BMs are required to define a robust boundary between the OP and the brain 

The authors must weigh this conclusion "Laminin γ1-dependent BMs serve to establish a straight boundary between the brain and OP, preventing local mixing and late convergence of the two OPs towards each other during flexion movement." Indeed, they don't really show any local mixing between the brain and OP cells. They would need to quantify in their images (Figure 5A-A' and Figure S4 A-A') the percentage of cells co-labelled by HuC and Tg(cldnb:GFP). 

We agree with the reviewer and thus replaced « reveal » by « suggest » in the conclusion of this section. 

(7) Role of Laminin γ1-dependent BMs in olfactory axon development 

An analysis of the retrograde extension movement in the axons of OMP+ ectopic neurons in the sly1 mutant condition would be useful to validate that the loss of laminin function does not play a role in this event. 

Indeed, even though we can visualise instances of retrograde extension occurring normally in sly mutants, we can not rule out that this process is affected in a subset of OP neurons, for instance in ectopic cells, which often show no axon or a misoriented axon. We added a sentence to mention this in the revised manuscript.

Minor comments and typos: 

Please check and mention the D-V/L-M or A-P/L-M orientation of the images in all figures. 

This has been checked.

Legend Figure 1: "distalmost" is missing a space "distal most". 

We checked and this word can be written without a space.

Figure 1 panel C: check the orientation (I am not sure that Dorsal is up). 

We double-checked and confirm that dorsal is up in this panel.

Movie 1 Legend: "aroung "the OP should be around the OP. 

Thanks to the reviewer for noticing the typo, we corrected it.

Reviewer #2 (Recommendations For The Authors):

The comments below are relatively minor and mostly raise questions regarding images and their presentation in the manuscript. 

• Figure 1, visualization of exit and entry points: It is a bit difficult to visualize the axon exit and entry points in these images, and in particular, to understand how the exit and entry points in C and D correspond to what is seen in F, F', H, and H'. There appears to be one resolvable break in the staining in C and D, whereas there are two distinct breaks in F-H'. Are these single optical sections? Is it possible to visualize these via 3-dimensional rendering? 

All the images presented in Figure 1 are single z-sections, which is now indicated in the Figure legend. As noticed by the reviewer, Laminin immunostainings on fixed embryos at 28 and 36 hpf suggested that the exit and entry points are facing each other, as shown in Figure 1C-D’. However, in our live imaging experiments we always observed that the exit point is slightly more ventral than the entry point (of about 10 to 20 µm). This discrepancy could be due to the fixation that precedes the immunostaining procedure, which could modify slightly the size and shape of cells/tissues. We added a sentence on this point in the text. In addition, we added new movies of the LamC1-sfGFP reporter with sparse red axonal labelling (Movie 3, see response to reviewer 1), as well as z-stacks presenting the organisation of exit and entry points in 3D (Movie 4), which should help to better illustrate the mechanisms of exit and entry point formation.

• Movie 2, p. 6, "small interruptions of the BM were already present near the axon tips, along the ventro-medial wall of the OP." This is a bit difficult to assess since the movie seems to show at least one other small interruption in the BM in addition to the exit point, in particular, one slightly dorsal to the exit point. Was this seen in other samples, or in different optical sections? 

Indeed the exit and entry points often appear as regions with several, small BM interruptions, rather than single holes in the BM. We now show in revised Movie 4 the two z-stacks (the merge and the single channel for green fluorescence) corresponding to the last time points of the movies showing exit and entry point formation in Movie 2, where several BM interruptions can be seen for both the exit and entry points. We had already mentioned this observation in the legend of Movie 2, and we added a sentence on this point in the main text of the revised manuscript. This is also represented for both exit and entry points in the new schematics in revised Fig. 1K and its legend. 

• Movie 2, p. 6, "The opening of the entry point through the brain BM was concomitant with the arrival of the RFP+ axons, suggesting that the axons degrade or displace BM components to enter the brain." Similar to the questions regarding the exit point, it was a bit difficult to evaluate this statement. There appears to be a broader region of BM discontinuity more dorsal to the arrowhead in Movie 2. A single-channel movie of just the laminin fluorescence might help to convey the extent of the discontinuity. As with above, was this seen in other samples, or in different optical sections?  

See our response to the previous comment.

• Figure 1H, I, "the distal tip of the RFP+ axons migrated in close proximity with the brain's BM." This is again a bit difficult to see, and quite different than what is seen in Figure 4A, in which the axons do not seem close to the BM in this section. Is it possible to visualize this via 3-dimensional rendering? 

In fixed embryos or in live imaging experiments, we observed that, once entered in the brain, the distal tips (the growth cones) of the axons are located close to the BM of the brain. However, this is not the case of the axon shafts which, as development proceeds, are located further away from the BM. This can clearly be seen at 36 hpf in Figure 1D’ and Figure 4A, as spotted by the reviewer. We modified the text to clarify this point.

• Figure 2J, J', p. 7, the gap between the OP and brain cells of sly mutants "was most often devoid of electron-dense material." It is difficult to see this loss of electron-dense material in 2J'. The thickness of the space is quantified well and is clearly smaller, but the change in electron-dense material is more difficult to see.  

We looked at Figure 2 again and it seems clear to us that there is electron-dense material between the plasma membranes in controls, which is practically not seen (rare spots) in the mutants. We added a sentence mentioning that we rarely see electron-dense spots in sly mutants.

• Figure 5E-F': There are concerns about evaluating the shape of a tissue based on nuclear position. Is there a way to co-stain for cell boundaries (maybe actin?), and then quantify distortion of the dlx+ cell population using the cell boundaries, rather than nuclear staining? 

We agree with the reviewer that it is not ideal to evaluate the shape of the OP/brain boundary based on a nuclear staining. As explained in the text, we could not use the Tg(eltC:GFP) or Tg(cldnb:Gal4; UAS:RFP) reporter lines for this analysis, due to ectopic or mosaic expression. However we are confident that the segmentation of the Dlx3b immunostaining reflects the organisation of the cells at the OP/brain tissue boundary: in other data sets in which we performed Dlx3b staining with membrane labelling independently of the present study and in the wild type context, we clearly see that cell membranes are juxtaposed to the Dlx3b nuclear staining (in other words, the cytoplasm volume of OP cells is very small). 

• Figure S5E: It would be helpful to see representative images for each of the categories (Proper axon bundle; Ventral projections; Medial projections) or a schematic to understand how the phenotypes were assessed. 

To address this point we added a schematic view to illustrate the phenotypes assessed in each column of the table in revised Figure S5G.

• Figure 6, p. 12, "Laminin gamma 1-dependent BMs are essential for growth and navigation of the axons...": What fraction of the tracked axons managed to exit the OP? Given the quantitative analyses in Figure 6, one might interpret this to mean that laminin gamma 1 is not essential for axon growth (speed and persistence are largely unchanged), but rather, primarily for navigation. 

As noticed by the reviewer, the speed and persistence of axonal growth cones are largely unchanged in the sly mutants (except for the reduced persistence in the 200-400 min window, and an increased speed in the 800-1000 min window), showing that the growth cones are still motile. However, as shown by the tracks, they tend to wander around within the OP, close to the cell bodies, which results in the end in a perturbed growth of the axons. The navigation issues are rather revealed by the analysis of fixed Tg(omp:meYFP) embryos presented in the table of Figure S5G. We modified the text to separate more clearly the conclusions of the two types of experiments (fixed, transgenic embryos versus live, mosaically labelled embryos).

Reviewer #3 (Recommendations For The Authors):

Testing the hypotheses mentioned in the public review will be interesting experiments for a follow-up study, but are not essential revisions for this manuscript. 

I have only a few minor suggestions for revisions: 

P8 subheading 'Role of Laminin γ1-dependent BMs in OP coalescence' - since no major role was demonstrated here, this heading should be reworded.  

We agree with the reviewer and replaced the previous title by « OP coalescence still occurs in the sly mutant ».

P11, line 3 - the authors conclude that the forebrain is smaller 'due to' the inward convergence of the OPs. I do not think it is possible to assign causation to this when the mutant disrupts Laminin γ1 systemically - it is equally possible that the OPs move inward due to a failure of the brain to form in the normal shape. Thus, the wording should be changed here. (In the Discussion on p15, the authors mention the 'apparent distortion' of the brain, and say that it is 'possibly due' to the inward migration of the placodes', but again this could be toned down.) 

We agree with the reviewer’s comment and changed the wording of our conclusions in the Results section.

P11 and Fig. S5 - The table and text seem to be saying opposite things here. The text on p11 (3rd paragraph) indicates that the normal exit point is ventral and that this is disrupted in the mutant, with axons exiting dorsally. However, in the table, at each time point there is a higher % of axons exiting ventrally in the mutant. Please clarify. The table does not provide a % value for axons exiting dorsally - it might help to add a column to show this value. 

We are grateful to the reviewer for pointing this out, and we apologize for the lack of clarity in the first version of the manuscript. We have modified the text and Figure S5 in order to clarify the different points raised by the reviewer in this comment. The Table in Fig. S5G does not represent the % of axons showing defects, but the % of embryos showing the phenotypes. In addition, an embryo is counted in the ventral or medial projection category if it shows at least one ventral or medial projection (even if its shows a proper bundle). This is now clearly indicated in the title of the columns in the table itself and in the legend. The embryos in which the axons exit dorsally in sly mutants are actually those counted in the left column of the Table (they exit dorsally and form a bundle), as shown by the new schematics added below the table. We also added this information in the title of the left column, and mention in the legend the pictures in which this dorsal exit can be observed in the article (Figures 4B and S3E’). Having more sly mutant embryos with axons exiting dorsally is thus compatible with more embryos showing at least one ventral projection.

Fig. S6, shows the lack of neural crest cells between the olfactory placode and the brain in both laminin γ1 mutants (without a basement membrane) and foxd3 mutants (which retain the membrane). Comparison of the two mutants here is a neat experiment and the result is striking, demonstrating that it is the basement membrane, and not the neural crest, that is required for correct morphology of the olfactory placode. I think this figure should be presented as a main figure, rather than supplementary.  

Our new live imaging characterisation of NCC migration in sly mutants and control siblings (Movie 9) revealed that at 32 hpf, in the vicinity of the OP, NCC (or their derivatives) are much more numerous than the subset of NCC showing crestin expression by in situ hybridisation (compare the end of our control movie – 32 hfp, with crestin ISH shown in Figure S6A for instance). 

Thus, the extent of the NCC migration defects should be analysed in more detail in the foxd3 mutant in the future (using live imaging or other NCC markers), and for this reason we chose to keep this dataset in the supplementary Figures.

One of the first topics covered in the Discussion section is the potential role of Collagen. I was surprised to see the description on P15 'the dramatic disorganization of the Collagen IV pattern observed by immunofluorescence in the sly mutant', as I hadn't picked this up from the Results section of the paper. I went back to the relevant figure (Fig. 2) and description on p7, which does not give the same impression: 'in sly mutants, Collagen IV immunoreactivity was not totally abolished'. This suggested to me that there was only minor (not dramatic) disorganisation of the Collagen IV. This needs clarification.  

The linear, BM-like Collagen IV staining was lost in sly mutants, but not the fibrous staining which remained in the form of discrete patches surrounding the OP. We modified the text in the Results section as well as in the Figure 2 legend to clarify our observations made on embryos immunostained for Collagen IV.

Typos etc 

P5 - '(ii) above of the neuronal rosette' - delete the word 'of'. 

P5 two lines below this - ensheathed. 

P10 - '3 distinct AP levels' (delete s from distincts). 

P10 - distortion (not distorsion) . 

P12 - 'From 14 hpf, they' should read 'From 14 hpf, neural crest cells'. 

P15, line 1 - 'is a consequence of' rather than 'is consecutive of'? 

P22 'When the data were not normal,' should read 'When the data were not normally distributed,'. 

We thank the reviewer for noticing these typos and have corrected them.

General 

Please number lines in future manuscripts for ease of reference. 

This has been done.

Author response:

Thank you for the positive and constructive feedback on our manuscript. We appreciate you highlighting the importance of our work advancing our understanding of the molecular etiology of acquired immunodeficiency syndrome (AIDS). To extend and further substantiate the observation that the CARD8 inflammasome is activated in response to viral protease during HIV-1 cell-to-cell transmission, we are in the process of completing additional experiments that are responsive to reviewer feedback, including:

• Primary CD4+ T cell to monocyte-derived macrophage (MDM) transmission:  We have now repeated the cell-to-cell experiments with HIV-1 transfer from primary CD4+ T cells to primary monocyte-derived macrophages, and our findings are consistent with CARD8-dependent IL-1β release from HIV-1-infected macrophages in this more physiologic context. We are in the process of repeating these experiments with additional donors and will add these results to the revised manuscript.

• Heterogeneity amongst blood donors: We have now repeated the cell-to-cell transfer and CARD8 knockout in MDMs with additional donors. While we continue to observe heterogeneity amongst donors, the key observation that CARD8 is require for inflammasome responses to HIV-1 infection is consistent. We note that some donors, including the one individual reported in the first submission, have markedly diminished CARD8 activity (to both HIV-1 and VbP).

• Time course experiments: We did conduct a time course experiment when initially establishing these assays. We have now repeated these experiments with additional timepoints and in the presence or absence of the RT inhibitor nevirapine. The results of these experiments will be included in the revised manuscript.

• The role of Gasdermin D: We are mostly interested in the release of IL-1β from the infected macrophages due to its potential contribution to myeloid-driven inflammation in PLWH. To date, there is no evidence that any other pore-forming protein other than GSDMD can initiate IL-1β release (and pyroptosis) downstream of CARD8. Nonetheless, we will attempt this experiment with the Gasdermin D inhibitor, disulfiram. 

We believe these and other experiments will further support the importance of the CARD8 inflammasome in myeloid-driven inflammation in PLWH and look forward to submitting the revision.

Author response:

Public Reviews: 

Reviewer #1 (Public review): 

Summary: 

The authors test whether the archerfish can modulate the fast response to a falling target.

We have not tested whether archerfish can 'modulate the fast response'. We quantitatively test specific hypotheses on the rules used by the fish. For this the accuracy of the decisions is analyzed with respect to specific points that can be calculated precisely in each experiment. The ill-defined term 'modulate' does in no way capture what is done here. This assessment might explain the question, raised by the reviewer, of 'what is the difference of this study and Reinel, 2016' (i.e. Reinel and Schuster, 2016). In that study, all objects were strictly falling ballistically, and latency and accuracy of the turn decisions were determined when the initial motion was not only horizontal but had an additional vertical component of speed. The question of that study was if the need to account to an additional variable (vertical speed) in the decision would affect its latency or accuracy. The study showed that also then archerfish rapidly turn to the later impact point. It also showed that accuracy and latency (defined in this study exactly as in the present study) were not changed by the added degree of freedom. This is a completely different question and by its very nature does not leave the realm of ballistics.

By manipulating the trajectory of the target, they claim

that the fish can modulate the fast response.

While it is clear from the result that the fish can modulate the fast response, the experimental support for the argument that the fish can do it for a reflex-like behavior is inadequate. 

This is disturbing: The manuscript is full of data that directly report response latency (a parameter that's critical in all experiments) and there are even graphical displays of the distribution of latency (Figs. 2, 5). How fast the responses are, can also already be seen in the first video. Most importantly, the nature of the 40 ms limit has been discovered and has been reported by our group in 2008 (Schlegel and Schuster, 2008, Fig. 4). For easy reference, we attach Schlegel and Schuster, 2008 with the relevant passages marked in yellow. But later studies also using high speed video (ie. typically 500 fps) and simultaneously evaluating accuracy and kinematics (in the same ways as used here!) to address various questions repeatedly report and even graphically represent minimum latencies of 40 ms, e.g. Krupczynski and Schuster, 2013 (e.g. Fig. 2); Reinel and Schuster, 2014; Reinel and Schuster, 2016;  Reinel and Schuster, 2018a, b (e.g. see Fig. 7 in the first part) and report how latency is increased as urgency is decreased (if the fish are too close or time of falling is increased), as temperature is decreased or as viewing conditions and their homogeneity across the tank change. Moreover, even a field study is available (Rischawy, Blum and Schuster, 2015) that shows why the speed is needed. This is because of massive competition with at least some of the competitor fish also be able to turn to the later impact point. So, speed is an absolute necessity if competitors are around. Interestingly, when the fish are isolated, latency goes up and eventually the fish will no longer respond with C-starts (Schlegel and Schuster, 2008).

Another aspect: considering the introduction it would not even have mattered if not 40 ms but instead 150 ms were the time needed for an accurate start (which is not the case). That would still be faster than an Olympic sprinter responds to a gun shot. Moreoever, please also note that we carefully talk of reflex-speed not of a reflex-behavior (which is as easy to verify as any other if the false statements made).

Strengths: 

Overall, the question that the authors raised in the manuscript is interesting. 

Given the statement of no difference between the present study and Reinel and Schuster, 2016, it is not clear what this assessment refers to.

Weaknesses: 

(1) The argument that the fish can modulate reflex-like behavior relies on the claim that the archerfish makes the decision in 40 ms. There is little support for the 40 ms reaction time.

The 'little support' is a paper in Science in which this important aspect is directly analyzed (Fig. 4 of that paper) and that has been praised by folks like Yadin Dudai (e.g . in Faculty 1000). The support is also data on latency as presented in the present paper. Furthermore, additional publications are available on the reaction time (see above).

The reaction time for the same behavior in Schlegel 2008, is 60-70 ms, and in Tsvilling 2012 about 75 ms, if we take the half height of the maximum as the estimated reaction time in both cases. If we take the peak (or average) of the distribution as an estimation of reaction time, the reaction time is even longer. This number is critical for the analysis the authors perform since if the reaction time is longer, maybe this is not a reflex as claimed.

See above.

In addition, mentioning the 40 ms in the abstract is overselling the result.

See above.

Just for completeness: Considering a very interesting point raised by reviewer 2 we add an additional panel to further emphasize the exciting point that accuracy and latency are unrelated in the start decisions. That point was already made in Fig.4 of the paper in Science but can be directly addressed.  

The title is also not supported by the results. 

No: the title is clearly supported by the results that are reported in the paper.

(2) A critical technical issue of the stimulus delivery is not clear.

The stimulus delivery is described in detail. Most importantly we emphasize (even mentioning frame rate) that all VR setups require experimental confirmation that they work for the species and for the behavior at hand. Ideally, they should elicit the same behavior (in all aspects) as a real stimulus does that the VR approach intends to mimic. Whether VR works in a given animal and for the behavior at hand in that animal cannot be known or postulated a priori. It must be shown in direct critical experiments. Such experiments and the need to perform them are described in detail in Figure 2 and in the text that is associated with that figure.

The frame rate is 120 FPS and the target horizontal speed can be up to 1.775 m/s. This produces a target jumping on the screen 15 mm in each frame. This is not a continuous motion. Thus, the similarity between the natural system where the target experiences ballistic trajectory and the experiment here is not clear. Ideally, another type of stimulus delivery system is needed for a project of this kind that requires fast-moving targets (e.g. Reiser, J. Neurosci.Meth. 2008).

See above. It is quite funny that one of the authors of the present study had been involved in developing a setup with a complete panorama of 6000 LEDs (Strauss, Schuster and Götz, 1997; and appropriately cited in Reiser) that has been the basis for Reiser. This panorama was also used to successfully implement VR in freely walking Drosophila (Schuster et al., Curr. Biol., 2002). However, an LED based approach was abandoned because of insufficient spatial resolution (that, in archerfish, is very different from that of Drosophila).

But the crucial point really is this: Just looking at Figure 2 shows that our approach could not have worked better in any way - it provided the input needed to cause turn decisions that are in all aspects just as those with real objects. Achieving this was not at all trivial and required enormous effort and many failed attempts. But it allows addressing our questions for the first time after 20 years of studying these interesting decisions.

In addition, the screen is rectangular and not circular, so in some directions, the target vanishes earlier than others. It must produce a bias in the fish response but there is no analysis of this type. 

Why 'must' it produce a bias? Is it not conceivable that you can only use a circular part of the screen? Briefly, and as could have been checked by quickly looking into the methods section, this is what we did. But still, why would it have mattered in our strictly randomized design? It could have mattered only in a completely silly way of running the experiments in which exclusively long trajectories are shown in one condition and exclusively short ones in another.

(3) The results here rely on the ability to measure the error of response in the case of a virtual experiment. It is not clear how this is done since the virtual target does not fall.

Well, of course it does not fall!!! That is the whole point that enables the study, and this is explained in connection with the glass plate experiment of Fig. 1 and quite some text is devoted to say that this is the starting point for the present analysis. The ballistic impact point is calculated (just as explained in our very first paper on the start decisions, Rossel, Corlija and Schuster, 2002) from the initial speed and height of the target, using simple high-school physics and the justification for that is also in that paper. This has been done already more than 20 years ago. How else could that paper have arrived at the conclusion that the fish turned to the virtual impact point even though nothing is falling? We also describe this for the readers of the present study, illustrate how accuracy is determined in Figures, in all videos and in an additional Supplementary Figure. Consulting the paper reveals that orientation of the fish is determined immediately at the end of stage 2 of its C-start and the error directly reports how close continuing in that direction would lead the fish to the (real or virtual) impact point. This measure has also been used since the first paper in 2002 in our lab and it is very useful because it provides an invariant measure that allows pooling all the different conditions (orientation and position of responding fish as well as direction, speed and height of target).

How do the authors validate that the fish indeed perceives the virtual target as the falling target?

See above. The fish produce C-starts (whose kinematics are analyzed and reported in Figures), whose latency is measured (from onset of target motion to onset of C-start) and whose accuracy in aligning them to the calculated virtual impact point is measured (see above). Additionally, the errors are also analyzed to other points of interest, for instance landmarks, the ballistic landing point in the re-trained fish or points calculated on the basis of specific hypotheses in the generalization experiments.

Since the deflection is at a later stage of the virtual trajectory, it is not clear what is the actual physics that governs the world of the experiment.

As explained in the text what we need is substituting the ballistic connection with another fixed relation between initial target motion and the landing point. This other relation needs to produce a large error in the aims when they remain based on the ballistic virtual landing point. It is directly shown in the key experiments that the fish need not see the deflection but can respond appropriately to the initial motion after training (Figs. 3, 5 and corresponding paragraphs in the text as well as additional movies). Please also note that after training the decision is based on the initial movement. This is shown in the interspersed experiments in which nothing than the initial (pre-deflection) movement was shown.

Overall, the experimental setup is not well designed. 

It is obviously designed well enough to mimic the natural situation in every aspect needed (see Fig. 2) and well enough to answer the questions we have asked.

Reviewer #2 (Public review): 

Summary: 

This manuscript studies prey capture by archer fish, which observe the initial values of motion of aerial prey they made fall by spitting on them, and then rapidly turn to reach the ballistic landing point on the water surface. The question raised by the article is whether this incredibly fast decision-making process is hardwired and thus unmodifiable or can be adjusted by experience to follow a new rule, namely that the landing point is deflected from a certain amount of the expected ballistic landing point. The results show that the fish learn the new rule and use it afterward in a variety of novel situations that include height, side, and speed of the prey, and which preserve the speed of the fish's decision. Moreover, a remarkable finding presented in this work is the fact that fish that have learned to use the new rule can relearn to use the ballistic landing point for an object based on its shape (a triangle) while keeping simultaneously the 'deflected rule' for an object differing in shape (a disc); in other words, fish can master simultaneously two decision-making rules based on the different shape of objects. 

Strengths: 

The manuscript relies on a sophisticated and clever experimental design that allows changing the apparent landing point of a virtual prey using a virtual reality system. Several robust controls are provided to demonstrate the reliability and usefulness of the experimental setup. 

Overall, I very much like the idea conveyed by the authors that even stimuli triggering apparently hardwired responses can be relearned in order to be associated with a different response, thus showing the impressive flexibility of circuits that are sometimes considered mediating pure reflexive responses.

Thank you so much for this precise assessment of what we have shown!

This is the case - as an additional example - of the main component of the Nasanov pheromone of bees (geraniol), which triggers immediate reflexive attraction and appetitive responses, and which can, nevertheless, be learned by bees in association with an electric shock so that bees end up exhibiting avoidance and the aversive response of sting extension to this odorant (1), which is a fully unnatural situation, and which shows that associative aversive learning is strong enough to override preprogrammed responding, thus reflecting an impressive behavioral flexibility. 

That's very interesting, thanks.

Weaknesses: 

As a general remark, there is some information that I missed and that is mandatory in the analysis of behavioral changes. 

Firstly, the variability in the performances displayed. The authors mentioned that the results reported come from 6 fish (which is a low sample size). How were the individual performances in terms of consistency? Were all fish equally good in adjusting/learning the new rule? How did errors vary according to individual identity? It seems to me that this kind of information should be available as the authors reported that individual fish could be recognized and tracked (see lines 620-635) and is essential for appreciating the flexibility of the system under study. 

Secondly, the speed of the learning process is not properly explained. Admittedly, fish learn in an impressive way the new rule and even two rules simultaneously; yet, how long did they need to achieve this? In the article, Figure 2 mentions that at least 6 training stages (each defined as a block of 60 evaluated turn decisions, which actually shows that the standard term 'Training Block' would be more appropriate) were required for the fish to learn the 'deflected rule'. While this means 360 trials (turning starts), I was left with the question of how long this process lasted. How many hours, days, and weeks were needed for the fish to learn? And as mentioned above, were all fish equally fast in learning? I would appreciate explaining this very important point because learning dynamics is relevant to understanding the flexibility of the system. 

First, it is very important to keep the question in mind that we wanted to clarify: Does the system have the potential to re-tune the decisions to other non-ballistic relations between the input variables and the output? This would have been established if one fish was found capable of doing that. However, we do have sufficient evidence to say that all six fish learned the new law and that at least one (actually four) individual was capable of simultaneously handling the two laws. We will explain this much better (hopefully) in our revised version. We also have to stress that not all archerfish might actually be able to do this and that not all archerfish might learn in the same way, at the same speed, or using the same strategies. These questions are extremely interesting and we therefore definitely will include all evidence that we have. If some individuals are better than others in quickly adjusting, then even observational learning could become a part of the story. However, we needed to make and document the first steps. Understanding these is essential and apparently is difficult enough.

Reference: 

(1) Roussel, E., Padie, S. & Giurfa, M. Aversive learning overcomes appetitive innate responding in honeybees. Anim Cogn 15, 135-141, doi:10.1007/s10071-011-0426-1 (2012). 

Thanks for this reference!

Author response:

Public Reviews:

Reviewer #1 (Public review):

This is an interesting manuscript tackling the issue of whether subcircuits of the cerebellum are differentially involved in processes of motor performance, learning, or learning consolidation. The authors focus on cerebellar outputs to the ventrolateral thalamus (VL) and to the centrolateral thalamus (CL), since these thalamic nuclei project to the motor cortex and striatum respectively, and thus might be expected to participate in diverse components of motor control and learning. In mice challenged with an accelerating rotarod, the investigators reduce cerebellar output either broadly, or in projection-specific populations, with CNO targeting DREADD-expressing neurons. They first establish that there are not major control deficits with the treatment regime, finding no differences in basic locomotor behavior, grid test, and fixed-speed rotarod. This is interpreted to allow them to differentiate control from learning, and their inter-relationships. These manipulations are coupled with chronic electrophysiological recordings targeted to the cerebellar nuclei (CN) to control for the efficacy of the CNO manipulation. I found the manuscript intriguing, offering much food for thought, and am confident that it will influence further work on motor learning consolidation. The issue of motor consolidation supported by the cerebellum is timely and interesting, and the claims are novel. There are some limitations to the data presentation and claims, highlighted below, which, if amended, would improve the manuscript.

We thank the reviewer for the positive comments and insightful critics.

(1.1) Statistical analyses: There is too little information provided about how the Deming regressions, mean points, slopes, and intercepts were compared across conditions. This is important since in the heart of the study when the effects of inactivating CL- vs VL- projecting neurons are being compared to control performance, these statistical methods become paramount. Details of these comparisons and their assumptions should be added to the Methods section. As it stands I barely see information about these tests, and only in the figure legends. I would also like the authors to describe whether there is a criterion for significance in a given correlation to be then compared to another. If I have a weak correlation for a regression model that is non-significant, I would not want to 'compare' that regression to another one since it is already a weak model. The authors should comment on the inclusion criteria for using statistics on regression models.

Currently the Methods indeed explain that groups are compared by testing differences of distributions of residuals of treatment and control groups around the Deming regression of the control groups: “To test if treatments altered the relationship between initial performance vs learning or daily vs overnight learning, we compared the distribution of signed distance to the control Deming regression line between groups.” But this shall indeed be explained in more details.

The performance on a given day depends on a cumulative process, so that the average measure of performance is not fully informative on what is learned or what is changed by a treatment (this is further explained in the text p9-10).The challenge is to deal with the multivariate relationships where initial performance, daily learning, and consolidated learning are interdependent. While in control groups these quantities show linear relationships, this is far less the case in treatment groups; this may indeed be due to the variability of the effect of the treatment (efficacy of viral injections) which adds up to the intrinsic variability in the absence of treatment.

Our choice to see if there is a shift in these relationships following treatments, is to see to which extent treatment points in bivariate comparisons (initial perf x daily learning, daily learning x consolidated learning) are evenly distributed around the control group regression line. We take the presence of a significant difference in the distribution of residuals between the control and treatment group as an indication that the process represented in group is disrupted by the treatment: e.g. if the residuals of the treatment group are lower than those of the control group in the initial performance * daily learning comparison, it indicates that learning is slower (or larger). If the residuals of the treatment group are lower than those of the control group in the daily learning * consolidated learning comparison, it indicates that consolidation is lower. This shall be clarified in a revised version.

(1.2a) The introduction makes the claim that the cerebellar feedback to the forebrain and cortex are functionally segregated. I interpreted this to mean that the cerebellar output neurons are known to project to either VL or CL exclusively (i.e. they do not collateralize). I was unaware of this knowledge and could find no support for the claim in the references provided (Proville 2014; Hintzer 2018; Bosan 2013). Either I am confused as to the authors' meaning or the claim is inaccurate. This point is broader however than some confusion about citation.

The references are not cited in the context of collaterals: “They [basal ganglia and cerebellum] send projections back to the cortex via anatomically and functionally segregated channels, which are relayed by predominantly non-overlapping thalamic regions (Bostan, Dum et al. 2013, Proville, Spolidoro et al. 2014, Hintzen, Pelzer et al. 2018). ” Indeed, the thalamic compartments targeted by the basal ganglia and cerebellum are distinct, and in the Proville 2014, we showed some functional segregation of the cerebello-cortical projections (whisker vs orofacial ascending projections). We do not claim that there is a full segregation of the two pathways, there is indeed some known degree of collateralization (see below).

(1.2b) The study assumes that the CN-CL population and CN-VL population are distinct cells, but to my knowledge, this has not been established. It is difficult to make sense of the data if they are entirely the same populations, unless projection topography differs, but in any event, it is critical to clarify this point: are these different cell types from the nuclei?; how has that been rigorously established?; is there overlap? No overlap? Etc. Results should be interpreted in light of the level of this knowledge of the anatomy in the mouse or rat.

Actually, the study does not assume that CL-projecting and VAL-projecting neurons are entirely separate populations (actually it is known that there is an overlap), but states that inhibition of neurons following retrograde infections from the CL and VAL do not produce identical results.

There is indeed a paragraph devoted to the discussion of this point (middle paragraph p20). “Interestingly, both Dentate and Interposed nuclei contain some neurons with collaterals in both VAL and CL thalamic structures (Aumann and Horne 1996, Sakayori, Kato et al. 2019), suggesting that the effect on learning could be mediated by a combined action on the learning process in the striatum (via the CL thalamus) and in the cortex (via the VAL thalamus). However, consistent with (Sakayori, Kato et al. 2019), we found that the manipulations of cerebellar neurons retrogradely targeted either from the CL or from the VAL produced different effects in the task. This indicates that either the distinct functional roles of VAL-projecting of CL-projecting neurons reported in our study is carried by a subset of pathway-specific neurons without collaterals, or that our retrograde infections in VAL and CL preferentially targeted different cerebello-thalamic populations even if these populations had axon terminals in both thalamic regions.”. In other words, we actually know from the literature that there is a degree of collateralization (CN neurons projecting to both VAL and CL, see refs cited above), but as the reviewer says, it does not seem logically possible that the exact same population would have different effects, which are very distinct during the first learning days. The only possible explanation is the CN-CL and CN-VAL retrograde infections recruit somewhat different populations of neurons. This could be due to differences in density of collaterals in CL and VAL of neurons with collaterals in both regions, or presence of CL-projecting neurons without collaterals in VAL, and VAL-projecting neurons without collaterals in CL in addition to the (established) population of neurons with collaterals in both regions. The lesional approach of CN-thalamus neurons in Sakayori et al. 2019 also observed separate effects for CL and VL injections consistent with the differential recruitment of CN populations by retrograde infections.

This should be improved in a revised version of the manuscript.

(1.3) It is commendable that the authors perform electrophysiology to validate DREADD/CNO. So many investigators don't bother and I really appreciate these data. Would the authors please show the 'wash' in Figure 1a, so that we can see the recovery of the spiking hash after CNO is cleared from the system? This would provide confidence that the signal is not disappearing for reasons of electrode instability or tissue damage/ other.

We do not have the wash data on the same day, but there is no significant change in the baseline firing rate across recording days.

(1.4) I don't think that the "Learning" and "Maintenance" terminology is very helpful and in fact may sow confusion. I would recommend that the authors use a day range " Days 1-3 vs 4-7" or similar, to refer to these epochs. The terminology chosen begs for careful validation, definitions, etc, and seems like it is unlikely uniform across all animals, thus it seems more appropriate to just report it straight, defining the epochs by day. Such original terminology could still be used in the Discussion, with appropriate caveats.

This shall be indeed corrected in a revised version.

(1.5) Minor, but, on the top of page 14 in the Results, the text states, "Suggesting the presence of a 'critical period' in the consolidation of the task". I think this is a non-standard use of 'critical period' and should be removed. If kept, the authors must define what they mean specifically and provide sufficient additional analyses to support the idea. As it stands, the point will sow confusion.

This shall be indeed corrected in a revised version

Reviewer #2 (Public review):

Summary:

This study examines the contribution of cerebello-thalamic pathways to motor skill learning and consolidation in an accelerating rotarod task. The authors use chemogenetic silencing to manipulate the activity of cerebellar nuclei neurons projecting to two thalamic subregions that target the motor cortex and striatum. By silencing these pathways during different phases of task acquisition (during the task vs after the task), the authors report valuable findings of the involvement of these cerebellar pathways in learning and consolidation.

Strengths:

The experiments are well-executed. The authors perform multiple controls and careful analysis to solidly rule out any gross motor deficits caused by their cerebellar nuclei manipulation. The finding that cerebellar projections to the thalamus are required for learning and execution of the accelerating rotarod task adds to a growing body of literature on the interactions between the cerebellum, motor cortex, and basal ganglia during motor learning. The finding that silencing the cerebellar nuclei after a task impairs the consolidation of the learned skill is interesting.

We thank the reviewer for the positive comments and insightful critics below.

Weaknesses:

(2.1) While the controls for a lack of gross motor deficit are solid, the data seem to show some motor execution deficit when cerebellar nuclei are silenced during task performance. This deficit could potentially impact learning when cerebellar nuclei are silenced during task acquisition.

One of our key controls are the tests of the treatment on fixed speed rotarod, which provides the closest conditions to the ones found in the accelerating rotarod (the main difference between the protocols being the slow steady acceleration of rod rotation [+0.12 rpm per s]- in the accelerating version).

In the CN experiments, we found clear deficits in learning and consolidation while there was no effect on the fixed speed rotarod (performance of the DREAD-CNO are even slightly better than some control groups), consistent with a separation of the effect on learning/consolidation from those on locomotion on a rotarod. However, small but measurable deficits are found at the highest speed in the fixed speed rotarod in the CN-VAL group; there was no significant effect in the CN-CL group, while the CN-CL actually shows lower performances from the second day of learning; we believe this supports our claim that the CN-CL inhibition impacted more the learning process than the motor coordination. In contrast the CN-VAL group only showed significantly lower performance on day 4 of the accelerating rotarod consistent with intact learning abilities. Of note, under CNO, CN-VAL mice could stay for more than a minute and half at 20rpm, while on average they fell from the accelerating rotarod as soon as the rotarod reached the speed of ~19rpm (130s).

The text currently states “The inhibition of CN-VAL neurons during the task also yielded lower levels of performance in the Maintenance stage,[[NB: day 5-7]] suggesting that these neurons contribute also to learning and retrieval of motor skills, although the mild defect in fixed speed rotarod could indicate the presence of a locomotor deficit, only visible at high speed.” Following the reviewers’ comment, we shall however revise the sentence above in the revised version of the MS to say that we cannot fully disambiguate the execution / learning-retrieval effect at high speed for these mice.

(2.2a) Separately, I find the support for two separate cerebello-thalamic pathways incomplete. The data presented do not clearly show the two pathways are anatomically parallel.

As explained above (point 1.2a), it is already known that these pathways overlap to some degree (discussion p 20), but yet their targeting differentially affects the behavior, consistent with separate contributions. A similar finding was observed for a lesional (irreversible) approach in Sakayori et al. 2019.

(2.2b) The difference in behavioral deficits caused by manipulating these pathways also appears subtle.

While we agree that after 3-4 days of learning the difference of performance between the groups becomes elusive, we respectfully disagree with the reviewer that in the early stages these differences are negligible and the impact of inhibition on "learning rate" (ie. amount of learning for a given daily initial performance) and consolidation (i.e. overnight retention of daily gain of performance) exhibit different profiles for the two groups (fig 3h vs 3k).

Reviewer #3 (Public review)

Summary:

Varani et al present important findings regarding the role of distinct cerebellothalamic connections in motor learning and performance. Their key findings are that:

(1) cerebellothalamic connections are important for learning motor skills

(2) cerebellar efferents specifically to the central lateral (CL) thalamus are important for short-term learning

(3) cerebellar efferents specifically to the ventral anterior lateral (VAL) complex are important for offline consolidation of learned skills, and

(4) that once a skill is acquired, cerebellothalamic connections become important for online task performance.

The authors went to great lengths to separate effects on motor performance from learning, for the most part successfully. While one could argue about some of the specifics, there is little doubt that the CN-CL and CN-VAL pathways play distinct roles in motor learning and performance. An important next step will be to dissect the downstream mechanisms by which these cerebellothalamic pathways mediate motor learning and adaptation.

Strengths:

(1) The dissociation between online learning through CN-CL and offline consolidation through CN-VAL is convincing.

(2) The ability to tease learning apart from performance using their titrated chemogenetic approach is impressive. In particular, their use of multiple motor assays to demonstrate preserved motor function and balance is an important control.

(3) The evidence supporting the main claims is convincing, with multiple replications of the findings and appropriate controls.

We thank the reviewer for the positive comments and insightful critics below.

Weaknesses:

(3.1) Despite the care the authors took to demonstrate that their chemogenetic approach does not impair online performance, there is a trend towards impaired rotarod performance at higher speeds in Supplementary Figure 4f, suggesting that there could be subtle changes in motor performance below the level of detection of their assays.

This is also discussed in point 2.1 above. In our view, the fixed speed rotarod is a control very close to the accelerating rotarod condition, with very similar requirements between the two tasks (yet unfortunately rarely tested in accelerating rotarod studies). We do not exclude the presence of motor deficits, but the main argument is that these do not suffice to explain the differences observed in the accelerating rotarod. No detectable deficit was found in the CN group while very clear deficits in learning/consolidation were observed. A mild deficit is only significant in the CN-VAL group, while the deficit is not significant in the fixed-speed rotarod for the CN-CL group which shows the strongest deficit in accelerating rotarod during the first days: e.g. on day 2, the CN-CL group is already below the control group with latencies to fall ~100s (corresponding to immediate fall at ~15rpm) while the fixed speed rotarod performances at 15s of the control and CNO-treated groups show an ability to stay more than 1 min at this speed. The text shall be improved to clarify this point.

(3.2) There is likely some overlap between CN neurons projecting to VAL and CL, somewhat limiting the specificity of their conclusions.

There is indeed published evidence for some degree of anatomical overlap, but also for some differential contribution of CN-VAL and CN-CL to the task. The answer to this point is developed in the points 1.2a 2.2a above. Although this point was exposed in the discussion (p20), the text shall be improved in a revised version of the MS to clarify our statement.

Author response:

Reviewer #1 (Public review):

Summary:

The authors successfully detected distinct mechanisms signalling prediction violations in the auditory cortex of mice. For this purpose, an auditory pure-tone local-global paradigm was presented to awake and anaesthetised mice. In awake rodents, the authors also evaluated interneuron cell types involved in responses to the interruption of the regularity imposed by local-global sequences. By performing two-photon calcium imaging and single-unit electrophysiology, the authors disentangled three phenomena underlying responses to violations of the distinct local-global regularity levels: Stimulus-specific adaptation, surprise and surprise adaptation. Both stimulus-specific adaptation and surprise-or deviant-evoked responses are observable under anaesthesia. Altogether, this work advances our understanding of distinct predictive processes computing prediction violations upon the complexity of the regularity imposed by the auditory sequence.

Strengths:

it is an elegant study beautifully executed.

Weaknesses:

No weaknesses were identified by this reviewer.

Reviewer #2 (Public review):

Summary:

Oddball responses are increases in sensory responses when a stimulus is encountered in an unexpected location in a sequence of predictable stimuli. There are two computational interpretations for these responses: stimulus-specific adaptation and prediction errors. In recent years, evidence has accumulated that a significant part of these sequence violation responses cannot be explained simply by stimulus-specific adaptation. The current work elegantly adds to this evidence by using a sequence paradigm based on two levels of sequence violations: "Local" sequence violations of repetitions of identical stimuli, and "global" sequence violations of stimulus sequence patterns. The authors demonstrate that both local and global sequence violation responses are found in L2/3 neurons of the mouse auditory cortex. Using sequences with different inter-stimulus intervals, they further demonstrate that these sequence violation responses cannot be explained by stimulus-specific adaption.

Strengths:

The work is based on a very clever use of a sequence violation paradigm (local-global paradigm) and provides convincing evidence for the interpretation that there are at least two types of sequence violation responses and that these cannot be explained by stimulus-specific adaption. Most of the conclusions are based on a large dataset, and are compelling.

Weaknesses:

The final part of the paper focuses on the responses of VIP and PV-positive interneurons. The responses of VIP interneurons appear somewhat variable and difficult to interpret (e.g. VIP neurons exhibit omission responses in the A block, but not the B block). The conclusions based on these data appear less solid.

We agree with the referee that the response modulations observed in  VIP and PV-Positive interneurons are weak and variable. This is indicated in the manuscript. Probably, larger scale recordings are necessary to ascertain fully the presence and distribution of omission responses.

Reviewer #3 (Public review):

Summary:

In their manuscript entitled "Parallel mechanisms signal a hierarchy of sequence structure violations in the auditory cortex", Jamali et al. provide evidence for cellular-level mechanisms in the auditory cortex of mice for the encoding of predictive information on different temporal and contextual scales. The study design separates more clearly than previous studies between the effects of local and global deviants and separates their respective effects on the neuronal responses clearly through the use of various contextual conditions and short and long time scales. Further, it identifies a contribution from a small set of VIP interneurons to the detection of omitted sounds, and shows the influence of isofluorane anesthesia on the neural responses.

Strengths:

(1) The study provides a rather encompassing set of experimental techniques to study the cellular level responses, using two complementary recording techniques in the same animal and similar cortical location.

(2) Comparison between awake and anesthetized states are conducted in the same animals, which allows for rather a direct comparison of populations under different conditions, thus reducing sampling variability.

(3) The set of paradigms is well developed and specifically chosen to provide appropriate and meaningful controls/comparisons, which were missing from previous studies.

(4) The addition of cell-type specific recordings is valuable and in particular in combination with the contrast of awake and anesthetized animals provides novel insights into the cellular level representation of deviant signals, such as surprise, prediction error, and general adaptation.

(5) The analysis and presentation of the data are clear and quite complete, yet remain succinct and perform insightful contrasts.

(6) The study will have an impact on multiple levels, as it introduces important variations in the paradigm and analytical contrasts that both human and animal researchers can pick up and improve their studies. The cell-type-specific results are particularly intriguing, although these would likely require replication before being completely reliable. Further, the study provides a substantial and diverse dataset that others can explore.

Weaknesses:

(1) The responses from cells recorded via Neuropixel and 2p differ qualitatively, as noted by the authors, with NP-recorded cells showing much more inhibited/reduced responses between acoustic stimulations. The authors briefly qualify these differences as potentially indicating a sampling issue, however, this matter deserves more detailed consideration in my opinion. Specifically, the authors could try to compare the different depths at which these neurons were sampled or relate the locations in the cortex to each other (as the Neuropixel recordings were collected in the same animals, a subset of the 2p recordings could be compared to the Neuropixel recordings.).

We agree with the referee that the sampling issue, which we propose as a possible explanation for the large difference between our Neuropixel and 2P imaging recordings, must be investigated more thoroughly. This is, however, largely outside of the scope of this study. We have reported the depth and location of Neuropixel recordings in Figure S2. The depth is similar for both techniques covering mostly layers 2, 3 and 4. The location spans mostly the primary auditory cortex for two photon imaging and Neuropixel recordings. One Neuropixel recording is located in the ventral secondary auditory cortex. We could not find any evidence that the response to global violations in Neuropixel data stems specifically from this particular recording. 

(2) The current study did not monitor the attentional state of the mouse in relation to the stimulus by either including a behavioral component or pupil monitoring, which could influence the neural responses to deviant stimuli and omissions.

As reported by Bekinschtein et al. 2009, the attentional state influences responses to global violation in human subjects. It is extremely difficult to precisely compare attentional states in mice and human subjects. We have performed recordings in mice that had to attend to sound to detect a white noise sound in between the sequence to obtain a reward. This did not lead to increased global violation response. However, as the sequence themselves did not predict reward in this context they may divert attention. Therefore, this result is inconclusive and not worth including in our manuscript. If the sequence predicts rewards, there is a potential confound between violation responses and reward expectations or motor preparation signals. Pupil monitoring could be an alternative which we did not investigate.

(3) Given the complexity and variety of the paradigms, conditions, and analyzed cell-types, the manuscript could profit from a more visual summary figure that provides an easy-to-access overview of what was found.

This is an excellent suggestion, although given the complexity and diversity of our observations it may be hard to fit everything in one understandable figure.

Author response:

We appreciate the insightful comments and suggestions, which will significantly improve our work. We will revise the manuscript to address the reviewer’s concerns. Here, we list some of the key aspects of those concerns and our preliminary plans to address them.

Both reviewers pointed out that we did not sufficiently justify the chosen optogenetic stimulation frequencies. We acknowledge and concur with their assessment, and will discuss it more extensively from a biological perspective (e.g., the neural firing rates in the olfactory bulb, OB, anterior olfactory nucleus, AON, and piriform cortex, Pir, under natural odor stimulation and respiration rhythm). Reviewer #1 suggested using beta values (b) rather than the area under the BOLD signal profile (AUC) to quantify the fMRI activations as they are more conventional for general linear model (GLM) analysis. We are aware of b and have used them for quantification of the amplitude of fMRI activations in our previous rodent fMRI studies1-3. However, in this study, we chose to utilize AUC as it offers a more comprehensive measure of BOLD signal change over time, including shape, duration, and magnitude, thereby capturing the bulk of neural activities and their dynamics throughout the stimulation period. b primarily represents the peak amplitude of BOLD responses (i.e., the % BOLD signal change)4 and can be constrained by the assumptions and limitations of the GLM analysis, such as the shape of the hemodynamic response function (HRF). AUC provides greater flexibility in capturing different aspects of neural responses across various brain regions, such as transient peaks and sustained responses.

As mentioned by reviewer #1, correlating the adaptation of BOLD and electrophysiology signals at the brain region level would better signify our findings. We will pursue additional analysis to address this in our forthcoming responses. Reviewer #2 would like us to clarify the image and signal quality of our echo planar imaging (EPI)-based fMRI data, especially in the regions close to the air-tissue interface such as OB, Pir, entorhinal cortex and amygdala, and the methodology for some of the experimental protocols implemented in our study. We will show the raw EPI fMRI images from a representative animal and revise the results, discussion, and methods sections of the manuscript to address reviewer #2's concerns.

In our forthcoming detailed responses to the reviewers' comments and recommendations, we will revise the text, figures, and captions accordingly to address and clarify the questions brought up by both reviewers.

References

(1) Gao, P.P., Zhang, J.W., Chan, R.W., Leong, A.T.L. & Wu, E.X. BOLD fMRI study of ultrahigh frequency encoding in the inferior colliculus. Neuroimage 114, 427-437 (2015).

(2) Leong, A.T.L., Wong, E.C., Wang, X. & Wu, E.X. Hippocampus Modulates Vocalizations Responses at Early Auditory Centers. Neuroimage 270, 119943 (2023).

(3) Gao, P.P., Zhang, J.W., Fan, S.J., Sanes, D.H. & Wu, E.X. Auditory midbrain processing is differentially modulated by auditory and visual cortices: An auditory fMRI study. Neuroimage 123, 22-32 (2015).

(4) Goddard, E. & Mullen, K.T. fMRI representational similarity analysis reveals graded preferences for chromatic and achromatic stimulus contrast across human visual cortex. Neuroimage 215, 116780 (2020).

Author response:

The following is the authors’ response to the original reviews.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The emergence of Drosophila EM connectomes has revealed numerous neurons within the associative learning circuit. However, these neurons are inaccessible for functional assessment or genetic manipulation in the absence of cell-type-specific drivers. Addressing this knowledge gap, Shuai et al. have screened over 4000 split-GAL4 drivers and correlated them with identified neuron types from the "Hemibrain" EM connectome by matching light microscopy images to neuronal shapes defined by EM. They successfully generated over 800 split-GAL4 drivers and 22 split-LexA drivers covering a substantial number of neuron types across layers of the mushroom body associative learning circuit. They provide new labeling tools for olfactory and non-olfactory sensory inputs to the mushroom body; interneurons connected with dopaminergic neurons and/or mushroom body output neurons; potential reinforcement sensory neurons; and expanded coverage of intrinsic mushroom body neurons. Furthermore, the authors have optimized the GR64f-GAL4 driver into a sugar sensory neuron-specific split-GAL4 driver and functionally validated it as providing a robust optogenetic substitute for sugar reward. Additionally, a driver for putative nociceptive ascending neurons, potentially serving as optogenetic negative reinforcement, is characterized by optogenetic avoidance behavior. The authors also use their very large dataset of neuronal anatomies, covering many example neurons from many brains, to identify neuron instances with atypical morphology. They find many examples of mushroom body neurons with altered neuronal numbers or mistargeting of dendrites or axons and estimate that 1-3% of neurons in each brain may have anatomic peculiarities or malformations. Significantly, the study systematically assesses the individualized existence of MBON08 for the first time. This neuron is a variant shape that sometimes occurs instead of one of two copies of MBON09, and this variation is more common than that in other neuronal classes: 75% of hemispheres have two MBON09's, and 25% have one MBON09 and one MBON08. These newly developed drivers not only expand the repertoire for genetic manipulation of mushroom body-related neurons but also empower researchers to investigate the functions of circuit motifs identified from the connectomes. The authors generously make these flies available to the public. In the foreseeable future, the tools generated in this study will allow important advances in the understanding of learning and memory in Drosophila.

Strengths:

(1) After decades of dedicated research on the mushroom body, a consensus has been established that the release of dopamine from DANs modulates the weights of connections between KCs and MBONs. This process updates the association between sensory information and behavioral responses. However, understanding how the unconditioned stimulus is conveyed from sensory neurons to DANs, and the interactions of MBON outputs with innate responses to sensory context remains less clear due to the developmental and anatomic diversity of MBONs and DANs. Additionally, the recurrent connections between MBONs and DANs are reported to be critical for learning. The characterization of split-GAL4 drivers for 30 major interneurons connected with DANs and/or MBONs in this study will significantly contribute to our understanding of recurrent connections in mushroom body function.

(2) Optogenetic substitutes for real unconditioned stimuli (such as sugar taste or electric shock) are sometimes easier to implement in behavioral assays due to the spatial and temporal specificity with which optogenetic activation can be induced. GR64f-GAL4 has been widely used in the field to activate sugar sensory neurons and mimic sugar reward. However, the authors demonstrate that GR64f-GAL4 drives expression in other neurons not necessary for sugar reward, and the potential activation of these neurons could introduce confounds into training, impairing training efficiency. To address this issue, the authors have elaborated on a series of intersectional drivers with GR64f-GAL4 to dissect subsets of labeled neurons. This approach successfully identified a more specific sugar sensory neuron driver, SS87269, which consistently exhibited optimal training performance and triggered ethologically relevant local searching behaviors. This newly characterized line could serve as an optimized optogenetic tool for sugar reward in future studies.

(3) MBON08 was first reported by Aso et al. 2014, exhibiting dendritic arborization into both ipsilateral and contralateral γ3 compartments. However, this neuron could not be identified in the previously published Drosophila brain connectomes. In the present study, the existence of MBON08 is confirmed, occurring in one hemisphere of 35% of imaged flies. In brains where MBON08 is present, its dendrite arborization disjointly shares contralateral γ3 compartments with MBON09. This remarkable phenotype potentially serves as a valuable resource for understanding the stochasticity of neurodevelopment and the molecular mechanisms underlying mushroom body lobe compartment formation.

Weaknesses:

There are some minor weaknesses in the paper that can be clarified:

(1) In Figure 8, the authors trained flies with a 20s, weak optogenetic conditioning first, followed by a 60s, strong optogenetic conditioning. The rationale for using this training paradigm is not explicitly provided.

These experiments were designed to test if flies could maintain consistent performance with repetitive and intense LED activation, which is essential for experiments involving long training protocols or coactivation of other neurons inside a brain.

In Figure 8E, if data for training with GR64f-GAL4 using the same paradigm is available, it would be beneficial for readers to compare the learning performance using newly generated split-GAL4 lines with the original GR64f-GAL4, which has been used in many previous research studies. It is noteworthy that in previously published work, repeating training test sessions typically leads to an increase in learning performance in discrimination assays. However, this augmentation is not observed in any of the split-GAL4 lines presented in Figure 8E. The authors may need to discuss possible reasons for this.

As the reviewer pointed out, many previous studies including ours used the original Gr64f-GAL4 in olfactory conditioning. Figure 1H of Yamada et al., 2023 (https://doi.org/10.7554/eLife.79042) showed such a result, where the first and second-order olfactory conditioning were assayed. Indeed, the first-order conditioning scores were gradually augmented over repeated training. In this experiment, we used low red LED intensity for the optogenetic activation. In the Figure 8E of the present paper, the first memory test was after 3x pairing of 20s odor with five 1s red LED without intermediate tests. Therefore, flies were already sufficiently trained to show a plateau memory level in “Test1”. In the revision of another recent report (Figure 1C-F of Aso et al., 2023; https://doi.org/10.7554/eLife.85756), we included the learning curve data of our best Gr64f-split-GAL4, SS87269. Under a less saturated training conditioning, SS87269 did show learning augmentation over repeated training.

(2) In line 327, the authors state that in all samples, the β'1 compartment is arborized by MBON09. However, in Figure 11J, the probability of having at least one β'1 compartment not arborized is inferred to be 2%. The authors should address and clarify this conflict in the text to avoid misunderstanding.

The chance of visualizing MBON08 in MCFO images was 21/209 in total (Figure 11I). If we assume that each of four cells adopt MBON08 development fate at this chance, we can calculate the probability for each case of MBON08/09 cell type composition. From this calculation, we inferred approximately 2% of flies would lack innervations to β'1 compartment in at least one hemisphere. However, we didn't observe a lack of β'1 arborizations in 169 sample flies. If these MBONs independently develop into MBON08 at 21/209 odds, the chance of never observing two MBON08s in either hemisphere of all 169 samples is 3.29%. Therefore, some developmental mechanisms may prevent the emergence of two MBON08 in the same hemisphere.

In the revised manuscript, we displayed these estimated probability for each case separately, and annotated actual observation on the right side.

(3) In general, are the samples presented male or female? This sample metadata will be shown when the images are deposited in FlyLight, but it would be useful in the context of this manuscript to describe in the methods whether animals are all one sex or mixed sex, and in some example images (e.g. mAL3A) to note whether the sample is male or female.

The samples presented in this study are mixed sex, except for Figure 11I, where genders are specified. We provided metadata information of the presented images in Supplemental File 7, and we added a paragraph in the in the method section:

“Most samples were collected from females, though typically at least one male fly was examined for each driver line. While we noticed certain lines such as SS48900, exhibited distinct expression patterns in females and males, we did not particularly focus on sexual dimorphism, which is analyzed elsewhere (Meissner et al. 2024). Therefore, unless stated otherwise, the presented samples are of mixed gender.

Detailed metadata, including gender information and the reporter used, can be found in Supplementary File 7.”

Reviewer #2 (Public Review):

Summary:

The article by Shuai et al. describes a comprehensive collection of over 800 split-GAL4 and split-LexA drivers, covering approximately 300 cell types in Drosophila, aimed at advancing the understanding of associative learning. The mushroom body (MB) in the insect brain is central to associative learning, with Kenyon cells (KCs) as primary intrinsic neurons and dopaminergic neurons (DANs) and MB output neurons (MBONs) forming compartmental zones for memory storage and behavior modulation. This study focuses on characterizing sensory input as well as direct upstream connections to the MB both anatomically and, to some extent, behaviorally. Genetic access to specific, sparsely expressed cell types is crucial for investigating the impact of single cells on computational and functional aspects within the circuitry. As such, this new and extensive collection significantly extends the range of targeted cell types related to the MB and will be an outstanding resource to elucidate MB-related processes in the future.

Strengths:

The work by Shuai et al. provides novel and essential resources to study MB-related processes and beyond. The resulting tools are publicly available and, together with the linked information, will be foundational for many future studies. The importance and impact of this tool development approach, along with previous ones, for the field cannot be overstated. One of many interesting aspects arises from the anatomical analysis of cell types that are less stereotypical across flies. These discoveries might open new avenues for future investigations into how such asymmetry and individuality arise from development and other factors, and how it impacts the computations performed by the circuitry that contains these elements.

Weaknesses:

Providing such an array of tools leaves little to complain about. However, despite the comprehensive genetic access to diverse sensory pathways and MB-connected cell types, the manuscript could be improved by discussing its limitations. For example, the projection neurons from the visual system seem to be underrepresented in the tools produced (or almost absent). A discussion of these omissions could help prevent misunderstandings.

We internally distributed efforts to produce split-GAL4 lines at Janelia Research Campus. The recent preprint (Nern et al., 2024; doi: https://doi.org/10.1101/2024.04.16.589741) described the full collection of split-GAL4 driver lines in the optic lobe including the visual projection neurons to the mushroom body. We cited this preprint in the revised manuscript by adding a short paragraph of discussion.

“Although less abundant than the olfactory input, the MB also receives visual information from the visual projection neurons (VPNs) that originate in the medulla and lobula and are targeted to the accessory calyx (Vogt et al. 2016; Li et al. 2020). A recent preprint described the full collection of split-GAL4 driver lines in the optic lobe, which includes the VPNs to the MB (Nern et al. 2024).”

Additionally, more details on the screening process, particularly the selection of candidate split halves and stable split-GAL4 lines, would provide valuable insights into the methodology and the collection's completeness.

The details of our split-GAL4 design and screening procedures were described in previous studies (Aso et al., 2014; Dolan et al., 2019). Available data and tools to design split-GAL4 changed over time, and we took different approaches accordingly. Many of split-GAL4 lines presented in this study were designed and screened in parallel to the lines for MBONs and DANs in 2010-2014 when MCFO images of GAL4 drivers and EM connectome were not yet available. With knowledge of where MBONs and DANs project, I (Y.A.) manually examined and annotated thousands of confocal stacks (Jenett et al., 2012; https://doi.org/10.1016/j.celrep.2012.09.011) to find candidate cell types that may concat with them.

Later I used more advanced computational tools (Otsuna et al., 2018; doi: https://doi.org/10.1101/318006) and MCFO images aligned to the standard brain volume (Meissner et al., 2023; DOI: 10.7554/eLife.80660.). Now, if one needs to further generate split-GAL4 lines for cell type identified in EM connectome data, neuron bridge website (https://neuronbridge.janelia.org/) can be very helpful to provide a list of GAL4 drivers that may label the neuron of interest.

Reviewer #3 (Public Review):

Summary:

Previous research on the Drosophila mushroom body (MB) has made this structure the best-understood example of an associative memory center in the animal kingdom. This is in no small part due to the generation of cell-type specific driver lines that have allowed consistent and reproducible genetic access to many of the MB's component neurons. The manuscript by Shuai et al. now vastly extends the number of driver lines available to researchers interested in studying learning and memory circuits in the fly. It is an 800-plus collection of new cell-type specific drivers target neurons that either provide input (direct or indirect) to MB neurons or that receive output from them. Many of the new drivers target neurons in sensory pathways that convey conditioned and unconditioned stimuli to the MB. Most drivers are exquisitely selective, and researchers will benefit from the fact that whenever possible, the authors have identified the targeted cell types within the Drosophila connectome. Driver expression patterns are beautifully documented and are publicly available through the Janelia Research Campus's Flylight database where full imaging results can be accessed. Overall, the manuscript significantly augments the number of cell type-specific driver lines available to the Drosophila research community for investigating the cellular mechanisms underlying learning and memory in the fly. Many of the lines will also be useful in dissecting the function of the neural circuits that mediate sensorimotor circuits.

Strengths:

The manuscript represents a huge amount of careful work and leverages numerous important developments from the last several years. These include the thousands of recently generated split-Gal4 lines at Janelia and the computational tools for pairing them to make exquisitely specific targeting reagents. In addition, the manuscript takes full advantage of the recently released Drosophila connectomes. Driver expression patterns are beautifully illustrated side-by-side with corresponding skeletonized neurons reconstructed by EM. A comprehensive table of the new lines, their split-Gal4 components, their neuronal targets, and other valuable information will make this collection eminently useful to end-users. In addition to the anatomical characterization, the manuscript also illustrates the functional utility of the new lines in optogenetic experiments. In one example, the authors identify a specific subset of sugar reward neurons that robustly promotes associative learning.

Weaknesses:

While the manuscript succeeds in making a mass of descriptive detail quite accessible to the reader, the way the collection is initially described - and the new lines categorized - in the text is sometimes confusing. Most of the details can be found elsewhere, but it would be useful to know how many of the lines are being presented for the first time and have not been previously introduced in other publications/contexts.

We revised the text as below.

“Among the 828 lines, a subset of 355 lines, collectively labeling at least 319 different cell types, exhibit highly specific and non-redundant expression patterns are likely to be particularly valuable for behavioral experiments. Detailed information, including genotype, expression specificity, matched EM cell type(s), and recommended driver for each cell type, can be found in Supplementary File 1. A small subset of 40 lines from this collection have been previously used in studies (Aso et al., 2023; Dolan et al., 2019; Gao et al., 2019; Scaplen et al., 2021; Schretter et al., 2020; Takagi et al., 2017; Xie et al., 2021; Yamada et al., 2023). All transgenic lines newly generated in this study are listed in Supplementary File 2 (Aso et al., 2023; Dolan et al., 2019; Gao et al., 2019; Scaplen et al., 2021; Schretter et al., 2020; Takagi et al., 2017; Xie et al., 2021; Yamada et al., 2023).”

And where can the lines be found at Flylight? Are they listed as one collection or as many?

They are listed as one collection - “Aso 2021” release. It is named “2021” because we released the images and started sharing lines in December of 2021 without a descriptive paper. We added a sentence in the Methods section.

“All splitGAL4 lines can be found at flylight database under “Aso 2021” release, and fly strains can be requested from Janelia or the Bloomington stock center.”

Also, the authors say that some of the lines were included in the collection despite not necessarily targeting the intended type of neuron (presumably one that is involved in learning and memory). What percentage of the collection falls into this category?

We do not have a good record of split-GAL4 screening to calculate the chance to intersect unintended cell types, but it was rather rare. Those unintended cell types can still be a part of circuits for associative learning (e.g. olfactory projection neurons) or totally unrelated cell types. For instance, among a new collection of split-LexA lines using Gr43a-LexADBD hemidriver (Figure 7-figure supplement 2), one line specifically intersected T1 neurons in the optic lobe despite that the AD line was selected to intersect sugar sensory neurons. We suspect that this is due to ectopic expression of Gr43a-LexADBD. Nonetheless, we included it in the paper because cell-type-specific Split-LexA driver for T1 will be useful irrespective of whether the expression of Gr43a gene is expressed in T1 or not.

And what about the lines that the authors say they included in the collection despite a lack of specificity? How many lines does this represent?

For a short answer, there are about 100 lines in the collection that lack the specificity for behavioral experiments.

We ranked specificity of split-GAL4 drivers in the Supplementary File 1. Rank 2 are the ideal lines, Rank 1 are less ideal but acceptable, and Rank 0 is not suitable for activation screening in behavioral experiments. Out of the 828 split-GAL4 lines reported here, there are 413, 305 and 103 lines in rank2, rank1 and rank0 categories respectively. 7 lines are not ranked for specificity because only flipout expression data are available.

Recommendations for the authors:

Reviewer #2 (Recommendations For The Authors):

As mentioned elsewhere and in addition to the minor points below, it is advisable for the authors to elaborate on the details of the screening process. Furthermore, a discussion about the circuits not targeted by their research, such as the visual projection neurons, would be beneficial.

See the response above to Reviewer #2’s public review.

Line 32-33: The citations are very fly-centric. the authors might want to consider reviews on the MB of other insect species regarding learning and memory.

We additionally cited Rybak and Menzel 2017’s book chapter on honey bee mushroom body.

Line 43-44: Citations should be added, e.g. Séjourné et al. (2011), Pai et al. (2013), Plaçais et al. (2013).

Citation added

Line 50-52: Citation Hulse et al. (2021) should be added.

Citation added

Line 162: In this part, it might be valuable for the reader to understand which of these PNs are actually connecting with KCs.

A full list of cell types within the MB were provided in Supplementary File 4 of the revised manuscript. See also response to Reviewer 3, Lines 150-1.

Line 179: Citation Burke et al. (2012) should be mentioned.

Citation added

Line 181: Thermogenic might be thermogenetic.

Corrected

Line 189: Citations add Otto et al. (2020) and Felsenberg et al. (2018).

Citations added

Line 208ff: The authors should consider discussing why they did not use other GR and IR promoters. For example, Gr5a is prominent in sugar-sensing, while Ir76b could be a reinforcement signal related to yeast food (Steck et al., 2018; Ganguly et al., 2017; see also Corfas et al., 2019 for local search).

We focused on the Gr64f promoter because of its relatively broad expression and successful use of Gr64f-GAL4 for fictive reward experiment. We added the Split-LexA lines with Gr43a and Gr66a promoters (Figure 7-figure supplement 2). Other gustatory sensory neurons also have the potential to be reinforcement signals, but we just did not have the bandwidth to cover them all.

Line 319: Consider citing Linneweber et al. (2020) for a neurodevelopmental account of such individuality.

We added a sentence and cited this reference.

“On the other hand, the neurodevelopmental origin of neuronal morphology appeared to have functional significance on behavioral individuality (Linneweber et al. 2020).”

Line 352: Citation add Hulse et al. (2021).

Citations added

Line 356ff: The utility and value of Split-LexA may not be apparent to non-expert readers. Moreover, how were LexADBDs chosen for creating these lines?

We have added an introductory sentence at the beginning of the paragraph and explained that these split-LexA lines were a conversion of split-GAL4 lines that were published in 2014 and frequently used in studying the mushroom body circuit.

“Split-GAL4 lines enable cell-type-specific manipulation, but some experiments require independent manipulation of two cell types. Split-GAL4 lines can be converted into split-LexA lines by replacing the GAL4 DNA binding domain with that of LexA (Ting et al., 2011). To broaden the utility of the split-GAL4 lines that have been frequently used since the publication in 2014 (Aso et al., 2014a), we have generated over 20 LexADBD lines to test the conversions of split-GAL4 to split-LexA. The majority (22 out of 34) of the resulting split-LexA lines exhibited very similar expression patterns to their corresponding original split-GAL4 lines (Figure 12).”

Line 374: Italicize Drosophila melanogaster.

Revised as suggested.

Reviewer #3 (Recommendations For The Authors):

Major Comments:

As mentioned in the Public Review, the drivers are nicely classified in the various subsections of the manuscript, but the statements in the text summarizing how many lines there are in specific categories are often confusing. For example, line 129 refers to "drivers encompassing 111 cell types that connect with the DANs and MBONs", but Figure 1E indicates that 46 new cell types downstream of MBONs and upstream of DANs have been generated. This seems like a discrepancy.

The 46 cell types in Figure 1E consider only the CRE/SMP/SIP/SLP area, where MBON downstreams and DAN upstreams are highly enriched, while the 111 cell types include all. To avoid confusion, we removed the “MBON downstream and DAN upstream” counting in Figure 1E in the revised manuscript.

Also, at line 75 the MBON lines previously generated by Rubin and Aso (2023) are referred to as though they are separate from the 828 described "In this report." Supplementary file 1 suggests, however, that they are included as part of this report.

Twenty five lines generated in Rubin and Aso (2023) were initially included in Supplementary file 1 for the convenience of users, but they were not counted towards the 828 new lines described in this report. To avoid confusion, we removed these 25 lines in the revised manuscript. Now all lines listed in Supplementary file 1 were generated in this study (“Aso 2021” release), and if a line has been used in earlier studies, or introduced in other contexts, for example the accompanying omnibus preprint (Meissener 2024, doi: 10.1101/2024.01.09.574419), the citations are listed in the reference column.

More generally, in lines 94-102 "828 useful lines based on their specificity, intensity and non-redundancy" are referred to, but they are subsequently subdivided into categories of lines with lower specificity (i.e. with off-target expression) and lines that did not target intended cell types (presumably ones unlikely to be involved in learning and memory). It would be useful to know how many lines (at least roughly) fall into these subcategories.

See the response above to Reviewer #3’s public review.

Finally, Figures 3B & C indicate cell types connected to DANs and MBONs and the number for which Split-Gal4 lines are available. The text (lines 136-7) states that the new collection covers 30 of these major cell types (Figure 3C)," but Figure 3C clearly has more than 30 dots showing the drivers available. Presumably existing and new driver lines are being pooled, but this should either be explained or the two should be distinguished.

“(Figure 3C)” was replaced with “(Supplementaryl File 3)” in the revised manuscript to correct the reference. Figure 3B & C are plots of all MB interneurons, not just the major cell types.

Minor Comments:

Although the paper is generally well written there are minor grammatical errors throughout (e.g. dropped articles, odd constructions, etc.) that somewhat detract from an otherwise smooth and enjoyable reading experience. A quick editing pass by a native speaker (i.e. any of several of the authors) could clean up these and numerous other small mistakes. A few examples: line 138 "presented" should be present; line 204: "contain off-targeted expressions" should be "have off-target expression;" line 219: "usage to substitute reward" is awkward at best and could be something like "use in generating fictive rewards"; line 326 "arborize[s]"; l. 331 "Based on the likelihood" should be something like "based on these observations"'; line 349 "[is] likely to appear"; l. 352 "extensive connection[s]"; line 353 "has [a] strong influence;" l. 963 "Projections" should be singular; etc.

All the mentioned examples have been corrected, and we have asked a native speaker to edit through the revised manuscript.

Lines 81-3: Is the lookup table referred to Suppl. File 1? A reference is desirable.

Yes, the lookup table referred to “Supplementary File 1” and a reference was added.

Lines 111-2: what is a "non-redundant set of...cell types?" Cell types that are represented by a single cell (or bilateral pair)? Or does this sentence mean that of the 828 lines, 355 are specific to a single cell type, and in total 319 cell types are targeted? The statement is confusing.

We revised the text as below.

“Figure 1E provides an overview of the categories of covered cell types. Among the 828 lines, a subset of 355 lines, collectively labeling at least 319 different cell types, exhibit highly specific and non-redundant expression patterns are likely to be particularly valuable for behavioral experiments. Detailed information, including genotype, expression specificity, matched EM cell type(s), and recommended driver for each cell type, can be found in Supplementary File 1. A small subset of 40 lines from this collection have been previously used in studies (Aso et al.,

2023; Dolan et al., 2019; Gao et al., 2019; Scaplen et al., 2021; Schretter et al., 2020; Takagi et al., 2017; Xie et al., 2021; Yamada et al., 2023). All transgenic lines newly generated in this study are listed in Supplementary File 2 (Aso et al., 2023; Dolan et al., 2019; Gao et al., 2019; Scaplen et al., 2021; Schretter et al., 2020; Takagi et al., 2017; Xie et al., 2021; Yamada et al., 2023).”

Line 148: "MB major interneurons" is a confusing descriptor for postsynaptic partners of MBONs.

We added a sentence to clarify the definition of the “MB major interneurons”.

“In the hemibrain EM connectome, there are about 400 interneuron cell types that have over 100 total synaptic inputs from MBONs and/or synaptic outputs to DANs. Our newly developed collection of split-GAL4 drivers covers 30 types of these ‘major interneurons’ of the MB (Supplementary File 3).”

Lines 150-1: Not sure what is meant by "have innervations within the MB." Sounds like cells are presynaptic to KCs, DANS, and MBONs, but Figure 3 Figure Supplement 1 indicates they include neurons that both provide and receive innervation to/from MB neurons. Please clarify.

For clarification, in the revised manuscript we have included a full list of cell types within the MB in Supplementary File 4. Included are all neurons with >= 50 pre-synaptic connections or with >=250 post-synaptic connections in the MB roi in the hemibrain (excluding the accessory calyx). The cell types include KCs, MBONs, DANs, PNs, and a few other cell types. The coverage ratio was updated based on this list.

Also, in line 152, what does it mean that they "may have been overlooked previously?" this seems unnecessarily ambiguous. Were they overlooked or weren't they?

Changed the text to “These lines offer valuable tools to study cell types that previously are not genetically accessible. Notably, SS85572 enables the functional study of LHMB1, which forms a rare direct pathway from the calyx and the lateral horn (LH) to the MB lobes (Bates et al., 2020). ”

Line 158 refers to PN cells within the MB, which are not mentioned in any place else as MB components.

What are these PNs and how do they differ from MBONs?

See responses to Lines 150-1 for clarification of cell types within the MB.

Line 188: not clear what is meant by "more continual learning tasks".

We rephrase it as “more complex learning tasks” to avoid jargon.

Line 235: Not clear why "extended training with high LED intensity" wouldn't promote the formation of robust memories. Is this for some reason unexpected based on previous experiments? Please explain.

See responses to weakness #1 of the same reviewer

Lines 317-9: It would be useful to state here that MB0N08 and MB0N09 are the two neurons labeled by MB083C.

Revised as suggested.

Line 368: Presumably the "lookup table" referred to is Supplementary File 1, but a reference here would be useful.

Yes, Supplementary File 1 and a reference was added.

Comments on Figures:

Figure 1C The "Dopamine Neurons" label position doesn't align with the Punishment and Reward labels, which is a bit confusing.

They are intentionally not aligned, because dopamine neurons are not reward/punishment per se. We intend to use the schematic to show that the punishment and reward are conveyed to the MB through the dopamine neuron layer, just as the output from the MB output neuron layer is used to guide further integration and actions. To keep the labels of “Dopamine neurons” and “MB Output Neurons” in a symmetrical position, we decide to keep the original figure unchanged. But we thank the reviewer for the kind suggestion.

Figure 1F and Figure 1 - Figure Supplement 1: the light gray labels presumably indicate the (EM-identified) neuron labeled by each line, but this should be explicitly stated in the figure legends. It would also be useful in the legends to direct the reader to the key (Supplementary File 1) for decoding neuronal identities.

Revised as suggested.

Figure 2: For clarity, I'd recommend titling this figure "LM-EM Match of the CRE011-specific driver SS45245". This reduces the confusion of mixing and matching the driver and cell-type names. Also, it would be helpful to indicate (e.g. with labels above the figure parts) that A & B represent the MCFO characterization step and C & D represent the LM-EM matching step of the pipeline. Revised as suggested.

Figure 6: For clarity, it would be useful to separately label the PN and sensory neuron groups. Also, for the sensory neurons at the bottom, what is the distinction between the cell names in gray and black font?

Figure 6 was updated to separate the non-olfactory PN and sensory neuron groups. The gray was intended for olfactory receptor neuron cell types that are additionally labeled in the driver lines. To avoid confusion, the gray cell types were removed in the revised figure, and a clarification sentence was added to the legend.

“Other than thermo-/hygro-sensory receptor neurons (TRNs and HRNs), SS00560 and MB408B also label olfactory receptor neurons (ORNs): ORN_VL2p and ORN_VC5 for SS00560, ORN_VL1 and ORN_VC5 for MB408B.”

Figure 7A: It's unclear why the creation of 6 Gr64f-LexADBD lines is reported. Aren't all these lines the same? If not, an explanation would be useful.

These six Gr64f-LexADBD lines are with different insertion sites, and with the presence or absence of the p10 translational enhancer. Explanation was added to legend. Enhanced expression level with p10 can be helpful to compensate for the general tendency that split-LexA is weaker than split-GAL4. Different insertions will be useful to avoid transvections with split-GAL4s, which are mostly in attP40 and attP2.

Figure 8F: It would help to include in the legend a brief description of each parameter being measured-essentially defining the y-axis label on the graphs as in Figure Supplement 2. Also, how is the probability of return calculated and what behavioral parameter does the change of curvature refer to?

We added a brief description to the behavioral parameters in the legend of Figure 8F.

“Return behavior was assessed within a 15-second time window. The probability of return (P return) is the percentage of flies that made an excursion (>10 mm) and then returned to within 3 mm of their initial position. Curvature is the ratio of angular velocity to walking speed.”

Figure 9E: What are the parenthetical labels for lines SS49267, SS49300, and SS35008?

They are EM bodyIDs. Figure legend was revised.

Author response:

The following is the authors’ response to the original reviews.

eLife Assessment

This study compiles a wide range of results on the connectivity, stimulus selectivity, and potential role of the claustrum in sensory behavior. While most of the connectivity results confirm earlier studies, this valuable work provides incomplete evidence that the claustrum responds to multimodal stimuli and that local connectivity is reduced across cells that have similar long-range connectivity. The conclusions drawn from the behavioral results are weakened by the animals' poor performance on the designed task.This study has the potential to be of interest to neuroscientists.

We thank the editor and the reviewers for their feedback on our work, which we have incorporated to help improve interpretation of our findings as outlined in the response below. While we agree with the editor that further work is necessary to provide a comprehensive understanding of claustrum circuitry and activity, this is true of most scientific endeavors and therefore we feel that describing this work as “incomplete” unfairly mischaracterizes the intent of the experiments performed which provide fundamental insights into this poorly understood brain region. Additionally, as identified in the main text, methods section, and our responses to the comments below, we disagree that the behavioral results are “weakened” by the performance of the animals. Our goal was to assess what information animals learned and used in an ambiguous sensory/reward environment, not to shape them toward a particular behavior and interpret the results solely based on their accuracy in performing the task.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The paper by Shelton et al investigates some of the anatomical and physiological properties of the mouse claustrum. First, they characterize the intrinsic properties of claustrum excitatory and inhibitory neurons and determine how these different claustrum neurons receive input from different cortical regions. Next, they perform in vitro patch clamp recordings to determine the extent of intraclaustrum connectivity between excitatory neurons. Following these experiments, in vivo axon imaging was performed to determine how claustrum-retrosplenial cortex neurons are modulated by different combinations of auditory, visual, and somatosensory input. Finally, the authors perform claustrum lesions to determine if claustrum neurons are required for performance on a multisensory discrimination task

Strengths:

An important potential contribution the authors provide is the demonstration of intra-claustrum excitation. In addition, this paper provides the first experimental data where two cortical inputs are independently stimulated in the same experiment (using 2 different opsins). Overall, the in vitro patch clamp experiments and anatomical data provide confirmation that claustrum neurons receive convergent inputs from areas of the frontal cortex. These experiments were conducted with rigor and are of high quality.

We thank the reviewer for their positive appraisal of our work.

Weaknesses:

The title of the paper states that claustrum neurons integrate information from different cortical sources. However, the authors did not actually test or measure integration in the manuscript. They do show physiological convergence of inputs on claustrum neurons in the slice work. Testing integration through simultaneous activation of inputs was not performed. The convergence of cortical input has been recently shown by several other papers (Chia et al), and the current paper largely supports these previous conclusions. The in vivo work did test for integration because simultaneous sensory stimulations were performed. However, integration was not measured at the single cell (axon) level because it was unclear how activity in a single claustrum ROI changes in response to (for example) visual, tactile, and visual-tactile stimulations. Reading the discussion, I also see the authors speculate that the sensory responses in the claustrum could arise from attentional or salience-related inputs from an upstream source such as the PFC. In this case, claustrum cells would not integrate anything (but instead respond to PFC inputs).

We thank the reviewer for raising this point. In response, we have provided a definition of “integration” in the manuscript text (lines 112-114, 353-354):

“...single-cell responsiveness to more than one input pathway, e.g. being capable of combining and therefore integrating these inputs.”

The reviewer’s point about testing simultaneous input to the claustrum is well made but not possible with the dual-color optogenetic stimulation paradigm used in our study as noted in the Results and Discussion sections (see also Klapoetke et al., 2014, Hooks et al., 2015). The novelty of our paper comes from testing these connections in single CLA neurons, something not shown in other studies to-date (Chia et al., 2020; Qadir et al., 2022), which average connectivity over many neurons.

Finally, we disagree with the reviewer regarding whether integration was tested at the single-axon level and provide data and supplementary figures to this effect (Fig. 6, Supp. Fig. S14, lines 468-511) . Although the possibility remains that sensory-related information may arise in the prefrontal cortex, as we note, there is still a large collection of studies (including this one) that document and describe direct sensory inputs to the claustrum (Olson & Greybeil, 1980; Sherk & LeVay, 1981; Smith & Alloway, 2010; Goll et al., 2015; Atlan et al., 2017; etc.). We have updated the wording of these sections to note that both direct and indirect sensory input integration is possible.

The different experiments in different figures often do not inform each other. For example, the authors show in Figure 3 that claustrum-RSP cells (CTB cells) do not receive input from the auditory cortex. But then, in Figure 6 auditory stimuli are used. Not surprisingly, claustrum ROIs respond very little to auditory stimuli (the weakest of all sensory modalities). Then, in Figure 7 the authors use auditory stimuli in the multisensory task. It seems that these experiments were done independently and were not used to inform each other.

The intention behind the current manuscript was to provide a deep characterisation of claustrum to inform future research into this enigmatic structure. In this case, we sought to test pathways in vivo that were identified as being weak or absent in vitro to confirm and specifically rule out their influence on computations performed by claustrum. We agree with the reviewer’s assessment that it is not surprising that claustrum ROIs respond weakly to auditory stimuli. Not testing these connections in vivo because of their apparent sparsity in vitro would have represented a critical gap in our knowledge of claustrum responses during passive sensory stimulation.

One novel aspect of the manuscript is the focus on intraclaustrum connectivity between excitatory cells (Figure 2). The authors used wide-field optogenetics to investigate connectivity. However, the use of paired patch-clamp recordings remains the ground truth technique for determining the rate of connectivity between cell types, and paired recordings were not performed here. It is difficult to understand and gain appreciation for intraclaustrum connectivity when only wide-field optogenetics is used.

We thank the reviewer for acknowledging the novelty of these experiments. We further acknowledge that paired patch-clamp recordings are the gold standard for assessing synaptic connectivity. Typically such experiments are performed in vitro, a necessity given the ventral location of claustrum precluding in vivo patching. In vitro slice preparations by their very nature sever connections and lead to an underestimate of connectivity as noted in our Discussion. Kim et al. (2016) have done this experiment in coronal slices with the understanding that excitatory-excitatory connectivity would be local (<200 μm) and therefore preserved. We used a variety of approaches that enabled us to explore connectivity along the longitudinal axis of the brain (the rostro-caudal, e.g. “long” axis of the claustrum), providing fresh insight into the circuitry embedded within this structure that would be challenging to examine using dual recordings. Further, our optogenetic method (CRACM, Petreanu et al., 2007), has been used successfully across a variety of brain structures to examine excitatory connectivity while circumventing artifacts arising from the slice axis.

In Figure 2, CLA-rsp cells express Chrimson, and the authors removed cells from the analysis with short latency responses (which reflect opsin expression). But wouldn't this also remove cells that express opsin and receive monosynaptic inputs from other opsin-expressing cells, therefore underestimating the connectivity between these CLA-rsp neurons? I think this needs to be addressed.

The total number of opsin-expressing CLA neurons in our dataset is 4/46 tested neurons. Assuming all of these neurons project to RSP, they would have accounted for 4/32 CLARSP neurons. Given the rate of monosynaptic connectivity observed in this study, these neurons would only contribute 2-3 additional connected neurons. Therefore, the exclusion of these neurons does not significantly impact the overall statistical accuracy of our connectivity findings.

In Figure 5J the lack of difference in the EPSC-IPSC timing in the RSP is likely due to 1 outlier EPSC at 30 ms which is most likely reflecting polysynaptic communication. Therefore, I do not feel the argument being made here with differences in physiology is particularly striking.

We thank the reviewer for their attention to detail about this analysis. We have performed additional statistics and found that leaving this neuron out does not affect the significance of the results (new p-value = 0.158, original p-value = 0.314, Mann-Whitney U test). We have removed this datapoint from the figure and our analysis.

In the text describing Figure 5, the authors state "These experiments point to a complex interaction ....likely influenced by cell type of CLA projection and intraclaustral modules in which they participate". How does this slice experiment stimulating axons from one input relate to different CLA cell types or intra-claustrum circuits? I don't follow this argument.

We have removed this speculation from the Results section.

In Figure 6G and H, the blank condition yields a result similar to many of the sensory stimulus conditions. This blank condition (when no stimulus was presented) serves as a nice reference to compare the rest of the conditions. However, the remainder of the stimulation conditions were not adjusted relative to what would be expected by chance. For example, the response of each cell could be compared to a distribution of shuffled data, where time-series data are shuffled in time by randomly assigned intervals and a surrogate distribution of responses generated. This procedure is repeated 200-1000x to generate a distribution of shuffled responses. Then the original stimulus-triggered response (1s post) could be compared to shuffled data. Currently, the authors just compare pre/post-mean data using a Mann-Whitney test from the mean overall response, which could be biased by a small number of trials. Therefore, I think a more conservative and statistically rigorous approach is warranted here, before making the claim of a 20% response probability or 50% overall response rate.

We appreciate the reviewer's thorough analysis and suggestion for a more conservative statistical approach. We acknowledge that responses on blank trials occur about 10% of the time, indicating that response probabilities around this level may not represent "real" responses. To address this, we will include the responses to the blank condition in the manuscript (lines 505-509). This will allow readers to make informed decisions based on the presented data.

Regarding Figure 6, a more conventional way to show sensory responses is to display a heatmap of the z-scored responses across all ROIs, sorted by their post-stimulus response. This enables the reader to better visualize and understand the claims being made here, rather than relying on the overall mean which could be influenced by a few highly responsive ROIs.

We apologize to the reviewer that our data in this figure was challenging to interpret. We have included an additional supplemental figure (Supp. Fig. S15) that displays the requested information.

For Figure 6, it would also help to display some raw data showing responses at the single ROI level and the population level. If these sensory stimulations are modulating claustrum neurons, then this will be observable on the mean population vector (averaged df/f across all ROIs as a function of time) within a given experiment and would add support to the conclusions being made.

We appreciate the reviewer’s desire to see more raw data – we would have included this in the figure given more space. However, the average df/f across all ROIs is shown as a time series with 95% confidence intervals in Fig. 6D.

As noted by the authors, there is substantial evidence in the literature showing that motor activity arises in mice during these types of sensory stimulation experiments. It is foreseeable that at least some of the responses measured here arise from motor activity. It would be important to identify to what extent this is the case.

While we acknowledge that some responses may arise from motor-related activity, addressing this comprehensively is beyond the scope of this paper. Given the extensive number of trials and recorded axonal segments, we believe that motor-related activity is unlikely to significantly impact the average response across all trials. Future studies focusing specifically on motor activity during sensory stimulation experiments would be needed to elucidate this aspect in detail.

All claims in the results for Figure 6 such as "the proportion of responsive axons tended to be highest when stimuli were combined" should be supported by statistics.

We have provided additional statistics in this section (lines 490-511) to address the reviewer’s comment.

In Figure 7, the authors state that mice learned the structure of the task. How is this the case, when the number of misses is 5-6x greater than the number of hits on audiovisual trials (S Figure 19). I don't get the impression that mice perform this task correctly. As shown in Figure 7I, the hit rate is exceptionally low on the audiovisual port in controls. I just can't see how control and lesion mice can have the same hit rate and false alarm rate yet have different d'. Indeed, I might be missing something in the analysis. However, given that both groups of mice are not performing the task as designed, I fail to see how the authors' claim regarding multisensory integration by the claustrum is supported. Even if there is some difference in the d' measure, what does that matter when the hits are the least likely trial outcome here for both groups.

We thank the reviewer for their comments and hope the following addresses their confusion about the performance of animals during our multimodal conditioning task.

Firstly, as pointed out by the reviewer, the hit-rate (HR) is lower than false-alarm-rate (FR) but crucially only when assessed explicitly within-condition (e.g. just auditory or just visual stimulation). Given the multimodal nature of the assay, HR and FR could also be evaluated across different trials, unimodal and multimodal, for both auditory and visual stimuli. Doing so resulted in a net positive d', as observed by the reviewer. From this perspective, and as documented in the Methods (Multimodal Conditioning and Reversal Learning) and Supplemental Figures, mice do indeed learn the conditioning task and perform at above-chance levels.

Secondly, as raised in the Discussion, an important caveat of this assay was that it was unnecessary for mice to learn the task structure explicitly but, rather, that they respond to environmental cues in a reward-seeking manner that indicated perception of a stimulus. "Performance" as it is quantified here demonstrates a perceptual difference between conditions that is observed through behavioral choice and timing, not necessarily the degree to which the mice have an understanding of the task per se.

In the discussion, it is stated that "While axons responded inconsistently to individual stimulus presentations, their responsivity remained consistent between stimuli and through time on average...". I do not understand this part of the sentence. Does this mean axons are consistently inconsistent?

The reviewer’s interpretation is correct – although recorded axons tended to have a preferred stimulus or combination of stimuli, they displayed variability in their responses (response probability), though little or no variability in their likelihood to respond over time (on average).

In the discussion, the authors state their axon imaging results contrast with recent studies in mice. Why not actually do the same analysis that Ollerenshaw did, so this statement is supported by fact? As pointed out above, the criteria used to classify an axon as responsive to stimuli were very liberal in this current manuscript.

While we appreciate this comment from the reviewer, we feel that it was not necessary to perform similar analyses to those of Ollerenshaw et al in order to appreciate that methodological differences between these studies would have confounded any comparisons made, as we note in the Discussion.

I find the discussion wildly speculative and broad. For example, "the integrative properties of the CLA could act as a substrate for transforming the information content of its inputs (e.g. reducing trial-to-trial variability of responses to conjunctive stimuli...)". How would a claustrum neuron responding with a 10% reliability to a stimuli (or set of stimuli) provide any role in reducing trial-to-trial variability of sensory activity in the cortex?

We thank the reviewer for their feedback. We acknowledge the reviewer's concern regarding the speculative nature of our discussion. To address the specific point raised, while a neuron with a 10% reliability might appear limited in reducing trial-to-trial variability in sensory activity, it's possible that such neurons are responsive to a combination of stimuli or conditions not fully controlled or recorded in our current setup. For instance, variables like the animal’s attentional or motivational states could influence the responsiveness of claustrum neurons, thus integrating these inputs could theoretically modulate cortical processing. We have refined this section to clarify these points (now lines 810-813).

Reviewer #2 (Public Review):

Summary:

In this manuscript, Shelton et al. explore the organization of the Claustrum. To do so, they focus on a specific claustrum population, the one projecting to the retrosplenial cortex (CLA-RSP neurons). Using an elegant technical approach, they first described electrophysiological properties of claustrum neurons, including the CLA-RSP ones. Further, they showed that CLA-RSP neurons (1) directly excite other CLA neurons, in a 'projection-specific' pattern, i.e. CLA-RSP neurons mainly excite claustrum neurons not projecting to the RSP and (2) receive excitatory inputs from multiple cortical territories (mainly frontal ones). To confirm the 'integrative' property of claustrum networks, they then imaged claustrum axons in the cortex during singleor multi-sensory stimulations. Finally, they investigated the effect of CLA-RSP lesion on performance in a sensory detection task.

Strengths:

Overall, this is a really good study, using state-of-the-art technical approaches to probe the local/global organization of the Claustrum. The in-vitro part is impressive, and the results are compelling.

We thank the reviewer for their positive appraisal of our work.

Weaknesses:

One noteworthy concern arises from the terminology used throughout the study. The authors claimed that the claustrum is an integrative structure. Yet, integration has a specific meaning, i.e. the production of a specific response by a single neuron (or network) in response to a specific combination of several input signals. In this study, the authors showed compelling results in favor of convergence rather than integration. On a lighter note, the in-vivo data are less convincing, and do not entirely support the claim of "integration" made by the authors.

We thank the reviewer for their clarity on this issue. We absolutely agree that without clear definition in the study, interpretation of our data could be misconstrued for one of several possible meanings. We have updated our Introduction, Results, and Discussion text to reflect the definition of ‘integration’ we used in the interpretation of our work and hope this clarifies our intent to the reader.

Reviewer #3 (Public Review):

The claustrum is one of the most enigmatic regions of the cerebral cortex, with a potential role in consciousness and integrating multisensory information. Despite extensive connections with almost all cortical areas, its functions and mechanisms are not well understood. In an attempt to unravel these complexities, Shelton et al. employed advanced circuit mapping technologies to examine specific neurons within the claustrum. They focused on how these neurons integrate incoming information and manage the output. Their findings suggest that claustrum neurons selectively communicate based on cortical projection targets and that their responsiveness to cortical inputs varies by cell type.

Imaging studies demonstrated that claustrum axons respond to both single and multiple sensory stimuli. Extended inhibition of the claustrum significantly reduced animals' responsiveness to multisensory stimuli, highlighting its critical role as an integrative hub in the cortex.

However, the study's conclusions at times rely on assumptions that may undermine their validity. For instance, the comparison between RSC-projecting and non-RSC-projecting neurons is problematic due to potential false negatives in the cell labeling process, which might not capture the entire neuron population projecting to a brain area. This issue casts doubt on the findings related to neuron interconnectivity and projections, suggesting that the results should be interpreted with caution. The study's approach to defining neuron types based on projection could benefit from a more critical evaluation or a broader methodological perspective.

We thank the reviewer for their attention to the methods used in our study. We acknowledge that there is an inherent bias introduced by false-negatives as a result of incomplete labeling but contend that this is true of most modern tracing experiments in neuroscience, irrespective of the method used. Moreover, if false-negative biases are affecting our results, then they likely do so in the direction of supporting our findings – perfect knowledge of claustrum connectivity would likely enhance the effects seen by increasing the pool of neurons for which we find an effect. For example, our cortico-claustal connectivity findings in Figure 3 likely would have shown even larger effects should false-negative CLARSP neurons have been positively identified.

Where appropriate we have provided estimates of variability and certainty in our experimental findings and do not claim any definitive knowledge of the true rate and scope of claustrum connectivity.

Nevertheless, the study sets the stage for many promising future research directions. Future work could particularly focus on exploring the functional and molecular differences between E1 and E2 neurons and further assess the implications of the distinct responses of excitatory and inhibitory claustrum neurons for internal computations. Additionally, adopting a different behavioral paradigm that more directly tests the integration of sensory information for purposeful behavior could also prove valuable.

We thank the reviewer for their outlook on the future directions of our work. These avenues for study, we believe, would be very fruitful in uncovering the cell-type-specific computations performed by claustrum neurons.

Recommendations for the authors:

Reviewing Editor (Recommendations for the Authors):

The editor recommends addressing the issues raised by the reviewers about the statistical significance of sensory response with respect to blank stimuli, and solving the issue generated by the exclusion of monosynaptically connected neurons in the connectivity study, to raise the assessment strength of evidence from incomplete to solid. Moreover, as the reported result stands, the behavioral task does not seem to be learned by the animals as the animals are above chance for visual and auditory but largely below chance level for multisensory. It seems that the animals do not perform a multisensory task. The authors should clarify this.

Reviewer #1 (Recommendations For The Authors):

Several references were missing from the manuscript, where mouse CLA-retrosplenial or CLA-frontal neurons were investigated and would be highly relevant to both the discussion of claustrum function and the context of the methodologies used here. (Wang et al., 2023 Nat Comm; Nair et al., 2023 PNAS, Marriott et al. 2024 Cell Reports ; Faig et al., 2024 Current

Biology).

Reviewer #2 (Recommendations For The Authors):

Let me be clear, this is an excellent study, using state-of-the-art technical approaches to probe the local/global organization of the Claustrum. However, the study is somehow disconnected, with a fantastic in-vitro part, and, in my opinion, a less convincing in-vivo one.

As stated in the public review, I'm concerned about the use of the term "integration", as, in my opinion, the data presented in this study (which I repeat are of excellent level) do not support that claim.

Below are my main points regarding the article:

(1) My main comment relates to the use of the term 'integration'. It might be a semantic debate, but I think that this is an important one. In my opinion, neural integration is the "summing of several neural input signals by a single neuron to produce an output signal that is some function of those inputs". As the authors state in the discussion, they were not able to "assess the EPSP response magnitude to the conjunction of stimuli due to photosensitivity of ChrimsonR opsins to blue light". Therefore, the authors did not specifically prove integration, but rather input convergence. This does not mean that the results presented are not important or of excellent quality, but I encourage the authors to either tone down the part on integration or to give a clear definition of what they call integration.

(2) The in vivo imaging data are somehow confusing. First, the authors image two claustral populations simultaneously (the CLA-RSP and the CLA-ACA axons). I may be missing the information, but there is no evidence that these cells overlap in the CLA (no data in the supplement and existing literature only support partial overlap). Second, in the results part, the authors claim that 96% of the sensory-responsive axons displayed multisensory response. This, combined with the 47% of axons responsive to at least one stimulus should lead to a global response of around 45% of the axons in multisensory trials. Yet, in Figures 6F-G, one can see that the response probability is actually low (closer to 20%). To be honest, I cannot really understand how to make sense of these results. At first, I thought that most of the multisensory responsive axons show no response during multisensory stimulus (but one in the unimodal stimulus). This hypothesis is however unlikely, as response AUC is biased toward positivity in Figure 6H. Overall, I'm not totally convinced by the imaging data, and I think that the authors should be more cautious about interpreting their results (as they are in the discussion part, but less in the results part).

(3) The TetTox approach used in the study ablates all neurons expressing the CRE in the CLA. If the hypothesis proposed by the authors is true, then ablating one subpopulation should not impact that much the functioning of the whole CLA, as other neurons will likely "integrate" information coming from multiple cortices (Figures 3 and 4), the local divergence (Figure 1) will then allow the broadcasting of this information back to multiples cortices. Do the authors think that such an approach deeply modified intra-claustral network connectivity? If this is not the case, shouldn't we expect less effect after lesioning a specific sub-population of CLA neurons?

(4) The behavioral protocol is also confusing. If I understand correctly, the aim of the task was to probe the D-Prime factor, as all trials, whatever the response of the animal are rewarded. From the Figure 7I, one can see that the mice cannot properly answer to the audiovisual cues, clearly indicating that both groups show impaired response to this type of trial. The whole conclusion of the authors is therefore drawn from the D-Prime calculation. However, even if D-Prime should represent a measure of sensitivity (i.e. is unaffected by response bias), two assumptions need to be met: (1) the signal and noise distributions should be both normal, and (2) the signal and noise distributions should have the same standard deviation. However, these assumptions cannot be tested in the task used by the authors (one would need rating tasks). The authors might want to use nonparametric measures of sensitivity such as A' (see Pollack and Norman 1964).

Reviewer #3 (Recommendations For The Authors):

While the study is comprehensive, some of its conclusions are based on assumptions that potentially weaken their validity. A significant issue arises in the comparison between neurons that project to the retrosplenial cortex (RSC) and those that do not. This differentiation is based on retrograde labeling from a single part of the RSC. However, CTB labeling, the technique used, does not capture 100% of the neurons projecting to a brain area. The study itself demonstrates this by showing that injecting the dye into three sections of the RSC results in three overlapping populations of neurons in the claustrum. Therefore, limiting the injection to just one of these areas inevitably leads to many false negatives-neurons that project to the RSC but are not marked by the CTB. This issue recurs in the analysis of neurons projecting to both the RSC and the prelimbic cortex (PL), where assumptions about interconnectivity are made without a thorough examination of overlap between these populations. The incomplete labeling complicates the interpretation of the data and draws firm conclusions from it.

Minor.

There is a reference to Figure 1D where claustrum->cortical connections are described. This should be 5D.

This is a correct reference pointing back to our single-cell characterizations of CLA morphoelectric types.

End of Page 22. Implies should be imply.

This has been resolved in the manuscript text.

Author response:

The following is the authors’ response to the original reviews.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

This is an interesting and valuable study that uses multiple approaches to understand the role of bursting involving voltage-gated calcium channels within the mediodorsal thalamus in the sedative-hypnotic effects of alcohol. Given its unique functional roles and connectivity pattern, the idea that the mediodorsal thalamus may have a fundamental role in regulating alcohol-induced transitions in consciousness state would be both important for researchers investigating thalamocortical dynamics and more broadly interesting for understanding brain function. In addition, the author's examination of the role of the voltage-gated calcium channel Cav3.1 provides some evidence that burst-firing mediated by this channel in the thalamus is functionally important for behavioral-state transitions. While many previous studies have suggested an analogous role for sleep-state regulation, the evidence for an analogous role of this type of bursting in sedative-induced transitions is more limited. Despite the importance of these results, however, there is some concern that the manipulations and recording approaches employed by the authors may affect other thalamic nuclei adjacent to the MD, such as the central lateral nucleus, which has also been implicated in controlling state transitions. The evidence for a specific role of the mediodorsal thalamus is therefore somewhat incomplete, and so additional validation is needed.

Strengths:

This study employs multiple, complementary research approaches including behavioral assays, sh-RNAbased localized knockdown, single-unit recordings, and patterned optogenetic interventions to examine the role of activity in the mediodorsal thalamus in the sedative-hypnotic effects of alcohol. Experiments and analyses included in the manuscript generally appear well conceived and are also generally well executed. Sample sizes are sufficiently large and statistical analysis appears generally appropriate though in some cases additional quantification would be helpful. The findings presented are novel and provide some interesting insight into the role of the thalamus as well as voltage-gated calcium channels within this region in controlling behavioral state transitions induced by alcohol. In particular, the observed effects of selective knockout along with recordings in total knockout of the voltage-gated calcium channel, Cav3.1, which has previously been implicated in bursting dynamics as well as state transitions, particularly in sleep, together suggest that the transition of thalamic neurons to a bursting pattern of firing from a more constant firing is important for transition to the sedated state produced by ethanol intoxication. While previous studies have similarly implicated Cav3.1 bursting in behavioral state transitions, the direct optogenetic interventions and single-unit recordings provide valuable new insight. These findings may also have interesting implications for the relationship between sleep process disruption associated with ethanol dependence, although the authors do not appear to examine this directly or extensively discuss these implications of their findings.

Weaknesses:

A key claim of the study is that the mediodorsal thalamus is specifically important for the sedative-hypnotic effect of ethanol and that a transition to a bursting pattern of firing in this circuit facilitates these effects due to a loss of a more constant tonic firing pattern. Despite the generally clear observed effects across the included experiments, however, the evidence presented does not fully support that the mediodorsal thalamus, in particular, is involved. This distinction is important because some previous studies have suggested that another thalamic nucleus which is very close to the mediodorsal thalamus, the central-lateral thalamus, has previously been suggested to play a role in preventing sedative-induced transitions. Despite its proximity to the mediodorsal thalamus, the central-lateral thalamus has a substantially different pattern of connectivity so distinguishing which region is impacted is important for understanding the findings in the manuscript. While sh- RNA knockdown appears to be largely centered in the mediodorsal thalamus in the example shown, (Figure 2) this is rather minimal evidence and it is also not well explained (indeed, the relevant panels do not even appear to be referenced in the text of the manuscript) and the consistency of the knockdown targeting is not quantified. Additional evidence should be provided to validate this approach. Similarly, while an example is shown for the expression of ChR2 (Fig. 5) there seems to be some spread of expression outside of the mediodorsal thalamus even in his example raising a concern about how regionally specific this effect.

The recordings targeting the mediodorsal thalamus could provide evidence of a direct association between changes in activity specifically in this part of the thalamus with the behavioral measures but there are currently some issues with making this link. One difficulty is that, although lesions are shown in Figure S5 to validate recording locations, this figure is relatively unclear and the examples appear to be taken from a different anterior/posterior location compared to the reference diagram. A larger image and improved visualization of the overall set of lesion locations that includes multiple anterior/posterior coronal sections would be helpful. Moreover, even for these example images, it is difficult to evaluate whether these are in the mediodorsal thalamus, particularly given the small size of the image shown. Ideally, an example image that is more obviously in the mediodorsal thalamus would also be included. Finally, an assessment of the relationship between the approximate locations of recorded neurons across the tetrode arrays and the behavioral measures would be very helpful in supporting the unique role of the mediodorsal thalamus. The lack of these direct links, in combination with the histological issues, reduces the insight that can be gained from this study.

In addition to the key experimental issues mentioned above, there are often problems in the text of the manuscript with reasoning or at least explanation as well as numerous minor issues with editing. The most substantial such issue is the lack of clarity in discussing the mediodorsal thalamus and other adjacent thalamic nuclei, such as the central-lateral nucleus, in the author's discussion of previous findings. Given that at last one of the manuscripts cited by the authors (Saalman, Front. Sys. Neuro. 2014) has directly claimed that central-lateral, rather than the mediodorsal, thalamus is important for arousal regulation related to a conscious state, this distinction should be addressed clearly in the discussion rather than papered over by grouping multiple thalamic nuclei as being medial. As part of this discussion, it would be important to consider additional relevant literature including Bastos et al., eLife, 2021 and Redinbaugh et al., Neuron, 2020 which are quite critical but currently do not appear to be cited. Considering additional literature relevant to the function of the mediodorsal thalamus would also be beneficial. While the methods employed generally seem sound, the description in the methods section is lacking in detail and is often difficult to follow. Analysis methods such as the burst index appear to only be given a brief explanation in the text and appear not to be mentioned in the methods section. Similarly, the staining method used in Figure 2 does not appear to be described in the methods section. The most substantial case is for the UMAP approach used in Figure 4-E which does not appear to be described in the methods or even described in the main text. The lack of detailed descriptions makes it difficult to evaluate the applicability and quality of the experimental and analytical approaches. Citations justifying the use of methods such as the approach to separate regular spiking and narrow spiking neuron subtypes are also needed.

Beyond the problems with content and reasoning discussed above, there are also some relatively minor issues with the clarity of writing throughout the paper (for example, in the abstract the authors refer to "the ethanol resistance behavior in WT mice" but it is difficult to parse what they mean by this statement. Similarly, the next sentence "These results support that the maintenance..." while clearer, is not well phrased. Though individually minor, issues like this re-occur throughout the manuscript and sometimes make it difficult to follow so the text should be revised to correct them. There are also some problems with labels such as the labels of A1/A2 in Figure 4, which appear to be incorrect. Also, S7 has no label] on the B panels. Finally, some references are not included (only a label of [ref]).

Reviewer #2 (Public Review):

In the current study, Latchoumane and collaborators focus on the Cav3.1 calcium channels in the mediodorsal thalamic nucleus as critical players in the regulation of brain-states and ethanol resistance in mice. By combining behavioural, electrophysiological, and genetic techniques, they report three main findings. First, KO Cav3.1 mice exhibit resistance to ethanol-induced sedation and sustained tonic firing in thalamocortical units. Second, knocked-down Cav3.1 mice reproduce the same behaviour when the mediodorsal, but not the ventrobasal, thalamic nucleus is targeted. Third, either optogenetic or electric stimulation of the mediodorsal thalamus reduces ethanol-induced sedation in control animals.

Overall, the study is well designed and performed, correctly controlled for confounds, and properly analysed. Nonetheless, it is important to address some aspects of the report. The results support the conclusions of the study. These results are likely to be relevant in the field of systems neuroscience, as they increase the molecular evidence showing how the thalamus regulates brain states.

Reviewer #1 (Recommendations For The Authors):

Aside from the additional quantification and clarification of the analysis discussed in the weakness section, in general, the experiments included in the manuscript seem reasonable. However, I would suggest one additional experiment as well as one control, both of which are relatively straightforward optogenetic experiments, that I feel would be helpful to further improve the study. First, as the authors note, the optogenetic interventions used do not directly address the relevance of the changes in bursting patterns observed in the knockout (KO), which are by far the most robust effect, with the changes in alcohol sensitivity. One approach that could help address this would be to use patterned suppression via inhibitory opsins (e.g. halorhodopsin) to "rescue" the periods of inhibition associated with bursting in the KO. Localizing this inhibition to the mediodorsal thalamus would also lend further credence to their claim that this nuclei is the relevant circuit for their observed effects. For the control, tonic activation of the ventrobasal nucleus, as the authors did for the mediodorsal nucleus, would be beneficial to rule out the possibility that the observed effect would occur with any thalamic nucleus. In addition to these experiments, I did not note the strategy for sharing data obtained through this study so this should be added.

R1 – 1: A key claim of the study is that the mediodorsal thalamus is specifically important for the sedative-hypnotic effect of ethanol and that a transition to a bursting pattern of firing in this circuit facilitates these effects due to a loss of a more constant tonic firing pattern. Despite the generally clear observed effects across the included experiments, however, the evidence presented does not fully support that the mediodorsal thalamus, in particular, is involved. This distinction is important because some previous studies have suggested that another thalamic nucleus which is very close to the mediodorsal thalamus, the central-lateral thalamus, has previously been suggested to play a role in preventing sedative-induced transitions. Despite its proximity to the mediodorsal thalamus, the central-lateral thalamus has a substantially different pattern of connectivity so distinguishing which region is impacted is important for understanding the findings in the manuscript.

R1-A1: The reviewer is right that CL has been pointed as another candidate structure with causal influence on arousal and consciousness. We have focused our efforts in including only recording single units that were from tetrode located in the MD specifically using the lesion code we explain in the method section and in response to R1 question#3. We also produced a quantification of Cav3.1 knock-down that clearly demonstrates that the KD experiment was itself specific to MD, bilaterally, and that CL to CM were minimally impacted by the knock-down process (Fig. 2C and D). Moreover, the optogenetic  (fiber incidence was 30 degrees guaranteeing a central coverage rather than lateral; Fiber optic NA = 0.22) and electric stimulation (bipolar twisted electrodes, 50uA) experiments were also very selective and specific to the MD (Fig.S5). It remains clear that MD might not be the sole structure involved in the brain state control towards sedation and “anesthetic states”, and CL might be a significant contributor as well, however, we show that CL manipulations were rather irrelevant in our experiments  (Fig. 2, S5, S9 and S11).

R1-2: While sh-RNA knockdown appears to be largely centered in the mediodorsal thalamus in the example shown, (Figure 2) this is rather minimal evidence and it is also not well explained (indeed, the relevant panels do not even appear to be referenced in the text of the manuscript) and the consistency of the knockdown targeting is not quantified. Additional evidence should be provided to validate this approach.

R1-A2: In order to address this important question, we have created an additional panel quantification to fig2D. We have then quantified the intensity per area of Cav3.1 expression in sub zones of 4 regions of interest: MD (left, right; 2 subzones each), Centro Medial (CM; 1 subzones in total), Centrolateral/Paraventricular nucleus (CL/PCN; left, right; 2 subzones each) and the submedial nucleus (SMT; left, right; used as a control for the intensity normalization; 1 subzones in total). This panel clearly illustrates that MD was knocked-down bilaterally (p<0.001). Moreover, CM (p<0.05) and CL (p<0.01) were also partially and unilaterally knocked down, as well. This analysis confirms that our KD had a high specificity to MD.

We added the relevant figure caption and text:

[Result section, Cav3.1 silencing in the MD, but not VB, increased ethanol resistance in mice, paragraph 3]

“We then characterized the change in Cav3.1 expression following the shControl and shCav3.1 knockdown injections in three test regions MD (left and right), CM (centromedial nucleus) and CL (centrolateral nuclei, left and right side) and a negative control region SMT (submedial thalamic nuclei, left and right side). The average intensity was obtained from two coronal brain slices for each mice used in the experiment (see Methods sections, Cav3.1 Intensity quantification). Our results show that the targeting of the knockdown was very specific to the bilateral MD (p<0.001; Fig. 2D). We noted that the CM (p<0.05) and a marginal unilateral knock-down of the CL were also observed (p<0.01). Notably, we tested the correlation between the level of knock-down in MD and the total time in LOM and observed a significant association (Fig. 2D inset; R = 0.599, p = 0.018). This result highlights that the Cav3.1 knock-down was specific to MD and with an intensity associated with ethanol-induced loss of motion.”

R1-3: One difficulty is that, although lesions are shown in Figure S5 to validate recording locations, this figure is relatively unclear and the examples appear to be taken from a different anterior/posterior location compared to the reference diagram. A larger image and improved visualization of the overall set of lesion locations that includes multiple anterior/posterior coronal sections would be helpful. Moreover, even for these example images, it is difficult to evaluate whether these are in the mediodorsal thalamus, particularly given the small size of the image shown. Ideally, an example image that is more obviously in the mediodorsal thalamus would also be included. Finally, an assessment of the relationship between the approximate locations of recorded neurons across the tetrode arrays and the behavioral measures would be very helpful in supporting the unique role of the mediodorsal thalamus.

R1-A3: Related to fig.S5, we re-distributed the position of the recordings from the tetrode electrode burned positions over 3 representative coronal planes that best represent the implant positions. We also provided additional snapshots of tetrode location. To identify the positions of four tetrodes in each animal, we encoded the positions with different electrical lesion strategies as follows: 1 lesion(tetrode 1), 2 lesions while we redrew the tetrode with 100 um interval (tetrode 2), 3 lesions with 200um interval (tetrode 3), 4 lesions with 50um intervals (tetrode4). Tetrodes that were found outside of the MD delimited region were discarded post analysis. A straight relationship between the closeness of the electrode is unfortunately not possible for tetrode recording, a straight silicone probe which maintains the spatial spacing in recording would have been a better approach in that case, but unfortunately, it was not performed in our study.

R1-4: In addition to the key experimental issues mentioned above, there are often problems in the text of the manuscript with reasoning or at least explanation as well as numerous minor issues with editing. The most substantial such issue is the lack of clarity in discussing the mediodorsal thalamus and other adjacent thalamic nuclei, such as the central-lateral nucleus, in the author's discussion of previous findings. Given that at last one of the manuscripts cited by the authors (Saalman, Front. Sys. Neuro. 2014) has directly claimed that central-lateral, rather than the mediodorsal, thalamus is important for arousal regulation related to a conscious state, this distinction should be addressed clearly in the discussion rather than papered over by grouping multiple thalamic nuclei as being medial. As part of this discussion, it would be important to consider additional relevant literature including Bastos et al., eLife, 2021 and Redinbaugh et al., Neuron, 2020 which are quite critical but currently do not appear to be cited. Considering additional literature relevant to the function of the mediodorsal thalamus would also be beneficial.

R1-A4: We thank the reviewer for his comments and suggestions. We agree that the added references mentioned by the reviewers are highly relevant and should be integrated in the manuscript. We have integrated the above-mentioned references and further developed on the discussion on the role of MD relative to other thalamic nuclei (ILN and CL in particular). We believe that this better-referenced and clarified text does improve the manuscript greatly.

[introduction section, paragraph 3]

“The centrolateral (CL) thalamic nucleus has been implicated in the modulation of arousal, behavior arrest 31, and improvement of level of consciousness during seizures 32. Notably, the direct electrical stimulation of the intralaminar nuclei (ILN) and, in particular CL, promoted hallmarks of arousal and awakening in primate under propofol and ketamine propofol anesthesia.”

[Discussion section, paragraph 1]

“In this work, we identified that the neural activity in MD plays a causal role in the maintenance of consciousness. Whole body Cav3.1 KO and MD-specific Cav3.1 KD mice showed resistance to loss of consciousness induced by hypnotic dose of ethanol. In WT mice, MD neurons demonstrated a reduced firing rate in natural (sleep) and ethanol-induced unconscious states compared to awake states. This neural activity reduction was impaired in KO mice. In particular, transition to an unconscious state was accompanied with a switch of firing mode from tonic firing to burst firing in WT mice whereas this modeshift disappeared in KO mice. Finally, optogenetic or electric stimulations of the MD after ethanol injection were sufficient to induce a resistance to loss of motion, supporting that the level of neural firing in the MD is critical to maintain conscious state and delay unconscious state. We showed that the expression of Cav3.1 t-type calcium channels in MD is a cellular modulator associated with this effect.”

[Discussion section, MD is a modulator of consciousness, paragraph 2 and 3]

“The MD is known to innervate limbic region, basal ganglia and medial prefrontal cortex 50 and increased activity in MD might modulate the stability of cortical UP states (e.g. awaken, aroused and attentive states) and synchronization 9,26. Thus, MD might be a major hub involved in cortical state control and brain state stabilization.

Supporting the brain state stabilization theory and the ethanol resistance of Cav3.1 mutants, Choi et al.34 demonstrated that the loss of Cav3.1 T-type calcium channel reduced the bilateral coherence between PFC and MD under ketamine anesthesia and ethanol hypnosis, especially in the delta frequency bands. More importantly, under propofol anesthesia, Bastos et al.35 showed that intralaminar nucleus and MD stimulation lead to increased wake-up subscore and arousal, together with an increased in cortico-cortico and thalamo-cortical slow (delta) frequency power.

In the present study, we observed that MD KD (Fig. 2A), but not VB KD (Fig. S3) of Cav3.1 increased and is associated (Fig. 2D) with ethanol resistance in mice. We found that MD neurons in Cav3.1 mutant mice exhibited tonic firing within range of wakefulness (Fig. 3 and 4), indicative of resistance to ethanol and wake-like brain state. In addition, we found a strong association between the normalized tonic firing in MD and the arousal through brain states (i.e. walk to wake to sleep states), supporting that MD tonic firing could be interpreted both as a thalamic readout and a modulator of the brain state 11 (Fig. 3). Finally, direct optogenetic and electric MD stimulation increased resistance to loss of consciousness in WT mice (Fig.5 and Fig. S10). To our knowledge, this is the first report demonstrating the causal involvement of mediodorsal thalamic nucleus in the modulation of wakefulness and the resistance to ethanol-induced loss of consciousness in mice.”

R1-5: While the methods employed generally seem sound, the description in the methods section is lacking in detail and is often difficult to follow. Analysis methods such as the burst index appear to only be given a brief explanation in the text and appear not to be mentioned in the methods section.

R1-A5: We have added a clear definition in the supplementary method following the original work used:

[Supplementary Method section, Single Unit recording, sorting and analysis, last paragraph]

“The bursting index was derived as described in (Royer et al. 2012). Namely, the burst index was estimated from the spike auto-correlogram (1-ms bin size) by subtracting the mean value between 40 and 50 ms (baseline) from the peak measured between 0 and 10 ms. Positive burst amplitudes were normalized to the peak and negative amplitudes were normalized to the baseline to obtain indexes ranging from −1 to 1.” We also edited its mention in the text for clarity:

[Result section, Lack of Ca3.1 in MD neurons removes thalamic burst in NREM sleep, paragraph 2]

“[…] and a clear reduction in total bursting represented as bursting index (Fig. 3-B; ratio of spikes count <10 ms and >50 ms based on auto-cross-correlogram).”

R1-6: Similarly, the staining method used in Figure 2 does not appear to be described in the methods section.

R1-A6: The staining method can be found in the supplementary method of the paper. [supplementary method, Immunohistochemistry]

R1-7: The most substantial case is for the UMAP approach used in Figure 4-E which does not appear to be described in the methods or even described in the main text.

R1-A7: Regarding the method, the UMAP approach is described in the supplementary method document [Uniform Manifold Approximation and Projection (UMAP)]. We believe that only a succinct description was needed here considering the extent of the analysis. Regarding the inserts in the main text, we agree that the main text was lacking the description of these results and we have amended the main text to reflect a clear description of this result and what it entails. The following paragraph was added:

[Result section, Under ethanol, MD neurons lacking Cav3.1 show no burst and a wake state-like neural activity, second to last paragraph]

“Finally, we asked whether the firing modes and properties (tonic firing rate, burst firing rate; see supplementary methods) of single MD neurons would form distinct qualitative representation of “brain stages” using a lowered dimensional UMAP representation (Uniform Manifold Approximation and Projection42 ). We observed that for awake and active (i.e. walk), the brain state representation formed two adjacent clusters that confounded both wild and mutant neurons (Fig. 4E, left panel). The REM and NREM states, the wild type neurons formed 2 additional interconnected clusters, whereas the mutant neurons tend to overlap with the clusters attributed to the “awake” brain state (Fig. 4E, second to left panel). Ethanol induced fLOM, similarly to REM and NREM clusters, was distinct from awake clusters in wild type mice and overlapped with the NREM clusters (Fig. 4E, third to left panel). Here also, mutant MD neurons showed overlap with the awake clusters rather than the “low consciousness” brain states. These results indicate that the firing mode and properties could define a brain state representation that shows distinctions in levels of consciousness. Moreover, the mutant showed a representation of “low consciousness” states overlapping with wild type “awake” states consistent with the hypothesis of resistance to loss of consciousness.”

R1-8: Citations justifying the use of methods such as the approach to separate regular spiking and narrow spiking neuron subtypes are also needed.

R1-A8: We have added two references related to the observation of the two subpopulations of spiking neurons [Schiff and Reyes, 2012; Destexhe, 2008].

R1-9: Beyond the problems with content and reasoning discussed above, there are also some relatively minor issues with the clarity of writing throughout the paper (for example, in the abstract the authors refer to "the ethanol resistance behavior in WT mice" but it is difficult to parse what they mean by this statement.

R1-A9: We addressed this issue by editing and revising the manuscript for clarity and flow.

R1-10: Similarly, the next sentence "These results support the maintenance..." while clearer, is not well phrased. Though individually minor, issues like this re-occur throughout the manuscript and sometimes make it difficult to follow so the text should be revised to correct them.

R1-A10: We thank the reviewer for highlighting this point. We have edited the overall text to improve clarity and flow.

[abstract] 

These results suggest that maintaining MD neural firing at a wakeful level is sufficient to induce resistance to ethanol-induced hypnosis in WT mice.

R1-11: There are also some problems with labels such as the labels of A1/A2 in Figure 4, which appear to be incorrect.

R1-A11: We noted this issue and have rectified the figure for clarity.

R1-12: Also, S7 has no label on the B panels.

R1-A12: We thank the reviewer for pointing out this lack. We have added the y-label on the panel for clarity.

R1-13: Finally, some references are not included (only a label of [ref]).

R1-A13: We have completed the missing reference and thank the reviewer for pointing that out.

Additional comments

R1-14: Aside from the additional quantification and clarification of the analysis discussed in the weakness section, in general, the experiments included in the manuscript seem reasonable. However, I would suggest one additional experiment as well as one control, both of which are relatively straightforward optogenetic experiments, that I feel would be helpful to further improve the study. First, as the authors note, the optogenetic interventions used do not directly address the relevance of the changes in bursting patterns observed in the knockout (KO), which are by far the most robust effect, with the changes in alcohol sensitivity. One approach that could help address this would be to use patterned suppression via inhibitory opsins (e.g. halorhodopsin) to "rescue" the periods of inhibition associated with bursting in the KO.

R1-A14: Here the reviewer proposes an interesting experiment which we have attempted to perform, however, poses several technical challenges. First, the KO do not have burst firing as they are depleted from Cav3.1 low-threshold calcium channel. Therefore, under ethanol, even if there might exist a rhythmic inhibition that activates Cav3.1 channels and causes a rebound burst, the KO are unable to have it. Therefore, an optogenetic inhibition would only accentuate the total inhibition and could potentially induce an overall decrease in MD firing, resulting in an increase in LOM features. Alternatively, we showed that in a WT with low ethanol dose (where LOM induction is harder), the increased rhythmic inhibition does indeed increase significantly LOM duration and marginally decreases latency to LOM (Fig. S12), indicating that increased inhibition could indeed explain the hypothesis: “ the stronger the decrease in MD firing, the faster and longer the LOM.” The only caveat of using WT here is that optogenetic inhibition might also include rebound burst post-inhibition. Injecting bursts only did not alter the response to ethanol (Fig. S10). These results point to the role of loss of firing in MD as a main factor for LOM, and potentially the contribution of burst necessitating a concurrent inhibition/loss of firing.

We agree that inhibition in KO would further validate this hypothesis, controlling for the role of burst. We regret that we are not in the capacity to perform additional experiments involving the KO mice.

R1-15: For the control, tonic activation of the ventrobasal nucleus, as the authors did for the mediodorsal nucleus, would be beneficial to rule out the possibility that the observed effect would occur with any thalamic nucleus.

R1-A15: We agree with the reviewer that we could have added an additional region control to the gain/loss of function experiments. We would even go further as to suggest that a better control nucleus would be a high order nucleus such as PO or an unrelated sensory relay nucleus such as LGN. VB being a motor relay nucleus, could also mediate movement initiation, which could be hard to interpret. Since the complete control study for all thalamic nuclei Cav3.1 KD is outside the scope of this study, we opted not to redo these experiments and keep the focus of the manuscript on the manipulation of MD activity rather than the various available thalamic nuclei. We also do not claim that MD is the sole center able to initiate a switch in the loss of consciousness, and a more in-depth study on that matter would be clearly needed.

R1-16: In addition to these experiments, I did not note the strategy for sharing data obtained through this study so this should be added.

R1-A16: We have uploaded data and code for most figures at the following repository and provided a clearer statement regarding data sharing. We thank the reviewer for pointing out this missing element.

The link for the repository is the following:

It contains:

- Excel spreadsheet file of all behavior values, including the newly quantified Cv3.1 expression in MD/CL/SMT

- Excel spreadsheet follow-up of all MD cells (single unit; tetrode) analyzed

- Folders for all groups studied with representative figures showing EEG power over time and normalized activity (WT vs KO for 2, 3 and 4 g/kg; MDshKD vs shCTR, VBshKD vs shCTR; CHR2 NOSTIM vs STIM; ESTIM Groups and ARCH NOSTIM vs STIM)

- A1G LORRvsLOM and OPEN FIELD Matlab data

- Matlab and ImageJ Codes: single unit analysis, characterization, brain state characterization, sleep stages, LOM, open field analysis and statistical analysis.

We have added the data sharing subsection in the acknowledgements:

“Part of the analyzed data and codes are available on the open access platform, mendeley:

Latchoumane, Charles-francois (2024), “Mediodorsal thalamic nucleus mediates resistance to ethanol through Cav3.1 T-type Ca2+ regulation of neural activity”, Mendeley Data, V1, doi: 10.17632/7fr427426m.1

Additional data (large size recording and images) can be provided upon reasonable requests.”

Reviewer #2 (Recommendations For The Authors):

R2-1. Consciousness is a contentious subject. Even in humans, there is still intense research on the topic, not to mention animals, about which we still know very little. Moreover, consciousness is not quantified in this study, as there is no standard metric to do so. Accordingly, talking about 'modulation', 'transition', ́level ', or 'reduction' of consciousness can be misleading. Hence, it is probably safer to strictly refer to brain-states and/or stages of the sleep-wake cycle in this study and reframe it entirely around these concepts.

R2-A1. The reviewer points to an important point and we appreciate this highlight. Agreeing that the definition of consciousness is rather loose and arguably difficult to pinpoint. Here, we settle on a definition that relies on the loss of motion and loss of righting reflex. This definition is widely accepted as the “verified” state in which the absence of responsiveness (to continuous stimuli, inducing reflex or discomfort) is observed and uninterrupted by jerks and spurious movements. Additional metrics needed would be the recording of EMG to quantify atonia and EEG to the settling of a dominantly slow-wave frequency (~4 Hz; ethanol-induced sedation at theta rhythm), as shown in Fig S1A. The driver of this 4Hz frequency and its correlation has been investigated previously (e.g. Choi et al, PNAS, 2012), leading to the accepted link between LOM/LORR and loss of consciousness. Our data present the advantage of showing single neuron recordings and that LOM is a state where the lowest firing activity is present (Fig S7AB) and comparable to deep sleep state activity (Fig3D). The first LOM is the most important as it highlights the deepest loss of consciousness before the ethanol starts to be metabolized and cleared, which would be consistent between animals.

As a result, we have edited the manuscript to clarify all mentions related to brain states and states of unconsciousness.

R2-2. It is not clear why the authors focus on the mediodorsal nucleus. This should be better explained in the introduction and developed in the discussion.

R2-A2. This comment converges with the Reviewer 1 comments and we are addressing this lack in the discussion as suggested. We have addressed it with this previous comment and believe it is now clearer.

R2-3. The discussion mentions that 'increased activity in MD might modulate the stability of cortical UP state and synchronization' (pg 21). This point should be either further developed and put into context, or removed. In its current state, it does not seem to contribute much to the discussion of results.

R2-A3. We understand that the working “UP state” might not be clear enough. We have modified this sentences as follows to clarify that UP state could be either a state of where the animal is awake, aroused or attentive:

[Discussion section, MD is a modulator of consciousness, first paragraph]

“The MD is known to innervate limbic region, basal ganglia and medial prefrontal cortex 50 and increased activity in MD might modulate the stability of cortical UP states (e.g. awaken, aroused and attentive states) and synchronization 9,26. Thus, MD might be a major hub involved in cortical state control and brain state stabilization.“

R2-4. The discussion states that 'mutant mice did not exhibit a decreased arousal level (i.e. increased locomotor activity)' (pg 23). This is confusing as decreased arousal should be reflected in decreased locomotor activity.

R2-A4. We understand that the formulation of this sentence may be confusing and we have edited this portion of the text to improve quality in the revised version of the manuscript. To clarify, mutant mice do not exhibit reduced or increased arousal (not quantified, just observational), they do have a phenotypic hyperlocomotion. This comes in contrast with a lower basal firing rate in the MD, which in our interpretation, is not synonymous with lower arousal. We believe that the relative change in MD determines the change in arousal, and that the absolute firing is not indicative of arousal in itself, only in comparison.

[Discussion section, The lower variability in MD Firing reflects Ethanol Resistance in Cav3.1 mutant mice, paragraph 2]

“Mutant RS neurons in MD showed an overall lower excitability and variability of firing in various natural conscious and unconscious states compared to wild type mice. Remarkably, Cav3.1 mutant mice exhibited a clear increased locomotor activity and an increased resistance to ethanol. The general lower firing rate and the high “arousal” observed in mutant mice suggests that the relative change from state to state in tonic firing in MD, and not the absolute value of firing, might be a better correlate of change in brain state in the mice.”

R2-5. The methods (pg 27) state that two genetic backgrounds (129/svjae and C57BL/6J ) were used in the study. Authors should show whether there were significant differences between those backgrounds in the key parameters assessed in the study (particularly resistance to ethanol sedation).

R2-A5. As mentioned in the method section, we only used the F1-background mice, which are the firstgeneration offspring produced by crossing 129/svjae and C57BL/6J strains. To produce F1 KO mice, we kept the heterozygote mice in two strains. We unfortunately did not study the particular difference of the respective KO of these two backgrounds; however, the pure C57BL/6J KO has been used in other studies by our group (Kim et al 2001; Na et al, 2008; Park et al., 2010). The F1 background allows us to work with mice that are less aggressive and can be handled with less inherent stress.

R2-6. It would be convenient to produce a supplementary figure associated with Figure 1C to show the same data with averages per mouse. That is, 9 points for control and 9 points for KO mice. This also applies to all cases where data is not presented per mouse but pooled between animals.

R2-A6. We have added a panel C in Figure S1, to show the scatter values for all the mice corresponding to the figure 1C. We have also generalized this presentation for all behavior graphics showing all the animals in the scatter plot next to the boxplot. We believe that this presentation increases further the transparency of the manuscript. We have then added the scatter plot for all mice in figure Fig1, Fig2, Fig5, Fig.S2, Fig.S3, Fig.S10 and Fig.S12.

R2-7. It would be informative to make a supplementary figure associated with Figure 1D to compare baseline raw activity levels (i.e., baseline walking recording) between control and KO mice. That is, do KO and control mice cover comparable distances and at similar speeds during baseline conditions? Figure 1D and Figure 4A suggest that the variability of locomotor activity is larger in KO mice. Hence, this parameter should be quantified and reported.

R2-A7. We thank the reviewer for this comment. We strived to answer to this question in the manuscript in two ways:

- We first measure the overall hyperlocomotion of the mice using the open field total distance parkoured in our mice cohorts (FigS4C). We did observe that the KO mutant showed hyperlocomotion, but not MD or VB knock-down mice. Which indicates that the hyperlocomotion component is not specific to the two thalamic nuclei studied.

- Using the forced walking task, we impose on the animal to keep a steady pace of roughly 6cm/s. This assay allows to normalize the general walking behavior to a relatively fixed pace making it comparable for all animals.

The reviewer suggested reporting the mean and variance in walking of WT and KO during baseline (prior to the ethanol I.P. injection). We believe that the two points mentioned above are sufficient to describe in a more quantitative way the WT vs KO locomotion differences. Moreover, by construction the normalized locomotion on the forced walking task will return similar means for the baseline, the standard deviation would, however, potentially show differences but would remain inconclusive.

R2-8. The legend in Figure 1 states that 'the loss of consciousness is evaluated using normalized moving index using either video analysis (differential pixel motion), on- head accelerometer-based motion, or neck electromyograms'. Authors should clarify whether these methods are equivalent and support it with data.

R2-A8. We understand the reviewer point and we have made a few modifications to the method description aligning better with what was done. For most mice, video analysis was used to obtain the moving index. When video recording was not available (2 mice), we had an accelerometer attached to the animal’s head stage which helped us derive a moving index that was similar to the video moving index. The neck electromyogram was rather used for animals implanted with the tetrodes to identify sleep stages based on local field potential frequency and muscle tone.  We have then clarified the method for this matter and Figure 1 to avoid this confusion. Since no concurrent recording of both video and accelerometer was performed, we do not have the data to compute the correlation between the two measures, however, no noticeable deviation from loss of motion was observed between the two methods. We realize that this may be a weak argument, however, our observations showed that video and accelerometers returned very similar timings for loss of motion (only a few comparative instances insufficient to present a statistical comparison).

R2-9. How were spike bursts defined? The authors should try different criteria and verify the consistency of results.

R2-A9 For in vivo single unit recording, we opted for a definition that is validated from our works and others as a silencing of at least 100 ms followed by a minimum of 3 spikes with:

- First spike pairs interspike interval less than 4 ms

- Remaining spike pairs interspike interval less than 20 ms

We have performed this analysis using a minimum of 2 spikes, and varied silencing periods between 50 and 100ms, without observing significant deviation of the results. As shown in Figure S6B, with this approach we observed that the burst distribution had a majority with <10 spikes per burst. Figure S6C indicated that with a clear distribution of ISI for first spike within 2-4ms as observed in previous works (Desai and Varela, 2021; Alitto et al, 2019), importantly, not clearly capped at 4 ms, showing that the range for the first ISI might indeed be lower than 4ms for thalamic burst. Within burst spike waveforms can become very variable and the choice of 3 over 2 spikes minimum per burst stems from the aim to reduce false positive detection of ultra-short bursts, which in single unit recording remains controversial (Gray et al. 1995).

Minor:

R2-10: Figure 4A2 'Cav3.1(+/+)' should presumably be Cav3.1(-/-).

R2-A10: this is correct and we have corrected the figure label [This sentence is ambiguous. What is ‘this’ that is correct?]

R2-11: Figure S2C legend states 'Post-hoc group comparison was performed using.' The sentence seems to be incomplete.

R2-A11: We have completed the sentence for clarity.

R2-12: In the methods (pg 29) virus concentration is reported as '107 TU/ul', which probably refers to 10e7.

R2-A12: We have corrected it by superscripting the power 7.

R2-13: Verify Fig 1C1 and correct Y-axis overlap between title and units.

R2-A13: We edited the figure for clarity, thank you.

R2-14: On page 24 there is a '[ref]' that probably stands for (a missing) reference.

R2-A14: the missing reference has been added.

Author response:

We are glad that the reviewers found our work to be interesting and appreciate its contribution to enhancing ecological validity of attention research. We also agree that much more work is needed to solidify this approach, and that some of the results should be considered “exploratory” at this point, but appreciate the recognition of the novelty and scientific potential of the approach introduced here.

We will address the reviewers’ specific comments in a revised version of the paper, and highlight the main points here:

· We agree that the use of multiple different neurophysiological measures is both an advantage and a disadvantage, and that the abundance of results can make it difficult to tell a “simple” story. In our revision, we will make an effort to clarify what (in our opinion) are the most important results and provide readers with a more cohesive narrative.

· Important additional discussion points raised by the reviewers, which will be discussed in a revised version are a) the similarities and differences between virtual and real classrooms; b) the utility of the methods and data to the community and c) the implication of these results for educational neuroscience and ADHD research.

· In the revision, we will also clarify several methodological aspects of the data analysis, as per the reviewers’ requests.

· After final publication, the data will be made available for other researchers to use.

Author response:

Reviewer #1 (Public review):

Weaknesses:

(1) The heatmaps (for example, Figure 3A, B) are challenging to read and interpret due to their size. Is there a way to alter the visualization to improve interpretability? Perhaps coloring the heatmap by general anatomical region could help? We feel that these heatmaps are critical to the utility of the registration strategy, and hence, clear visualization is necessary.

We thank the reviewers for this point on aesthetic improvement, and we agree that clearer visualization of our correlation heatmaps is important. To address this point, we have incorporated the capability of grouping “child” subregions in anatomical order by their more general “parent” region into the package function, plot_correlation_heatmaps(). Parent regions will be visually represented as smaller sub-facets in the heatmaps, and we will be submitting our full revised manuscript with these visual changes.

(2) Additional context in the Introduction on the use of immediate early genes to label ensembles of neurons that are specifically activated during the various behavioral manipulations would enable the manuscript and methodology to be better appreciated by a broad audience.

We thank the reviewers for this suggestion and will be revising parts of our Introduction to reflect the broader use and appeal of immediate early genes (IEGs) for studying neural changes underlying behavior.

(3) The authors mention that their segmentation strategies are optimized for the particular staining pattern exhibited by each reporter and demonstrate that the manually annotated cell counts match the automated analysis. They mention that alternative strategies are compatible, but don't show this data.

We thank the reviewers for this comment. We also appreciate that integration with alternative strategies is a major point of interest to readers, given that others may be interested in compatibility with our analysis and software package, rather than completely revising their own pre-existing workflows.

This specific point on segmentation refers to the import_segmentation_custom()function in the package. As there is currently not a standard cell segmentation export format adopted by the field, this function still requires some data wrangling into an import format saved as a .txt file. However, we chose not to visually demonstrate this capability in the paper for a few reasons.

i. A figure showing the broad testing of many different segmentation algorithms, (e.g., Cellpose, Vaa3d, Trainable Weka Segmentation) would better demonstrate the efficacy of segmentation of these alternative approaches, which have already been well-documented. However, demonstrating importation compatibility is more of a demonstration of API interface, which is better shown in website documentation and tutorial notebooks.

ii. Additionally, showing importation with one well-established segmentation approach is still a demonstration of a single use case. There would be a major burden-of-proof in establishing importation compatibility with all potential alternative platforms, their specific export formats, which may be slightly different depending on post-processing choices, and the needs of the experimenters (e.g., exporting one vs many channels, having different naming conventions, having different export formats). For example, output from Cellpose can take the form of a NumPy file (_seg.npy file), a .png, or Native ImageJ ROI archive output, and users can have chosen up to four channels. Until the field adopts a standardized file format, one flexible enough to account for all the variables of experimental interest, we currently believe it is more efficient to advise external groups on how to transform their specific data to be compatible with our generic import function.

Internally, in collaborative efforts, we have validated the ability to import datasets generated from completely different workflows for segmentation and registration. We intend on releasing this documentation in coming updates on our package website, which we believe will be more demonstrative on how to take advantage of our analysis package, without adopting our entire workflow.

(4) The authors provided highly detailed information for their segmentation strategy, but the same level of detail was not provided for the registration algorithms. Additional details would help users achieve optimal alignment.

We apologize for this lack of detail. The registration strategy depends upon the WholeBrain package for registration to the Allen Mouse Common Coordinate Framework. While this strategy has been published and documented elsewhere, we will be revising our methods to better incorporate details of this approach.

Reviewer #2 (Public review):

Weaknesses:

(1) While I was able to install the SMARTR package, after trying for the better part of one hour, I could not install the "mjin1812/wholebrain" R package as instructed in OSF. I also could not find a function to load an example dataset to easily test SMARTR. So, unfortunately, I was unable to test out any of the packages for myself. Along with the currently broken "tractatus/wholebrain" package, this is a good example of why I would strongly encourage the authors to publish SMARTR on either Bioconductor or CRAN in the future. The high standards set by Bioc/CRAN will ensure that SMARTR is able to be easily installed and used across major operating systems for the long term.

We thank reviewers for pointing out this weakness; long-term maintenance of this package is certainly a mutual goal. Loading an .RDATA file is accomplished by either double-clicking directly on the file in a directory window, or by using the load() function, (e.g., load("directory/example.RData")). We will explicitly outline these directions in the online documentation and in our full revision.

Moreover, we will submit our package to CRAN. Currently, SMARTR is not dependent on the WholeBrain package, which remains optional for the registration portion of our workflow. Ultimately, this independence will allow us to maintain the analysis and visualization portion of the package independently, and allow for submission to a more centralized software repository such as CRAN.

(2) The package is quite large (several thousand lines include comments and space). While impressive, this does inherently make the package more difficult to maintain - and the authors currently have not included any unit tests. The authors should add unit tests to cover a large percentage of the package to ensure code stability.

We appreciate this feedback and will add unit testing to improve the reliability of our package in the full revision.

(3) Why do the authors choose to perform image segmentation outside of the SMARTR package using ImageJ macros? Leading segmentation algorithms such as CellPose and StarMap have well-documented APIs that would be easy to wrap in R. They would likely be faster as well. As noted in the discussion, making SMARTR a one-stop shop for multi-ensemble analyses would be more appealing to a user.

We appreciate this feedback. We believe parts of our response to Reviewer 1, comment 3, are relevant to this point. Interfaces for CellPose and ClusterMap (which processes in situ transcriptomic approaches like STARmap) are both in python, and currently there are ways to call python from within R (https://rstudio.github.io/reticulate/index.html). We will certainly explore incorporating these APIs from R. However, we would anticipate this capability is more similar to “translation” between programming languages, but would not currently preclude users from the issue of still needing some familiarity with the capabilities of these python packages, and thus with python syntax.

(4) Given the small number of observations for correlation analyses (n=6 per group), Pearson correlations would be highly susceptible to outliers. The authors chose to deal with potential outliers by dropping any subject per region that was> 2 SDs from the group mean. Another way to get at this would be using Spearman correlation. How do these analyses change if you use Spearman correlation instead of Pearson? It would be a valuable addition for the author to include Spearman correlations as an option in SMARTR.

We thank reviewers for this suggestion and will provide a supplementary analysis of our results using Spearman correlations.

(5) I see the authors have incorporated the ability to adjust p-values in many of the analysis functions (and recommend the BH procedure) but did not use adjusted p-values for any of the analyses in the manuscript. Why is this? This is particularly relevant for the differential correlation analyses between groups (Figures 3P and 4P). Based on the un-adjusted p-values, I assume few if any data points will still be significant after adjusting. While it's logical to highlight the regional correlations that strongly change between groups, the authors should caution which correlations are "significant" without adjusting for multiple comparisons. As this package now makes this analysis easily usable for all researchers, the authors should also provide better explanations for when and why to use adjusted p-values in the online documentation for new users.

We appreciate the feedback and will more explicitly outline that in our paper, our dataset is presented as a more demonstrative and exploratory resource for readers and, as such, we accept a high tolerance for false positives, while decreasing risk of missing possible interesting findings. As noted by Reviewer #2, it is still “logical to highlight the regional correlations that strongly change between groups.” We will further clarify in our methods that we chose to present uncorrected p-values when speaking of significance. We will also include more statistical detail on our online documentation regarding FDR correction. Ultimately, the decision to correct for multiple comparisons and FDR choice of threshold, should still be informed by standard statistical theory and user-defined tolerance for inclusion of false-positives and missing of false-negatives. This will be influenced by factors, such as the nature and purpose of the study, and quality of the dataset.  

(6) The package was developed in R3.6.3. This is several years and one major version behind the current R version (4.4.3). Have the authors tested if this package runs on modern R versions? If not, this could be a significant hurdle for potential users.

We thank reviewers for pointing out concerns regarding versioning. Analysis and visualization capabilities are currently supported using R version 4.1+. The recommendation for R 3.6.3 is primarily for users interested in using the full workflow, which requires installation of the WholeBrain package. We anticipate supporting of visualization and network analysis capabilities with updated packages and R versions, and maintaining a legacy version for the full workflow presented in this paper.

Author response:

The following is the authors’ response to the previous reviews.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The authors present a new application of the high-content image-based morphological profiling Cell Painting (CP) to single cell type classification in mixed heterogeneous induced pluripotent stem cellderived mixed neural cultures. Machine learning models were trained to classify single cell types according to either "engineered" features derived from the image or from the raw CP multiplexed image. The authors systematically evaluated experimental (e.g., cell density, cell types, fluorescent channels) and computational (e.g., different models, different cell regions) parameters and convincingly demonstrated that focusing on the nucleus and its surroundings contains sufficient information for robust and accurate cell type classification. Models that were trained on mono-cultures (i.e., containing a single cell type) could generalize for cell type prediction in mixed co-cultures, and describe intermediate states of the maturation process of iPSC-derived neural progenitors to differentiation neurons.

Strengths:

Automatically identifying single-cell types in heterogeneous mixed-cell populations holds great promise to characterize mixed-cell populations and to discover new rules of spatial organization and cell-cell communication. Although the current manuscript focuses on the application of quality control of iPSC cultures, the same approach can be extended to a wealth of other applications including an in-depth study of the spatial context. The simple and high-content assay democratizes use and enables adoption by other labs.

The manuscript is supported by comprehensive experimental and computational validations that raise the bar beyond the current state of the art in the field of high-content phenotyping and make this manuscript especially compelling. These include (i) Explicitly assessing replication biases (batch effects); (ii) Direct comparison of feature-based (a la cell profiling) versus deep-learning-based classification (which is not trivial/obvious for the application of cell profiling); (iii) Systematic assessment of the contribution of each fluorescent channel; (iv) Evaluation of cell-density dependency; (v) Explicit examination of mistakes in classification; (vi) Evaluating the performance of different spatial contexts around the cell/nucleus; (vii) Generalization of models trained on cultures containing a single cell type (mono-cultures) to mixed co-cultures; (viii) Application to multiple classification tasks.

I especially liked the generalization of classification from mono- to co-cultures (Figure 4C), and quantitatively following the gradual transition from NPC to Neurons (Figure 5H).

The manuscript is well-written and easy tofollow.

Thank you for the positive appreciation of our work and constructive comments. 

Weaknesses:

I am not certain how useful/important the specific application demonstrated in this study is (quality control of iPSC cultures), this could be better explained in the manuscript. 

To clarify the importance we have added an additional explanation to the introduction (page 3) and also come back to it in the discussion (page 17).

Text from the introduction:

“However, genetic drift, clonal and patient heterogeneity cause variability in reprogramming and differentiation efficiency10,11. The differentiation outcome is further strongly influenced by variations in protocol12. This can significantly impact experimental outcomes, leading to inconsistent and potentially misleading results and consequently, it hinders the use of iPSC-derived cell systems in systematic drug screening or cell therapy pipelines. This is particularly true for iPSC-derived neural cultures, as their composition, purity and maturity directly affect gene expression and functional activity, which is essential for modelling neurological conditions13,14. Thus, from a preclinical perspective, there is the need for a fast and cost-effective QC approach to increase experimental reproducibility and cell type specificity15. From a clinical perspective in turn, robust QC is required for safety and regulatory compliance (e.g., for cell therapeutic solutions). This need for improved standardization and QC is underscored by large-scale collaborative efforts such as the International Stem Cell Banking Initiative16, which focusses on clinical quality attributes and provides recommendations for iPSC validation testing for use as cellular therapeutics, or the CorEuStem network, aiming to harmonize iPSC practices across core facilities in Europe.”

Text from the discussion: 

“Many groups highlight the difficulty of reproducible neural differentiation and attribute this to culture conditions, cultivation time and variation in developmental signalling pathways in the source iPSC material43,44. Spontaneous neural differentiation has previously been shown to require approximately 80 days before mature neurons arise that can fire action potentials and show neural circuit formation. Although these differentiation processes display a stereotypical temporal sequence34, the exact timing and duration might vary. This variation negatively affects the statistical power when testing drug interventions and thus prohibits the application of iPSC-culture derivatives in routine drug screening. Current solutions (e.g., immunocytochemistry, flow cytometry, …) are often cost-ineffective, tedious, and incompatible with longitudinal/multimodal interrogation. CP is a much more cost-effective solution and ideally suited for this purpose. Routine CP-based could add confidence to and save costs for the drug discovery pipeline. We have shown that CP can be leveraged to capture the morphological changes associated with neural differentiation.”

Another issue that I feel should be discussed more explicitly is how far can this application go - how sensitively can the combination of cell painting and machine learning discriminate between cell types that are more subtly morphologically different from one another?

Thank you for this interesting question. The fact that an approach based on a subregion not encompassing the whole cell (the “nucleocentric” approach) can predict cell types equally well, suggests that the cell shape as such is not the defining factor for accurate cell type profiling. And, while clearly neural progenitors, neurons or glia have vastly different cell shapes. We have shown that cells with closer phenotypes such as 1321N1 vs. SH-SY5Y or astrocytes vs. microglia can be distinguished with equal performance. However, triggered by the reviewers’ question, we have now tested additional conditions with more subtle phenotypes, including the classification of 1321N1 vs. two related retinal pigment epithelial cells with much more similar morphology (ARPE and RPE1 cells). We found that the CNN could discriminate these cells equally well and have added the results on page 8 and in Fig. 3D. To address this question from a different angle, we have also performed an experiment in which we changed cell states to assess whether discriminatory power remains high. Concretely, we exposed co-cultures of neurons and microglia to LPS to trigger microglial activation (more subtly visible as cytoskeletal changes and vacuole formation). This revealed that our approach still discriminates both cell types (neurons vs. microglia) with high accuracy, regardless of the microglial state. Furthermore, using a two-step approach, we could also distinguish LPS-treated (assumed to be activated) from unchallenged microglia (assumed to be more homeostatic), albeit with a lower accuracy. This experiment has been added as an extra results section (Cell type identification can be applied to mixed iPSC-derived neuronal cultures regardless of activation state, p12) and Fig. 7c. Finally, we have also added our take on what the possibilities could be for future applications in even more complex contexts such as tissue slice, 3D and live cell applications (page 17-18). 

Regarding evaluations, the use of accuracy, which is a measure that can be biased by class imbalance, is not the most appropriate measurement in my opinion. The confusion matrices are a great help, but I would recommend using a measurement that is less sensitive for class imbalance for cell-type classification performance evaluations.  

Across all CNNs trained in this manuscript, the sample size of the input classes has always been equalized, ruling out any effects of class imbalance. Nevertheless, to follow the reviewers’ recommendation, we have now used the F-score to document performance as it is insensitive to such imbalance. For clarity, we have now also mentioned the input number (ROIs/class) in every figure.

Another issue is that the performance evaluation is calculated on a subset of the full cell population - after exclusion/filtering. Could there be a bias toward specific cell types in the exclusion criteria? How would it affect our ability to measure the cell type composition of the population?

As explained in the M&M section, filtering was performed based on three criteria:

(1) Nuclear size: values below a threshold of 160, objects are considered to represent debris;

(2) DAPI intensity: values below a threshold of 500 represent segmentation errors;

(3) IF staining intensity: gates were set onto the intensity of the fluorescent markers used with posthoc IF to only retain cells that are unequivocally positive for either marker and to avoid inclusion of double positive (or negative) cells in the ground truth training. 

One could argue that the last criterion introduces a certain bias in that it does not consider part of the cell population. However, this is also not the purpose of our pioneering study that aims at identifying unique cell types for which ground truth is as pure and reliable as possible. Not filtering out these cells with a ‘dubious’ IF profile (e.g., cells that might be transitioning or are of a different type) would negatively affect the model by introducing noise. It is correct that the predictions are based only on these inputs and so cells of a subsequent test set will only be classified according to these labels. For example, in the neuronal differentiation experiment (Fig. 6G-H), cells are either characterized as NPC or as neurons, which leaves the transitioning (or undefined) cells in either category. Despite this simplification, the model adequately predicted the increase in neuron/NPC ratio with culture age. In future iterations, one could envision defining more refined cell (sub-)types in a population based on richer post-hoc information (e.g., through cyclic immunofluorescence or spatial single cell transcriptomics) or longitudinal follow-up of cell-state transitions using live imaging. This notion has been added to page 17 of the manuscript.

I am not entirely convinced by the arguments regarding the superiority of the nucleocentric vs. the nuclear representations. Could it be that this improvement is due to not being sensitive/ influenced by nucleus segmentation errors?

The reviewer has a valid point that segmentation errors may occur. However, the algorithm we have used (Stardist classifier), is very robust to nuclear segmentation errors. To verify the performance, we have now quantified segmentation errors in 20 images for 3 different densities and found a consistently low error rate (0.6 -1.6%) without correlation to the culture density. Moreover, these errors include partial imperfections (e.g., a missed protrusion or bleb) as well as over- (one nucleus detected as more) or under- (more nuclei detected as one) segmentations. The latter two will affect both the nuclear and nucleocentric predictions and should thus not affect the prediction performance. In the case of imperfect segmentations, there may be a specific impact on the nucleus-based predictions (which rely on blanking the non-nuclear part), but this alone cannot explain the significantly higher gain in accuracy for nucleocentric predictions (>5%). Therefore, we conclude that segmentation errors may contribute in part, but not exclusively, to the overall improved performance of nucleocentric input models. We have added this notion in the discussion (pages 14-15 and Suppl. Fig. 1E).

GRADCAM shows cherry-picked examples and is not very convincing.

To help convince the reviewer and illustrate the representativeness of selected images, we have now randomly selected for each condition and density 10 images (using random seeds to avoid cherrypicking) and added these in a Suppl. Fig. 3.

There are many missing details in the figure panels, figure legend, and text that would help the reader to better appreciate some of the technical details, see details in the section on recommendations for the authors.

Please see further for our specific adaptations.

Reviewer #2 (Public Review):

This study uses an AI-based image analysis approach to classify different cell types in cultures of different densities. The authors could demonstrate the superiority of the CNN strategy used with nucleocentric cell profiling approach for a variety of cell types classification. The paper is very clear and well-written. I just have a couple of minor suggestions and clarifications needed for the reader.

The entire prediction model is based on image analysis. Could the authors discuss the minimal spatial resolution of images required to allow a good prediction? Along the same line, it would be interesting to the reader to know which metrics related to image quality (e.g. signal to noise ratio) allow a good accuracy of the prediction.

Thank you for the positive and relevant feedback.

The reviewer has a good point that it is important to portray the imaging conditions that are required for accurate predictions. To investigate this further we have performed additional experiments that give a better view on the operating window in terms of resolution and SNR (manuscript page 7-8 and new figure panels Fig. 3B-C). The initial image resolution was 0.325 µm/pixel. To understand the dependency on resolution we performed training and classifications for image data sets that were progressively binned. We found that a two-fold reduction in resolution did not significantly affect the F-score, but further degradation decreased the performance. At a resolution of 6,0 µm/pixel (20-fold binning), the F-score dropped to 0.79±0.02, comparable to the performance when only the DAPI (nuclear) channel was used as input. The effect of reduced image quality was assessed in a similar manner, by iteratively adding more Gaussian noise to the image. We found that above an SNR of 10 the prediction performance remains consistent but below it starts to degrade. While this exercise provides a first impression of the current confines of our method, we do believe it is plausible that its performance can be extended to even lower-quality images for example by using image restoration algorithms. We have added this notion in the discussion (page 14).

The authors show that nucleocentric-based cell feature extraction is superior to feeding the CNN-based model for cell type prediction. Could they discuss what is the optimal size and shape of this ROI to ensure a good prediction? What if, for example, you increase or decrease the size of the ROI by a certain number of pixels?

To identify the optimal input, we varied the size of the square region around the nuclear centroid from 0.6 to 150 µm for the whole dataset. Within the nuclear-to-cell window (12µm- 30µm) the average Fscore is limited, but an important observation is the increasing error and differences in precision and recall with increasing nucleocentric patch sizes, which will become detrimental in cases of class imbalance. The F-score is maximal for a box of 12-18µm surrounding the nuclear centroid. In this “sweet spot”, the precision and recall are also in balance. Therefore, we have selected this region for the actual density comparison experiment. We have added our results to the manuscript (page 9 and 15).

It would be interesting for the reader to know the number of ROI used to feed each model and know the minimal amount of data necessary to reach a high level of accuracy in the predictions.

The figures have now been adjusted so that the number of ROIs used as input to feed the model are listed. The minimal number of ROIs required to obtain high level accuracy is tested in Figure 2C. By systematically increasing the number of input ROIs for both RF and CNN, we found that a plateau is reached at 5000 input ROIs (per class) for optimal prediction performance. This is also documented in the results section page 6.

From Figure 1 to Figure 4 the author shows that CNN based approach is efficient in distinguishing 1321N1 vs SH-SY5Y cell lines. The last two figures are dedicated to showing 2 different applications of the techniques: identification of different stages of neuronal differentiation (Figure 5) and different cell types (neurons, microglia, and astrocytes) in Figure 6. It would be interesting, for these 2 two cases as well, to assess the superiority of the CNN-based approach compared to the more classical Random Forest classification. This would reinforce the universal value of the method proposed.

To meet the reviewer’s request, we have now also compared CNN to RF for the classification of cells in iPSC-derived models (Figures 6 and 7). As expected, the CNN performed better in both cases. We have now added these results in Fig. 6 D and 7 C and pages 12 and 13 of the manuscript.

Reviewer #3 (Public Review):

Induced pluripotent stem cells, or iPSCs, are cells that scientists can push to become new, more mature cell types like neurons. iPSCs have a high potential to transform how scientists study disease by combining precision medicine gene editing with processes known as high-content imaging and drug screening. However, there are many challenges that must be overcome to realize this overall goal. The authors of this paper solve one of these challenges: predicting cell types that might result from potentially inefficient and unpredictable differentiation protocols. These predictions can then help optimize protocols.

The authors train advanced computational algorithms to predict single-cell types directly from microscopy images. The authors also test their approach in a variety of scenarios that one may encounter in the lab, including when cells divide quickly and crowd each other in a plate. Importantly, the authors suggest that providing their algorithms with just the right amount of information beyond the cells' nuclei is the best approach to overcome issues with cell crowding.

The work provides many well-controlled experiments to support the authors' conclusions. However, there are two primary concerns: (1) The model may be relying too heavily on the background and thus technical artifacts (instead of the cells) for making CNN-based predictions, and (2) the conclusion that their nucleocentric approach (including a small area beyond the nucleus) is not well supported, and may just be better by random chance. If the authors were to address these two concerns (through additional experimentation), then the work may influence how the field performs cell profiling in the future.

Thank you very much for confirming the potential value of our work and raising these relevant items. To better support our claims we have now performed additional validations, which we detail below. 

(1) The model may be relying too heavily on the background and thus technical artifacts (instead of the cells) for making CNN-based predictions 

To address the first point, we have adapted the GradCAM images to show an overlay of the input crop and GradCAM heatmap to give a better view of the structures that are highlighted by the CNN. We further investigated the influence of the background on the prediction performance. Our finding that a CNN trained on a monoculture retains a relatively high performance on cocultures implies that the CNN uses the salient characteristics of a cell to recognize it in more complex heterogeneous environments. Assuming that the background can vary between experiments, the prediction of a pretrained CNN on a new dataset indicates that cellular characteristics are used for robust prediction.  When inspecting GradCAM images obtained from the nucleocentric CNN approaches (now added in Suppl. Fig. 3), we noticed that the nuclear periphery typically contributed the most (but not exclusively) to the prediction performance. When using only the nuclear region as input, GradCAMs were more strongly (but again not exclusively) directed to the background surrounding the nuclei. To train the latter CNN, we had cropped nuclei and set the background to a value of zero. To rule out that this could have introduced a bias, we have now performed the exact same training and classification, but setting the background to random noise instead (Suppl. Fig. 2). While this effectively diverted the attention of the GradCAM output to the nucleus instead of the background, the prediction performance was unaltered. We therefore assume that irrespective of the background, when using nuclear crops as input, the CNN is dominated by features that describe nuclear size. We observe that nuclear size is significantly different in both cell types (although intranuclear features also still contribute) which is also reflected in the feature map gradient in the first UMAP dimension (Suppl. Fig. 2). This notion has been added to the manuscript (page 9) and Suppl. Fig. 2. 

(2) The conclusion that their nucleocentric approach (including a small area beyond the nucleus) is not well supported, and may just be better by random chance. 

To address this second concern, which was also raised by reviewer 2, we have performed a more extensive analysis in which the patch size was varied from 0.6 to 120µm around the nuclear centroid (Fig. 4E and page 9 of the manuscript). We observed that there is little effect of in- or decreasing patch size on the average F-score within the nuclear to cell window, but that the imbalance between the precision and recall increases towards the larger box sizes (>18µm). Under our experimental conditions, the input numbers per class were equal, but this will not be the case in situations where the ground truth is unknown (and needs to be predicted by the CNN). Therefore, a well-balanced CNN is of high importance. This notion has been added to page 15 of the manuscript.

The main advantage of nucleocentric profiling over whole-cell profiling in dense cultures is that it relies on a more robust nuclear segmentation method and is less sensitive to differences in cell density (Suppl. Fig. 1D). In other words, in dense cultures, the segmentation mask will contain similar regional input as the nuclear mask and the nucleocentric crop will contain more perinuclear information which contributes to the prediction accuracy. Therefore, at high densities, the performance of the CNN on whole-cell crops decreases owing to poorer segmentation performance. A CNN that uses nucleocentric crops, will be less sensitive to these errors. This notion has been added to pages 14-15 of the manuscript. 

Additionally, the impact of this work will be limited, given the authors do not provide a specific link to the public source code that they used to process and analyze their data.

The source code is now available on the Github page of the DeVos lab, under the following URL: https://github.com/DeVosLab/Nucleocentric-Profiling

Recommendations for the authors:  

Reviewing Editor (Recommendations For The Authors):

Evaluation summary

The authors present a new application of the high-content image-based morphological profiling Cell Painting (CP) to single cell type classification in mixed heterogeneous induced pluripotent stem cellderived mixed neural cultures. Machine learning models were trained to classify single cell types according to either "engineered" features derived from the image or from the raw CP multiplexed image. The authors systematically evaluated experimental (e.g., cell density, cell types, fluorescent channels, replication biases) and computational (e.g., different models, different cell regions) parameters and argue that focusing on the nucleus and its surroundings contains sufficient information for robust and accurate cell type classification. Models that were trained on mono-cultures (i.e., containing a single cell type) could generalize for cell type prediction in mixed co-cultures, and describe intermediate states of the maturation process of iPSC-derived neural progenitors to differentiation neurons.

Strengths:

Automatically identifying single-cell types in heterogeneous mixed-cell populations is an important application and holds great promise. The simple and high-content assay democratizes use and enables adoption by other labs. The manuscript is supported by comprehensive experimental and computational validations. The manuscript is well-written and easy to follow.

Weaknesses:

The conclusion is that the nucleocentric approach (including a small area beyond the nucleus) is not well supported, and may just be better by random chance. If better supported by additional experiments, this may influence how the field performs cell profiling in the future. Model interpretability (GradCAM) analysis is not convincing. The lack of a public source code repository is also limiting the impact of this study. There are missing details in the figure panels, figure legend, and text that would help the reader to better appreciate some of the technical details.

Essential revisions:

To reach a "compelling" strength of evidence the authors are requested to either perform a comprehensive analysis of the effect of ROI size on performance, or tune down statements regarding the superior performance of their "nucleocentric" approach. Further addition of a public and reproducible source code GitHub repository will lead to an "exceptional" strength of evidence.

To answer the main comment, we have performed an experiment in which we varied the size of the nucleocentric patch and quantified CNN performance. We have also evaluated the operational window of our method by varying the resolution and SNR and we have experimented with different background blanking methods. We have expanded our examples of GradCAM images and now also made our source code and an example data set available via GitHub.

Reviewer #1 (Recommendations For The Authors):

I think that an evaluation of how the excluded cells affect our ability to measure the cell type composition of the population would be helpful to better understand the limitations and practical measurement noise introduced by this approach. A similar evaluation of the excluded cells can also help to better understand the benefit of nucleocentric vs. cell representations by more convincingly demonstrating the case for the nucleocentric approach. In any case, I recommend discussing in more depth the arguments for using the nucleocentric representation and why it is superior to the nuclear representation.

The benefits of nucleocentric representation over nuclear and whole-cell representation are discussed more in depth at pages 14-15 of the manuscript. 

“The nucleocentric approach, which is based on more robust nuclear segmentation, minimizes such mistakes whilst still retaining input information from the structures directly surrounding the nucleus. At higher cell density, the whole-cell body segmentation becomes more error-prone, while also loosing morphological information (Suppl. Fig. 1D). The nucleocentric approach is more consistent as it relies on a more robust segmentation and does not blank the surrounding region. This way it also buffers for occasional nuclear segmentation errors (e.g., where blebs or parts of the nucleus are left undetected).”

It is not entirely clear to me why Figure 5 moves back to "engineered" features after previous figures showed the superiority of the deep learning approach. Especially, where Figure 6 goes again to DL. Dimensionality reduction can be also applied to DL-based classifications (e.g., using the last layer).

Following up on the reviewers’ interesting comment, we extracted the embeddings from the trained CNN and performed UMAP dimensionality reduction. The results are shown in Fig. 3D, 6F and supplementary figure 1B and added to the manuscript on pages 6, 8 and 12. 

We concluded that unsupervised dimensionality reduction using the feature embeddings could separate cell type clusters, where the distance between the clusters reflected the morphological similarity between the cell lines. 

I would recommend including more comprehensive GRADCAM panels in the SI to reduce the concern of cherry-picking examples. What is the interpretation of the nucleocentric area?

A more extensive set of GradCAM images have now been included in supplementary material (Supplementary figure 3) using the same random seeds for all conditions, thus avoiding any cherry picking. We interpret the GradCAM maps on the nucleocentric crops as highlighting the structures surrounding the nucleus (reflecting ER, mitochondria, Golgi) indicating their importance in correct cell classification. This was added to the manuscript on pages 9 and 15.

Missing/lacking details and suggestions in the figure panels and figure legend:

- Scale bars missing in some of the images shown (e.g., Figure 2F, Figure 3D, Figure 4, Supplementary Figure 4), what are the "composite" channels (e.g., Figure 2F), missing x-label in Figure 3B. 

These have now been added.

- Terms that are not clear in the figure and not explained in the legend, such as FITC and cy3 energy (Figure 1C). 

The figure has been adapted to better show the region, channel and feature. We have now added a Table (Table 5), detailing the definition of each morphological feature that is extracted. On page 27, information on feature extraction is noted.

- Details that are missing or not sufficiently explained in the figure legends such as what each data point represents and what is Gini importance (Figure 1D) 

We have added these explanations to the figure legends. The Gini importance or mean decrease in impurity reflects how often this feature is used in decision tree splits across all random forest trees.

Is it the std shown in Figure 2C?

Yes, this has now been added to the legend.  

It is not fully clear what is single/mixed (Figure 2D)

Clarification is added to the legend and in the manuscript on page 6.

explain what is DIV 13-90 in the legend (Figure 5).

DIV stands for days in vitro, here it refers to the days in culture since the start of the neural induction process. This has been added in the legend.

and state what are img1-5 (Supplementary Figures 1B-C) Clarification has been added to the legend.

- Supplementary Figure 1. What is the y-axis in panel C and how do the results align with the cell mask in panel B?

The y-axis represents the intersection over union (IoU). The IoU quantifies the overlap between ground truth (manually segmented ROI) and the ROI detected by the segmentation algorithm. It is defined as the area of the overlapping region over the total area. This clarification has been added to the legend.

- Supplementary Figure 1 and Methods. Please explain when CellPose and when StarDist were applied.

Added to supplementary figure and methods at page 24. In the case of nuclear segmentation (nucleus and nucleocentric crops), Stardist was used. For whole-cell crops, cell segmentation using Cellpose was used.

- Supplementary Figure 4C - the color code is different between nuclear and nucleocentric - this is confusing.

We have changed to color code to correspond in both conditions in Fig. 1A.

- Figure 3B - better to have a normalized measure in the x-axis (number of cells per area in um^2)

We agree and have changed this.

Suggestions and missing/lacking details in the text:

• Line #38: "we then applied this" because it is the first time that this term is presented.

This has been rephrased.

• Line #88: a few words on what were the features extracted would be helpful.

Short description added to page 26-27 and detailed definition of all features added in table 5.

-  Line #91: PCA analysis - the authors can highlight what (known) features were important to PC1 using the linear transformation that defined it.

The 5 most important features of PC1 were (in order of decreasing importance): channel 1 dissimilarity, channel 1 homogeneity, nuclear perimeter, channel 4 dissimilarity and nuclear area.  

- Line #92: Order of referencing Supplementary Figure 4 before referencing Supplementary Figure 13.

The order of the Supplementary images was changed to follow the chronology. 

• Line #96: Can the authors show the data supporting this claim?

The unsupervised UMAP shown in fig. 1B is either color coded by cell type (left) or replicate (right). Based on this feature map, we observe clustering along the UMAP1 axis to be associated with the cell type. Variations in cellular morphology associated with the biological replicate are more visible along the UMAP2 axis. When looking at fig. 1C, the feature map reflecting the cellular area shows a gradient along the UMAP1 direction, supporting the assumption that cell area contributes to the cell type separation. On the other hand, the average intensity (Channel 2 intensity) has a gradient within the feature map along the UMAP2 direction. This corresponds to the pattern associated with the inter-replicate variability in panel B.

- Line #108: what is "nuclear Cy3 energy"?

This represents the local change of pixel intensities within the ROI in the nucleus in the 3rd channel dimension. This parameter reflects the texture within the nuclear region for the phalloidin and WGA staining. The definitions of all handcrafted features are added in table 5 of the manuscript.

- Line #110-112: Can the authors show the data supporting this claim?

The figure has been changed to include the results from a filtered and unfiltered dataframe (exclusion and inclusion of redundant features). Features could be filtered out if the correlation was above a threshold of 0.95. This has been added to page 6 of the manuscript and fig. 1D.  

- Line #115-116: please state the size of the mask.

Added to the text (page 6). We used isotropic image crops of 60µm centred on individual cell centroids.

- Lines 120-122: more details will make this more clear (single vs. mixed).

This has been changed on page 6 of the manuscript.

• Line #142: "(mimics)" - is it a typo?

Tissue mimics refers to organoids/models that are meant to replicate the physiological behaviour.

• Line #159: the bounding box for nucleocentric analysis is 15x15um (and not 60), as stated in the Methods.

Thank you for pointing out this mistake. We have adapted this.

- Line #165: what is the interpretation of what was important for the nucleocentric classification?

The colour code in GradCAM images is indicative of the attention of the CNN (the more to the red, the more attention). In fig. 4D and Suppl. Fig. 3 the structures directly surrounding the nucleus receive high attention from the CNN trained on nucleocentric crops. This has been added to the manuscript page 9 and 15.

• Section starting in line #172: not explicitly stated what model was used (nucleocentric?).

Added in the legend of fig. 5. For these experiments, the full cell segmentation was still used. 

- Section starting in line #199: why use a feature-based model rather than nucleocentric? A short sentence would be helpful.

For CNN training, nucleocentric profiling was used. In response to a legitimate question of one of the reviewers, the feature-based UMAP analysis was replaced with the feature embeddings from the CNN. 

- Line #213: Fig. 5B does not show transitioning cells.

Thank you for pointing this out, this was a mistake and has been changed.

Lines #218-220: not fully clear to some readers (culture condition as a weak label), more details can be helpful.

We changed this at page 11 of the manuscript for clarity. 

“This gating strategy resulted in a fractional abundance of neurons vs. total (neurons + NPC) of 36,4 % in the primed condition and 80,0% in the differentiated condition (Fig. 6C). We therefore refer to the culture condition as a weak label as it does not take into account the heterogeneity within each condition (well).”

-  Line #230: "increasing dendritic outgrowth" - what does it mean? Can you explicitly highlight this phenotype in Figure 5G?

When the cells become more mature during differentiation, the cell body becomes smaller and the neurons form long, thin ramifications. This explanation has been added to page 12 of the manuscript.

• Line #243: is it the nucleocentric CNN?

Yes.

• Lines #304-313, the authors might want to discuss other papers dealing with continuous (non-neural) differentiation state transitions (eg PMID: 38238594).  

A discussion of the use of morphological profiling for longitudinal follow-up of continuous differentiation states has been added to the manuscript at page 18. 

- Line #444: cellpose or stardist? How did the authors use both?

Clarification has been added to supplementary figure 1 and methods at page 24. Stardist was used for nuclear segmentation, whereas Cellpose was used for whole-cell segmentation. 

• Line #470-474: I would appreciate seeing the performance on the full dataset without exclusions.

Cells have been excluded based on 3 arguments: the absence of DAPI intensity, too small nuclear size and absence of ground truth staining. The first two arguments are based on the assumption that ROIs that contain no DAPI signal or are too small are errors in cell segmentation and therefore should not be taken along in the analysis. The third filtering step was based on the ground-truth IF signal. Not filtering out these cells with a ‘dubious’ IF profile (e.g., cells that might be transitioning or are of a different type) would negatively affect the model by introducing noise. It is correct that the predictions are based only on these inputs and so cells of a subsequent test set will only be classified according to these labels which might introduce bias. However, the model could predict increase in neuron/NPC ratio with culture age in absence of ground-truth staining (and thus IF-based filtering).

Reviewer #2 (Recommendations For The Authors):

Figure 1A: it would be interesting to the reader to see the SH-SY5Y data as well.

This has been added in fig. 1A.

Figure 3A: 95-100% image: showing images with the same magnification as the others would help to appreciate the cell density.

Now fig. 4A. The figure has been changed to make sure all images have the same magnification. 

Figure Supp 4 (line 132) is referred to before Figure Supp1 (line 152).

The image order and numbering has been changed to solve this issue.

Figure Supp 2 & 3 are not referred to in the text.

This has been adjusted.

Line 225: a statistical test would help to convince of the accuracy of these results (Figure 5C vs Figure 5F)?

These figures represent the total ROI counts and thus represent a single number.

Line 227: Could you explain to the reader, in a few words, what a dual SMAD inhibition is?

This has been added to the manuscript at page 20. 

“This dual blockade of SMAD signalling in iPSCs is induces neural differentiation by synergistically causing the loss of pluripotency and push towards neuroectodermal lineage.”

Reviewer #3 (Recommendations For The Authors):

I have a few concerns and several comments that, if addressed, may strengthen conclusions, and increase clarity of an already technically sound paper.

Concerns

• The results presented in Figure 3 panel D, may indicate a critical error in data processing and interpretation that the authors must address. The GradCAM method highlights the background as having the highest importance. While it can be argued in the nucleocentric profiling method that GradCAM focuses on the nuclear membrane, the background is highly important even for the nuclear profiling method, which should provide little information. What procedure did the authors use for mask subtraction prior to CNN training? Could the segmentation algorithm be performing differently between cell lines? The authors interpret the GradCAM results to indicate a proxy for nuclear size, but then why did the CNN perform so much better than random forest using hand-crafted features that include this variable? The authors should also present size distributions between cell lines (and across seeding densities, in case one of the cell lines has different compaction properties with increasing density).

Perhaps clarifying this sentence (lines 166-168) would help as well: "As nuclear area dropped with culture density, the dynamic range decreased, which could explain the increased error rate of the CNN for high densities unrelated to segmentation errors (Suppl. Fig. 4B)." What do the authors mean by "dynamic range" and it is not clear how Supplementary Figure 4B provides evidence for this? 

The dynamic range refers to the difference between the minimum and maximum nuclear area. We expect the difference to decrease at highe rdensity owing to the crowding that forces all nuclei to take on a more similar (smaller) size.

More clarification on this has been added to page 9 of the manuscript.

I certainly understand that extrapolating the GradCAM concern to the remaining single-cell images using only four (out of tens of thousands of options) is also dangerous, but so is "cherry-picking" these cells to visualize. Finally, I also recommend that the authors quantitatively diagnose the extent of the background influence according to GradCAM by systematically measuring background influence in all cells and displaying the results per cell line per density.

To avoid cherry picking of GradCAM images, we have now randomly selected for each condition and density 10 images (using random seeds to avoid cherry-picking) and added these in a Suppl. Fig. 3.

In answer to this concern, we refer to the response above: 

“To address the first point, we have adapted the GradCAM images to show an overlay of the input crop and GradCAM heatmap to give a better view of the structures that are highlighted by the CNN. We further investigated the influence of the background on the prediction performance. Our finding that a CNN trained on a monoculture retains a relatively high performance on cocultures implies that the CNN uses the salient characteristics of a cell to recognize it in more complex heterogeneous environments. Assuming that the background can vary between experiments, the prediction of a pretrained CNN on a new dataset indicates that cellular characteristics are used for robust prediction.  When inspecting GradCAM images obtained from the nucleocentric CNN approaches (now added in Suppl. Fig. 3), we noticed that the nuclear periphery typically contributed the most (but not exclusively) to the prediction performance. When using only the nuclear region as input, GradCAMs were more strongly (but again not exclusively) directed to the background surrounding the nuclei. To train the latter CNN, we had cropped nuclei and set the background to a value of zero. To rule out that this could have introduced a bias, we have now performed the exact same training and classification, but setting the background to random noise instead (Suppl. Fig. 2). While this effectively diverted the attention of the GradCAM output to the nucleus instead of the background, the prediction performance was unaltered. We therefore assume that irrespective of the background, when using nuclear crops as input, the CNN is dominated by features that describe nuclear size. We observe that nuclear size is significantly different in both cell types (although intranuclear features also still contribute) which is also reflected in the feature map gradient in the first UMAP dimension (Suppl. Fig. 2). This notion has been added to the manuscript (page 9) and Suppl. Fig. 2.”

• The data supporting the conclusion about nucleocentric profiling outperforming nuclear and full-cell profiling is minimal. I am picking on this conclusion in particular, because I think it is a super cool and elegant result that may change how folks approach issues stemming from cell density disproportionately impacting profiling. Figures 3B and 3C show nucleocentric slightly outperforming full cell, and the result is not significant. The authors state in lines 168-170: "Thus, we conclude that using the nucleocentric region as input for the CNN is a valuable strategy for accurate cell phenotype identification in dense cultures." This is somewhat of a weak conclusion, that, with additional analysis, could be strengthened and add high value to the community. Additionally, the authors describe the nucleocentric approach insufficiently. In the methods, the authors state (lines 501-503): "Cell crops (60μm whole cell - 15μm nucleocentric/nuclear area) were defined based on the segmentation mask for each ROI." This is not sufficient to reproduce the method. What software did the authors use?

Presumably, 60μm refers to a box size around cytoplasm? Much more detail is needed. Additionally, I suggest an analysis to confirm the impact of nucleocentric profiling, which would strengthen the authors' conclusions. I recommend systematically varying the subtraction (-30μm, -20μm, -10μm, 5μm, 0, +5μm, +10μm, etc.) and reporting the density-based analysis in Figure 3B per subtraction. I would expect to see some nucleocentric "sweet spot" where performance spikes, especially in high culture density. If we don't see this difference, then the non-significant result presented in Figures 3B and C is likely due to random chance. The authors mention "iterative data erosion" in the abstract, which might refer to what I am recommending, but do not describe this later.

More detail was added to the methods describing the image crops given as input to the CNN (page 28 of the manuscript). 

“Crops were defined based on the segmentation mask for each ROI. The bounding box was cropped out of the original image with a fixed patch size (60µm for whole cells, 18µm for nucleus and nucleocentric crops) surrounding the centroid of the segmentation mask. For the whole cell and nuclear crops, all pixels outside of the segmentation mask were set to zero. This was not the case for the nucleocentric crops. Each ROI was cropped out of the original morphological image and associated with metadata corresponding to its ground truth label.”

To address this concern, we also refer to the answer above. 

“We have performed a more extensive analysis in which the patch size was varied from 0.6 to 120µm around the nuclear centroid (Fig. 4E and page 9 of the manuscript). We observed that there is little effect of in- or decreasing patch size on the average F-score within the nuclear to cell window, but that the imbalance between the precision and recall increases towards the larger box sizes (>18µm). Under our experimental conditions, the input numbers per class were equal, but this will not be the case in situations where the ground truth is unknown (and needs to be predicted by the CNN). Therefore, a well-balanced CNN is of high importance. This notion has been added to page 12 of the manuscript.

The main advantage of nucleocentric profiling over whole-cell profiling in dense cultures is that it relies on a more robust nuclear segmentation method and is less sensitive to differences in cell density (Suppl. Fig. 1D). In other words, in dense cultures, the segmentation mask will contain similar regional input as the nuclear mask and the nucleocentric crop will contain more perinuclear information which contributes to the prediction accuracy. Therefore, at high densities, the performance of the CNN on whole-cell crops decreases owing to poorer segmentation performance. A CNN that uses nucleocentric crops, will be less sensitive to these errors. This notion has been added to pages 14-15 of the manuscript.“

Comments

• There is a disconnect between the abstract and the introduction. The abstract highlights the nucleocentric model, but then it is not discussed in the introduction, which focuses on quality control. The introduction would benefit from some additional description of the single-cell or whole-image approach to profiling.

We highlight the importance of QC of complex iPSC-derived neural cultures as an application of morphological profiling. We used single-cell profiling to facilitate cell identification in these mixed cultures where the whole-image approach would be unable to deal with the heterogeneity withing the field of view. In the introduction, we added a description of the whole-image vs. single-cell approach to profiling (page 4). In the discussion (page 18), we further highlight the application of this single-cell profiling approach for QC purposes. 

- Comments on Figure 1. It is unclear how panel B shows "without replicate bias". 

In response to this comment, we refer to the answer above: “The unsupervised UMAP shown in fig. 1B is either color coded by cell type (left) or replicate (right). Based on this feature map, we observe clustering along the UMAP1 axis to be associated with the cell type. Variations in cellular morphology associated with the biological replicate are more visible along the UMAP2 axis. When looking at fig. 1C, the feature map reflecting the cellular area shows a gradient along the UMAP1 direction, supporting the assumption that cell area contributes to the cell type separation. On the other hand, the average intensity (Channel 2 intensity) has a gradient within the feature map along the UMAP2 direction. This corresponds to the pattern associated with the inter-replicate variability in panel B.” We added this notion to page 5 of the manuscript.

The paper would benefit from a description of how features were extracted sooner.

Information on the feature extraction was added to the manuscript at page 27. An additional table (table 5) has been added with the definition of each feature.  

- Comments on Supplementary Figure 4. The clustering with PCA is only showing 2 dimensions, so it is not surprising UMAP shows more distinct clustering.

We used two components for UMAP dimensionality reduction, so the data was also visualized in two dimensions. However, we agree that UMAP can show more distinct clustering as this method is non-linear.

Why is Figure S4 the first referenced Supplementary Figure?

This has been changed. 

• Comments on Figure 2. Need discussion of the validation set - how was it determined? Panel E might have the answer I am looking for, but it is difficult to decipher exactly what is being done. The terminology needs to be defined somewhere, or maybe it is inconsistent. It is tough to tell. For example, what exactly are the two categories of model validation (cross-validation and independent testing)?

Additional clarification has been added to the manuscript at pages 6-7 and figure 2.

The metric being reported is accuracy for the independent replicate if the other two are used to train?

Yes. 

Panel C is a very cool analysis. Panel F needs a description of how those images were selected, randomly?

Added in the methods section (page 29). GradCAM analysis was used to visualize the regions used by the CNN for classification. This map is specific to each cell. Images are selected randomly out the full dataset for visualization.  

They also need scale bars.

Added to the figures. 

Panel G would benefit from explicit channel labels (at least a legend would be good!).

Explanation has been added to the legend. All color code and channel numbering are consistent with fig. 1A. 

What do the dots and boxplots represent? The legend says, "independent replicates", but independent replicates of, I assume, different model initializations?

Clarification has been added to the figure legends. For plots showing the performance of a CNN or RF classifier, each dot represents a different model initialization. Each classifier has been initialized at least 3 times. When indicated, the model training was performed with different random seeds for data splitting.

• Comments on Figure 3. Panel A needs scale bar. See comment on Panel D in concern #1 described above. 

This has been added.

• Comments on Supplementary Figure 1. A reader will need a more detailed description in panel C. I assume that the grey bar is the average of the points, and the points represent different single cells?

How many cells? How were these cells selected? 

This information on the figure (now Suppl. Fig. 1D), has been added to the legend.

“Left: Representative images of 1321N1 cells with increasing density alongside their cell and nuclear mask produced using resp. Cellpose and Stardist. Images are numbered from 1-5 with increasing density. Upper right: The number of ROIs detected in comparison to the ground truth (manual segmentation). A ROI was considered undetected when the intersection over union (IoU) was below 0,15. Each bar refers to the image number on the left. The IoU quantifies the overlap between ground truth (manually segmented ROI) and the ROI detected by the segmentation algorithm. It is defined as the area of the overlapping region over the total area. IoU for increasing cell density for cell and nuclear masks is given in the bottom right. Each point represents an individual ROI. Each bar refers to the image number on the left.”

• Comments on Figure 4. More details on quenching are needed for a general audience. The markers chosen (EdU and BrdU) are generally not specific to cell type but to biological processes (proliferation), so it is confusing how they are being used as cell-type markers. 

The base analogues were incorporated into each cell line prior to mixing them, i.e.  when they were still growing in monoculture so they could be labelled and identified after co-seeding and morphological profiling. Additional clarification has been added to the manuscript (page 26) 

It is also unclear why reducing CV is an important side-effect of finetuning. CV of what? The legend says, "model iterations", but what does this mean? 

The dots in the violinplot are different CNN initializations. A lower variability between model initializations is an indicator of certainty of the results. Prior to finetuning, the results of the CNN were highly variable leading to a high CoV between the different CNNs. This means the outcome after finetuning is more robust.

• Comments on Figure 5. This is a very convincing and well-described result, kudos! This provides another opportunity to again compare other approaches (not just nucleocentric). Additionally, since the UMAP space uses hand-crafted features. The authors could consider interpreting the specific morphology features impacted by the striking gradual shift to neuron population by fitting a series of linear models per individual feature. This might confirm (or discover) how exactly the cells are shifting morphology.

The supervised UMAP on the handcrafted features did not highlight any features contributing to the separation. Using the supervised UMAP, the clustering is dominated by the known cell type. Unsupervised UMAP on the handcrafted features does not show any clustering. In response to a previous comment, we adapted the figure to show UMAP dimensionality reduction using the feature embeddings from the cell-based CNN. This unsupervised UMAP does show good cell type separation, but it does not use any directly interpretable shape descriptors.

• General comments on Methods. The section on "ground truth alignment" needs more details. Why was this performed? 

Following sequential staining and imaging rounds, multiple images were captured representing the same cell with different markers. Lifting the plate of the microscope stage and imaging in sequential rounds after several days results in small linear translations in the exact location of each image. These linear translations need to be corrected to align (or register) morphological with ground truth image data within the same ROI. This notion has been added to the manuscript at page 26. 

Handcrafted features extracted using what software? 

The complete analysis was performed in python. All packages used are listed in table 4. Handcrafted features were extracted using the scikit-image package (regionprops and GLCM functions). This has been added to the manuscript at page 27.

Software should be cited more often throughout the manuscript. 

Lastly, the GitHub URL points to the DeVosLab organization, but should point to a specific repository. Therefore, I was unable to review the provided code. A well-documented and reproducible analysis pipeline should be included.

A test dataset and source code are available on GitHub:  https://github.com/DeVosLab/Nucleocentric-Profiling

Author response:

The following is the authors’ response to the original reviews.

Reviewer 1:

Comment 1. In Figure 1, the MafB antibody (Sigma) was used to identify Renshaw cells at P5. However, according to the supplementary Figure 3D, the specificity of the MafB antibody (Sigma) is relatively low. The image of MafB-GFP, V1-INs, and MafB-IR at P5 should be added to the supplementary figure. The specificity of MaFB-IR-Sigma in V1 neurons at P5 should be shown. This image also might support the description of the genetically labeled MafB-V1 distribution at P5 (page 8, lines 28-32). 

We followed the reviewer’s suggestion and moved analyses of the MafB-GFP mouse to a supplemental figure (Fig S3). The characterization of MafB immunoreactivities is now in supplemental Figure S2 and the related text in results was also moved to supplemental to reduce technicalities in the main text. We added confocal images of MafB-GFP V1 interneurons at P5 showing immunoreactivities for both MafB antibodies, as suggested by the reviewer (Fig S2A,B). We agree with the reviewer that this strengthens our comparisons on the sensitivity and specificity of the two MafB antibodies used in this study. 

As explained in the preliminary response we cannot show lack of immunoreactivity for MafB antibodies in MafB GFP/GFP knockout mice at P5 because MafB global KOs die at birth. This is why we used tissues from late embryos to check MafB immunoreactivities (Figure S2C and S2D). We made this point clearer in the text and supplemental figure legends.

Comment 2. The proportion of genetically labeled FoxP2-V1 in all V1 is more than 60%, although immunolabeled FoxP2-V1 is approximately 30% at P5. Genetically labeled Otp-V1 included other nonFoxP2 V1 clades (Fig. 8L-M). I wonder whether genetically labeled FoxP2-V1 might include the other three clades. The authors should show whether genetically labeled FoxP2-V1 expresses other clade markers, such as pou6f2, sp8, and calbindin, at P5. 

We included the requested data in Figure 3E-G. Lineage-labeled Foxp2-V1 neurons in our genetic intersection do not include cells from other V1-clades.

Reviewer 2:

Comment 1. The current version of the paper is VERY hard to read. It is often extremely difficult to "see the forest for the trees" and the reader is often drowned in methodological details that provide only minor additions to the scientific message. Non-specialists in developmental biology, but still interested in the spinal cord organization, especially students, might find this article challenging to digest and there is a high risk that they will be inclined to abandon reading it. The diversity of developmental stages studied (with possible mistakes between text and figures) adds a substantial complexity in the reading. It is also not clear at all why authors choose to focus on the Foxp2 V1 from page 9. Naively, the Pou6f2 might have been equally interesting. Finally, numerous discrepancies in the referencing of figures must also be fixed. I strongly recommend an in-depth streamlining and proofreading, and possibly moving some material to supplement (e.g. page 8, and elsewhere).

The whole text was re-written and streamlined with most methodological discussion (including the section referred to by the reviewer) transferred to supplemental data. Nevertheless, enough details on samples, stats and methods were retained to maintain the rigor of the manuscript. 

The reasons justifying a focus on Foxp2-V1 interneurons were fully explained in our preliminary response. Briefly, we are trying to elucidate V1 heterogeneity, and prior data showed that this is the most heterogeneous V1 clade (Bikoff et al., 2016), so it makes sense it was studied further. We agree that the Pou6f2 clade is equally interesting and is in fact the subject of several ongoing studies.

Comment 2. … although the different V1 populations have been investigated in detail regarding their development and positioning, their functional ambition is not directly investigated through gain or loss of function experiments. For the Foxp2-V1, the developmental and anatomical mapping is complemented by a connectivity mapping (Fig 6s, 8), but the latter is fairly superficial compared to the former. Synapses (Fig 6) are counted on a relatively small number of motoneurons per animal, that may, or may not, be representative of the population. Likewise, putative synaptic inputs are only counted on neuronal somata. Motoneurons that lack of axo-somatic contacts may still be contacted distally. Hence, while this data is still suggestive of differences between V1 pools, it is only little predictive of function.

We fully answered the question on functional studies in the preliminary response. Briefly, we are currently conducting these studies using various mouse models that include chronic synaptic silencing using tetanus toxin, acute partial silencing using DREADDs, and acute cell deletion using diphtheria toxin. Each intervention reveals different features of Foxp2-V1 interneuron functions, and each model requires independent validation. Moreover, these studies are being carried out at three developmental stages: embryos, early postnatal period of locomotor maturation and mature animals. Obviously, this is all beyond the goals and scope of the present study. The present study is however the basis for better informed interpretations of results obtained in functional studies.

Regarding the question on synapse counts, we explained in the preliminary results fully why we believe our experimental designs for synapse counting at the confocal level are among the most thorough that can be found in the literature. We counted a very large number of motoneurons per animal when adding all motor column and segments analyzed in each animal. Statistical power was also enough to detect fundamental variation in synaptic density among motor columns.

We focus our analyses on motoneuron cells bodies because analysis of full dendritic arbors on all motor columns present throughout all lumbosacral segments is not feasible. Please see Rotterman et al., 2014 (J. of Neuroscience; doi: 10.1523/JNEUROSCI.4768-13.2014) for evaluation of what this entails for a single motoneuron. We agree with the reviewer that analyses of V1 synapses over full dendrite arbors in specific motoneurons will be very relevant in further studies. These should be carried out now that we know which motor columns are of high interest. Nevertheless, inhibitory synapses exert the most efficient modulation of neuronal firing when they are on cell bodies, and our analyses clearly suggest a difference in in cell body inhibitory synapses targeting between different V1 interneuron types that we find very relevant.

Comment 3. I suggest taking with caution the rabies labelling (Figure 8). It is known that this type of Rabies vectors, when delivered from the periphery, might also label sensory afferents and their postsynaptic targets in the cord through anterograde transport and transneuronal spread (e.g., Pimpinella et al., 2022). Yet I am not sure authors have made all controls to exclude that labelled neurons, presumed here to be premotoneurons, could rather be anterogradely labelled from sensory afferents. 

Over the years, we performed many extensive controls and validation of rabies virus transsynaptic tracing methods. These were presented at two SfN meetings (Gomez-Perez et al., 2015 and 2016; Program Nos. 242.08 and 366.06). Our validation of this technique was fully explained in our preliminary response. We also pointed out that the methods used by Pimpinella et al. have a very different design and therefore their results are not comparable to ours. In this study we injected the virus at P15 into leg muscles, and not directly into the spinal cord. In our hands, and as cited in Pimpinella et al., the rabies virus loses tropism for primary afferents with age when injected in muscle. The lack of primary afferent labeling in key lumbosacral segments (L4 and L5) is now illustrated in a new supplemental figure (Figure S6). This figure also shows some starter motoneurons. As explained in the text and in our previous response, these are few in number because of the reduced infection rate when using this method in mature animals (after P10).  

Comment 4. The ambition to differentiate neuronal birthdate at a half-day resolution (e.g., E10 vs E10.5) is interesting but must be considered with caution. As the author explains in their methods, animals are caged at 7pm, and the plug is checked the next morning at 7 am. There is hence a potential error of 12h. 

We agree with the reviewer, and we previously explicitly discussed these temporal resolution caveats. We have now further expanded on this in new text (see middle paragraph in page 5). Nevertheless, the method did reveal the temporal sequence of neurogenesis of V1 clades with close to 12-hour resolution.

As explained in text and preliminary response this is because we analyzed a sufficient number of animals from enough litters and utilized very stringent criteria to count EdU positives. 

Moreover, our results fit very well with current literature. The data agree with previous conclusions from Andreas Sagner group (Institut für Biochemie, Friedrich-Alexander-Universität Erlangen-Nürnberg), on spinal interneurons (including V1s) birthdates based on a different methodology (Delile J et al.

Development. 2019 146(12):dev173807. doi: 10.1242/dev.173807. PMID: 30846445; PMCID: PMC6602353). In the discussion we compared in detail both the data and methods between Delile article and our results. We also cite Sagner 2024 review as requested later in the reviewer’s detailed comments. Our results also confirmed our previous report on the birthdates of V1-derived Renshaw cells and Ia inhibitory interneurons (Benito-Gonzalez A, Alvarez FJ J Neurosci. 2012 32(4):1156-70. doi: 10.1523/JNEUROSCI.3630-12.2012. PMID: 22279202; PMCID: PMC3276112). Finally, we recently received a communication notifying us that our neurogenesis sequence of V1s has been replicated in a different vertebrate species by Lora Sweeney’s group (Institute of Science and Technology Austria; direct email from this lab) and we shared our data with them for comparison. This manuscript is currently close to submission. Therefore, we are confident that despite the limitations of EdU birthdating we discussed, the conclusions we offered are strong and are being validated by other groups using different methods and species. We also want to acknowledge the positive comments of reviewer 3 regarding our birthdating study, indicating it is one the most rigorous he or she has ever seen.

Reviewer 3:

Comment 1. My only criticism is that some of the main messages of the paper are buried in technical details. Better separation of the main conclusions of the paper, which should be kept in the main figures and text, and technical details/experimental nuances, which are essential but should be moved to the supplement, is critical. This will also correct the other issue with the text at present, which is that it is too long.

Similar to our response to comment 1 from Reviewer 2 we followed the reviewers’ recommendations and greatly summarized, simplified and removed technical details from the main text, trying not to decrease rigor.  

Reviewer #1 (Recommendations For The Authors):

In Figure 1, the definition of the area to analyze MafB ventral and MafB dorsal is unclear. It should be described.

This has been clarified in both text and supplemental figure S3.

“We focused the analyses on the brighter dorsal and ventral MafB-V1 populations defined by boxes of 100 µm dorsoventral width at the level of the central canal (dorsal) or the ventral edge of the gray matter (ventral) (Supplemental Figure S3B).”

Problems with figure citation.

We apologize for the mistakes. All have been corrected. 

Reviewer #2 (Recommendations For The Authors):

As indicated in the public review, I'd recommend to substantially revise the writing, for clarity. As such, the paper is extremely hard to read. I would also recommend justifying the focus on Foxp2 neurons.

Also, the scope of the present paper is not clearly stated in the introduction (page 4).

Done. We also modified the introduction such that the exact goals are more clearly stated.

I would also recommend toning down the interpretation that V1 clades constitute "unique functional subsets" (discussion and elsewhere). Functional investigation is not performed, and connectomic data is partial and only very suggestive.

We include the following sentence at the end of the 1st paragraph in the discussion:

“This result strengthens the conclusion that these V1 clades defined by their genetic make-up might represent distinct functional subtypes, although further validation is necessary in more functionally focused studies.”

Different post-natal stages are used for different sections of the manuscript. This is often confusing, please justify each stage. From the beginning even, why is the initial birthdating (Figure 1) done here at p5, while the previous characterization of clades was done at p0? I am not sure to understand the justification that this was chosen "to preserve expression of V1 defining TFs". Isn't the sooner the better?

The birthdating study was carried out at P5. P5 is a good time point because there is little variation in TF expression compared to P0, as demonstrated in the results. Furthermore, later tissue harvesting allows higher replicability since it is difficult to consistently harvest tissue the day a litter is born (P0). Also technically, it is easier to handle P5 tissue compared to P0. The analysis of VGUT1 synapses was also done at P5 rather than later ages. This has two advantages: TFs immunoreactivities are preserved at this age, and also corticospinal projections have not yet reached the lumbar cord reducing interpretation caveats on the origins of VGUT1 synapses in the ventral horn (although VGLUT1 synapses are still maturing at this age, see below).

Other parts of the study focus on different ages selected to be most adequate for each purpose. To best study synaptic connectivity, it is best to study mature spinal cords after synaptic plasticity of the first week. For the tracing study we thoroughly explain in the text the reasons for the experimental design (see also below in detailed comments). For counting Foxp2-V1 interneurons and comparing them to motor columns we analyze mature animals. For testing our lineage labeling we use animals of all ages to confirm the consistency of the genetic targeting strategy throughout postnatal development and into adulthood.

Figure 5: wouldn't it be worth quantifying and illustrating cellular densities, in addition to the average number of Foxp2 neurons, across lumbar segments (panel D & E)? Indeed, the size of - and hence total number of cells within - each lumbar segment might not be the same, with a significant "enlargement" from L2 to L4 (this is actually visible on the transverse sections). Hence, if the total number of cells is in the higher in these enlarged segments, but the total number of Foxp2-V1 is not, it may mean that this class is proportionally less abundant.

We believe the critical parameter is the ratio of Foxp2-V1s to motoneurons. This informs how Foxp2-V1 interneurons vary according to the size of the motor columns and the number of motoneurons overall.

The question asked by the reviewer would best be answered by estimating the proportion of Foxp2-V1 neurons to all NeuN labeled interneurons. This is because interneuron density in the spinal cord varies in different segments. We are not sure what this additional analysis will contribute to the paper.

Why, in the Rabies tracing scheme (Fig 8), the Rabies injection is performed at p15? As the authors explain in the text, rabies uptake at the neuromuscular junction is weak after p10. It is not clear to me why such experiments weren't done all at early postnatal stages, with a "classical" co-injection of TVA and Rabies.

First, we do not need TVA in this experiment because we are using B19-G coated virus and injecting it into muscles, not into the spinal cord directly.

Second, enhanced tracing occurs when the AAV is injected a few days before rabies virus. This is because AAV transgene expression is delayed with respect to rabies virus infection and replication. We have performed full time courses and presented these data in one abstract to SfN: Gomez-Perez et al., 2015 Program Nos. 242. We believe full description of these technical details is beyond the scope of this manuscript that has already been considered too technical.

Third, the justification of P15 timing of injections for anterograde primary afferent labeling and retrograde monosynaptic labeling of interneurons is fully explained in the text. 

“To obtain transcomplementation of RVDG-mCherry with glycoprotein in LG motoneurons, we first injected the LG muscle with an AAV1 expressing B19-G at P4. We then performed RVDG and CTB injections at P15 to optimize muscle targeting and avoid cross-contamination of nearby muscles. Muscle specificity was confirmed post-hoc by dissection of all muscles below the knee. Analyses were done at P22, a timepoint after developmental critical windows through which Ia (VGLUT1+) synaptic numbers increase and mature on V1-IaINs (Siembab et al., 2010)” 

Furthermore, CTB starts to decrease in intensity 7 days after injection because intracellular degradation and rabies virus labeling disappears because cell death. Both limit the time of postinjection for analyses.

Likewise, I am surprised not to see a single motoneuron in the rabies tracing (Fig 8, neither on histology nor on graphs (Fig 8). How can authors be certain that there was indeed rabies uptake from the muscle at this age, and that all labelled cells, presumed to be preMN, are not actually sensory neurons? It is known that Rabies vectors, when delivered from the periphery, might also label sensory afferents and their post-synaptic targets through anterograde transport and transneuronal spread (e.g., Pimpinella et al., 2022). This potential bias must be considered.

This is fully explained in our previous response to the second reviewer’s general comments. We have also added a confocal image showing starter motoneurons as requested (Figure S6A).

Please carefully inspect the references to figures and figure panels, which I suspect are not always correct.

Thank you. We carefully revised the manuscript to correct these deficiencies and we apologize for them.

Reviewer #3 (Recommendations For The Authors):

Figure 1: Data here is absolutely beautiful and provides one of the most thorough studies, in terms of timepoints, number of animals analyzed, and precision of analysis, of edU-based birth timing that has been published for neuron subtypes in the spinal cord so far. My only suggestion is to color code the early and late born populations (in for example, different shades of green for early; and blue for late, to better emphasize the differences between them). It is very difficult to differentiate between the purple, red and black colors in G-I, which this would also fix. The antibody staining for Pou6f2 (F) is also difficult to see; gain could be increased on these images or insets added for clarity.

The choice of colors is adapted for optimal visualization by people with different degrees of color blindness. Shades of individual colors are always more difficult to discriminate. This is personally verified by the senior corresponding author of this paper who has some color discrimination deficits. Moreover, each line has a different symbol for the same purpose of easing differentiation.

Figure 2: This is also a picture-perfect figure showing further diversity by birth time even within a clade. One small aesthetic comment is that the arrows are quite unclear and block the data. Perhaps the contours themselves could be subdivided by region and color coded by birth time-such that for example the dorsal contours that emerge in the MafB clade at E11 are highlighted in their own color. Some quantification of the shift in distribution as well as the relative number of neurons within each spatially localized group would also be useful. For MafB, for example, it looks as though the ventral cells (likely Renshaw) are generated at all times in the contour plots; in the dot plots however, it looks like the most ventral cells are present at e10.5. This is likely because the contours are measuring fractional representations, not absolute number. An independent measure of absolute number of ventral and dorsal, by for example, subdividing the spinal cord into dorsoventral bins, would be very useful to address this ambiguity.

We believe density plots already convey the message of the shift in positions with birthdate. We are not sure how we can quantify this more accurately than showing the differences in cellular density plots. We used dorsoventral and mediolateral binning in our first paper decades ago (Avarez et al., 2005). This has now been replaced by more rigorous density profiles that describe better cell distributions. Unfortunately, to obtain the most accurate density profiles we need to pool all cells from all animals precluding statistical comparisons. This is because for some groups there have very few cells per animal (for example early born Sp8 or Foxp2 cells).

Figure 3 and Figure 4: These, and all figures that compare the lineage trace and antibody staining, should be moved to the supplement in my opinion-as they are not for generalist readers but rather specialists that are interested in these exact tools. In addition, the majority of the text that relates to these figures should be transferred to the supplement as well. Figure 5: Another great figure that sets the stage for the analysis of FoxP2V1-to-MN synaptic connectivity, and provides basic information about the rostrocaudal distribution of this clade, by analyzing settling position by level. I have only minor comments. The grid in B obscures the view of the cells and should be removed. The motor neuron cell bodies in C would be better visible if they were red.

We moved some of the images to supplemental (see new supplemental Fig S4). However, we also added new data to the figure as requested by reviewers (Fig 3E-G). We preserved our analyses of Foxp2 and non-Foxp2 V1s across ages and spinal segments because we think this information is critical to the paper. Finally, we want to prevent misleading readers into believing that Foxp2 is a marker that is unique to V1s. Therefore, we also preserved Figures 3H to 3J showing the non-V1 Foxp2 population in the ventral horn. 

Figure 6: Very careful and quantitative analysis of V1 synaptic input to motor neurons is presented here.  For the reader, a summary figure (similar to B but with V1s too) that schematizes V1 FoxP2 versus Renshaw cell connectivity with LMC, MMC, and PGC motor neurons are one level would be useful.

Thanks for the suggestion. A summary figure has now been included (Figure 5G). 

Figure 7: The goal of this figure is to highlight intra-clade diversity at the level of transcription factor expression (or maintenance of expression), birth timing and cell body position culminating in the clear and concise diagram presented in G. In panels A-F however, it takes extra effort to link the data shown to these I-IV subtypes. The figure should be restructured to better highlight these links. One option might be to separate the figure into four parts (one for each type): with the individual spatial, birth timing and TF data for each population extracted and presented in each individual part.

We agree with the reviewer that this is a very busy figure. We tried to re-structure the figure following the suggestions of the reviewer and also several alternative options. All resulted in designs that were more difficult to follow than the original figure. We apologize for its complexity, but we believe this is the best organization to describe all the data in the simplest form.

Figure 8: in A-D, the main point of the figure - that V1FoxP2Otp preferentially receive proprioceptive synapses is buried in a bunch of technical details. To make it easier for the reader, please:

(1) add a summary as in B of the %FoxP2-V1 Otp+ cells (82%) with Vglut1 synapses to make the point stronger that the majority of these cells have synapses.

We added this graph by extending the previous graph to include lineage labeled Foxp2-V1s with OTP or Foxp2 immunoreactivity. It is now Figure 7B.

(2) Additionally, add a representative example that shows large numbers of proximal synapses on an FoxP2-V1 Otp+.

The image we presented before as Figure 8A was already immunostained for OTP, so we just added the OTP channel to the images. Now all this information is in panels that are subparts of Figure 7A.

(3) Move the comparison between FoxP2-V1 and FoxP2AB+V1s to the supplement.

We preserved the quantitative data on Foxp2-V1 lineage cells with Foxp2-immunoreactivity but made this a standalone figure, so it is not as busy.

(4) Move J-M description of antibody versus lineage trace of Otp to supplement as ending with this confuses the main message of the paper (see comment above).

All results for the Otp-V1 mouse model have now been placed in a supplemental figure (Figure 5S).

Discussion: A more nuanced and detailed discussion of how the temporal pattern of subtype generation presented here aligns with the established temporal transcription factor code (nicely summarized in Sagner 2024) would be helpful to place their work in the broader context of the field.

This aspect of the discussion was expanded on pages 20 and 21. We replaced the earlier cited review (Sagner and Briscoe, 2019, Development) with the updated Sagner 2024 review and further discussed the data in the context of the field and neurogenesis waves throughout the neural tube, not only the spinal cord. We previously carefully compared our data with the spinal cord data from Sagner’s group (Delile et, 2019, Development). We have now further expanded this comparison in the discussion.

Author response:

The following is the authors’ response to the original reviews.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The study characterized the cellular and molecular mechanisms of spike timing-dependent long-term depression (t-LTD) at the synapses between excitatory afferents from lateral (LPP) and medial (MPP) perforant pathways to granule cells (GC) of the dentate gyrus (DG) in mice.

Strengths:

The electrophysiological experiments are thorough. The experiments are systematically reported and support the conclusions drawn.

This study extends current knowledge by elucidating additional plasticity mechanisms at PP-GC synapses, complementing existing literature.

We thank the reviewer for the positive assessment of our work and the constructive suggestions to improve the manuscript.

Weaknesses:

To more conclusively define the pivotal role of astrocytes in modulating t-LTD at MPP and LPP GC synapses through SNARE protein-dependent glutamate release, as posited in this study, the authors could adopt additional methods, such as alternative mouse models designed to regulate SNARE-dependent exocytosis, as well as optogenetic or chemogenetic strategies for precise astrocyte manipulation during t-LTD induction. This would provide more direct evidence of the influence of astrocytic activity on synaptic plasticity.

We thank the reviewer for the suggestion. As stated in the manuscript and in figure 4, we already used two different approaches (aBAPTA to interfere with astrocyte calcium signalling and dnSNARE mice (that have vesicular release impaired) to determine the involvement of astrocytes in the discovered forms of LTD, and both approaches clearly indicated the requirement of astrocytes for t-LTD. In BAPTA-treated astrocytes and in dnSNARE mice, t-LTD was prevented. Notwithstanding this, and as suggested by the reviewer, we used two additional approaches to confirm astrocyte participation. We loaded astrocytes with the light chain of the tetanus toxin (TeTxLC), which is known to block exocytosis by cleaving the vesicle-associated membrane protein, an important part of the SNARE complex (Schiavo et al., 1992, Nature 359, 832-835). In this experimental condition, we observed a clear lack of t-LTD at both (lateral and medial) pathways, thus confirming the requirement of astrocytes and the SNARE complex and vesicular release for both types of t-LTD. In addition, to gain more insight into the fact that glutamate is released by astrocytes, we blocked glutamate release from astrocytes by loading the astrocytes with Evans blue, known to interfere with glutamate uptake into vesicles as it inhibits the vesicular glutamate transporter (VGLUT). In this experimental condition, again t-LTD was prevented, indicating that t-LTD requires Ca2+dependent exocytosis of glutamate from astrocytes.

Reviewer #2 (Public Review):

Summary:

This work reports the existence of spike timing-dependent long-term depression (t-LTD) of excitatory synaptic strength at two synapses of the dentate gyrus granule cell, which are differently connected to the entorhinal cortex via either the lateral or medial perforant pathways (LPP or MPP, respectively). Using patch-clamp electrophysiological recording of tLTD in combination with either pharmacology or a genetically modified mouse model, they provide information on the differences in the molecular mechanism underlying this t-LTD at the two synapses.

Strengths:

The two synapses analyzed in this study have been understudied. This new data thus provides interesting new information on a plasticity process at these synapses, and the authors demonstrate subtle differences in the underlying molecular mechanisms at play. Experiments are in general well controlled and provide robust data that are properly interpreted.

We thank the reviewer for the positive assessment of our work and the constructive suggestions to improve the manuscript.

Weaknesses:

• Caution should be taken in the interpretation of the results to extrapolate to adult brain as the data were obtained in P13-21 days old mice, a period during which synapses are still maturing and highly plastic.

We thank the reviewer for noticing this. In fact, our experiments were intentionally performed in young animals (P13-21), just knowing that this is a critical period of plasticity. We indicate that in the methods, results, and discussion (where we discuss that in some detail) sections.

• In experiments where the drug FK506 or thapsigargin are loaded intracellularly, the concentrations used are as high as for extracellular application. Could there be an error of interpretation when stating that the targeted actors are necessarily in the post-synaptic neuron? Is it not possible for the drug to diffuse out of the cell as it is evident that it can enter the cell when applied extracellularly?

We thank the reviewer for rising this point. While it would be possible that these compounds cross the cell membranes, to do it and to pass to other cells, this would, in principle, require a relatively long time to occur. Additionally, to have any effect, the same concentration or a relatively high concentration of that we put into the pipette has to reach other cells. Furthermore, even if a compound is able to cross a cell membrane during the duration of an experiment, after this, it may be exposed to the extracellular fluid where will be diluted and most probably washed out. For all these reasons, we do not see this very plausible. Notwithstanding this, and as suggested, we have repeated the experiments using lower concentrations of thapsigargin (1 uM) and FK506 (1 uM), and have obtained the same results. These data are now included in the figure 3 and in the text.

• The experiments implicating glutamate release from astrocytes in t-LTD would require additional controls to better support the conclusions made by the authors. As the data stand, it is not clear, how the authors identified astrocytes to load BAPTA and if dnSNARE expression in astrocytes does not indirectly perturb glutamate release in neurons.

We thank the reviewer for rising this point. We now indicate how astrocytes have been identified to load BAPTA. We reply to this in detail in the “Recommendations for the authors” from reviewer 2.

Significance:

While this is the first report of t-LTD at these synapses, this plasticity process has been mechanistically well investigated at other synapses in the hippocampus and in the cortex. Nevertheless, this new data suggests that mechanistic differences in the induction of t-LTD at these two DG synapses could contribute to the differences in the physiological influence of the LPP and MPP pathways.

Reviewer #3 (Public Review):

Coatl et al. investigated the mechanisms of synaptic plasticity of two important hippocampal synapses, the excitatory afferents from lateral and medial perforant pathways (LPP and MPP, respectively) of the entorhinal cortex (EC) connecting to granule cells of the hippocampal dentate gyrus (DG). They find that these two different EC-DG synaptic connections in mice show a presynaptically expressed form of long-term depression (LTD) requiring postsynaptic calcium, eCB synthesis, CB1R activation, astrocyte activity, and metabotropic glutamate receptor activation. Interestingly, LTD at MPP-GC synapses requires ionotropic NMDAR activation whereas LTD at LPP-GC synapse is NMDAR independent. Thus, they discovered two novel forms of t-LTD that require astrocytes at EC-GC synapses. Although plasticity of EC-DG granule cell (GC) synapses has been studied using classical protocols, These are the first analysis of the synaptic plasticity induced by spike timing dependent protocols at these synapses. Interestingly, the data also indicate that t-LTD at each type of synapse require different group I mGluRs, with LPP-GC synapses dependent on mGluR5 and MPP-GC t-LTD requiring mGluR1.

The authors performed a detailed analysis of the coefficient of variation of the EPSP slopes, miniature responses and different approaches (failure rate, PPRs, CV, and mEPSP frequency and amplitude analysis) they demonstrate a decrease in the probability of neurotransmitter release and a presynaptic locus for these two forms of LTD at both types of synapses. By using elegant electrophysiological experiments and taking advantage of the conditional dominant-negative (dn) SNARE mice in which doxycycline administration blocks exocytosis and impairs vesicle release by astrocytes, they demonstrate that both LTD forms require the release of gliotransmitters from astrocytes. These data add in an interesting way to the ongoing discussion on whether LTD induced by STDP participates in refining synapses potentially weakening excitatory synapses under the control of different astrocytic networks. The conclusions of this paper are mostly well supported by data, but some aspects the results must be clarified and extended.

We thank the reviewer for the positive assessment of our work and the constructive suggestions to improve the manuscript.

(1) It should be clarified whether present results are obtained with or without the functional inhibitory synapse activation. It is not clear if GABAergic synapses are blocked or not. If GABAergic synapses are not blocked authors must discuss whether the LTD of the EPSPs is due to a decrease in glutamatergic receptor activation or an increase in GABAergic receptor activation. Moreover, it should be recommended to analyze not only the EPSPs but also the EPSCs to address whether the decrease in synaptic transmission is caused by a decrease in the input resistance or by a decrease in the space constant (lambda).

We thank the reviewer for rising these points. GABAergic inhibition was not blocked in our experiments. The observed forms of t-LTD seem to be due to a decrease in glutamate release probability as indicated in the manuscript, mediated by the mechanism we uncover and describe here. To determine and clarify whether GABA receptors have any role in these forms of t-LTD, we repeated the experiments in the presence of the GABAA and GABAB receptors antagonists bicuculline and SCH50911, respectively. Blocking GABA receptors do not prevent or affect t-LTD at LPP- or MPP-GC synapses, that is still present and with a similar magnitude that controls. These results indicating that these receptors are not involved in these forms of t-LTD. These results are now included in the text in the results section (page 8) and as a new figure S1. In our experiments, no changes in input resistance or space constant were observed, and importantly, no changes were observed in the amplitude/slopes of EPSP in the control pathway that does not undergo plasticity protocol that we routinely use in our experiments.

(2) Authors show that Thapsigargin loaded in the postsynaptic neuron prevents the induction of LTD at both synapses. Analyzing the effects of blocking postsynaptic IP3Rs (Heparin in the patch pipette) and Ryanodine receptors (Ruthenium red in the patch pipette) is recommended for a deeper analysis of the mechanism implicated in the induction of this novel forms of LTD in the hippocampus.

We thank the reviewer for this suggestion. We repeated the experiments loading the postsynaptic cell with heparin and ruthenium red using the path pipette. In these experimental conditions, we observed that t-LTD was not affected by the heparin treatment (discharging a role of IP3Rs), but that it was prevented by the ruthenium red treatment (indicating the requirement of ryanodine receptors). We include now this data in the text (page 12) and in the Figure 3a, b, e, f.

(3) Authors nicely demonstrate that CB1R activation is required in these forms of LTD by blocking CB1Rs with AM251, however an interesting unanswered question is whether CB1R activation is sufficient to induce this synaptic plasticity. This reviewer suggests studying whether applying puffs of the CB1R agonist, WIN 55,212-2, could induce these forms of LTD.

We thank the reviewer for this suggestion. We repeated the experiments adding WIN55, 212-2 as suggested.  The activation of CB1R by puffs of the agonist WIN 55, 212-2 to the astrocyte, directly induced LTD at both LPP- and MPP-GC synapses. We include now this data in the text (page 14) and in the Figure 3c, d, g, h.

(4) Finally, adding a last figure with a cartoon summarizing the proposed model of action in these novel forms of LTD would add a positive value and would help the reading of the manuscript, especially in those aspects related with the discussion of the results.

We thank the reviewer for the suggestion. We include now a figure showing the proposed mechanisms (Figure 5).

The extension of these results would improve the manuscript, which provides interesting results showing two novel forms of presynaptic t-LTD in the brain synapses with different action mechanisms probably implicated in the different aspects of information processing.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

There are just a few aspects that could be clarified to bolster the authors' conclusions.

The author centered the conclusion of their study on the role of astrocytic activity in regulating these two forms of plasticity (see title). To strengthen the evidence that astrocytes are key regulators of t-LTD at MPP and LPP GC synapses by regulating SNARE protein-dependent glutamate release, additional complementary approaches should be considered, such as other mouse models enabling the control of SNARE-dependent exocytosis and/or optogenetic/chemogenetic tools to selectively manipulate astrocytes during the induction of t-LTD, thereby directly assessing the impact of astrocytic activity on synaptic plasticity. Implementing calcium imaging or glutamate sensors to visualize the dynamics of astrocytic calcium signaling and glutamate release during t-LTD could be also considered.

We thank the reviewer for the suggestion. As stated in the manuscript and in figure 4, we already used two different approaches (aBAPTA to interfere with astrocyte calcium signalling and dnSNARE mice (that have vesicular release impaired) to determine the involvement of astrocytes in the discovered forms of LTD, and both approaches clearly indicated the requirement of astrocytes for t-LTD. In BAPTA-treated astrocytes and in dnSNARE, t-LTD was prevented. Notwithstanding this, and as suggested by the reviewer, we used two additional approaches to confirm astrocytes participation. We loaded astrocytes with the light chain of the tetanus toxin (TeTxLC), which is known to block exocytosis by cleaving the vesicle-associated membrane protein, an important part of the SNARE complex (Schiavo et al., 1992, Nature 359, 832-835). In this experimental condition, we observed a clear lack of t-LTD at both (lateral and medial) pathways, thus confirming the requirement of astrocytes and the SNARE complex and vesicular release for both types of t-LTD. In addition, to gain more insight into the fact that glutamate is released by astrocytes, we blocked glutamate release from astrocytes by loading the astrocytes with Evans blue, known to interfere with glutamate uptake into vesicles as it inhibits the vesicular glutamate transporter (VGLUT). In this experimental condition, again t-LTD was prevented, indicating that t-LTD requires Ca2+-dependent exocytosis of glutamate from astrocytes. This information is now included in the text, pages 14 and 15 and in figure 4.

• How were astrocytes identified to be loaded with BAPTA? The author should clarify this methodological aspect and provide confocal images of patched astrocytes situated 50-100 um from the recorded neuron.

We thank the reviewer for the comment. We include now this information in the Methods section (page 6) and in figure S3. Astrocytes were identified by their rounded morphology under differential interference contrast microscopy, and were characterized by low membrane potential, low membrane resistance and passive responses (they do not show action potentials) to both negative and positive current injection.

• Please provide confocal images of EGFP expression in the DG astrocytes of dnSNARE mice both on and off Dox, to verify transgene expression in astrocytes

We thank the reviewer for this suggestion. We now include an image of GFP expression in the DG astrocytes of off Dox dnSNARE mice. We did not provide the animals with doxycycline since birth and thus the gene was constantly expressed. We now show this image in Fig. S3. All the pups and mice are not DOX fed, meaning that the transgenes are continuously being expressed and therefore the exocytosis should be blocked in astrocytes.

Minor points:

Lines 250-253: It is mentioned that TTX is added at baseline, washed out for the t-LTD experiment, and then reapplied post t-LTD. I suggest clarifying the timing and rationale for this application for a broad audience.

We thank the reviewer for the suggestion. We now include some information related to the timing and rationale of the experiment phases (page 9).

The discussion is quite detailed and provides a comprehensive overview of the study's findings. To enhance clarity and impact, the authors might consider to,

• add subheadings and bullet points for key findings. This will improve readability.

• this section could benefit from streamlining to avoid redundancy.

• some sentences could be made more concise without losing meaning.

We thank the reviewer for these suggestions. We now include subheadings in the discussion section to improve readability and have made some sentences more concise and simple without losing meaning.

In figure legends, consistency with capitalization should be maintained, for example in the statistical significance notation, ***P < 0.001" or ***p < 0.001")

We now include p<0.001 in the figure legend 4 for consistency.

Reviewer #2 (Recommendations For The Authors):

Major:

• All results were obtained in young still quite immature synapses. To strengthen the significance of the findings, the authors could repeat some of the main experiments in adult mice (8 weeks and beyond). If not, they should state clearly that these mechanisms were only evidenced in early post-natal conditions.

We thank the reviewer for noticing this. In fact, our experiments were intentionally performed in young animals (P13-21), just knowing that this is a critical period of plasticity. As the reviewer suggests, we indicate that in the methods (page 5), results (page 8), and discussion (page 19) (where we discuss that in some detail) sections.

• Lines 246-249 and fig 1f,p: Authors need to perform a statistical test on these two graphs to support their claim that 'A plot of CV-2 versus the change in the mean evoked EPSP 246 slope (M) before and after t-LTD mainly yielded points below the diagonal line at LPP-GC and MPP-GC synapses'.

That could not be clear in the previous version. We observed an error in the points (with some points missing) of one of the graphs that we have corrected. In addition, and as suggested by the reviewer we performed a regression analysis that confirms the conclusions stated. This is now included in the text (page 9). Thus, we have added information about mean values ± SEM in the text and the linear regression of the data for LPP-GC (Mean = 0.607 ± 0.054 vs 1/CV2 = 0.439 ± 0.096, R2 = 0.337; n = 14) and MPP-GC synapses (Mean = 0.596 ± 0.056 vs 1/CV2 = 0.461 ± 0.090, R2 = 0.168; n = 13), respectively. Data yielded on the dotted horizontal line, 1/CV2 = 1, indicates no change in the probability of release, in contrast, data yielded below the dotted diagonal line is suggestive of a change in the probability of release parameters (for review, see Brock et al., 2020, Front Synaptic Neurosci 12, 11).

• We are not sure that the experiment with the MK801 provided in the patch pipet can be interpreted correctly (Figure 2 a,b and e,f). How sure are the authors that, when applying MK801 in the patch pipet, it can reach its binding site within the pore? The concentration of MK801 is also very high (500 microM) and used at the same concentration extracellularly and intracellularly. Why did the authors not use lower concentration when applied intracellularly?

We thank the reviewer for rising this point. MK801 in the pipette is reaching the pore when loaded postsynaptically as when we record NMDA currents from postsynaptic neurons loaded with MK801, these currents are blocked. We include now a control experiment showing the effect of postsynaptic MK801 on NMDA current in the text (page 10). NMDA currents has been recorded at +40 mV, blocking AMPAR and GABAR with NBQX and bicuculline. Related to the concentration, it has been described that the affinity from the internal site is much lower (several orders of magnitude) than from the extracellular side(Sun et al., 2018 Neuropharmacology, 143, 122-129) and the concentrations used have been extensively used in previous studies. It is clear that the concentrations used in the present work blocked NMDAR currents but did not prevent LTD.

• Linked to the point above, for the intracellular application of FK506 and thapsigargin, the concentrations used extracellularly and intracellularly are identical. The authors could have used lower concentrations for the intracellular application. Also, how can they be sure of the correct interpretation of these data as the drug essentially reaching a post-synaptic target when applied intracellularly? If the drug can enter the neuron, why could it not diffuse out of the neuron especially when loaded at a high concentration? Maybe using a lower concentration when applied intracellularly could at least partially address this issue.

It is evident that it can enter the cell when applied extracellularly?

We thank the reviewer for rising this point. While it would be possible that these compound cross the cell membranes, to do it and to pass to other cells, this would, in principle, require a relatively long time to occur. Additionally, to have any effect, the same concentration or a relatively high concentration of that we put into the pipette has to reach other cells. Furthermore, even if a compound is able to cross a cell membrane during the duration of an experiment, after this, it may be exposed to the extracellular fluid where it will be diluted and most probably washed out. For all these reasons, we do not see this very plausible. Notwithstanding this, we have repeated the experiments using lower concentrations of thapsigargin (1 uM) and FK506 (1 uM) and have obtained the same results. These data are now included in the figure 3 and the numbers in the text have been updated (pages 12-13).

• The data supporting the possibility of glutamate release by astrocytes as a main source of glutamate to promote t-LTD needs to be strengthened. In experiment Figure a-h, it is not clear how the authors recognize astrocytes to patch. No details are provided in the methods or in the main text. If we understand correctly, it is only by performing a current steps protocol to ensure that the patched cell did not produce action potentials. If this was the case, the authors need to be more specific and provide details of this protocol. More importantly, the one trace that was provided in Figures 4a and 4f suggests, albeit by a rough estimation that we made with a ruler, that the highest current step only depolarized the cell to about -40 mV. This is not sufficient to ensure that the recorded cell is not a neuron. The authors should increase their steps to high depolarizing currents to ensure that the patched cell is not a neuron. Better yet, they should load the cell with an dye to process the slice after the electrophysiological recording for immunohistochemistry to ensure that it was indeed an astrocyte. Alternatively, they can try to aspirate the cell content at the end of the recording to perform a qPCR for astrocyte markers eg. GFAP.

We thank the reviewer for the comment. We include now information regarding how astrocytes were identified (also raised by reviewer 1) in the Methods section (page 6) and in figure S3. Astrocytes were identified by their rounded morphology under differential interference contrast microscopy, eGFP fluorescence (astrocytes from dnSNARE mice), and were characterized by low membrane potential, low membrane resistance and passive responses (they do not show action potentials) to both negative and positive current injection.

We agree with the reviewer that in figure 4a and 4f, the step protocol might not be completely clear. For this, we revised that and now include in a clearer way that we applied pulses that depolarized astrocytes beyond -20 mV, with no action potentials found at any point. We also include now this in figure S3.

• Related to the point above, the use of the model expressing dnSNARE in astrocytes is elegant. Yet, to really interpret the data obtained in these slices as a lack of vesicle release (and most importantly glutamate) we think that the authors should ensure that glutamate release from nearby neurons is not impacted. They could patch nearby neurons in dnSNARE slices and test PPR or synaptic fatigue when stimulating either the LPP or MPP. The authors should avoid overinterpretation of these results. As it stands, it is not evident that dnSNARE expression does not perturb other mechanisms within the astrocyte that in turn perturb pre-synaptic glutamate release. Adding back glutamate as puffs does not help to disentangle this issue.

To gain more insight into the fact that glutamate is released by astrocytes we blocked glutamate release from astrocytes by loading the astrocytes with Evans blue, known to interfere with glutamate uptake into vesicles as it inhibits the vesicular glutamate transporter (VGLUT). In this experimental condition, as indicated above, t-LTD was prevented, indicating that t-LTD requires Ca2+-dependent exocytosis of glutamate from astrocytes. This is included in the text (page 15) and in figure 4d,e, i, j.

In addition, we loaded astrocytes with the light chain of the tetanus toxin (TeTxLC) which is known to block exocytosis by cleaving the vesicle-associated membrane protein, an important part of the SNARE complex (Schiavo et al., 1992, Nature 359, 832-835). In this experimental condition, we observed a clear lack of t-LTD at both (lateral and medial) pathways, thus confirming the requirement of astrocytes and the SNARE complex and vesicular release for both types of t-LTD. These data indicate that t-LTD requires Ca2+-dependent exocytosis of glutamate from astrocytes. This information is now included in the text, page 14 and in figure 4.

Minor points:

• line 107, did the authors mean t-LTP and t-LTD? we don't understand STDP mentioned here.

We meant to say t-LTP. This is now corrected.

• line 108: should STDP be replaced by t-LTD as the authors only focused on this plasticity mechanism.

We agree, we indicate now t-LTD.

• line 131-132 : it is not clear when the animals were fed with doxycycline. If it was from birth, then the 'not' should be removed. Otherwise the authors should clearly state when the doxycyline was provided.

DOX was not provided and that means that the transgene was continuously expressed and therefore the exocytosis should be blocked in astrocytes. We express that clearer in page 5, methods section.

• line 223 : which hippocampal synapses? needs to be stated

As suggested this is now included in the text as for cortical synapses. Synapses are Schaffer collaterals SC-CA1 for hippocampus and layer L4-L2/3 for cortical synapses (page 8).

• line 273: what do the authors mean when writing 'from'? We don't understand the data provided on this line.

We thank the reviewer for noticing this. That refers to the amplitude of NMDAR-mediated currents average before and after D-AP5 or MK801. We express this now in a clearer way (page 10, from 57±8 pA to 6±5 pA).

• line 286 : why do the authors point out work on GluN2B and GluN3A only here when they first investigate GluN2A contribution to t-LTD? what about previous data on GluN2A?

We have now expressed this in a different way to make it clear. We wanted to indicate that the available data for presynaptic NMDAR at MPP-GC synapses has been indicated to contain GluN2B and GluN3A subunits and to our knowledge, no data indicate that they contain GluN2A subunits.

• line 428 : what do the authors mean by 'not least' ?

This is a typo and we have removed that from the text.

Reviewer #3 (Recommendations For The Authors):

My only suggestion for improving data presentation in the manuscript would be to split some figures of the paper. In my opinion, the figures are too dense and therefore difficult to follow for the broad audience of eLife readers. In addition, a real image of the recorded dentate granule cells in the slice showing also the location of the real stimulation electrodes would significantly improve the presentation of Figure 1.

We thank the reviewer for the suggestion, but we would prefer to let the figures as they are organized, as while we agree in some cases they are a bit big, in this way it is easier to compare lateral and medial pathways. For this, it could be better to let information regarding the two pathways in the same figure. Nevertheless, we try now to make figures clearer to use a columnar organization of the figures for each pathway what we think, would make easier to compare pathways. As the reviewer suggests we include now a real image of the recorded dentate granule cells in the slice showing also the location of the real stimulation electrodes in Figure 1, that we agree will improve the presentation of this figure and thank the reviewer for the suggestion.

Author response:

The following is the authors’ response to the current reviews.

We thank the Reviewer for all their effort and suggestions over multiple drafts. Their comments have encouraged us to read and think more deeply about the issue under discussion (BLA spiking in response to CS/US inputs), and to find the papers whose contents we think provide a potential solution. We agree that there is more to understand about the mechanisms underlying associative learning in the BLA. We offer our paper as providing a new way of understanding the role of circuit dynamics (rhythms) in guiding associative learning via STDP. As we pointed out in our response to the previous review, the issue highlighted by the Reviewer is an issue for the entire field of associative learning in BLA: our discussion of the issue suggests why the experimentally observed BLA spiking in response to CS inputs, performed in the absence of US inputs (as done in the papers cited by the Reviewer), may not be what occurs in the presence of the US. Since our explanation involves the role of neuromodulators, such as ACh and dopamine, the suggestion is open to further testing.

The following is the authors’ response to the original reviews.

Reviewer #1:

Public Review’s only objection: “Deficient in this study is the construction of the afferent drive to the network, which does elicit activities that are consistent with those observed to similar stimuli. It still remains to be demonstrated that their mechanism promotes plasticity for training protocols that emulate the kinds of activities observed in the BLA during fear conditioning.”

Recommendations for the Authors: “The authors have successfully addressed most of my concerns. I commend them for their thorough response. The one nagging issue is the unrealistic activation used to drive CS and US activation in their network. While I agree that their stimulus parameters are consistent with a contextual fear task, or one that uses an olfactory CS, this was not the focus of their study as originally conceived. Moreover, the types of activation observed in response to auditory cues, which is the focus of their study, do not follow what is reported experimentally. Thus, I stand by the critique that the proposed mechanism has not been demonstrated to work for the conditioning task which the authors sought to emulate (Krabbe et al. 2019). Frustratingly, addressing this is simple: run the model with ECS neurons driven so that they fire bursts of action potentials every ~1 sec for 30 sec, and with the US activation noncontiguous with that. If the model does not produce plasticity in this case, then it suggests that the mechanisms embedded in the model are not sufficient, and more work is needed to identify them. While 'memory' effects are possible that could extend the temporal contiguity of the CS and US, the authors need to provide experimental evidence for this occurring in the BLA under similar conditions if they want to invoke it in their model. 

(1) Fair response. I accept the authors arguments and changes. 

(2) The authors rightly point out that the simulated afferents need not perfectly match the time courses of the peripheral inputs, since what the amygdala receives them indirectly via the thalamus, cortex, etc. However, it is known how amygdala neurons respond to such stimuli, so it behooves the authors to incorporate that fact into their model. 

Quirk et al. 1997 show that the response to the tone plummets after the first 100 ms in Figs 5A and 6B. The Herry et al. 2007 paper emphasizes the transient response to tone pips, with spiking falling back to a poisson low firing rate baseline outside of the time when the pip is delivered. 

Regarding potential metabotropic glutamate activation, the stimulus in Whittington et al. 1995 was electrical stimulation at 100 Hz that would synchronously activate a large volume of tissue, which is far outside the physiological norm. I appreciate that metabotropic glutamate receptors may play a role here, but ultimately the model depends upon spiking activity for the plastic process to occur, and to the best of my knowledge the spiking activity in BLA in response to a sustained, unconditioned tone, is brief (see also Quirk, Repa, and Ledoux 1995). Perhaps a better justification for the authors would be Bordi and Ledoux 1992, which found that 18% of auditory responsive neurons showed a 'sustained' response, but the sustained response neurons appear to show much weaker responses than those with transient ones (Fig 2).  I am willing to say that their paper IS relevant to contextual fear, but that is not what the authors set out to do. 

(3) Fair response. 

(4) Very good response! 

Minor points: All points were addressed.”

We thank Reviewer 1 (R1) for the positive feedback and also for pointing out that, in R1’s opinion, there is still a nagging issue related to the activation in response to CS we modeled. In (Krabbe et al., 2019), CS is a pulsed input and US is delivered right after the CS offset. The current objection of R1 is that instead, we are modeling CS and US as continuous and overlapping. R1 suggested that we add the actual input and see if they will produce the desired outputs. The answer is simple: it will not work because we need the effects of CS and US on pyramidal cells to overlap. We note that the fear learning community appears to agree with us that such contingency is necessary for synaptic plasticity (Sun et al., 2020; Palchaudhuri et al., 2024). To the best of our understanding, the source of that overlap is not understood in the community, and the gap has been much noticed (Sun et al., 2020). We do note, however, that STDP may not be the only kind of plasticity in fear learning (Li et al., 2009; Kim et al., 2013, 2016).

It is important to emphasize that it is not the aim of our paper to model the origin of the overlap. Rather, our intent is to demonstrate the roles of brain rhythms in producing the appropriate timing for STDP, assuming that ECS and F cells can continue to be active after the offset of CS and US, respectively. This assumption is very close to how the field now treats the plasticity, even for auditory fear conditioning (Sun et al., 2020). Thus, our methodology does not contradict known results. However, the question raised by R1 is indeed very interesting, if not the point of our paper. Hence, below we give details about why our hypothesis is reasonable.

Several papers (Quirk, Repa and LeDoux, 1995; Herry et al, 2007; Bordi and Ledoux 1992) show that the pips in auditory fear conditioning increase the activity of some BLA neurons: after an initial transient, the overall spike rate is still higher than baseline activity. As R1 points out, we did not model the transient increase in BLA spiking activity that occurs in response to each pip in the auditory fear conditioning paradigm. However, we did model the low-level sustained activity that occurs in between pips of the CS in the absence of US (Quirk, Repa and LeDoux, 1995, Fig. 2) and after CS offset (see Fig. 2B, left hand part of our manuscript). We read the data of Quirk et al., 1995 as suggesting that the low-level activity can be sustained for some indefinite time after a pip (cut off of recording was at 500 ms with no noticeable decrease in activity). As such, even if the pips and the US do not overlap in time, as in (Krabbe et al., 2019), the spiking of the ECS can be sustained after CS offset and thus overlap with US, a condition necessary in our model for plasticity through STDP. In Herry et al., 2007 Fig. 3 shows that BLA neurons respond to a pip at the population level with a transient increase in spiking and return to a baseline Poisson firing rate. However, a subset of cells continues to fire at an increased-over-baseline rate after the transient effect wears off (Fig. 3C, top few neurons) and this increased rate extends to the end of the recording time (here ~ 300 ms). These are the cells we consider to be ECS in our model. In Quirk et al., 1997, Fig. 5A also shows sustained low level activity of neurons in BLA in response to a pip. The low-level activity is shown to increase after fear learning, as is also the case in our model since ECS now entrains F so that there are more pyramidal cells spiking in response to CS. The question remains as to whether the spiking is sustained long enough and at a high enough rate for STDP to take place when US is presented sometime after the stop of the CS. 

Experimental recordings cannot speak to the rate of spiking of BLA neurons during US due to recording interference from the shock. However, evidence seems to suggest that ECS activity should increase during the US due to the release of acetylcholine (ACh) from neurons in the basal forebrain (BF) (Rajebhosale et al., 2024). Pyramidal cells of the BLA robustly express M1 muscarinic ACh receptors (Muller et al., 2013; McDonald and Mott, 2021). Thus, ACh from BF should elicit a depolarization in pyramidal cells. Indeed, the pairing of ACh with even low levels of spiking of BLA neurons results in a membrane depolarization that can last 7 – 10 s (Unal et al., 2015). This should induce higher spiking rates and more sustained activity in the ECS and F neurons during and after the presentation of US, thus ensuring a concomitant activation of ECS and fear (F) neurons necessary for STDP to take place. Other modulators, including dopamine, may also play a role in producing the sustained activity. Activation of US leads to increased dopamine release in the BLA (Harmer and Phillips, 1999; Suzuki et al., 2002). D1 receptors are known to increase the membrane excitability of BLA projection neurons by lowering their spiking threshold (Kröner et al., 2005). Thus, the activation of the US can lead to continued and higher firing rates of ECS and F. The effect of dopamine can last up to 20 minutes (Kröner et al., 2005). For CS-positive neurons, the ACh modulation coming from the firing of US may lead to a temporary extension of firing that is then amplified and continued by dopaminergic effects.

Hence, we suggest that a solution to the problem raised by R1 may be solved by considering the roles of ACh and dopamine in the BLA. The involvement of neuromodulators is consistent with the suggestion of (Sun et al., 2020). The model we have may be considered a “minimal” model that puts in by hand the overlap in activity due to the neuromodulation without explicitly modeling it. As R1 says, it is important for us to give the motivation of our hypotheses. We have used the simplest way to model overlap without assumptions about timing specificity in the overlap.

To account for these points in the manuscript, we first specified that we consider the effects of the US and CS inputs on the neuronal network as overlapping, while the actual inputs may not overlap. To do that, we added the following text:

(1) In the introduction: 

“In this paper, we aim to show 1) How a variety of BLA interneurons (PV, SOM and VIP) lead to the creation of these rhythms and 2) How the interaction of the interneurons and the rhythms leads to the appropriate timing of the cells responding to the US and those responding to the CS to promote fear association through spike-timing-dependent plasticity (STDP). Since STDP requires overlap of the effects of the CS and US, and some conditioning paradigms do not have overlapping US and CS, we include as a hypothesis that the effects of the CS and US overlap even if the CS and US stimuli do not. In the Discussion, we suggest how neuromodulation by ACh and/or dopamine can provide such overlap. We create a biophysically detailed model of the BLA circuit involving all three types of interneurons and show how each may participate in producing the experimentally observed rhythms and interacting to produce the necessary timing for the fear learning.”

(2) In the Result section “With the depression-dominated plasticity rule, all interneuron types are needed to provide potentiation during fear learning”:

“The 40-second interval we consider has both ECS and F, as well as VIP and PV interneurons, active during the entire period: an initial bout of US is known to produce a long-lasting fear response beyond the offset of the US (Hole and Lorens, 1975) and to induce the release of neuromodulators. The latter, in particular acetylcholine and dopamine that are known to be released upon US presentation (Harmer and Phillips, 1999; Suzuki et al., 2002; Rajebhosale et al., 2024), may induce more sustained activity in the ECS, F, VIP, and PV neurons during and after the presentation of US, thus ensuring a concomitant activation of those neurons necessary for STDP to take place (see “Assumptions and predictions of the model” in the Discussion).”

(3) In the Discussion section “Synaptic plasticity in our model”:

“Synaptic plasticity is the mechanism underlying the association between neurons that respond to the neutral stimulus CS (ECS) and those that respond to fear (F), which instantiates the acquisition and expression of fear behavior. One form of experimentally observed long-term synaptic plasticity is spike-timing-dependent plasticity (STDP), which defines the amount of potentiation and depression for each pair of pre- and postsynaptic neuron spikes as a function of their relative timing (Bi and Poo, 2001; Caporale and Dan, 2008). All forms of STDP require that there be an overlap in the firing of the pre- and postsynaptic cells. In some fear learning paradigms, the US and the CS do not overlap. We address this below under “Assumptions and predictions of the model”, showing how the effects of US and CS on the spiking of the relevant neurons can overlap even in the absence of overlap of US and CS.”

To fully present our reasoning about the origin of the overlap of the effects of US and CS, we modified and added to the last paragraph of the Discussion section “Assumptions and predictions of the model”, which now reads as follows:

“Finally, our model requires the effect of the CS and US inputs on the BLA neuron activity to overlap in time in order to instantiate fear learning through STDP. Such a hypothesis, that learning uses spike-timing-dependent plasticity, is common in the modeling literature (Bi and Poo, 2001; Caporale and Dan, 2008; Markram et al., 2011). Current paradigms of fear conditioning include examples in which the CS and US stimuli do not overlap (Krabbe et al., 2019). Such a condition might seem to rule out the mechanisms in our paper. Nevertheless, the argument below suggests that the effects of the CS and US can cause an overlap in neuronal spiking of ECS, F, VIP, and SOM, even when CS and US inputs do not overlap.

Experimental recordings cannot speak to the rate of spiking of BLA neurons during US due to recording interference from the shock. However, evidence suggests that ECS activity should increase during the US due to the release of acetylcholine (ACh) from neurons in the basal forebrain (BF) (Rajebhosale et al., 2024). Pyramidal cells of the BLA robustly express M1 muscarinic ACh receptors (McDonald and Mott, 2021). Thus, ACh from BF should elicit a depolarization in pyramidal cells. Indeed, the pairing of ACh with even low levels of spiking of BLA neurons results in a membrane depolarization that can last 7 – 10 s (Unal et al., 2015).   Other modulators, including dopamine, may also play a role in producing the sustained activity. Activation of US leads to increased dopamine release in the BLA (Harmer and Phillips, 1999; Suzuki et al., 2002). D1 receptors are known to increase the membrane excitability of BLA projection neurons by lowering their spiking threshold (Kröner et al., 2005). Thus, neuromodulator release should induce higher spiking rates and more sustained activity in the ECS and F neurons during and after the presentation of US, thus ensuring a concomitant activation of ECS and fear (F) neurons necessary for STDP to take place. Thus, the activation of the US can lead to continued and higher firing rates of ECS and F. The effect of dopamine can last up to 20 minutes (Kröner et al., 2005). For CS-positive neurons, the ACh modulation coming from the firing of US may lead to a temporary extension of firing that is then amplified and continued by dopaminergic effects.

Hence, we suggest that a solution to the problem apparently posed by the non-overlap US and CS in some paradigms of auditory fear conditioning (Krabbe et al., 2019) may be solved by considering the roles of ACh and dopamine in the BLA. The model we have may be considered a “minimal” model that puts in by hand the overlap in activity due to the neuromodulation without explicitly modeling it. We have used the simplest way to model overlap without assumptions about timing specificity in the overlap. We note that, even though ECS and F neurons have the ability to fire continuously when ACh and dopamine are involved, the participation of the interneurons enforces periodic silence needed for the depression-dominated STDP.”

In the Discussion (in section “Involvement of other brain structures”), we also acknowledged that the overlap between the effects of US and CS in the BLA may be provided by other brain structures by writing the following:

“In our model, the excitatory projection neurons and VIP and PV interneurons show sustained activity during and after the US presentation, thus allowing potentiation through STDP to take place. The medial prefrontal cortex and/or the hippocampus may provide the substrates for the continued firing of the BLA neurons after the 2-second US stimulation. We also discuss below that this network sustained activity may originate from neuromodulator release induced by US (see section “Assumptions and predictions of the model” in the Discussion).”

We also improved our discussion about the (Grewe et al., 2017) paper, which questions Hebbian plasticity in the context of fear conditioning based on several critiques. We included a new section in the Discussion entitled “Is STDP needed in fear conditioning?” to discuss those critiques and how our model may address them, which reads as follows:

“Is STDP needed in fear conditioning? The study in (Grewe et al., 2017) questions the validity of the Hebbian model in establishing associative learning during fear conditioning. There are several critiques we discuss here. The first critique is that Hebbian plasticity does not explain the experimental finding showing that both upregulation and downregulation of stimulus-evoked responses are present between coactive neurons. The upregulation is provided by our model, so the issue is the downregulation, which is not addressed by our model. However, our model highlights that coactivity alone does not create potentiation; the fine timing of the pre- and postsynaptic spikes determines whether there is potentiation or depression. Here, we find that PING networks are instrumental in setting up the fine timing for potentiation. We suggest that networks not connected to produce the PING may undergo depression when coactive.

The second critique raised by (Grewe et al., 2017) is that Hebbian plasticity alone does not explain why most of the cells exhibiting enhanced responses to the CS did not react to the US before fear conditioning. They suggest that neuromodulators may provide a third condition (besides the activity of the pre- and postsynaptic neurons) that changes the plasticity rule. Our model also does not explicitly address this experimental finding since it requires F to be initially activated by US in order for the fear association to be established. We agree that the fear cells described in (Grewe et al. 2017) may be depolarized by the US without reaching the spiking threshold; however, with neuromodulation provided during the fear training, the same input can lead to spiking, enabling the conditions for Hebbian plasticity. Our discussions above about how neuromodulators affect excitability are relevant to this point. We do not exclude that other forms of plasticity may play a role during fear conditioning in cells not initially activated by the US, but this is not the topic of our modeling study.

The third critique raised by (Grewe et al., 2017) is that Hebbian plasticity cannot explain why the majority of cells that were US- and CS-responsive before training have a reduced CS-evoked response afterward. The reduced response happens over multiple exposures of CS without US; this can involve processes similar to those present in fear extinction, which require plasticity in further networks, especially involving the infralimbic cortex (Milad and Quirk, 2002; Burgos-Robles et al., 2007). An extension of our model could investigate such mechanisms. In the fourth critique, (Grewe et al., 2017) suggests that the Hebbian plasticity rule cannot easily account for the reduction of the responses of many CS+-responsive cells, but not of the CS−-responsive cells. We suggest that the circuits involving paradigms similar to fear extinction do not involve the CS- cells.

Overall, we agree with (Grewe et al., 2017) that neuromodulators play a crucial role in fear conditioning, especially in prolonging the US- and CS-encoding activity as discussed in (see section “Assumptions and predictions of the model” in the Discussion), or even participating in changing the details of the plasticity rule. A possible follow-up of our work involves investigating how fear ensembles form and modify through fear conditioning and later stages. This follow-up work may involve using a tri-conditional rule, as suggested in (Grewe et al., 2017), in which the potential role of neuromodulators is taken into account in the plasticity rule in addition to the pre- and postsynaptic neuron activity. Another direction is to investigate a possible relationship between neuromodulation and a depression-dominated Hebbian rule.”

Finally, we made additional minor changes to the manuscript:

(1) In the Result section “Interneurons interact to modulate fear neuron output”, we specified the following:

“The US input on the pyramidal cell and VIP interneuron is modeled as a Poisson spike train at ~ 50 Hz and an applied current, respectively. In the rest of the paper, we will use the words “US” as shorthand for “the effects of US”.” 

(2) In the Result section “Interneuron rhythms provide the fine timing needed for depression dominated STDP to make the association between CS and fear”, we also reported the following:

“Similarly to the US, in the rest of the paper, we will use the words “CS” as shorthand for “the effects of CS”. In our simulations, CS is modeled as a Poisson spike train at ~ 50 Hz, independent of the US input. Thus, we hypothesize that the time structure of the inputs sometimes used for the training (e.g., a series of auditory pips) is not central to the formation of the plasticity in the network.”  

Reviewer #2 (Public Reviews):

The authors of this study have investigated how oscillations may promote fear learning using a network model. They distinguished three types of rhythmic activities and implemented an STDP rule to the network aiming to understand the mechanisms underlying fear learning in the BLA. 

After the revision, the fundamental question, namely, whether the BLA networks can or cannot intrinsically generate any theta rhythms, is still unanswered. The author added this sentence to the revised version: "A recent experimental paper, (Antonoudiou et al., 2022), suggests that the BLA can intrinsically generate theta oscillations (3-12 Hz) detectable by LFP recordings under certain conditions, such as reduced inhibitory tone." In the cited paper, the authors studied gamma oscillations, and when they applied 10 uM Gabazine to the BLA slices observed rhythmic oscillations at theta frequencies. 10 uM Gabazine does not reduce the GABA-A receptor-mediated inhibition but eliminates it, resulting in rhythmic populations burst driven solely by excitatory cells. Thus, the results by Antonoudiou et al., 2022 contrast with, and do not support, the present study, which claims that rhythmic oscillations in the BLA depend on the function of interneurons. Thus, there is still no convincing evidence that BLA circuits can intrinsically generate theta oscillations in intact brain or acute slices. If one extrapolates from the hippocampal studies, then this is not surprising, as the hippocampal theta depends on extrahippocampal inputs, including, but not limited to the entorhinal afferents and medial septal projections (see Buzsaki, 2002). Similarly, respiratory related 4 Hz oscillations are also driven by extrinsic inputs. Therefore, at present, it is unclear which kind of physiologically relevant theta rhythm in the BLA networks has been modelled. 

In our public reply to the Reviewer’s point, we reported the following:

(1) We kindly disagree that (Antonoudiou et al., 2022) contrasts with our study. (Antonoudiou et al., 2022) is a slice study showing that the BLA theta power (3-12 Hz) increases with gabazine compared to baseline. With all GABAergic currents omitted due to gabazine, the LFP is composed of excitatory currents and intrinsic currents. In our model, the high theta (6-12 Hz) comes from the spiking activity of the SOM cells, which increase their activity if the inhibition from VIP cells is removed. Thus, the model produces high theta in the presence of gabazine (see Fig. 1 in our replies to the Reviewers’ public comments). The model also shows that a PING rhythm is produced without gabazine, and that this rhythm goes away with gabazine because PING requires feedback inhibition from PV to fear cells. Thus, the high theta increase and gamma reduction with gabazine in the (Antonoudiou et al., 2022) paper can be reproduced in our model.

(2) We agree that (Antonoudiou et al., 2022) alone is not sufficient evidence that the BLA can produce low theta (3-6 Hz); we discussed a new paper (Bratsch-Prince et al., 2024) that provides further evidence of BLA ability to produce low theta and under what circumstances. The authors reported that intrinsic BLA theta is produced in slices with ACh stimulation (without needing external glutamate input) which, in vivo, would be provided by the basal forebrain (Rajebhosale et al., eLife, 2024) in response to salient stimuli. The low theta depends on muscarinic activation of CCK interneurons, a group of interneurons that overlaps with the VIP neurons in our model (Krabbe 2017; Mascagni and McDonald, 2003). We suspect that the low theta produced in (Bratsch-Prince et al., 2024) is the same as the low theta in our model. In future work, we will aim to show that ACh activates the BLA VIP cells, which are essential to the low theta generation in the network.

In the manuscript, we added to and modified the Discussion section “Where the rhythms originate, and by what mechanisms”. This text aims to better discuss (Antonoudiou et al. 2022) and introduce (Bratsch-Prince et al., 2024) with its connection to our hypothesis that the theta oscillations can be produced within the BLA. The new version is:

“Where the rhythms originate, and by what mechanisms. A recent experimental paper (Antonoudiou et al., 2022) suggests that the BLA can intrinsically generate theta oscillations (312 Hz) detectable by LFP recordings when inhibition is totally removed due to gabazine application. They draw this conclusion in mice by removing the hippocampus, which can volume conduct to BLA, and noticing that other nearby brain structures did not display any oscillatory activity. In our model, we note that when inhibition is removed, both AMPA and intrinsic currents contribute to the network dynamics and the LFP. Thus, interneurons with their specific intrinsic currents (i.e., D-current in the VIP interneurons, and NaP- and H- currents in SOM interneurons) can indeed affect the model LFP and support the generation of theta and gamma rhythms (Fig. 6G). 

Another slice study, (Bratsch-Prince et al., 2024), shows that BLA is intrinsically capable of producing a low theta rhythm with ACh stimulation and without needing external glutamate input. ACh is produced in vivo by the basal forebrain in response to US (Rajebhosale et al., 2024). Although we did not explicitly include the BF and ACh modulation of BLA in our model, we implicitly include the effect of ACh in BLA by increasing the activity of the VIP cells, which then produce the low theta rhythm. Indeed, low theta in the BLA is known to depend on the muscarinic activation of CCK interneurons, a group of interneurons that overlaps with the class of VIP neurons in our model (Mascagni and McDonald, 2003; Krabbe et al., 2018). 

Although the BLA can produce these rhythms, this does not rule out that other brain structures also produce the same rhythms through different mechanisms, and these can be transmitted to the BLA. Specifically, it is known that the olfactory bulb produces and transmits the respiratoryrelated low theta (4 Hz) oscillations to the dorsomedial prefrontal cortex, where it organizes neural activity (Bagur et al., 2021). Thus, the respiratory-related low theta may be captured by BLA LFP because of volume conduction or through BLA extensive communications with the prefrontal cortex. Furthermore, high theta oscillations are known to be produced by the hippocampus during various brain functions and behavioral states, including during spatial exploration (Vanderwolf, 1969) and memory formation/retrieval (Raghavachari et al., 2001), which are both involved in fear conditioning. Similarly to the low theta rhythm, the hippocampal high theta can manifest in the BLA. It remains to understand how these other rhythms may interact with the ones described in our paper. However, we emphasize that there is also evidence (as discussed above) that these rhythms arise within the BLA.”

Reviewer #2 (Recommendations for the Authors):

(1) Three different types of VIP interneurons with distinct firing patterns have been revealed in the BLA (Rhomberg et al., 2018). Does the generation of rhythmic activities depend on the firing features of VIP interneurons? Does it matter whether VIP interneurons fire burst of action potentials or they discharge more regularly?  

(2) The authors used data for modeling SST interneurons obtained e.g., in the hippocampus. However, there are studies in the BLA where the intrinsic characteristics of SST interneurons have been reported (Unal et al., 2020; Guthman et al., 2020; Vereczki et al., 2021). Have the authors considered using results of studies that were conducted in the BLA? 

We thank the Reviewer for their questions, which have helped us further improve our manuscript in response to similar queries from Reviewer 3 in the previous review round. More in detail:

(1) Although other electrophysiological types exist (Sosulina et al., 2010), we hypothesized that the electrophysiological type of VIP neurons that display intrinsic stuttering is the type that would be involved in mediating low theta oscillations during fear conditioning. This is because VIP intrinsic stuttering in cortical neurons is thought to involve the D-current, which helps create low theta bursting oscillations in the neuronal spiking patterns (Chartove et al., 2020). We think that the other subtypes of VIP interneurons are not essential for the low theta oscillatory dynamics observed during fear conditioning and, thus, did not provide an essential constraint for the phenomena we are trying to capture. VIP interneurons in our network must fire bursts at low theta to be effective in creating the pauses in ECS and F spiking needed for potentiation; single spikes at theta are not sufficient to create these pauses.

(2) In our model, we used the results conducted in a BLA study (Sosulina et al., 2010). SOM cells in the BLA display several physiologic types. We chose to include in our model the type showing early adaptation in response to a depolarizing current and inward (outward) rectification upon the initiation (release) of a hyperpolarizing current. We hypothesize that this type can produce high theta oscillations, a prominently observed rhythm in the BLA. Unal et al., 2020 (Unal et al., 2020) found two populations of SOM cells in the BLA, which have been previously recorded in (Sosulina et al., 2010), including the one type we chose to model. This SOM cell type shows a low threshold spiking profile characterized by spike frequency adaptation and voltage sag indicative of an H-current used in our model. Guthman et al., 2020, (Guthman et al., 2020), also found a population of SOM cells with hyperpolarization induced sag.

Our model also uses a NaP-current for which there is no data in the BLA. However, it is known to exist in hippocampal SOM cells and that NaP- and H- currents can produce such a high theta in hippocampal cells. It is a standard practice in modeling to use the best possible replacement for unknown currents. Of course, it is unfortunate to have to do this. We also note that models can be considered proof of principle, that can be proved or disproved by further experimental work. Both (Guthman et al., 2020) and (Vereczki et al., 2021) also uncover further heterogeneity among BLA SOM interneurons involving more than electrophysiology. We hypothesize that such a level of heterogeneity revealed by these three studies is not key to the question we are asking (where crucial ingredients are the rhythms) and, therefore, was not included in our minimal model.

We modified the Discussion section titled “Assumptions and predictions of the model” as follows:

“Our model, which is a first effort towards a biophysically detailed description of the BLA rhythms and their functions, does not include the neuron morphology, many other cell types, conductances, and connections that are known to exist in the BLA; models such as ours are often called “minimal models” and constitute most biologically detailed models. For example, although there is considerable variability in the activity patterns of both VIP cells and SOM cells (Sosulina et al., 2010; Guthman et al., 2020; Ünal et al., 2020; Vereczki et al., 2021), our focus was specifically on those subtypes that generate critical rhythms within the BLA. Such minimal models are used to maximize the insight that can be gained by omitting details whose influence on the answers to the questions addressed in the model are believed not to be qualitatively important. We note that the absence of these omitted features constitutes hypotheses of the model: we hypothesize that the absence of these features does not materially affect the conclusions of the model about the questions we are investigating. Of course, such hypotheses can be refuted by further work showing the importance of some omitted features for these questions and may be critical for other questions. Our results hold when there is some degree of heterogeneity of cells of the same type, showing that homogeneity is not a necessary condition.”

(3) The authors may double-check the reference list, as e.g., Cuhna-Reis et al., 2020 is not listed. 

We thank the Reviewer for spotting this. We checked the reference list and all the references are now listed.

Finally, we wanted to acknowledge that we made other changes to the manuscript unrelated to the reviewers’ questions with the purpose of gaining clarity. More specifically:

(1) We included a section titled “Significance” after the abstract and keywords, which reads as follows:

“Our paper accounts for the experimental evidence showing that amygdalar rhythms exist, suggests network origins for these rhythms, and points to their central role in the mechanisms of plasticity involved in associative learning. It is one of the few papers to address high-order cognition with biophysically detailed models, which are sometimes thought to be too detailed to be adequately constrained. Our paper provides a template for how to use information about brain rhythms to constrain biophysical models. It shows in detail, for the first time, how multiple interneurons help to provide time scales necessary for some kinds of spike-timing-dependent plasticity (STDP). It spells out the conditions under which such interactions between interneurons are needed for STDP and why. Finally, our work helps to provide a framework by which some of the discrepancies in the fear learning literature might be reevaluated. In particular, we discuss issues about Hebbian plasticity in fear learning; we show in the context of our model how neuromodulation might resolve some of those issues. The model addresses issues more general than that of fear learning since it is based on interactions of interneurons that are prominent in the cortex, as well as the amygdala.”

(2) The Result section “Physiology of the interneuron types is critical to their role in depression-dominated plasticity”, which is now titled “Mechanisms by which interneurons contribute to potentiation in depression-dominated plasticity”, now reads as follows:

“Mechanisms by which interneurons contribute to potentiation during depressiondominated plasticity. The PV cell is necessary to induce the correct pre-post timing between ECS and F needed for long-term potentiation of the ECS to F conductance. In our model, PV has reciprocal connections with F and provides lateral inhibition to ECS. Since the lateral inhibition is weaker than the feedback inhibition, PV tends to bias ECS to fire before F. This creates the fine timing needed for the depression-dominated rule to instantiate plasticity. If we used the classical Hebbian plasticity rule (Bi and Poo, 2001) with gamma frequency inputs, this fine timing would not be needed and ECS to F would potentiate over most of the gamma cycle, and thus we would expect random timing between ECS and F to lead to potentiation (Fig. S4). In this case, no interneurons are needed (See Discussion “Synaptic plasticity in our model” for the potential necessity of the depression-dominated rule). 

In this network configuration, the pre-post timing for ECS and F is repeated robustly over time due to coordinated gamma oscillations (PING, as shown in Fig. 4A, Fig. 1C) arising through the reciprocal interactions between F and PV (Feng et al., 2019). PING can arise only when PV is in a sufficiently low excitation regime such that F can control PV activity (Börgers et al., 2005), as in Fig. 4A. However, although such a low excitation regime establishes the correct fine timing for potentiation, it is not sufficient to lead to potentiation (Fig. 4A, Fig. S2C): the depression-dominated rule leads to depression rather than potentiation unless the PING is periodically interrupted. During the pauses, made possible only in the full network by the presence of VIP and SOM, the history-dependent build-up of depression decays back to baseline, allowing potentiation to occur on the next ECS/F active phase. (The detailed mechanism of how this happens is in the Supplementary Information, including Fig. S2). Thus, a network without the other interneuron types cannot lead to potentiation. Though a low excitation level for a PV cell is necessary to produce a PING, a higher excitation level is necessary to produce a pause in the ECS and F. This higher excitation level is consistent with the experimental literature showing a strong activation of PV after the onset of CS (Wolff et al., 2014). The higher excitation happens when the VIP cell is silent, whereas a low excitation level is achieved when the VIP cell fires and partially inhibits the PV cell (Fig. 4B, Fig. S2D). The interruption in the ECS and F activity requires the participation of another interneuron, the SOM cell (Figs. 2B, S2): the pauses in inhibition from the VIP periodically interrupt ECS and F firing by releasing PV and SOM from inhibition and thus indirectly silencing ECS and F. Without these pauses, depression dominates (see SI section “ECS and F activity patterns determine overall potentiation or depression”).”

We also removed a supplementary figure (Fig. S2).

(3) We wanted to be clear and motivate our choice to extend the low theta range to 2-6 Hz and the high theta range to 6-14 Hz, compared to the 3-6 Hz and 6-12 Hz, respectively in the BLA experimental literature. Our main reason for extending the ranges was because the peaks of low and high theta power in the VIP and SOM cells, respectively, (the cells that generate these oscillations) occurred at the borders of the experimental ranges. Thus, in order to include the peaks of the model LFP, we lowered the low theta range by 1 Hz and increased the high theta range by 2 Hz.

We present a new supplementary figure (Fig. S1) containing the power spectra of VIP, which is the source of low theta in our model, and SOM interneuron, which is the source of high theta:

We mention Fig. S1 in the Result section “Rhythms in the BLA can be produced by interneurons”, where we added the following text: o “In the baseline condition, the condition without any external input from the fear conditioning paradigm (Fig. 1B, top), our VIP neurons exhibit short bursts of gamma activity (~38 Hz) at low theta frequencies (~2-6 Hz) (peaking at ~3.5 Hz) (see Fig. S1A).” o “In our baseline model, SOM cells have a natural frequency of ~12 Hz (Fig. 1B, middle; Fig. S1B), which is at the upper limit of the experimental high theta range; this motivates our choice to extend the high theta range up to 14 Hz in order to include the peak.” 

Knowing the natural frequencies of VIP and SOM interneurons from the Result section “Rhythms in the BLA can be produced by interneurons”, we specified more clearly that we quantify the change of power in the low and high theta range around the power peaks in those ranges. Specifically, we changed some sentences in the first paragraph of the Result section “Increased low-theta frequency is a biomarker of fear learning” as follows:

“We find that fear conditioning leads to an increase in low theta frequency power of the network spiking activity compared to the pre-conditioned level (Fig. 6 A,B); there is no change in the high theta power. We also find that the LFP, modeled as the linear sum of all the AMPA, GABA, NaP-, D-, and H- currents in the network, similarly reveals a low theta power increase when considering the peak of the low theta power, and no significant variation in the high theta power again when considering the peak of the high theta power (Fig. 6 C,D,E).”

Finally, we made a few other small changes:

In the Introduction, we mention the following: “We also note that there is not uniformity on the exact frequencies associated with low and high theta, e.g., ((Lorétan et al., 2004) used 2-6 Hz for low theta). Here, we use 2-6 Hz for the theta range and 6-14 Hz for the high theta range.”

In Fig. 6DE (reported below point 3)), we reran the statistics using a smaller interval for high theta (11.5-13 Hz) to focus around the peak. Our initial result showing significant change in low theta between pre and post fear conditioning and no change in high theta still holds.

In Fig. 6 of the Result section “Increase low-theta frequency is a biomarker of fear learning”, we switched the order of panels F and G. This change allows us to first focus on the AMPA currents, which are the major contributors of the low theta power increase, and to specify what AMPA current drives that increase. After that, we present the power spectrum of the GABA currents, as well.

The corresponding text in the Result section, now reads as follows:

“We find that fear conditioning leads to an increase in low theta frequency power of the network spiking activity compared to the pre-conditioned level (Fig. 6 A,B); there is no change in the high theta power. We also find that the LFP, modeled as the linear sum of all the AMPA, GABA, NaP-, D-, and H- currents in the network, similarly reveals a low theta power increase when considering the peak of the low theta power, and no significant variation in the high theta power again when considering the peak of the high theta power (Fig. 6 C,D,E). These results are consistent with the experimental findings in (Davis et al., 2017). Specifically, the newly potentiated AMPA synapse from ECS to F ensures F is active after fear conditioning, thus generating strong currents in the PV cells to which it has strong connections (Fig. 6F). It is the AMPA currents to the PV interneurons that are directly responsible for the low theta increase; it is the newly potentiated ECS to F synapse that paces the AMPA currents in the PV interneurons to go at low theta. Thus, the low theta increase is due to added excitation provided by the new learned pathway.”

(4) In the Discussion section “Assumptions and predictions of the model”, we specified the following:

“Our model predicts that blockade of D-current in VIP interneurons (or silencing VIP interneurons) will both diminish low theta and prevent fear learning. Finally, the model assumes the absence of significantly strong connections from the excitatory projection cells ECS to PV interneurons, unlike the ones from F to PV. Including those synapses would alter the PING rhythm created by the interactions between F and PV, which is crucial for fine timing between ECS and F needed for LTP.”

(5) Finally, to broaden the potential interest of our study, we added the following sentences:

At the conclusion of the abstract:

“The model makes use of interneurons commonly found in the cortex and, hence, may apply to a wide variety of associative learning situations.” - At the conclusion of the introduction:

“Finally, we note that the ideas in the model may apply very generally to associative learning in the cortex, which contains similar subcircuits of pyramidal cells and interneurons: PV, SOM and VIP cells.” 

Also, changes in the emphasis of the paper led us to remove the following from the abstract: “Finally, we discuss how the peptide released by the VIP cell may alter the dynamics of plasticity to support the necessary fine timing.”

Author response:

The following is the authors’ response to the original reviews.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

The manuscript could be improved by addressing the following issues.

(1) Fig. 3: The analgesic effects after astrocyte ablation appear to recover after one week. Is this due to repopulation of astrocytes?

Although we did not detect the proliferation of astrocytes, we hypothesized that it was likely related to the microglia phagocytosis of astrocyte debris after astrocyte ablation. Microglia are known to have the function of phagocytosis of cell debris. Diphtheria toxin-mediated cell ablation caused AAV2/5-GfaABC1D-Cre labeled astrocytes death and cell fragmentation. We hypothesized that the microglia could phagocyte the astrocyte fragments and were stimulated to activate type I interferon signal. When microglia phagocyte debris ended, the activation of type I interferon signal was also declined. Reduced activation of type I interferon signal may also be accompanied by recurrence of pain.

(2) Fig. 3: Please justify the large sample size of n=30-36. Is this sample size based on previous studies or statistical estimation?

The number of mice was based on our previous report [1], and the increased number of mice may also ensure that the pain data would also be reliable. Not only did we explore the differences between the sexes, and we also needed to obtain samples at different times for different experiments.

(3) Please try to plot individual data points for some critical time points to demonstrate data distribution. It is also helpful to plot male and female data points separately for some time points.

Individual data have been plotted as your request and added in the supplementary material.

(4) It is unclear if the same number of males and females were used in this study, as females were typically used for SCI studies. I wonder if you can use repeated measures Two-Way ANOVA for statistical analysis.

According to our observations, the number of males and females was not the same, while both of them were sufficient for statistical analysis. In addition, in the process of breeding transgenic mice, we would obtain both male and female mice, and rational use of mice may be better for us. Indeed, previous studies have shown that female mice are more commonly used in pain studies. Although we did not observe a gender difference in this study, it has been reported in the previous studies that gender is one of the factors for pain differences. According to your suggestion, we adopted the Two-Way ANOVA for statistical analysis and updated it in the part of statistical methods, but the statistical results were consistent with the previous results, so we did not modify the statistical results of the pictures.

(5) Fig. 3C, D: The effects of astrocyte ablation on mechanical pain are mild, compared to thermal pain. Electronic von Frey apparatus may be difficult for mice. It works very well for rats and large animals.

Since the animals involved in this study were all mice, we did not know how electronic von Frey was used in rats and large animals. But after the using of electronic von Frey, it seems to us that electronic von Frey is very suitable for mouse experiments. Best of all, our electronic von Frey can achieve accuracy as low as 0.01g. This allows us to detect very sensitive pain data, which may be more accurate and intuitive than before.

(6) Fig. 2B: In the figure legend it states n = 3 biological repeats. There are many more dots in each column. Are these individual animals or spinal cord sections?

As we describe in our method, n = 3 biological repeats represented three biological repeats per group, i.e., three mice/group with three IF per mouse. We take three or more values in each ascending tract (depending on the partition size of the different ascending tracts of lumbar enlargements). So, we would get more data as shown in Figure 2, which could be also more reliable.

(7) Fig. 4C: It appears that GFAP is increased by toxin treatment. Please explain this result.

This figure was calculated for astrocyte activation in the lesion area (T9-10), but not for the lumbar enlargement.

Reviewer #2 (Recommendations For The Authors):

Specific Comments:

RNA-Sequencing Analysis: The strength of the RNA-sequencing data in elucidating the impact of astrocyte elimination is compelling. While the focus on IFN signaling is well-supported, the manuscript overlooks other differentially expressed genes. A deeper analysis or at least a discussion of these genes could enrich the study's conclusions, offering a more holistic view of the underlying mechanisms.

Although we did not focus more on other relevant differential genes, we focused on the most significant differential genes, for these differential genes have a more significant effect on pain.

Q2: Figure Presentation: Consolidating Figures 1-3 could increase the clarity of the result presentation, reducing distractions from the main narrative. Certain aspects, such as the comparison of different tracts in Figure 2B and the body weight data in Figure 3C, seem tangential and might be better suited for supplementary materials.

The comparison of astrocyte activation in different ascending tracts of lumbar enlargements explained the relationships between astrocyte activation and pain, and laid the foundation for the subsequent astrocyte elimination. The weight data is also important, reflecting not only the changes in the overall recovery process after spinal cord injury, but also the effect of astrocyte elimination on the overall effect of mice. Thus, the weight data together with the pain test results will be more intuitive for the reader to understand the change of overall conditions of mice after astrocyte elimination.

Q3: Schematic Clarity: The schematic in Figure 1A is confusing, particularly in distinguishing between transgenic mice and viral constructs. The inconsistent naming of Cre recombinase (alternatively referred to as Cre, CRE, and sometimes DRE) further complicates understanding. Standardizing these elements would greatly enhance clarity for the readers.

As we described in the part of method, Gt(ROSA)26Sorem1(CAG-LSL-RSR-tdTomato-2A-DTR)Smoc mice contain both Loxp-stop-Loxp sequence and Rox-stop-Rox sequence. In the process of reproduction, Gt(ROSA)26Sorem1(CAG-LSL-RSR-tdTomato-2A-DTR)Smoc mice crossed with C57BL/6JSmoc-Tg(CAG-Dre)Smoc mice could remove the Rox-stop-Rox sequence, which could further crossed with mice containing Cre recombinase, or with AAV2/5-GfaABC1D-Cre intervention to remove the Loxp-stop-Loxp sequence and induce the expression of tdTomato and DTR.

Q4: Pathway Analysis: The discussion of the signal pathway analysis in Figure 8 leans heavily on speculation without direct evidence from the study. Distinguishing clearly between findings and literature-derived hypotheses is crucial. A more detailed discussion that properly cites sources for each pathway element would strengthen the manuscript.

According to your question, we have added this figure to the supplementary picture.

Q5: Statistical Analysis: The use of one-way ANOVA, despite presenting data in groups, is misaligned with the data's structure. Employing two-way ANOVA followed by post-hoc comparisons is appropriate for statistical analysis.

According to your suggestions, we adopted the Two-Way ANOVA for statistical analysis and updated it in the part of statistical methods, but the statistical results are consistent with the previous ones. Therefore, we did not modify the statistical results of the pictures.

Author response:

Public Reviews:

Reviewer #1 (Public review):

[…] Strengths:

The study has several important strengths: (i) the work on GDA stability and competition of GDA with point mutations is a very promising area of research and the authors contribute new aspects to it, (ii) rigorous experimentation, (iii) very clearly written introduction and discussion sections. To me, the best part of the data is that deletion of lon stimulates GDA, which has not been shown with such clarity until now.

Weaknesses:

The minor weaknesses of the manuscript are a lack of clarity in parts of the results section (Point 1) and the methods (Point 2).

We thank the reviewer for their comments and suggestions on our manuscript. We also appreciate the succinct summary of key findings that the Reviewer has taken cognisance of in their assessment, in particular the association of the Lon protease with the propensity for GDAs as well as its impact on their eventual fate. Going ahead, we plan to revise the manuscript for greater clarity as suggested by Reviewer #1.

Reviewer #2 (Public review):

[…] The study does what any bold and ambitious study should: it contains large claims and uses multiple sorts of evidence to test those claims.

Weaknesses:

While the general argument and conclusion are clear, this paper is written for a bacterial genetics audience that is familiar with the manner of bacterial experimental evolution. From the language to the visuals, the paper is written in a boutique fashion. The figures are even difficult for me - someone very familiar with proteostasis - to understand. I don't know if this is the fault of the authors or the modern culture of publishing (where figures are increasingly packed with information and hard to decipher), but I found the figures hard to follow with the captions. But let me also consider that the problem might be mine, and so I do not want to unfairly criticize the authors.

For a generalist journal, more could be done to make this study clear, and in particular, to connect to the greater community of proteostasis researchers. I think this study needs a schematic diagram that outlines exactly what was accomplished here, at the beginning. Diagrams like this are especially important for studies like this one that offer a clear and direct set of findings, but conduct many different sorts of tests to get there. I recommend developing a visual abstract that would orient the readers to the work that has been done.

Next, I will make some more specific suggestions. In general, this study is well done and rigorous, but doesn't adequately address a growing literature that examines how proteostasis machinery influences molecular evolution in bacteria.

While this paper might properly test the authors' claims about protein quality control and evolution, the paper does not engage a growing literature in this arena and is generally not very strong on the use of evolutionary theory. I recognize that this is not the aim of the paper, however, and I do not question the authors' authority on the topic. My thoughts here are less about the invocation of theory in evolution (which can be verbose and not relevant), and more about engagement with a growing literature in this very area.

The authors mention Rodrigues 2016, but there are many other studies that should be engaged when discussing the interaction between protein quality control and evolution.

A 2015 study demonstrated how proteostasis machinery can act as a barrier to the usage of novel genes: Bershtein, S., Serohijos, A. W., Bhattacharyya, S., Manhart, M., Choi, J. M., Mu, W., ... & Shakhnovich, E. I. (2015). Protein homeostasis imposes a barrier to functional integration of horizontally transferred genes in bacteria. PLoS genetics, 11(10), e1005612

A 2019 study examined how Lon deletion influenced resistance mutations in DHFR specifically: Guerrero RF, Scarpino SV, Rodrigues JV, Hartl DL, Ogbunugafor CB. The proteostasis environment shapes higher-order epistasis operating on antibiotic resistance. Genetics. 2019 Jun 1;212(2):565-75.

A 2020 study did something similar: Thompson, Samuel, et al. "Altered expression of a quality control protease in E. coli reshapes the in vivo mutational landscape of a model enzyme." Elife 9 (2020): e53476.

And there's a new review (preprint) on this very topic that speaks directly to the various ways proteostasis shapes molecular evolution:

Arenas, Carolina Diaz, Maristella Alvarez, Robert H. Wilson, Eugene I. Shakhnovich, C. Brandon Ogbunugafor, and C. Brandon Ogbunugafor. "Proteostasis is a master modulator of molecular evolution in bacteria."

I am not simply attempting to list studies that should be cited, but rather, this study needs to be better situated in the contemporary discussion on how protein quality control is shaping evolution. This study adds to this list and is a unique and important contribution. However, the findings can be better summarized within the context of the current state of the field. This should be relatively easy to implement.

We thank the reviewer for their encouraging assessment of our manuscript. We appreciate that the manuscript may not be accessible for a general readership in its present form. We plan to revise the manuscript, in part by modifying figures and adding schematics, to afford greater clarity. We also appreciate the concern regarding situating this study in the context of other published work that relates proteostasis and molecular evolution. Indeed, this was a particularly difficult aspect for us given the different kinds of literature that were needed to make sense of our study. We plan on revising the manuscript by incorporating the references that the Reviewer has pointed out.

Reviewer #3 (Public review):

[…] Strengths:

The major strength of this paper is identifying an example of antibiotic resistance evolution that illustrates the interplay between the proteolytic stability and copy number of an antibiotic target in the setting of antibiotic selection. If the weaknesses are addressed, then this paper will be of interest to microbiologists who study the evolution of antibiotic resistance.

Weaknesses:

Although the proposed mechanism is highly plausible and consistent with the data presented, the analysis of the experiments supporting the claim is incomplete and requires more rigor and reproducibility. The impact of this finding is somewhat limited given that it is a single example that occurred in a lon strain and compensatory mutations for evolved antibiotic resistance mechanisms are described. In this case, it is not clear that there is a functional difference between the evolution of copy number versus any other mechanism that meets a requirement for increased "expression demand" (e.g. promoter mutations that increase expression and protein stabilizing mutations).

We thank the reviewer for their in-depth assessment of our work and appreciate their concerns regarding reproducibility and rigor in analysis of our data. We will incorporate this feedback and provide the necessary clarifications in the revised version of our manuscript.

Author Response:

We would like thank reviewers for your comprehensive and insightful reviews of our manuscript. We highly value your constructive comments and suggestions and are preparing revisions that will enhance both the clarity and robustness of our study. Below is an outline of the changes we will implement in response to the points you raised.

All three reviewers expressed concerns regarding the robustness of our conclusions about the relationship between task-related theta activity and aperiodic changes. We will revise the manuscript to present these conclusions more cautiously, stating that the findings indicate a potential contribution of aperiodic activity to what is traditionally interpreted as theta activity. While our results emphasize the importance of distinguishing between periodic and aperiodic components, further research is necessary to fully understand this relationship. We will conduct additional control analyses, including a comparison of the scalp topographies of theta and aperiodic components, to better understand the relationship between aperiodic and periodic (theta) activity.

In response to Reviewer #1's request for greater transparency in our reporting of methodological details, we will provide key clarifications. We will add a clear statement noting that the primary results are based on data from middle-aged to older adults, some of whom had subjective cognitive complaints (SCC). However, it is important to note that no differences were observed between the SCC group and the control group regarding periodic or aperiodic changes in power. Additionally, the main findings were replicated in a sample of middle-aged adults.

To address potential confounding factors, we will include an analysis contrasting response-related ERPs with the identified aperiodic components. However, we do not entirely agree with the assertion that this will necessarily clarify the results. ERPs are not inherently distinct from aperiodic (or periodic) activity; they may reflect changes in aperiodic (or periodic) power. In our view, examining aperiodic and periodic power, ERPs, or time-frequency decomposition with baseline correction provides different perspectives on the same data. Nonetheless, the combined analyses and their results are intended to guide future researchers toward the most suitable approach for interpreting this data.

Reviewer #3 raised concerns regarding the task's effectiveness in evoking theta power and the ability of spectral parameterization method (specparam) to adequately quantify background activity around theta bursts. To address these concerns, we will include additional visualizations demonstrating that the task reliably elicited theta (and delta) activity. Regarding the reviewer's concerns about specparam and theta bursts, it is important to clarify that specparam, in the form we used, does not incorporate time information; rather, it can be applied to any power spectral density (PSD), independent of how the PSD is derived. Specparam’s performance depends on the methods used to estimate frequency content. For time-frequency decomposition, we employed superlets (https://doi.org/10.1038/s41467-020-20539-9), which have been shown to resolve short bursts of activity more effectively than other methods. To our knowledge, superlets provide the highest resolution in terms of both time and frequency. Moreover, to improve stability, we performed spectral parameterization on trial-averaged power (in contrast to the approach in https://doi.org/10.7554/eLife.77348). Nonetheless, we will conduct a simulation to test whether specparam can reliably resolve low-frequency peaks over the 1/f activity.

Reviewer #2 suggested that the manuscript would benefit from a more detailed account of the effects. In response, we will include more detailed quantifications of the analyzed effects, such as model error and R² values.

We believe that the planned revisions will strengthen the manuscript and address the primary concerns raised by the reviewers. We sincerely appreciate your thoughtful feedback and look forward to submitting an improved version of the manuscript soon.

Once again, thank you for your time and expertise in reviewing our work.

Sincerely,

Andraž Matkovič & Tisa Frelih

Author response:

The following is the authors’ response to the original reviews.

We greatly appreciate reviewer 2 comments with both insightful and clearly evaluated assessments of this study that include, much appreciated reframing and evaluation of the study’s advances in the sleep field. It is a constructive review and provides considerable added value to this study in better defining the biological significance of the findings, including both advances and limitations.  

Reviewer 2 nicely summarized the work as “…highlight(ing) the accumulation and resolution of sleep need centered on the strength of excitatory synapses onto excitatory neurons.”. The reviewer succinctly placed one of the main electrophysiological findings in context of one of the sleep field’s most prevalent views, “that LTP associated with wake, leads to the accumulation of sleep need by increasing neuronal excitability, and by the "saturation" of LTP capacity.” It has been speculated that “This saturation subsequently impairs the capacity for further ongoing learning. This new data provides a satisfying mechanism of this saturation phenomenon (and its restoration by recovery sleep) by introducing the concept of silent synapses.” We want to emphasize that sleep need and its resolution involves more than just homeostasis of excitatory synaptic strength but may also be extended to include homeostasis of excitatory synaptic potential to undergo LTP (a homeostasis of meta-plasticity), with implications for learning and memory.   

Reviewer 2 also identified another advance made by this study, summarized as, “The new snRNAseq dataset indicates the sleep need is primarily seen (at the transcriptional level) in excitatory neurons, consistent with a number of other studies.” References for these studies are nicely provided by the reviewer. Our analysis of this data extends the evidence for transcriptional sleep-need-driven changes, observed by us and others in excitatory neurons to more particularly involve the excitatory neurons in layers 2-5, targeting  intra-telencephalic neurons.  

Reviewer 2, importantly noted, “New snRNAseq analysis indicates that SD drives the expression of synaptic shaping components (SSCs) consistent with the excitatory synapse as a major target for the restorative basis of sleep function”, and that “SD-induced gene expression is also enriched for autism spectrum disorder (ASD) risk genes”. These comments are well appreciated as they emphasize that beyond identification of the major target cell type of sleep function, the major sleep-target, gene-ontological characteristics are starting to be addressed.

Reviewer 2 commented on the molecular sleep model, making a key observation that “SDinduced gene expression in excitatory neurons overlaps with genes regulated by the transcription factor MEF2C and HDAC4/5 (Figure 4),” and accurately discusses the significance with respect to the proposed model.

We are in complete agreement with the observation that the molecular sleep model presented is not “definitively supported by the new data and in this regard should be viewed as a perspective…”. One of the more glaring gaps in supporting evidence is the absence of understanding of the role of HDAC4/5 (part of the SIK3-HDAC4/5 pathway) in sleep need modulation of excitatory synapses. Resolution of this issue might be approached by assessment of the synaptic effects of constitutively nuclear HDAC4/5. The current study provides a first step in the assessment by showing a correlation between HDAC4/5 and MEF2c target genes and a subset of differentially expressed synaptic shaping component (SSC) genes that modulate excitatory synapse strength and phenotype. However, the functional studies have yet to be completed. Complimentary studies on SD-induced SSC-DEGs (identified in this study) are also needed for follow-up characterization of their sleep need induced functional impact (both strength and meta-plasticity modulation) on the most relevant excitatory synapses (as identified in the current study).

We agree with both reviewers 1 and 2 that, “Additional work is also needed to understand the mechanistic links between SIK3-HDAC4/5 signaling and MEF2C activity”. Reviewer 2 clarifies the key unresolved issue as, “cnHDAC4/5 suppresses NREM amount and NREM SWA but had no effect on the NREM-SWA increase following SD (Zhou et al., Nature 2022). Loss of MEF2C in CaMKII neurons had no effect on NREM amount and suppressed the increase in NREM-SWA following SD (Bjorness et al., 2020)”. One may conclude with reviewer 2, “These instances indicate that cnHDAC4/5 and loss of MEF2C do not exactly match suggesting additional factors are relevant in these phenotypes.”

An understanding of the mechanism(s) responsible for the relationship between sleep need and SWA are critical to the evaluation of sleep need’s correlation with sleep DEGs and synaptic transmission, including “additional factors” as suggested by reviewer 2. SWA might result from a decrease of cortical glutamatergic neurotransmission below some threshold, which might occur in response to prolonged waking (possibly in response to waking activity-induced local increases of adenosine?), rather than being a cause of, or, being intimately involved in resolving sleep need.  

An increase of SWA in association with SD can result directly from an acute SD-induced increase in local adenosine concentration. This will elicit an ADORA1-mediated down-regulation of glutamate excitatory neurotransmission in the cortex (Bjorness et al., 2016) and in cholinergic arousal centers (Rainnie et al., 1994; Porkka-Heiskanen et al., 1997; Portas et al., 1997; Li et al., 2023). When MEF2c is derepressed by chronic loss of HDAC4 function, SWA is facilitated (Kim et al., 2022). It is plausible that loss of HDAC4 function contributes to the increased SWA by downscaling glutamate excitatory transmission (independent of sleep need). This is expected to result from derepressed, MEF2c mediated sleep-gene expression.  

Similarly, over-expression of constitutively active HDAC4 (cnHD4) can contribute to chronic upscaling of cortical glutamate synaptic strength to depress SWA (again, independent of sleep need). Thus, facilitation or depression of SWA correlates with up or down scaling effects on cortical glutamate neurotransmission, respectively, even in the absence of  a direct effects on sleep need (Figure 4D). Many reagents that reduce the excitability of glutamate pyramidal cells by various mechanisms, including anesthetics like isoflurane, barbiturates or benzodiazepines in addition to those activating ADORA1, increase SWA. Finally, it is important to acknowledge that direct evidence for this proposed link of SWA to cortical glutamate transmission remains in need of further investigation. Thus, SWA may reflect generalized cortical glutamate synaptic activity whether modulated by sleep function or by other agents.

Still, other factors that can have a role mediating some of the mis-match between cnHD4/5 DEGs and Mef2c-cKO DEGs, include the broader over-expression of AAV-cnHD4 compared to CamKII- driven Cre KO of Mef2c. The cnHD4 overexpression can increase arousal center activity in the hypothalamus and other arousal areas to interfere with SWA, but not to the exclusion of SD-DEG repression resulting from a repression of MEF2c-mediated sleep gene expression.

The critique by reviewer 1 raises a number of important technical issues with this study. A key, potentially critical issue raised by reviewer 1, is that of our method of experimental sleep deprivation (ESD). The reviewer suggests that “…neuronal activity/induction of plasticity”, peculiar to the ESD methodology employed in this study, “…rather than sleep/wake states are responsible for the observed results…”.  

In this study, a slow-moving treadmill (SMTM; 0.1km/hour, as stated in the methods), requiring locomotion to avoid bumping into the backwall of a false bottomed plexiglass cage was used to induce ESD. A mouse, in its home cage, typically moves much faster than 0.1km/hour and the mouse is able to eat and drink freely while in the cage (see file: video 1). Furthermore, our observations using a beam-break cage, indicate that mice spontaneously travel for comparable to longer distances over 6 hours than the treadmill moves (during the ESD of 6 hours). Finally, our EEG recordings of mice on the active treadmill show 100% waking while it is on (Bjorness et al., 2009), whereas prevention of NREM sleep (including transition time) using the “gentle handling”  (GH) technique occurs depending on the diligence of the experimenter.  

The accommodation (one week prior to ESD) included exposure to the treadmill-on for 30minutes ~ZT=2 & ZT= 14 hours (now spelled out in the “Materials & Methods” section). Thus, the likelihood of motor learning seems vanishingly small.  

As with all ESD methods, there must be some associated increase in sensory and motor neuronal activity to drive arousal and prevent transition to sleep. For example, the more widely employed GH method of ESD involves sensory stimulation (tactile and or auditory) of sufficient intensity to induce postural change from that associated with sleep to that associated with wake (often involving some locomotion). Like the SMTM, both sensory and motor systems are likely to be engaged. Unlike the SMTM method, the stimulation used in GH is variably-intermittent from mouse to mouse and from experimenter to experimenter as it is applied only when the experimenter judges the mouse to be falling asleep. . It can even be argued that the varied and unpredictable ways in which these interactions happen cause plastic changes with a higher likelihood than the constant slow motion of a treadmill – the mice know how to walk, after all. In other protocols, novel objects are introduced to the animals – those will certainly trigger plastic processes –something that is avoided using a slow-running treadmill to which the mouse has been accommodated, for sleep deprivation.  

The changes induced by SMTM technique are reproducible and induce arousal by somatic stimulation of sufficient intensity to induce natural motor activity as with GH. All ESD methods induce motor activity and it is reasonable to speculate that induced, motor activity is essential for effective ESD for the prolonged durations (>4 hours in mice) that elicit high sleep need. Electrophysiological assessment of SD-evoked increases in mEPSC amplitude and frequency using GH-ESD (Liu et al., 2010) are similar in all respects to our observations of the response to SMTMESD (Bjorness et al., 2020). Further studies might directly address a comparison of SMTM-ESD to GH-ESD as suggested by reviewer 1 but are regrettably outside the scope and resources of our study.

The model presented in Figure 4C is consistent with the experimental findings with respect to the observed electrophysiological changes (including loss of silent synapses and increased AMPA/NMDA ratio after ESD of 6 hours) and altered gene expression that includes enrichment of SSC genes, many of which (7 candidates are listed) can affect both AMPA/NMDA ratio and silent synapses. No claim of mechanism linking the changed expression to altered AMPAR or NMDAR activity can be made at this point, even as to polarity of gene expression, related to electrophysiological outcome. Furthermore, some transcripts may involve receptor trafficking while others more directly affect activated receptor function. To help illustrate the complexity of interpreting gene up-regulation, consider the following hypothetical scenario. If a gene like upregulated Grin3a acts rapidly, it may facilitate reduction of NMDAR function (decreasing plasticity) during ESD, whereas upregulation of a gene like Kif17, if acting in a more delayed manner, might enhance NMDAR surface expression and activity (increasing silent synapses) in response to ESD, during recovery sleep. Relevant references, consistent with these various outcomes are supplied in the manuscript but further investigation is clearly needed, or as reviewer 2 so aptly commented, this work “…provides a framework to stimulate further research and advances on the molecular basis of sleep function”.  

Several issues are raised by reviewer 1 concerning the electrophysiological methodology and statistical assessment. In regard to the former, we closely followed established protocols employed in the frontal neocortex (Myme et al., 2003). We did not include the details for series resistance monitoring. Series resistance values ranged between 8 and 15 MOhm and experiments with changes larger than 25% not used for further analyses. Thank you for bringing this  oversight on our part, to our attention. This essential information, that is unfailingly gathered for all our whole cell recordings, is now added to the version of record.

The -90 mV holding potential was chosen according to precedent (Myme et al., 2003). It increases driving force and permits lower stimulus strength for the same response size – reducing the likelihood for polysynaptic responses. Experiments with multiple response peaks at -90 mV were not included in the analysis. The -90 mV holding potential also increases NMDA receptor Mg++ block resulting in a minimally contaminated AMPA response. This information is now added to our submitted version of record.

The statistical assessments shown in Table 1 refer to two sets of data measured from 3X2=6 different cohorts for each sleep condition (CS, SD, RS): 1) AMPA & NMDA EPSCs and 2) AMPA/NMDA FR ratios (FRR; now bolded in row 1, second tab, Table S1). As stated in the results section, “A two-way ANOVA analysis showed a significant interaction between AMPA matched to NMDA EPSC response for each neuron, and sleep condition (F (2, 21) = 7.268, p<0.004; Figure 1 A, C, E). When considered independently, neither the effect of sleep condition nor of EPSC subtype reached significance at p<0.05 (Figure 1 C)”.  

As noted by reviewer 1, we inadvertently dropped one of the data points from the RS FR and FR ratio (FRR) statistical analysis (raw data in the third tab of Table S1, statistical data in fourth and fifth tab and illustrated in figure 1 F). Thanks to this appreciated, rigorous review, we can correct the oversight (using raw data unchanged in Table S1, third tab). The Table S1 and figure 1 F are now corrected for the version of record. For better clarity, we now use two tabs, the fourth and fifth tabs, respectively of Table S1, for separate stat analyses of FR and FRR data.

The significance of the AMPA/NMDA FRR across sleep conditions was assessed with the KruskalWallis test, a non-parametric method. The two-stage linear step-up procedure of Benjamini, Krieger, and Yekutieli (BKY) was used to control for the FDR across multiple sleep conditions, in the non-parametric Kruskal-Wallis test but it is usually less powerful than tests presuming normal distributions like the one-way ANOVA and Holm-Sidak’s test. We have now added re-analyzed  FRR across CS, SD and RS conditions using a normal one-way ANOVA (Table S1, tab5). The results now read, “The difference between  sleep conditions and FRR is significant (F (2, 19) = 11.3, Table S1, tab5). Multiple comparisons (Holm-Sidak, Table S1, tab5) indicate the near absence of silent synapses was reversed by either CS or RS (SD/CS; p<0.0011 and SD/RS: p<0.0006; Table S1, tab 5; Figure 1 F).”. These analyses compare well to the non-parametric assessment using the  KruskalWallis test (significant at p= 0.0006) with BYK correction for multiple comparison analysis to give for CS-SD, p<= 0.0262 and for RS-SD, p<= 0.0006 (statistics also shown in Table S1, tab5). [Also shown in tab5 is the “standard approach of correcting for family wise error rate”, namely, Dunn’s test. It is more conservative but less powerful than the BYK correction- in general the tradeoff of greater power/ less conservative is better tolerated when many comparisons are made, however, it can be argued that in the present analysis type 2 errors are also potentially misleading and thus not well tolerated.]  The modifications of our statistical analyses, inspired by reviewer 1,  did not affect the interpretation of the data nor the conclusions.  

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Author response

We appreciate the positive comments and constructive suggestions from the editors and reviewers, which will help us improve our manuscript. We will implement the changes as requested by the reviewers, focusing primarily on revising and clarifying the following aspects:

First, we will clarify the use of biological and technical replicates in each experiment and provide more details about the statistical analyses conducted. Additionally, we plan to include a schematic representation of the experimental design.

Second, we will explain the experiment conducted to rule out hormonal effects or differences in the oocyte maturation method used. We will also indicate the concentration of OVGP1 in the oviduct and explain why we selected OVGP1 as the probable cause of species specificity.

Third, by addressing all of the reviewers' suggestions, we aim to resolve any concerns, inconsistencies, or minor errors identified by the reviewers.

We are committed to addressing all the issues raised by the reviewers and believe that the manuscript will greatly benefit from the insightful suggestions and invaluable contributions of the editors and reviewers.

Author response:

The following is the authors’ response to the original reviews.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The paper begins with phenotyping the DGRP for post-diapause fecundity, which is used to map genes and variants associated with fecundity. There are overlaps with genes mapped in other studies and also functional enrichment of pathways including most surprisingly neuronal pathways. This somewhat explains the strong overlap with traits such as olfactory behaviors and circadian rhythm. The authors then go on to test genes by knocking them down effectively at 10 degrees. Two genes, Dip-gamma and sbb, are identified as significantly associated with post-diapause fecundity, and they also find the effects to be specific to neurons. They further show that the neurons in the antenna but not the arista are required for the effects of Dip-gamma and sbb. They show that removing the antenna has a diapause-specific lifespan-extending effect, which is quite interesting. Finally, ionotropic receptor neurons are shown to be required for the diapause-associated effects.

Strengths and Weaknesses:

Overall I find the experiments rigorously done and interpretations sound. I have no further suggestions except an ANOVA to estimate the heritability of the post-diapause fecundity trait, which is routinely done in the DGRP and offers a global parameter regarding how reliable phenotyping is. A minor point is I cannot find how many DGRP lines are used.

Thank you for the suggestions. We screened 193 lines and we will add that information to the methods. Additionally, we will add the heritability estimate of the post-diapause fecundity trait.

Reviewer #2 (Public Review):

Summary

In this study, Easwaran and Montell investigated the molecular, cellular, and genetic basis of adult reproductive diapause in Drosophila using the Drosophila Genetic Reference Panel (DGRP). Their GWAS revealed genes associated with variation in post-diapause fecundity across the DGRP and performed RNAi screens on these candidate genes. They also analyzed the functional implications of these genes, highlighting the role of genes involved in neural and germline development. In addition, in conjunction with other GWAS results, they noted the importance of the olfactory system within the nervous system, which was supported by genetic experiments. Overall, their solid research uncovered new aspects of adult diapause regulation and provided a useful reference for future studies in this field.

Strengths:

The authors used whole-genome sequenced DGRP to identify genes and regulatory mechanisms involved in adult diapause. The first Drosophila GWAS of diapause successfully uncovered many QTL underlying post-diapause fecundity variations across DGRP lines. Gene network analysis and comparative GWAS led them to reveal a key role for the olfactory system in diapause lifespan extension and post-diapause fecundity.

Weaknesses:

(1) I suspect that there may be variation in survivorship after long-term exposure to cold conditions (10ºC, 35 days), which could also be quantified and mapped using genome-wide association studies (GWAS). Since blocking Ir21a neuronal transmission prevented flies from exiting diapause, it is possible that natural genetic variation could have a similar effect, influencing the success rate of exiting diapause and post-diapause mortality. If there is variation in this trait, could it affect post-diapause fecundity? I am concerned that this could be a confounding factor in the analysis of post-diapause fecundity. However, I also believe that understanding phenotypic variation in this trait itself could be significant in regulating adult diapause.

We agree that it is possible that the ability to endure cool temperatures per se may influence post-diapause fecundity. However, cool temperature is the essential diapause-inducing condition in Drosophila, so it is not obvious how to separate those effects experimentally, and we agree that phenotypic variation in the cool-sensitivity trait itself could be significant in regulating diapause.

(2) On p.10, the authors conclude that "Dip-𝛾 and sbb are required in neurons for successful diapause, consistent with the enrichment of this gene class in the diapause GWAS." While I acknowledge that the results support their neuronal functions, I remain unconvinced that these genes are required for "successful diapause". According to the RNAi scheme (Figure 4I), Dip-γ and sbb are downregulated only during the post-diapause period, but still show a significant effect, comparable to that seen in the nSyb Gal4 RNAi lines (Figure 4K).

Our definition of successful diapause is the ability to produce viable adult progeny post-diapause, which requires that the flies enter, maintain, and exit diapause, alive and fertile. We will restate our conclusion to say that Dip-γ and sbb are required for post-diapause fecundity.

In addition, two other RNAi lines (SH330386, 80461) that did not show lethality did not affect post-diapause fecundity.

We interpret those results to mean that those RNAi lines were not effective since Dip-γ and sbb are known to be essential.

Notably, RNAi (27049, KK104056) substantially reduced non-diapause fecundity, suggesting impairment of these genes affects fecundity in general regardless of diapause experience. Therefore, the reduced post-diapause fecundity observed may be a result of this broader effect on fecundity, particularly in a more "sensitized" state during the post-diapause period, rather than a direct regulation of adult diapause by these genes.

Ubiquitous expression of RNAi lines #27049 or #KK104056 was lethal, so we included the tubGAL80ts repressor to prevent RNAi from taking effect during development. Flies had to be shifted to 30 °C to inactivate the repressor and thereby activate the RNAi. At 30 °C, fecundity of the controls (GFP RNAi lines #9331, KK60102) were also lower (average non-diapause fecundity = 12 and 19 respectively) and similar to #27049 or #KK104056. We also assessed the knockdown using Repo GAL4 and nSyb GAL4 and did not find a significant difference/decline in the non diapause fecundity for #27049 and #KK104056 as compared to a nonspecific RNAi control (#54037).

(3) The authors characterized 546 genetic variants and 291 genes associated with phenotypic variation across DGRP lines but did not prioritize them by significance. They did prioritize candidate genes with multiple associated variants (p.9 "Genes with multiple SNPs are good candidates for influencing diapause traits."), but this is not a valid argument, likely due to a misunderstanding of LD among variants in the same gene. A gene with one highly significantly associated variant may be more likely to be the causal gene in a QTL than a gene with many weakly associated variants in LD. I recommend taking significance into account in the analysis.

We agree with the reviewer, and in Supplemental Table S3 we list top-associated SNPs in order from the lowest (most significant) p-value. Most of the top-associated genes from this analysis were uncharacterized CG numbers for which there were insufficient tools available for validation purposes. Nevertheless, there is overlap amongst the highly significant genes by p-value and those with multiple SNPs. Amongst the top 15 genes with multiple associated SNPs- CG18636 & CR15280 ranked 3rd by p-value, CG7759 ranked 4th, CG42732 ranked 10th, and Drip ranked 30th (all above the conservative Bonferroni threshold of 4.8e-8) while three Sbb-associated SNPs also appear in Table 3 above the standard e-5 threshold.

Reviewer #3 (Public Review):

Summary:

Drosophila melanogaster of North America overwinters in a state of reproductive diapause. The authors aimed to measure 'successful' D. melanogaster reproductive diapause and reveal loci that impact this quantitative trait. In practice, the authors quantified the number of eggs produced by a female after she exited 35 days of diapause. The authors claim that genes involved with olfaction in part contribute to some of the variation in this trait.

Strengths:

The work used the power platform of the fly DRGP/GWAS. The work tried to verify some of the candidate loci with targeted gene manipulations.

Weaknesses:

Some context is needed. Previous work from 2001 established that D. melanogaster reproductive diapause in the laboratory suspends adult aging but reduces post-diapause fecundity. The work from 2001 showed the extent fecundity is reduced is proportional to diapause duration. As well, the 2001 data showed short diapause periods used in the current submission reduce fecundity only in the first days following diapause termination; after this time fecundity is greater in the post-diapause females than in the non-diapause controls.

The 2001 paper by Tatar et al. reports the number of eggs laid after 3, 6, or 9 weeks in diapause conditions. Thus the diapause conditions used in this study (35 days or 5 weeks) are neither short nor long, rather intermediate. Does the reviewer have a specific concern?

In this context, the submission fails to offer a meaningful concept for what constitutes 'successful diapause'. There is no biological rationale or relationship to the known patterns of post-diapause fecundity. The phenotype is biologically ambiguous.

We have unambiguously defined successful diapause as the ability to produce viable adult progeny post-diapause. Other groups have measured % of flies that arrest ovarian development or % of post-diapause flies with mature eggs in the ovary, or # eggs laid post-diapause; however we suggest that # of viable adult progeny produced post-diapause is more meaningful than the other measurements from the point of view of perpetuating the species.

I have a serious concern about the antenna-removal design. These flies were placed on cool/short days two weeks after surgery. Adults at this time will not enter diapause, which must be induced soon after eclosion. Two-week-old adults will respond to cool temperatures by 'slowing down', but they will continue to age on a time scale of day-degrees. This is why the control group shows age-dependent mortality, which would not be seen in truly diapaused adults. Loss of antennae increases the age-dependent mortality of these cold adults, but this result does not reflect an impact on diapause.

We carried out the lifespan study under two different conditions. We either removed the antenna and moved the flies directly to 10 °C or we removed the antenna and allowed a “wound healing” period prior to moving the flies to 10 °C (out of concern that the flies might die quickly because wound healing may be impaired at 10 °C). In both cases, antenna removal shortened lifespan. Furthermore the lifespan extension at 10 °C was similar regardless of whether flies had experienced two weeks at 25 °C or not.

• Appraisal of whether the authors achieved their aims, and whether the results support their conclusions.

The work falls well short of its aim because the concept of 'successful diapause' is not biologically established. The paper studies post-diapause fecundity, and we don't know what that means. The loci identified in this analysis segregate for a minimally constructed phenotype. The results and conclusions are orthogonal.

It is unclear to us why the reviewer has such a negative opinion of measuring post-diapause fecundity, specifically the ability to produce viable progeny post-diapause. The value of this measurement seems obvious from the point of view of perpetuating the species.

• The likely impact of the work on the field, and the utility of the methods and data to the community.

The work will have little likely impact. Its phenotype and operational methods are weakly developed. It lacks insight based on the primary literature on post-diapause. The community of insect diapause investigators are not likely to use the data or conclusions to understand beneficial or pest insects, or the impact of a changing climate on how they over-winter.

The reviewer has not explained why his/her opinion is so negative.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

(1) Perform an ANOVA to estimate heritability.

We will do this.

(2) List the number of DGRP lines tested.

193

Reviewer #2 (Recommendations For The Authors):

[Minor suggestions]

(1) Check Drosophila italics

We will do this.

(2) It would be informative to include the number of DGRP lines used in this study in the Results and Methods section.

We will include the information that we assessed 193 DGRP lines.

(3) Figure 1C - several dots are missing at the top of the line.

We will correct.

(4) Figures 1E, F - Why use a discontinuous histogram for continuous distribution? Consider using a continuous histogram (e.g. Lafuente et al. (2018) Figure 1C).

We will do this.

(5) Figure 1F - Why have fewer bins than panel E?

Figure 1F is normalized post-diapause fecundity. Individual post-diapause fecundity was normalized to the mean non-diapause fecundity. Then the normalized individual post-diapause fecundity was averaged to get the mean normalized post-diapause fecundity for the DGRP line. So the bins are different in panel E. Please refer to Supplemental Table S1.

(6) Figure 2D - It would be informative to have fold enrichment stats.

The following will be added in the methods section: The Gene Ontology (GO) categories and Q-values from the false discovery rate (FDR)-corrected hypergeometric test for enrichment are reported. Additionally, coverage ratios for the number of annotated genes in the displayed network versus the number of genes with that annotation in the genome are provided. GeneMANIA estimates Q-values using the Benjamini-Hochberg procedure.

(7) Supplementary table (Table S5) or supplemental table (other supplementary tables)? Need consistency (to Supplementary?)

We will change ‘Supplementary Table S5’ to ‘Supplemental Table S5’.

(8) Figure 5D,E - unused ticks on the x-axis.

The unused ticks on the x-axis will be removed from Figures 5D and E.

Reviewer #3 (Recommendations For The Authors):

• Suggestions for improved or additional experiments, data or analyses.

The authors cannot redo the GWAS with an alternative trait that might better reflect 'successful diapause', and I am not even sure what such a trait would involve or mean. Given this limitation, the authors should consider how they can conduct additional experiments to better define, justify, and elaborate how post-diapause reproduction relates to the mechanisms, processes, depth, and 'success' of diapause.

We agree that it is entirely unclear what trait would be a better measure of successful diapause. Other investigators might have chosen to measure something different but there is no reason why a different choice would be a better choice. We do not believe that this is a “limitation.” We believe that we have unambiguously defined and justified  post-diapause reproduction as a measurement of successful diapause with respect to perpetuating the species through a stressful period.

• Recommendations for improving the writing and presentation.

The mechanics of the writing are fine, aside from some typos/grammar issues. But, the paper is conceptually superficial and tautological. It claims to provide a 'stringent criterion' for 'successful diapause', then measures an unjustified trait, then claims this demonstrates variation for 'successful diapause'.

We respectfully disagree with this opinion.

This story is conducted without reference to prior, primary literature or on the mechanisms of reproductive diapause. The presentation may be improved by considering the literature and precedence for what and how reproductive diapause is induced, maintained, and terminated ... in many insects as well as Drosophila

We will revisit our citations of the literature and apologize for any inadvertent omissions.

Author response:

The following is the authors’ response to the original reviews.

In our initial submission, reviewers highlighted that the major limitations of our study were related to both the number of minibinders tested as well as the number of optimizations we evaluated for improving minibinder function. In this revision, we have focused on expanding the minibinders tested. To do so, we selected two previously published minibinders against the epidermal growth factor receptor (EGFR). Selection of EGFR as a target enabled us to evaluate two minibinders that bind at different sites, unlike the previously evaluated binders LCB1 and LCB3 which both bind the same interface on SARS-CoV-2 Spike. Further, using EGFR as a target enabled us to qualitatively compare the efficacy of minibinder-coupled chimeric antigen receptors against an existing anti-EGFR CAR. We believe the results here demonstrate broader generalizability of our approach across binding sites, targets, and minibinders. We hope this addition is sufficient to convince future would-be users of these tools to attempt synthetic receptor engineering using minibinders against their protein of choice.

Reviewers made comments about the presentation of flow data and the use of statistics throughout the manuscript. We did not modify how flow data are presented as the density plots we used are common throughout the field. We have opted to not include statistics – we believe that in the case of most of the experiments we show, our findings are obvious. In cases where statistics would be helpful for discerning whether subtle effects are real – for example, comparing the linker-based optimizations or comparing the anti-EGFR CARs – we believe that other experimental factors like construct expression are sufficient confounds that even in the presence of statistically significant effects we would be leading readers astray to make such claims about our data. As such, we have sought to limit the claims we make and hope that reviewers and audience agree we do not over interpret our data without statistical support.

On more minor points, both reviewers addressed the differences in Figure 5A and 5C, which we addressed in our figure legend and in the previous response to reviews is the result of these data originating from different time points of the same assay. Reviewer #2 believed we should be more staid in our comments about linker optimality, which we have addressed by changing the referenced line in the discussion. Otherwise, we have made no modifications to figures or text beyond the addition of new data.

Author response:

The following is the authors’ response to the previous reviews.

We addressed the issue of “tolerability” in our answers to Reviewer 2 and in the revised manuscript where we had added data concerning tolerability, see the paragraph in the Results Section, page 11:

"Finally, tolerability studies were performed with the administration of up to 20 and 40 mg/kg eq. NT (i.e. 25.8 and 51.6 mg/kg of VH-N412) with n=3 for these doses. The rectal temperature of the animals did not fall below 32.5 to 33.2°C, similar to the temperature induced with the 4 mg/kg eq. NT dose. We observed no mortality or notable clinical signs other than those associated with the rapid HT effect such as a decrease in locomotor activity. We thus report a very interesting therapeutic index since the maximal tolerated dose (MTD) was > 40 mg/kg eq. NT, while the maximum effect is observed at a 10x lower dose of 4 mg/kg eq. NT and an ED50 established at 0.69 mg/kg as shown in Figure 1G.”

We have slightly modified the paragraph above to emphasize that the tolerability studies were performed in “naïve mice”. 

"Finally, tolerability studies were performed in naïve mice with the administration of up to 20 and 40 mg/kg eq. NT (i.e. 25.8 and 51.6 mg/kg of VH-N412) with n=3 for these doses. The rectal temperature of the animals did not fall below 32.5 to 33.2°C, similar to the temperature induced with the 4 mg/kg eq. NT dose. We observed no mortality or notable clinical signs other than those associated with the rapid HT effect such as a decrease in locomotor activity. We thus report a very interesting therapeutic index since the maximal tolerated dose (MTD) was > 40 mg/kg eq. NT, while the maximum effect is observed at a 10x lower dose of 4 mg/kg eq. NT and an ED50 established at 0.69 mg/kg as shown in Figure 1G.”

We propose to add a sentence in the Results section, page 11, relative to the fact that we can also induce severe hypothermia in rats using conjugates similar to VH-N412.

We also added in the Discussion section (page 38) that we could induce hypothermia with different conjugates in mice, rats and pigs.

Author response:

The following is the authors’ response to the original reviews.

Reviewer #1 (Public Review):

(1) Some of the figures are of rather poor quality. For example, the H&E and Sirius Red stainings in Figures 3 and 4 are quite poor so it is difficult to see what is going on in the muscles. The authors should take note of another publication on dy3K/dy3K mice of similar age (PMID: 31586140) where such images are of much higher quality. Similarly, the Western blot for laminin-alpha2 (Figure 4B) of the wild-type mouse needs improvement. If the single laminin-alpha2 protein is not detected, there is an issue with the denaturation buffer used to load the protein.

Thank you for the valuable suggestions. We have read the study on dy3K/dy3K mice of similar age (PMID: 31586140) which showed dystrophic changes in dy3K/dy3K muscle throughout the disease course with the whole muscle and representative muscle area. We have generated new figures with higher quality including the whole muscle and representative muscle area for the H&E and Sirius Red stainings.  However, due to the large images, we have added them in the new Figure supplement 2 and Figure supplement 3. Also, we have changed the denaturation buffer used to load the protein, and performed Western blot of laminin α2, the result of the laminin α2 protein of the wild-type mice (n =3) and dyH/dyH mice (n =3) detected by Western blot has been showed in Figure 4B.

(2) My biggest concern is, however, the many overstatements in the manuscript and the over-interpretation of the data. This already starts with the first sentence in the abstract where the authors write: "Understanding the underlying pathogenesis of LAMA2- related muscular dystrophy (LAMA2-MD) have been hampered by lack of genuine mouse model." This is not correct as the dy3K/dy3K, generated in 1997 (PMID: 9326364), are also Lama2 knockout mice; there are also other strains (dyW/dyW mice) that are severely affected and there are the dy2J/dy2J mice that represent a milder form of LAMA2-MD. Similarly, the last two sentences of the abstract "This is the first reported genuine model simulating human LAMA2-MD. We can use it to study the molecular pathogenesis and develop effective therapies." are a clear overstatement. The mechanisms of the disease are well studied and the above-listed mouse models have been amply used to develop possible treatment options. The overinterpretation concerns the results from transcriptomics. The fact that Lama2 is expressed in particular cell types of the brain does not at all imply that Lama2 knockout mice have a defect in the blood-brain barrier as the authors state. If there are no functional data, this cannot be stated. Indications for a blood-brain barrier defect come from work in dy3K/dy3K mice (PMID: 25392494) and this needs to be written like this.

Thank you for your comment and sorry for the overstatements in the manuscript. We have carefully considered our previous statements and corrected them accordingly. We have changed the first sentence in the abstract into "Our understanding of the molecular pathogenesis of LAMA2-related muscular dystrophy (LAMA2-MD) requires improving". Also, we have changed the last two sentences in the abstract with "In summary, this study provided useful information for understanding the molecular pathogenesis of LAMA2-MD".

We also agree that "Lama2 is expressed in particular cell types of the brain does not at all imply that Lama2 knockout mice have a defect in the blood-brain barrier", and the indications for a blood-brain barrier defect come from work in dy3K/dy3K mice (PMID: 25392494). Therefore, we have corrected the overstatement according to the suggestion with "It was reported that the deficiency of laminin α2 in astrocytes and pericytes was associated with a defective blood-brain barrier (BBB) in the dy3K/dy3K mice (Menezes et al., 2014). The defective BBB presented with altered integrity and composition of the endothelial basal lamina, reduced pericyte coverage, and hypertrophic astrocytic endfeet lacking appropriately polarized aquaporin4 channels."

(3) Finally, the bulk RNA-seq data also needs to be presented in a disease context. The authors, again, mix up changes in expression with functional impairment. All gene expression changes are interpreted as direct evidence of an involvement of the cytoskeleton. In fact, changes in the cytoskeleton are more likely a consequence of the severe muscle phenotype and the delay in muscle development. This is particularly possible as muscle samples from 14-day-old mice are compared; a stage at which muscle still develops and grows tremendously. Thus, all the data need to be interpreted with caution.

Thank you for your comment. We have changed the over-interpretation of the bulk RNA-seq data, and have corrected the last sentence in the Result with "These observations important data for the impaired muscle cytoskeleton and abnormal muscle development which were associated with the muscle pathology consequence of severe dystrophic changes in the dyH/dyH mice.".

(4) In summary, the authors need to improve data presentation and, most importantly, they need to tone down the interpretation and they must be fully aware that their work is not as novel as they present it.

Thank you for your comments and valuable suggestions, and we have changed the previous overstatements and interpretation of the results. We are sorry that we failed to clearly present our rational of making this mouse model. Indeed, there were many existing mouse models, which were all important to the research in the field. One of the reasons why we wished to create dyH/dyH is to make a mouse model without any trace of engineering (e.g., inserted bacterial elements for knockout). By doing so, we were hoping to provide a novel model suited for gene-editing-based gene therapy development. To this end, dyH/dyH was created to reflect the hot mutation region in the Chinese population. Hopefully, you will agree with our points and see that we were not trying to belittle previous models but were simply trying to provide a different option. The overstatements were largely rooted from language barriers, and we have tried to make our statements more cautious and acceptable to the readers.

Reviewer #2 (Public Review):

(1) The major weakness is the manuscript reads like this was the first-ever knockout mouse model generated for LAMA2-CMD. There are in fact many Lama2 knockout mice (dy, dy2J, dy3k, dyW, and more) which have all been extensively studied with publications. It is important for the authors to comment on these other published studies that have generated these well-studied mouse lines. Therefore, there is a lack of background information on these other Lama2 null mice.

Thank you for your comment. We have added background information on these other Lama2 null mice with the sentences "The most common mouse models for LAMA2-MD are the dy/dy, dy3k/dy3k, dyw/dyw and dy2J/dy2J mice (Xu et al., 1994; Michelson et al., 1995; Miyagoe et al., 1997; Kuang et al., 1998; Sunada et al., 1995). Among them, the dy/dy, dy3k/dy3k, dyw/dyw mice present severe muscular dystrophy, and dy2J/dy2J mice show mild muscular dystrophy and peripheral neuropathy (Gawlik and Durbeej, 2020). The mutation of the dy/dy mice has been still unclear (Xu et al., 1994; Michelson et al., 1995). The dy3k/dy3k mice were generated by inserting a reverse Neo element in the 3' end of exon 4 of Lama2 gene in 1997 (Miyagoe et al., 1997), and the dyw/dyw mice were created with an insertion of lacZ-neo in the exon 1 of Lama2 gene in 1998 (Kuang et al., 1998). The dy2J/dy2J mice were generated in 1970 by a spontaneous splice donor site mutation which resulted in a predominant transcript with a 171 base in-frame deletion, leading to the expression of a truncated laminin α2 with a 57 amino acid deletion (residues 34-90) and a substitution of Gln91Glu (Sunada et al., 1995). They were established in the pre-gene therapy era, leaving trace of engineering, such as bacterial elements in the Lama2 gene locus, thus unsuitable for testing various gene therapy strategies. Moreover, insufficient transcriptomic data of the muscle and brain of LAMA2-CMD mouse models limits the understanding of disease hallmarks. Therefore, there is a need to create new appropriate mouse models for LAMA2-CMD based on human high frequently mutated region using the latest gene editing technology such as clustered regularly interspaced short palindromic repeats (CRISPR)-Cas9."

(2) The phenotypes of dyH/dyH are similar to, if not identical to dy/dy, dy2J/dy2J, dy3k/dy3k, dyW/dyW including muscle wasting, muscle weakness, compromised blood-brain barrier, and reduced life expectancy. This should be addressed, and a comparison made with Lama2 deficient mice in published literature.

Thank you for your comment. We have added Table supplement 3 to make a comparison between dyH/dyH with other Lama2 deficient mice. We aslo have added the statement in Discussin with "Compared with other Lama2 deficient mice including dy/dy, dy2J/dy2J, dy3k/dy3k and dyW/dyW, the phenotype of the dyH/dyH mice presented with a very severe muscular dystrophy, which was similar to that of the dy3k/dy3k mice (Table supplement 3)."

(3) Recent published studies (Chen et al., Development (2023), PMID 36960827) show loss of Itga7 causes disruption of the brain-vascular basal lamina leading to defects in the blood-brain barrier. This should be referenced in the manuscript since this integrin is a major Laminin-211/221 receptor in the brain and the mouse model appears to phenocopy the dyH/dyH mouse model.

Thank you for your great suggestion. We have cited the published studies (Chen et al., Development (2023), PMID 36960827) and added statements in Discussion with "As reported, the aberrant BBB function was also associated with the adhesion defect of alpha7 integrin subunit in astrocytes to laminins in the Itga_7-/- mice (_Chen et al., 2023). In this study, loss of communications involving the laminins’ pathway between laminin α2 and integrins were predicted between vascular and leptomeningeal fibroblasts and astrocytes in the dyH/dyH brain, providing more evidence for the impaired BBB due to laminin α2 deficiency."

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

(1) Improve the data presentation (as mentioned above). Make a new picture of the histology; repeat the Western blots. Discuss the RNA-seq data with more caution and present it in a more attractive way. Tone down the wording.

Thank you for your recommendations. We have revised the overstatements and improved the RNA-seq data interpretation as suggested. Also,we have made a new picture of the histology, and repeated the Western blots.

Reviewer #2 (Recommendations For The Authors)

(1) There are many grammatical errors within the manuscript. The manuscript should be carefully proofread.

Thank you for your recommendations. We have carefully corrected the grammatical errors within the manuscript.

(2) Figure 2: The animal numbers used in this analysis were not indicated. Please include this number in the figure legend.

Thank you for your recommendations. We have added animal numbers in the figure legends wherever applicable.

(3) Figure 2: The forelimb grip strength is informative but has limitations. Ex vivo or in vivo muscle contractility is the gold standard for measuring muscle strength.

Thank you for your recommendations. We do agree that the ex vivo or in vivo muscle contractility is the gold standard for measuring muscle strength, and we really want to finish this experiment. However, we feel sorry that this test has not been finished due to the following reasons: (1) The forelimb grip strength for measuring muscle strength is a classic method and remains a commonly used method for measuring mouse muscle strength in the studies of different muscular dystrophies, such as LAMA2-MD (Amelioration of muscle and nerve pathology of Lama2-related dystrophy by AAV9-laminin-αLN linker protein. JCI Insight. 2022;7(13):e158397. PMID: 35639486), Duchenne muscular dystrophy (Investigating the role of dystrophin isoform deficiency in motor function in Duchenne muscular dystrophy. J Cachexia Sarcopenia Muscle. 2022;13(2):1360-1372. PMID: 35083887), facioscapulohumeral muscular dystrophy (Systemic delivery of a DUX4-targeting antisense oligonucleotide to treat facioscapulohumeral muscular dystrophy. Mol Ther Nucleic Acids. 2021;26:813-827. PMID: 34729250), and etc. (2) The forelimb grip strength for measuring muscle strength is also used in the human studies (PMID: 32366821; PMID: 29313844; PMID: 34499663, and etc). In view of reasons above, for measuring muscle strength, we used the forelimb grip strength, and have not finished the supplementary experiment of ex vivo or in vivo muscle contractility.

(4) Figure 3: Muscle fibrosis should be measured with a hydroxyproline assay.

Thank you for your recommendations. We do agree that the hydroxyproline assay is one of the most classic method to evaluate collagen content for measuring muscle fibrosis. However, we performed Sirius Red staining for measuring muscle fibrosis due to the following reasons: (1) Muscle fibrosis measured by Sirius Red staining can be observed more directly, and the other pathological features also can be observed, and compared through muscle pathology. (2) Sirius Red staining is also a classic method and remains a commonly used method for measuring muscle fibrosis, which has been previously reported in the mouse studies of muscle disorders, such as PMID: 22522482 (Losartan, a therapeutic candidate in congenital muscular dystrophy: studies in the dy(2J) /dy(2J) mouse. Ann Neurol. 2012;71(5):699-708.), PMID: 34337906 (Aging-related hyperphosphatemia impairs myogenic differentiation and enhances fibrosis in skeletal muscle. J Cachexia Sarcopenia Muscle. 2021;12(5):1266-1279.), PMID: 28798156 (Phosphodiesterase 4 inhibitor and phosphodiesterase 5 inhibitor combination therapy has antifibrotic and anti-inflammatory effects in mdx mice with Duchenne muscular dystrophy. FASEB J. 2017;31(12):5307-5320.), and etc. Therefore, we used Sirius Red staining to measure muscle fibrosis in this study.

(5) Figure 8: The N=3 is very low which could result in type I or II statistical errors. A larger sample size will reduce the chance of statistical errors.

Thank you for your recommendations. We have increased the number of animals to reduce the chance of statistical errors. We have performed the supplementary experiment, the number of animals for each group has been increased to 6 (3 male and female each).  The results were consistent with previous data in Figure 8.

(6) Power analysis to estimate experimental animal numbers should be reported in the manuscript.

Thank you for your recommendations. Refer to previous study (Power and sample size. Nature Methods. 2013;10:1139–1140), “The distributions show effect sizes d = 1, 1.5 and 2 for n = 3 and α = 0.05. Right, power as function of d at four different a values for n = 3”, and “If we average seven measurements (n = 7), we are able to detect a 10% increase in expression levels (μ_A = 11, _d = 1) 84% of the time with α = 0.05.”, the experimental animal numbers estimated were 3 to 7. Moreover, if the increased number of experimental animals could be available, we would retain data.

(7) It is unclear if the studies were performed with adequate rigor. Were those scoring outcome measures blinded to the treatment groups?

Thank you for your recommendations. We performed the studies with those scoring outcome measures not blinded to the treatment groups, the groups were based on their genotype. Actually, it was easy to discriminate the dyH/dyH groups from the WT/Het mice due to their small body shape.

(8) Authors should appropriately cite previous studies that have generated Lama2 null mice.

Thank you for your recommendations. We have cited previous studies that have generated Lama2 null mice with the sentence “The most common mouse models for LAMA2-MD are the dy/dy, dy3k/dy3k, dyw/dyw and dy2J/dy2J mice (Xu et al., 1994; Michelson et al., 1995; Miyagoe et al., 1997; Kuang et al., 1998; Sunada et al., 1995)”.

(9) The number of animals should be increased to reduce the chance of statistical error.

Thank you for your recommendations. We have performed the supplementary experiment, the number of animals for each group has been increased to reduce the chance of statistical error.

(10) A power analysis should be performed to determine the number of experimental animals.

Thank you for your recommendations. We have performed a power analysis to determine the number of experimental animals as mentioned above.

(11) There are many grammatical errors within the manuscript. The manuscript should be carefully proofread.

Thank you for your recommendations. We have carefully corrected the grammatical errors within the manuscript.

Author Response:

Reviewer #1 (Public review):

Summary:

Fallah and colleagues characterize the connectivity between two basal ganglia output nuclei, the SNr and GPe, and the pedunculopontine nucleus, a brainstem nucleus that is part of the mesencephalic locomotor region. Through a series of systematic electrophysiological studies, they find that these regions target and inhibit different populations of neurons, with anatomical organization. Overall, SNr projects to PPN and inhibits all major cell types, while the GPe inhibits glutamatergic and GABAergic PPN neurons, and preferentially in the caudal part of the nucleus. Optogenetic manipulation of these inputs had opposing effects on behavior - SNr terminals in the PPN drove place aversion, while GPe terminals drove place preference.

Strengths:

This work is a thorough and systematic characterization of a set of relatively understudied circuits. They build on the classic notions of basal ganglia connectivity and suggest a number of interesting future directions to dissect motor control and valence processing in brainstem systems.

We thank the reviewers for these positive comments.

Weaknesses:

Characterization of the behavioral effects of manipulations of these PPN input circuits could be further parsed, for a better understanding of the functional consequences of the connections demonstrated in the ephys analyses.

We will further analyze our behavioral data to reveal more nuanced functional effects.

All the cell type recording studies showing subtle differences in the degree of inhibition and anatomical organization of that inhibition suggest a complex effect of general optogenetic manipulation of SNr or GPe terminals in the PPN. It will be important to determine if SNr or GPe inputs onto a particular cell type in PPN are more or less critical for how the locomotion and valence effects are demonstrated here.

This is a really interesting future direction and we will expand on these points in the discussion.

Reviewer #2 (Public review):

Summary:

Fallah et al carefully dissect projections from SNr and GPe - two key basal ganglia nuclei - to the PPN, an important brainstem nucleus for motor control. They consider inputs from these two areas onto 3 types of downstream PPN neurons: GABAergic, glutamatergic, and cholinergic neurons. They also carefully map connectivity along the rostrocaudal axis of the PPN.

Strengths:

The slice electrophysiology work is technically well done and provides useful information for further studies of PPN. The optogenetics and behavioral studies are thought-provoking, showing that SNr and GPe projections to PPN play distinct roles in behavior.

We appreciate the reviewer’s positive evaluation.

Weaknesses:

Although the optogenetics and behavioral studies are intriguing, they are somewhat difficult to fit together into a specific model of circuit function. Perhaps the authors can work to solidify the connection between these two arms of the work.

We will expand on these topics in the discussion.

(1) Male and female mice are used, but the authors do not discuss any analysis of sex differences. If there are no sex differences, it is still useful to report data disaggregated by sex in addition to pooled data.

While we do not have sufficient n for a well-powered analysis of sex differences in behavior, we find that both male and female mice increase movement in response to SNr axon stimulation and decrease movement in response to GPe axon stimulation. We will expand on this further in the revised manuscript.

(2) There is some lack of clarity in the current manuscript on the ages used - 2-5 months vs "at least 7 weeks." Is 7 weeks the time of virus injection surgery, then recordings 3 weeks later (at least 10 weeks)? Please clarify if these ages apply equally to electrophysiological and behavioral studies. If the age range used for the test is large, it may be useful to analyze and report if there are age-related effects.

7 weeks is the youngest age at which mice used for electrophysiology were injected, and all were used for electrophysiology between 2-5 months. For behavior, the youngest mice used were 11 weeks old at time of behavior (8 weeks old at injection). Mice in the GPe-stimulated condition were 110 ± 7.4 SEM days old and mice in the SNr-stimulated condition 132 ± 23.4 SEM days old. We will add these details to the revised manuscript.

In addition, we have correlated distance traveled at baseline and during stimulation with age for both SNr and GPe stimulated conditions. Baseline distance traveled did not correlate with age, but there was a trend toward more movement during stimulation with older mice in the SNr axon stimulation group. We will discuss this in the revised manuscript.

(3) Were any exclusion criteria applied, e.g. to account for missed injections?

All injection sites and implant sites were within our range of acceptability, so we did not exclude any mice for missed injections.

(4) 28-34degC is a fairly wide range of temperatures for electrophysiological recording, which could affect kinetics.

This is an important consideration. We have checked our main measurement of current amplitude in the condition where we found significant differences between rostral and caudal PPN (SNr to Vglut2 PPN neurons) against temperature and found no correlation (Pearson’s r value = -0.0076). Similarly, we found no correlation between baseline (pre-opto) firing frequency and temperature (r = -0.068).

(5) It would be good to report the number of mice used for each condition in addition to n=cells. Statistically, it would be preferable not to assume that each cell from the same mouse is an independent measurement and to use a nested ANOVA.

For electrophysiology, the number of mice used in each experiment was 6 (3 male, 3 female). In the manuscript ‘N’ represents number of mice and ‘n’ represents number of cells. Because of the unpredictability of how many healthy cells can be recorded from one mouse, our data were planned to be collected with n=cells, and are underpowered for a nested ANOVA. However, rostral and caudal data were collected from the same mice. While we do not have sufficient paired data for each parameter, analyzing one of our main and most important findings with a paired comparison (with biological replicates being mice) shows a statistically significant difference in the inhibitory effect of SNr axon stimulation on firing rate between rostral and caudal glutamatergic neurons (p=0.031, Wilcoxon signed rank test).

Reviewer #3 (Public review):

Summary:

The study by Fallah et al provides a thorough characterization of the effects of two basal ganglia output pathways on cholinergic, glutamatergic, and GABAergic neurons of the PPN. The authors first found that SNr projections spread over the entire PPN, whereas GPe projections are mostly concentrated in the caudal portion of the nucleus. Then the authors characterized the postsynaptic effects of optogenetically activating these basal ganglia inputs and identified the PPN's cell subtypes using genetically encoded fluorescent reporters. Activation of inputs from the SNr inhibited virtually all PPN neurons. Activation of inputs from the GPe predominantly inhibited glutamatergic neurons in the caudal PPN, and to a lesser extent GABAergic neurons. Finally, the authors tested the effects of activating these inputs on locomotor activity and place preference. SNr activation was found to increase locomotor activity and elicit avoidance of the optogenetic stimulation zone in a real-time place preference task. In contrast, GPe activation reduced locomotion and increased the time in the RTPP stimulation zone.

Strengths:

The evidence of functional connectivity of SNr and GPe neurons with cholinergic, glutamatergic, and GABAergic PPN neurons is solid and reveals a prominent influence of the SNr over the entire PPN output. In addition, the evidence of a GPe projection that preferentially innervates the caudal glutamatergic PPN is unexpected and highly relevant for basal ganglia function.

Opposing effects of two basal ganglia outputs on locomotion and valence through their connectivity with the PPN.

Overall, these results provide an unprecedented cell-type-specific characterization of the effects of basal ganglia inputs in the PPN and support the well-established notion of a close relationship between the PPN and the basal ganglia.

We thank the reviewer for their positive comments.

Weaknesses:

The behavioral experiments require further analysis as some motor effects could have been averaged out by analyzing long segments.

We will further analyze our motor effects in the revised manuscript.

Additional controls are needed to rule out a motor effect in the real-time place preference task.

This is an important point. Our use of unilateral stimulation in the RTPP task reduces potential motor effects, and our supplemental videos show that the mice can easily escape and enter the stimulated zone. However, we can't completely rule out a motor component. To delve into this further, we analyzed mouse speed in the RTPP task. We find that in both SNr and GPe stimulation conditions, the maximum speed of the mouse is not different in the stimulated vs unstimulated zone. We will further analyze mouse speed at the transition into and out of the stimulated zone to identify any acute motor effects in this experiment.

Importantly, the location of the stimulation is not reported even though this is critical to interpret the behavioral effects.

The implant locations were generally over the middle-to-rostral PPN and we will clarify this in the revised manuscript. These locations are shown in figure 7B.

There are some concerns about the possible recruitment of dopamine neurons in the SNr experiments.

We are very interested in this possibility and plan to discuss this with more clarity in a revised manuscript.

Author response:

The following is the authors’ response to the previous reviews.

Reviewer #1 (Recommendations For The Authors): 

This is not a recommendation. While reading old literature, I found some interesting facts. The shape of the neurocranium in monotremes, birds, and mammals, at least in early stages, resembles the phenotype of 'dact'1/2, wnt11f2, or syu mutants. For more details, see DeBeer's: 'The Development of the Vertebrate Skull, !937' Plate 137. 

Thank you for pointing this out. It is indeed interesting.

Minor Comments: 

• Lines 64, 66, and 69: same citation without interruption: Heisenberg, Brand et al. 1996

Revised line 76. 

• Lines 101 and 102: same citation without interruption: Li, Florez et al. 2013 

Revised line 118.

• Lines 144, 515, 527, and 1147: should be wnt11f2 instead of wntllf2 - if not, then explain 

Revised lines 185, 625, 640,1300.

• Lines 169 and 171: incorrect figure citation: Fig 1D - correct to Fig 1F 

Revised lines 217, 219.

• Line 173: delete (Fig. S1) 

Revised line 221.

• Line 207: indicate that both dact1 and dact2 mRNA levels increased, noting a 40% higher level of dact2 mRNA after deletion of 7 bp in the dact2 gene 

Revised line 265.

• Line 215: Fig 1F instead of Fig 1D 

Revised line 217.

• Line 248: unify naming of compound mutants to either dact1/2 or dact1/dact2 compound mutants 

Revised to dact1/2 throughout.

• Line 259: incorrect figure citation: Fig S1 - correct to Fig S2D/E 

Revised line 324.

• Line 302: correct abbreviation position: neural crest (NCC) cell - change to neural crest cell (NCC) population 

Revised line 380.

• Line 349: repeating kny mut definition from line 70 may be unnecessary 

Revised line 434.

• Line 351: clarify distinction between Fig S1 and Fig S2 in the supplementary section 

Revised line 324.

• Line 436: refer to the correct figure for pathways associated with proteolysis (Fig 7B) 

Revised line 530.

• Line 446-447: complete the sentence and clarify the relevance of smad1 expression, and correct the use of "also" in relation to capn8 

Revised line 567.

• Line 462: clarify that this phenotype was never observed in wildtype larvae, and correct figure reference to exclude dact1+/- dact2+/- 

Revised line 563, 568.

• Line 463: explain the injection procedure into embryos from dact1/2+/- interbreeding 

Revised line 565.

• Lines 488 and 491: same citation without interruption: Waxman, Hocking et al. 2004 

Revised line 591.

• Line 502: maintain consistency in referring to TGF-beta signaling throughout the article 

Revised throughout.

• Line 523: define CNCC; previously used only NCC 

Revised to cranial NCC throughout.

• Line 1105: reconsider citing another work in the figure legend 

Revised line 1249.

• Line 1143: consider using "mutant" instead of "mu" 

Revised line 1295.

• Fig 2A/B: indicate the number of animals used ("n") 

N is noted on line 1274.

• Fig 2C, D, E: ensure uniform terminology for control groups ("wt" vs. "wildtype") 

Revised in figure.

• Fig 7C: clarify analysis of dact1/2-/- mutant in lateral plate mesoderm vs. ectoderm 

Revised line 1356.

• Fig 8A: label the figure to indicate it shows capn8, not just in the legend 

Revised.

• Fig 8D: explain the black/white portions and simplify to highlight important data 

Revised.

• Fig S2: add the title "Figure S2" 

Revised.

• Consider omitting the sentence: "As with most studies, this work has contributed some new knowledge but generated more questions than answers." 

Revised line 720.

Reviewer #2 (Recommendations For The Authors): 

Major comments: 

(1) The authors have addressed many of the questions I had, including making the biological sample numbers more transparent. It might be more informative to use n = n/n, e.g. n = 3/3, rather than just n = 3. Alternatively, that information can be given in the figure legend or in the form of penetrance %. 

The compound heterozygote breeding and phenotyping analyses were not carried out in such a way that we can comment on the precise % penetrance of the ANC phenotype, as we did not dissect every ANC and genotype every individual that resulted from the triple heterozygote in crossings. We collected phenotype/genotype data until we obtained at least three replicates.

We did genotype every individual resulting from dact1/2 dHet crosses to correlate genotype to the phenotype of the embryonic convergent extension phenotype and narrowed ethmoid plate (Fig. 2A, Fig. 3) which demonstrated full penetrance.

(2) The description of the expression of dact1/2 and wnt11f2 is not consistent with what the images are showing. In the revised figure 1 legend, the author says "dact2 and wnt11f2 transcripts are detected in the anterior neural plate" (line 1099)", but it's hard to see wnt11f2 expression in the anterior neural plate in 1B. The authors then again said " wnt11f2 is also expressed in these cells", referring to the anterior neural plate and polster (P), notochord (N), paraxial and presomitic mesoderm (PM) and tailbud (TB). However, other than the notochord expression, other expression is actually quite dissimilar between dact2 and wnt11f2 in 1C. The authors should describe their expression more accurately and take that into account when considering their function in the same pathway. 

We have revised these sections to more carefully describe the expression patterns. We have added references to previous descriptions of wnt11 expression domains.

(3) Similar to (2), while the Daniocell was useful in demonstrating that expression of dact1 and dact2 are more similar to expression of gpc4 and wnt11f2, the text description of the data is quite confusing. The authors stated "dact2 was more highly expressed in anterior structures including cephalic mesoderm and neural ectoderm while dact1 was more highly expressed in mesenchyme and muscle" (lines 174-176). However, the Daniocell seems to show more dact1 expression in the neural tissues than dact2, which would contradict the in situ data as well. I think the problem is in part due to the dataset contains cells from many different stages and it might be helpful to include a plot of the cells at different stages, as well as the cell types, both of which are available from the Daniocell website. 

We have revised the text to focus the Daniocell analysis on the overall and general expression patterns. Line 220.

(4) The authors used the term "morphological movements" (line 337) to describe the cause of dact1/2 phenotypes. Please clarify what this means. Is it cell movement? Or is it the shape of the tissues? What does "morphological movements" really mean and how does that affect the formation of the EP by the second stream of NCCs? 

We have revised this sentence to improve clarity. Line 416.

(5) In the first submission, only 1 out of 142 calpain-overexpressing animals phenocopied dact1/2 mutants and that was a major concern regarding the functional significance of calpain 8 in this context. In the revised manuscript, the authors demonstrated that more embryos developed the phenotype when they are heterozygous for both dact1/2. While this is encouraging, it is interesting that the same phenomenon was not observed in the dact1-/-; dact2+/- embryos (Fig. 6D). The authors did not discuss this and should provide some explanation. The authors should also discuss sufficiency vs requirement tested in this experiment. However, given that this is the most novel aspect of the paper, performing experiments to demonstrate requirements would be important. 

We have added a statement regarding the non-effect in dact1-/-;dact2+/- embryos. Line 568-570. We have also added discussion of sufficiency vs necessity/requirement testing. Line 676-679.

(6) Related to (5), the authors cited figure 8c when mentioning 0/192 gfp-injected embryos developed EP phenotypes. However, figure 8c is dact1/2 +/- embryos. The numbers also doesn't match the numbers in Figure 8d either. Please add relevant/correct figures. 

The text has been revised to distinguish between our overexpression experiment in wildtype embryos (data not shown) versus overexpression in dact1/2 double het in cross embryos (Fig 8).

Minor comments: 

(1) Fig 1 legend line 1106 "the midbrain (MP)" should be MB 

Revised line 1250.

(2) Wntllf2, instead of wnt11f2, (i.e. the letter "l" rather than the number "1") was used in 4 instances, line 144, 515, 527, 1147 

Revised lines 185, 625, 640,1300.

(3) The authors replaced ANC with EP in many instances, but ANC is left unchanged in some places and it's not defined in the text. It's first mentioned in line 170.

Revised line 218.

Author response:

The following is the authors’ response to the original reviews.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The manuscript gives a broad overview of how to write NeuroML, and a brief description of how to use it with different simulators and for different purposes - cells to networks, simulation, optimization, and analysis. From this perspective, it can be an extremely useful document to introduce new users to NeuroML.

We are glad the reviewer found our manuscript useful.

However, the manuscript itself seems to lose sight of this goal in many places, and instead, the description at times seems to target software developers. For example, there is a long paragraph on the board and user community. The discussion on simulator tools seems more for developers, not users. All the information presented at the level of a developer is likely to be distracting to eLife readership.

To make the paper less developer focussed and more accessible to the end user we have shortened the long paragraphs on the board and user community (and moved some of this text to the Methods section; lines: 524-572 in the document with highlighted changes). We have also made the discussion on simulator tools more focussed on the user (lines 334-406). However, we believe some information on the development and oversight of NeuroML and its community base are relevant to the end user, so we have not removed these completely from the main text.

Strengths:

The modularity of NeuroML is indeed a great advantage. For example, the ability to specify the channel file allows different channels to be used with different morphologies without redundancy. The hierarchical nature of NeuroML also is commendable, and well illustrated in Figures 2a through c.

The number of tools available to work with NeuroML is impressive.

The abstract, beginning, and end of the manuscript present and discuss incorporating NeuroML into research workflows to support FAIR principles.

Having a Python API and providing examples using this API is fantastic. Exporting to NeuroML from Python is also a great feature.

We are glad the reviewer appreciated the design of NeuroML and its support for FAIR principles.

Weaknesses:

Though modularity is a strength, it is unclear to me why the cell morphology isn't also treated similarly, i.e., specify the morphology of a multi-compartmental model in a separate file, and then allow the cell file to specify not only the files containing channels, but also the file containing the multi-compartmental morphology, and then specify the conductance for different segment groups. Also, after pynml_write_neuroml2_file, you would not have a super long neuroML file for each variation of conductances, since there would be no need to rewrite the multi-compartmental morphology for each conductance variation.

We thank the reviewer for highlighting this shortcoming in NeuroML2. We have now added the ability to reference externally defined (e.g. in another file) <morphology> and <biophysicalProperties> elements from <cells>. This has enabled the morphologies and/or specification of ionic conductances to be separated out and enables more streamlined analysis of cells with different properties, as requested. Simulators NEURON, NetPyNE and EDEN already support this new form. Information on this feature has been added to https://docs.neuroml.org/Userdocs/ImportingMorphologyFiles.html#neuroml2 and also mentioned in the text (lines 188-190).

This would be especially important for optimizations, if each trial optimization wrote out the neuroML file, then including the full morphology of a realistic cell would take up excessive disk space, as opposed to just writing out the conductance densities. As long as cell morphology must be included in every cell file, then NeuroML is not sufficiently modular, and the authors should moderate their claim of modularity (line 419) and building blocks (551).

We believe the new functionality outlined above addresses this issue, as a single file containing the <morphology> element could be referenced, while a much smaller file, containing the channel distributions in a <biophysicalProperties> element would be generated and saved on each iteration of the optimisation.

In addition, this is very important for downloading NeuroML-compliant reconstructions from NeuroMorpho.org. If the cell morphology cannot be imported, then the user has to edit the file downloaded from NeuroMorpho.org, and provenance can be lost.

While the NeuroMorpho.Org website does support converting reconstructed morphologies in SWC format to NeuroML, this export feature is no longer supported on most modern browsers due to it being based on Java Applet technologies. However, a desktop version of this application, CVApp, is actively maintained

(https://github.com/NeuroML/Cvapp-NeuroMorpho.org), and we have updated it to support export of the SWC to the standalone <morphology> element form of NeuroML discussed above. Additionally, a new Python application for conversion of SWC to NeuroML is in development and will be incorporated into PyNeuroML (Google Summer of Code 2024). Our documentation has been updated with the recommended use of SWC in NeuroML based modelling here: https://docs.neuroml.org/Userdocs/Software/Tools/SWC.html

We have also included URLs to the tool and the documentation in the paper (lines: 473-474).

SWC files, however, cannot be used “as is” for modelling since they only include information (often incomplete—for example a single point may represent a soma in SWC files) on the points that make the cell, but not on the sections/segments/cables that these form. Therefore, NeuroML and other simulation tools, including NEURON, must convert these into formats suitable for simulation. The suggested pipeline for use of NeuroMorpho SWC files would therefore be to convert them to NeuroML, check that they represent the intended compartmentalisation of the neuron and then use them in models.

To ensure that provenance is maintained in all NeuroML models (including conversions from other formats), NeuroML supports the addition of RDF annotations using the COMBINE annotation specifications in model files:

https://docs.neuroml.org/Userdocs/Provenance.html. We have added this information to the paper (lines: 464-465).

Also, Figure 2d loses the hierarchical nature by showing ion channels, synapses, and networks as separate main branches of NeuroML.

While an instance of an ion channel is on a segment, in a cell, in a population (and hence there is a hierarchy between them), in terms of layout in a NeuroML file the ion channel is defined at the “top level” so that it can be referenced and used by multiple cells, the cell definitions are also defined top level, and used in multiple populations, etc. There are multiple ways to depict these relationships between entities, and we believe Fig 2d complements Fig 2a-c (which is more hierarchical), by emphasising the different categories of entities present in NeuroML files. We have modified the caption of Figure 2d to clarify that it shows the main categories of elements included in the NeuroML standard in their respective hierarchies.

In Figure 5, the difference between the core and native simulator is unclear.

We have modified the figure and text (lines: 341) to clarify this. We now say “reference” simulators instead of “core”. This emphasises that jNeuroML and pyLEMS are intended as reference implementations in each of their languages of how to interpret NeuroML models, as opposed to high performance simulators for research use. We have also updated the categorization of the backends in the text accordingly.

What is involved in helper scripts?

Simulators such as NetPyNE can import NeuroML into their own internal format, but require some boilerplate code to do this (e.g. the NetPyNE scripts calls the importNeuroML2SimulateAnalyze() method with appropriate parameters). The NeuroML tools generate short scripts that use this boilerplate code. We have renamed “helper scripts” to “import scripts'' for clarity (Figure 5 and its caption).

I thought neurons could read NeuroML? If so, why do you need the export simulator-specific scripts?

The NEURON simulator does have some NeuroML functionality (it can export cells, though not the full network, to NeuroML 2 through its ModelView menu), but does not natively support reading/importing of NeuroML in its current version. But this is not a problem as jNeuroML/PyNeuroML translates the NeuroML model description into NEURON’s formats: Python scripts/HOC/Nmodl which NEURON then executes.

As NEURON is the simulator which allows simulation of the widest range of NeuroML elements, we have (in agreement with the NEURON developers) concentrated on incorporating the best support for NeuroML import/export in the latest (easy to install/update) releases of PyNeuroML, rather than adding this to the Neuron source code. NEURON’s core features have been very stable for years and many versions of the simulator are used by modellers - installing the latest PyNeuroML gives them the latest NEURON support without having to reinstall the latter.

In addition, it seems strange to call something the "core" simulation engine, when it cannot support multi-compartmental models. It is unclear why "other simulators" that natively support NeuroML cannot be called the core.

We agree that this terminology was confusing. As mentioned above, we have changed “core simulator” to “reference simulator”, to emphasise the roles of these simulation engine options.

It might be more helpful to replace this sort of classification with a user-targeted description. The authors already state which simulators support NeuroML and which ones need code to be exported. In contrast, lines 369-370 mention that not all NeuroML models are supported by each simulator. I recommend expanding this to explain which features are supported in each simulator. Then, the unhelpful separation between core and native could be eliminated.

As suggested, we have grouped the simulators in terms of function and removed the core/ non-core distinction. We have also added a table (Table 3) in the appendices that lists what features each simulation engine supports and updated the text to be more user focussed (lines: 348-394).

The body of the manuscript has so much other detail that I lose sight of how NeuroML supports FAIR. It is also unclear who is the intended audience. When I get to lines 336-344, it seems that this description is too much detail for the eLife audience. The paragraph beginning on line 691 is a great example of being unclear about who is the audience. Does someone wanting to develop NeuroML models need to understand XSD schema? If so, the explanation is not clear. XSD schema is not defined and instead explains NeuroML-specific aspects of XSD. Lines 734-735 are another example of explaining to code developers (not model developers).

We have modified these sentences to be more suitable for the general eLife audience: we have moved the explanation of how the different simulator backends are supported to the more technically detailed Methods section (lines 882-942).

While the results sections focus on documenting what users can do with NeuroML, the Methods sections include information on “how” the NeuroML and software ecosystem function. While the information in the methods sections may not be required by users who want to use the standard NeuroML model elements, those users looking to extend NeuroML with their own model entities and/or contribute these for inclusion in the NeuroML standard will require some understanding of how the schema and component types work.

We have tried to limit this information to the bare minimum, pointing to online documentation where appropriate. XSD schemas are, for example, briefly introduced at the beginning of the section “The NeuroML XML Schema”. We have also included a link to the W3C documentation on XSD schemas as a footnote (line 724).

Reviewer #2 (Public Review):

Summary:

Developing neuronal models that are shareable, reproducible, and interoperable allows the neuroscience community to make better use of published models and to collaborate more effectively. In this manuscript, the authors present a consolidated overview of the NeuroML model description system along with its associated tools and workflows. They describe where different components of this ecosystem lay along the model development pathway and highlight resources, including documentation and tutorials, to help users employ this system.

Strengths:

The manuscript is well-organized and clearly written. It effectively uses the delineated model development life cycle steps, presented in Figure 1, to organize its descriptions of the different components and tools relating to NeuroML. It uses this framework to cover the breadth of the software ecosystem and categorize its various elements. The NeuroML format is clearly described, and the authors outline the different benefits of its particular construction. As primarily a means of describing models, NeuroML also depends on many other software components to be of high utility to computational neuroscientists; these include simulators (ones that both pre-date NeuroML and those developed afterwards), visualization tools, and model databases.

Overall, the rationale for the approach NeuroML has taken is convincing and well-described. The pointers to existing documentation, guides, and the example usages presented within the manuscript are useful starting points for potential new users. This manuscript can also serve to inform potential users of features or aspects of the ecosystem that they may have been unaware of, which could lower obstacles to adoption. While much of what is presented is not new to this manuscript, it still serves as a useful resource for the community looking for information about an established, but perhaps daunting, set of computational tools.

We are glad the reviewer appreciated the utility of the manuscript.

Weaknesses:

The manuscript in large part catalogs the different tools and functionalities that have been produced through the long development cycle of NeuroML. As discussed above, this is quite useful, but it can still be somewhat overwhelming for a potential new user of these tools. There are new user guides (e.g., Table 1) and example code (e.g. Box 1), but it is not clear if those resources employ elements of the ecosystem chosen primarily for their didactic advantages, rather than general-purpose utility. I feel like the manuscript would be strengthened by the addition of clearer recommendations for users (or a range of recommendations for users in different scenarios).

To make Table 1 more accessible to users and provide recommendations we have added the following new categories: Introductory guides aimed at teaching the fundamental

NeuroML concepts; Advanced guides illustrating specific modelling workflows; and Walkthrough guides discussing the steps required for converting models to NeuroML. Box 1 has also been improved to clearly mark API and command line examples.

For example, is the intention that most users should primarily use the core NeuroML tools and expand into the wider ecosystem only under particular circumstances? What are the criteria to keep in mind when making that decision to use alternative tools (scale/complexity of model, prior familiarity with other tools, etc.)? The place where it seems most ambiguous is in the choice of simulator (in part because there seem to be the most options there) - are there particular scenarios where the authors may recommend using simulators other than the core jNeuroML software?

The interoperability of NeuroML is a major strength, but it does increase the complexity of choices facing users entering into the ecosystem. Some clearer guidance in this manuscript could enable computational neuroscientists with particular goals in mind to make better strategic decisions about which tools to employ at the outset of their work.

As mentioned in the response to Reviewer 1, the term “core simulator” for jNeuroML was confusing, as it suggested that this is a recommended simulation tool. We have changed the description of jNeuroML to a “reference simulator” to clarify this (Figure 5 and lines 341, 353).

In terms of giving specific guidance on which simulator to use, we have focussed on their functionality and limitations rather than recommending a specific tool (as simulator independent standards developers we are not in a position to favour particular simulators). While NEURON is the most widely used simulator currently, other simulation opinions (e.g. EDEN) have emerged recently which provide quite comprehensive NeuroML support and similar performance. Our approach is to document and promote all supported tools, while encouraging innovation and new developments. The new Table 3 in the Appendix gives a guide to assist users in choosing which simulator may best suit their needs and we have updated the text to include a brief description (lines 348-394).

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

I do not understand what the $comments mean in Box 1. It isn't until I get further in the text that I realize that those are command line equivalents to the Python commands.

We thank the reviewer for highlighting this confusion. We’ve now explicitly marked the API usage and command line usage example columns to make this clearer. We have also used “>” instead of “$” now to indicate the command line,

In Figure 9 Caption "Examples of analysis functions ..", the word analysis seems a misnomer, as these graphs all illustrate the simulation output and graphing of existing variables. I think analysis typically refers to the transformation of variables, such as spike counts and widths.

To clarify this we have changed the caption to “Examples of visualizing biophysical properties of a NeuroML model neuron”.

Figure 10: Why is the pulse generator part of a model? Isn't that the input to a model?

Whether the input to the model is described separately from the NeuroML biophysical description or combined with it is a choice for the researcher. This is possible because in NeuroML any entity which has time varying states can be a NeuroML element, including the current pulse generator. In this simple example the input is contained within the same file (and therefore <neuroml> element) as the cell. However, this does not need to be the case. The cell could be fully specified in its own NeuroML file and then this can be included in other files which add different inputs to facilitate different simulation scenarios. The Python scripting interface facilitates these types of workflows.

In the interest of modularity, can stim information be stored in a separate file and "included"?

Yes, as mentioned above, the stimulus could be stored in a separate file.

I find it strange to use a cell with mostly dimensionless numbers as an example. I think it would be more helpful to use a model that was more physiological.

In choosing an example model type to use to illustrate the use of LEMS (Fig 12), NeuroML (Fig 10), XML Schema (Fig 11), the Python API (Fig 13) and online documentation (Fig 15), we needed an example which showed a sufficiently broad range of concepts (dimensional parameters, state variables, time derivatives), but which is sufficiently compact to allow a concise depiction of the key elements in figures, that fit in a single page (e.g. Fig 12). We felt that the Hindmarsh Rose model, while not very physiological, was well suited for this purpose (explaining the underlying technologies behind the NeuroML specification). The simplicity of the Hindmarsh Rose model is counterbalanced in the manuscript by the detailed models of neurons and circuits in Figures 7 & 9. The latter shows a morphologically and biophysically detailed cortical L5b pyramidal cell model.

In lines 710-714, it is unclear what is being validated. That all parameters are defined? Using the units (or lack thereof) defined in the schema?

Validation against the schema is “level 1” validation where the model structure, parameters, parameter values and their units, cardinality, and element positioning in the model hierarchy are checked. We have updated the paragraph to include this information and to also point to Figure 6 where different levels of validation are explained.

Lines 740 to 746 are confusing. If 1-1 between XSD and LEMS (1st sentence) then how can component types be defined in LEMS and NOT added to the standard? Which is it? 1-1 or not 1-1?

For the curated model elements included in the NeuroML standard, there will be a 1-1 correspondence between their component type definitions in LEMS and type definitions in the XSD schema. New user defined component types (e.g. a new abstract cell model) can be specified in LEMS as required, and these do not need to be included in the XSD schema to be loaded/simulated. However, since they are not present in the schema definition of the core/curated elements, they cannot be validated against it (level 1 validation). We have modified the text to make this clearer (line: 778).

Nonetheless, if the new type is useful for the wider community, it can be accepted by the Editorial Board, and at that stage it will be incorporated into the core types, and added to the Schema, to be part of “valid NeuroML”.

Figure 12. select="synapses[*]/i" is not explained. Does /i mean that iSyn is divided by i, which is current (according to the sentence 3 lines after 766) or perhaps synapse number?

We thank the reviewer for highlighting this confusion. We have now explained the construct in the text (lines 810-812). It denotes “select the i (current) values from all Attachments which have the id ‘synapses’”. These multiple values should be reduced down to a single value through addition, as specified by the attribute: reduce=”add”.

The line after 766 says that "DerivedVariables, variables whose values depend on other variables". You should add "and that are not derivatives, which are handled separately" because by your definition derivatives are derived variables.

Thank you. We have updated the text with your suggestion

Reviewer #2 (Recommendations For The Authors):

- Figure 9: I found it somewhat confusing to have the header from the screenshot at the top ("Layer 5 Burst Accommodating Double Bouquet Cell (5)") not match the morphology shown at the bottom. It's not visually clear that the different panels in Figure 9 may refer to unrelated cells/models.

Thank you for pointing this out. We have replaced the NeuroML-DB screenshot with one of the same Layer 5b pyramidal cells shown in the panels below it.

Additional change:

Figure 7c (showing the NetPyNE-UI interface) has been replaced. Previously, this displayed a 3D model which had been created in NetPyNE itself, but now shows a model which has been created in NeuroML and imported for display/simulation in NetPyNE-UI, and therefore better illustrates NeuroML functionality.

Author response:

To Reviewer #1:

Thank you for your kind words regarding the novelty, study design, and evidence presented. We will clarify our language when describing fuzzy local-linear regression discontinuity analysis. We thank you for this feedback as our goals are to introduce these methods to a neuroscientific audience. Lastly, we will respond and clarify the methodological points, including post-selection inference, bandwidths, and Bayesian analysis in version 2.

To Reviewers #2 and #3:

We thank you both for your constructive feedback, specifically in highlighting 1) the scope of the intervention and 2) the UKB-neuro healthy volunteer bias. In the next manuscript version, we will expand our discussion of plausible reasons for not finding an effect – weighing up the strengths and limitations of our study in 3 aspects; statistical (RD power), design-based (lack of representativeness vs. large sample), and mechanistic (the impact/or lack thereof of one-year of education on neural plasticity decades later). As we believe the approach of natural experiments with RD designs has considerable promise for the field of population cognitive neuroscience beyond this particular study, we will address each of these points within a broader section focused on considerations on how to optimize the insight, power, and inferences gained in future work within and beyond Biobank. Moreover, we will situate our discussion on the magnitude of the educational intervention among a broader discussion of cognitive training versus education, and short - versus long-term effects. We believe revising the manuscript will improve interpretation for the reader and thank you for your in-depth feedback. Lastly, we will provide a point-by-point response in the next version.

Author response:

Public Reviews: 

Reviewer #1 (Public review): 

The conserved AAA-ATPase PCH-2 has been shown in several organisms including C. elegans to remodel classes of HORMAD proteins that act in meiotic pairing and recombination. In some organisms the impact of PCH-2 mutations is subtle but becomes more apparent when other aspects of recombination are perturbed. Patel et al. performed a set of elegant experiments in C. elegans aimed at identifying conserved functions of PCH-2. Their work provides such an opportunity because in C. elegans meiotically expressed HORMADs localize to meiotic chromosomes independently of PCH-2. Work in C. elegans also allows the authors to focus on nuclear PCH-2 functions as opposed to cytoplasmic functions also seen for PCH-2 in other organisms. 

The authors performed the following experiments: 

(1) They constructed C. elegans animals with SNPs that enabled them to measure crossing over in intervals that cover most of four of the six chromosomes. They then showed that doublecrossovers, which were common on most of the four chromosomes in wild-type, were absent in pch-2. They also noted shifts in crossover distribution in the four chromosomes. 

(2) Based on the crossover analysis and previous studies they hypothesized that PCH-2 plays a role at an early stage in meiotic prophase to regulate how SPO-11 induced double-strand breaks are utilized to form crossovers. They tested their hypothesis by performing ionizing irradiation and depleting SPO-11 at different stages in meiotic prophase in wild-type and pch-2 mutant animals. The authors observed that irradiation of meiotic nuclei in zygotene resulted in pch-2 nuclei having a larger number of nuclei with 6 or greater crossovers (as measured by COSA-1 foci) compared to wildtype. Consistent with this observation, SPO11 depletion, starting roughly in zygotene, also resulted in pch-2 nuclei having an increase in 6 or more COSA-1 foci compared to wild type. The increased number at this time point appeared beneficial because a significant decrease in univalents was observed. 

(3) They then asked if the above phenotypes correlated with the localization of MSH-5, a factor that stabilizes crossover-specific DNA recombination intermediates. They observed that pch-2

mutants displayed an increase in MSH-5 foci at early times in meiotic prophase and an unexpectedly higher number at later times. They conclude based on the differences in early MSH-5 localization and the SPO-11 and irradiation studies that PCH-2 prevents early DSBs from becoming crossovers and early loading of MSH-5. By analyzing different HORMAD proteins that are defective in forming the closed conformation acted upon by PCH-2, they present evidence that MSH-5 loading was regulated by the HIM-3 HORMAD. 

(4) They performed a crossover homeostasis experiment in which DSB levels were reduced. The goal of this experiment was to test if PCH-2 acts in crossover assurance. Interestingly, in this background PCH-2 negative nuclei displayed higher levels of COSA-1 foci compared to PCH-2 positive nuclei. This observation and a further test of the model suggested that "PCH-2's presence on the SC prevents crossover designation." 

(5) Based on their observations indicating that early DSBS are prevented from becoming crossovers by PCH-2, the authors hypothesized that the DNA damage kinase CHK-2 and PCH2 act to control how DSBs enter the crossover pathway. This hypothesis was developed based on their finding that PCH-2 prevents early DSBs from becoming crossovers and previous work showing that CHK-2 activity is modulated during meiotic recombination progression. They tested their hypothesis using a mutant synaptonemal complex component that maintains high CHK-2 activity that cannot be turned off to enable crossover designation. Their finding that the pch-2 mutation suppressed the crossover defect (as measured by COSA-1 foci) supports their hypothesis. 

Based on these studies the authors provide convincing evidence that PCH-2 prevents early DSBs from becoming crossovers and controls the number and distribution of crossovers to promote a regulated mechanism that ensures the formation of obligate crossovers and crossover homeostasis. As the authors note, such a mechanism is consistent with earlier studies suggesting that early DSBs could serve as "scouts" to facilitate homolog pairing or to coordinate the DNA damage response with repair events that lead to crossing over. The detailed mechanistic insights provided in this work will certainly be used to better understand functions for PCH-2 in meiosis in other organisms. My comments below are aimed at improving the clarity of the manuscript. 

We thank the reviewer for their concise summary of our manuscript and their assessment of our work as “convincing” and providing “detailed mechanistic insight.”

Comments 

(1) It appears from reading the Materials and Methods that the SNPs used to measure crossing over were obtained by mating Hawaiian and Bristol strains. It is not clear to this reviewer how the SNPs were introduced into the animals. Was crossing over measured in a single animal line? Were the wild-type and pch-2 mutations made in backgrounds that were isogenic with respect to each other? This is a concern because it is not clear, at least to this reviewer, how much of an impact crossing different ecotypes will have on the frequency and distribution of recombination events (and possibly the recombination intermediates that were studied). 

We will clarify these issues in the Materials and Methods of an updated preprint. The control and pch-2 mutants were isogenic in either the Bristol or Hawaiian backgrounds. Control lines were the original Bristol and Hawaiian lines and pch-2 mutants were originally made in the Bristol line and backcrossed at least 3 times before analysis. Hawaiian pch-2 mutants were made by backcrossing pch-2 mutants at least 7 times to the Hawaiian background and verifying the presence of Hawaiian SNPs on all chromosomes tested in the recombination assay. To perform the recombination assays, these isogenic lines were crossed to generate the relevant F1s.

(2) The authors state that in pch-2 mutants there was a striking shift of crossovers (line 135) to the PC end for all of the four chromosomes that were tested. I looked at Figure 1 for some time and felt that the results were more ambiguous. Map distances seemed similar at the PC end for wildtype and pch-2 on Chrom. I. While the decrease in crossing over in pch-2 appeared significant for Chrom. I and III, the results for Chrom. IV, and Chrom. X. seemed less clear. Were map distances compared statistically? At least for this reviewer the effects on specific intervals appear less clear and without a bit more detail on how the animals were constructed it's hard for me to follow these conclusions. 

We hope that the added details above makes the results of these assays more clear. Map distances were compared and did not satisfy statistical significance, except where indicated. While we agree that the comparisons between control animals and pch-2 mutants may seem less clear with individual chromosomes, we argue that more general patterns become clear when analyzing multiple chromosomes. Indeed, this is why we expanded our recombination analysis beyond Chromosome III and the X Chromosomes, as reported in Deshong, 2014. 

(3) Figure 2. I'm curious why non-irradiated controls were not tested side-by-side for COSA-1 staining. It just seems like a nice control that would strengthen the authors' arguments. 

We will add these controls in the updated preprint.

(4) Figure 3. It took me a while to follow the connection between the COSA-1 staining and DAPI staining panels (12 hrs later). Perhaps an arrow that connects each set of time points between the panels or just a single title on the X-axis that links the two would make things clearer. 

We will make changes in the updated preprint to make this figure more clear.

Reviewer #2 (Public review): 

Summary: 

This paper has some intriguing data regarding the different potential roles of Pch-2 in ensuring crossing over. In particular, the alterations in crossover distribution and Msh-5 foci are compelling. My main issue is that some of the models are confusingly presented and would benefit from some reframing. The role of Pch-2 across organisms has been difficult to determine, the ability to separate pairing and synapsis roles in worms provides a great advantage for this paper. 

Strengths: 

Beautiful genetic data, clearly made figures. Great system for studying the role of Pch-2 in crossing over. 

We thank the reviewers for their constructive and useful summary of our manuscript and the analysis of its strengths. 

Weaknesses: 

(1) For a general audience, definitions of crossover assurance, crossover eligible intermediates, and crossover designation would be helpful. This applies to both the proposed molecular model and the cytological manifestation that is being scored specifically in C. elegans. 

We will make these changes in an updated preprint.

(2) Line 62: Is there evidence that DSBs are introduced gradually throughout the early prophase? Please provide references. 

We will reference Woglar and Villeneuve 2018 and Joshi et. al. 2015 to support this statement in the updated preprint.

(3) Do double crossovers show strong interference in worms? Given that the PC is at the ends of chromosomes don't you expect double crossovers to be near the chromosome ends and thus the PC? 

Despite their rarity, double crossovers do show interference in worms. However, the PC is limited to one end of the chromosome. Therefore, even if interference ensures the spacing of these double crossovers, the preponderance of one of these crossovers toward one end (and not both ends) suggest something functionally unique about the PC end.

(4) Line 155 - if the previous data in Deshong et al is helpful it would be useful to briefly describe it and how the experimental caveats led to misinterpretation (or state that further investigation suggests a different model etc.). Many readers are unlikely to look up the paper to find out what this means. 

We will add this to the updated preprint.

(5) Line 248: I am confused by the meaning of crossover assurance here - you see no difference in the average number of COSA-1 foci in Pch-2 vs. wt at any time point. Is it the increase in cells with >6 COSA-1 foci that shows a loss of crossover assurance? That is the only thing that shows a significant difference (at the one time point) in COSA-1 foci. The number of dapi bodies shows the loss of Pch-2 increases crossover assurance (fewer cells with unattached homologs). So this part is confusing to me. How does reliably detecting foci vs. DAPI bodies explain this? 

We apologize for the confusion and will make this more clear in an updated perprint. The reviewer is correct that we do not see a difference in the average number of GFP::COSA1 foci at all time points in this experiment, even though we do see a difference in the number of DAPI stained bodies (an increase in crossover assurance in pch-2 mutants). What we meant to convey is that because of PCH-2’s dual role in regulating crossover formation (inhibiting it in early prophase, guaranteeing assurance later), the average number of GFP::COSA-1 foci at all time points also reflects this later role, resulting in this average being lower than if PCH-2 only inhibited crossovers early in meiotic prophase. We have shown that this later role does not significantly affect the average number of DAPI stained bodies, allowing us to see the role of PCH-2 in early meiotic prophase on crossover formation more clearly.

(6) Line 384: I am confused. I understand that in the dsb-2/pch2 mutant there are fewer COSA-1 foci. So fewer crossovers are designated when DSBs are reduced in the absence of PCH-2.

How then does this suggest that PCH-2's presence on the SC prevents crossover designation? Its absence is preventing crossover designation at least in the dsb-2 mutant. 

We will also make this more clear in an updated preprint, as well as provide additional evidence to support this claim. In this experiment, we had identified three possible explanations for why PCH-2 persists on some nuclei that do not have GFP::COSA-1 foci: 1) PCH-2 removal is coincident with crossover designation; 2) PCH-2 removal depends on crossover designation; and 3) PCH-2 removal facilitates crossover designation. The decrease in the number of GFP::COSA-1 foci in dsb-2::AID;pch-2 mutants argues against the first two possibilities, suggesting that the third might be correct. We have additional evidence that we will include in an updated preprint that should provide stronger support and make this more clear.

(7) Discussion Line 535: How do you know that the crossovers that form near the PCs are Class II and not the other way around? Perhaps early forming Class I crossovers give time for a second Class II crossover to form. In budding yeast, it is thought that synapsis initiation sites are likely sites of crossover designation and class I crossing over. Also, the precursors that form class I and II crossovers may be the same or highly similar to each other, such that Pch-2's actions could equally affect both pathways. 

We do not know that the crossovers that form near the PC are Class II but hypothesize that they are based on the close, functional relationship that exists between Class I crossovers and synapsis and the apparent antagonistic relationship that exists between Class II crossovers and synapsis. We agree that Class I and Class II crossover precursors are likely to be the same or highly similar, exhibit extensive crosstalk that may complicate straightforward analysis and PCH-2 is likely to affect both, as strongly suggested by our GFP::MSH-5 analysis. We present this hypothesis based on the apparent relationship between PCH-2 and synapsis in several systems but agree that it needs to be formally tested. We will make this argument more clear in an updated preprint.

Reviewer #3 (Public review): 

Summary: 

This manuscript describes an in-depth analysis of the effect of the AAA+ ATPase PCH-2 on meiotic crossover formation in C. elegant. The authors reach several conclusions, and attempt to synthesize a 'universal' framework for the role of this factor in eukaryotic meiosis. 

Strengths: 

The manuscript makes use of the advantages of the 'conveyor' belt system within the c.elegans reproductive tract, to enable a series of elegant genetic experiments. 

We thank this reviewer for the useful assessment of our manuscript and the articulation of its strengths.

Weaknesses: 

A weakness of this manuscript is that it heavily relies on certain genetic/cell biological assays that can report on distinct crossover outcomes, without clear and directed control over other aspects and variables that might also impact the final repair outcome. Such assays are currently out of reach in this model system. 

In general, this manuscript could be more generally accessible to non-C.elegans readers. Currently, the manuscript is hard to digest for non-experts (even if meiosis researchers). In addition, the authors should be careful to consider alternative explanations for certain results. At several steps in the manuscript, results could ostensibly be caused by underlying defects that are currently unknown (for example, can we know for sure that pch-2 mutants do not suffer from altered DSB patterning, and how can we know what the exact functional and genetic interactions between pch-2 and HORMAD mutants tell us?). Alternative explanations are possible and it would serve the reader well to explicitly name and explain these options throughout the manuscript. 

We will make the manuscript more accessible to non-C. elegans readers and discuss alternate explanations for specific results in an updated preprint.

Author response:

The following is the authors’ response to the original reviews.

A summary of changes

(1) Line 93: “positive effect” to “positive contribution”, as suggested by reviewer 2.

(2) Line 147-148: the null hypothesis to test “equal interspecific and intraspecific interactions”, as indicated by reviewers 2 and 4.

(3) Lines 155-162: removed to reduce duplication with the additive partitioning, as suggested by reviewer 2.

(4) Lines 186-188: added “the estimated competitive growth response would also include the effects of density-dependent pests, pathogens, or microclimates”, as suggested by reviewer 3.  

(5) Lines 219-222: added “The community positive effect can be further partitioned by mechanisms of positive interactions (resource partitioning and facilitation), and facilitative effect can be classified as mutualism (+/+), commensalism (+/0), or parasitic (+/–) based on species specific assessments”.  

(6) Lines 377-386: added options for determining maximum competitive growth response in some extreme scenarios of species mixtures.

(7) Figure 1: modified to show the variations of competitive growth response with relative competitive ability from minimum (null expectation) to maximum (competitive exclusion).    

A summary of four reviewers’ questions and authors’ response

(1) A summary of authors’ responses. Reviewers did not seem to understand our work. They indicated that our model is inadequate for hypothesis testing. The fact is, as we note below, that our model allows for more hypothesis testing than the additive partitioning model. They suggested that one of our model components, the competitive growth response, needs to be further partitioned. However, this term represents only the competition effect and can not be split any further. Reviewers criticized us for misunderstanding the additive components while they suggested the same logic to test some intuitive ideas. They did not seem to know that the effects of competitive interactions vary with assessment methods, which differ between competition and biodiversity research. Our work seeks to harmonise definitions between these two fields and bridge the gap. The reviewers acknowledged that the additive components (i.e., the selection effect and complementarity effect) do not have clear biological meanings; however, they did not acknowledge that the additive components are used extensively for determining mechanisms of species interactions in biodiversity research. There is hardly any research that uses the additive partitioning model without linking the additive components to specific mechanisms of species interactions (i.e., positive SE to competition and positive CE to positive interactions).

(2) Additive partitioning and underlying mechanisms. Some reviewers acknowledged that additive partitioning is not meant for determining mechanisms of species interactions and therefore argued that the additive partitioning should not be criticized for lack of biological meanings with the additive components. However, they insisted that additive partitioning is useful in quantifying net biodiversity effects against the null hypothesis that there is no difference between intraspecific and interspecific interactions or testing the idea that “niche complementarity mitigates competition” or “competitively superior species dominate mixtures”. Are these views contradictory each other? How can the additive partitioning that is not designed for determining mechanisms of species interactions provide meaningful explanations for outputs of species interactions, e.g., “niche complementarity mitigates competition” or “competitively superior species dominate mixtures”?

Reviewers did not seem to realize that these ideas are equivalent to the suggestions that CE represents for the effects of positive interactions and SE for the effects of competitive interactions, that the quantification of net biodiversity effects does not require the two additive components, and that the null hypothesis exists long before the additive partitioning (see de Wit, 1960, de Wit et al., 1966). It is generally agreed that CE and SE result from mathematical calculations and do not have clear biological meanings in terms of linkages to specific mechanisms of species interactions responsible for observed net biodiversity effects or changes in ecosystem function (Loreau and Hector, 2012; Bourrat et al., 2023). Calling some mixed effects of species interactions as mechanisms (e.g., CE and SE) is misleading.        

Model structure: incomplete or inadequate for hypothesis testing. Other than positive, negative, and competition interactions, two reviewers wanted to have more specific interactions such as microclimate amelioration and negative feedback from species-specific pests and pathogens. The determination of these specific mechanisms requires more investigations and cannot be simply made through partitioning growth and yield data. However, the effects of these interactions will be captured in our definition of species interactions.  Reviewers did not seem to know that the additive partitioning would also not allow identifying these specific positive species interactions.

Inspired by the mathematical form of additive partitioning, two reviewers suggested that our model (presumably equation 4) is incomplete and the second term, i.e., competitive growth response needs to be further explored or partitioned. The second term represents deviations from the null expectation, due to species differences in growth and competitive ability or competition effect. We do not know why and how this term can be further partitioned and what any subcomponents would mean.   

Our competitive partitioning model is based on two hypotheses: first, the null hypothesis to test the equivalence of interspecific and intraspecific interactions. This hypothesis is the same as the additive partitioning model. Second, the competitive hypothesis, which tests the dominance of positive or negative species interactions in a community. Thus, our model allows for more hypothesis testing than the current additive partitioning model.     

(3) Types of species interactions. We follow the definition of species interactions generally used in biodiversity research (see Loreau and Hector, 2001), i.e., positive interactions (or complementarity) include resource partitioning and facilitation, negative interactions include interference competition, and competitive interactions include resource competition. One reviewer suggested that resource partitioning is byproduct of competition and should not be part of positive species interactions, which may be true for long-term evolution of species co-existence but not for biodiversity experiments of decade duration at most. Two reviewers suggested that positive interactions should also include microclimate amelioration or negative feedback from species-specific pests and pathogens. We agree and these are included in our definition. 

(4) Significance of partial density monocultures. We used partial and full density monocultures and species competitive ability to determine what species can possibly achieve in mixture under the competitive hypothesis that constituent species share an identical niche but differ in growth and competitive ability. We did not use partial monocultures to test the effects of density on biodiversity effects. As with the additive partitioning, the competitive partitioning model is not designed for comparing yields across different densities. We added at lines 186-188 to indicate that the estimated competitive growth response would also include the effects of density-dependent pests, pathogens, or microclimates.  

Similarly, we do not use the partial density monoculture to  supplant the replacement series design. Partial density monocultures only supplement the “replacement series” design that does not provides estimates of facilitative effects and competitive growth responses that would occur in mixtures. It is crucial to know that one experimental approach is simply not enough for determining underlying mechanisms of species interactions responsible for changes in ecosystem function.  

(5) Competition effect in competition and biodiversity research. Due to different methods used, competition effect in competition research has different ecological meanings from that in biodiversity research. In competition research, species performance in mixture are compared with their partial density monocultures and therefore competition effect is generally negative, as suggested by reviewer 4. In biodiversity research, comparison is between mixture and full density monocultures. The resulting competition effect can be positive or negative for both individual species and community productivity defined by species composition and full density monoculture yields.     

Therefore, we cannot use the results of competition research based on additive series design to describe effects of competitive interactions on ecosystem productivity based replacement series design.

Reviewer #1 (Public Review):

[Editors' note: this is an overall synthesis from the Reviewing Editor in consultation with the reviewers.]

The three reviews expand our critique of this manuscript in some depth and complementary directions. These can be synthesized in the following main points (we point out that there is quite a bit more that could be written about the flaws with this study; however, time constraints prevented us from further elaborating on the issues we see):

(1) It is unclear what the authors want to do.

As indicate by the title, our objective is to “partition changes in ecosystem productivity by effects of species interactions”, i.e., partitioning net biodiversity effects estimated from the null expectation into components associated with positive, negative, or competition interspecific interactions.

It seems their main point is that the large BEF literature and especially biodiversity experiments overstate the occurrence of positive biodiversity effects because some of these can result from competition.

We demonstrated through ecological theories and simulation/experiment data that competition is a major source of the net biodiversity effects estimated with additive partitioning model. We know that competition effect varies with mixture attributes. Future research will determine average effect of competitive interactions on biodiversity effects in large BEF literature.   

Because reduced interspecific relative to intraspecific competition in mixture is sufficient to produce positive effects in mixtures (if interspecific competition = 0 then RYT = S, where S is species richness in mixture -- this according to the reciprocal yield law = law of constant final yield), they have a problem accepting NE > 0 as true biodiversity effect (see additive partitioning method of Loreau & Hector 2001 cited in manuscript).

We have no problem to accept NE>0 as true positive biodiversity effect. However, NE>0 can also result from competitive interactions based on the null expectation and needs to be partitioned by effects of species interactions.

(2) The authors' next claim, without justification, that additive partitioning of NE is flawed and theoretically and biologically meaningless.

The additive partitioning model is based on Covariance equation (or Price equation) that has nothing to do with biodiversity partitioning (Bourrat et al., 2023). Biological meaning was arbitrarily assigned to CE and SE. We made clear that the additive partitioning model is mathematically sound but does not have biological meanings that it has been used for.   

They misinterpret the CE component as biological niche partitioning and the SE component as biological dominance.

We did not. Loreau and Hector (2001) clearly indicated positive CE for positive interactions and positive SE for competitive interactions, which is generally what has been used for in the last twenty years.

They do not seem to accept that the additive partitioning is a logically and mathematically sound derivation from basic principles that cannot be contested.

We do not have problem with mathematical form of additive partitioning but only oppose ecological meanings assigned to CE and SE, simply because CE and SE both result from all species interactions (see Loreau and Hector, 2001; Bourrat et al., 2023). The reviewer seemed to have a contradictory thinking that the additive components are biologically meaningless but derived from biological basic principles.       

(3) The authors go on to introduce a method to calculate species-level overyielding (RY > 1/S in replacement series experiments) as a competitive growth response and multiply this with the species monoculture biomass relative to the maximum to obtain competitive expectation. This method is based on resource competition and the idea that resource uptake is fully converted into biomass (instead of e.g. investing it in allelopathic chemical production).

Correct, but we did not assume “resource uptake is fully converted into biomass”.

(4) It is unclear which experiments should be done, i.e. are partial-density monocultures planted or simply calculated from full-density monocultures? At what time are monocultures evaluated? The framework suggests that monocultures must have the full potential to develop, but in experiments, they are often performing very poorly, at least after some time. I assume in such cases the monocultures could not be used.

Both partial and full density monocultures are needed, along with mixtures to separate NE by species interactions. Calculating competitive growth responses from density-size relationships can be an alternative, given the lack of partial density monocultures in current biodiversity experiments, but is not preferred.

Similar to additive partitioning, our model can (and should) be applied to all developmental stages of an experiment to examine how interactions evolve through time.   

(5) There are many reasons why the ideal case of only resource competition playing a role is unrealistic. This excludes enemies but also differential conversion factors of resources into biomass and antagonistic or facilitative effects. Because there are so many potential reasons for deviations from the null model of only resource competition, a deviation from the null model does not allow conclusions about underlying mechanisms.

The competitive expectation is only a hypothesis, just as the null expectation. The difference between competitive and null expectations represents a competitive effect resulting from species differences in growth and competitive ability, while the deviation of observed yields from the competitive expectation indicates positive or negative effect (see lines 201-219).

Furthermore, this is not a systematically developed partitioning, but some rather empirical ad hoc formulation of a first term that is thought to approximate competitive effects as understood by the authors (but again, there already are problems here). The second residual term is not investigated. For a proper partitioning approach, one would have to decompose overyielding into two (or more) terms and demonstrate (algebraically) that under some reasonable definitions of competitive and non-competitive interactions, these end up driving the respective terms.

The first term represents the null expectation assuming equal interspecific and intraspecific interactions, i.e., absence of positive, negative, and competition effects. The second residual term represents competition effect, due to species differences in growth and competitive ability. The meaning of second residual term is clear and does not need to be further partitioned or investigated.

In fact, our competitive partitioning also has several components including null expectation, competitive growth response, and observed yield, plus partial density monocultures for species assessment, or null expectations, competitive expectations, and observed yields for community level assessment, although different from the additive partitioning.

(6) Using a simplistic simulation to test the method is insufficient. For example, I do not see how the simulation includes a mechanism that could create CE in additive partitioning if all species would have the same monoculture yield. Similarly, they do not include mechanisms of enemies or antagonistic interactions (e.g. allelopathy).

The simulation model we used is developed from real world data and can only do what are available in the model in terms of species and their growth under different conditions. We can not go beyond data limitation. The model is empirical and has been shown to accurately estimate yield in the aspen-spruce forest condition. We would also note that we do also use experimental data (Table 2).  

(7) The authors do not cite relevant literature regarding density x biodiversity experiments, competition experiments, replacement-series experiments, density-yield experiments, additive partitioning, facilitation, and so on.

We cited literature relevant to biodiversity partitioning since we are not aiming to cover everything. The reviewer may not be aware that most of the research areas listed are actually included in our work, such as additive and replacement-series experiment designs, additive partitioning, facilitation, competition studies, and density-yield relationships. Our competitive model partitioning is based on biological principles, while the additive partitioning model is based only on a mathematical equation.   

Overall, this manuscript does not lead further from what we have already elaborated in the broad field of BEF and competition studies and rather blurs our understanding of the topic.

The results of competition studies based on additive series design are not really used in the broad field of BEF based on replacement series design. The effects of competitive interactions on BEF are never clearly defined using the results of competition studies. Our work is filling that gap.  

Reviewer #2 (Public Review):

This manuscript is motivated by the question of what mechanisms cause overyielding in mixed-species communities relative to the corresponding monocultures. This is an important and timely question, given that the ultimate biological reasons for such biodiversity effects are not fully understood.

As a starting point, the authors discuss the so-called "additive partitioning" (AP) method proposed by Loreau & Hector in 2001. The AP is the result of a mathematical rearrangement of the definition of overyielding, written in terms of relative yields (RY) of species in mixtures relative to monocultures. One term, the so-called complementarity effect (CE), is proportional to the average RY deviations from the null expectations that plants of both species "do the same" in monocultures and mixtures. The other term, the selection effect (SE), captures how these RY deviations are related to monoculture productivity. Overall, CE measures whether relative biomass gains differ from zero when averaged across all community members, and SE, whether the "relative advantage" species have in the mixture, is related to their productivity. In extreme cases, when all species benefit, CE becomes positive. When large species have large relative productivity increases, SE becomes positive. This is intuitively compatible with the idea that niche complementarity mitigates competition (CE>0), or that competitively superior species dominate mixtures and thereby driver overyielding (SE>0).

The reviewer needs to know that these ideas are based on the same logic that positive CE represents the effects of positive interactions and positive SE represents the effects of competitive interactions. CE>0 or SE>0 can result from many different scenarios of species interactions, not necessarily “niche complementarity mitigates competition” or “competitively superior species dominate mixtures”. CE>0 and SE>0 can occur alone or together. We simply can not tell underlying mechanisms of overyielding from mathematical calculations (CE and SE), as suggested by this reviewer later.

The reviewer criticizes us while using the same logic themselves.

However, it is very important to understand that CE and SE capture the "statistical structure" of RY that underlies overyielding. Specifically, CE and SE are not the ultimate biological mechanisms that drive overyielding, and never were meant to be. CE also does not describe niche complementarity. Interpreting CE and SE as directly quantifying niche complementarity or resource competition, is simply wrong, although it sometimes is done. The criticism of the AP method thus in large part seems unwarranted. The alternative methods the authors discuss (lines 108-123) are based on very similar principles.

The reviewer actually supports our point. However, CE and SE have been largely used as biological mechanisms, positive CE as the results of complementary interactions and positive SE as the results of competitive interactions (see Loreau and Hector, 2001).  

We do not have problem with the "statistical structure" of AP; it is simply a covariance equation. It is important to know that CE and SE do not provide additional information on overyielding than NE in terms of underlying mechanisms of species interactions. Any attempt to investigate mechanism of overyielding with CE or SE can easily go wrong.

Our competitive partitioning model incorporates effects of competitive interactions into the conventional null expectation and allows for separating different effects of species interactions. In comparison, the additive partitioning model does not have this capacity, not even designed for this purpose, as suggested by this and other reviewers.         

The authors now set out to develop a method that aims at linking response patterns to "more true" biological mechanisms.

Assuming that "competitive dominance" is key to understanding mixture productivity, because "competitive interactions are the predominant type of interspecific relationships in plants", the authors introduce "partial density" monocultures, i.e. monocultures that have the same planting density for a species as in a mixture. The idea is that using these partial density monocultures as a reference would allow for isolating the effect of competition by the surrounding "species matrix".

Correct.

The authors argue that "To separate effects of competitive interactions from those of other species interactions, we would need the hypothesis that constituent species share an identical niche but differ in growth and competitive ability (i.e., absence of positive/negative interactions)." - I think the term interaction is not correctly used here, because clearly competition is an interaction, but the point made here is that this would be a zero-sum game.

We did not say that competition is not an interaction; we only want to separate the effect of competition from those of other species interactions.

The authors use the ratio of productivity of partial density and full-density monocultures, divided by planting density, as a measure of "competitive growth response" (abbreviated as MG). This is the extra growth a plant individual produces when intraspecific competition is reduced.

Correct.

We added at lines 377-386 to discuss options to determine MG in some uncommon scenarios of species mixtures.

Here, I see two issues: first, this rests on the assumption that there is only "one mode" of competition if two species use the same resources, which may not be true, because intraspecific and interspecific competition may differ. Of course, one can argue that then somehow "niches" are different, but such a niche definition would be very broad and go beyond the "resource set" perspective the authors adopt. Second, this value will heavily depend on timing and the relationship between maximum initial growth rates and competitive abilities at high stand densities.

First, the "competitive effect" focusses on resource competition and other forms of competition (presumably interference competition) are included in the negative interactions.

Second, competitive growth response varies over time and with density, and so do NE, CE, SE, and interspecific interactions.

The authors then progress to define relative competitive ability (RC), and this time simply uses monoculture biomass as a measure of competitive ability. To express this biomass in a standardized way, they express it as different from the mean of the other species and then divide by the maximum monoculture biomass of all species.

I have two concerns here: first, if competitive ability is the capability of a species to preempt resources from a pool also accessed by another species, as the authors argued before, then this seems wrong because one would expect that a species can simply be more productive because it has a broader niche space that it exploits. This contradicts the very narrow perspective on competitive ability the authors have adopted. This also is difficult to reconcile with the idea that specialist species with a narrow niche would outcompete generalist species with a broad niche. Second, I am concerned by the mathematical form. Standardizing by the maximum makes the scaling dependent on a single value.

First, growth conditions are controlled in biodiversity experiments, i.e., both monocultures and mixtures are the same in resource space. Species do not have opportunity to exploit resources outside experimental area. For example, if less productive species on normal soils outperform more competitive species on saline/alkaline soil, these “less productive species” are considered “more productive”.    

Second, as discussed in our paper (lines 367-376; Figure 1), more research is needed to determine relationships between species traits (biomass or height) and relative competitive ability. By then, scaling by the maximum would not be needed. There has been quite a lot of research on such relationships; we should leave this to subject experts to determine what would be mostly appropriate for species studied.

As a final step, the authors calculate a "competitive expectation" for a species' biomass in the mixture, by scaling deviations from the expected yield by the product MG ⨯ RC. This would mean a species does better in a mixture when (1) it benefits most from a conspecific density reduction, and (2) has a relatively high biomass.

Put simply, the assumption would be that if a species is productive in monoculture (high RC), it effectively does not "see" the competitors and then grows like it would be the sole species in the community, i.e. like in the partial density monoculture.

Correct, if species competitive ability differs substantially, the more competitive species in the mixture would grow like partial density monoculture. This extra growth should not be treated as sources of positive biodiversity effects, simply because it does not result from positive species interactions.   

Overall, I am not very convinced by the proposed method.

(1) The proposed method seems not very systematic but rather "ad hoc". It also is much less a partitioning method than the AP method because the other term is simply the difference. It would be good if the authors investigated the mathematical form of this remainder and explored its properties.. when does complementarity occur? Would it capture complementarity and facilitation?

AP is, by no means, systematic. Remember, AP is based on covariance equation (or Price equation) that has nothing to do with species interactions, other than nice-looking mathematical form (Bourrat et al., 2023). Ecological meanings are subjectively given to CE and SE. Therefore,  CE and SE reflect what we call them, not what they really mean.    

The remainder measures deviations from the null expectation, due to only competition effect, and can not be partitioned any further. The remainder would be positive for more competitive species and negative for less competitive species in mixture relative to their full density monoculture. The deviation of observed yields from competitive expectations indicates dominance of positive or negative species interactions. All these are clearly outlined at lines 201-221.   

(2) The justification for the calculation of MG and RC does not seem to follow the very strict assumptions of what competition (in the absence of complementarity) is. See my specific comments above.

We do not see why not.

(3) Overall, the manuscript is hard to read. This is in part a problem of terminology and presentation, and it would be good to use more systematic terms for "response patterns" and "biological mechanisms".

To help understand the variations of competitive growth response with relative competitive ability, the x axis of Figure 1 is labelled with null expectation, competitive expectation, and competitive exclusion from minimum to maximum deviation of competitive ability from community average.

We have followed terms used in biodiversity partitioning and changing terms can be confusing.  

Examples:

- on line 30, the authors write that CE is used to measure "positive" interactions and SE to measure "competitive interactions", and later name "positive" and "negative" interactions "mechanisms of species interactions". Here the authors first use "positive interaction" as any type of effect that results in a community-level biomass gain, but then they use "interaction" with reference to specific biological mechanisms (e.g. one species might attract a parasite that infests another species, which in turn may cause further changes that modify the growth of the first and other species).

There are some differences in meaning, but that is what CE and SE have been generally used for. Using different terms can be confusing and does not help understanding the problems with AP.

- on line 70, the authors state that "positive interaction" increases productivity relative to the null expectation, but it is clear that an interaction can have "negative" consequences for one interaction partner and "positive" ones for the other. Therefore, "positive" and "negative" interactions, when defined in this way, cannot be directly linked to "resource partitioning" and "facilitation", and "species interference" as the authors do. Also, these categories of mechanisms are still simple. For example, how do biotic interactions with enemies classify, see above?

We are explaining effects of competitive interactions on species yield, and ultimately on community yield that can be linked to “resource partitioning" and "facilitation", and "species interference".

More specific species interactions require detailed biological investigation and cannot be determined through partitioning of biomass production.  

- line 145: "Under the null hypothesis, species in the mixture are assumed to be competitively equivalent (i.e., absence of interspecific interactions)". This is wrong. The assumption is that there are interspecific interactions, but that these are the same as the intraspecific ones. Weirdly, what follows is a description of the AP method, which does not belong here. This paragraph would better be moved to the introduction where the AP method is mentioned. Or omitted, since it is basically a repetition of the original Loreau & Hector paper.

As suggested, “absence of interspecific interactions” was replaced with “equal interspecific and intraspecific interactions”.

We have removed lines 155-162 to reduce duplication. However, our method is based on null expectation that needs to be introduced, despite it is part of AP.

Other points:

- line 66: community productivity, not ecosystem productivity.

Both community productivity and ecosystem productivity are used in biodiversity research, although meaning can be slightly different. Comparatively, ecosystem productivity is more common.

- line 68: community average responses are with respect to relative yields - this is important!

- line 64: what are "species effects of species interactions"?

We searched and did not find “species effects of species interactions”.

- line 90: here "competitive" and "productive" are mixed up, and it is important to state that "suffers more" refers to relative changes, not yield changes.

It, in fact, refers to yield changes. For example, less productive species, at active growth, are more responsive to changes in competition, while more productive species, at inactive growth (i.e., aging), are less responsive to changes in competition.   

- line 92: "positive effect of competitive dominance": I don't understand what is meant here.

The phrase was modified to “positive contribution of competitive dominance to ecosystem productivity based on the null expectation”.

Reviewer #3 (Public Review):

Summary:

This manuscript by Tao et al. reports on an effort to better specify the underlying interactions driving the effects of biodiversity on productivity in biodiversity experiments. The authors are especially concerned with the potential for competitive interactions to drive positive biodiversity-ecosystem functioning relationships by driving down the biomass of subdominant species. The authors suggest a new partitioning schema that utilizes a suite of partial density treatments to capture so-called competitive ability. While I agree with the authors that understanding the underlying drivers of biodiversity-ecosystem functioning relationships is valuable - I am unsure of the added value of this specific approach for several reasons.

Strengths:

I can find a lot of value in endeavouring to improve our understanding of how biodiversity-ecosystem functioning relationships arise. I agree with the authors that competition is not well integrated into the complementarity and selection effect and interrogating this is important.

Weaknesses:

(1) The authors start the introduction very narrowly and do not make clear why it is so important to understand the underlying mechanisms driving biodiversity-ecosystem functioning relationships until the end of the discussion.

There are different ways to start introduction; we believe that starting with the problems of the current approach is the most effective for outlining the study’s objective.  

(2) The authors criticize the existing framework for only incorporating positive interactions but this is an oversimplification of the existing framework in several ways:

We did not criticize the existing framework for only incorporating positive interactions. We criticize the existing framework, because it is not based on mechanisms of species interactions, but is extensively used to determine underlying mechanisms driving biodiversity-ecosystem functioning relationships.

a. The existing partitioning scheme incorporates resource partitioning which is an effect of competition.

Resource partitioning means that species utilize resources differently, while competition means species use the same resources. “resource partitioning is an effect of competition” is not true in biodiversity experiments that are often short in duration and controlled in conditions.  

b. The authors neglect the potential that negative feedback from species-specific pests and pathogens can also drive positive BEF and complementarity effects but is not a positive interaction, necessarily. This is discussed in Schnitzer et al. 2011, Maron et al. 2011, Hendriks et al. 2013, Barry et al. 2019, etc.

We did not. The feedback effect will be reflected in the differences between observed yields and competitive expectations if species in mixtures have different pests and pathogens relative to monocultures. The additive partitioning does not identify these feedback effects either.

c. Hector and Loreau (and many of the other citations listed) do not limit competition to SE because resource partitioning is a byproduct of competition.

Positive SE has been largely interpreted as the result of competition including Hector and Loreau (2001) and many others. It needs to be clear that neither of the additive components can be linked to specific mechanisms of species interactions. 

Does “resource partitioning is a byproduct of competition” mean that species change their niche to avoid competition? If this is what the reviewer means, it may occur through long-term evolution, but not in short-term biodiversity experiments. Hector and Loreau (2001) clearly indicated that their complementarity effect includes both resource partitioning and facilitation.   

(3) It is unclear how this new measure relates to the selection effect, in particular. I would suggest that the authors add a conceptual figure that shows some scenarios in which this metric would give a different answer than the traditional additive partition. The example that the authors use where a dominant species increases in biomass and the amount that it increases in biomass is greater than the amount of loss from it outcompeting a subdominant species is a general example often used for a selection effect when exactly would you see a difference between the two?:<br /> a. Just a note - I do think you should see a difference between the two if the species suffers from strong intraspecific competition and has therefore low monoculture biomass but this would tend to also be a very low-density monoculture in practice so there would potentially be little difference between a low density and high-density monoculture because the individuals in a high-density monoculture would die anyway. So I am not sure that in practice you would really see this difference even if partial density plots were incorporated.

Linking new measure to SE or CE would be difficult (see many comparisons in Tables and Figures in our manuscript), as SE and CE are derived from mathematical equation and do not represent specific mechanisms of species interactions (Hector and Loreau 2012; Bourrat et al., 2023).

(4) One of the tricky things about these endeavors is that they often pull on theory from two different subfields and use similar terminology to refer to different things. For example - in competition theory, facilitation often refers to a positive relative interaction index (this seems to be how the authors are interpreting this) while in the BEF world facilitation often refers to a set of concrete physical mechanisms like microclimate amelioration. The truth is that both of these subfields use net effects. The relative interaction index is also a net outcome as is the complementarity effect even if it is only a piece of the net biodiversity effect. Trying to combine these two subfields to come up with a new partitioning mechanism requires interrogating the underlying assumptions of both subfields which I do not see in this paper.

Agree, microclimate amelioration is also part of positive effect and will be reflected in the difference between observed yield and competitive expectation. We can not separate the two mechanisms of positive species interactions without investigating influences of microclimate on growth and yield.

(5) The partial density treatment does not isolate competition in the way that the authors indicate. All of the interactions that the authors discuss are density-dependent including the mechanism that is not discussed (negative feedback from species-specific pests and pathogens). These partial density treatment effects therefore cannot simply be equated to competition as the authors indicate.:

We use partial density monoculture to determine maximum competitive growth response, effect of density-dependent intraspecific interactions, and species competitive ability to determine the level of maximum competitive growth response species can achieve in mixtures. There may be changes in species-specific pests and pathogens from partial to full density monocultures, which will be captured in competitive growth responses of individuals. We added at lines 186-188 to indicate that the maximum competitive growth response estimated would also include the effects of density-dependent pests, pathogens, or microclimates.   

a. Additionally - the authors use mixture biomass as a stand-in for competitive ability in some cases but mixture biomass could also be determined by the degree to which a plant is facilitated in the mixture (for example).

We used monoculture biomass, not mixture biomass, to assess competitive ability

(6) I found the literature citation to be a bit loose. For example, the authors state that the additive partition is used to separate positive interactions from competition (lines 70-76) and cite many papers but several of these (e.g. Barry et al. 2019) explicitly do not say this.

Barry et al. (2019) defined CE as overproduction from monocultures, an effect of positive interactions.  

(7) The natural take-home message from this study is that it would be valuable for biodiversity experiments to include partial density treatments but I have a hard time seeing this as a valuable addition to the field for two reasons:

a. In practice - adding in partial density treatments would not be feasible for the vast majority of experiments which are already often unfeasibly large to maintain.

The reviewer suggested that quantity is more important than quality. Without partial density monocultures no one can separate different effects of species interactions, as suggested by Loreau and Hector, reviewers, and many others that effects of species interactions can not be clearly differentiated with replacement series design. Unreliable scientific findings are not valuable.

b. The density effect would likely only be valuable during the establishment phase of the experiment because species that are strongly limited by intraspecific competition will die in the full-density plots resulting in low-density monocultures. You can see this in many biodiversity experiments after the first years. Even though they are seeded (or rarely planted) at a certain density, the density after several years in many monocultures is quite low.

True. High or low density also depends on individual size; if individuals do not get enough resources, density is high. Therefore, density effect can be strong even as density drops substantially from initial levels.  

Reviewer #4 (Public Review):

Summary:

This manuscript claims to provide a new null hypothesis for testing the effects of biodiversity on ecosystem functioning. It reports that the strength of biodiversity effects changes when this different null hypothesis is used. This main result is rather inevitable. That is, one expects a different answer when using a different approach. The question then becomes whether the manuscript’s null hypothesis is both new and an improvement on the null hypothesis that has been in use in recent decades.

It needs to be clear that we use two hypotheses, null hypothesis that is currently used with AP, and competitive hypothesis that is new with this manuscript. The null hypothesis helps determine changes in ecosystem productivity from all species interactions, while the competitive hypothesis helps partition changes in ecosystem productivity by mechanisms of species interactions, i.e., positive, negative, or competitive interactions.    

Strengths:

In general, I appreciate studies like this that question whether we have been doing it all wrong and I encourage consideration of new approaches.

Weaknesses:

Despite many sweeping critiques of previous studies and bold claims of novelty made throughout the manuscript, I was unable to find new insights. The manuscript fails to place the study in the context of the long history of literature on competition and biodiversity and ecosystem functioning. The Introduction claims the new approach will address deficiencies of previous approaches, but after reading further I see no evidence that it addresses the limitations of previous approaches noted in the Introduction. Furthermore, the manuscript does not reproducibly describe the methods used to produce the results (e.g., in Table 1) and relies on simulations, claiming experimental data are not available when many experiments have already tested these ideas and not found support for them. Finally, it is unclear to me whether rejecting the ‘new’ null hypothesis presented in the manuscript would be of interest to ecologists, agronomists, conservationists, or others. I will elaborate on each of these points below.

First, there are many biodiversity experiments but those with partial density monocultures are rare. We found only one greenhouse experiment. We have to use simulation to illustrate different scenarios of species interactions to demonstrate how our approach works and how different it is from the AP.  

Because of different methods used, the results of long history competition research (generally based on additive series design) cannot be used to define effects of competitive interactions in biodiversity research (generally based on replacement series design). This may be the reason that few competition researchers were cited in Loreau and Hector (2001).

Our approach requires two hypotheses, null and competitive, and the meaning of deviation from these hypotheses are outlined at lines 201-221 for both individual species and community level assessments. Distinguishing changes in ecosystem productivity by species interactions would be of great interest to “ecologists, agronomists, conservationists, or others”.

The critiques of biodiversity experiments and existing additive partitioning methods are overstated, as is the extent to which this new approach addresses its limitations. For example, the critique that current biodiversity experiments cannot reveal the effects of species interactions (e.g., lines 37-39) isn't generally true, but it could be true if stated more specifically. That is, this statement is incorrect as written because comparisons of mixtures, where there are interspecific and intraspecific interactions, with monocultures, where there are only intraspecific interactions, certainly provide information about the effects of species interactions (interspecific interactions). These biodiversity experiments and existing additive partitioning approaches have limits, of course, for identifying the specific types of interactions (e.g., whether mediated by exploitative resource competition, apparent competition, or other types of interactions). However, the approach proposed in this manuscript gets no closer to identifying these specific mechanisms of species interactions. It has no ability to distinguish between resource and apparent competition, for example. Thus, the motivation and framing of the manuscript do not match what it provides. I believe the entire Introduction would need to be rewritten to clarify what gap in knowledge this proposed approach is addressing and what would be gained by filling this knowledge gap.

Our approach helps determine underlying mechanisms of species interactions, i.e., positive (resources partitioning or facilitation), negative, or competitive interactions. I am not sure how much we need to go further in identifying more specific mechanisms. If resource and apparent competition refers to resource and interference competition, our approach can tease apart them.

I recommend that the Introduction instead clarify how this study builds on and goes beyond many decades of literature considering how competition and biodiversity effects depend on density. This large literature is insufficiently addressed in this manuscript. This fails to give credit to previous studies considering these ideas and makes it unclear how this manuscript goes beyond the many previous related studies. For example, see papers and books written by de Wit, Harper, Vandermeer, Connolly, Schmid, and many others. Also, note that many biodiversity experiments have crossed diversity treatments with a density treatment and found no significant effects of density or interactions between density and diversity (e.g., Finn et al. 2013 Journal of Applied Ecology). Thus, claiming that these considerations of density are novel, without giving credit to the enormous number of previous studies considering this, is insufficient.

A misunderstanding here. Our approach is not designed to test density effect. The same density is held across full density monocultures and mixtures. We use partial density monocultures to determine what species may competitively achieve in full density mixture, without positive or negative interspecific interactions.  

Replacement series designs emerged as a consensus for biodiversity experiments because they directly test a relevant null hypothesis. This is not to say that there are no other interesting null hypotheses or study designs, but one must acknowledge that many designs and analyses of biodiversity experiments have already been considered. For example, Schmid et al. reviewed these designs and analyses two decades ago (2002, chapter 6 in Loreau et al. 2002 OUP book) and the overwhelming consensus in recent decades has been to use a replacement series and test the corresponding null hypothesis.

Some wrong impressions. We are not trying to supplant “replacement series” with “additive series”; we use “additive series” designs to supplement “replacement series” design for partitioning changes in ecosystem productivity by mechanisms of species interactions, which would not be possible with “replacement series” design alone, as suggested by many including reviewers.   

It is unclear to me whether rejecting the 'new' null hypothesis presented in the manuscript would be of interest to ecologists, agronomists, conservationists, or others. Most biodiversity experiments and additive partitions have tested and quantified diversity effects against the null hypothesis that there is no difference between intraspecific and interspecific interactions. If there was no less competition and no more facilitation in mixtures than in monocultures, then there would be no positive diversity effects. Rejecting this null hypothesis is relevant when considering coexistence in ecology, overyielding in agronomy, and the consequences of biodiversity loss in conservation (e.g., Vandermeer 1981 Bioscience, Loreau 2010 Princeton Monograph). This manuscript proposes a different null hypothesis and it is not yet clear to me how it would be relevant to any of these ongoing discussions of changes in biodiversity.

Our method begins with the null expectation: that intraspecific and interspecific interactions are equivalent. We then propose the competitive hypothesis as a second non-exclusive hypothesis which tests the dominance of positive or negative specific interactions. As shown by its name, the additive partitioning model has been advocated for partitioning biodiversity effects by some ecological mechanisms (CE and SE). The ecological meaning of deviation from the two hypotheses are outlined at lines 201-221 for both individual species and community level assessments.   

The claim that all previous methods 'are not capable of quantifying changes in ecosystem productivity by species interactions and species or community level' is incorrect. As noted above, all approaches that compare mixtures, where there are interspecific interactions, to monocultures, where there are no species interactions, do this to some extent. By overstating the limitations of previous approaches, the manuscript fails to clearly identify what unique contribution it is offering, and how this builds on and goes beyond previous work.

The reviewer implies that a partial truth equals the whole truth. The same argument can also be applied to the additive partitioning if relative yield total or response ratio provides a kind of comparison between mixture and monocultures. Our statement is correct in the way that previous approaches are not designed to separate changes in ecosystem productivity by species interactions, as indicated by other reviewers. The additive partitioning is built on Price equation (covariance equation) that has never been biologically demonstrated for relevance in biodiversity partitioning (Bourrat et al., 2023).  

We made clear that our work is built on and beyond the null expectation with addition of competitive expectation.

The manuscript relies on simulations because it claims that current experiments are unable to test this, given that they have replacement series designs (lines 128-131). There are, however, dozens of experiments where the replacement series was repeated at multiple densities, which would allow a direct test of these ideas. In fact, these ideas have already been tested in these experiments and density effects were found to be nonsignificant (e.g., Finn et al. 2013).

Out of point. Again, we are not testing density effect. Partial density is used to determine competitive growth responses that species may achieve in mixture based on their relative competitive ability. We used simulations, as partial density monocultures are used only in one experimental study that has been included in our study.  

It seems that the authors are primarily interested in trees planted at a fixed density, with no opportunity for changes in density, and thus only changes in the size of individuals (e.g., Fig. 1). In natural and experimental systems, realized density differs from the initial planted density, and survivorship of seedlings can depend on both intraspecific and interspecific interactions. Thus, the constrained conditions under which these ideas are explored in this manuscript seem narrow and far from the more complex reality where density is not fixed.

We use fixed density only for convenience. In biodiversity experiments, density can increase or decrease over time from initial levels. However, initial density is generally used in evaluation of species interactions. If interest is community productivity, density change does not need to be considered. Again, we are not testing density effects.    

Additional detailed comments:

It is unclear to me which 'effects' are referred to on line 36. For example, are these diversity effects or just effects of competition? What is the response variable?

It means the effect of competitive interactions on productivity and should be clear based on previous sentences.

The usefulness of the approach is overstated on line 52. All partitioning approaches, including the new one proposed here, give the net result of many types of species interactions and thus cannot 'disentangle underlying mechanisms of species interactions.'

Not sure how many types of species interactions the reviewer referred to. If mechanisms of species interactions are grouped in three categories (positive, negative, and competitive) as has been in biodiversity research, our approach can tease them apart.   

The weaknesses of previous approaches are overstated throughout the manuscript, including in lines 60-61. All approaches provide some, but not all insights. Sweeping statements that previous approaches are not effective, without clarifying what they can and can't do, is unhelpful and incorrect. Also, these statements imply that the approach proposed here addresses the limitations of these previous approaches. I don't yet see how it does so.

The weaknesses of previous approaches are not overstated in terms of separating changes in ecosystem productivity by species interactions. As pointed by other reviewers, none of the previous approaches are designed for quantifying changes in ecosystem productivity by species interactions.   

The definitions given for the CE and SE on line 71 are incorrect. Competition affects both terms and CE can be negative or have nothing to do with positive interactions, as noted in many of the papers cited.

We are not trying to define CE and SE but only point out how CE and SE have been generally used in biodiversity research (see recent publication by Feng et al., 2022).

The proposed approach does not address the limitations noted on lines 73 and 74.

It does in terms of sources of net biodiversity effect, whether from positive, negative or competitive interactions.

The definition of positive interactions in lines 77 and 78 seems inconsistent with much of the literature, which instead focuses on facilitation or mutualism, rather than competition when describing positive interactions.

Much of the literature supports our definition (see Loreau and Hector, 2001). In biodiversity research, positive interactions include resource partitioning and facilitation. What we are trying to point out is that competition affects species and community level assessments based on the null expectation and needs to be separated.

Throughout the manuscript, competition is often used interchangeably with resource competition (e.g., line 82) and complementarity is often attributed to resource partitioning (e.g., line 77). This ignores apparent competition and partitioning enemy-free niche space, which has been found to contribute to biodiversity effects in many studies.

If apparent competition refers to interference competition, it is included in negative interaction. Changes in species-specific pests and pathogens in mixture will be captured in positive or negative effects through facilitation or interference.  

In what sense are competitive interactions positive for competitive species (lines 82-83)? By definition, competition is an interaction that has a negative effect. Do you mean that interspecific competition is less than intraspecific competition? I am having a very difficult time following the logic.

I am glad the reviewer raised this question that may confuse many others and has never been clearly discussed. It all depends on how comparison is made. If species performance in mixture are compared with that in partial density monocultures, as is in competition research, competition effect is negative for all species. If comparison is made between mixture and full density monocultures, as is done in biodiversity research, competition effect should be positive for more competitive species and negative for less competitive species, with resources flowing from less to more competitive species in mixture relative to full density monocultures.   

Therefore, the definitions of competitive interactions based on additive series design in competition research cannot be used to describe competitive interactions based on replacement series design in biodiversity research. In biodiversity research, the effects of competitive interactions are never clearly defined at species or community level and mixed up with those of other species interactions.      

Results are asserted on lines 93-95, but I cannot find the methods that produced these results. I am unable to evaluate the work without a repeatable description of the methods.

We have added references on sources of these data.

The description of the null hypothesis in the common additive partitioning approach on lines 145-146 is incorrect. In the null case, it does not assume that there are no interspecific interactions, but rather that interspecific and intraspecific interactions are equivalent.

Correct, changes have been made as suggested.

Recommendations for the authors:

Reviewer #2 (Recommendations For The Authors):

I recommend to:

- re-organize the presentation of the material (see my concerns in the public review section). The manuscript is very difficult to read.

Changes have been made to help with understanding of our approach. Figure 1 was modified to show the variations of competitive growth response with relative competitive ability from minimum (null expectation) to maximum (competitive exclusion).

- explore the mathematical form the the remainder term. It seems important to understand that the remainder capture terms unrelated to competition as defined in the present scope.

The remainder measures deviations from the null expectation, due to species differences in growth and competitive ability or competition effect. The term has clear meaning, positive for more competitive species and negative for less competitive species (lines 202-204), and does not need to be further explored or partitioned. The deviations of observed yields from competitive expectations are outlined in lines 205-221.  

Reviewer #4 (Recommendations For The Authors):

The authors should be sure to include reproducible methods and share any data and code.

Both simulation and experimental data are shared through supplementary tables. Calculations are included in excel spreadsheets and do not require program coding.

Author response:

The following is the authors’ response to the original reviews.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

This research offers an in-depth exploration and quantification of social vocalization within three families of Mongolian gerbils. In an enlarged, semi-natural environment, the study continuously monitored two parent gerbils and their four pups from P14 to P34. Through dimensionality reduction and clustering, a diverse range of gerbil call types was identified. Interestingly, distinct sets of vocalizations were used by different families in their daily interactions, with unique transition structures exhibited across these families. The primary results of this study are compelling, although some elements could benefit from clarification

Strengths:

Three elements of this study warrant emphasis. Firstly, it bridges the gap between laboratory and natural environments. This approach offers the opportunity to examine natural social behavior within a controlled setting (such as specified family composition, diet, and life stages), maintaining the social relevance of the behavior. Secondly, it seeks to understand short-timescale behaviors, like vocalizations, within the broader context of daily and life-stage timescales. Lastly, the use of unsupervised learning precludes the injection of human bias, such as pre-defined call categories, allowing the discovery of the diversity of vocal outputs.

Weaknesses:

(1) While the notable differences in vocal clusters across families are convincing, the drivers of these differences remain unclear. Are they attributable to "dialect," call usage, or specific vocalizing individuals (e.g., adults vs. pups)? Further investigation, via a literature review or additional observation, into acoustic differences between adult and pup calls is recommended. Moreover, a consistent post-weaning decrease in the bottom-left cluster (Fig. S3) invites interpretation: could this reflect drops in pup vocalization?

Thank you for bringing up this point of clarification. Without knowledge of individual vocalizers, we are unable to rigorously assess pronunciation differences between individuals, however we can get a clear proxy for dialect through observing usage differences between families. We’ve added the following text (blue) in the Discussion to help clarify:

“To address whether gerbils also exhibit family specific vocal features, we compared GMM-labeled vocal cluster usages across the three recorded families and showed differences in vocal type usage (Figure 3). The differences in this study align with the definition of human vocal dialect, which is a regional or social variety of language that can differ in pronunciation, grammatical, semantic and/or language use differences (Henry et al., 2015). This definition of dialect is inclusive of both pronunciation differences (e.g. a Bostonian’s characteristic pronunciation of “car” as “cah”) and usage differences (e.g. a Bostonian’s preferential usage of the words “Go Red Sox” vs. a New Yorker’s preferential usage of the words “Go Yankees”). In our case, vocal clusters can be rarely observed in some families yet highly over-expressed in others (e.g. analogous to language usage differences in humans), or highly expressed in both families, but contain subtle spectrotemporal variations (Figure 3D, Family 1 cluster 11 vs. Family 3 clusters 2, 18, 30; e.g. analogous to pronunciation differences in humans).”

Indeed, our recordings obtained after pup removal could suggest that adults may use fewer low frequency calls (bottom left cluster in UMAP). However, this dataset does not permit a proper assessment of post-weaning pup calls. In fact, our results and the literature shows that adults are likely to use low frequency calls, but only during social interactions with pups or other adults. For example, Furuyama et al. 2022 describe a number of low frequency call types used by adults in agonistic social interactions, which look similar to a low frequency call type used by pups described in Silberstein et al. 2023. Similarly, Ter-Mikaelian et al. 2012 (their Figure 6) recorded several types of sonic vocalizations during adult social interaction. To our knowledge, it has not been shown whether gerbil pups and adults produce distinct call types. It is a challenging problem to solve, as animals placed in isolation (i.e. an experimental condition for which the identity of the vocalizer is known) vocalize infrequently and of the limited number they might emit, they do not use the full range of vocalizations described in the literature (RP personal observations). To properly address this question, one would need to elicit full use of the vocal repertoire through free social interaction, then attribute calls to individual vocalizers via sound source localization and/or head-mounted microphones — we are currently pursuing both of these technical challenges, but this is outside the scope of this manuscript.

Although the literature reflects the limitations discussed above, we have added a brief paragraph to the Discussion (limitations section) that addresses the reviewer’s question about the development of vocalizations:

“Although we were not able to attribute vocalizations to individual family members, we did seek to determine the importance of family structure by comparing audio recordings before and after removal of the pups at P30. The results show a clear effect of family integrity, and the sudden reduction of sonic calls following pup removal (Figure S3) could suggest that these vocalizations are produced selectively by pups.

However, there is ample evidence that adult gerbils also produce sonic vocalizations. For example, a number of low frequency call types are used by adults during a range of social interactions (Ter-Mikaelian et al., 2012; Furuyama et al., 2022), some of which are similar to a low frequency call type used by pups (Silberstein et al., 2023). Vocalization patterns of developing gerbils depend on isolation or staged interactions. Thus, when gerbil pups are recorded during isolation, ultrasonic vocalization rate declines and sonic vocalizations increase for animals that are in a high arousal state (De Ghett 1974, Silberstein et al., 2023). As gerbils progress from juvenile to adolescent development (P17-55) a significant increase in ultrasonic vocalization rate is observed during dyadic social encounters, with a distinct change in usage pattern that depends upon the sex of each animal (Holman & Seale 1991, Holman et al. 1995). The development of vocalization types has been assessed in another member of the Gerbillinae subfamily, called fat-tailed gerbils (Pachyuromys duprasi), during isolation and handling. Here, the number of ultrasonic vocalization syllable types increase from neonatal to adult animals (Zaytseva et al. 2019), while some very low frequency sonic call types were rarely observed after P20 (Zaytseva et al. 2020). By comparison, mouse syllable usage changes during development, but pups produced 10 of the 11 syllable types produced by adults (Grimsley et al. 2011). In summary, our understanding of the maturation of vocalization usage remains limited by our inability to obtain longitudinal data from individual animals within their natural social setting. For example, when recorded in their natural environment, chimpanzees display a prolonged maturation of vocalization complexity, such as the probability of a unique utterance in a sequence, with the greatest changes occuring when animals begin to experience non-kin social interactions (Bortolato et al. 2023).”

(2) Developmental progression, particularly during pre-weaning periods when pup vocal output remains unstable, might be another factor influencing cross-family vocal differences. Representing data from this non-stationary process as an overall density map could result in the loss of time-dependent information. For instance, were dominating call types consistently present throughout the recording period, or were they prominent only at specific times? Displaying the evolution of the density map would enhance understanding of this aspect.

This is a great suggestion. Thank you for bringing it up. To address this, we have added an additional figure (Figure 4) to the main text (Note that the former Figure 4 is now Figure 5). New text associated with this new figure was added to the Results and Discussion sections:

Results

“Vocal usage differences remain stable across days of development It is possible that the observed vocal usage differences could result from varying developmental progression of vocal behavior or overexpression of certain vocal types during specific periods within the recording. To assess the potential effect of daily variation on family specific vocal usage, we visualized density maps of vocal usage across days for each of the families (Figure 4A). There are two noteworthy trends: 1.) the density map remains coarsely stable across days (rows) and 2.) the maps look distinct across families on any given day (columns). This is a qualitative approximation for the repertoire’s stability, but does not take into account variation of call type usage (as defined by GMM clustering of the latent space). Figure 4B, shows the normalized usage of each cluster type over development for each family. Cluster usages during the period of “full family, shared recording days” (postnatal days beneath the purple bars) are stable across days within families – as is apparent by the horizontal striations in the plot – though each family maintains this stability through using a unique set of call types. This is addressed empirically in Figure 4C, which shows clearly separable PCA projections of the cluster usages shown in Figure 4B (purple days). Finally, we computed the pairwise Mean Max Discrepancy (MMD) between latent distributions of vocalizations from individual recording days for each of the families (Figure 4D). This shows that across-family repertoire differences are substantially larger than within-family differences. This is visualized in a multidimensional scaling projection of the MMD matrix in Figure 4E.”

Discussion

“The described family differences collapse data from multiple days into a single comparison, however it’s possible that factors such as vocal development and/or high usage of particular vocal types during specific periods of the recording could explain family differences. Therefore, we took advantage of the longitudinal nature of our dataset to assess whether repertoire differences remain stable across time. First, we visualized vocal repertoire usage across days as either UMAP probability density maps (Figure 4A) or daily GMM cluster usages (Figure 4B). Though qualitative, one can appreciate that family repertoire usage remains stable across days and appears to differ on a consistent daily basis across families. To formally quantify this, we first projected GMM cluster usages from Figure 4B into PC space and show that family GMM cluster usage patterns are highly separable, regardless of postnatal day (Figure 4C). If families had used a more overlapping set of call types, then the projections would have appeared intermixed. Next, we performed a cluster-free analysis by computing the pairwise MMD distance between VAE latent distributions of vocalizations from each family and day (Figure 4D). This analysis shows very low MMD values across days within a family (i.e. the repertoire is highly consistent with itself), and high MMD values across families/days (greater than would be expected by chance; see shuffle control in Figure S2D). The relative differences in this matrix are made clear in Figure 4E, which provides additional evidence that family vocal repertoires remain stable across days and are consistently different from other families. Taken together, we believe that this is compelling evidence that differences in vocal repertoires between families are not driven by dominating call types during specific phases in the recording period; rather, families consistently emit characteristic sets of call types across days. This opens up the possibility to assess repertoire differences over much shorter time periods (e.g. 24 hours) in future studies.”

(3) Family-specific vocalizations were credited to the transition structure, a finding that may seem obvious if the 1-gram (i.e., the proportion of call types) already differs. This result lacks depth unless it can be demonstrated that, firstly, the transition matrix provides a robust description of the data, and secondly, different families arrange the same set of syllables into unique sequences.

Thank you for these important suggestions. We agree that it is true that the 2-gram transition structure must vary based on the 1-gram structure. To determine whether this influences the interpretation of the finding, we have added Figure S5 and the following text in the Results section:

“To determine whether differences in 1-gram structure contribute to differences in the transition (2-gram) structure, we performed a number of controls. Although subtle, vertical streaks are clearly present in shuffled transition matrices that correspond to 1-gram usages (Figure S5A-B). Given the shuffled data structure, we sought to determine whether the observed transition probabilities differed significantly from chance levels. We randomly shuffled label sequences 1000 times independently for each family to generate a null transition matrix distribution. Using these null distributions and the observed transition probabilities, we computed a p-value for each transition using a one-sample t-test and created a binary transition matrix indicating which transitions happen above chance levels (Figure S5C, black pixels, p <= 0.05 after post hoc Benjamini-Hochberg multiple comparisons correction). As is made clear in Figure S5C, most transitions for each family occur significantly above chance levels, despite the inherent 1-gram structure. Moreover, by looking at transitions from a highly usage cluster type used roughly the same proportion across families (cluster 12), we show that families arrange the same sets of vocal clusters into unique sequences (Figure S5D). We believe that this provides compelling evidence that the 1-gram structure does not change the interpretation of the main claim that transition structure varies by family. “””

To address your second point, we inspected frequent transitions from individual syllables to all other syllables using bigram transition probability graphs. This revealed a common trend that across all families, many shared and unshared transitions existed, suggesting that families use the same sets of syllables to make unique transition patterns. Figure S5D shows a single syllable example of the phenomenon, with red lines indicating the shared transition types between families and black showing transition patterns not shared between families (i.e. unique family-specific transitions, or lack thereof).”

Reviewer #2 (Public Review):

Peterson et al., perform a series of behavioral experiments to study the repertoire and variance of Mongolian gerbil vocalizations across social groups (families). A key strength of the study is the use of a behavioral paradigm which allows for long term audio recordings under naturalistic conditions. This experimental set-up results in the identification of additional vocalization types. In combination with state of the art methods for vocalization analysis, the authors demonstrate that the distribution of sound types and the transitions between these sound types across three gerbil families is different. This is a highly compelling finding which suggests that individual families may develop distinct vocal repertoires. One potential limitation of the study lies in the cluster analysis used for identifying distinct vocalization types. The authors use a Gaussian Mixed Model (GMM) trained on variational auto Encoder derived latent representation of vocalizations to classify recorded sounds into clusters. Through the analysis the authors identify 70 distinct clusters and demonstrate a differential usage of these sound clusters across families. While the authors acknowledge the inherent challenges in cluster analysis and provide additional analyses (i.e. maximum mean discrepancy, MMD), additional analysis would increase the strength of the conclusions. In particular, analysis with different cluster sizes would be valuable. An additional limitation of the study is that due to the methodology that is used, the authors can not provide any information about the bioacoustic features that contribute to differences in sound types across families which limits interpretations about how the animals may perceive and react to these sounds in an ethologically relevant manner.

The conclusions of this paper are well supported by data, but certain parts of the data analysis should be expanded and more fully explained.

• Can the authors comment on the potential biological significance of the 70 sound clusters? Does each cluster represent a single sound type? How many vocal clusters can be attributed to a single individual? Similarly, can the authors comment on the intra-individual and inter-individual variability of the sound types within and across families?

Previous work documenting the Mongolian gerbil repertoire (Ter-Mikaelian 2012, Kobayasi 2012) has revealed ~12 vocalization types that vary with social context. Our thinking is that we are capturing these ~12 (plus a few more, as illustrated in Figure 2C) as well as individual or family-specific variations of some call types. Although the number of discrete call types is likely less than 70, it’s plausible that variation due to vocalizer identity pushes some calls into unique clusters. This idea is supported by the fact that both naked mole rats and Mongolian gerbils have been shown to exhibit individual-specific variation in vocalizations, though only in single call types (Barker 2021, Figure 1; Nishiyama 2011, Table I). The current study is not ideal to test this prediction, as we cannot attribute each vocalization to individual family members. Using our 4-mic array, we attempted to apply established sound source localization techniques to assign vocalizations to individuals (Neunuebel 2015), but the technique failed, presumably due to high amounts of reverberation in the arena. We are currently developing a custom deep learning based sound localization algorithm, and had hoped to extract individual animal vocalizations from our data set (part of the reason why this manuscript has taken longer than expected to return!), but the performance is not yet satisfactory for large groups of animals. We have added text to the Methods sections with the context outlined above to further justify the use of ~70 clusters.

• As a main conclusion of the paper rests on the different distribution of sound clusters across families, it is important to validate the robustness of these differences across different cluster parameters. Specifically, the authors state that "we selected 70 clusters as the most parsimonious fit". Could the authors provide more details about how this was fit? Specifically, could the authors expand upon what is meant by "prior domain knowledge about the number of vocal types...". If the authors chose a range of cluster values (i.e. 10, 30, 50, 90) does the significance of the results still hold?

Thank you for the suggestion, this is an important point that we have addressed with new analyses in the revision (see GMM clustering methods and new Figure S4). The prior domain knowledge referenced is with respect to the information known about the Mongolian gerbil vocal types provided in the response above. We have made this more clear in the discussion.

We mainly based our selection of the number of clusters using the elbow method on GMM held-out log likelihood (Figure S2C). Around 70 clusters is when the likelihood begins to plateau, though it’s clear that there are a number of reasonable cluster sizes. To assess whether cluster size has an effect on interpretation of the family differences result, we added Figure S5, where we varied the number of GMM clusters used and compared cluster usage differences across families (Figure S4A). We quantified pairwise family differences in cluster usage by computing the sum of the absolute value of differential cluster usages, for each GMM cluster value (Figure S4B). We find that relative usage differences remain unchanged across the range of cluster values used, indicating that GMM cluster size does bias the finding.

• While VAEs are powerful tools for analyzing complex datasets in this case they are restricted to analysis of spectrogram images. Have the authors identified any acoustic differences (i.e. in pitch, frequency, and other sound components) across families?

Though it’s true that this VAE is limited to spectrograms, the VAE latent space has been shown to correspond to real acoustic features such as frequency and duration, and contain a higher representational capacity than traditional acoustic features (Goffinet 2021, Figure 2). Therefore, clustering of the latent space necessarily means that vocalizations with similar acoustic features are clustered together regardless of their family identity.

Despite this, your point is well taken that there could be systematic differences in certain acoustic features for specific call types. We are not able to ascertain this with the current dataset. This is addressed in Barker 2021 by recording a single call type (soft chirp) from individuals within and across families. Mongolian gerbils have been shown to exhibit individual differences in the initial, terminal, minimum, and maximum frequency of the ultrasonic up-frequency modulated call type (Figure 2, top right green; Nishiyama 2011, Figure 1A ). Therefore it’s possible that family-specific differences exist for that particular call type. To assess whether other call types show family or individual differences, it’s necessary to either 1.) elicit all call types from an animal in isolation or 2.) determine vocalizer identity in social-vocal interactions. The problem with the former idea is that gerbils only produce up-frequency modulated USVs in isolation and there is no known way to elicit the full vocal repertoire in single animals. The latter idea would allow for full use of the vocal repertoire, but requires invasive techniques (e.g., skull-implanted microphones, or awake-behaving laryngeal nerve recordings) that permit assignment of vocalizations to individuals during a natural social interaction. We are actively exploring solutions to both problems.

It’s likely that future studies will look deeper into acoustic differences between individuals and families. Therefore, we have added acoustic feature quantification of vocalizations in each of the GMM clusters as a reference (Figure S6).

Reviewer #3 (Public Review):

Summary:

In this study, Peterson et al. longitudinally record and document the vocal repertoires of three Mongolian gerbil families. Using unsupervised learning techniques, they map the variability across these groups, finding that while overall statistics of, e.g., vocal emission rates and bout lengths are similar, families differed markedly in their distributions of syllable types and the transitions between these types within bouts. In addition, the large and rich data are likely to be valuable to others in the field.

Strengths:

- Extensive data collection across multiple days in multiple family groups.

-  Thoughtful application of modern analysis techniques for analyzing vocal repertoires. - Careful examination of the statistical structure of vocal behavior, with indications that these gerbils, like naked mole rats, may differ in repertoire across families.

Weaknesses:

- The work is largely descriptive, documenting behavior rather than testing a specific hypothesis.

- The number of families (N=3) is somewhat limited.

We agree that the number of families is relatively small. However, our new analysis of vocal repertoire by postnatal day (Figure 4) demonstrates that the finding is quite robust. A high sample-size study was outside the scope of this initial observational study given the difficulty of obtaining and processing longitudinal data of this scale. In light of new analyses in Figure 4, we are confident that future studies will not need so much data to characterize family-specific differences. A single 24-hour recording should be sufficient, making comparison of many more families relatively straightforward.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Several minor concerns:

(1) The three thresholds used for vocalization segmentation lack explanation.

Figure 1C's first vocal event appears to define the first gap via the gray threshold (th_2, as the trace does not cross the black line) and the second gap via the black threshold (th_1 or th_3). And this is not addressed in the Methods section.

Thank you for bringing this to our attention. We agree, this is presented in an unnecessarily complicated way. We have updated the methods section describing the thresholding procedure.

“Sound onsets are detected when the amplitude exceeds 'th_3' (black dashed line, Figure 1C), and sound offset occurs when there is a subsequent local minimum e.g., amplitude less than 'th_2' (gray dashed line, Figure 1C), or 'th_1' (black dashed line, Figure 1C), whichever comes first. In this specific use case, th_2 (5) will always come before th_1 (2), therefore the gray dashed line will always be the offset. A subsequent onset will be marked if the sound amplitude crosses th_2 or th_3, whichever comes first. For example, the first sound event detected in Figure 1C shows the sound amplitude rising above the black dashed line (th_3) and marks an onset. Subsequently, the amplitude trace falls below the gray dashed line (th_2) and an offset is marked. Finally, the amplitude rises above th_2 without dipping below th_3 and an onset for a new sound event is marked. Had the amplitude dipped below th_3, a new sound event onset would be marked when the amplitude trace subsequently exceeded th_3 (e.g. between sound event 2 and 3, Figure 1C). The maximum and minimum syllable durations were selected based on published duration ranges of gerbil vocalizations (Ter-Mikaelian et al. 2012, Kobayasi & Riquimaroux, 2012).”

(2) The determination of multi-syllabic calls could be explained further. In Figure 1C, for instance, do syllables separated by short gaps (e.g., the first syllable and the rest of the first group, and the third group in this example) belong to the same call or different calls?

We have added an operational definition of mono vs. multisyllabic calls in the Results section:

“Vocalizations occur as either single syllables bounded by silence (monosyllabic) or consist of combinations of single syllables without a silent interval (multisyllabic).”

Under this definition, the examples you mentioned in Figure 1C are considered monosyllabic. One could reasonably expand the definition to include calls separated by less than X ms of silence for example, however we choose not to do that in this study. A deeper understanding of the phonation mechanisms for different gerbil vocalization types would be helpful to more rigorously determine the distinction between mono vs. multisyllabic vocalizations.

(3) Labeling the calls shown in Fig. 3D in the latent feature space would help highlight within-family diversity and between-family similarities.

Great suggestion. We have updated Figure 3 to include where in UMAP space each family’s preferred clusters are.

(4) In the introduction, the statement, "Therefore, our study considers the possibility that there is a diversity of vocalizations within the gerbil family social group" doesn't naturally follow from the previous example. This could be rephrased.

Agreed, thank you. We revised this section of the introduction to flow better.

Reviewer #2 (Recommendations For The Authors):

While outside the scope of the current study the authors may consider the following experiments and analysis for future studies:

• Do vocal repertories retain their family signatures across subsequent generations of pups? (i.e. if vocalizations are continually monitored during second or third litters of the same parents).

• Do the authors observe any long-term changes in family repertoires related to the developmental trajectory of the pups? Are there changes in individual pup vocal features or sound type usage throughout development?

Thank you for these great suggestions. Given that naked mole rats learn vocalizations through cultural transmission, it would be interesting to see whether other subterranean species with complex social structures (gerbils, voles, rats) have similar abilities. A straightforward way to assess this possibility could be as you suggest — are latent distributions of vocalizations from multi-generational families closer together than cross-family differences? If true, this would provide compelling evidence to investigate further.

We partially address your second suggestion in our response to Reviewer 1 and in Figure S4, which shows that the family repertoire remains stable throughout this particular period of development. This doesn’t rule out the possibility that there could be other phases of development that undergo more vocal change. Your final suggestion is an area that we are actively researching and eager to know the answer to. A follow-up question: could differences in pup vocal features contribute to differential care by parents?

Reviewer #3 (Recommendations For The Authors):

In all, I found the paper clearly written and the figures easy to follow. One small suggestion:

Figure 1: I can't see the black and gray thresholds described in the caption very well. Perhaps a zoom-in to the first 0.15s or so of the normalized amplitude plot would better display these.

Agreed, thank you. We added a zoom-in to Figure 1.

Author response:

The following is the authors’ response to the original reviews.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

In this manuscript, Unckless and colleagues address the issue of the maintenance of genetic diversity of the gene diptericin A, which encodes an antimicrobial peptide in the model organism Drosophila melanogaster.

Strengths:

The data indicate that flies homozygous for the dptA S69 allele are better protected against some bacteria. By contrast, male flies homozygous for the R69 allele better resist starvation than flies homozygous for the S69 allele.

Weaknesses:

-I am surprised by the inconsistency between the data presented in Fig. 1A and Fig. S2A for the survival of male flies after infection with P. rettgeri. I am not convinced that the data presented support the claim that females have lower survival rates than males when infected with P. rettgeri (lines 176-182).

The two figures are pasted above (1A left, S2A right). The reviewer is correct that the two experiments look different in terms of overall outcomes for males, though qualitatively similar. These two experiments were performed by different researchers, and as much as we attempt to infect consistently from researcher to researcher, some have heavier hands than others. It is true that the genotype that has the largest sex effect is the arginine line (blue) where females (in this experiment) are as bad as the null allele, and males are more intermediate. Also note that the experiments in S2A (male and female) were done in the same block so they are the better comparison. We’ve reflected this in the manuscript.

- The data in Fig. 2 do not seem to support the claim that female flies with either the dptA S69 or the R69 alleles have a longer lifespan than males (lines 211-215). A comment on the [delta] dpt line, which is one of the CRISPR edited lines, would be welcome.

We’ve reworded this section based on these comments.

- The data in Fig. 2B show that male flies with the dptA S69 or R69 alleles have the same lifespan when poly-associated with L. plantarum and A. tropicalis, which contradicts the claim of the authors (lines 256-260).

This is correct – the effect is only in females. It has been corrected.

Reviewer #2 (Public Review):

Summary: In this study, the authors delve into the mechanisms responsible for the maintenance of two diptericin alleles within Drosophila populations. Diptericin is a significant antimicrobial peptide that plays a dual role in fly defense against systemic bacterial infections and in shaping the gut bacterial community, contributing to gut homeostasis.

Strengths: The study unquestionably demonstrates the distinct functions of these two diptericin alleles in responding to systemic infections caused by specific bacteria and in regulating gut homeostasis and fly physiology. Notably, these effects vary between male and female flies.

Weaknesses: Although the findings are highly intriguing and shed light on crucial mechanisms contributing to the preservation of both diptericin alleles in fly populations, a more comprehensive investigation is warranted to dissect the selection mechanisms at play, particularly concerning diptericin's roles in systemic infection and gut homeostasis. Unfortunately, the results from the association study conducted on wild-caught flies lack conclusive evidence.

This is true that the wild fly association study is mostly a negative result. We’ve backed off the claim about the Morganella association.

Major Concerns:

Lines 120-134: The second hypothesis is not adequately defined or articulated. Please revise it to provide more clarity. Additionally, it should be explicitly stated that the first part of the first hypothesis (pathogen specificity), i.e., the superior survival of the S allele in Providencia infections compared to the R allele, has been previously investigated and supported by the results in the Unckless et al. 2016 paper. The current study aims to additionally investigate the opposite scenario: whether the R allele exhibits better survival in a different infection. Please consider revising to emphasize this point.

We’ve reworded this section and added references to both the Unckless et al. 2016 and Hanson et al. 2023 papers.

Figures and statistical analyses: It is essential to present the results of significant differences from the statistical analyses within Figures 1B, 2B, and 3. Additionally, please include detailed descriptions of the statistical analysis methods in the figure legends. Specify whether the error bars represent standard error or standard deviation, particularly in Figure 3, where assays were conducted with as few as 3 flies.

We have added statistical details as requested.

Lines 317-318 (as well as 320-328): The data related to P. rettgeri appear somewhat incomplete, and the authors acknowledge that bacterial load varies significantly, and this bacterium establishes poorly in the gut. These data may introduce more noise than clarity to the study. Please consider revising these sections by either providing more data, refining the presentation, or possibly removing them altogether.

The fact that P. rettgeri establishes poorly in the gut in wildtype flies is the result of several unpublished experiments in the Lazzaro and Unckless labs. We don’t have this as a figure because it was not directly tested in these experiments. We’ve added a note that it is personal observation and we’ve reworked the discussion in the second section.

Lines 335-387 and Figure 4: Although these results are intriguing and suggest interactions between functional diptericin and fly physiology, some mediated by the gut microbiome, they remain descriptive and do not significantly contribute to our understanding of the mechanism that maintains the diptericin alleles.

While the reviewer is correct that these experiments do not elucidate mechanism, they do strongly suggest (based on the controlled nature of the experiments) that the physiological tradeoffs are due to Diptericin genotype. The disagreement is the level of “mechanism”. At the evolutionary level, the demonstration of a physiological cost of a protective immune allele is sufficient to explain the maintenance of alleles. However, we have not determined (and did not attempt to determine) why Diptericin genotype influences these traits. That will have to wait for future experiments.

Lines 399-400: The contrast between this result and statement and the highly reproducible data presented in Figures 2-4 should be discussed.

We’ve added some discussion to this section including a reference to the “inconstancy” of the Drosophila gut microbiome.

Lines 422-429 and Figure 5D: The conclusion regarding an association between diptericin alleles and Morganellaceae bacteria is not clearly supported by Figure 5D and lacks statistical evidence.

We’ve changed this to just be suggestive.

Reviewer #3 (Public Review):

Summary:

This paper investigates the evolutionary aspects around a single amino acid polymorphism in an immune peptide (the antimicrobial peptide Diptericin A) of Drosophila melanogaster. This polymorphism was shown in an earlier population genetic study to be under long-term balancing selection. Using flies with different AA at this immune peptide it was found that one allelic form provides better survival of systemic infections by a bacterial pathogen, but that the alternative allele provides its carriers a longer lifespan under certain conditions (depending on the microbiota). It is suggested that these contrasting fitness effects of the two alleles contribute to balance their long-term evolutionary fate.

Strengths:

The approach taken and the results presented are interesting and show the way forward for studying such polymorphisms experimentally.

Weaknesses:

(1) A clear demonstration (in one experiment) that the antagonistic effect of the two selection pressures isolated is not provided.

The study is overwhelming with many experiments and countless statistical tests. The overall conclusion of the many experiments and tests suggests that "dptS69 flies survive systemic infection better, while dptS69R flies survive some opportunistic gut infections better." (line 444-446). Given the number of results, different experiments, and hundreds of tests conducted, how can we make sure that the result is not just one of many possible combinations? I suggest experimentally testing this conclusion in one experiment (one may call this the "killer-experiment") with the relevant treatments being conducted at the same time, side by side, and the appropriate statistical test being conducted by a statistical test for a treatment x genotype interaction effect.

This is a nice idea but would not work in practice since the fly lines used are different (gnotobiotic vs conventional) and gnotobiotics have to be derived from axenic lines that need a few generations to recover from the bleaching treatment.

(2) The implication that the two forms of selection acting on the immune peptide are maintained by balancing selection is not supported.

The picture presented about how balancing selection is working is rather simplistic and not convincing. In particular, it is not distinguished between fluctuating selection (FL) and balancing selection (BL). BL is the result of negative frequency-dependent selection. It may act within populations (e.g. Red Queen type processes, mating types) or between populations (local adaptation). FL is a process that is sometimes suggested to produce BL, but this is only the case when selection is negative frequency dependent. In most cases, FL does not lead to BL.

The presented study is introduced with a framework of BL, but the aspects investigated are all better described as FL (as the title says: "A suite of selective pressures ..."). The two models presented in the introduction (lines 62 to 69; two pathogens, cost of resistance) are both examples for FL, not for BL.

We’ve added a discussion of how fluctuating selection and balancing selection relate at the end of the discussion.

Finally, no evidence is presented that the different selection pressures suggested to select on the different allelic forms of the immune peptide are acting to produce a pattern of negative frequency dependence.

We are not arguing for negative frequency dependent selection. We assume throughout that Dpt allele does not drive overall frequency of P. rettgeri in populations since it is a ubiquitous microbe. So evolution within D. melanogaster therefore has little to no effect on density of the pathogen.

Recommendations for the authors:

Reviewer #2 (Recommendations For The Authors):

Minor Comments:

Line 31: Rewrite the sentence mentioning "homozygous serine" for improved clarity, especially since the S/R polymorphism of Diptericin has not been introduced yet.

This has been changed to be vague in terms of specific alleles and just refers to “one allele” vs the other.

Lines 87-94: Consider reorganizing this paragraph to maintain a logical flow of the discussion on the Drosophila immune system and the IMD pathway.

We explored other orders, but we think that as is (IMD to AMPs in general to AMPs in Drosophila) makes the most sense here.

Line 99: Provide an explanation of balancing selection for a broader readership, differentiating it from other modes of selection.

We added a brief discussion but note that the intro has significant discussion of balancing selection.

Lines 105-106: Please provide a proper reference. Additionally, ensure that the Unkless et al. 2016 paper is correctly referenced, both in lines 111 and 138-141.

This has been added.

Lines 138-141: It would be beneficial to state that the previous study by Unkless et al. 2016 did not control for genetic background, which is why the assay was redone with gene editing.

This has been added.

Lines 296-303: Clarify the source of the survival observations and consider incorporating this data into Figure 2 for improved visualization.

We’ve clarified that this is Figure 2.

Lines 390-394: Explain the distinctions between vials and cages, particularly in terms of food consumption, exposure to bacteria, etc., which can be relevant to gut homeostasis.

We’ve added a discussion of why these two approaches are complementary.

Reviewer #3 (Recommendations For The Authors):

Statistics

Statistical results are limited to the presentation of p-values (several hundred of them!). For a proper assessment of the statistical analyses, one would also want to see the models used and the test statistics obtained.

The statistical tests done are often unclear. For example, in several experiments, pools of 3 trials (blocs) of multiple animals were tested. The blocs need to be included in the model. Likewise, it seems that multiple delta-dpt fly genotypes were produced. Apparently, they were not distinguished later. Were they considered in the statistical analyses? By contrast, two lines of dptS69R flies were reported to show differences. What concept was applied to test for line difference in some cases and not in others?

In the same dataset (i.e. data resulting from one experiment), it seems that mostly multiple tests were done. For example, in one case each treatment was contrasted to the dptS69 flies. It is generally not acceptable to break down one dataset in multiple subsets and conduct tests with each subtest. One single model for each experiment should be done. This may then be followed by post-hoc tests to see which treatments differ from each other.

We’ve attempted to clarify these statistical approaches throughout.

Minor points

In the legend of Figure 3 it says: "A) monoassociations where each plot represents a different experiment,". This is unclear to me. First, how many plots are there: 3 or 12? Second, what means "experiment"? Are these treatments, or entirely different experiments? How was this statistically taken into account?

We’ve changed this to “different condition” which is clearer. We performed statistical analysis independently for each condition and we’ve now discussed that.

Fig. 5D. It is suggested in the text ("Most intriguing", line 426) and the figure legend that the abundance of Morganellaceae in wild-caught flies differs among genotypes. This is not visible in the figure and not convincingly shown in the text. No stats are given.

We’ve now added that these differences are not significant.

Line 458-461: This sentence is unclear.

We’ve attempted to clarify.

What is a "a traditional adaptive immune system"?

We’ve reworded to “an adaptive immune system”.

There are several typos in the manuscript. Please correct.

We’ve attempted to fix typos throughout.

Bold statements are often without references.

We’ve attempted to add appropriate references throughout.

Author response:

The following is the authors’ response to the original reviews.

Public Reviews:

Reviewer #1 (Public Review):

In the manuscript, the authors explore the mechanism by which Taenia solium larvae may contribute to human epilepsy. This is extremely important question to address because T. solium is a significant cause of epilepsy and is extremely understudied. Advances in determining how T. solium may contribute to epilepsy could have significant impact on this form of epilepsy. Excitingly, the authors convincingly show that Taenia larvae contain and release glutamate sufficient to depolarize neurons and induce recurrent excitation reminiscent of seizures. They use a combination of cutting-edge tools including electrophysiology, calcium and glutamate imaging, and biochemical approaches to demonstrate this important advance. They also show that this occurs in neurons from both mice and humans. This is relevant for pathophysiology of chronic epilepsy development. This study does not rule out other aspects of T. solium that may also contribute to epilepsy, including immunological aspects, but demonstrates a clear potential role for glutamate.

Strengths:

- The authors examine not only T. solium homogenate, but also excretory/secretory products which suggests glutamate may play a role in multiple aspects of disease progression.

- The authors confirm that the human relevant pathogen also causes neuronal depolarization in human brain tissue

- There is very high clinical relevance. Preventing epileptogenesis/seizures possibly with Glu-R antagonists or by more actively removing glutamate as a second possible treatment approach in addition to/replacing post-infection immune response.

- Effects are consistent across multiple species (rat, mouse, human) and methodological assays (GluSnFR AND current clamp recordings AND Ca imaging)

- High K content (comparable levels to high-K seizure models) of larvae could have also caused depolarization. Adequate experiments to exclude K and other suspected larvae contents (i.e. Substance P).

Weaknesses:

- Acute study is limited to studying depolarization in slices and it is unclear what is necessary/sufficient for in vivo seizure generation or epileptogenesis for chronic epilepsy. - There is likely a significant role of the immune system that is not explored here. This issue is adequately addressed in the discussion, however, and the glutamate data is considered in this context.

Discuss impact:

- Interfering with peri-larval glutamate signaling may hold promise to prevent ictogenesis and chronic epileptogenesis as this is a very understudied cause of epilepsy with unknown mechanistic etiology.

Additional context for interpreting significance:

- High medical need as most common adult onset epilepsy in many parts of the world

We thank Reviewer 1 for their positive and thorough assessment of our manuscript. We have elected to respond to and address the following aspects from their “Recommendations For The Authors” below:

Reviewer #1 (Recommendations For The Authors):

Additional experiments/analysis:

-   Fig 4a-c: Larva on a slice and not next to it? Negative results maybe because its E/S products are just washed away (assuming submerged recording chamber/conditions)? Experiments and negative results described here do not seem conclusive. Should be discussed at least?

We agree with the reviewer and have added the following sentence to the relevant section of the Results: ‘Our submerged recording setup might have led to swift diffusion or washout of released glutamate, possibly explaining the lack of observable changes.’

Writing & presentation:

- Data is not always reported consistently in text and figures, examples:

- Results in text are reported varyingly without explanation:

- Mean and/or median? SEM or SD and/or IQR? Stat info included in text or not? i.e. lines 130/131 vs. 160/161

Results and data are now presented in a more uniform fashion. We report medians and IQRs, sample size, statistical test result, statistical test used in that order.

- Larval release data interrupts reading flow, lines 246-252 double up results presented in Fig 5F.

This section has now been significantly abbreviated and reads as follows: ‘T. crassiceps larvae released a relatively constant median daily amount of glutamate, ranging from 41.59 – 60.15 ug/20 larvae, which showed no statistically significant difference across days one to six. Similarly, T. crassiceps larvae released a relatively constant median daily amount of aspartate, ranging from 9.431 – 14.18 ug/20 larvae, which showed no statistically significant difference across days one to six.’

- Results in figures are reported in different styles:

Results have now been made uniform, reporting medians and IQRs and: sample size, p test result, statistical test used, figure # reported in that order.

- Fig 6: E/S glu concentration seems to be significantly higher in solium vs crassiceps (about 6fold higher in solium). Should be discussed at least.

Given the small sample size from T. solium (see response below), we do not draw attention to this difference and instead simply make the point that T. solium larvae contain and release glutamate.

- In this context - N=1 may be sufficient for proof of principle (release) but seems too small of a cohort to describe non-constant release of glu over days (Fig 6D). Is initial release on day 1, no release and recovery in the following days reproducible? Is very high glu content of E/S content (15-fold higher in comparison to solium homogenate AND 6-fold higher in comparison to crassiceps homogenate and E/S content). Not sure if Fig 6D is adding relevant information, especially since it is based on n = 1

We agree that a N=1 is only sufficient for proof of principle. However it is worth noting that the measurements still reflect the cumulative release from 20 larvae. Nonetheless, the statement in text has been simplified to say: ‘These results demonstrate that T. solium larvae continually release glutamate and aspartate into their immediate surroundings.’ As this focusses on the point that the larvae release glutamate and aspartate continuously and that we can’t draw conclusions about the variability over days.

Methods:

- Human slices, mention cortex - what part, patient data would be interesting. I.e. etiology of epilepsy, epilepsy duration 

In the Materials and Methods section “Brain slice preparation” we have now added a table with the requested information.

- For Taenia solium: How were they acquired and used in these experiments?

In the Materials and Methods section “Taenia maintenance and preparation of whole cyst homogenates and E/S products” we describe how Taenia solium larvae were acquired and used.

- Was access resistance monitored? Add exclusion criteria for patch experiments

Figure supplement tables containing the basic properties for each cell recording have been added for each figure and the following statements were added to the electrophysiology section of the Methods: ‘Basic properties of each cell were recorded (supplementary files 1, 2, 3, 4, 6).’ and ‘Cells were excluded from analyses if the Ra was greater than 80 Ω or if the resting membrane potential was above –40 mV.’  

- Cannot see any reference to mouse slices in methods? Also, mouse organotypic cultures (for AAV?)? Or only acute slices from mice and organotypic hip cultures from rats? Seems to have been mouse and rat organotypic cultures? But not clear with further clarification in methods.

We have now added the following clarification to the methods: ‘For experiments using calcium and glutamate imaging mouse hippocampal organotypic brain slices were used. For all other experiments rat hippocampal organotypic brain slices were used. A subset of experiments used acute human cortical brain slices and are specified.’

- How long after the wash-in phase was the wash-out phase data collected?

For wash-in recordings drugs were washed in for 8 mins before recordings were made. Drugs were washed out for at least 8 mins before wash-out recordings were made. This information has been added to the Materials and Methods section.

- In general, the M&M section seems to have been written hastily - author's internal remarks "supplier?" are still present.

The M&M section has been thoroughly proofread for errors and internal remarks removed or corrected.

- A little more information on the clinical subjects would be appreciated. I.e. duration of epilepsy? Localization? What cortex? Usual temporal lobe or other regions?

We have now added a table with this information to the Materials and Methods section “Brain slice preparation”.

Minor corrections text/figures:

- i.e. 3D,F,H,J show individual data points, thats great, but maybe add mean/median marker (as results are reported like this in text)  like in fig 4G,I and others

Figures 3D,F,H & J have been revised to include median and IQR.

- Only one patient mentioned in acknowledgements, but 2 in methods and text

We apologize for this oversight and now acknowledge both patients in the acknowledgements.

- Fig 1 B-F individual puffs are described as increasing - consistent with cellular effects (1st puff depolarizes, 2nd puff elicits 1 AP, 3rd puff elicits AP burst)  However, dilution ratio of homogenate or puff concentrations are not mentioned (or potentially longer than 20 ms puffs for 2nd and 3rd stimulus?) in text or figures. Seems to be enough space to indicate in figure as well (i.e. multiple or thicker arrows for subsequent puffs or label with homogenate dilution/concentration in figure).

We state in the results section associated with Fig. 1 that increasing the amount of homogenate delivered was achieved by increasing the pressure applied to the ejection system. We now include this information in the figure legend.

- Figure legend describes 30 ms puff for Ca imaging whereas ephys data (from text) is 20 ms puff. Was Ca imaging performed in acute mouse hippocampal slices (as figure text suggests) or were those organotypic hippocampal cultures from mice?

Ca2+  imaging was performed in mouse hippocampal organotypic brain slice cultures. The figure text for Fig. 1 E) states “widefield fluorescence image of neurons in the dentate gyrus of a mouse hippocampal organotypic brain slice culture expressing the genetically encoded Ca2+ reporter GCAMP6s...”

- 11.4 mM K is reported for homogenate in text only. How variable is that? How many n? No SD reported in text and no individual data points reported since this experiment is not represented as a figure.

This has been clarified in the text by adding (N = 1, homogenate prepared from >100 larvae).

- Same results (effect of 11.4 mM K on Vm) described twice in one paragraph, compare lines 126-131 with 131-136.

The repetition has been removed.

- Line 182 - example for consistency: decide IQR or SD/SEM

To improve consistency, we have changed to median and IQR throughout.

- Neuronal recordings are reported as hippocampal pyramidal neurons (i.e. line 222) but some recordings were made from dentate granule cells - please clarify which neurons were recorded in ephys, ca imaging, GluSnFr imaging

For each experiment we describe which type of neurons were recorded from. For rodent recordings these were hippocampal pyramidal neurons except in the case of the Ca2+ imaging example where the widefield recording was over the dentate gyrus subfield.

- Line 309: "should" seems to be an extra word

We have removed the word ‘should’ and made the sentence shorter and clearer. It now reads: ‘Given our finding that cestode larvae contain and release significant quantities of glutamate, it is possible that homeostatic mechanisms for taking up and metabolizing glutamate fail to compensate for larvalderived glutamate in the extracellular space. Therefore, similar glutamate-dependent excitotoxic and epileptogenic processes that occur in stroke, traumatic brain injury and CNS tumors are likely to also occur in NCC.’

Reviewer #2 (Public Review):

Since neurocysticercosis is associated with epilepsy, the authors wish to establish how cestode larvae affect neurons. The underlying hypothesis is that the larvae may directly excite neurons and thus favor seizure genesis.

To test this hypothesis, the authors collected biological materials from larvae (from either homogenates or excretory/secretory products), and applied them to hippocampal neurons (rats and mice) and human cortical neurons.

This constitutes a major strength of the paper, providing a direct reading of larvae's biological effects. Another strength is the combination of methods, including patch clamp, Ca, and glutamate imaging.

We thank the Reviewer 2 for their review of the strength and weaknesses of our manuscript. We respond to the identified weaknesses below.

There are some weaknesses:

(1) The main one relates to the statement: "Together, these results indicate that T. crassiceps larvae homogenate results not just in a transient depolarization of cells in the immediate vicinity of application, but can also trigger a wave of excitation that propagates through the brain slice in both space and time. This demonstrates that T. crassiceps homogenate can initiate seizurelike activity under suitable conditions."

The only "evidence" of propagation is an image at two time points. It is one experiment, and there is no quantification. Either increase n's and perform a quantification, or remove such a statement.

We acknowledge that the data is from one experiment, with the intention of demonstrating that it is plausible for intense depolarization of a subset of neurons to result in the initiation and propagation of seizure-like activity to nearby neurons under suitable conditions. However, we agree that it is prudent to remove this statement and have done so.

Likewise, there is no evidence of seizure genesis. A single cell recording is shown. The presence of a seizure-like event should be evaluated with field recordings.

In this experiment the Ca2+ imaging demonstrates activity spreading from the site of the restricted homogenate puff to all surrounding neurons. Furthermore, the whole-cell recoding is typical of a slice wide seizure-like event.  

(2) Control puff experiments are lacking for Fig 1. Would puffing ACSF also produce a depolarization, and even firing, as suggested in Fig. 2D? This is needed for at least one species.

We agree and have added this data for the rat and mouse neuron in a new Figure 1-figure supplement 1.

(3) What is the rationale to use a Cs-based solution? Even in the presence of TTX and with blocking K channels, the depolarization may be sufficient to activate Ca channels (LVGs), which would further contribute to the depolarization. Why not perform voltage clamp recordings to directly the current?

The intention of the Cs-based solution was to block K+ channels and reduce the effect of moderately raised K+ in the homogenate to isolate the contribution of other causative agents of depolarization (i.e. glutamate / aspartate). We agree that performing voltage clamp recordings would have been useful for directly recording the currents responsible for depolarization. 

(4) Why did you use organotypic slices? Since you wish to model adult epilepsy, it would have been more relevant to use fresh slices from adult rats/mice. At least, discuss the caveat of using a network still in development in vitro.

Recordings were performed 6–14 days post culture, which is equivalent to postnatal Days (P) 12 to 22. Previous work has shown that neurons in the organotypic hippocampal brain slice are relatively mature (Gähwiler et al., 1997). For example they possess mature Cl- homeostasis mechanisms at this point, as evidenced by their hyperpolarizing EGABA (Raimondo et al., 2012).  

(5) Please include both the number of slices and number of cells recorded in each condition. This is the standard (the number of cells is not enough).

This has now been added to all relevant sections of the results text.  

(6) Please provide a table with the basic properties of cells (Rin, Rs, etc.). This is standard to assess the quality of the recordings.

Tables containing the basic properties for each cell recording have been created for each figure (as Figure supplements) and the following statement was added to the electrophysiology section of the Methods: ‘Basic properties of each cell were recorded (see Figure supplements).’

(7) Please provide a table on patient's profile. This is standard when using human material. Were these TLE cases (and "control" cortex) or epileptogenic cortex?

We have now added a basic table on the patient’s profiles to the Materials and Methods section.

Globally, the authors achieved their aims. They show convincingly that larvae material can depolarize neurons, with glutamate (and aspartate) as the most likely candidates.

This is important not only because it provides mechanistic insight but also potential therapeutic targets. The result is impactful, as the authors use quasi-naturalistic conditions, to assess what might happen in the human brain. The experimental design is appropriate to address the question. It can be replicated by any interested person.

We thank the Reviewer 2 for their enthusiastic and constructive assessment of our manuscript. We have elected to respond to and address the following aspects from their “Recommendations For The Authors” below:

Reviewer #2 (Recommendations For The Authors):

lines 132 and following are a repetition of those above

These have been removed.

line 151 Fig "2" missing

This has been added.

187, 190 should be E, F not C, D

This has been changed in the text.  

481, 482 supplier?

This has been corrected and the correct suppliers described.

Reviewer #3 (Public Review):

This paper has high significance because it addresses a prevalent parasitic infection of the nervous system, Neurocysticercosis (NCC). The infection is caused by larvae of the parasitic cestode Taenia solium It is a leading cause of epilepsy in adults worldwide

To address the effects of cestode larvae, homogenates and excretory/secretory products of larvae were added to organotypic brain slice cultures of rodents or layer 2/3 of human cortical brain slices from patients with refractory epilepsy.

We thank Reviewer 3 for their helpful comments and suggestions for improvement which we address below.

A self-made pressure ejection system was used to puff larvae homogenate (20 ms puff) onto the soma of patched neurons. The mechanical force could have caused depolarizaton so a vehicle control is critical. On line 150 they appear to have used saline in this regard, and clarification would be good. Were the controls here (and aCSF elsewhere) done with the low Mg2+o aCSF like the larvae homogenates?

We agree and have added examples where aCSF alone was pressure ejected onto the same rat and mouse neurons in a new Figure 1-figure supplement 1. In Figure 1, the same aCSF as that was used to bathe the slices was used. In Figure 2D-G, either PBS (which larval homogenates were prepared in) or growth medium (which contain larval E/S products) were used as comparative controls.

They found that neurons depolarized after larvae homogenate exposure and the effect was mediated by glutamate but not nicotinic receptors for acetylcholine (nAChRs), acid-sensing channels or substance P. To address nAChRs, they used 10uM mecamyline, and for ASICs 2mM amiloride which seems like a high concentration. Could the concentrations be confirmed for their selectivity? 

We did not independently verify the selectivity of the antagonist concentrations used in our study. However, the persistence of depolarizations despite the use of high concentrations of mecamylamine (10 μM) and amiloride (2 mM) provides strong evidence that neither nAChRs nor ASICs are primarily responsible for mediating these responses. The high concentrations used, while potentially raising concerns about specificity, actually strengthen our conclusion that these receptor types are not involved in the observed effect.

Glutamate receptor antagonists, used in combination, were 10uM CNQX, 50uM DAP5, and 2mM kynurenic acid. These concentrations are twice what most use. Please discuss. 

We intentionally used higher-than-typical concentrations of glutamate receptor antagonists in our experimental design. Our rationale for this approach was to ensure maximal blockade of glutamate receptors, thereby minimizing the possibility of residual receptor activity confounding our results.

Also, it would be very interesting to know if the glutamate receptor is AMPA, Kainic acid, or NMDA. Were metabotropic antagonists ever tested? That would be logical because CNQX/DAPR/Kynurenic acid did not block all of the depolarization.

We appreciate the reviewer's interest in the specific glutamate receptor subtypes involved in our study. Our research primarily focused on ionotropic glutamate receptors as a group, without differentiating the individual contributions of AMPA, Kainate, and NMDA receptors. This approach, while broad, allowed us to establish the involvement of glutamatergic signalling in the observed effects. We acknowledge that we did not investigate metabotropic glutamate receptors in this study. Importantly, we demonstrate later in our manuscript that the larval products contain both glutamate and aspartate. Therefore the precise nature of the glutamate-dependent depolarization observed using a particular experimental preparation would depend on the specific types of neurons exposed to the homogenate and the expression profile of different glutamate receptor subtypes on these neurons.

They also showed the elevated K+ in the homogenate (~11 mM) could not account for the depolarization. However, the experiment with K+ was not done in a low Mg2+o buffer (Or was it -please clarify). 

The experiment where 11.39 mM K+ as well as the experiment with T. crass. Homogenate with a cesium internal and added TTX were all done in standard 2 mM Mg2+ containing aCSF.

They also confirmed that only small molecules led to the depolarization after filtering out very large molecules. That supports the conclusion that glutamate - which is quite small - could be responsible. It is logical to test substance P because the Intro points out prior work links the larvae and seizures by inflammation and implicates substance P. However, why focus on nAChRs and ASIC?

These were chosen as they are ionotropic receptors which mediate depolarization and hence could conceivably be responsible for the homogenate-induced depolarization we observed.

The depolarizations caused seizure-like events in slices. The slices were exposed to a proconvulant buffer though- low Mg2+o. This buffer can cause spontaneous seizure-like events so it is important to know what the buffer did alone.

We agree that a low M2+ buffer solution can elicit seizure-like events in organotypic slices alone. However, the timing of the onset of the seizure-like event in the example presented in Figure 1 strongly suggests that it was triggered by the T. crass homogenate puff. Nonetheless, on the suggestion of the other reviewers we have reduced emphasis on our experimental evidence for the ability of T. crass. homogenate to illicit seizure-like events.  

They suggest the effects could underlie seizure generation in NCC. However, there is only one event that is seizure-like in the paper and it is just an inset. Were others similar? How frequency were they? How long?

Please see the response above as well as our response to Reviewer 1 who raised a similar concern.

Using Glutamate-sensing fluorescent reporters they found the larvae contain glutamate and can release it, a strength of the paper.

Fig. 4. Could an inset be added to show the effects are very fast? That would support an effect of glutamate.

We have not added an inset. However, given the scale bar (500 ms) for the trace provided, the response is very fast.  

Why is aspartate relatively weak and glutamate relatively effective as an agonist?

Glutamate generally has a higher affinity for glutamate receptors compared to aspartate. This is particularly true for AMPA and kainate receptors, where glutamate is the primary endogenous agonist. Similarly iGluSnFR has a higher sensitivity for glutamate over aspartate (Marvin et al., 2013).

Could some of the variability in Fig 4G be due to choice of different cell types? That would be consistent with Fig 5B where only a fraction of cells in the culture showed a response to the larvae nearby. 

Whilst differences in cell types could contribute to the variability in Fig 4G, all the responses were recorded from hippocampal pyramidal neurons and hence it is more likely that the variability is a function of other sources of variation including differences in iGluSnFR expression, depth of the cell imaged, the proximity of the puffer pipette etc. In Fig. 5B we think the lack of response may be due to the fact that any released glutamate by the live larvae was not able reach the iGluSnFR neurons at sufficient concentrations due to the nature of our submerged recording setup. We have added the following sentence to the results. ‘Our submerged recording setup might have led to swift diffusion or washout of released glutamate, possibly explaining the lack of observable changes.’

On what basis was the ROI drawn in Fig. 5B.

The ROI drawn in Fig. 5B was selected to include all iGluSnFR expressing neurons in the brain slice. which were captured in the field of view.

Also in 5B, I don't see anything in the transmitted image. What should be seen exactly?

We agree that it is difficult to resolve much in the transmitted image. However, both the brain slice on the left as well as a T. crass. larva on the right is visible and outlined with a green or orange dashed line respectively.

Human brain slices were from temporal cortex of patients with refractory epilepsy. Was the temporal cortex devoid of pathology and EEG abnormalities? This area may be quite involved in the epilepsy because refractory epilepsy that goes to surgery is often temporal lobe epilepsy. Please discuss the limitations of studying the temporal cortex of humans with epilepsy since it may be more susceptible to depolarizations of many kinds, not just larvae.

We acknowledge the important limitations of using temporal cortex tissue from patients with refractory epilepsy. While we aimed to use visually normal tissue, we recognize that the tissue may have underlying pathology or functional abnormalities not visible to the naked eye. It may also be more susceptible to induced depolarizations due to epilepsy-related changes in neuronal excitability. Despite these limitations, we believe our human tissue data still provides valuable data that the larval homogenates can induce depolarization in human as well as rodent neurons.  

Please discuss the limitations of the cultures - they are from very young animals and cultured for 6-14 days.

We acknowledge the potential limitations of our experimental model using organotypic hippocampal slice cultures from young animals. The use of relatively immature tissue may not fully represent the adult nervous system due to developmental differences in receptor expression, synaptic connections, and network properties. The 6-14 day culture period, while allowing some maturation, may induce changes that differ from the in vivo environment, including alterations in cellular physiology and network reorganization. Despite these limitations, this model provides a valuable balance between preserved local circuitry and experimental accessibility. Future studies comparing results with acute adult slices and in vivo models would be beneficial to validate and extend our findings.

References:

Gähwiler, B.H. et al. (1997) ‘Organotypic slice cultures: a technique has come of age.’, Trends in neurosciences, 20(10), pp. 471–7.

Marvin, J.S. et al. (2013) ‘An optimized fluorescent probe for visualizing glutamate neurotransmission.’, Nature methods, 10(2), pp. 162–70. Available at: https://doi.org/10.1038/nmeth.2333.

Raimondo, J.V. et al. (2012) ‘Optogenetic silencing strategies differ in their effects on inhibitory synaptic transmission.’, Nat. Neurosci., 15(8), pp. 1102–4. Available at: https://doi.org/10.1038/nn.3143.

Author response:

Public Reviews:

Reviewer #1 (Public review):

Summary:

The authors describe a method to probe both the proteins associated with genomic elements in cells, as well as 3D contacts between sites in chromatin. The approach is interesting and promising, and it is great to see a proximity labeling method like this that can make both proteins and 3D contacts. It utilizes DNA oligomers, which will likely make it a widely adopted method. However, the manuscript over-interprets its successes, which are likely due to the limited appropriate controls, and of any validation experiments. I think the study requires better proteomic controls, and some validation experiments of the "new" proteins and 3D contacts described. In addition, toning down the claims made in the paper would assist those looking to implement one of the various available proximity labeling methods and would make this manuscript more reliable to non-experts.

Strengths:

(1) The mapping of 3D contacts for 20 kb regions using proximity labeling is beautiful.

(2) The use of in situ hybridization will probably improve background and specificity.

(3) The use of fixed cells should prove enabling and is a strong alternative to similar, living cell methods.

Weaknesses:

(1) A major drawback to the experimental approach of this study is the "multiplexed comparisons". Using the mtDNA as a comparator is not a great comparison - there is no reason to think the telomeres/centrosomes would look like mtDNA as a whole. The mito proteome is much less complex. It is going to provide a large number of false positives. The centromere/telomere comparison is ok, if one is interested in what's different between those two repetitive elements. But the more realistic use case of this method would be "what is at a specific genomic element"? A purely nuclear-localized control would be needed for that. Or a genomic element that has nothing interesting at it (I do not know of one). You can see this in the label-free work: non-specific, nuclear GO terms are enriched likely due to the random plus non-random labeling in the nucleus. What would a Telo vs general nucleus GSEA look like? (GSEA should be used for quantitative data, no GO). That would provide some specificity. Figures 2G and S4A are encouraging, but a) these proteins are largely sequestered in their respective locations, and b) no validation by an orthogonal method like ChIP or Cut and Run/Tag is used.

You can also see this in the enormous number of "enriched" proteins in the supplemental volcano plots. The hypothesis-supporting ones are labeled, but do the authors really believe all of those proteins are specific to the loci being looked at? Maybe compared to mitochondria, but it's hard to believe there are not a lot of false positives in those blue clouds. I believe the authors are more seeing mito vs nucleus + Telo than the stated comparison. For example, if you have no labeling in the nucleus in the control (Figures 1C and 2C) you cannot separate background labeling from specific labeling. Same with mito vs. nuc+Telo. It is not the proper control to say what is specifically at the Telo.

I would like to see a Telo vs nuclear control and a Centromere vs nuc control. One could then subtract the background from both experiments, then contrast Telo vs Cent for a proper, rigorous comparison. However, I realize that is a lot of work, so rewriting the manuscript to better and more accurately reflect what was accomplished here, and its limitations, would suffice.

(2) A second major drawback is the lack of validation experiments. References to literature are helpful but do not make up for the lack of validation of a new method claiming new protein-DNA or DNA-DNA interactions. At least a handful of newly described proximal proteins need to be validated by an orthogonal method, like ChIP qPCR, other genomic methods, or gel shifts if they are likely to directly bind DNA. It is ok to have false positives in a challenging assay like this. But it needs to be well and clearly estimated and communicated.

(3) The mapping of 3D contacts for 20 kb regions is beautiful. Some added discussion on this method's benefits over HiC-variants would be welcomed.

(4) The study claims this method circumvents the need for transfectable cells. However, the authors go on to describe how they needed tons of cells, now in solution, to get it to work. The intro should be more in line with what was actually accomplished.

(5) Comments like "Compared to other repetitive elements in the human genome...." appear to circumvent the fact that this method is still (apparently) largely limited to repetitive elements. Other than Glopro, which did analyze non-repetitive promoter elements, most comparable methods looked at telomeres. So, this isn't quite the advancement you are implying. Plus, the overlap with telomeric proteins and other studies should be addressed. However, that will be challenging due to the controls used here, discussed above.

We thank the Reviewer for their careful reading of manuscript and constructive suggestions. We plan to substantially revise the framing and presentation of manuscript to address the concerns raised by all three reviewers.

Reviewer #2 (Public review):

Summary

Liu and MacGann et al. introduce the method DNA O-MAP that uses oligo-based ISH probes to recruit horseradish peroxidase for targeted proximity biotinylation at specific DNA loci. The method's specificity was tested by profiling the proteomic composition at repetitive DNA loci such as telomeres and pericentromeric alpha satellite repeats. In addition, the authors provide proof-of-principle for the capture and mapping of contact frequencies between individual DNA loop anchors.

Strengths

Identifying locus-specific proteomes still represents a major technical challenge and remains an outstanding issue (1). Theoretically, this method could benefit from the specificity of ISH probes and be applied to identify proteomes at non-repetitive DNA loci. This method also requires significantly fewer cells than other ISH- or dCas9-based locus-enrichment methods. Another potential advantage to be tested is the lack of cell line engineering that allows its application to primary cell lines or tissue.

Weaknesses

The authors indicate that DNA O-MAP is superior to other methods for identifying locus-specific proteomes. Still, no proof exists that this method could uncover proteomes at non-repetitive DNA loci. Also, there is very little validation of novel factors to confirm the superiority of the technique regarding specificity.

The authors first tested their method's specificity at repetitive telomeric regions, and like other approaches, expected low-abundant telomere-specific proteins were absent (for example, all subunits of the telomerase holoenzyme complex). Detecting known proteins while identifying noncanonical and unexpected protein factors with high confidence could indicate that DNA O-MAP does not fully capture biologically crucial proteins due to insufficient enrichment of locus-specific factors. The newly identified proteins in Figure 1E might still be relevant, but independent validation is missing entirely. In my opinion, the current data cannot be interpreted as successfully describing local protein composition.

Finally, the authors could have discussed the limitations of DNA O-MAP and made a fair comparison to other existing methods (2-5). Unlike targeted proximity biotinylation methods, DNA O-MAP requires paraformaldehyde crosslinking, which has several disadvantages. For instance, transient protein-protein interactions may not be efficiently retained on crosslinked chromatin. Similarly, some proteins may not be crosslinked by formaldehyde and thus will be lost during preparation (6).

(1) Gauchier M, van Mierlo G, Vermeulen M, Dejardin J. Purification and enrichment of specific chromatin loci. Nat Methods. 2020;17(4):380-9.

(2) Dejardin J, Kingston RE. Purification of proteins associated with specific genomic Loci. Cell. 2009;136(1):175-86.

(3) Liu X, Zhang Y, Chen Y, Li M, Zhou F, Li K, et al. In Situ Capture of Chromatin Interactions by Biotinylated dCas9. Cell. 2017;170(5):1028-43 e19.

(4) Villasenor R, Pfaendler R, Ambrosi C, Butz S, Giuliani S, Bryan E, et al. ChromID identifies the protein interactome at chromatin marks. Nat Biotechnol. 2020;38(6):728-36.

(5) Santos-Barriopedro I, van Mierlo G, Vermeulen M. Off-the-shelf proximity biotinylation for interaction proteomics. Nat Commun. 2021;12(1):5015.

(6) Schmiedeberg L, Skene P, Deaton A, Bird A. A temporal threshold for formaldehyde crosslinking and fixation. PLoS One. 2009;4(2):e4636.

We thank the Reviewer for their constructive feedback on our work. As noted above, we plan to substantially revise the framing and presentation of manuscript to address the concerns raised by all three reviewers.

Reviewer #3 (Public review):

Significance of the Findings:

The study by Liu et al. presents a novel method, DNA-O-MAP, which combines locus-specific hybridisation with proximity biotinylation to isolate specific genomic regions and their associated proteins. The potential significance of this approach lies in its purported ability to target genomic loci with heightened specificity by enabling extensive washing prior to the biotinylation reaction, theoretically improving the signal-to-noise ratio when compared with other methods such as dCas9-based techniques. Should the method prove successful, it could represent a notable advancement in the field of chromatin biology, particularly in establishing the proteomes of individual chromatin regions - an extremely challenging objective that has not yet been comprehensively addressed by existing methodologies.

Strength of the Evidence:

The evidence presented by the authors is somewhat mixed, and the robustness of the findings appears to be preliminary at this stage. While certain data indicate that DNA-O-MAP may function effectively for repetitive DNA regions, a number of the claims made in the manuscript are either unsupported or require further substantiation. There are significant concerns about the resolution of the method, with substantial biotinylation signals extending well beyond the intended target regions (megabases around the target), suggesting a lack of specificity and poor resolution, particularly for smaller loci. Furthermore, comparisons with previous techniques are unfounded since the authors have not provided direct comparisons with the same mass spectrometry (MS) equipment and protocols. Additionally, although the authors assert an advantage in multiplexing, this claim appears overstated, as previous methods could achieve similar outcomes through TMT multiplexing. Therefore, while the method has potential, the evidence requires more rigorous support, comprehensive benchmarking, and further experimental validation to demonstrate the claimed improvements in specificity and practical applicability.

We thank the Reviewer for providing detailed critiques of our manuscript. As noted above, we plan to substantially revise the framing and presentation of manuscript to address the concerns raised by all three reviewers.

Author response:

Reviewer #1 (Public review):

Summary:

The crystal structure of the Sld3CBD-Cdc45 complex presented by Li et al. is a novel contribution that significantly advances our understanding of CMG formation during the rate-limiting step of DNA replication initiation. This structure provides insights into the intermediate steps of CMG formation. The study builds upon previously known structures of Sld3 and Cdc45 and offers new perspectives into how Cdc45 is loaded onto MCM DH through Sld3-Sld7. The most notable finding is the structural difference in Sld3CBD when bound to Cdc45, particularly the arrangement of the α8-helix, which is essential for Cdc45 binding and may also pertain to its metazoan counterpart, Treslin. Additionally, the conformational shift in the DHHA1 domain of Cdc45 suggests a possible mechanism for its binding to MCM2NTD.

Strengths:

The manuscript is generally well-written, with a precise structural analysis and a solid methodological section that will significantly advance future studies in the field. The predictions based on structural alignments are intriguing and provide a new direction for exploring CMG formation, potentially shaping the future of DNA replication research.

Weaknesses:

The main weakness of the manuscript lies in the lack of experimental validation for the proposed Sld3-Sld7-Cdc45 model. Specifically, the claim that Sld3 binding to Cdc45-MCM does not inhibit GINS binding, a finding that contradicts previous research, is not sufficiently substantiated with experimental evidence. To strengthen their model, the authors must provide additional experimental data to support this mechanism. Also, the authors have not compared the recently published Cryo-EM structures of the metazoan CMG helicases with their predicted models to see if Sld3/Treslin does not cause any clash with the GINS when bound to the CMG. Still, the work holds great potential in its current form but requires further experiments to confirm the authors' conclusions.

We appreciate the reviewers’ careful reading and the comments.

The structure of Sld3CBD-Cdc45 showed that the binding site of Cdc45 to Sld3CBD was distinct from the binding ranges of Cdc45 to GINS and MCM, indicating that the Sld3CBD, MCM, and GINS bind to separate sites of Cdc45 on the CMG complex. The SCMG-DNA model confirmed such a binding situation but did not show whether the binding of Sld3 to Cdc45 affects the recruitment of GINS (by GINS-Dbp11-Sld2) for CMG formation. We will modify our manuscript and discuss this point. Also, we will check the recently published Cryo-EM structures of the metazoan CMG helicases with their predicted models to confirm our conclusions. We will try to conduct the experiments as suggested.

Reviewer #2 (Public review):

Summary

The manuscript presents valuable findings, particularly in the crystal structure of the Sld3CBD-Cdc45 interaction and the identification of additional sequences involved in their binding. The modeling of the Sld7-Sld3CBD-CDC45 subcomplex is novel, and the results provide insights into potential conformational changes that occur upon interaction. However, the work remains incomplete as several main claims are only partially supported by experimental data, particularly the proposed model for Sld3 interaction with GINS on the CMG. Additionally, the single-stranded DNA binding data from different species do not convincingly advance the manuscript's central arguments.

Strengths

(1) The Sld3CBD-Cdc45 structure is a novel contribution, revealing critical residues involved in the interaction.

(2) The model structures generated from the crystal data are well presented and provide valuable insights into the interaction sequences between Sld3 and Cdc45.

(3) The experiments testing the requirements for interaction sequences are thorough and conducted well, with clear figures supporting the conclusions.

(4) The conformational changes observed in Sld3 and Cdc45 upon binding are interesting and enhance our understanding of the interaction.

(5) The modeling of the Sld7-Sld3CBD-CDC45 subcomplex is a new and valuable addition to the field.

Weaknesses

(1) The proposed model for Sld3 interacting with GINS on the CMG needs more experimental validation and conflicts with published findings. These discrepancies need more detailed discussion and exploration.

(2) The section on the binding of Sld3 complexes to origin single-stranded DNA needs significant improvement. The comparisons between Sld3-CBD, Sld3CBD-Cdc45, and Sld7-Sld3CBD-Cdc45 involve complexes from different species, limiting the comparisons' value.

(3) The authors' model proposing the release of Sld3 from CMG based on its binding to single-stranded DNA is unclear and needs more elaboration.

We appreciate your positive comments. As suggested, we will try to improve the experiments and manuscript and discuss in more detail, including the interaction between Sld3 and GINS on the CMG, ssDNA-binding section, and the explanations of why we use different species for comparison and more elaboration on the Sld3-release proposal.

Reviewer #3 (Public review):

Summary:

The paper by Li et al. describes the crystal structure of a complex of Sld3-Cdc45-binding domain (CBD) with Cdc45 and a model of the dimer of an Sld3-binding protein, Sld7, with two Sld3-CBD-Cdc45 for the tethering. In addition, the authors showed the genetic analysis of the amino acid substitution of residues of Sld3 in the interface with Cdc45 and biochemical analysis of the protein interaction between Sld3 and Cdc45 as well as DNA binding activity of Sld3 to the single-strand DNAs of the ARS sequence.

Strengths:

The authors provided a nice model of an intermediate step in the assembly of an active Cdc45-MCM-GINS (CMG) double hexamers at the replication origin, which is mediated by the Sld3-Sld7 complex. The dimer of the Sld3-Sld7 complexes tethers two MCM hexamers together for the recruitment of GINS-Pol epsilon on the replication origin.

Weaknesses:

The biochemical analysis should be carefully evaluated with more quantitative ways to strengthen the authors' conclusion.

We thank your positive assessment. We will provide more quantitative information and try to quantify the experiments as suggested.

Author response:

Reviewer 1:

(1) I think the article is a little too immature in its current form. I'd recommend that the authors work on their writing. For example, the objectives of the article are not completely clear to me after reading the manuscript, composed of parts where the authors seem to focus on SGCs, and others where they study "engram" neurons without differentiating the neuronal type (Figure 5). The next version of the manuscript should clearly establish the objectives and sub-aims.

Our overarching focus was to identify whether intrinsic physiology and circuit connectivity of SGCs contribute to their unique overrepresentation in neurons labeled as part of a behaviorally relevant dentate engram. Since our systematic analysis of “engram SGCs” did not support the proposal that engram SGCs drive robust feedforward excitation of engram GCs or feedback inhibition of non-engram GCs, we examined an alternative hypothesis that inputs drive recruitment of neurons, regardless of subtype (in figure 5). These are sparsely labeled neurons, with mixed populations of GCs and SGCs undergoing paired recordings. Since the focus of the experiment was input correlation between two simultaneously recorded neurons, we did not report the individual cell types. We regret that this caused confusion and will clarify this issue in the revised manuscript.

(2) In addition, some results are not entirely novel (e.g., the disproportionate recruitment as well as the distinctive physiological properties of SGCs), and/or based on correlations that do not fully support the conclusions of the article. In addition to re-writing, I believe that the article would benefit from being enriched with further analyses or even additional experiments before being resubmitted in a more definitive form.

We would like to note that while we and others have previously reported the distinctive SGC physiology, this study is the first to compare physiological properties of SGCs labeled as part of an engram to unlabeled SGCs. That was the thrust of the data presented which may have been missed and will be emphasized in the revision. Similarly, while others have shown higher SGC recruitment in dentate engrams, we had to validate this in the dentate dependent behaviors that we adopted in this study. We also note that the proportional SGC recruitment in our study, based on morphometric classification, differs from what was reported previously. These aspects of study, which were considered confirmatory, represent the necessary validation needed to proceed with the novel cell-type specific paired recordings and optogenetic analyses of engram neurons presented in subsequent sections of the manuscript. We will emphasize these considerations in the revised manuscript.

Reviewer 2:

(1) The authors conclude that SGCs are disproportionately recruited into cfos assemblies during the enriched environment and Barnes maze task given that their classifier identifies about 30% of labelled cells as SGCs in both cases and that another study using a different method (Save et al., 2019) identified less than 5% of an unbiased sample of granule cells as SGCs. To make matters worse, the classifier deployed here was itself established on a biased sample of GCs patched in the molecular layer and granule cell layer, respectively, at even numbers (Gupta et al., 2020). The first thing the authors would need to show to make the claim that SGCs are disproportionately recruited into memory ensembles is that the fraction of GCs identified as SGCs with their own classifier is significantly lower than 30% using their own method on a random sample of GCs (e.g. through sparse viral labelling). As the authors correctly state in their discussion, morphological samples from patch-clamp studies are problematic for this purpose because of inherent technical issues (i.e. easier access to scattered GCs in the molecular layer).

We regret that there seems to be some confusion about use of a classifier. We did NOT use any automated classifier in this study. All cell type classifications in the study were conducted by experienced investigators examining cell morphology and classifying cells based on established morphometric criteria. In our prior study (Gupta et al., 2020) we had conducted an automated cluster analysis that was able to classify GCs and SGCs as different cell types. The principal components underlying the automated clustering in Gupta et al 2020 were consistent with the major criteria identified in prior morphology-based analyses by us and others (including Williams et al 2010 and Save et al., 2019). To date, in the absence of a validated molecular marker, morphometry from recorded and filled cells or sparsely labeled neurons is the only established method to classify SGCs. This was the approach we adopted, and this will be further clarified in the revisions.

(2) The authors claim that recurrent excitation from SGCs onto GCs or other SGCs is irrelevant because they did not find any connections in 32 simultaneous recordings (plus 63 in the next experiment). Without a demonstration that other connections from SGCs (e.g. onto mossy cells or interneurons) are preserved in their preparation and if so at what rates, it is unclear whether this experiment is indicative of the underlying biology or the quality of the preparation. The argument that spontaneous EPSCs are observed is not very convincing as these could equally well arise from severed axons (in fact we would expect that the vast majority of inputs are not from local excitatory cells). The argument on line 418 that SGCs have compact axons isn't particularly convincing either given that the morphologies from which they were derived were also obtained in slice preparations and would be subject to the same likelihood of severing the axon. Finally, even in paired slice recordings from CA3 pyramidal cells the experimentally detected connectivity rates are only around 1% (Guzman et al., 2016). The authors would need to record from a lot more than 32 pairs (and show convincing positive controls regarding other connections) to make the claim that connectivity is too low to be relevant.

As noted in our discussion, we are fully cognizant that potential SGC to GC connections may have been missed by the nature of slice physiology experiments and made every effort to limit this possibility. As noted in the manuscript, we only analyzed GC/SGC pairs where hilar axon collaterals of the neurons were recovered. We do not claim that SGC to GC/SGC connections are irrelevant, rather, we indicate that these connections, if present, are sparse and unlikely to drive engram refinement. Interestingly, wide field optical stimulation, designed to activate multiple labeled engram neurons and axon terminals including those of SGCs whose somata were outside the slice, did not lead to EPSCs in other unlabeled GCs or SGCs suggesting the lack of robust SGC to GC/SGC synaptic connectivity. While we have previously published paired recordings from interneurons to GCs (Proddutur  et al 2023) , we agree that recordings demonstrating the presence of SGC/GC to hilar neuron synapses would serve as an added control in the revised manuscript.

(3) Another troubling sign is the fact that optogenetic GC stimulation rarely ever evokes feedback inhibition onto other cells which contrasts with both other in vitro (e.g. Braganza et al., 2020) and in vivo studies (Stefanelli et al., 2016) studies. Without a convincing demonstration that monosynaptic connections between SGCs/GCs and interneurons in both directions is preserved at least at the rates previously described in other slice studies (e.g. Geiger et al., 1997, Neuron, Hainmueller et al., 2014, PNAS, Savanthrapadian et al., 2014, J. Neurosci), the notion that this setting could be closer to naturalistic memory processing than the in vivo experiments in Stefanelli et al. (e.g. lines 443-444) strikes me as odd. In any case, the discussion should clearly state that compromised connectivity in the slice preparation is likely a significant confound when comparing these results.

We would like to note that our data are consistent with Braganza 2020 study, as we explain below. Moreover, we would like to point out that the demonstration of “feedback inhibition” in the Stefanelli study was NOT in engram or behaviorally labeled neurons nor was it in vivo. As we explain below, the physiological assay in Stefanelli was in slices and in a cohort of GCs with virally driven ChR2 expression. Thus, we are fully confident that our experimental paradigm better reflects a behavioral engram. As noted in response (2, we have previously published paired monosynaptic connections from interneurons to GCs (Proddutur  et al 2023) and find the connectivity consistent with published data. However, we agree that recordings demonstrating the presence of SGC/GC to hilar neuron synapses  or recruitment of feedback inhibition by focal activation of GCs would serve to allay concerns regarding slice preparation. We also submit that we already discuss the potential concerns regarding compromised connectivity in slice preparations.

Regarding the lack of optically evoked feedback inhibition, we would like to point out that the Braganza 2020 study examined focal optogenetic activation of GCs, where a high density of GCs was labeled using a Prox-cre line. They reported that about 2-4% of these densely labeled cells need to be recruited to evoke feedback IPSCs. Our experimental condition, where ChR2 was expressed in behaviorally labeled neurons, leads to sparse labeling much less than the focal 4% needed to evoke IPSCs in the Braganza study. We do not claim that feedback inhibition cannot be activated by focal activation of a cohort of GCs and even show an example of paired recording with feedback GC inhibition of an SGC. Our conclusion is that the few sparsely labeled neurons during a behavioral episode do not support robust feedback inhibition proposed to mediate engram refinement. We submit that our findings are fully consistent with the sparse GC driven feedback inhibition, and the need to activate a cohort of focal GCs to recruit feedback inhibition, reported in Braganza 2020

Regarding the Stefanelli study, we maintain that our behaviorally relevant in vivo labeling approach is more naturalistic than the DREADD and Channelrhodopsin driven artificial “engrams” generated in the Stefanelli study. Of note, we used cFOS driven TRAP mice to label, in vivo, neurons active during a behavior and then undertook slice physiology studies in these mice a week later. In contrast, the slice physiology data demonstrating putative feedback inhibition in the Stefanelli study (Fig 5) used wildtype mice injected with AAV CAMKII-cre and AAV-DIO-ChR2. Thus, unlike our study, the physiological data demonstrating feedback inhibition in the Stefanelli study was not performed in a behaviorally labeled engram. Apart from the one set of histological experiments using AAV-SARE-GFP to demonstrate increased GFP labeling of SST neurons in behavior, all other data presented in the Stefanelli study are generated based on artificially generated engrams where optogenetic activation or silencing on granule cells was used to manipulate the numbers of neurons active during a task followed by histological analysis of cFOS staining or behaviors. Thus, the physiological experiments in the Stefanelli et al (2016) generated by wide field activation of a large cohort of GCs labeled by focal virally driven ChR2 expression, were similar to wide field optical stimulation studies in the Braganza 2020 study, and were NOT conducted in a behavioral engram. The strength of our study is in the use of a behaviorally tagged engram neurons for analysis and our findings in sparsely labeled neurons are consistent with the reports in Braganza 2020. We will further clarify in our discussion that the data presented in the Stefanelli study do NOT represent a natural behavior generated engram.

(4) Probably the most convincing finding in this study is the higher zero-time lag correlation of spontaneous EPSCs in labelled vs. unlabeled pairs. Unfortunately, the fact that the authors use spontaneous EPSCs to begin with, which likely represent a mixture of spontaneous release from severed axons, minis, and coordinated discharge from intact axon segments or entire neurons, makes it very hard to determine the meaning and relevance of this finding. At the bare minimum, the authors need to show if and how strongly differences in baseline spontaneous EPSC rates between different cells and slices are contributing to this phenomenon. I would encourage the authors to use low-intensity extracellular stimulation at multiple foci to determine whether labelled pairs really share higher numbers of input from common presynaptic axons or cells compared to unlabeled pairs as they claim. I would also suggest the authors use conventional Cross correlograms (CCG; see e.g. English et al., 2017, Neuron; Senzai and Buzsaki, 2017, Neuron) instead of their somewhat convoluted interval-selective correlation analysis to illustrate co-dependencies between the event time series. The references above also illustrate a more robust approach to determining whether peaks in the CCGs exceed chance levels.

We appreciate the comment can provide additional data on the EPSC frequency in individual labeled and unlabeled cells in the revised manuscript. As indicated in the manuscript, we constrained our analysis to cell pairs with comparable EPSC frequency in order to avoid additional confounds in analysis. We have additional experiments to show that over 50% of the sEPSCs represent action potential driven events which we will include in the revised manuscript. We thank the reviewer for the suggestion to explores alternative methods of analyses including CCGs to further strengthen our findings.

(5) Finally, one of the biggest caveats of the study is that the ensemble is labelled a full week before the slice experiment and thereby represents a latent state of a memory rather than encoding consolidation, or recall processes. The authors acknowledge that in the discussion but they should also be mindful of this when discussing other (especially in vivo) studies and comparing their results to these. For instance, Pignatelli et al 2018 show drastic changes in GC engram activity and features driven by behavioral memory recall, so the results of the current study may be very different if slices were cut immediately after memory acquisition (if that was possible with a different labelling strategy), or if animals were re-exposed to the enriched environment right before sacrificing the animal.

As noted by the reviewer, we fully acknowledge and are cognizant of the concern that slices prepared a week after labeling may not reflect ongoing encoding. Although our data show that labeled cells are reactivated in higher proportion during recall, we have discussed this caveat and will include alternative experimental strategies in the discussion.

Reviewer 3:

(1) Engram cells are (i) activated by a learning experience, (ii) physically or chemically modified by the learning experience, and (iii) reactivated by subsequent presentation of the stimuli present at the learning experience (or some portion thereof), resulting in memory retrieval. The authors show that exposure to Barnes Maze and the enriched environment-activated semilunar granule cells and granule cells preferentially in the superior blade of the dentate gyrus, and a significant fraction were reactivated on re-exposure. However, physical or chemical modification by experience was not tested. Experience modifies engram cells, and a common modification is the Hebbian, i.e., potentiation of excitatory synapses. The authors recorded EPSCs from labeled and unlabeled GCs and SGCs. Was there a difference in the amplitude or frequency of EPSCs recorded from labeled and unlabeled cells?

We agree that we did not examine the physical or chemical modifications by experience. Although we constrained our sEPSC analysis to cell pairs with comparable sEPSC frequency, we will include data on sEPSC parameters in labeled and unlabeled cells in the revised manuscript.

(2) The authors studied five sequential sections, each 250 μm apart across the septotemporal axis, which were immunostained for c-Fos and analyzed for quantification. Is this an adequate sample? Also, it would help to report the dorso-ventral gradient since more engram cells are in the dorsal hippocampus. Slices shown in the figures appear to be from the dorsal hippocampus.

We thank the reviewer for the comment. We analyzed sections along the dorso-ventral gradient. As explained in the methods, there is considerable animal to animal variability in the number of labeled cells which was why we had to use matched littermate pairs in our experiments This variability could render it difficult to tease apart dorsoventral differences.

(3) The authors investigated the role of surround inhibition in establishing memory engram SGCs and GCs. Surprisingly, they found no evidence of lateral inhibition in the slice preparation. Interneurons, e.g., PV interneurons, have large axonal arbors that may be cut during slicing. Similarly, the authors point out that some excitatory connections may be lost in slices. This is a limitation of slice electrophysiology.

We agree that slice physiology has limitations and discuss this caveat. As noted in response (2, we have previously published paired monosynaptic connections from interneurons to GCs (Proddutur  et al 2023) and find the connectivity consistent with published data. However, we agree that recordings demonstrating the presence of SGC/GC to hilar neuron synapses  or recruitment of feedback inhibition by focal activation of GCs would serve to allay concerns regarding slice preparation.

Author response:

The following is the authors’ response to the original reviews.

Reviewer #1 (Public Review):

The study by Chikermane and colleagues investigates the functional, structural, and dopaminergic network substrates of cortical beta oscillations (13-30 Hz). The major strength of the work lies in the methodology taken by the authors, namely a multimodal lesion network mapping. First, using invasive electrophysiological recordings from healthy cortical territories of epileptic patients they identify regions with the highest beta power. Next, they leverage open-access MRI data and PET atlases and use the identified high-beta regions as seeds to find (1) the whole-brain functional and structural maps of regions that form the putative underlying network of high-beta regions and (2) the spatial distribution of dopaminergic receptors that show correlation with nodal connectivity of the identified networks. These steps are achieved by generating aggregate functional, structural, and dopaminergic network maps using lead-DBS toolbox, and by contrasting the results with those obtained from high-alpha regions.

The main findings are:

(1) Beta power is strongest across frontal, cingulate, and insular regions in invasive electrophysiological data, and these regions map onto a shared functional and structural network. (2) The shared functional and structural networks show significant positive correlations with dopamine receptors across the cortex and basal ganglia (which is not the case for alpha, where correlations are found with GABA).

Nevertheless, a few clarifications regarding the choice of high-power electrodes and distributions of functional connectivity maps (i.e., strength and sign across cortex and sub-cortex) can help with understanding the results.

We thank the reviewer for this critical expert assessment. 

Reviewer #1 (Recommendations For The Authors):

To potentially enhance the quality of the manuscript in the current version, I kindly ask the authors to address the following points:

Major:

(A) Power analysis of electrophysiological data

(1) How were significant peaks identified exactly? I understand that the authors used FOOOF methodology to estimate periodic components of brain activity.

Thank you for pointing us to this lack of clarity. The application of FOOOF consists of the fitting of a one-over-f curve that delineates the aperiodic component followed by the definition of gaussians to fit periodic activity. This allows for extraction of periodic peak power estimates that are corrected for offset and exponent of the one-over-f or non-oscillatory aperiodic component in the spectrum (further information can be found here https://fooof-tools.github.io/fooof/auto_tutorials/plot_02-FOOOF.html). We included all peaks that could be fitted using the process.

How about aperiodic components (Figure 1, PSD plots)? 

We share the interest in aperiodic activity with the reviewer. However, given that the primary aim of this study was the description of beta oscillations and the methodology and results presentation is already very complex, we did not include the analysis of aperiodic activity in this manuscript. This could be done in the future and it would surely be interesting to visualize the whole brain connectomic fingerprints of aperiodic exponent and offset. With regard to the purely anatomical description of nonoscillatory aperiodic activity we would like to refer to Figure 8 in Frauscher et al. Brain 2018 (https://doi.org/10.1093/brain/awy035) where this is described. We have decided not to include additional information on this matter, because a) we felt that this would further convolute the results and discussion without directly addressing any of the hypotheses and aims that we set out to tackle and b) the interpretation of aperiodic activity is still a matter of intense research with conflicting results, which warrants very careful considerations of many aspects that again would go beyond the scope of this paper. 

In addition, to what degree would the results change if one identified the peaks relative to sites with no peak, similar to Frauscher et al. 

Beta activity, the oscillation of interest in our analysis is ubiquitous in the brain. In fact, of 1772 channels, only 21 channels did not exhibit a beta peak detectable with FOOOF. Thus, a comparison of 1751 against 21 would not yield meaningful results. We have therefore decided to focus on the channels in which beta activity is the strongest and dominant observable oscillation. 

If the FOOOF approach has some advantages, these should be pointed out or discussed.

FOOOF indeed has the advantage that it provides an objective and reproducible estimation of peak oscillatory activity that accounts for differences in aperiodic activity. To the best of our knowledge, there is no other approach that is nearly as well documented, validated and computationally reproducible. 

Changes in manuscript: We have now further clarified the definition of peak amplitudes in the results and methods section and have discussed the use of alternative measures in the limitations section of our manuscript.

Results: “The frequency band with the highest peak amplitude was identified using the extracted peak parameter (pw) for each channel and depicted as the dominant rhythm for the respective localisation (Figure 1).”

Methods: “Peak height was extracted using the pw parameter, which depicts peak amplitude after subtraction of any aperiodic activity.”

Discussion: “Alternative approaches could yield different results, e.g. reusing channels for each peak that is observable and contrasting them to channels where such peak was not present. However, in our study the majority of channels exhibited beta activity, even if peaks were of low amplitude, which we believe would have led to less interpretable results.”

(2) How exactly do the authors deal with channels with more than one peak? Some elaboration on this and how this could potentially impact the results would be appreciated. Sorry if I have missed it.

Indeed, a description of this was lacking so we are very thankful that the reviewer pointed this out. The maximum peak amplitude method was a winner-takes-all approach where in the case of multiple peaks, the peak with the higher amplitude was chosen. This method of course has drawbacks in the form of lost or disregarded peaks and remains a limitation to this study. 

Changes in manuscript: We have now clarified this in the methods and results sections, which now read: 

Methods: “In case of multiple peaks within the same region, we used only the highest peak amplitude.”

Results: “In case of multiple peaks within the same frequency band, we focused the analysis on the peak with the highest amplitude.”

And added the following to the Limitations section of the discussion: 

“Another limitation in our study is the fact that the statistical approach for the comparison of beta and alpha networks and even for multiple peaks within the same frequency band follows a winner takes all logic that is, by definition, a simplification, as most areas will contribute to more than one spatiospectrally distinct oscillatory network. Specifically, while multiple peaks within or across frequency bands could be present in each channel, we decided to allocate this channel to only the frequency band containing the highest peak amplitude.” 

(B) Network mapping

(1) Knowing that fMRI data are preprocessed by regressing the global signal, there are negative correlations across the functional networks. Unfortunately, the distribution, sign, and strength of the correlations are not quantitatively shown in any of the plots. Thus, it is unclear whether, e.g., corticocortical vs. subcortico-cortical correlations differ in strength and/or sign. I think this additional information is important for better understanding the up/down-regulation of beta, e.g., by DA signaling. Some discussion around this point in addition would be insightful, I think.

The referee is touching upon a very important and difficult point, which we have considered very carefully. Global signal regression is a controversial topic and the neurophysiological basis of negative correlations remains to be elucidated. We can justify our use of this approach based on an expert consensus described in Murphy & Fox 2017 (https://doi.org/10.1016%2Fj.neuroimage.2016.11.052), which highlights that global signal regression can improve the specificity of positive correlations, improve the correspondence to anatomical connectivity. The truth however is that, we relied on it, because it is the more commonly used and validated approach used in lesion network and DBS connectivity mapping and implemented in the Lead Mapper pipeline. Indeed all connectivity estimates are shown in Supplementary figure 3. We remain hesitant to raise the focus to these points, because of the uncertain underlying neural correlates. However, when looking at the values, it is interesting to note that most key regions of interest exhibit positive connectivity values. 

Changes in manuscript: We now point to the supplement containing all connectivity values in the results section more prominently: “All connectivity values including their sign are shown in figures as brain region averages parcellated with the automatic anatomical labelling atlas in supplementary figures 2&3.”

(2) I assume no thresholding is applied to the functional connectivity maps (in a graph-theoretical sense). Please clarify (this is also related to the comment above, in particular, the strength of correlations.

Indeed, we demonstrate SPM maps using family wise error corrected stats in figure 2, but all further analyses were performed on unthresholded maps as correctly pointed out by the referee. 

Changes in manuscript: 

Results: “Specifically, we analysed to what degree the spatial uptake patterns of dopamine, as measurable with fluorodopa (FDOPA; cohort average of 12 healthy subjects) and other dopamine signalling related tracers that bind D1/D2 receptors (average of N=17/44 respectively healthy subjects) or the dopamine transporter (DAT; cohort average of N=180 healthy subjects) were correlated with the unthresholded MRI connectivity maps.”

Methods: “This parcellation was applied to both PET and unthresholded structural and functional connectivity maps using SPM and custom code.”

Minor

(1) Methods, Connectivity analysis: The description of (mass-univariate) GLM analysis is confusing. The maps underwent preprocessing? Which preprocessing steps are meant here? What is the dependent variable and what are the predictors exactly?

We thank the reviewer for catching this error in our methods. We apologise for the confusion and mistake and thank the reviewer for catching it. Indeed, we have used t-tests without further preprocessing instead of a GLM. 

Changes in manuscript: The respective section has been removed from the methods section and intermediate steps have been clarified. The section now reads: “To investigate differences between beta dominant and alpha dominant functional connectivity networks, a two sample t-test was calculated for the condition where beta was greater than alpha and vice versa using SPM. Here, the connectivity maps from each dominant channel (1005 beta functional connectivity maps and 397 alpha connectivity maps) Estimation of model parameters yielded t-values for each voxel, indicating the strength and direction of differences between the two contrasts (beta > alpha, alpha > beta). To address the issue of multiple comparisons, we applied Family-Wise Error (FWE) correction, adjusting significance thresholds such that only voxels with p < 0.05 would be included.”

(2) I encourage the authors to find a better (visual) way of reporting Table 1, to make the main observations easier to grasp and compare (maybe a two-dimensional bar plot? Or color-coding the cells?)

Reply: Thank you for your suggestion to improve the table, the new table is adjusted to the recommended changes to make it more readable.

Reviewer #2 (Public Review):

Summary:

This is a very interesting paper that leveraged several publicly available datasets: invasive cortical recording in epilepsy patients, functional and structural connectomic data, and PET data related to dopaminergic and gaba-ergic synapses. These were combined to create a unified hypothesis of beta band oscillatory activity in the human brain. They show that beta frequency activity is ubiquitous, not just in sensorimotor areas, and cortical regions where beta predominated had high connectivity to regions high in dopamine re-uptake.

Strengths:

The authors leverage and integrate three publicly available human brain datasets in a creative way. While these public datasets are powerful tools for human neuroscience, it is innovative to combine these three types of data into a common brain space to generate novel findings and hypotheses. Findings are nicely controlled by separately examining cortical regions where alpha predominates (which have a different connectivity pattern). GABA uptake from PET studies is used as a control for the specificity of the relationship between beta activity and dopamine uptake. There is much interest in synchronized oscillatory activity as a mechanism of brain function and dysfunction, but the field is short on unifying hypotheses of why particular rhythms predominate in particular regions. This paper contributes nicely to that gap. It is ambitious in generating hypotheses, particularly that modulation of beta activity may be used as a "proxy" for modulating phasic dopamine release.

Weaknesses:

As the authors point out, the use of normative data is excellent for exploring hypotheses but does not address or explore individual variations which could lead to other insights. It is also biased to resting state activity; maps of task-related activity (if they were available) might show different findings.

The figures, results, introduction, and methods are admirably clear and succinct but the discussion could be both shorter and more convincing.

Reviewer #2 (Recommendations For The Authors):

The tone of the discussion is excessively lofty and abstract, and hard to follow in places. Specific examples in comments to authors below.

We thank the reviewer for their positive assessment and their constructive feedback on the discussion. Also in light of the other reviewers we have made a sincere effort to shorten, restructure and improve the discussion. Additionally, we have addressed all the specific comments the reviewer had below. We appended each change to the manuscript where appropriate below and have addressed all comments in the main text. Having that said, we see this paper and discussion to provide our most up-to-date and personal perspective on a correct concept on the interplay of beta oscillations and dopamine that is generalizable. Providing a concept that is so generalizable is very challenging and so far very few authors have even attempted this. One notable exception is the “status quo” concept by Fries & Engel. While we will do our very best to address the comments, we have decided not to deviate from our initial ambition to provide a discussion on a generalizable concept. Naturally such a concept must be very complex and therefore it will be hard to understand in parts. Through the revision, we hope that the readability and comprehensibility has improved, while it provides an in-depth perspective and hypothesis on how beta oscillations, dopamine and their brain circuits may facilitate brain function. Nevertheless, we want to express our honest gratitude for the thoroughness with which the reviewer has read and scrutinized our paper. The review clearly tells that the reviewer had the ambition to follow and understand what we were trying to convey, which can be rare nowadays. We are truly thankful for this.

The first sentence is not quite true, as invasive neurophysiology was not, and cannot be, done in healthy humans. "The present study combined three openly available datasets of invasive neurophysiology, MRI connectomics, and molecular neuroimaging in healthy humans to characterise the spatial distribution of brain regions exhibiting resting beta activity, their shared circuit architecture, and its correlation with molecular markers of dopamine signaling in the human brain."

Changes in manuscript: We have now removed the “healthy” from the respective sentence.

"Our results motivate to conceptualise the capacity to generate.... This is not clear.

Changes in manuscript: “Our results suggest that one common denominator of brain regions that generate beta activity, is their affiliation with beta oscillations as a feature that arises from a largescale global brain network that is modulated by dopamine.”

"Similarly, the robust beta modulation that is elicited by voluntary action in sensorimotor cortex and its correlation with motor symptoms of Parkinson's disease is long known" - the association between movement-related cortical beta desynchronization and Parkinson's motor signs is not well described - could the authors specify and reference this?

We thank the reviewer for pointing out this lack of clarity. We meant that independently beta is known for “movement” and for “movement disorders” and not “movement in movement disorders”. Having that said, there are some studies that suggest that beta ERD is altered in PD (e.g.https://doi.org/10.1093/cercor/bht121), but saying that this is “long known” would be an overstatement and was not our intention. We rephrased this sentence accordingly.

Changes in manuscript: The sentence now reads: “Moreover, the robust beta modulation that is elicited by voluntary action in sensorimotor cortex and its correlation with motor symptoms of Parkinson’s disease is long known.”

"...first fast-cyclic voltammetry experiments that allowed for combined measurement of dopamine release with invasive neurophysiology have provided first evidence that beta band oscillations in healthy non-human primates can differentially link dopamine release, beta oscillations and reward and motor control, depending on the contextual information and striatal domain" - This is not very clear - not sure what "differentially link" signifies.

I think the fact that this is not easy to understand signifies the complexity that we and the authors of the cited paper from Ann Graybiel’s lab aimed to communicate. In fact, we stayed very close to the phrasing used in their paper to try and avoid confusion (Title: Dopamine and beta-band oscillations differentially link to striatal value and motor control” - https://doi.org/10.1126/sciadv.abb9226). The specific results go beyond the scope of the discussion but are very interesting, so I would be happy if our paper would inspire readers to look it up. 

Changes in manuscript: We have now adapted the sentence to “In line with this more complex picture, direct measurement of dopamine concentration in non-human primates revealed specific interactions between dopamine release, beta oscillations, reward value and motor control, depending on contextual information and striatal domain. This shows that the relationship of dopamine and beta activity is not solely associated with either reward or movement and depends on where in the striatum beta activity is recorded.”

"In fact, one could argue that it can be contextualised in a recently described framework of neural reinforcement, that serves to orchestrate the re-entrance and refinement of neural population dynamics for the production of neural trajectories" - this is not clear - for example what is a neural trajectory? What is meant by "re-entrance and refinement"?

A neural trajectory refers to the path that the activity of a neural population takes through a high-dimensional space over time. It can be obtained through multivariate analysis of population activity with dimensionality reduction techniques, such as PCA. The concept of low-dimensional representations of high-dimensional neural activity has gained a lot of attention in computational neuroscience ever since high-channel count recordings of neural population activity have become available (an early and prominent example is Churchland et al., 2012 Nature https://doi.org/10.1038/nature11129 , while a more recent example is Safaie et al., Nature 2023 https://doi.org/10.1038/s41586-023-06714-0). The review we refer to by Rui Costa and colleagues (Athalye, V. R., Carmena, J. M. & Costa, R. M. Neural reinforcement: re-entering and refining neural dynamics leading to desirable outcomes. Curr Opin Neurobiol 60, 145–154 (2020) https://doi.org/10.1016/j.conb.2019.11.023) suggests that dopamine may serve to modulate the likelihood of a specific pattern to emerge and re-enter the cortex – basal ganglia loop, for the “reliable production of neural trajectories driving skillful behavior on-demand”. We believe that this concept could be revolutionary in our understanding of dopaminergic modulation and disoroders and together with colleague Alessia Cavallo have written an invited perspective on this topic (https://doi.org/10.1111/ejn.16222), which may help further clarify the topic. 

Changes in manuscript: We realize that this aspect may sound a bit unclear or far away from the data in this manuscript. However, given that we have spent more than a decade thinking about beta oscillations and how they can be conceptualized, we would prefer not to entirely change our points and rather bet on the possibility that the concepts become more widely accepted and well-known. Nevertheless, we have now adapted the text to make this a bit more clear:

“We hypothesise that, this “status quo” hypothesis could be equally or maybe even more adequately posed on the neural level. Namely, it could provide insights to what degree a certain activity pattern or synaptic connection is to be strengthened or weakened, in light of neural learning. We propose that this putative function can be contextualised in a recently described framework of neural reinforcement, that serves to orchestrate the re-entrance and refinement of neural population dynamics for the production of neural trajectories.”

"....after which it was quickly translated to first experimental studies using cortical or subcortical beta signals in human patients44." - reference 44 only deals with the use of subcortical beta, not cortical, in adaptive control.

The reviewer is right, in fact there is no study using motor cortex beta for adaptive DBS yet, but different studies have used different markers (especially gamma) since then. 

Changes in manuscript: We have rephrased and added citations accordingly: “This approach, also termed adaptive DBS, was first demonstrated based on cortical beta activity that was used to adapt pallidal DBS in the MPTP non-human primate model of PD43. It was quickly translated to first experimental studies using subcortical beta signals in human patients44, followed by further research using more complex cortical and subcortical sensing setups and biomarker combinations45,46.”

The paragraph headed " Implications for neurotechnology" is quite long and should be condensed and focused. It doesn't seem to support the last sentence, "....targeted interventions that can increase and decrease beta activity, as recently shown through phase specific modulation45 could be utilised to mimic phasic dopamine release as a neuroprosthetic approach to alter neural reinforcement38." - I don't quite follow the logic. The authors have clearly shown that beta-related circuits tend to be those linked to dopamine modulation, and may subserve tasks for which reinforcement learning is an important mechanism. However the logic of how modulation of beta activity can "substitute" for modulation of dopamine isn't clear. That would seem to require that the mechanism by which dopamine produces reinforcement, is via an effect on beta oscillation properties (phase, amplitude, frequency). Is there evidence for this? If so it should be better spelled out.

We realize that this is very speculative at this point. Indeed, we believe that subthalamic DBS can mimic dopaminergic control and in the future there may be new treatment avenues, e.g. using neurochemical using neurochemical interfaces for which beta could be informative to mimic dopamine release but ultimately explaining this would be very complex, so we have removed the sentence. With regard to the remaining text in the section, we considered shortening / condensing but felt that this paragraph is highly relevant for the ongoing development of neurotechnology and therefore decided to only remove the first and last sentences.

Changes in manuscript: We have removed the first and last sentences.

"While the abovementioned prospects are promising we should cautiously consider the limitations of our study." - an unnecessary sentence to start a "limitations" section, its clearly a paragraph about limitations. In general, authors should go thru discussion and reduce verbosity; it is not nearly as well edited as the rest of the paper.

Agreed. 

Changes in manuscript: We removed the sentence. 

Reviewer #3 (Public Review):

Summary:

In this paper, Chikermane et al. leverages a large open dataset of intracranial recordings (sEEG or ECoG) to analyze resting state (eyes closed) oscillatory activity from a variety of human brain areas. The authors identify a dominant proportion of channels in which beta band activity (12-30Hz) is most prominent and subsequently seek to relate this to anatomical connectivity data by using the sEEG/ECoG electrodes as seeds in a large set of MRI data from the human connectome project. This reveals separate regions and white matter tracts for alpha (primarily occipital) and beta (prefrontal cortex and basal ganglia) oscillations. Finally, using a third available dataset of PET imaging, the authors relate the parcellated signals to dopamine signaling as estimated by spatial uptake patterns of dopamine, and reveal a significant correlation between the functional connectivity maps and the dopamine reuptake maps, suggesting a functional relationship between the two.

Strengths:

Overall, I found the paper well justified, focused on an important topic, and interesting. The authors' use of 3 different open datasets was creative and informative, and it significantly adds to our understanding of different oscillatory networks in the human brain, and their more elusive relation with neuromodulator signaling networks by adding to our knowledge of the association between beta oscillations and dopamine signaling. Even my main comments about the lack of a theta network analysis and discussion points are relatively minor, and I believe this paper is valuable and informative.

Weaknesses:

The analyses were adequate, and the authors cleverly leveraged these different datasets to build an interesting story. The main aspect I found missing (in addition to some discussion items, see below) was an examination of the theta network. Theta oscillations have been involved in a number of cognitive processes including spatial navigation and memory, and have been proposed to have different potential originating brain regions, and it would be informative to see how their anatomical networks (e.g. as in Figure 2) look like under the author's analyses.

The authors devote a significant portion of the discussion to relating their findings to a popular hypothesis for the function of beta oscillations, the maintenance of the "status quo", mostly in the context of motor control. As the authors acknowledge, given the static nature of the data and lack of behavior, this interpretation remains largely speculative and I found it a bit too far-reaching given the data shown in the paper. In contrast, I missed a more detailed discussion on the growing literature indicating a role for beta in mood (e.g. in Kirkby et al. 2018), especially given the apparent lack of hippocampal and amygdala involvement in the paper, which was surprising.

We thank the reviewer for their insightful review of our manuscript. One of the aims of our paper was to provide the ground for a circuit-based conceptualization of beta activity, which does not primarily relate to behavior. Practically we have the ambition to provide a generalizable concept that can be applied to all behavioral domains including mood. The reason we focus on the “status quo” hypothesis, is that it is one of the very few if not only generalizable concept of the function of beta oscillations. Through our paper and the discussion, we have to redirect this concept towards a less cognitive/behavioral and more anatomical network based domain, while acknowledging principles that may overlap. We realize that this is very ambitious and this endeavour is necessarily very complex and not easy to communicate. In light of the reviewers comments, we have made an effort to improve the discussion as best we could without trailing too far away from what our initial aim was. We are thankful for the suggested reference, which we have now added to the discussion in the section where we have previously discussed beta as biomarker for mood, also noting the absence of beta dominant channels in amygdala and hippocampus. Here it should be clarified however, that a) only three channels were located in the amygdala of which one exhibited beta activity, we should be cautious to not overinterpret this result and b) most channels exhibited beta and just because beta wasn’t dominant, it doesn’t mean that beta is not present or important in these brain areas. Absence of evidence is not evidence for absence with the way we approached the analysis. We are thankful for the interesting reference, which we have now included our discussion. Notably the study used a complex network analysis, which we could not perform because we did not have parallel recordings from these areas in multiple patients. This is now noted in the limitations. 

Changes in manuscript: “For example, it was shown that beta is implicated in working memory28, utilisation of salient sensory cues29, language processing30, motivation31, sleep32, emotion recognition33, mood34 and may even serve as a biomarker for depressive symptom severity in the anterior cingulate cortex35” and “One impactful study reported that beta oscillatory sub-networks of Amygdala and hippocampus could reflect human variations in mood 34. This is interesting, but highlights another relevant limitation of our study, namely that recordings in different areas were stemming from different patients and thus, such sub-network analyses on the oscillatory level could not be conducted.” 

Major comment:

• Although the proportion of electrodes with theta-dominant oscillations was lower (~15%) than alpha (~22%) or beta (~57%), it would be very valuable to also see the same analyses the authors carried out in these frequency bands extended to theta oscillations.

We agree with the reviewer and appreciate the interest in other frequency bands; theta, alpha and gamma. Our primary interest was to provide a network concept of beta activity, but anticipated that interest would go beyond that frequency band. However, we also had to limit ourselves to what is communicable and comprehensible. The key aim for us was to provide a data-driven circuit description of beta activity that can lay ground for a generalizable concept of where beta oscillations emerge. Reproducing all analyses for every frequency band would clutter both the results and the discussion. Moreover, the honest truth is that funding and individual career plans of the researchers currently do not allow to allocate time for a reanalysis of all data which would be a significant effort. Therefore, we have decided to just add the topography of theta and gamma channels as a supplement. In case the reviewer is interested on a collaboration on extending this project to other frequency bands and circuits, we would like to invite them to get in touch and perhaps this could be a new collaborative project. Until then, we have extended our limitation that this would be important work for the future. 

Changes in manuscript: 

We have added and cited the new supplementary figure for the results from theta in the results section, which now reads: 

“Further information on the topography of theta channels are shown in supplementary figure 1.”

We would like to add that a sensible interpretation of results from gamma dominant channels is unlikely to be possible given the low count of channels with prominent resting activity in this frequency band. We have added the following text to the limitations section: “The aim of this study was to elucidate the circuit architecture of beta oscillations, which is why insights from this study for other frequency bands are limited. Future research investigating the specific circuits of theta, alpha and gamma oscillations and their relationship with neurotransmitter uptake could yield new important insights on the networks underlying human brain rhythms.“ 

Reviewer #3 (Recommendations For The Authors):

Minor comments:

• Results: "we performed non-parametric Spearman's correlations between the structural and functional connectivity maps of beta networks with neurotransmitter uptake". This is a significantly complex analysis that requires more detail for the reader to evaluate. There is more detail in the Figure 3 legend but still insufficient. The Methods offer more detail, but I found the description of the parcellation to be vague and I would appreciate a more detailed description.

We thank the reviewer for bringing the insufficient explanation of the methods used to calculate the correlations in analysis to our attention. We have now made an effort to provide more level of detail in the relevant paragraphs. 

Changes in manuscript: We have now made changes to both the Results and Methods sections and added the following explanations respectively:

Results: “Next, we resliced the beta network map and the PET images to allow for a meaningful comparison, using a combined parcellation with 476 brain regions that include cortex19, basal ganglia20, and cerebellum21. Here, each parcel – which was a collection of voxels belonging to a particular brain region – from the connectivity map was correlated with the same parcel containing average neurotransmitter uptake from the respective PET scan (see Figure 3A). In this way nonparametric Spearman’s correlations between PET intensity and structural and functional connectivity maps of beta networks were obtained, which indicate to what degree the spatial distribution of connectivity is similar to the distribution of neurotransmitter uptake.“

Methods: “A custom master parcellation in MNI space was created in Matlab using SPM functions by combining three existing parcellations to include cortical regions19, structures of the basal ganglia20 and cerebellar regions21. Regions that were (partially) overlapping between the atlases were only selected once. The final compound parcellation had 476 regions in total. This parcellation was applied to both PET and structural and functional connectivity maps using SPM and custom code. This allowed for the calculation of spatial correlations, providing a statistical measure of spatial similarity of the PET intensity and MRI connectivity distributions. For this, Spearman’s ranked correlations were used to calculate correlations between the PET images, such as the dopamine aggregate map and both functional and structural beta connectivity networks (Figure 3). The analysis was repeated for individual tracers showing similar results Supplementary figure 2. Finally, to validate these results, a control analysis was performed using a GABA PET scan from the same open dataset of neurotransmitter uptake following the same pipeline (Figure 2A, 2B).”

• All of the recordings were taken in an eyes-closed condition. This is likely to affect the power of alpha oscillations; the authors should comment on this.

We agree with the reviewer that this will likely have influenced the results. However, given that the key result of our paper is the abundance and circuit topography of beta oscillations, it is unlikely that increased alpha in some channels will have led to false positive results for beta. If anything, it may have increased the contrast leading to a more conservative estimate of which channels truly show strong beta dominance. On the other hand, we should acknowledge that this limitation can affect the interpretation of the alpha result. Another reason for us to primarily focus on beta in the discussion and results presentation. 

Changes in manuscript: We now comment on this in the results:

“It should be noted that that alpha recordings were performed in eyes closed which is known to increase alpha power, which may influence the generalizability of the alpha maps to an eyes open condition. However, given that our primary use of alpha was to act as a control, we believe that this should not affect the interpretability of the key findings of our study.” 

• Although the relative proportion of theta and gamma channels is lower, it would be interesting to see the distribution of channels in a SOM figure.

As described above, we have now added supplementary figure 1 that accommodates the topography but not the network analyses.

• Figure legend - typo - "Neither, alpha nor beta" - no comma needed.

Now fixed, thank you for pointing is to this lapse!

• Results: " ere, we aimed to investigate the whole brain circuit representation of beta activity, which is impossible with current neurophysiology approaches" not entirely accurate; suggest rephrasing it to "Here, we aimed to investigate the whole brain circuit representation of beta activity, which is impossible with non-invasive neurophysiology approaches "

Thank you for suggesting the alternative formulation. 

Changes in manuscript: The text has been modified as per the suggestion and now reads “Here, we aimed to investigate the whole brain circuit representation of beta activity, which is impossible with non-invasive neurophysiology approaches”.

• Results - typo - "cortical brain areas, that exhibit resting beta activity share a common brain network" - no comma needed.

Thank you for the suggestion, the comma has been removed to better the flow of the sentence structure as suggested.

Author response:

eLife Assessment

This useful study presents the first detailed and comprehensive description of brain sulcus anatomy of a range of carnivoran species based on a robust manual labeling model allowing species comparisons. Although the database is recognized and the method for reconstructing cortical surfaces is convincing, the evidence supporting the conclusions is incomplete due to the lack of appropriate quantitative measurements and analyses. Considering additional specimens to assess intraspecies variations, as well as exploring the functional correlates of interspecies differences would increase the scope of the study. Setting an instructive foundation for comparative anatomy, this study will be of interest to neuroscientists and neuroimaging researchers interested in that field, as well as in brain morphology and sulcal patterns, their phylogeny, and ontogeny in relation to functional development and behaviour. 

We are pleased that our primary objective of creating a comprehensive framework to navigate carnivoran brains is considered as successfully achieved and that our work is expected to be of broad interest to various disciplines, as it provides the foundation for future investigations into carnivoran brain organization.

As we will set out below, a description of the major sulci is an appropriate measure for large-scale comparative anatomy — it is stable enough in the population of each species to not require a large N, provides a suitable variability across species, and can be related to other aspects of between-species diversity. We will include a number of additional species to increase the scope of the study, as suggested. Although a quantitative assessment of functional correlates is, in principle, beyond the scope of this first foundational paper, we will provide a first start of this as well. We emphasize, however, that this was a secondary outcome, emerging after first application of the framework.

Public Reviews: 

Reviewer #1 (Public review): 

Summary: 

The paper by Boch and colleagues, entitled Comparative Neuroimaging of the Carnivore Brain: Neocortical Sulcal Anatomy, compares and describes the cortical sulci of eighteen carnivore species, and sets a benchmark for future work on comparative brains. 

Based on previous observations, electrophysiological, histological and neuroimaging studies and their own observations, the authors establish a correspondence between the cortical sulci and gyri of these species. The different folding patterns of all brain regions are detailed, put into perspective in relation to their phylogeny as well as their potential involvement in cortical area expansion and behavioral differences. 

Strengths: 

This is a pioneering article, very useful for comparative brain studies and conducted with great seriousness and based on many past studies. The article is well-written and very didactic. The different protocols for brain collection, perfusion, and scanning are very detailed. The images are self-explanatory and of high quality. The authors explain their choice of nomenclature and labels for sulci and gyri on all species, with many arguments. The opening on ecology and social behavior in the discussion is of great interest and helps to put into perspective the differences in folding found at the level of the different cortexes. In addition, the authors do not forget to put their results into the context of the laws of allometry. They explain, for example, that although the largest brains were the most folded and had the deepest folds in their dataset, they did not necessarily have unique sulci, unlike some of the smaller, smoother brains. 

Weaknesses: 

The article is aware of its limitations, not being able to take into account inter-individual variability within each species, inter-hemispheric asymmetries, or differences between males and females. However, this does not detract from their aim, which is to lay the foundations for a correspondence between the brains of carnivores so that navigation within the brains of these species can be simplified for future studies. This article does not include comparisons of morphometric data such as sulci depth, sulci wall surface, or thickness of the cortical ribbon around the sulci. 

We thank the reviewer for their overwhelmingly positive evaluation of our work. As noted by the reviewer, our primary aim was to establish a framework for navigating carnivoran brains to lay the foundation for future research. We are pleased that this objective is deemed as successfully achieved.

As the reviewer points out, we do not quantify within-species intraindividual differences. This is a conscious choice; we aimed to emphasize breadth of species over individuals, as is standard in large-scale comparative anatomy (cf. Heuer et al., 2023, eLife; Suarez et al., 2022, eLife). Following the logic of phylogenetic relationships, the presence of a particular sulcus in related species is also a measure of reliability. We felt safe in this choice, as previous work in both primates and carnivorans has shown that differences across major sulci across individuals are a matter of degree rather than a case of presence or absence (Connolly, 1950, External morphology of the primate brain, C.C. Thomas; Hecht et al., 2019 J Neurosci; Kawamuro 1971 Acta Anat., Kawamuro & Naito, 1977, Acta Anat.). In our revised manuscript, we aim to include some additional individuals of selected species as supplementary material, further illustrating this point.

We feel that measures such as sulci depth, sulci wall surface, or thickness of the cortical ribbon are measures that vary more across individuals and we have therefore not included them in the study. In addition, these are measures that are not generally used as between-species comparative measures, whereas sulcal patterning is (cf. Amiez et al., 2019, Nat Comms; Connolly, 1950; Miller et al., 2021, Brain Behav Evol; Radinsky 1975, J Mammal; Radinsky 1969, Ann N Y Acad Sci; Welker & Campos 1963 J. Comp Neurol).

Reviewer #2 (Public review): 

Summary: 

The authors have completed MRI-based descriptions of the sulcal anatomy of 18 carnivoran species that vary greatly in behaviour and ecology. In this descriptive study, different sulcal patterns are identified in relation to phylogeny and, to some extent, behaviour. The authors argue that the reported differences across families reflect behaviour and electrophysiology, but these correlations are not supported by any analyses. 

Strengths: 

A major strength of this paper is using very similar imaging methods across all specimens. Often papers like this rely on highly variable methods so that consistency reduces some of the variability that can arise due to methodology. 

The descriptive anatomy was accurate and precise. I could readily follow exactly where on the cortical surface the authors referring. This is not always the case for descriptive anatomy papers, so I appreciated the efforts the authors took to make the results understandable for a broader audience. 

I also greatly appreciate the authors making the images open access through their website. 

Weaknesses: 

Although I enjoyed many aspects of this manuscript, it is lacking in any quantitative analyses that would provide more insights into what these variations in sulcal anatomy might mean. The authors do discuss inter-clade differences in relation to behaviour and older electrophysiology papers by Welker, Campos, Johnson, and others, but it would be more biologically relevant to try to calculate surface areas or volumes of cortical fields defined by some of these sulci. For example, something like the endocast surface area measurements used by Sakai and colleagues would allow the authors to test for differences among clades, in relation to brain/body size, or behaviour. Quantitative measurements would also aid significantly in supporting some of the potential correlations hinted at in the Discussion. 

Although quantitative measurements would be helpful, there are also some significant concerns in relation to the specimens themselves. First, almost all of these are captive individuals. We know that environmental differences can alter neocortical development and humans and nonhuman animals and domestication affects neocortical volume and morphology. Whether captive breeding affects neocortical anatomy might not be known, but it can affect other brain regions and overall brain size and could affect sulcal patterns. Second, despite using similar imaging methods across specimens, fixation varied markedly across specimens. Fixation is unlikely to affect the ability to recognize deep sulci, but variations in shrinkage could nevertheless affect overall brain size and morphology, including the ability to recognize shallow sulci. Third, the sample size = 1 for every species examined. In humans and nonhuman animals, sulcal patterns can vary significantly among individuals. In domestic dogs, it can even vary greatly across breeds. It, therefore, remains unclear to what extent the pattern observed in one individual can be generalized for a species, let alone an entire genus or family. The lack of accounting for inter-individual variability makes it difficult to make any firm conclusions regarding the functional relevance of sulcal patterns. 

We thank the reviewer for their assessment of our work. The primary aim of this study was to establish a framework for navigating carnivoran brains by providing a comprehensive overview of all major neocortical sulci across eighteen different species. Given the inconsistent nomenclature in the literature and the lack of standardized criteria (“recipes”) for identifying the major sulci, we specifically focused on homogenizing the terminology and creating recipes for their identification. Moreover, we also generated digital surfaces of all brains and will also add sulcal masks to further facilitate future research building on our framework. We are pleased to hear that we succeeded in our primary objective.

We respectfully disagree with the reviewer on two accounts, where we believe the reviewer is not judging the scope of the current work.

The first is with respect to individual differences. To the best of our knowledge, differences between captive and wild animals, or indeed between individuals, do not affect the presence or absence of any major sulci. No differences in sulcal patterns were detected between captive and (semi-)wild macaques (cf. Sallet et al., 2011, Science; Testard et al., 2022, Sci Adv), different dog breeds (Hecht et al., 2019 J Neurosci) or foxes selectively bred to simulate domestication, compared to controls (Hecht et al., 2021 J. Neurosci). Indeed, we do not find major differences between wolf-like canid species, suggesting that a difference between individuals of the same species is even more unlikely. Nevertheless, we agree with the reviewer that building up a database like ours will benefit from providing as much information about the samples as possible to enable these issues to be tested. We, therefore, will update our table to include if the animals were from captive or wild populations. Moreover, we aim, where possible, to include both wild and captive animals of the same species if they are available in our revision.

The second is in the quantification of structure/function relationships. We believe the sulci atlases themselves are the main deliverables of this project. We felt it prudent to include some qualitative descriptions of the relationship between sulci as we observed them and behaviours as known from the literature as an illustration of the possibilities that this foundational work opens us. This approach also allowed us to confirm previous findings based on observations from a less diverse range of carnivoran species and families (Radinsky 1968 J Comp Neurol; Radinsky 1969, Ann N Y Acad Sci; Welker & Campos 1963 J Comp Neurol; Welker & Seidenstein, 1959 J Comp Neurol). However, a full statistical framework for analysis is beyond the scope of this paper. Our group has previously worked on methods to quantitatively compare brain organization across species — indeed, we have developed a full framework for doing so (Mars et al., 2021, Annu Rev Neurosci), based on the idea that brains that differ in size and morphology should be compared based on anatomical features in a common feature space. Previously, we have used white matter anatomy (Mars et al., 2018, eLife) and spatial transcriptomics (Beauchamp et al., 2021, eLife). The present work presents the foundation for this approach to be expanded to sulcal anatomy, but the full development of this approach will be the topic of future communications.

Nevertheless, we aim to include a first step quantitative analysis of the relationship between the presence and absence of particular sulci and the two behaviours of interest in our manuscript.

We also would like to emphasize that we strongly believe that looking at measures of brain organization at a more detailed level than brain size or relative brain size is informative. Indeed, studies looking at correlations between brain size and particular behavioural variables, although very prominent in the literature, have found it very difficult to distinguish between competing behavioural hypotheses (Healy, 2021, Adaptation and the brain, OUP). In contrast, connectivity has a much more direct relationship to behavioural differences across species (Bryant et al., 2024, bioRxiv), as does sulcal anatomy (Amiez et al., 2019, Nat Comms; Miller et al., 2021, Brain Behav Evol). Moreover, such measures are less sensitive to the effects of fixation since that will affect brain size but not the presence or absence of a sulcus.

Following the reviewer’s recommendations, we will endeavour to include an even broader range of species in the revised version.

Author response:

Public Reviews: 

Reviewer #1 (Public review): 

Summary: 

The authors address a fundamental question for cell and tissue biology using the skin epidermis as a paradigm and ask how stratifying self-renewing epithelia induce diCerentiation and upward migration in basal dividing progenitor cells to generate suprabasal barrier-forming cells that are essential for a functional barrier formed by such an epithelium. The authors show for the first time that an increase in intracellular actomyosin contractility, a hallmark of barrier-forming keratinocytes, is suCicient to trigger terminal diCerentiation. Hence the data provide in vivo evidence of the more general interdependency of cell mechanics and diCerentiation. The data appear to be of high quality and the evidences are strengthened through a combination of diCerent genetic mouse models, RNA sequencing, and immunofluorescence analysis. 

To generate and maintain the multilayered, barrier-forming epidermis, keratinocytes of the basal stem cell layer diCerentiate and move suprabasally accompanied by stepwise changes not only in gene expression but also in cell morphology, mechanics, and cell position. Whether any of these changes is instructive for diCerentiation itself and whether consecutive changes in diCerentiation are required remains unclear. Also, there are few comprehensive data sets on the exact changes in gene expression between diCerent states of keratinocyte diCerentiation. In this study, through genetic fluorescence labeling of cell states at diCerent developmental time points the authors were able to analyze gene expression of basal stem cells and suprabasal diCerentiated cells at two diCerent stages of maturation: E14 (embryonic day 14) when the epidermis comprises mostly two functional compartments (basal stem cells and suprabasal so-called intermediate cells) and E16 when the epidermis comprise three (living) compartments where the spinous layer separates basal stem cells from the barrier-forming granular layer, as is the case in adult epidermis. Using RNA bulk sequencing, the authors developed useful new markers for suprabasal stages of diCerentiation like MafB and Cox1. The transcription factor MafB was then shown to inhibit suprabasal proliferation in a MafB transgenic model. 

The data indicate that early in development at E14 the suprabasal intermediate cells resemble in terms of RNA expression, the barrier-forming granular layer at E16, suggesting that keratinocytes can undergo either stepwise (E16) or more direct (E14) terminal diCerentiation. 

Previous studies by several groups found an increased actomyosin contractility in the barrier-forming granular layer and showed that this increase in tension is important for epidermal barrier formation and function. However, it was not clear whether contractility itself serves as an instructive signal for diCerentiation. To address this question, the authors use a previously published model to induce premature hypercontractility in the spinous layer by using spastin overexpression (K10-Spastin) to disrupt microtubules (MT) thereby indirectly inducing actomyosin contractility. A second model activates myosin contractility more directly through overexpression of a constitutively active RhoA GEF (K10Arhgef11CA). Both models induce late diCerentiation of suprabasal keratinocytes regardless of the suprabasal position in either spinous or granular layer indicating that increased contractility is key to induce late diCerentiation of granular cells. A potential weakness of the K10-spastin model is the disruption of MT as the primary eCect which secondarily causes hypercontractility. However, their previous publications provided some evidence that the eCect on diCerentiation is driven by the increase in contractility (Ning et al. cell stem cell 2021). Moreover, the data are confirmed by the second model directly activating myosin through RhoA. These previous publications already indicated a role for contractility in diCerentiation but were focused on early diCerentiation. The data in this manuscript focus on the regulation of late diCerentiation in barrier-forming cells. These important data help to unravel the interdependencies of cell position, mechanical state, and diCerentiation in the epidermis, suggesting that an increase in cellular contractility in most apical positions within the epidermis can induce terminal diCerentiation. Importantly the authors show that despite contractility-induced nuclear localization of the mechanoresponsive transcription factor YAP in the barrier-forming granular layer, YAP nuclear localization is not suCicient to drive premature diCerentiation when forced to the nucleus in the spinous layer. 

Overall, this is a well-written manuscript and a comprehensive dataset. Only the RNA sequencing result should be presented more transparently providing the full lists of regulated genes instead of presenting just the GO analysis and selected target genes so that this analysis can serve as a useful repository. The authors themselves have profited from and used published datasets of gene expression of the granular cells. Moreover, some of the previous data should be better discussed though. The authors state that forced suprabasal contractility in their mouse models induces the expression of some genes of the epidermal diCerentiation complex (EDC). However, in their previous publication, the authors showed that major classical EDC genes are actually not regulated like filaggrin and loricrin (Muroyama and Lechler eLife 2017). This should be discussed better and necessitates including the full list of regulated genes to show what exactly is regulated. 

We thank all the reviewers for their suggestions and comments.

Thank you especially for the reminder to include gene lists. We had an excel document with all this data but neglected to upload it with the initial manuscript decision. This includes all the gene signatures for the diCerent cell compartments across development. We will also include a page that lists all EDC genes and whether they were up-regulated in intermediate cells and cells in which contractility was induced. Further, we note that all the RNA-Seq datasets are available for use on GEO. 

In our previous publication, we indeed included images showing a lack of change in loricrin and filaggrin in the embryos where spastin was expressed in the diCerentiated epidermis. Consistent with this, there is no change in Lor mRNA levels by RNA-Seq, (it is one of the rare EDC genes that is unchanged). In contrast, Flg mRNA was up in the RNASeq, though we didn’t see a dramatic change in protein levels. We have not further pursued whether this reflects translational regulation. That said, our data clearly show that other genes associated with granular fate were increased in the contractile skin.  

Reviewer #2 (Public review): 

Summary: 

The manuscript from Prado-Mantilla and co-workers addresses mechanisms of embryonic epidermis development, focusing on the intermediate layer cells, a transient population of suprabasal cells that contributes to the expansion of the epidermis through proliferation. Using bulk-RNA they show that these cells are transcriptionally distinct from the suprabasal spinous cells and identify specific marker genes for these populations. They then use transgenesis to demonstrate that one of these selected spinous layer-specific markers, the transcription factor MafB is capable of suppressing proliferation in the intermediate layers, providing a potential explanation for the shift of suprabasal cells into a non-proliferative state during development. Further, lineage tracing experiments show that the intermediate cells become granular cells without a spinous layer intermediate. Finally, the authors show that the intermediate layer cells express higher levels of contractilityrelated genes than spinous layers and overexpression of cytoskeletal regulators accelerates the diCerentiation of spinous layer cells into granular cells. 

Overall the manuscript presents a number of interesting observations on the developmental stage-specific identities of suprabasal cells and their diCerentiation trajectories and points to a potential role of contractility in promoting diCerentiation of suprabasal cells into granular cells. The precise mechanisms by which MafB suppresses proliferation, how the intermediate cells bypass the spinous layer stage to diCerentiate into granular cells, and how contractility feeds into these mechanisms remain open. Interestingly, while the mechanosensitive transcription factor YAP appears deferentially active in the two states, it is shown to be downstream rather than upstream of the observed diCerences in mechanics. 

Strengths: 

The authors use a nice combination of RNA sequencing, imaging, lineage tracing, and transgenesis to address the suprabasal to granular layer transition. The imaging is convincing and the biological eCects appear robust. The manuscript is clearly written and logical to follow. 

Weaknesses: 

While the data overall supports the authors' claims, there are a few minor weaknesses that pertain to the aspect of the role of contractility, The choice of spastin overexpression to modulate contractility is not ideal as spastin has multiple roles in regulating microtubule dynamics and membrane transport which could also be potential mechanisms explaining some of the phenotypes. Use of Arghap11 overexpression mitigates this eCect to some extent but overall it would have been more convincing to manipulate myosin activity directly. It would also be important to show that these manipulations increase the levels of F-actin and myosin II as shown for the intermediate layer. It would also be logical to address if further increasing contractility in the intermediate layer would enhance the diCerentiation of these cells. 

We agree with the reviewer that the development of additional tools to precisely control myosin activity will be of great use to the field. That said, our series of publications has clearly demonstrated that ablating microtubules results in increased contractility and that this phenocopies the eCects of Arhgef11 induced contractility (Ning et al, Cell Stem Cell 2021). Further, we showed that these phenotypes were rescued by myosin inhibition with blebbistatin. Our prior publications also showed a clear increase in junctional acto-myosin through expression of either spastin or Arhgef11, as well as increased staining for the tension sensitive epitope of alpha-catenin (alpha-18) (also in Ning et al, 2021).  We are not aware of tools that allow direct manipulation of myosin activity that currently exist in mouse models.  

The gene expression analyses are relatively superficial and rely heavily on GO term analyses which are of course informative but do not give the reader a good sense of what kind of genes and transcriptional programs are regulated. It would be useful to show volcano plots or heatmaps of actual gene expression changes as well as to perform additional analyses of for example gene set enrichment and/or transcription factor enrichment analyses to better describe the transcriptional programs 

We will include an excel document that lists all the gene signatures. Additionally, all of our data are deposited in GEO for others to perform their own analyses.  

Claims of changes in cell division/proliferation changes are made exclusively by quantifying EdU incorporation. It would be useful to more directly look at mitosis. At minimum Y-axis labels should be changed from "% Dividing cells" to % EdU+ cells to more accurately represent findings 

We will change the axis label to precisely match our analysis.  

Despite these minor weaknesses the manuscript is overall of high quality, sheds new light on the fundamental mechanisms of epidermal stratification during embryogenesis, and will likely be of interest to the skin research community. 

Reviewer #3 (Public review): 

Summary: 

This is an interesting paper by Lechler and colleagues describing the transcriptomic signature and fate of intermediate cells (ICs), a transient and poorly defined embryonic cell type in the skin. ICs are the first suprabasal cells in the stratifying skin and unlike laterdeveloping suprabasal cells, ICs continue to divide. Using bulk RNA seq to compare ICs to spinous and granular transcriptomes, the authors find that IC-specific gene signatures include hallmarks of granular cells, such as genes involved in lipid metabolism and skin barrier function that are not expressed in spinous cells. ICs were assumed to diCerentiate into spinous cells, but lineage tracing convincingly shows ICs diCerentiate directly into granular cells without passing through a spinous intermediate. Rather, basal cells give rise to the first spinous cells. They further show that transcripts associated with contractility are also shared signatures of ICs and granular cells, and overexpression of two contractility inducers (Spastin and ArhGEF-CA) can induce granular and repress spinous gene expression. This contractility-induced granular gene expression does not appear to be mediated by the mechanosensitive transcription factor, Yap. The paper also identifies new markers that distinguish IC and spinous layers and shows the spinous signature gene, MafB, is suCicient to repress proliferation when prematurely expressed in ICs. 

Strengths: 

Overall this is a well-executed study, and the data are clearly presented and the findings convincing. It provides an important contribution to the skin field by characterizing the features and fate of ICs, a much-understudied cell type, at high levels of spatial and transcriptomic detail. The conclusions challenge the assumption that ICs are spinous precursors through compelling lineage tracing data. The demonstration that diCerentiation can be induced by cell contractility is an intriguing finding and adds a growing list of examples where cell mechanics influence gene expression and diCerentiation. 

Weaknesses: 

A weakness of the study is an over-reliance on overexpression and suCiciency experiments to test the contributions of MafB, Yap, and contractility in diCerentiation. The inclusion of loss-of-function approaches would enable one to determine if, for example, contractility is required for the transition of ICs to granular fate, and whether MafB is required for spinous fate. Second, whether the induction of contractility-associated genes is accompanied by measurable changes in the physical properties or mechanics of the IC and granular layers is not directly shown. The inclusion of physical measurements would bolster the conclusion that mechanics lies upstream of diCerentiation. 

We agree that loss of function studies would be useful. For MafB, these have been performed in cultured human keratinocytes, where loss of MafB and its ortholog cMaf results in a phenotype consistent with loss of spinous diCerentiation (Lopes-Pajares, Dev Cell 2015). Due to the complex genetics involved, generating these double mutant mice is beyond the scope of this study. Loss of function studies of myosin are also complicated by genetic redundancy of the non-muscle type II myosin genes, as well as the role for these myosins in actin cross linking in addition to contractility. In addition, we have found that these myosins are quite stable in the embryonic intestine, with loss of protein delayed by several days from the induction of recombination. Therefore, elimination of myosins by embryonic day e14.5 with our current drivers is not likely possible. Thus, generation of inducible inhibitors of contractility is a valuable future goal. 

A number of recent papers have used AFM of skin sections to probe tissue rigidity. We have not attempted these studies and are unclear about the spatial resolution and whether, in the very thin epidermis at these stages we could spatially resolve diCerences. That said, we previously assessed the macro-contractility of tissues in which myosin activity was induced and demonstrated that there was a significant increase in this over a tissue-wide scale (Ning et al, Cell Stem Cell, 2021).  

Finally, whether the expression of granular-associated genes in ICs provides them with some sort of barrier function in the embryo is not addressed, so the role of ICs in epidermal development remains unclear. Although not essential to support the conclusions of this study, insights into the function of this transient cell layer would strengthen the overall impact.  

By traditional dye penetration assays, there is no epidermal barrier at the time that intermediate cells exist. One interpretation of the data is that cells are beginning to express mRNAs (and in some cases, proteins) so that they are able to rapidly generate a barrier as they become granular cells. We have attempted experiments to ablate intermediate cells with DTA expression - this resulted in ineCicient and delayed cell death and thus did not yield strong conclusions. Our findings that transcriptional regulators of granular diCerentiation (such as Grhl3 and Hopx) are also present in intermediate cells, should allow future analysis of the eCects of their ablation on the earliest stages of granular diCerentiation from intermediate cells.

Author response:

The following is the authors’ response to the original reviews.

Reviewer #1 (Public Review):

This paper aims to address the establishment and maintenance of neural circuitry in the case of a massive loss of neurons. The authors used genetic manipulations to ablate the principal projection neurons, the mitral/tufted cells, in the mouse olfactory bulb. Using diphtheria toxin (Tbx21-Cre:: loxP-DTA line) the authors ablated progressively large numbers of M/T cells postnatally. By injecting diphtheria toxin (DT) into the Tbx21-Cre:: loxP-iDTR line, the authors were able to control the timing of the ablation in the adult stage. Both methods led to the successful elimination of a majority of M/TCs by 4 months of age. The authors made a few interesting observations. First, they found that the initial pruning of the remaining M/T cell primary dendrite was unaffected. However, in adulthood, a significant portion of these cells extended primary dendrites to innervate multiple glomeruli. Moreover, the incoming olfactory sensory neuron (OSN) axons, as examined for those expressing the M72 receptor, showed a divergent innervation pattern as well. The authors conclude that M/T cell density is required to maintain the dendritic structures and the olfactory map. To address the functional consequences of eliminating a large portion of principal neurons, the authors conducted a series of behavioral assays. They found that learned odor discrimination was largely intact. On the other hand, mating and aggression were reduced. The authors concluded that learned behaviors are more resilient than innate ones.

The study is technically sound, and the results are clear-cut. The most striking result is the contrast between the normal dendritic pruning during early development and the expanded dendritic innervation in adulthood. It is a novel discovery that can lead to further investigation of how the single-glomerulus dendritic innervation is maintained. The authors conducted a

few experiments to address potential mechanisms, but it is inconclusive, as detailed below. It is also interesting to see that the massive neuronal loss did not severely impact learned odor discrimination. This result, together with previous studies showing nearly normal odor discrimination in the absence of large portions of the olfactory bulb or scrambled innervation patterns, attests to the redundancy and robustness of the sensory system. The discussion should take into account these other studies in a historical context.

Main comments:

(1) In previous studies, it has been concluded that dendritic pruning unfolds independently, regardless of the innervation pattern or activity of the OSNs. The new observation bolsters this conclusion by showing that a loss of neighboring M/T cells does not affect the developmental process. A more nuanced discussion comparing the results of these studies would strengthen the paper.

We thank the reviewer for the suggestion. We now include an extended discussion citing relevant previous works in the manuscript (Lines 351-374).

(2) The authors propose that a certain density of M/T is required to prevent the divergent innervation of primary dendrites, but the evidence is not sufficient to support this proposal. The experiment with low-dose DT injection to ablate a smaller portion of M/T cells did not change the percentage of cells innervating two or more glomeruli. The authors suggest that a threshold must be met, but this threshold is not determined.  

In our experiments using high-dose DT, we hypothesized that there may be many empty glomeruli (glomeruli not innervated by M/T cells), and as a result, that some of the remaining M/T cells could branch their apical dendrite tuft into multiple empty glomeruli. To test this hypothesis, we carried out another experiment using a lower dose of DT. In this experiment, the fraction of remaining M/T cells was 25% (~10,000 M/T cells), which was higher than with the high DT dose (5%, or around 2,000 M/T cells) , but still significantly lower than wild type mice (~40,000 cells M/T cells). With around 2,000 glomeruli and 10,000 M/T per bulb, it could be expected that each glomerulus would be innervated by ~5 M/T cells (on average). However, we found that the percentage of M/T cells projecting to multiple glomeruli (around 40%) was similar when either 10,000 or 2,000 of M/T remained in the bulb. In addition, it is important to emphasize that even in wt animals with a full set of M/T cells, a small percentage of M/T cells still innervate more than one glomerulus (Lin et al., 2000). Together, these observations suggest that the innervation of multiple glomeruli by M/T cells is not simply due to the presence of empty glomeruli, and that our hypothesis was not correct.

We have added a comment explaining this issue in the Results section (Lines 200-203).

(3) The authors suggest that neural activity is not required for this plasticity. The evidence was derived primarily from naris occlusion and neuronal silencing using Kir2.1. While the results are consistent with the notion, it is a rather narrow interpretation of how neural activity affects circuit configuration. Perturbation of neural activity also entails an increase in firing. Inducing the activity of the neurons may alter this plasticity. Silencing per se may induce a homeostatic response that expands the neurite innervation pattern to increase synaptic input to compensate for the loss of activity. Thus, further silencing the cells may not reduce multiglomerular innervation, but an increased activity may.

The experiments with Kir2.1 demonstrate that the structural plasticity observed after reducing the total number of M/T cells in an animal is not regulated by the firing action potentials in the remaining cells. Instead, this experiment indicates that the observed structural plasticity may be regulated by other types of mechanisms (including increased synaptic excitation as suggested by the reviewer) that do not require the firing of action potentials in M/T cells. 

We now have included a comment regarding this point (Lines 243-247).  

(4) There is a discrepancy between this study and the one by Fujimoto et al. (Developmental Cell; 2023), which shows that not only glutamatergic inputs to the primary dendrite can facilitate pruning of remaining dendrites but also Kir2.1 overexpression can significantly perturb dendritic pruning. This discrepancy is not discussed by the authors.

We agree that it would be useful to contrast these two works.

In our experiments, performed in adult animals, we blocked sensory input by performing naris occlusion before we induced ablation of M/T cells. In a separate experiment, also in adult animals, we expressed the Kir2.1 channel, to reduce the ability of neurons to fire action potentials. With both types of manipulations, we observed that the ablation of a large fraction of M/T cells still caused the remaining M/T cells to maintain a single apical dendrite that sprouts several new tufts towards multiple glomeruli. A recent paper (Fujimoto et al., 2023)) in which Kir2.1 was expressed in a large percentage of M/T starting during embryonic development showed that these “silent” M/T cells failed to prune their arbors to a single dendrite. In aggregate, these observations indicate that action potentials are necessary for the normal pruning that occurs during perinatal development (Fujimoto et al., 2023), but are not required for the expansion of dendritic trees caused by ablating a large fraction of M/T cells in adult animals (our current manuscript).

We have now explained the differences between both studies in the manuscript (Lines 427-439).

(5) An alternative interpretation of the discrepancy between the apparent normal pruning by p10 and expanded dendritic innervation in adulthood is that there are more cells before P10, when ~25% of M/T cells are present, but at a later date only 1-3% are present. 

The relationship between the number of M/T cells and single glomerulus innervation has not been explored during postnatal development. It would be important to test this hypothesis.

We agree with this comment, and in lines 375-381 we discuss the discrepancy between normal refinement during development, and dendritic sprouting in adults.

Cre is expressed in M/T cells and it induces DTA expression starting around P0. The elimination of M/T cells starts at this time, and continues until by P10, when more than 75% of M/T have been eliminated. At P21 more than 90% of M/T have been eliminated, and their number remains stable thereafter.

Pruning of the dendrites of M/T cells starts at P0 and it is mostly complete by P10. Therefore, it is possible that between P0 and P7, when dendrites are being pruned, the number of M/T cells remaining in the bulb is still over a threshold that does not interfere with the process of normal dendrite pruning. We agree that it would be very informative to perform additional experiments in the future where a large set of M/T cells could be ablated before pruning occurs (ideally before P0). 

(6) The authors attribute the change in the olfactory map to the loss of M/T cells. Another obvious possibility is that the diffused projection is a response to the change in the olfactory bulb size. With less space to occupy, the axons may be forced to innervate neighboring glomeruli. It is not known how the total number of glomeruli is affected. This question could be addressed by tracking developmental changes in bulb volume and glomerular numbers.

Certainly, this is a possibility, and we have now included a comment on this regard in the manuscript (Lines 473-480). 

We believe that there are three likely scenarios that could account for these observations:

(a) After ablating M/T cells, the tufts of the remaining M/T cells sprout into multiple glomeruli, and this causes the axons of OSNs to project into multiple glomeruli.

(b) Ablating M/T cells may cause changes in other OB cells that make synapses in the glomeruli (ETCs, PGCs, sAC, etc…), and the misrouting of OSN axons that we observed in our experiments may be a secondary effect caused by the elimination of M/T cells.

(c) After ablating the majority of M/T cells, the olfactory bulb gets reduced in size, and the axons of OSNs find it difficult to precisely converge on a target that now has become smaller. As a result, the axons of OSNs fail to converge on single glomeruli.

(7) The retained ability to discriminate odors upon reinforced training is not surprising in light of a number of earlier studies. For example, Slotnick and colleagues have shown that rats losing ~90% of the OB can retain odor discrimination. Weiss et al have shown that humans without an olfactory bulb can perform normal olfactory tasks. Gronowitz et al have used theoretical prediction and experimental results to demonstrate that perturbing the olfactory map does not have a major impact on olfactory discrimination. Fleischmann et al have shown that mice with a monoclonal nose can discriminate odors. The authors should discuss their results in these contexts.

We apologize for this important oversight - we now include a more elaborate discussion including the relevant references as suggested in the manuscript (Line 483-496).

(8) It should be noted that odor discrimination resulting from reinforcement training does not mean normal olfactory function. It is a highly artificial situation as the animals are overtrained. It should not be used as a measure of the robustness of the olfactory sense. Natural odor discrimination (without training), detection threshold, and innate appetitive/aversive response to certain odors may be affected. These experiments were not conducted.

We agree that the standard tests commonly used to measure olfactory function require substantial training, and thus, are quite artificial. However, these tests are used because they allow a more precise quantification of olfactory function than those relying on natural behaviors.  

We have now included a few sentences to address this point in the results (Lines 321322) and discussion sections (Lines 541-543).

(9) The social behaviors were conducted using relatively coarse measures (vaginal plug and display of aggression). Moreover, these behaviors are most likely affected by the disruption of the AOB mitral cells and have little to do with the dendritic pruning process described in the paper. It is misleading to lump social behaviors with innate responses to odors.

This point follows the same logic as the previous one. The olfactory tests that rely on natural behaviors are quite coarse and difficult to quantify. In contrast, the olfactory tests using apparatuses such as olfactometers can be quantified with precision, but they are artificial. We agree that some of the naturalistic behaviors that we studied such as mating or aggression may depend to a large extent on the AOB (although it is possible that the MOB may also be involved in these tasks to a degree). In our initial version of the manuscript, we commented on the anticipated relative involvement of the MOB and AOB in the studied tasks, but we have now added some additional sentences to make this point clearer. In addition, we now add a comment indicating that it is possible that the abnormal behaviors could simply be due to a reduction in the number of AOB M/T cells (~98.5% and ~ 85% elimination of M/T cells in the AOB in Tbx::DTA and Tbx::iDTR mice, respectively), regardless of the abnormal dendritic pruning of main OB M/T cells (Lines 530-534).

See Figure 5E - M/T cells in AOB (Lines 1238-1239). 

Reviewer #2 (Public Review):

The authors make the interesting observation that the developmental refinement of apical M/T cell dendrites into individual glomeruli proceeds normally even when the majority of neighboring M/T cells are ablated. At later stages, the remaining neurons develop additional dendrites that invade multiple glomeruli ectopically, and similarly, OSN inputs to glomeruli lose projection specificity as well. The authors conclude that the normal density of M/T neurons is not required for developmental refinement, but rather for maintaining specific connectivity in adults.

The observations are indeed quite striking; however, the authors' conclusions are not entirely supported by the data.

(1) It is unclear whether the expression of diphtheria toxin that eventually leads to the ablation of the large majority of M/T neurons compromises the cell biology of the remaining ones.

DT is an extremely potent toxin that kills cells by inhibiting proteins translation, and it has been demonstrated that the presence of a single DT molecule in a cell is sufficient to kill it, because of its highly efficient catalytic activity. Accordingly, previous experiments have shown that DT kills cells within a few hours after its appearance in the cytoplasm (Yamaizumi et al., 1978). In other words, all the published evidence suggests that if a cell is exposed to the action of DT, that cell will die shortly. There is no evidence that cells exposed to DT can survive and experience long-term effects. Finally, previous works have not observed any long-term changes in neurons directly caused by the actions of DT (Johnson et al., 2017).

(2) The authors interpret the growth of ectopic dendrites later in life as a lack of maintenance of dendrite structure; however, maybe the observed changes reflect actually adaptations that optimize wiring for extremely low numbers of M/T neurons. The finding that olfactory behavior was less affected than predicted supports this interpretation.

We do not know the cellular or molecular mechanisms that explain why reducing the density of M/T cells is followed by the growth of ectopic dendrites from the remaining M/T cells. We agree that the functional outcome of growing ectopic dendrites may result in an optimization of wiring in the bulb and could explain why olfactory function is relatively preserved. We now include a comment regarding this possibility (Lines 513-525).   

(3) The number of remaining M/T neurons is much higher at P10 than later. Can the relatively large number of remaining neurons (or their better health status) be the reason that dendrites refine normally at the early developmental stages rather than a (currently unknown) developmental capacity that preserves refinement?

We thank the reviewer for the suggestion, which was also raised by reviewer 1. 

We agree with this comment, and in lines 375-381 we discuss the discrepancy between normal refinement during development, and dendritic sprouting in adults.

Cre is expressed in M/T cells and it induces DTA expression starting around P0. The elimination of M/T cells starts at this time, and continues until by P10, when more than 75% of M/T have been eliminated. At P21 more than 90% of M/T have been eliminated, and their number remains stable thereafter.

Pruning of the dendrites of M/T cells starts at P0 and it is mostly complete by P10. Therefore, it is possible that between P0 and P7, when dendrites are being pruned, the number of M/T cells remaining in the bulb is still over a threshold that does not interfere with the process of normal dendrite pruning. We agree that it would be very informative to perform additional experiments in the future where a large set of M/T cells could be ablated before pruning occurs (ideally before P0). 

(4) While the effect of reduced M/T neuron density on both M/T dendrites and OSN axons is described well, the relationship between both needs to be characterized better: Is one effect preceding the other or do they occur simultaneously? Can one be the consequence of the other?

Previous works have demonstrated that disrupting the topographic projection of the OSN axons has no effect on the structure of the apical dendrite of M/T cells (Ma et al., 2014; Nishizumi et al., 2019). Our experiments ablating a large fraction of M/T cells suggest that they are necessary for the correct targeting of OSN axons into the bulb. However, our experiments do not allow us to tell apart these 2 scenarios: 

(a) the ablation of a large fraction of M/T cells directly causes the sprouting of the apical dendrite of M/T cells, and that this sprouting in turn causes the abnormal projection of OSN axons onto the bulb. 

(b) the ablation of a large fraction of M/T cells first causes the axons of OSN to project abnormally onto multiple glomeruli in the bulb, and this in turn causes the dendrite of remaining M/T cells to sprout onto multiple glomeruli. 

We now include a comment on the manuscript explaining this point. (Lines 473-492)

(5) Page 7: the observation that not all neurons develop additional dendrites is not a sign of differences between cell types, it may be purely stochastic.

This is correct, and we mention these 2 scenarios in the discussion (Line 407-408). 

(6) Page 8: the fact that activity blockade did not affect the formation of ectopic dendrites does not suggest that the process is not activity-dependent: both manipulations have the same effect and may just mask each other.

The experiments with Kir2.1 demonstrate that the structural plasticity observed after reducing the total number of M/T cells in an animal is not regulated by the firing action potentials in the remaining cells. Instead, this experiment indicates that the observed structural plasticity may be regulated by other types of mechanisms (including increased synaptic excitation as suggested by the reviewer) that do not require the firing of action potentials in M/T cells. 

We now have included a comment regarding this point (Lines 243-247).  

(7) It remains unclear how the observed structural changes can explain the behavioral effects.

We agree that the relationship between structural changes and behavior was not appropriately explained in our manuscript. Our manipulations cause two major changes in the olfactory system, one primary, and several secondary. The primary change is a large reduction in the number of M/T cells both in the MOB and AOB. This reduction in M/T cell number triggers significant secondary changes in the connectivity of the bulb, including an abnormal projection of OSNs onto the OB, and the growth of ectopic dendrites from the remaining M/T cells into multiple glomeruli.

The behavioral abnormalities displayed by these mice is ultimately caused by the reduction in the number of M/T cells, but it is likely that the secondary structural changes could regulate some of the behavioral phenomena that we observed. For example, in principle, it is possible that the ectopic dendrites innervating several glomeruli could help the bulb to perceive smells with a much reduced number of M/T cells. On the other hand, this promiscuous growth of dendrites into multiple glomeruli could make it more difficult for the animals to discriminate between smells. The same argument could be made about the fact that OSN axons project onto multiple glomeruli: we simply do not know if this change helps or makes it more difficult for the animal to detect smells.  

We now include a comment regarding this issue (Lines 513-525).   

Reviewer #1 (Recommendations For The Authors):

Additional experiments and a more thorough discussion of the results, as suggested in the public review, would significantly strengthen the paper. Below are some specific parts that need to be addressed.

There is a lack of information on how M/T cell numbers are quantified. Without the information, it is difficult to evaluate the claim. Using the tdTomato signal may miss cells that are not labeled due to the transgenic effect. 

Although we cannot conclude that we are identifying the complete set of M/T cells (because the transgenic lines may fail to label some M/T cells), the number of M/T cells that we observed is similar to that previously reported (Richard et al., 2010). This concern has been included in the Results section (Lines 121-124).

A more detailed description about M/T cells quantification has been added into the method section (Lines 627-632).

There is a lack of information on the timeline of treatment and how measurement of the olfactory bulb volume is conducted.

We now include a more detailed description of how the volume of the OB was measured in the methods (Lines 621-623).

The volume measurement is inconsistent with the pictures shown. In Figure 1, supplemental data 2 panels B and C, it appears that the bulbs in DTA and DTR mice are about half in length in each dimension. This would translate into ~1/8 of the volume of the control mice.

We measured the volume of the bulbs based on the Neurolucida reconstructions, and we observed that in both DTA and iDTR mice the volumes of their bulbs are roughly 50% compared to a wild type mouse. In Figure 1 - figure supplement 2 the sections that were shown for wild type, DTA and iDTR mice were not taken at the same position in the bulb, and this gave the impression that the bulbs from DTA and iDTR were much smaller than they really are. We now show sections for these three animals at equivalent positions in the bulb. 

Figure 1 E and F have no legend.

We apologize for this mistake - we have now added the legend for Figures 1E and F (Lines 1009-1013).

Figure 3, supplemental data 2, it is not clear what the readers should be looking at. The data is confusing even for experts in the field. The authors should describe the figures more clearly, pointing out what they are supposed to show.

We apologize for this, and we have now added a more detailed description of Figure3 – figure supplement 2 (Lines 1153-1167).

In several figures, it is not clearly written what the comparisons were for where there are indications of statistical significance above the bars.

We have now included a more detailed description of the statistics comparison in the figure legends.

AAV serotype should be specified.

The AAV serotype used to label M/T cells was the AAV-PHP.eB. We have added this information in the methods section of the manuscript. 

Reviewer #2 (Recommendations For The Authors):

Minor points

Page 5, para 2: "The decrease in neuronal plasticity with age": it is unclear what "the decrease" refers to.

We have changed this sentence in the text to make it clear:

“The decrease in structural plasticity of M/T cells after apical dendrite refinement (Mizrahi and Katz, 2003),….”

Line 146-148

Is there a quantification of the effect of Kir2.1 overexpression alone (example shown in Figure 3D)?

We did an experiment in IDTR animals in which a fraction of M/T cells expressed Kir2.1, and we split these animals in 2 groups: (a) animals that received an injection of DT, and (b) animals that did not receive any DT. We quantified the effect of Kir2.1 on M/T cells from animals that received DT injection (with an ablation of around of 90% of M/T cells) and we did not observe any clear statistically significant differences between cells expressing Kir2.1 or neurons that did not express Kir2.1 from other iDTR animals that also received DT injections. We did not quantify the possible effects of kir2.1 in the group of animals that did not receive DT because on a first inspection we did not observe any clear differences between Kir2.1 cells and neighboring wild type cells. 

References

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Johnson RE, Tien N-W, Shen N, Pearson JT, Soto F, Kerschensteiner D. 2017. Homeostatic plasticity shapes the visual system’s first synapse. Nat Commun 8:1220. doi:10.1038/s41467-017-01332-7

Lin DM, Wang F, Lowe G, Gold GH, Axel R, Ngai J, Brunet L. 2000. Formation of precise connections in the olfactory bulb occurs in the absence of odorant-evoked neuronal activity. Neuron 26:69–80. doi:10.1016/s0896-6273(00)81139-3

Ma L, Wu Y, Qiu Q, Scheerer H, Moran A, Yu CR. 2014. A developmental switch of axon targeting in the continuously regenerating mouse olfactory system. Science 344:194–197. doi:10.1126/science.1248805

Nishizumi H, Miyashita A, Inoue N, Inokuchi K, Aoki M, Sakano H. 2019. Primary dendrites of mitral cells synapse unto neighboring glomeruli independent of their odorant receptor identity. Commun Biol 2:1–12. doi:10.1038/s42003-018-0252-y

Richard MB, Taylor SR, Greer CA. 2010. Age-induced disruption of selective olfactory bulb synaptic circuits. Proc Natl Acad Sci U S A 107:15613–15618. doi:10.1073/pnas.1007931107

Yamaizumi M, Mekada E, Uchida T, Okada Y. 1978. One molecule of diphtheria toxin fragment A introduced into a cell can kill the cell. Cell 15:245–250. doi:10.1016/0092-8674(78)90099-5

Author response:

The following is the authors’ response to the original reviews.

Reviewer #1 (Public Review):

Summary:

In their manuscript, "Nicotine enhances the stemness and tumorigenicity in intestinal stem cells via Hippo-YAP/TAZ and Notch signal pathway", authors Isotani et al claimed that this study identifies a NIC-triggered pathway regulating the stemness and tumorigenicity of ISCs and suggest the use of DBZ as a potential therapeutic strategy for treating intestinal tumors. However, the presented data do not support the primary claims.

Weaknesses:

My main reservation is that the quality of the results presented in the manuscript may not fully substantiate their conclusions. For instance, in Figure 2 A and B, it is challenging to discern a healthy organoid. This is significant, as the entirety of Figure 2 and several panels in Figures 3 - 5 are based on these organoid assays. Additionally, there seems to be a discrepancy in the quality of results from the western blot, as the lanes of actin do not align with other proteins (Figure 6B).

We directly count organoids under microscopy as described previously (Igarashi M et.al., Cell.2016 Igarashi M et.al., Aging Cell.2019). When we count the number of organoids, we exactly can discern which are alive or dead organoids under microscope. Hence, we will detail the method and show which are alive or dead organoids using arrows in our revised version (Figure2A and B).

Moreover, as reviewer1 pointed out, the number of organoids originated from intestinal or colonic crypts can be affected by dead organoids as in Figure2A and 2B. However, almost all colonies from isolated intestinal stem cells (ISCs) (Figure 2C and D) are alive, so the number of colonies are less affected by dead colonies in those experiments using isolated ISCs. Since all organoid data in Figure 3-5 are based on the same method as that of Figure2C and D, the data quality of Figures 3-5 cannot be affected by dead colonies.

Finally, to improve data quality of Figure6B, we repeated this experiments and replaced it by new figures.

Reviewer #2 (Public Review):

Summary:

The manuscript by Isotani et al characterizes the hyperproliferation of intestinal stem cells (ISCs) induced by nicotine treatment in vivo. Employing a range of small molecule inhibitors, the authors systematically investigated potential receptors and downstream pathways associated with nicotine-induced phenotypes through in vitro organoid experiments. Notably, the study specifically highlights a signaling cascade involving α7-nAChR/PKC/YAP/TAZ/Notch as a key driver of nicotine-induced stem cell hyperproliferation. Utilizing a Lgr5CreER Apcfl/fl mouse model, the authors extend their findings to propose a potential role of nicotine in stem cell tumorgenesis. The study posits that Notch signaling is essential during this process.

Strengths and Weaknesses:

One noteworthy research highlight in this study is the indication, as shown in Figure 2 and S2, that the trophic effect of nicotine on ISC expansion is independent of Paneth cells. In the Discussion section, the authors propose that this independence may be attributed to distinct expression patterns of nAChRs in different cell types. To further substantiate these findings, it is suggested that the authors perform tissue staining of various nAChRs in the small intestine and colon. This additional analysis would provide more conclusive evidence regarding how stem cells uniquely respond to nicotine. It is also recommended to present the staining of α7-nAChR from different intestinal regions. This will provide insights into the primary target sites of nicotine in the gut tract. Additionally, it is recommended that the authors consider rephrasing the conclusion in this section (lines 123-124). The current statement implies that nicotine does not affect Paneth cells, which may be inaccurate based on the suggestion in line 275 that nicotine might influence Paneth cells through α2β4-nAChR. Providing a more nuanced conclusion would better reflect the complexity of nicotine's potential impact on Paneth cells.

It was difficult to obtain nAchRs antibodies usable in immunostaining. Hence, we instead performed qPCR of nAchRs in ISCs and Paneth cells from isolated whole small intestine (new Figure3C), although we cannot know the difference of the nAchRs expression in different intestinal regions by this method. Although the comparatively high expression was observed in α7-nAChR and α8nAChR in both ISCs and Paneth cells, the significant difference between ISCs and Paneth cells were not observed (Figure3C). 

Interestingly, nicotine up-regulated only the expression of α7-nAChR in ISCs, suggesting the specifical response of α7-nAChR to nicotine (Figures 3C and D). We paraphrased the conclusion of the paragraph according to reviewer’s suggestion.

As shown in the same result section, the effect of nicotine on ISC organoid formation appears to be independent of CHIR99021, a Wnt activator. Despite this, the authors suggest a potential involvement of Wnt/β-catenin activation downstream of nicotine in Figure 4F. In the Lgr5CreER Apcfl/fl mouse model, it is known that APC loss results in a constitutive stabilization of β-catenin, thus the hyperproliferation of ISCs by nicotine treatment in this mouse model is likely beyond Wnt activation. Therefore, it is recommended that the authors reconsider the inclusion of Wnt/β-catenin as a crucial signaling pathway downstream of nicotine, given the experimental evidence provided in this study.

We appreciate for this important suggestion. Certainly, Wnt/β-catenin was activated in Nicotine treated ISCs. However, as reviewer points out, the hyperproliferation of ISCs by nicotine treatment is likely beyond Wnt activation.  According to the reviewer’s suggestion, we removed Wnt/β-catenin as a crucial signaling pathway downstream of nicotine (Figure 5G).

In Figure 4, the authors investigate ISC organoid formation with a panPKC inhibitor, revealing that PKC inhibition blocks nicotine-induced ISC expansion. It's noteworthy that PKC inhibitors have historically been used successfully to isolate and maintain stem cells by promoting self-renewal. Therefore, it is surprising to observe no effect or reversal effect on ISCs in this context. A previous study demonstrated that the loss of PKCζ leads to increased ISC activity both in vivo and in vitro (DOI: 10.1016/j.celrep.2015.01.007). Additionally, to strengthen this aspect of the study, it would be beneficial for the authors to present more evidence, possibly using different PKC inhibitors, to reproduce the observed results with Gö 6983. This could help address potential concerns or discrepancies and contribute to a more comprehensive understanding of the role of PKC in nicotine-induced ISC expansion.

Gö 6983 is a pan-PKC inhibitor against for PKCα, PKCβ, PKCγ, PKCδ and PKCζ with IC50 of 7 nM, 7 nM, 6 nM, 10 nM and 60 nM, respectively. Since we used Gö 6983 at the concentration of 10nM in our experiment, we consider PKCζ may not be possible target of nicotine. Additionally, we treated using 5nM Sotrastaurin, another pan-PKC inhibitor, which is supposed not to affect PKCζ. The observed result with Gö 6983 was reproduced by Sotrastaurin (Supplemental Figure 3E).

An additional avenue that could enhance the clinical relevance of the study is the exploration of human datasets. Specifically, leveraging scRNA-seq datasets of the human intestinal epithelium (DOI: 10.1038/s41586-021-03852-1) could provide valuable insights. Analyzing the expression patterns of nAChRs across diverse regions and cell types in the human intestine may offer a potential clinical implication.

We analyzed distribution pattern nAChRs of by scRNA-seq datasets of the human intestinal epithelium (DOI: 10.1038/s41586-021-03852-1). In consistent with mouse data (Figure3C), the expression of human α7-nAChR is higher than that of other nAChRs. The difference of the expression between ISCs and Paneth cells is not clear as in that of mouse (Supplemental Figure4A and B). From mouse and human data, we speculate the induction of specific nAChR by nicotine is essence of ISC response to nicotine, rather than the distribution of nAChRs.

Reviewer #2 (Recommendations For The Authors):

The manuscript could benefit from addressing a few minor points to enhance its quality before publication:

(1) Ensure all images are presented in higher resolution to improve visual clarity.

We replaced all images by those with higher resolution.

(2) Quantify Western blot results accurately for rigor and precision in data representation.

We quantified all blots.

(3) Include error bars in control groups where missing, particularly in Figures 3C and 4D, to enhance data interpretation.

We included error bars in control groups in new Figure 3C and 4D.

(4) The layout of Figure S3B, S4A and S4B should be corrected.

We corrected the layout of those Figures.

Author response:

The following is the authors’ response to the original reviews.

Reviewer #1 (Public Review): 

Petty and Bruno investigate how response characteristics in the higher-order thalamic nuclei POm (typically somatosensory) and LP (typically visual) change when a stimulus (whisker air puff or visual drifting grating) of one or the other modality is conditioned to a reward. Using a two-step training procedure, they developed an elegant paradigm, where the distractor stimulus is completely uninformative about the reward, which is reflected in the licking behavior of trained mice. While the animals seem to take on to the tactile stimulus more readily, they can also associate the reward with the visual stimulus, ignoring tactile stimuli. In trained mice, the authors recorded single-unit responses in both POm and LP while presenting the same stimuli. The authors first focused on POm recordings, finding that in animals with tactile conditioning POm units specifically responded to the air puff stimulus but not the visual grating. Unexpectedly, in visually conditioned animals, POm units also responded to the visual grating, suggesting that the responses are not modality-specific but more related to behavioral relevance. These effects seem not be homogeneously distributed across POm, whereas lateral units maintain tactile specificity and medial units respond more flexibly. The authors further ask if the unexpected cross-modal responses might result from behavioral activity signatures. By regressing behavior-coupled activity out of the responses, they show that late activity indeed can be related to whisking, licking, and pupil size measures. However, cross-modal short latency responses are not clearly related to animal behavior. Finally, LP neurons also seem to change their modality-specificity dependent on conditioning, whereas tactile responses are attenuated in LP if the animal is conditioned to visual stimuli.

The authors make a compelling case that POm neurons are less modality-specific than typically assumed. The training paradigm, employed methods, and analyses are mostly to the point, well supporting the conclusions. The findings importantly widen our understanding of higher-order thalamus processing features with the flexibility to encode multiple modalities and behavioral relevance. The results raise many important questions on the brain-wide representation of conditioned stimuli. E.g. how specific are the responses to the conditioned stimuli? Are thalamic cross-modal neurons recruited for the specific conditioned stimulus or do their responses reflect a more global shift of attention from one modality to another? 

To elaborate on higher-order thalamic activity in relationship to conditioned behavior, a trialby-trial analysis would be very useful. Is neuronal activity predictive of licking and at which relative timing? 

To elaborate on the relationship between neuronal activity and licking, we have created a new supplementary figure (Figure S1), where we present the lick latency of each mouse on the day of recording. We also perform more in-depth analysis of neural activity that occurs before lick onset, which is presented in a new main figure (new Figure 4). 

Furthermore, I wonder why the (in my mind) major and from the data obvious take-away, "POm neurons respond more strongly to visual stimuli if visually conditioned", is not directly tested in the summary statistics in Figure 3h.

We have added a summary statistic to Figure 3h and to the Results section (lines 156-157) comparing the drifting grating responses in visually and tactilely conditioned mice.  

The remaining early visual responses in POm in visually conditioned mice after removing behavior-linked activity are very convincing (Figure 5d). It would help, however, to see a representation of this on a single-neuron basis side-by-side. Are individual neurons just coupled to behavior while others are independent, or is behaviorally coupled activity a homogeneous effect on all neurons on top of sensory activity?

In lieu of a new figure, we have performed a new analysis of individual neurons to classify them as “stimulus tuned” and/or “movement tuned.” We find that nearly all POm cells encode movement and arousal regardless of whether they also respond to stimuli. This is presented in the Results under the heading “POm correlates with arousal and movement regardless of conditioning” (Lines 219-231).

The conclusions on flexible response characteristics in LP in general are less strongly supported than those in POm. First, the differentiation between POm and LP relies heavily on the histological alignment of labeled probe depth and recording channel, possibly allowing for wrong assignment. 

We appreciate the importance in differentiating between POm, LP, and surrounding regions to accurately assign a putative cell to a brain region. The method we employed (aligning an electrode track to a common reference atlas) is widely used in rodent neuroscience, especially in regions like POm and LP which are difficult to differentiate molecularly (for example, see Sibille, Nature Communications, 2022; and Schröder, Neuron, 2020). 

Furthermore, it seems surprising, but is not discussed, that putative LP neurons have such strong responses to the air puff stimuli, in both conditioning cases. In tactile conditioning, LP air puff responses seem to be even faster and stronger than POm. In visual conditioning, drifting grating responses paradoxically seem to be later than in tactile conditioning (Fig S2e). These differences in response changes between POm and LP should be discussed in more detail and statements of "similar phenomena" in POm and LP (abstract) should be qualified.  

We have further developed our analysis and discussion of LP activity. Our analysis of LP stimulus response latencies are now presented in greater detail in Figure S3, and we have expanded the results section accordingly (lines 266-275). We have also expanded the discussion section to both address these new analyses and speculate on what might drive these surprising “tactile responses” in LP.

Reviewer #2 (Public Review): 

Summary  

This manuscript by Petty and Bruno delves into the still poorly understood role of higherorder thalamic nuclei in the encoding of sensory information by examining the activity in the Pom and LP cells in mice performing an associative learning task. They developed an elegant paradigm in which they conditioned head-fixed mice to attend to a stimulus of one sensory modality (visual or tactile) and ignore a second stimulus of the other modality. They recorded simultaneously from POm and LP, using 64-channel electrode arrays, to reveal the contextdependency of the firing activity of cells in higher-order thalamic nuclei. They concluded that behavioral training reshapes activity in these secondary thalamic nuclei. I have no major concerns with the manuscript's conclusions, but some important methodological details are lacking and I feel the manuscript could be improved with the following revisions.

Strengths 

The authors developed an original and elegant paradigm in which they conditioned headfixed mice to attend to a stimulus of one sensory modality, either visual or tactile, and ignore a second stimulus of the other modality. As a tactile stimulus, they applied gentle air puffs on the distal part of the vibrissae, ensuring that the stimulus was innocuous and therefore none aversive which is crucial in their study. 

It is commonly viewed that the first-order thalamus performs filtering and re-encoding of the sensory flow; in contrast, the computations taking place in high-order nuclei are poorly understood. They may contribute to cognitive functions. By integrating top-down control, high-order nuclei may participate in generating updated models of the environment based on sensory activity; how this can take place is a key question that Petty and Bruno addressed in the present study.

Weaknesses  

(1) Overall, methods, results, and discussion, involving sensory responses, especially for the Pom, are confusing. I have the feeling that throughout the manuscript, the authors are dealing with the sensory and non-sensory aspects of the modulation of the firing activity in the Pom and LP, without a clear definition of what they examined. Making subsections in the results, or a better naming of what is analyzed could convey the authors' message in a clearer way, e.g., baseline, stim-on, reward.  

We thank Reviewer 2 for this suggestion. We have adjusted the language throughout the paper to more clearly state which portions of a given trial we analyzed. We now consistently refer to “baseline,” “stimulus onset,” and “stimulus offset” periods. 

In line #502 in Methods, the authors defined "Sensory Responses. We examined each cell's putative sensory response by comparing its firing rate during a "stimulus period" to its baseline firing rate. We first excluded overlapping stimuli, defined as any stimulus occurring within 6 seconds of a stimulus of a different type. We then counted the number of spikes that occurred within 1 second prior to the onset of each stimulus (baseline period) and within one second of the stimulus onset (stimulus period). The period within +/-50ms of the stimulus was considered ambiguous and excluded from analysis." 

Considering that the responses to whisker deflection, while weak and delayed, were shown to occur, when present, before 50 ms in the Pom (Diamond et al., 1992), it is not clear what the authors mean and consider as "Sensory Responses"? 

We have addressed this important concern in three ways. First, we have reanalyzed our data to include the 50ms pre- and post-stimulus time windows that were previously excluded. This did not qualitatively change our results, but updated statistical measurements are reflected in the Results and the legends of figures 3 and 7. Second, we have created a new figure (new Figure 4) which provides a more detailed analysis of early POm stimulus responses at a finer time scale. Third, we have amended the language throughout the paper to refer to “stimulus responses” rather than “sensory responses” to reflect how we cannot disambiguate between bottom-up sensory input and top-down input into POm and LP with our experimental setup. We refer only to “putative sensory responses” when discussing lowlatency (<100ms) stimulus responses.

Precise wording may help to clarify the message. For instance, line #134: "Of cells from tactilely conditioned mice, 175 (50.4%) significantly responded to the air puff, as defined by having a firing rate significantly different from baseline within one second from air puff onset (Figure 3d, bottom)", could be written "significantly responded to the air puff" should be written "significantly increased (or modified if some decreased) their firing rate within one second after the air puff onset (baseline: ...)". This will avoid any confusion with the sensory responses per se.

We have made this specific change suggested by the reviewer (lines 145-146) and made similar adjustments to the language throughout the manuscript to better communicate our analysis methods. 

(2) To extend the previous concern, the latency of the modulation of the firing rate of the Pom cells for each modality and each conditioning may be an issue. This latency, given in Figure S2, is rather long, i.e. particularly late latencies for the whisker system, which is completely in favor of non-sensory "responses" per se and the authors' hypothesis that sensory-, arousal-, and movement-evoked activity in Pom are shaped by associative learning. Latency is a key point in this study. 

Therefore, 

- latencies should be given in the main text, and Figure S2 could be considered for a main figure, at least panels c, d, and e, could be part of Figure 3. 

- the Figure S2b points out rather short latency responses to the air puff, at least in some cells, in addition to late ones. The manuscript would highly benefit from an analysis of both early and late latency components of the "responses" to air puffs and drafting grating in both conditions. This analysis may definitely help to clarify the authors' message. Since the authors performed unit recordings, these data are accessible.

- it would be highly instructive to examine the latency of the modulation of Pom cells firing rate in parallel with the onset of each behavior, i.e. modification of pupil radius, whisking amplitude, lick rate (Figures 1e, g and 3a, b). The Figure 1 does not provide the latency of the licks in conditioned mice.

- the authors mention in the discussion low-latency responses, e.g., line #299: "In both tactilely and visually conditioned mice, movement could not explain the increased firing rate at air puff onset. These low-latency responses across conditioning groups is likely due in part to "true" sensory responses driven by S1 and SpVi."; line #306: "Like POm, LP displayed varied stimulus-evoked activity that was heavily dependent on conditioning. LP responded to the air puff robustly and with low latency, despite lacking direct somatosensory inputs."  But which low-latency responses do the authors refer to? Again, this points out that a robust analysis of these latencies is missing in the manuscript but would be helpful to conclude.

We have moved our analysis of stimulus response latency in POm to new Figure 4 in the main text and have expanded both the Results and Discussion sections accordingly. We have also analyzed the lick latency on the day of recording, included in a new supplemental Figure S1. 

(3) Anatomical locations of recordings in the dorsal part of the thalamus. Line #122 "Our recordings covered most of the volume of POm but were clustered primarily in the anterior and medial portions of LP (Figure 2d-f). Cells that were within 50 µm of a region border were excluded from analysis." 

How did the authors distinguish the anterior boundary of the LP with the LD nucleus just more anterior to the LP, another higher-order nucleus, where whisker-responsive cells have been isolated (Bezdudnaya and Keller, 2008)? 

Cells within 50µm of any region boundary were excluded, including those at the border of LP and LD. We also reviewed our histology images by eye and believe that our recordings were all made posterior of LD. 

(4) The mention in the Methods about the approval by an ethics committee is missing.  All the surgery (line #381), i.e., for the implant, the craniotomy, as well as the perfusion, are performed under isoflurane. But isoflurane induces narcosis only and not proper anesthesia. The mention of the use of analgesia is missing. 

We thank Reviewer 2 for drawing our attention to this oversight. All experiments were conducted under the approval of the Columbia University IACUC. Mice were treated with the global analgesics buprenorphine and carprofen, the local analgesic bupivacaine, and anesthetized with isoflurane during all surgical procedures. We have amended the Methods section to include this information (Lines 458-470).

Reviewer #3 (Public Review): 

Petty and Bruno ask whether activity in secondary thalamic nuclei depends on the behavioral relevance of stimulus modality. They recorded from POm and LP, but the weight of the paper is skewed toward POm. They use two cohorts of mice (N=11 and 12), recorded in both nuclei using multi-electrode arrays, while being trained to lick to either a tactile stimulus (air puff against whiskers, first cohort) or a visual stimulus (drifting grating, second cohort), and ignore the respective other. They find that both nuclei, while primarily responsive to their 'home' modality, are more responsive to the relevant modality (i.e. the modality predicting reward). 

Strengths: 

The paper asks an important question, it is timely and is very well executed. The behavioral method using a delayed lick index (excluding impulsive responses) is well worked out. Electrophysiology methods are state-of-the-art with information about spike quality in Figure S1. The main result is novel and important, convincingly conveying the point that encoding of secondary thalamic nuclei is flexible and clearly includes aspects of the behavioral relevance of a stimulus. The paper explores the mapping of responses within POm, pointing to a complex functional structure, something that has been reported/suggested in earlier studies. 

Weaknesses: 

Coding: It does not become clear to which aspect of the task POm/LP is responding. There is a motor-related response (whisking, licking, pupil), which, however, after regressing it out leaves a remaining response that the authors speculate could be sensory.

Learning: The paper talks a lot about 'learning', although it is only indirectly addressed. The authors use two differently (over-)trained mice cohorts rather than studying e.g. a rule switch in one and the same mouse, which would allow us to directly assess whether it is the same neurons that undergo rule-dependent encoding. 

We disagree that our animals are “overtrained,” as every mouse was fully trained within 13 days. We agree that it would be interesting to study a rule-switch type experiment, but such an experiment is not necessary to reveal the profound effect that conditioning has on stimulus responses in POm and LP. 

Mapping: The authors treat and interpret the two nuclei very much in the same vein, although there are clear differences. I would think these differences are mentioned in passing but could be discussed in more depth. Mapping using responses on electrode tracks is done in POm but not LP.

The mapping of LP responses by anatomical location is presented in the supplemental Figure S4 (previously S3). We have expanded our discussion of LP and how it might differ from POm.

Reviewer #1 (Recommendations For The Authors):  

Minor writing issues: 

122 ...67 >LP< cells?

301 plural "are”

We have fixed these typos.

Figure issues

*  3a,b time ticks are misaligned and the grey bar (bottom) seems not to align with the visual/tactile stimulus shadings.

*  legend to Figure 3b refers to Figure 1c which is a scheme, but if 1g is meant, this mouse does not seem to have a session 12? 

*  3c,e time ticks slightly misaligned. 

*  5e misses shading for the relevant box plots, assuming it should be like Figure 3h.  

We thank Reviewer 1 for pointing out these errors. We have adjusted Figures 1, 3, and 5 accordingly.

Analyses 

I am missing a similar summary statistics for LP as in Figure 3h 

We have added a summary box chart of LP stimulus responses (Figure 7g), similar to that of POm in Figure 3. We have also performed similar statistical analyses, the results of which are presented in the legend for Figure 7. 

Reviewer #2 (Recommendations For The Authors): 

More precisions are required for the following points: 

(1) The mention of the use of analgesia is missing and this is not a minor concern. Even if the recordings are performed 24 hours after the surgery for the craniotomy and screw insertion and several days after the main surgery for the implant, taking into account the pain of the animals during surgeries is crucial first for ethical reasons, and second because it may affect the data, especially in Pom cells: pain during surgery may induce the development of allodynia and/or hyperalgesia phenomenae and Pom responses to sensory stimuli were shown to be more robust in behavioral hyperalgesia (Masri et al., 2009).  

We neglected to include details on the analgesics used during surgery and post-operation recovery in our original manuscript. Mice were administered buprenorphine, carprofen, and bupivacaine immediately prior to the head plate surgery and were treated with additional carprofen during recovery. Mice were similarly treated with analgesics for the craniotomy procedure. Mice were carefully observed after craniotomy, and we saw no evidence of pain or discomfort. Furthermore, mice performed the behavior at the same level pre- and postcraniotomy (now presented in Figure 1j), which also indicates that they were not in any pain. 

(2) The head-fixed preparation is only poorly described.

Line #414: "Prior to conditioning, mice were habituated to head fixation and given ad libitum water in the behavior apparatus for 15-25 minutes." 

And line #425 "Mice were trained for one session per day, with each session consisting of an equal number of visual stimuli and air puffs. Sessions ranged from 20-60 minutes and about 40-120 of each stimulus. " 

More details should be given about the head-fixation training protocol. Are 15-25 minutes the session time duration, 60 minutes, or other time duration? How long does it take to get mice well trained to the head fixation, and on which criteria?  

Line #389: "Mice were then allowed to recover for 24 hours, after which the sealant was removed and recordings were performed. At the end of experiments,"

The timeline is not clear: is there one day or several days of recordings? 

We have expanded on our description of the head fixation protocol in the Methods. We describe in more detail how mice were habituated to head fixation, the timing of water restriction, and the start of conditioning/training (Habituation and Conditioning, lines 492-500).

(4) Line #411: "Mice were deprived of water 3 days prior to the start of conditioning" followed by line #414 "Prior to conditioning, mice were habituated to head fixation and given ad libitum water in the behavior apparatus for 15-25 minutes".

If I understood correctly, the mice were then not fully water-deprived for 3 days since they received water while head-fixed. This point may be clarified. 

We addressed these concerns in the changes to the Methods section mentioned in the preceding point (3).

(5) Line #157: "Modality selectivity varies with anatomical location in Pom" while the end of the previous paragraph is "This suggests that POm encoding of reward and/or licking is insensitive to task type, an observation we examine further below."

The authors then come to anatomical concerns before coming back to what the Pom may encode in the following section. This makes the story quite confusing and hard to follow even though pretty interesting.  

We have reordered our Figures and Results to improve the flow of the paper and remove this point of confusion. We now present results on the encoding of movement before analyzing the relationship between POm stimulus responses and anatomical location. What was old Figure 5 now precedes what was old Figure 4.

(6) Licks Analysis. Line #99 "However, this mouse also learned that the air puff predicted a lack of reward in the shaping task, as evidenced by withholding licking upon the onset of the air puff. The mouse thus displayed a positive visual lick index and a negative tactile lick index, suggesting that it attended to both the tactile and visual stimuli (Figure 1f, middle arrow)."

Line #105 "All visually conditioned mice exhibited a similar learning trajectory (Figure 1i left, 1j left)". 

Interestingly, the authors revealed that mice withheld licking upon the onset of the air puff in the visual conditioning, which they did not do at the onset of the drifting grating in the tactile conditioning. This withholding was extinguished after the 8th session, which the authors interpret as the mice finally ignoring the air puff. Is this effect significant, is there a significant withholding licking upon the onset of the air puff on the 12 tested mice? 

The withholding of licking was significant (assessed with a sign-rank test) in visually conditioned mice prior to switching to the full version of the task. Indeed, it was the abolishment of this effect after conditioning with the full version of the task that was our criterion for when a mouse was fully trained. We have elaborated on this in the Habituation and Conditioning section in the Methods.

(1) Throughout the manuscript "Touch" is used instead of passive whisker deflection, and may be confusing with "active touch" for the whisker community readers. I recommend avoiding using "touch" instead of "passive whisker deflection".

We appreciate that “touch” can be an ambiguous term in some contexts. However, we have limited our use of the word to refer to the percept of whisker deflection; we do not describe the air puff stimulus as a “touch.” We respectfully would like to retain the use of the word, as it is useful for comparing somatosensory stimuli to visual stimuli.

(2) Line #395: "Air puffs (0.5-1 PSI) were delivered through a nozzle (cut p1000 pipet tip, approximately 3.5mm diameter aperture)".

Are air puffs of <1 PSI applied, not <1 bar?  

We thank Reviewer 3 for pointing out this inaccuracy. The air puffs were indeed between 0.5 and 1 bar, not PSI. We have addressed this in the Methods.

(3) Line #441: "In the full task, the stimuli and reward were identical, but stimuli were presented at uncorrelated and less predictable intervals."  Do the authors mean that all stimuli are rewarded?  

The stimuli and reward were identical between the shaping and full versions of the task. In the full version of the task, the unrewarded stimulus was truly uncorrelated with reward, rather than anticorrelated. 

(4) Line #445 "for a mean ISI of 20 msec." ISI is not defined, I guess that it means interstimulus interval. Even if pretty obvious, to avoid any confusion for future readers, I would recommend using another acronym, especially in a manuscript about electrophysiology, since ISI is a dedicated acronym for inter-spike interval. 

We have defined the acronym ISI as “inter-stimulus interval” when first introduced in the results (Line 82) and in the Methods (Line 511).

(5) Line #416 "In the first phase of conditioning ("shaping"), mice were separated into two cohorts: a "tactile" cohort and a "visual" cohort. Mice were presented with tactile stimuli (a two-second air puff delivered to the distal whisker field) and visual stimuli (vertical drifting grating on a monitor). Throughout conditioning, mice were monitored via webcam to ensure that the air puff only contacted the whiskers and did not disturb the facial fur nor cause the mouse to blink, flinch, or otherwise react - ensuring the stimulus was innocuous. The stimulus types were randomly ordered. In the visual conditioning cohort, the visual stimulus was paired with a water reward (8-16µL) delivered at the time of stimulus offset. In the tactile conditioning cohort, the reward was instead paired with the offset of the air puff. Regardless of the type of conditioning, stimulus type was a balanced 50:50 with an inter-stimulus interval of 8-12 seconds (uniform distribution)." 

The mention of the "full version of the task" will be welcome in this paragraph to clarify what the task is for the mouse in the Methods part.

We have more clearly defined the full version of the task in a later paragraph (line 506). We believe this addresses the potential confusion caused by the original description of the conditioning paradigm. 

(6) Line #467: "Units were assigned to the array channel on which its mean waveform was largest". 

Should it read mean waveform "amplitude"? 

This is correct, we have adjusted the statement accordingly. 

(7) Line #482 "The eye camera was positioned on the right side of the face and recorded at 60 fps." Then line #487 "The trace of pupil radius over time was smoothed over 5 frames (8.3 msec).” 5 frames, with a 60fps, represent then 83 ms and not 8.3 ms.

We have corrected this error.  

(8) Line #121: "257 POm cells and 67 cells from 12 visually conditioned mice" 

67 LP cells, LP is missing 

We have corrected this error. 

(9) Line #354: "A consistent result of attention studies in humans and nonhuman primates is the enhancement of cortical and thalamic sensory responses to an attended visual stimuli. Here, we show not just enhancement of sensory responses to stimuli within a single modality, but also across modalities. It is worth investigating further how secondary thalamus and high-order sensory cortex encode attention to stimuli outside of their respective modalities. Our surprising conclusion that the nuclei are equivalently activated by behaviorally relevant stimuli is nevertheless compatible with these previous studies."  Since higher-order thalamic nuclei are integrative centers of many cortical and subcortical inputs, they cannot be viewed simply as relay nuclei, and there is therefore no "surprising" conclusion in these results. Not surprising, but still an elegant demonstration of the contextdependent activity/responses of the Pom/LP cells. 

We disagree. Visual stimuli activating strong POm responses and tactile stimuli activating strong LP responses - however they do it - is a surprising result. We agree that higher-order thalamic nuclei are integrative centers, but exactly what they integrate and what the integrated output means is still poorly understood.

Author response:

The following is the authors’ response to the original reviews.

Reviewer #1 (Public Review):

The models described are not fundamentally novel, essentially a random intercept model (with a warping function), and some flexible covariate effects using splines (i.e., additive models).

We respectfully but strongly disagree with the reviewer’s assessment of the novelty of our work. The models referred to by the reviewer as “random intercept models … and some flexible covariate effects” seem to relate to the estimation of normative models derived cross-sectionally as developed in and adopted from previous work, not to the work presented here. To be clear, the contributions of this work are: (i) a principled methodology to make statistical predictions for individual subjects in longitudinal studies based on a novel z-diff score, (ii) an approach to transfer information large scale normative models estimated on large scale cross-sectional data to longitudinal studies (iii) an extensive theoretical analysis of the properties of this approach and (iv) empirical evaluation on an unpublished psychosis dataset. Put simply, we provide the ability to estimate within subject change in normative models which until now only provide the ability to show a subject's position in the normative range at a given timepoint. With the exception of the reference [13] cited in the main text, we are not aware of any methods available that can achieve this. Based on this feedback combined with the feedback of the Reviewer 2, we now improved our introduction and clearly state our contribution right from the outset of the manuscript whilst also shortening the introduction to make it more concise. In this work, we are trying to be very transparent in showing to the reader that our method builds on a previously peer-reviewed model.

The assumption of constant quantiles is very strong, and limits the utility of the model to very short term data.

We now provide an extensive theoretical analysis of our approach (section 2.1.3), where we show that this assumption is actually not strictly necessary and that our approach yields valid inferences even under much milder assumptions. More specifically, we first provide a mathematical grounding for the assumption we made in the initial submission, then generalise our method to a wider class of residual processes and show that our original assumption of constant quantiles is not too restrictive. We also provide a simulation study to show how the practitioner can evaluate the validity and implications of this assumption on a case-by-case basis. This generalisation is described in depth in section 2.1.3.

The schizophrenia example leads to a counter-intuitive normalization of trajectories, which leads to suspicions that this is driven by some artifact of the data modeling/imaging pipelines.

We understand that the observed normalisation effects might appear surprising. As we outlined in our provisional response, we would like to emphasise that there is increasing evidence that the old neurodegenerative view of psychosis is an oversimplification and that trajectories of cortical thickness are highly variable across different individuals after the first psychotic episode. More specifically, we have shown in an independent sample and with different methodology that individuals treated with second-generation antipsychotics and with careful clinical follow-up can show normalisation of cortical thickness atypicalities after the first episode (https://www.medrxiv.org/content/10.1101/2024.04.19.24306008v2, now accepted in Schizophrenia Bulletin). These results are well-aligned with the results we show in this manuscript. We now added remarks on this topic into the discussion. We would also like to re-emphasise that the data were processed with the utmost rigour using state of the art processing pipelines including quality control, which we have reported as transparently as possible. The confidence that the results are not ‘driven by some artifact of the data modeling/imaging pipelines’ is also supported by the fact that analysis of a group of healthy controls did not show any significant z-diffs (see Discussion section), neither frontally nor elsewhere. If the reviewer believes there are additional quality control checks that would further increase confidence in our findings, we would welcome the reviewer to provide specific details.

The method also assumes that the cross-sectional data is from a "healthy population" without describing what this population is (there is certainly every chance of ascertainment bias in large scale studies as well as small scale studies). This issue is completely elided over in the manuscript.

Indeed, we do not describe the cross-sectional population used for training the models, as these models were already trained and published with in-depth description of the datasets used for the training (https://elifesciences.org/articles/72904). We now make this more explicit in the section 2.1.1. of the manuscript (page 7), and also more explicitly acknowledge the possibility of ascertainment bias in the simulation section 2.1.4. However, we would like to emphasise that such ascertainment bias is not in any way specific to the analyses we report. In fact it is present in all studies that utilise large scale cohorts such as UK Biobank. Indeed, we are currently working on another manuscript to address this question in detail, but given the complexity of this problem and the fact that many publicly available legacy studies simply do not record sufficient demographic information, e.g. to assess racial bias properly, we believe that this is beyond the scope of the current work.

Reviewer #2 (Public Review):

The organization and clarity of this manuscript need enhancement for better comprehension and flow. For example, in the first few paragraphs of the introduction, the wording is quite vague. A lot of information was scattered and repeated in the latter part of the introduction, and the actual challenges/motivation of this work were not introduced until the 5th paragraph.

As noted above in our response to Reviewer 1, we significantly pruned the introduction, stating our objective in the first paragraph and elaborating on the topic later in the text. We hope that it is now less repetitive and easier to follow.

There are no simulation studies to evaluate whether the adjustment of the crosssectional normative model to longitudinal data can make accurate estimations and inferences regarding the longitudinal changes. Also, there are some assumptions involved in the modeling procedure, for example, the deviation of a healthy control from the population over time is purely caused by noise and constant variability of error/noise across x_n, and these seem to be quite strong assumptions. The presentation of this work's method development would be strengthened if the authors can conduct a formal simulation study to evaluate the method's performance when such assumptions are violated, and, ideally, propose some methods to check these assumptions before performing the analyses.

This comment encouraged us to zoom out from our original assumption and generalise our method to a wider class of residual processes (stationary Gaussian processes) in section 2.1.3. We now present a theoretical analysis of our model to show that our original assumption (of stable quantiles plus noise) is actually not necessary for valid inference in our method, which broadens the applicability of our method. Of course, we also discuss in what way the original assumption is restrictive and how it aligns with the more general dynamics. We also include a simulation study to evaluate the method's performance and elucidate the role of the more general dynamics in section 2.1.4.

The proposed "z-diff score" still falls in the common form of z-score to describe the individual deviation from the population/reference level, but now is just specifically used to quantify the deviation of individual temporal change from the population level. The authors need to further highlight the difference between the "z-score" and "z-diff score", ideally at its first mention, in case readers get confused (I was confused at first until I reached the latter part of the manuscript). The z-score can also be called a measure of "standardized difference" which kind of collides with what "z-diff" implies by its name.

We added the mention of the difference between z-score and z-diff score into the last paragraph of introduction.

Explaining that one component of the variance is related to the estimation of the model and the other is due to prediction would be helpful for non-statistical readers.

We now added an interpretation of the z-score in the original model below equation 7.

It would be easier for the non-statistical reader if the authors consistently used precision or variance for all variance parameters. Probably variance would be more accessible.

This was a very useful observation, we unified the notation and now only use variance.

The functions psi were never explicitly described. This would be helpful to have in the supplement with a reference to that in the paper.

Indeed, while describing the original model we had to make choices about how to condense the necessary information from the original model so that we can build upon it. As the phi function is only used for data transformation in the original model, we did not further elaborate on it, however, we now refer to the specific section of the original paper of Fraza et al. 2021 where it is described more in detail (https://www.sciencedirect.com/science/article/pii/S1053811921009873).

What is the goal of equations (13) and (14)? The authors should clarify what the point of writing these equations is prior to showing the math. It seems like it is to obtain an estimate of \sigma_{\ksi}^2, which the reader only learns at the end.

We corrected the formatting.

What is the definition of "adaption" as used to describe equation (15)? In this equation, I think norm on subsample was not defined.

We added a more detailed description of the adaptation after equation 15.

"(the sandwich part with A)" - maybe call this an inner product so that it is not confused with a sandwich variance estimator. This is a bit unclear. Equation (8) does have the inner product involving A and \beta^{-1} does include variability of \eta. It seems like you mean that equation (8) incorrectly includes variability of \eta and does not have the right term vector component of the inner product involving A, but this needs clarifying.

We now changed the formulation to be less confusing and also explicitly clarified the caveat regarding the difference of z-scores.

One challenge with the z-diff score is that it does not account for whether a person sits above or below zero at the first time point. It might make it difficult to interpret the results, as the results for a particular pathology could change depending on what stage of the lifespan a person is in. I am not sure how the authors would address those challenges.

We agree with the outlined limitation in interpretation of overall trends when the position in the visit one is different between the subjects. However, this is a much broader challenge and is not specific to our approach. This effect is generally independent of the lifespan, but may further interact with the typical lifespan of disease. rWhen the z scores are taken in the context of the cross-sectional normative models, it does make it possible to identify what the overall trend of an illness is across the lifespan, and individual patient’s z-diffs not in line (with what would this typical group trajectory predicts) may e.g. correspond to early/late onset of their individual atrophy. We now make these considerations explicitly in the discussion section.

Reviewer #2 (Recommendations For The Authors):

Other minor suggestions to help improve the text:...

We thank Reviewer #2 for the list of minor suggestions to improve the text, which we all implemented in the manuscript.