aber jeder fucking andere term hot er bisher kennt??

boa des pisst mi scho a kle a. des erste kapitel und es isch einfach so 'wow i bin so cool und special, sogar die lüt in da welt finden i bin krass'

VORALLEM wenn des so krass isch tät i gern wissa wie se des gmacht hot und i will o ned ewig warta mit ussafinda

I merk o gad, der oane hot a fucking warehouse ausserhalb vom 3. also kanns ned si dass niaamand dean valosst

Asprosin dysregulation is implicated in human metabolic disorders including type 2 diabetes, obesity, and polycystic ovary syndrome (PCOS). Also, mutations in the parent FBN1 gene are cited as the cause of Marfan syndrome and related connective tissue disorders.

Should be a hyperlink to the reference list so that it is easier to for the reader to follow and factcheck the sentence with the source.

Needs referencing which published study?

Yes, Jamestown was named after King James, a simple accuracy but a rarity nonetheless in the film -Ellie Irwin

The settlers would not be this clean, especially not on the ship. Similar to their time in Jamestown, many settlers died on the voyage to the New World. Supplies dwindled, and the voyage was off course, not nearly as smooth as the film depicts -Ellie Irwin https://www.pbs.org/wnet/secrets/death-jamestown-background/1428/

Surprising amount of self-awareness, and while a funny line, I do not think a settler would say this. It's true the settler invaded the land and dug up their earth, but they also spread disease to the natives, among other horrific actions that a Disney movie would not depict -Ellie Irwin https://www.nps.gov/jame/learn/historyculture/pocahontas-her-life-and-legend.htm -Ellie Irwin

Pocahontas rarely left the village in reality unless otherwise sent. She stayed around the village, helping other women with growing crops and forging. Being a child, it is unlikely she would just leave to explore on her own. The only times she would leave was to negotiate with the settlers for Powatan prisoners -Ellie Irwin https://www.pbs.org/wnet/secrets/death-jamestown-background/1428/

There is accuracy here, as the settlers did suffer the first few months, having no food. But still, the film glosses over the hardship; they were starving, and they got sick, and a lot of them died (more than half) -Ellie Irwin https://www.pbs.org/wnet/secrets/death-jamestown-background/1428/

There is accuracy in how the settlers would jump to violence, as well as their mentality toward natives. However, during first interactions, the natives helped the settlers by giving them supplies and food -Ellie Irwin https://www.nps.gov/jame/learn/historyculture/pocahontas-her-life-and-legend.htm

Pocahontas was not a young adult at the time. She was thought to be 10-11 years old, while Smith was a grown man -Ellie Irwin https://www.nps.gov/jame/learn/historyculture/pocahontas-her-life-and-legend.htm

Pocahontas was only a nickname meaning "playful one". Her real name was Amonute, with her private name being Matoaka -Ellie Irwin https://www.nps.gov/jame/learn/historyculture/pocahontas-her-life-and-legend.htm

Pocahontas was not the only daughter of Chief Powhatan, let alone his only child. Chief Powhatan had multiple children -Ellie Irwin (McClurken, "Pocahontas Lecture, 9/1/26 and 9/3/26)

The settlers definitely did ravage the land for resources, so this is accurate. Though I am not entirely sure they would be this self-aware or use the term "ravish"<br /> -Ellie Irwin

The references should be hyperlinked so that it would be possible to click on it and get moved to the reference in the sources section.

There is no source here so how can I see Creswell's point? Or which of his points?

Needs referencing

Should be referenced

Should be referenced? Perhaps a reference to a source that explains what research is? Or maybe referenced to the "Last session"? Cause it is not clear which session it is talked about here.

eLife Assessment

This is a very helpful study that presents an easy to use and reliable alternative to conventional and UV-based tetramers for the identification of antigen-specific CD8 T cells. The authors demonstrate that tetramers for a number of HLA alleles can be subjected to specific temperatures that facilitate peptide exchange whilst maintaining structural integrity, thereby generating detection reagents. They provide convincing evidence for this method, and the approach is likely to provide a useful alternative to generating reagents for examining T cell specificities.

Reviewer #1 (Public review):

[Editors' note: this version has been assessed by the Reviewing Editor without further input from the original reviewers. The authors have addressed the comments raised in the previous round of review.]

Summary:

A fundamental technique for the identification of peptide-specific CD8 T cells is the use of fluorophore-conjugated and peptide loaded MHC tetramers. Classically, refolding of specific peptides with MHC monomers can be labour intensive, and not optimal for screening large numbers of different peptides. Hence, UV-exchanged tetramers have been developed to upscale this, however, still has some associated challenges such as UV-mediated damage to peptide complexes. Here, Pothast, C.R. et al demonstrate the efficacy of using temperature exchanged tetramers for the prevalent alleles HLA-A*03:01, A*11:01, B*07:02, and C*07:02. Building upon their previous work with HLA-A*02:01, H-2Kb, and HLA-E. They first demonstrate the complex stability of tetramers with different affinity peptides at high temperature, showing complex destabilisation can be rescued with higher affinity peptides. This is followed by an optimisation of peptide exchange temperatures, tailored for each allele. The authors then demonstrate successful binding to clonal T cell lines, and then a step further with viral peptides against PBMCs from individuals with confirmed infection history. For the latter they compare to conventional tetramers and demonstrate comparable signal.

Due to the prevalence of these 4 alleles, the ease-of-handling, and short time requirements, these tetramers are likely to show high utility.

Strengths:

The manuscript is well-written and results are solid, although more detail may add clarity to some of the results, in particular figure 1 and 2. Other than the points reported below, the study uses accurate controls to demonstrate the specificity of the tetramers, and the data is convincing.

Overall, their interpretation of the results is accurate and the discussion thorough, additional comments may be included to cover potential tetramer batch variability, and differences in the stability of different alleles. Specifically, whether certain alleles require higher affinity peptides to be stable, compared to others.

Comments on revised version.

The authors addressed the weaknesses highlighted in the previous version, specifically addressing that they do not conclude that thermal peptide exchange is not superior to existing technologies, but may be more feasible, as well as including a section on the stability of the different alleles and template peptides. The authors also updated the labelling and description of figures, making them much more interpretable. Addition of the LILRB1 reporter cell line to demonstrate successful peptide exchange and stability adds extra validity to the results.

Reviewer #2 (Public review):

Summary:

This work highlights a new approach to build an array of highly stable peptide MHC-I using temperature exchange, which can be used to identify antigen specific CD8+ T cells.

Strengths:

In this work, the authors have proposed an alternative method to reload the peptide MHC-I molecule. Their temperature-exchange approach is distinct from current reloadable peptide MHC technologies involving photolabile peptide, empty MHC-I (Nat Commun 11, 1314 (2020). https://doi.org/10.1038/s41467-020-14862-4), tapasin/TAPBPR chaperone-assisted (eLife 7:e40126.), enzyme exchangeable (https://pubmed.ncbi.nlm.nih.gov/31201791/) and small alcohol (Curr Res Immunol. 2022 Aug 18;3:167-174. doi: 10.1016/j.crimmu.2022.08.002) approaches

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

Summary:

A fundamental technique for the identification of peptide-specific CD8 T cells is the use of fluorophore-conjugated and peptide-loaded MHC tetramers. Classically, refolding of specific peptides with MHC monomers can be labour-intensive and not optimal for screening large numbers of different peptides. Hence, UV-exchanged tetramers have been developed to upscale this, however, still has some associated challenges such as UV-mediated damage to peptide complexes. Here, Pothast, C.R. et al demonstrate the efficacy of using temperature-exchanged tetramers for the prevalent alleles HLA-A*03:01, A*11:01, B*07:02, and C*07:02. Building upon their previous work with HLA-A*02:01, H-2Kb, and HLA-E. They first demonstrate the complex stability of tetramers with different affinity peptides at high temperature, showing complex destabilisation can be rescued with higher affinity peptides. This is followed by an optimisation of peptide exchange temperatures, tailored for each allele. The authors then demonstrate successful binding to clonal T cell lines, and then a step further with viral peptides against PBMCs from individuals with confirmed infection history. For the later they compare to conventional tetramers and demonstrate comparable signal.

Due to the prevalence of these 4 alleles, the ease of handling, and short me requirements, these tetramers are likely to show high utility.

Strengths:

The manuscript is well-written and results are solid, although more detail may add clarity to some of the results, in particular figure 1 and 2. Other than the points reported below, the study uses accurate controls to demonstrate the specificity of the tetramers, and the data is convincing.

Overall, their interpretation of the results is accurate and the discussion thorough, additional comments may be included to cover potential tetramer batch variability, and differences in the stability of different alleles. Specifically, whether certain alleles require higher affinity peptides to be stable, compared to others.

Comments on revised version.

The authors addressed the weaknesses highlighted in the previous version, specifically addressing that they do not conclude that thermal peptide exchange is not superior to existing technologies, but may be more feasible, as well as including a section on the stability of the different alleles and template peptides. The authors also updated the labelling and description of figures, making them much more interpretable. Addition of the LILRB1 reporter cell line to demonstrate successful peptide exchange and stability adds extra validity to the results.

We thank the reviewer for the careful evaluation of our work and for the positive assessment of the manuscript. We are pleased that the reviewer finds the study well written, the data convincing, and the approach likely to be of practical utility for the analysis of antigen-specific T-cell responses.

Reviewer #2 (Public review):

Summary:

This work highlights a new approach to build an array of highly stable peptide MHC-I using temperature exchange, which can be used to identify antigen specific CD8+ T cells.

Strengths:

In this work, the authors have proposed an alternative method to reload the peptide MHC-I molecule. Their temperature-exchange approach is distinct from current reloadable peptide MHC technologies involving photo labile peptide, empty MHC-I (Nat Community 11, 1314 (2020). https://doi.org/10.1038/s41467-020-14862-4), tapasin/TAPBPR chaperone-assisted (eLife 7:e40126.), enzyme exchangeable (https://pubmed.ncbi.nlm.nih.gov/31201791/) and small alcohol (Curr Res Immunol. 2022 Aug 18;3:167-174. doi: 10.1016/j.crimmu.2022.08.002) approaches

Weaknesses:

The temperature-exchange approach identifies antigen-specific T cells at the same frequency of the photolabile peptide MHC-I molecules. The time saved using temperature-exchange protocol may not be a pull factor as the photolabile peptide MHC-I approach is not unreasonably laborious.

As with other exchange systems, performance can vary by HLA allele and complex stability, so each new allele needs empirical optimization.

In short, temperature-based p-MHC exchange is a practical and fast loading strategy with good utility for multimer generation, but its main limitation is dependence on thermal stability and careful template selection.

We thank the reviewer for the positive evaluation of our work and for recognizing temperature-mediated peptide exchange as a practical approach for the generation of peptide–MHCI multimers.

We appreciate the reviewer placing our methodology within the broader context of existing peptide loading technologies, including photolabile peptide, empty MHC-I, chaperone-assisted, enzyme-mediated, and small alcohol-based exchange systems. In response to comments raised during review, we have expanded the Introduction to include a more comprehensive discussion of these alternative approaches and their respective strengths and limitations.

We agree that the primary advantage of thermal peptide exchange is not necessarily improved sensitivity or detection of antigen-specific T cells compared with established methodologies. Rather, its value lies in providing an additional peptide-exchange platform that is simple to implement, requires only standard laboratory equipment, and enables rapid generation of peptide–MHC multimers. We also agree that successful implementation requires empirical optimization for each HLA allele and relies on the identification of suitable template peptides.

We are grateful for the reviewer's constructive comments, which have helped us improve the clarity and balance of the manuscript.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

No additional experiments required.

Resolution of figures could be improved unless it is a caveat of the software used.

We thank the reviewer for this positive assessment and constructive suggestion. High-resolution versions of all figures have been included in the revised manuscript

Reviewer #2 (Recommendations for the authors):

The following sentences are incorrect.

Line 101: "The MHC-multimers are multimerized and fluorescently labeled prior to peptide exchange" is incorrect. Instead, combinatorial peptide MHC multimers involves peptide exchange prior to multimerization during barcoding and not vice versa (https://pubmed.ncbi.nlm.nih.gov/23748502/).

We thank the reviewer for carefully noting this inaccuracy. As suggested, we have revised the text to better reflect the different workflows used across MHC multimer platforms. The sentence now reads: “Depending on the platform, peptide exchange may be performed either before or after multimerization and labeling.”

Line 296: "Quantifying the efficiency of UV-induced peptide destruction and subsequent peptide exchange remains challenging" is incorrect. The thermal stability of empty MHC-I vs loaded peptide MHC-I after UV irradiation or thermal exchange can be measured using either gel filtration chromatography or SyPro orange thermal shift.

The sentence now reads: “While the stability of UV-exchanged p-MHC complexes can be assessed using methods such as thermal shift assays and size-exclusion chromatography, the efficiency of peptide exchange is more difficult to quantify directly.”

toby invents the strength and skill of both fighters to terrify Andrew and Viola. He builds a false reputation for each of them, just like Maria built a false letter. It’s another invented identity, but this time it puts real swords in their hands.

Shakespeare breaks the fourth wall and makes fun of his own script. By admitting the story is unbelievable, he disarms the criticism and makes the scene work better. It also fits a play where every character is acting a part.

Malvolio thinks Olivia is expressing her feelings, while she understands nothing and believes he’s crazy. The cruel part is that she asks Sir Toby to take care of him — the man behind the whole plot. By trying to protect him, she gives him to his tormentors.

So this information lacks 2 information elements. First one is that there is one obligatory assignment which the students has to pass in order to get a grade at all. Also it does not state that the annotation of this script has to be passed in order to get a grade at all.

Second is that: Are all the essays obligatory to pass the subject? Meaning is it possible to just do the script part along with the first assignment and the one of the essays? Which gives a total of 20%+30%=50%=D? Or does every single assignment need to get a pass in order to get a grade?

Here UiA's AI guidelines should be referenced. UiA's AI guidelines state that the use of AI needs to be written.

你好

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This act of total trust prepares the coming confusion: Antonio will later mistake Viola for Sebastian, be refused his own money and believe he has been betrayed. The purse also contrasts with Toby, who empties Andrew’s purse out of calculation while Antonio gives his away.

Antonio knows he could be arrested in these streets and comes anyway. His love is the only one in the play that costs something real: Orsino sighs comfortably at home and Olivia sends rings, while Antonio risks his freedom for a man he barely knows.

Antonio describes his feelings with a very strong image: a desire sharper than steel pushing him forward. It’s much more than ordinary friendship — his attachment is physical and painful, and it makes him act even when he knows the city is dangerous for him.

eLife Assessment<br /> <br /> This study provides valuable insights into the role of thalamic nuclei in associative threat and extinction learning, underpinned by a large dataset and rigorous, multipronged analyses. The evidence provided is solid, supporting the main conclusions. The manuscript will be of broad interest to researchers in learning and memory, fear, thalamic circuitry, and related mental health conditions.

Reviewer #1 (Public review):

Summary:

Badarnee and colleagues analyse fMRI data collected during an associative threat-learning task. They find evidence for parallel processes mediated by the mediodorsal, LGn and pulvinar nuclei of the thalamus. The evidence for these conclusions is promising, but limited by a lack of clarity regarding the preprocessing and statistical methods.

Strengths:

The approach is inventive and novel, providing information about thalamocortical interactions that are scant in the current literature.

Comments on revised version.

No further comments. The authors have adequately addressed my concerns.

Reviewer #3 (Public review):

Summary:

The present work was aimed at investigating the specific contributions of thalamic nuclei to associative threat learning and extinction. Using fMRI, it examined activation patterns across pulvinar divisions, the lateral geniculate nucleus (LGN), and the mediodorsal thalamus (MD) during threat acquisition, extinction, and recall. It goals was to uncover whether distinct thalamic systems support different modes of learning-automatic survival mechanisms versus more deliberate processes-and to propose a hierarchical pulvinar model of fear conditioning. The manuscript also tried to refine current neuroanatomical models of threat learning and memory, highlighting the role of thalamic nuclei in it.

Strengths:

(1) Valuable theoretical elaboration and modeling regarding the differential role of pulvinar subdivisions on feedforward (inferior, lateral) and higher-order integration (anterior), and their functional interplay with other relevant subcortical and cortical structures in associative threat and extinction learning.

(2) Large sample sizes and multipronged analytical approaches were used for hypothesis testing.

(3) Exhaustive literature review in the field of associative threat, as well as regarding the role of thalamic nuclei and other brain structures in it.

Comments on revised version:

My three main concerns remain insufficiently addressed or only partially addressed in the study limitations. First, using 3 mm isotropic voxels limits anatomical and functional specificity when investigating small thalamic nuclei. Higher-resolution fMRI protocols and probabilistic thalamic atlases that allow subject-specific segmentation have been available for several years and could have been used to improve anatomical precision. Second, although analyses in normalized space are widely accepted for group-level analyses, normalization can distort the size and boundaries of small thalamic nuclei. Analyses in native space, using subject-specific segmentation, would help address this limitation, particularly given the relatively low functional resolution. Finally, standard preprocessing procedures and the inclusion of motion parameters as covariates do not fully exclude residual motion-related effects or potential differences in motion across experimental conditions. The authors do not report additional motion quality-control procedures, such as excluding participants with excessive motion or substantial translational or rotational drift. Taken together, these concerns prevent me from upgrading my assessment.

Author response:

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

eLife Assessment:

This study provides valuable insights into the role of thalamic nuclei in associative threat and extinction learning, underpinned by a large dataset and rigorous, multipronged analyses. The evidence provided is solid, supporting the main conclusions. Minor analytical refinements notwithstanding, the manuscript will be of broad interest to researchers in learning and memory, fear, thalamic circuitry, and related mental health conditions.

We sincerely thank the editors and the reviewer for their careful evaluation of the revised manuscript and for their constructive comments. We greatly appreciate the time and effort invested in reviewing our work, and we have carefully considered each suggestion to further improve the clarity, presentation, and interpretation of the manuscript.

Author’s note:

We note that several comments refer to figure numbers and manuscript text from an earlier version of the manuscript (e.g., Figure 7 in the earlier version corresponds to Figure 8 in the current revised manuscript). For clarity, our responses below refer to the corresponding figures and sections in the current revised manuscript while addressing each comment individually.

Public Reviews:

Reviewer #1 (Public review):

Summary:

Badarnee and colleagues analyse fMRI data collected during an associative threat-learning task. They find evidence for parallel processes mediated by the mediodorsal, LGn and pulvinar nuclei of the thalamus. The evidence for these conclusions is promising, but limited by a lack of clarity regarding the preprocessing and statistical methods.

We thank the reviewer for these encouraging comments and for recognizing the novelty of our approach and its potential contribution to advancing our understanding of thalamocortical interactions during associative threat learning. We also appreciate the reviewer’s constructive suggestions regarding the methodological description, which have helped us improve the clarity of the manuscript.

Strengths:

The approach is inventive and novel, providing information about thalamocortical interactions that are scant in the current literature.

Weaknesses:

(1) There are not sufficient details present to allow for the direct interrogation of the methods used in the study.

Thank you for raising this point. As described in our previous revision, we substantially expanded the Experimental Procedure section by adding three dedicated subsections describing the fear learning procedure, shock calibration, and participant instructions. These additions included details regarding the stimuli, experimental context, stimulus and shock durations, average inter-trial interval, pseudorandomized stimulus presentation, and the procedure used to determine each participant’s shock intensity.

In the current revision, we have further clarified the Methods by specifying electrical stimulator that we used and specified the usage of the gel and placement of the electrodes. We believe these additions provide sufficient methodological detail to allow readers to understand and replicate the experimental procedure (page 40):

“Electrical stimulation was delivered using a BIOPAC stimulation system with BIOPAC electrodes and conductive gel. The electrodes were attached to the palm of the participant's right hand and positioned in close proximity without making contact with one another. The electric stimulation began…”

(2) The figures do not contain sufficiently granular details, making it challenging to determine whether the observed effects were robust to individual differences.

Thank you for this suggestion. To better illustrate the underlying data distribution and between-participant variability, we revised Fig. 1 and 4-6 by adding individual participant data points for the block-level analyses and kernel density estimate (KDE) distributions for the trial-level analyses. These additions allow readers to better assess the distribution of the data and the extent to which the reported effects are consistent across participants while maintaining figure readability. An example of the revised Fig. 1.

Comments on revisions:

I continue to recommend the plotting of individual data points. While there may be individual variance, it is important to quantify this in publication so that future studies can appreciate the uncertainty surrounding test statistics.

Thank you for this recommendation. As described in our response to Weakness 2 above, we revised the figures to better illustrate the underlying data distribution and between-participant variability. Specifically, we added individual participant data points for the block-level analyses and kernel density estimate (KDE) distributions for the trial-level analyses. We believe these revisions improve the transparency of the underlying data while maintaining figure readability. An example of a revised figure is included above.

Reviewer #2 (Public review):

Summary:

The authors quantify human fMRI BOLD responses in pulvinar and mediodorsal thalamic nuclei during a fear conditioning and extinction task across two days, in a large sample size (hundreds of participants). They show that the BOLD responses in these areas differentiate the conditioned (CS+) and safety (CS-) stimulus. Additionally this changes with repeated trials which could be a neural correlate of fear learning. They show that the anterior pulvinar is most correlated with the MD, and that this is not due to anatomical proximity. They perform graph analysis on the pulvinar sub nuclei which suggests that the medial pulvinar is a hub between the sensory (lateral/inferior) and associative (anterior) pulvinar. They show different patterns of thalamic activity across conditioning, extinction, recall, and renewal.

We thank the reviewer for this thoughtful summary of our work and for recognizing the scope of the study and the value of the findings. We appreciate the reviewer’s careful evaluation and constructive feedback, which helped improve the clarity and interpretation of the manuscript throughout the revision process. Below, we respond to each of the points raised under the Weaknesses and Comments on revisions sections and describe the corresponding revisions made to the manuscript.

Strengths:

The data has a large sample size (n=293 in some measures, n=412 in others). This is a validated human fear conditioning/extinction task that Dr Milad's group has been working with for several years. Few labs have investigated the thalamus activity during fear conditioning and extinction, particularly with a large sample size. There is an independent replication of the pulvinar network structure (Fig. 3), which suggests that the processing in the more sensory-related inferior and lateral pulvinar is relayed to the anterior pulvinar (and possibly thereby to more action-related prefrontal areas) via an intermediate step in the medial pulvinar - potentially a novel discovery but that needs more validation.

Weaknesses:

(1) The authors cannot make causal claims about their results based on correlational neuroimaging evidence. Causal claims should be pared back. E.g. Sentence 1 in results "The anterior pulvinar and MD contribute to early associative threat learning, as evidenced by increased functional activation in response to CS+ compared to CS- at the block level (Fig. 1b-c)." needs to be reworded to something like 'the anterior pulvinar and MD have increased functional activation... This suggests that these areas may contribute to early associate threat learning"

We agree with the reviewer that causal claims cannot be made based on correlational neuroimaging evidence. Accordingly, we carefully revised the manuscript throughout to avoid causal language and instead use wording that reflects statistical associations and interpretations. Representative examples of these revisions are provided below.

Representative examples of these revisions are shown below:

Page 7: “At the block level, both the anterior pulvinar and MD showed increased activation to CS+ vs. CS− (anterior pulvinar: t₍₂₉₂₎ = 4.41, p = 0.00001, d = 0.25; MD: t₍₂₉₂₎ = 6.41, p = 5.83x10^-10, d = 0.37; Fig. 1b–c), suggesting a possible involvement of these regions in early associative threat learning.”

Page 13: “A data-driven approach suggests a hierarchical computational organization of pulvinar divisions during associative threat learning.”

Page 13: “we integrated findings from complementary analyses to propose a computational framework describing the functional relationships among pulvinar divisions during associative threat learning.”

Page 13-14: “The network plot identified a statistical association between the lateral and anterior pulvinar, together with pathways linking the lateral and inferior pulvinar to the anterior pulvinar through the medial pulvinar (Fig. 3b).”

Page 14: “The medial pulvinar statistically mediates the functional relationships within the pulvinar network and is associated with elevated centrality values. Together, these findings are consistent with a possible hub-like functional role of the medial pulvinar, consistent with higher-order integration of CS+ information, relative to the more sensory-related functional profiles in the inferior and lateral pulvinar. This possible hierarchical organization is consistent with previous evidence showing that the inferior and lateral pulvinar are associated with processing basic sensory information (Berman and Wurtz, 2010, 2008; Cortes et al., 2024), while the medial pulvinar is implicated in higher-order processing, including attention and working memory (Homman-Ludiye and Bourne, 2019).”

Page 15: “This data-driven analyses provide a possible new perspective on the functional specialization of pulvinar divisions during associative threat learning. Based on these observations, we hypothesized a computational model in which activation in the medial pulvinar statistically mediates the functional relationships between the inferior and lateral pulvinar and the anterior pulvinar (Fig. 3e). To evaluate this hypothesis, we performed a mediation analysis and assessed the robustness of the model using k-fold cross-validation.”

Page 25: “The vmPFC connectivity may reflect involvement of prefrontal processes related to evaluation and regulation of threat-related information.”

Page 27: “The medial pulvinar may statistically mediate the functional relationships between the inferior and lateral divisions and the anterior pulvinar, consistent with a hierarchical computational model.”

Page 29: “Together, these findings are consistent with the possibility that the anterior pulvinar and MD contribute in parallel to associative threat learning, extending the two-system.”

Page 32: “Using statistical mediation analysis, we found that activation in the medial pulvinar statistically mediated the relationship between activation in the inferior/lateral pulvinar and activation in the anterior pulvinar. This statistical mediation is consistent with, but does not establish, a hierarchical computational organization.”

(2) Fig .1 The fact that the difference in BOLD activity between CS+ and CS- goes away on the third trial is not addressed. This is a very large effect in the data.

Thank you for raising this important point. We have addressed this issue in both the Discussion and the Limitations sections, as follows:

Discussion (page 31):

“The attenuated CS+ vs. CS− differentiation on the third trial specifically in the pulvinar may reflect a decreased requirement for differential thalamic engagement once the initial association has been acquired. Notably, because the MD sustained the BOLD response to the CS+ in the third trial which may indicate involvement of this nucleus in continued processing or maintenance of the learned association. This aligns with the well-established MD-PFC circuit involved in cognitive processes (Wolff and Halassa, 2024). Additionally, in a previous study using a similar paradigm, we observed sustained CS+ vs. CS− differentiation on the third trial in the nucleus reuniens, as well (Tuna et al., 2025). These findings suggest that trial-dependent learning dynamics may vary across thalamic nuclei rather than reflecting a uniform thalamic learning signal. Together, while our paradigm does not inherently distinguish between different stages of learning, such as early acquisition and stabilization, our findings are consistent with stronger associative learning-related engagement during the first two trials, with a reduced differential response by the third trial that may reflect the involvement of different neural processes.”

Limitations (page 36):

“The reduced CS+ vs. CS− differentiation observed in the pulvinar subdivisions during the third trial may reflect a transition from the rapid associative learning processes engaged during the first two trials to a different stage of threat processing. However, the present experimental design does not allow us to determine the specific psychological or neural mechanisms underlying this change. Importantly, this attenuation was not observed uniformly across thalamic nuclei, suggesting that it does not reflect a general thalamic response but may instead indicate nucleus-specific functional dynamics. Future studies specifically designed to dissociate different stages of threat learning will be required to determine the functional significance of this trial-dependent effect.”

(3) Fig. 3 Could the observed network structure be due to anatomical proximity? Perhaps the authors should do an analogous analysis to what they did in Fig. 2 for this intra-pulvinar analysis. This analysis doesn't take into account the indirect connections through corticothalamic and thalamocortical connections with visual cortex and the pulvinar. There is an implicit assumption that there are interconnections between the pulvinar sub nuclei, but there are few strong excitatory projections between these sub nuclei to my knowledge. If visual areas are included in the graph, it would make things more complex, but would probably dramatically change the story. In this way, the message is somewhat constructed or arbitrary.

We thank the reviewer for this thoughtful suggestion. We performed an analysis analogous to that presented in Figure 2. Specifically, we computed partial correlations between pulvinar divisions while controlling for activation in the other thalamic nuclei included in the study (LGN and MD). The relationships between pulvinar divisions remained significant after accounting for shared variance with these nuclei (Supplementary Figure 4), suggesting that the observed associations are not solely explained by activation in neighboring thalamic regions. This analysis is particularly relevant for the LGN because of its close anatomical proximity to the pulvinar and its central role within the visual thalamic system.

Additionally, we statistically compared the strength of the partial correlations among all pairs of pulvinar divisions (Author response table 1). These analyses demonstrated substantial heterogeneity in the correlation strengths, with several pairwise differences remaining significant after FDR correction, indicating that the functional relationships among pulvinar divisions are heterogeneous rather than uniformly correlated, making a simple anatomical-proximity explanation less likely.

Author response table 1.

Pairwise comparisons demonstrate heterogeneous functional relationships among pulvinar subdivisions after controlling for MD and LGN activation.

Note. All values are partial correlations controlling for MD and LGN activation (N = 293). Differences between correlations were tested using Steiger’s test for dependent correlations measured in the same participants. The sample size entered into the test was 293 - 2 = 291 as two variables (MD and LGN) were partialled out. The test itself then uses 291 - 3 = 288, the standard adjustment applied whenever correlations are converted to Fisher z scores. Steiger provides two versions of the same equation: one for comparisons in which the two correlations share a subdivision, and one for comparisons in which they share none (marked †). Analyses were conducted using the cocor package in R.

The p-values for the 15 comparisons were corrected using the Benjamini-Hochberg false discovery rate (FDR) procedure; 9 of 15 comparisons remained significant after correction. Correlations ranged from 0.182 to 0.718. PuA = anterior pulvinar; PuI = inferior pulvinar; PuM = medial pulvinar; PuL = lateral pulvinar.

The two correlations being compared share no subdivision.

Finally, we agree that pulvinar function is embedded within broader corticothalamic and thalamocortical circuits involving visual cortical regions. However, a comprehensive characterization of pulvinar-visual cortical interactions at the level of specific pulvinar divisions was beyond the scope of the current study. To acknowledge this limitation and avoid implying that the observed relationships represent isolated direct interactions among pulvinar divisions, we revised the conceptual model in Fig. 8 by adding bidirectional dashed gray arrows between the pulvinar divisions and visual cortex (see Fig 8). Additionally, we clarified this point in the Figure notes and the study limitations:

Limitations (page 36):

“The observed relationships among pulvinar divisions during conditioning are purely functional and do not distinguish direct inter-nuclear interactions from indirect coupling mediated by corticothalamic and thalamocortical pathways, including visual cortical regions. Thus, the proposed functional pulvinar model may reflect indirect corticothalamic and thalamocortical loops, currently undocumented internuclear interactions, or a combination of these mechanisms.”

(4) In the results section describing Fig. 4-7, there are no statistics supporting the claims made. There needs to be a set of graphs comparing the results across the study sessions and days, with statistical comparisons between the different experiments to confirm differences.

We thank the reviewer for this thoughtful comment. As stated in the scope of the study (page 6 in the manuscript), our objective was to characterize thalamic activation and connectivity within each learning phase separately. Accordingly, our statistical analyses were designed to test CS+ vs. CS− differentiation within each phase rather than to directly compare activation or connectivity across phases (e.g., conditioning, extinction, extinction recall, and threat renewal).

This phase-specific approach was selected because each learning phase probes a distinct psychological process with different experimental conditions. Therefore, direct comparisons of activation or connectivity between phases would address a different research question than the one investigated in the present study were not part of our predefined hypotheses or statistical design.

To address the reviewer’s concern, we carefully revised the Results and Discussion to make this analytical framework explicit and to avoid wording that could be interpreted as implying direct statistical comparisons between learning phases. Representative examples of revisions made throughout the manuscript to clarify that the statistical analyses and interpretations are phase-specific (CS+ vs. CS− within each phase) and do not involve direct statistical comparisons across learning phases.

Representative examples of these revisions are shown below:

Page 17: “Pulvinar divisions and MD show engagement during extinction learning and context-dependent threat memory processing.”

Page 21: “The threat renewal phase, in which participants were exposed to contextual cues similar to those used during threat conditioning, was associated with.”

Page 23: “At the block level, activation was greater for CS+ than CS− within each phase separately: conditioning…”

Page 23: “Collectively, these findings suggest that the LGN exhibited early trial-dependent responses within each learning phase separately, consistent with its proposed role in feedforward visual processing.”

Page 28: “Figure 8b title: Conceptual model of thalamic-dependent ‘toggle switching’ between safe and threat memory retrieval across contextual cues”

“Legend: (b) Conceptual model illustrating a possible thalamic-dependent “toggle switch” underlying context-dependent safety and threat memory expression. The observed MDdACC and anterior pulvinar-vmPFC connectivity patterns are consistent with possible preferential engagement of prefrontal circuits during extinction recall and threat renewal, respectively. This framework represents a theoretical integration of the present findings rather than evidence of causal neural mechanisms. It is based on the findings observed within each phase separately and should not be interpreted as a direct comparison between phases.”

Page 25: “Interestingly, within each phase separately, the findings suggest that thalamic connectivity patterns vary with contextual conditions during extinction recall and threat renewal. Specifically, the MD and anterior pulvinar showed distinct CS+E vs. CS− connectivity patterns during these phases, respectively.”

(5) FIg. 7 does not include the major corticothalamic and thalamocortical projections from early, mid-level, and higher visual cortex to the different pulvinar nuclei. I doubt that there are strong direct projections between the pulvinar nuclei, rather the functional connections are probably mediated through interconnections with cortical visual areas.

Thank you for pointing out this important point. As described in our response to Weakness 3 above, we agree that pulvinar function is embedded within broader corticothalamic and thalamocortical circuits involving visual cortical regions. To better acknowledge this, we revised the conceptual model by adding bidirectional dashed gray arrows between the pulvinar and visual cortex and retained a note in the figure legend clarifying that the schematic is not intended to represent a comprehensive anatomical connectivity map. We also emphasize in the manuscript that the proposed intrapulvinar relationships should be interpreted as a functional/computational model based on statistical dependencies rather than evidence of direct monosynaptic anatomical connections. An updated version of the figure is provided. See Fig 8.

(6) Stylistic: There are a lot of hypotheses and interpretations presented in this primary literature paper which may be better suited for a review or perspective piece.

Thank you for this comment. Our intention here is to integrate the empirical findings within an existing conceptual framework rather than present isolated observations. We believe that placing our results in the context of current models helps highlight their potential implications while remaining grounded in the experimental data. Nevertheless, we reviewed the Discussion again and ensured that speculative statements are clearly identified as hypotheses or possible interpretations and are appropriately qualified throughout the manuscript.

(7) In the discussion there is an assumption that the fMRI BOLD responses to CS+ and CS- need to be different to indicate that an area is processing these distinctly, but the BOLD signal can only detect large scale changes in overall activity. It's easy to imagine that an area could be involved in processing these two stimuli distinctly without showing an overall difference in the gross amount of activity.

Thank you for raising this important limitation. We agree that the absence of differential BOLD responses does not necessarily imply the absence of stimulus-specific neural processing. We explicitly acknowledged this limitation in the manuscript (page 35):

“Although our findings suggest distinct patterns of thalamic involvement during threat learning, fMRI cannot fully capture the complexity of thalamic organization. Pulvinar divisions, MD, and LGN each contain diverse neuronal subtypes and finer anatomical subdivisions that may serve distinct functions. Importantly, the absence of CS+ vs. CS− differences in BOLD activation should not be interpreted as a lack of stimulus-specific processing, as such distinctions may occur without detectable changes in overall BOLD activation…”

We reviewed this section again and believe it appropriately addresses the limitation raised by the reviewer.

(8) There is strong evidence that the BOLD responses to the threat-related and safety-related stimuli are different, modest evidence for their claims of learning/plasticity in these pathways, and circumstantial evidence supporting their hypothesized graph network models. Overall most of the claims made in the discussion are better considered possible interpretations rather than proven findings - this is not a criticism, as these experiments and subject matter are extremely complex.

We thank the reviewer for this thoughtful perspective. We agree that our findings provide varying levels of evidence across different aspects of the study and that the proposed network models should be interpreted as hypotheses informed by our empirical observations rather than definitive mechanistic conclusions. Accordingly, throughout the current revisions, we have carefully revised the Results and Discussion sections to distinguish empirical findings from interpretations by using more cautious language and explicitly acknowledging the limitations of our experimental approach. All revisions are in the manuscript, and representative examples of the changes can be found above.

(9) This study continues to validate the power and utility of this in human fear conditioning/extinction paradigm, and extends this paradigm to investigating fear learning beyond the traditional limbic system pathways. It's possible that their models for the pulvinar nuclei interconnections could guide future neuromodulation or DBS studies that could provide more causal evidence for their hypotheses.

We thank the reviewer for these encouraging comments and for recognizing the potential utility of this paradigm and the possible implications of our findings for future mechanistic and neuromodulation studies.

Comments on revisions:

The reviewers addressed my major concerns appropriately in the modified manuscript. As long as the MRI analysis concerns of Reviewer 3 are satisfied (MRI analysis is not my expertise), I am satisfied with the modified manuscript.

We sincerely thank the reviewer for the positive assessment of our revised manuscript and for recognizing that the major concerns were appropriately addressed. We appreciate the reviewer's constructive feedback throughout the review process, which has helped improve the quality and clarity of the manuscript.

Reviewer #3 (Public review):

Summary:

The present work was aimed at investigating the specific contributions of thalamic nuclei to associative threat learning and extinction. Using fMRI, it examined activation patterns across pulvinar divisions, the lateral geniculate nucleus (LGN), and the mediodorsal thalamus (MD) during threat acquisition, extinction, and recall. It goals was to uncover whether distinct thalamic systems support different modes of learning-automatic survival mechanisms versus more deliberate processes-and to propose a hierarchical pulvinar model of fear conditioning. The manuscript also tried to refine current neuroanatomical models of threat learning and memory, highlighting the role of thalamic nuclei in it.

We thank the reviewer for this accurate summary of the aims and scope of our study. We also appreciate the thoughtful and constructive feedback provided throughout the review process, which has helped us improve the methodological description, interpretation of the findings, and overall clarity of the manuscript. Below, we respond to each of the points raised under the Weaknesses and Recommendations sections.

Strengths:

(1) Valuable theoretical elaboration and modeling regarding the differential role of pulvinar subdivisions on feedforward (inferior, lateral) and higher-order integration (anterior), and their functional interplay with other relevant subcortical and cortical structures in associative threat and extinction learning.

(2) Large sample sizes and multipronged analytical approaches were used for hypothesis testing.

(3) Exhaustive literature review in the field of associative threat, as well as regarding the role of thalamic nuclei and other brain structures in it.

Weaknesses:

(1) The manuscript has improved methodologically and analytically after the review. Several weaknesses remain, in my opinion, but still findings are valuable and the evidence can be considered as convincing.

(a) fMRI data have low resolution (3 cubic mm), which certainly limits the examination of small nuclei such as the ones investigated here, and especially the examination of the LGN and inferior pulvinar.

Thank you! We have acknowledged this limitation in the manuscript (page 35):

“Pulvinar divisions, MD, and LGN each contain diverse neuronal subtypes and finer anatomical subdivisions that may serve distinct functions. Importantly, the absence of CS+ versus CS− differences in BOLD activation should not be interpreted as a lack of stimulus-specific processing, as such distinctions may occur without detectable changes in overall BOLD activation. Future advances in higher-resolution imaging and human brain atlases may further improve anatomical and functional precision.”

(b) fMRI was normalized to standard space. Analyzing the data in individual-subject space would have given you the options of avoiding altering every participant's brain and of using more precise atlases than the normalized AAL for ROI selection.

We thank the reviewer for this thoughtful suggestion. Analyses performed in native subject space and standardized MNI space each have methodological advantages. In the present study, we adopted a standardized MNI-space pipeline because it provides consistent anatomical localization and ROI definition across participants, facilitating group-level analyses and comparisons across individuals. We have added this consideration to the Limitations section (page 35).

“The present analyses were performed in standardized MNI space to enable consistent group-level analyses and ROI definition across participants. Although this is a widely accepted approach in human neuroimaging, future studies may examine the generalizability of the present findings using complementary approaches, including analyses performed in native subject space.”

(c) Motion during scanning was poorly controlled. Including the motion parameters as covariates of no interest in the GLM/analysis does not fully guarantee that motion is not influencing the results, and that motion is not differentially influencing some experimental conditions more than others.

We thank the reviewer for highlighting the importance of controlling for head motion in fMRI analyses. We agree and acknowledge that no preprocessing strategy can completely eliminate all potential residual motion-related effects in fMRI data.

For the activation analyses, the data were preprocessed using the standard fMRIPrep (v20.0.2) pipeline, which included motion correction through rigid-body realignment prior to statistical modeling. The first-level SPM analyses included the six estimated head-motion parameters (three translations and three rotations) as nuisance regressors. White matter and cerebrospinal fluid signals were also included as nuisance regressors to reduce non-neuronal sources of variance.

For the functional connectivity analyses, we applied CONN’s denoising pipeline, which included regression of motion parameters, regression of identified outlier scans (scrubbing), component-based physiological noise correction (CompCor), and temporal filtering prior to generalized psychophysiological interaction (gPPI) analyses. The combination of motion correction, nuisance regression, and additional denoising procedures, provides a multistep approach to reducing potential motion-related and non-neuronal confounds in the reported findings. We have further clarified these processes in the manuscript (pages 44-45 in the manuscript):

“Functional connectivity was computed using the CONN toolbox (version 22.a) for MATLAB (NietoCastanon and Whitfield-Gabrieli, 2022; Whitfield-Gabrieli and Nieto-Castanon, 2012). Anatomical images were segmented into gray matter, white matter, and CSF. Functional images were preprocessed following CONN’s default pipeline, including realignment, slice-timing correction, and coregistration. Functional images were then denoised using a component-based noise correction approach (CompCor), including regression of estimated motion parameters, regression of identified outlier scans using artifact detection procedures (scrubbing), and regression of white matter and CSF signals. The denoised BOLD time series were subsequently band-pass filtered between 0.008 and 0.09 Hz.

Following these denoising procedures – including regression of motion parameters, artifact-related outlier scans (scrubbing), physiological noise correction (CompCor), and temporal filtering – condition-dependent connectivity effects were inferred from subject-level generalized psychophysiological interaction (gPPI) contrast estimates rather than from raw time-series correlations…”

Recommendations for the authors:

Reviewer #3 (Recommendations for the authors):

(1) Shock methods: how long was the shock? Which electrical stimulator was used? Where were the shock electrodes placed? Was gel used?

Thank you for pointing this out. The duration of the electrical stimulation was already specified in the manuscript. In the current revision, we have further clarified the shock delivery procedure by adding details regarding the stimulation system, electrodes, use of conductive gel, and electrode placement. These details have now been added to the Experimental Procedures section (page 40):

“…Electrical stimulation was delivered using a BIOPAC stimulation system with BIOPAC electrodes and conductive gel. The electrodes were attached to the palm of the participant's right hand and positioned in close proximity without making contact with one another. The stimulation began…”

(2) Fig. 3G: needs details about the independent sample. Was this cohort held out for cross-validation? etc.

Thank you for raising this point. We added this information to Fig. 3 notes and to the results section.

Fig. 3 notes:

“(g) Replication of the mediation model in an independent sample. This independent cohort (N = 114) was collected as part of a separate research project that included the same Day 1 fear-conditioning paradigm used in the present study. The cohort was used to assess whether the mediation findings could be replicated independently.”

Results (page 15):

“...we applied the mediation analysis to an independent cohort (N = 114) collected as part of a separate research project using the same Day 1 fear-conditioning paradigm. The mediation findings were largely replicated in this independent cohort (Fig. 3f-3g).”

и тут ))

Если подписка продлилась, а приложение не активировалось:

Проверьте, что в приложении вы вошли в тот аккаунт My Kaspersky, на который оформлена подписка.

Отправьте подписку на нужное устройство по инструкции.

Упростим: После продления подписка продолжит работать на устройствах, подключенных к аккаунту My Kaspersky. Заново активировать приложение не нужно.

Тут надо пояснить, что имеется в виду. Упростить и разъяснить.

Упростим: Подписка продлится автоматически: оплата спишется до конца текущего периода. За 1–2 недели до списания на указанный email придёт уведомление о продлении и его стоимости.

Давай тут тоже упростим Для продления выбранным способом достаточно средств.

давай напишем маркетплейсах или магазинах электроники

Потому что там еще есть разные варианты магазинов

Давай добавим "при такой установке"

Давай везде упрощать, как в предыдущей статье =))

В приложении вы вошли в тот аккаунт My Kaspersky, на который оформлена подписка (с email, указанным при покупке)

Давай заменим на "что"

может заменим на "если"?

Давай упростим в стиле твоей прошлой статьи по b2c:

После преобразвания кода на всех нужных устройствах:

Не поняла эту ссылку. Она никакой полезной информации как будто и не несет...

давай упростим - а пытаетесь активировать его в России.

а можно выделить болдом "не на сайте"? или технически нельзя?

Давай переформулируем, смысл - код активации не придет не в письмах, а в принципе, если ты купил на сайте ЛК. А то сейчас получается, что не в этом письме. И если не придет, иди в поддержку.

А вообще, я бы не добавляла эту фразу =))

Давай сделаем "как активировать, если приложение еще не установлено", чтобы поставить Юлины сео-галочки

Это не совсем верно. Клиент ничего не создает. Он указывает email при оформлении заказа и на этот email создается автоматически аккаунт в MуК. Именно поэтому очень важно указывать правильную почту при оформлении заказа.

А если он уже для этой почты есть, то да, подписка просто добавится.

Кажется, тут должно быть "и" или "а также"

!

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Otras notas que tomé del audio: Este proyecto liderado por Green Capital está proyectado para consumir 3 gigawatts (el 3% de los 100 gigawatts que hoy se calcula que requieren todos los centros de datos en funcionamiento). Sin embargo, hablan de que, para una primera etapa, requerirá solo 300 megawatts de energía: Este proyecto, ubicado en la ciudad de Trelew, provincia de Chubut en la Patagonia argentina, todavía no tiene cliente y no tiene inversor. Esta información es relevante si se revisa en comparación con un proyecto anterior, en la provincia de Neuquén, que tenía como cliente a OpenAI, y sin embargo no se concretó. Este proyecto no tiene nada concreto, quizás negocios que tienen que ver con la concesión sobre la tierra, por lo que se puede tratar un proyecto para vender humo por parte del gobernador de la provincia, en una de las ciudades más pobres de Argentina. En una mirada más global, o general, es importante ver que el mercado de la IA (y de los centros de datos que se requieren para su desarrollo) se está deteniendo, debido a que cada nuevo modelo ocupa 4 veces la energía del anterior, y los productos no están generando la utilidad esperada, así que se está perdiendo inversión en este sector.

这个场景改成Chloe用崇拜、敬佩的眼神看着伊森，并且对他竖起大拇指，展现出甜美女孩又甜又可爱又崇拜的模样。

这句话删掉

伊森发出胜利者的笑容，但是骨子里根本没把Kevin这种蝼蚁放在眼里

伊森冷漠地盯着Kevin。

应该是紧握着这张纸，准备把它撕成两半

这句删掉

这句在短剧中作用不大

可以补一个伊森运筹帷幄的神态，他抬手拍了拍Chloe的手，似在安慰她

这位浓妆艳抹的员工

所有人的嘴巴吃惊地张开

律师专业而不带感情地说

有人的脸上是好奇，有人是不屑，有人是幸灾乐祸

员工殷勤而谄媚地对凯文说，贼眉鼠眼

改成集团大小姐Chloe

伊森冷漠地看着

露出不屑的神情

这样男主也太憋屈了，改成在他胸口拍了两下就足够

凯文阴阳怪气地说

他把冰拿铁的杯子对着伊森的鼻子。

vergoeding - er wordt normaal gesproken betaald voor de dienst

伊森冷眼看着他们，鼻腔中发出哼地一声冷笑

员工发出幸灾乐祸的声音，好像发现了什么新大陆似的震惊

突出时男主奔跑时的模样，和之前形成callback

应该有一个伊森走入公司玻璃大门的场面，并打量了四周后，皱了皱眉，停下了脚步

伊森露出神秘高智的微笑

对伊森露出迷妹般崇拜的眼神和期待的笑容

伊森停下擦头发的动作，转过头认真看着Chloe，微微皱眉

Chloe一开始懵住了，但她很快调整好了表情，要特别体现她那种被拆穿后的害羞和傲娇

伊森却游刃有余地挺直了身子，将手从Chloe周围撤开

不停地眨着她水润润的大眼睛

要加上Chloe因为距离过近而连脸红的描写

布莱克面带笑容地站起来

在此之前布莱克已经睁开眼睛了，这里应该是想解释、眨了眨眼睛，被拆穿的尴尬和害羞使他脸红着喘气解释

这里是布莱克的脸上有微笑，格兰特表情冷冷的

确保这里是内心声音

这个形容词会不会略微中二

这时候两人是有距离的，一个在走廊这头，一个在走廊那头

run是想表示落荒而逃，会不会有歧义

恶霸盯着格兰特和那个对他表白的omega

这里想的是布莱克背对格兰特，布莱克面对梅森，梅森越过布莱克肩头能和格兰特对视的站位，需要描述吗？同时梅森这里是接过水和毛巾后，揽过布莱克的肩膀和他一起走。

格兰特点头说Saturday或者“可以”

Chloe目不转睛地盯着他走进，脸红

这时，男主的脚上虽然还有疤痕，但是已经不流血了

嘴角扬起一丝邪魅调情的微笑

删掉这句话

Chloe本来懒洋洋地、悠闲地坐在沙发上刷手机，抬眼一看，差点把嘴里的草莓汁喷出来

删掉这句话

改成伊森打量着四周环境，犹豫片刻，抬脚走进了客厅

门一开，两人站在客厅外。Chloe率先向伊森说，进去吧，这里很安全

删掉这段

删掉这句话，改成，“成交，你得到钱，我得到真相”

我觉得这里可以加一下伊森的内心独白：天上不会掉馅饼，但是我现在别无选择了。

听到这句话后，伊森的眉头稍微舒展了一点，虽然仍然是戒备地看着Chloe，但是手上的力道松了

不能说成是want，应该是想独吞财产

伊森仿佛听到了什么禁语，眉头皱了起来

她的笑容缓缓消失，从甜美单纯的模样变得聪明智慧

Chloe许是感到有点疼痛，轻呼了一声，但是却没有挣脱

伊森的后背紧贴着车后门，眼睛微微眯着，满眼戒备地盯着Chloe

Mattel and the intellectual property of Barbie

The Claddagh ring and symbol has been co-opted by both small and large business to convey some sense of 'Irishness'

eLife Assessment

This valuable study compares orthogonal approaches for detecting RNA chemical modifications and provides a helpful framework for improving the reliability of RNA modification identification by direct RNA sequencing. The evidence supporting the technical benchmarking claims is solid, with multiple complementary controls, including in vitro-transcribed RNA, chemical inhibition of METTL3, and orthogonal m6A mapping. However, claims regarding relative method accuracy and cell-type- and isoform-specific biological differences remain less well supported due to limited biological replication and validation.

Reviewer #1 (Public review):

The authors have substantially strengthened the manuscript in response to my comments. In particular, they have improved the treatment of non-canonical calls, added further illustrative examples at the level of individual RNA variants, repeated pathway enrichment using an appropriate expression-matched background, assessed treatment-associated cell viability, and moderated several biological claims. The revised manuscript is now most compelling as a technical benchmarking and best-practice resource for high-confidence prediction of a common RNA chemical modification from nanopore direct RNA sequencing.

The matched-input comparison of two methods, use of unmodified RNA controls, and inhibitor treatment are important strengths. Together, these experiments provide solid support for the central conclusion that careful filtering and appropriate controls can substantially reduce likely false-positive calls and yield a reproducible set of high-confidence predictions. The added analyses also provide useful illustrations of the capacity of long-read sequencing to assign predictions to individual RNA variants, although they do not establish broad superiority over the short-read comparison method.

A few claims remain insufficiently supported. First, the discussion of non-DRACH sites should cite Cancer mutations rewire the RNA methylation specificity of METTL3-METTL14 (Science Advances, DOI: 10.1126/sciadv.ads4750). In particular, Figure 3C of that study shows that the normal METTL3-METTL14 enzyme retains measurable activity on non-DRACH substrates; this provides a useful precedent for describing the inhibitor-sensitive GGAUU calls as plausible candidates rather than treating non-DRACH calls, especially those detected only by GLORI, as presumptively artefactual. The revised manuscript appropriately notes technical and biological explanations for non-canonical calls and identifies the GGAUU sites as candidates requiring further validation, but this additional context would improve interpretation.

Second, the comparison between Dorado and GLORI remains overinterpreted. The matched-input design and inhibitor perturbation are important strengths, but the current analysis does not establish that sites detected only by GLORI are false positives or that Dorado is a reference standard. This would require, for example, direct comparison of inhibitor responsiveness among GLORI-only and shared sites, while accounting for coverage, conversion efficiency, and other method-specific biases; therefore, phrases such as "GLORI exhibits reduced specificity," "largely spurious," and "validated by an orthogonal approach" are not fully supported by the data presented.

Similarly, the thresholding approach should not be described as "unbiased." The analysis is useful and empirically informed, but it relies on selected parameters, a manually specified penalty, and predetermined decision criteria. Describing the procedure as "data-driven" or "empirically calibrated against matched unmodified RNA controls" would more accurately convey both its value and its limitations.

The additional RNA-variant examples are helpful, but they demonstrate an advantage of long-read data rather than broadly proving that Dorado "outperforms" GLORI for RNA-variant-level quantification. A more accurate formulation would be: "Direct RNA sequencing provides RNA-variant-resolved predictions that are not readily assignable using short-read GLORI data, as illustrated at several loci."

Finally, the authors now acknowledge that the functional significance of cell-type differences remains unresolved, but the abstract still presents the results as evidence of "finely tuned" regulation, and the discussion links them to neuronal function. The data provide solid evidence for differences in predicted modification proportions between cell types, but the evidence is incomplete for claims about functional consequences; these statements should therefore be framed as context or hypotheses for future study rather than as inferences from the present experiments.

Reviewer #2 (Public review):

The revised manuscript has improved substantially. The authors have addressed the main concerns raised during the previous round by introducing an IVT-based calibration framework, evaluating background calls across probability thresholds, examining basecalling mismatches, and replacing "stoichiometry" with the more appropriate term "predicted modification proportion." The expanded discussion of non-DRACH predictions is also useful and appropriately balanced. Overall, the study now provides a valuable practical framework for Dorado-based RNA modification analysis.

I have only a few remaining comments, all of which should be addressable through clarification and minor textual revisions.

(1) The comparison between NHDF and HD10.6 cells does not appear to include independent biological replicates. The observed differences in predicted m6A/A proportions should therefore be presented as differences between the two datasets rather than as general cell-type-specific effects. The interpretation is already cautious in the Discussion, but similar wording should be used in the Abstract, Results heading, and title if applicable.

(2) The direction of the comparison in Figure 3D should be clarified. The percentages represent the fraction of GLORI sites that is also detected by Dorado. Thus, the analysis shows that Dorado increasingly recovers high-proportion GLORI sites. It does not directly demonstrate that high-proportion Dorado predictions are increasingly validated by GLORI. The text should reflect this distinction, and the figure should clearly identify which circle represents each method.

(3) The ratio of calls detected in IVT relative to biological samples is a useful empirical measure of background, but it is not formally a false-positive rate because the true status and testability of every site are not known. A term such as "IVT-to-sample call ratio" or "empirical background-call ratio" would be more precise and would not alter the main conclusion regarding the value of stringent filtering.

(4) The description of the change-point framework still contains the placeholder "at least X-fold more signal." The actual enrichment threshold should be provided. The authors should also ensure that a log2 ratio threshold of 1.5 is not described as 1.5-fold enrichment, since these values are not equivalent.

These are relatively minor issues. The authors have responded constructively to the previous reviews, and I do not consider further experimental work necessary.

Author response:

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

We thank you all for the careful evaluation of our manuscript and for the numerous constructive comments and suggestions. As noted by the reviewers, the study directly compares two distinct approaches for detecting m⁶A modifications using the same RNA input material. The inclusion of IVT and STM2457-treated RNA as negative controls, as well as the high-depth long-read datasets from two different cellular systems, enable robust analysis and interpretation of the data, providing a rigorous and reproducible analysis workflow and a valuable reference for the rapidly expanding community aiming to use Oxford Nanopore Technologies’ (ONT) latest flow cell chemistry and Dorado base calling tool.

We have revised the manuscript according to this feedback and believe the changes have greatly improved the clarity and utility of the work. We have, where possible, addressed all the points raised and have further discussed or acknowledged those that involve broader technical considerations beyond the scope of the present study.

Reviewer #1 (Public review):

Summary:

The authors set out to evaluate how accurately direct sequencing of RNA can identify and quantify several chemical modifications on RNA molecules, focusing primarily on m6A. A central goal of the work is to compare this approach with an independent chemical-based method (glyoxal and nitrite-mediated deamination of unmethylated adenosines (GLORI), using the same RNA samples, in order to assess reproducibility, false-positive signals, and sensitivity across a range of detection strategies. The authors further aim to demonstrate the biological utility of this approach by applying it to two human cell types, primary human fibroblasts and HD10.6 neurons. While the manuscript also reports detection of additional RNA modifications (pseudouridine and m5C), the depth of analysis and strength of controls are greatest for m6A, which forms the primary focus of the study.

Strengths:

A strength of this work is the direct comparison of two distinct measurement approaches performed on the same RNA input material; this has not been done in other recently published benchmarking studies evaluating the utility of the recent direct RNA sequencing for calling m6A. The authors systematically test multiple analysis models and show that, when appropriate filtering is applied, detection of modified sites is reproducible across software versions. The use of synthetic RNA standards and METTL3 inhibitors as negative controls helps to reinforce the overall results. The data show good agreement between the two methods at higher m6A modification levels, supporting the conclusion that direct RNA sequencing can reliably detect high-confidence modification sites. The authors also demonstrate that this approach can, in principle, provide information at the level of individual RNA variants (although only one example was provided), which is difficult to achieve with short-read methods. The methodology described here is likely to be useful to others seeking to apply similar approaches to identify and quantify m6A. The study also explores the detection of other RNA modifications, which highlights the broader potential of the approach, although these analyses are necessarily more exploratory given the more limited controls and data available.

Weaknesses:

Despite these strengths, several issues limit the interpretation of the results and should be clarified for readers.

First, the authors appropriately address false-positive signals by estimating expected false-positive rates and by quantitatively comparing sequence motif enrichment before and after filtering. These analyses provide important support for the use of stoichiometry-based thresholds and demonstrate that filtering substantially improves specificity. However, even after filtering, a subset of detected sites remains outside the expected sequence context. It therefore remains unclear to what extent these non-canonical sites reflect genuine biology versus residual technical artifacts.

The detected sites that were outside of the expected sequence context (0.1% of the sites per cell type) exhibited sensitivity to STM2457 treatment, supporting the conclusion that these sites represent genuine signals. This point has been clarified in the text (lines 424-455 and 563-578) and additional supporting data have been included in the supplementary figures (Supplementary Fig. S23 and S24).

Second, claims regarding the ability of direct RNA sequencing to resolve modification patterns across different RNA variants are supported by very limited evidence. The conclusion that this approach provides superior isoform-level quantification relative to short-read methods is based largely on a single gene example. While this case is interesting, it does not establish how widespread or general this advantage is. A broader analysis indicating how many genes show isoform-specific modification patterns detectable by this method, and how often these are missed by the comparison approach, would be necessary to support a general claim.

We thank the reviewer for this valuable comment. To strengthen this point, we have added two additional examples illustrating isoform-specific m⁶A modification patterns, as well as one monoexonic gene for comparison (new Supplementary Figure S22). KRTAP1-5 is a monoexonic gene with a single detectable isoform and shows that Dorado detects more m⁶A sites than GLORI. HSPA8, with five detectable isoforms, is a good example to show that two isoforms carry additional m⁶A sites not resolvable by GLORI. CITED2 has three detectable isoforms, and differential m⁶A patterns can only be resolved with Dorado. We adjusted the text accordingly in lines 395-401.

Third, the biological interpretation of cell type-specific differences in modification levels remains underdeveloped. Although differences in modification stoichiometry are reported between fibroblasts and neuron-derived cells, the functional consequences of these differences are not addressed. It is unclear whether changes in modification levels are associated with differences in RNA abundance, stability, or translation. As a result, statements suggesting that these modifications fine-tune core cellular pathways are speculative and should either be supported with additional analyses or framed more cautiously.

We have updated the text (lines 477-481) to ensure a more cautious interpretation of the observations.

Related to this point, differences in gene expression between the two cell types are a potential confounding factor. The pathway enrichment patterns presented appear biased toward particular functional categories, but without clear control for differential gene expression, it is difficult to determine whether the observed enrichment reflects cell type-specific regulation of RNA modification or simply differences in which genes are expressed. Clarifying how background gene sets were defined for these analyses would help readers interpret the results.

We thank the reviewer for raising this important point. For the KEGG pathway enrichment analysis, we used genes harboring shared m⁶A sites in both NHDF and HD10.6 cells as the input gene list, irrespective of their expression levels. We initially did not use a custom background gene set, which is also a valid point brought up by the reviewer. Hence, we repeated the analysis and used all genes expressed in both cell types (~13,000 genes) as background (i.e. excluding those only expressed in one cell type). We have revised the results (lines 467-468) and methods section (lines 740-749) accordingly. We replaced the plot in Figure 4H with the new plot.

The manuscript also suggests broader differences in overall modification levels between cell types, but this is not validated using an independent global assay. An orthogonal measurement of total modification levels on polyadenylated RNA (for example, dot blot) would help place site-specific stoichiometry differences in a clearer biological context.

We tried to assess global changes in modification levels using a dot blot assay. However, even when loading 2 µg of RNA per sample, we did not detect visible differences between DMSO- and STM2457-treated samples in either cell type, which we expected to serve as a positive control. This most likely indicates that the dot blot assay is much less sensitive than our DRS-based sequencing approach. However, as we agree that for making a statement about broader differences between cell types, orthogonal validation would be necessary. Accordingly, we have adjusted our statements in lines 44-46 and 477-481.

Finally, the effects of the METTL3 inhibitor on these cell types are not fully characterized. While changes in m6A modification patterns are reported following treatment, the manuscript does not address whether the treatment affects cell growth or viability.

Cell viability following STM2457 treatment was determined in NHDF and HD10.6 cells using the CellTiter-Glo® assay. HD10.6 cells are terminally differentiated and post-mitotic; therefore, proliferation/growth is not applicable to this cell type, and cell viability (assessed by CTG assay) is the relevant endpoint. We added the results, which showed no significant differences in either cell type, as a supplementary figure (Fig. S1). While we ensured that NHDFs showed normal growth behaviour when treating them with STM2457, because HD10.6 cells are non-proliferative, we assessed cellular health in each of the experiments conducted. We updated a description of the assay in the methods section, lines 637-643, and in the results section (lines 137-138).

Appraisal of conclusions and impact:

Overall, the study provides an informative technical assessment of direct RNA sequencing for modification detection and establishes clear conditions under which the method performs well. The evidence strongly supports conclusions related to technical benchmarking, reproducibility, and the importance of filtering and controls, particularly for m6A. In contrast, conclusions regarding isoform-specific regulation and cell type-specific biological roles of RNA modification are less well supported by the data currently presented, and would benefit from either additional analysis or more restrained interpretation.

The work is likely to have a meaningful impact as a practical reference for researchers using direct RNA sequencing, particularly by clarifying sources of false positives and the value of appropriate controls. With clearer limits placed on biological interpretation or more data presented in support of the biological interpretation, the study would serve as a valuable reference for users seeking to apply these technologies reliably.

We thank the reviewer for this positive evaluation of our work. To address major concerns, we have adjusted our interpretation of the biological findings throughout the manuscript to better reflect the scope of the presented data. We believe these revisions more clearly distinguish the technical benchmarking aspects of the study from the biological insights that need further investigation.

Reviewer #2 (Public review):

Summary:

In this study, the authors aim to establish a calibrated framework for detecting RNA modifications using long-read sequencing and apply it to compare modification patterns between fibroblasts and neuron-like cells. The work combines long-read sequencing, in vitro transcribed controls, methyltransferase inhibition, and comparison to an orthogonal sequencing-based method in an attempt to derive filtering strategies that reduce false positive modification calls. The authors further apply this framework to explore differences in modification levels between the two cell types. The resulting dataset may be of interest to researchers working on RNA modification detection using long-read sequencing technologies. Independent datasets across additional cellular systems can be useful for benchmarking computational methods and evaluating the behavior of modification detection models. However, the conceptual advance of the analytical framework presented here remains somewhat unclear, as many aspects of the analysis closely resemble strategies that have already been described in recent benchmarking studies.

Strengths:

A clear strength of the study is the generation of a relatively large long-read sequencing dataset together with several useful experimental controls, including in vitro transcribed RNA and pharmacological inhibition of the methyltransferase enzyme responsible for installing this modification. These controls are helpful for illustrating the challenges associated with distinguishing high-confidence modification sites from background signals. The inclusion of two different human cellular systems also provides an additional dataset that may be useful for benchmarking and cross-validation in the field. The study addresses a practically relevant question for the community, namely, how to reduce false positive calls in long-read sequencing-based RNA modification analyses.

Weaknesses:

The main weakness of the manuscript is its limited methodological novelty. Much of the analytical framework presented here closely follows benchmarking strategies that have already been described in recent studies of RNA modification detection using long-read sequencing. Several previous studies have evaluated modification-aware basecalling approaches, discussed the need for stringent filtering strategies, and compared long-read sequencing-based predictions with orthogonal mapping approaches. The manuscript would therefore benefit from a deeper engagement with the recent benchmarking literature and a clearer explanation of what conceptual or methodological advance the present study provides beyond these earlier analyses.

We thank the reviewer for this constructive comment. At the time our study was conducted, relatively few benchmarking studies addressing these methodological aspects were available, and the analytical approach and thresholds applied in our study were therefore established independently of these reports. Several relevant studies (some initially as preprints) subsequently emerged within a relatively short period. Now that these studies are available, we have considered their findings, including the thresholds and filtering criteria applied, and have incorporated the relevant comparisons and methodological considerations into the revised manuscript. We have rewritten the introduction section (lines 99-117) and the discussion (lines 537-546, 547-549, 553-562) to incorporate newer studies and correctly integrate our study into the field. Furthermore, we consider these findings not to be supportive of our work, as they strengthen the overall interpretations and guidelines for ONT DRS users.

A second concern relates to the filtering strategy that forms the core of the proposed workflow. The manuscript applies several thresholds, including modification probability, stoichiometry, and read coverage cutoffs, but it is not clearly explained how these thresholds were determined. It remains unclear whether these cutoffs were derived from statistical calibration, empirical optimization using the presented dataset, or adopted from previous studies. Because the downstream conclusions depend strongly on these filtering choices, a clearer methodological justification would strengthen the work and help readers assess the robustness of the proposed framework.

We thank the reviewer for raising this important point. For the modification probability thresholds, we compared the distribution of modification probabilities in the biological samples with that observed in the IVT controls. In the revised manuscript, we have integrated a new statistical framework that enables an unbiased determination of modification probability cutoffs for each dataset (see lines 174184, Methods, as well as Fig 2 and Supplementary Figs 5, 8, 9 and 10). We further report error rates relative to IVT across different modification probability cutoffs and probability ranges (lines 186-200). The validread coverage threshold was introduced as an additional safeguard against sites supported predominantly by reads that were neither confidently called as the canonical nucleotide nor confidently assigned as modified (since valid coverage includes only reads supporting the canonical base or the corresponding modification above the selected probability threshold (≥0.98 or ≥0.99, depending on the modification) (lines 233-240). Finally, the stoichiometry cutoff was also informed by comparison with the IVT controls. Although stringent modification-probability filtering substantially reduced background, the majority of residual sites detected in the IVT controls occurred at low modification proportions, particularly below 10%. We therefore introduced the stoichiometry threshold to further distinguish biological modification signal from low-level background calls observed in the unmodified IVT reference (lines 241-249).

The interpretation of the comparison between the two modification detection approaches also appears somewhat overstated. Differences between the methods are frequently interpreted as evidence that one approach produces large numbers of false positive calls, but the analyses presented do not fully exclude alternative explanations such as differences in sensitivity, sequencing depth, or methodological biases. A more cautious interpretation of these discrepancies would therefore be appropriate.

Some discussion points also appear speculative. In particular, certain interpretations propose mechanistic explanations without presenting analyses that would allow these possibilities to be distinguished. Such interpretations would benefit from either additional supporting analyses or more cautious phrasing.

We have revised the relevant sections accordingly to provide a more cautious interpretation of these findings (lines 583-592).

From a methodological perspective, the statistical robustness of the thresholds used throughout the analysis could also be discussed in more detail. Given the relatively modest read coverage cutoff applied in the study, low stoichiometry estimates may be strongly influenced by sampling noise, and fixed stoichiometry thresholds may therefore not correspond to a consistent level of confidence across sites. In addition, the manuscript relies heavily on fixed modification probability cutoffs to define high-confidence calls, but it does not discuss whether these scores are statistically calibrated or how they relate to expected error rates. Neural network outputs are often not well-calibrated probabilities, and interpreting these values as direct confidence estimates can therefore be problematic. Finally, modification detection models trained on known modification sites may capture sequence-context patterns present in the training data, meaning that motif enrichment or positional distributions along transcripts may partly reflect model biases rather than purely biological signals. A brief discussion of these limitations would help readers better interpret the robustness of the proposed filtering strategy and the downstream biological conclusions.

We thank the reviewer for raising these important methodological considerations. We acknowledge that modification proportion estimates, particularly at sites with relatively low coverage and low modification proportions, may be influenced by sampling variability and may not accurately reflect the true modification proportion.

In terms of our filtering strategy, we performed false-positive rate analyses across multiple modification probability thresholds using repeated subsampling of biological and modification-free IVT datasets. In addition, we performed an independent change-point analysis to identify where modification signals in biological samples diverged from the IVT background. These analyses, together with the reduction in high-probability m⁶A calls following METTL3 inhibition, provide complementary empirical support for the applied filtering criteria.

We also acknowledge that sequence-context features learned by modification-detection models may influence predicted motif distributions. We therefore performed additional analyses of non-DRACH predictions, identifying both technical sources of erroneous calls and non-canonical sites consistent with genuine biological modifications, including STM2457-sensitive sites.

Overall, while the dataset may be of interest to the community, the extent to which the study advances current methodological understanding beyond recent benchmarking efforts remains limited.

We thank the reviewer for their comments and critiques. The revised version of the manuscript has addressed these points (as detailed above), and we consider the fact that other recently published benchmarking studies come to broadly similar conclusions provide additional support to our work.

Notably, however, our study remains the only one to use the same RNA as input for distinct sequencing approaches (i.e. nanopore vs. GLORI-Seq).

Minor comments:

The discussion of the "DRACH" versus "all-context" outputs would benefit from greater technical precision. The statement that the number of sites within DRACH motifs identified by the all-context approach was nearly identical to the number reported by the DRACH model may suggest that these outputs derive from fundamentally different predictive models. As I understand it, the underlying neural network is the same, whereas the distinction lies primarily in the classification context. Clarifying this explicitly in the manuscript would improve interpretability and avoid potential confusion for readers.

We have updated the text accordingly to ensure greater technical precision (lines 342-350).

The manuscript compares results obtained with different basecalling and modification settings but refers primarily to Dorado software versions. This may be misleading, as software version and model version are not necessarily equivalent. Different basecalling or modification models can be used with the same software release, and newer software versions may still use older models. For clarity and reproducibility, the authors should report the exact basecalling and modification model names used in the analyses rather than referring only to the Dorado software version.

We thank the reviewer for highlighting this distinction. We have revised the manuscript and figures accordingly and clarified the specific Dorado software version as well as the individual models used for each analysis (lines 315-330).

Reviewer #2 (Recommendations for the authors):

(1) To strengthen the methodological robustness of the proposed framework, the authors should clarify how the filtering thresholds used throughout the analysis were determined. If these thresholds were derived empirically from the present dataset, the authors could consider providing additional analyses demonstrating their robustness (for example, by showing how key results change across different probability or stoichiometry cutoffs). If the thresholds were adopted from previous studies, the relevant sources and the rationale for using them should be explicitly cited.

We thank the reviewer for this recommendation. As outlined before, the filtering thresholds used in the analysis were empirically informed by comparison of the biological samples with the unmodified IVT controls. In the revised manuscript, we have implemented a statistical framework that enables the identification of appropriate modification probability cutoffs on a per-sample basis. Specifically, we report error rates relative to the IVT controls across different modification probability cutoffs and probability ranges (lines 186-200), together with statistically supported identification of modification probability cutoffs for each biological sample (by comparing with the corresponding IVT reference, lines 220-230). These analyses support the modification probability thresholds applied in the downstream analyses.

We have also clarified the rationale for the additional filtering criteria, such as valid coverage and stoichiometry cutoffs. (lines 233-240 and 241-249).

(2) Because the analysis relies strongly on modification probability scores, the authors may also wish to discuss how these scores relate to expected error rates. If possible, providing an analysis of how false positive rates behave across probability thresholds using the IVT controls included in the study could help readers better interpret the meaning of the chosen cutoff values.

We thank the reviewer for this suggestion. As described above, we have now directly assessed how false-positive rates vary across modification probability thresholds using the unmodified IVT controls. In the revised manuscript, we report error rates relative to the IVT controls across a range of modification probability cutoffs and within discrete probability intervals (lines 203–213). These analyses demonstrate that false-positive rates decrease with increasing modification probability and provide an empirical basis for interpreting the selected probability thresholds.

(3) For the comparison between the two modification detection approaches, the authors may consider providing additional analyses that clarify the source of discrepancies between the methods. For example, examining whether differences depend on read coverage, sequence context, or site stoichiometry could help readers better understand the conditions under which the two approaches converge or diverge.

Thank you for this suggestion. We agree that analyses of read coverage, sequence context, and site stoichiometry could help explain the differences between the two methods. However, our current results suggest that the main reason for the discrepancy is the limited conversion efficiency of the GLORI dataset (see Fig. 3D). To properly assess the effects of coverage or sequence context, we would need a higher-quality GLORI dataset with better conversion efficiency. Generating such a dataset and repeating the analysis was beyond the scope of this study. We have therefore revised the text to make this limitation clearer and adjusted our interpretation accordingly (lines 343-361 and 583-593).

(4) The interpretation of modification patterns between fibroblasts and neuron-like cells could also be strengthened by additional analyses that account for potential confounding factors such as differences in transcript abundance, transcript coverage, or gene expression levels between the two datasets.

We thank the reviewer for this important suggestion. Our comparison was restricted to m⁶A sites shared between both cell types, minimizing potential biases from cell type-specific transcript detection. We agree that transcript abundance, coverage, and the effects of m⁶A on transcript stability and translation should be considered to assess the biological significance of these differences, but such analyses are beyond the scope of this study. We have therefore rephrased our statements more cautiously (lines 478-482).

(5) In the description of the modification detection models, the manuscript would benefit from clearer reporting of the exact basecalling and modification model names used in the analyses. Because software version and model version are not necessarily equivalent, specifying the model identifiers would improve clarity and reproducibility.

As mentioned above, we have revised the manuscript and figures accordingly and clarified the specific basecalling and modification models used for each analysis (lines 315-330).

(6) The manuscript would also benefit from clarifying the relationship between the "DRACH" and "allcontext" outputs, particularly whether these represent distinct predictive models or the same model applied in different classification contexts.

As mentioned above, we have revised the corresponding paragraph to avoid ambiguity regarding the models used in each analysis (lines 315-330).

(7) Finally, the manuscript would strongly benefit from a more detailed discussion of the relationship between the present analysis and previous benchmarking studies of long-read RNA modification detection, which would help readers understand how the current work relates to and extends existing efforts in the field.

We thank the reviewer for this helpful suggestion. We have expanded the discussion to better place our findings in the context of new benchmarking studies (538-547, 554-563).

Reviewer #3 (Public review):

In this study, the authors aim to establish a calibrated framework for identifying RNA chemical marks from direct RNA sequencing data using a modification-aware basecalling workflow, with a particular focus on N6-methyladenosine. By combining native RNA sequencing with an unmodified control transcriptome, enzyme inhibition, comparison across multiple software versions, and orthogonal validation using an independent mapping approach, the authors seek to define a best-practice pipeline for reducing falsepositive calls and improving confidence in quantitative interpretation across cell types.

A major strength of the work is the rigor of the benchmarking strategy. In particular, the inclusion of an unmodified control transcriptome is both important and useful, and the study provides compelling evidence that this control remains necessary for robust interpretation, despite being omitted in many current workflows. The comparison across software versions and the matched analysis with an independent sequencing-based approach also substantially strengthen the evidence presented. The work therefore makes a valuable contribution to the community by offering a more stringent analytical framework that will likely be broadly useful to groups applying native RNA sequencing to study RNA chemical marks.

The evidence supporting the main conclusions is solid overall. The authors convincingly show that stringent filtering substantially reduces false-positive calls and improves agreement with orthogonal approaches, particularly at highly modified sites. The observation that many sites are conserved across cell types, while showing differences in relative modification levels, is also supported by the presented analyses.

At the same time, several conceptual issues limit the strength of some downstream interpretations. Most importantly, the manuscript repeatedly refers to the reported values as "stoichiometry," whereas the underlying software output is more appropriately interpreted as a statistical estimate of the proportion of aligned reads classified as modified. This distinction is important because the conclusions regarding celltype differences rely on quantitative comparisons of these values. In addition, the current calling framework depends on successful canonical base assignment before modification calling, which raises an important limitation: sites with the strongest signal deviations may be underrepresented if they are more likely to be miscalled during basecalling. This issue may be especially relevant for RNA marks that induce stronger mismatch signatures than N6-methyladenosine and should be more explicitly discussed. Overall, the authors largely achieve their primary aim of establishing a more rigorous and broadly applicable analytical framework for direct RNA sequencing-based modification detection. The work is likely to have a meaningful impact on the field, particularly by reinforcing the importance of appropriate negative controls and benchmarking standards. With clearer framing of the quantitative outputs and explicit discussion of current software limitations, this study will serve as a highly useful resource for the community.

Reviewer #3 (Recommendations for the authors):

I have several conceptual and methodological concerns.

(1) Please avoid the term "stoichiometry". A major concern is the repeated use of the term "stoichiometry." In the context of Dorado-based nanopore calling, this is fundamentally a statistical estimate of the proportion of aligned reads classified as modified, rather than a direct biochemical measurement of true molecular stoichiometry or occupancy. I strongly recommend replacing this term throughout the manuscript with:

- predicted m6A/A proportion

- relative modification proportionThis distinction is especially important because the manuscript draws biological conclusions from differences in these values across cell types. At present, the language risks overstating the quantitative certainty of the method.

The reviewer is correct, and where applicable (text, figures), we have replaced the word “stoichiometry” with “predicted m⁶A/A proportion”.

(2) Canonical basecalling dependency requires explicit discussion.

A key conceptual concern is that the modification caller requires the base to first be correctly called as A before assigning it as m6A. This point needs to be explicitly addressed in the manuscript. Because the proportion is being calculated only from reads that are first aligned and called as canonical A, I strongly encourage the authors to include a mismatch analysis and explicitly frame the reported values as proportions derived only from correctly aligned A-containing reads. This raises an important question: are the strongest signal-distorting sites being systematically excluded from analysis because they are more likely to be basecalled incorrectly? This is an important limitation of the ONT Dorado/Modkit framework and should be discussed directly.

We thank the reviewer for raising this important point. We agree that the reported modification proportions do not capture all reads overlapping a given position. The proportions are calculated from reads in which the corresponding base is basecalled as an A and is confidently assigned as either canonical A or m⁶A above the selected modification-probability threshold. Reads in which the position is basecalled as a different nucleotide, contain a deletion, are assigned another modification, or have an m⁶A probability below the selected threshold are therefore not included in the proportion estimate. For the DRACH context model, reads containing a non-matching base at other positions within the required motif context are likewise excluded from modification calling. We have now clarified this limitation explicitly in the revised manuscript (lines 285-296). In addition, to assess the potential magnitude of this effect, we performed a mismatch analysis examining the percentage of reads in which the reference A was basecalled as another nucleotide and evaluated its relationship with the predicted m⁶A/A proportion across sites in our filtered dataset (lines 297-313). This analysis provides information that is not captured by the predicted modification proportions.

(3) The concern above (2) is likely stronger for Pseudouridine and Inosine RNA editing.

I think this issue is likely even more important for Pseudouridine and Inosine RNA editing than for m6A. For these modifications, the basecalling error at the modified site is expected to be substantially stronger than for m6A, which may leave very few aligned reads available for downstream proportion calculations. This could potentially explain why the authors require a higher modification probability threshold for these marks. I would strongly encourage the authors to explicitly discuss whether this basecalling dependency contributes to the need for the {greater than or equal to}0.99 threshold and whether this introduces a systematic detection bias.

To investigate the potential impact of basecalling errors on modification proportion estimates, we examined alternative nucleotide calls at modification sites, including pseudouridine and inosine sites, focusing on positions where non-reference basecalls accounted for ≥25% of the reads overlapping the site (lines 297-305). At pseudouridine sites, the predominant alternative basecall was C, whereas at inosine sites, G was most frequently observed. These results indicate that modification-associated basecalling errors may lead to the exclusion of a fraction of reads from downstream modification proportion calculations. Consequently, the reported modification proportions may be underestimated at sites where the presence of a modification strongly affects canonical basecalling, introducing a potential detection bias.

Importantly, however, the modification probability thresholds used in our analysis were derived empirically by comparing modification probability distributions in the biological samples with those observed in the corresponding IVT and STM2457-treated controls. Basecalling accuracy was not considered in establishing these thresholds.

(4) Need better framing of multiple modifications on the same transcript.

In the Introduction, I would encourage the authors to better position nanopore DRS relative to short-read approaches by explicitly noting that short-read methods are also limited in their ability to resolve multiple modifications on the same transcript molecule. This is an important conceptual advantage of native longread sequencing and deserves clearer emphasis early in the manuscript.

We agree that the ability to examine the co-occurrence of multiple modifications on individual RNA molecules represents an important distinction between nanopore DRS and short-read approaches. We have therefore revised the Introduction to explicitly note the limitation (lines 79-80, 83-84).

(5) Clarify unclear experimental step.

The stoichiometry distribution experiment (lines 189-195) in the Results section is difficult to follow.

We thank the reviewer for pointing this out. We have revised and restructured this section to more clearly describe the stoichiometry analysis (lines 255-274).

(6) Discussion of non-DRACH sites.

The manuscript interprets many non-DRACH GLORI sites as likely false positives. While this is a reasonable interpretation, I would like the authors to also discuss whether true cellular m6A may exist at non-DRACH motifs, and whether any non-canonical sites identified by nanopore DRS may represent biologically real events rather than technical artifacts.

We agree and have added a new analysis (Fig. S23 and S24) addressing this point. Most non-DRACH calls derive from technical causes (SNPs, homopolymers, proximity to true DRACH sites), but a subset conforming to DRAC motifs, e.g., are STM2457-sensitive, indicating they likely represent genuine METTL3-dependent m⁶A sites. We have added a description of the new figures in the results section 425456) and discuss this further in lines 564-579.

This is especially important given emerging evidence that non-canonical motifs can occasionally occur in vivo.

We thank the reviewer and have added corresponding data and discussion, as pointed out above. While most all-context non-DRACH calls reflect similar technical artifacts, we identify STM2457-sensitive GGAUU sites, e.g., and a distinct STM2457-insensitive, METTL16-like class (e.g., ACAGA in MAT2A), supporting that some non-canonical calls represent true m⁶A sites.

Regarding lines 236-240: Are there also sites from nanopore sequencing that are unexpectedly positive?

We thank the reviewer for this question and refer to our newly added figures (Figure S23 and S24) and explanations described above.

(7) Strongest point of the paper. I want to emphasize that one of the strongest contributions of this study is its clear demonstration that the modification-free transcriptome control is necessary. This should be stated even more explicitly as a best-practice recommendation, as the field increasingly trends toward analyses that omit such controls.

We thank the reviewer for highlighting this important aspect of our study. We have strengthened this point in the Discussion (lines 610-611) and now more explicitly recommend using a modification-free transcriptome control as a best practice for reliable RNA modification detection by DRS.

Overall, I find this to be a strong manuscript, with its methodological rigor being a major strength. Addressing the concerns above would substantially improve both the clarity and interpretability of the work.

We thank the reviewer for their positive assessment and constructive feedback. We believe that the revisions have improved the clarity of the manuscript and the interpretability of our findings.

eLife Assessment

In this important study, the authors demonstrate that generative AI techniques (restricted Boltzmann machine) can be used effectively to design and characterize mutational pathways of WW domains with different binding specificities. The computational studies are complemented by experimental validations, and the results provide convincing evidence supporting the idea that sequence landscape holds significance in understanding protein evolution from a transition path perspective. The minor weakness of the study has been largely addressed in the revision. The work will likely have a major impact on research aimed at understanding how evolution navigates fitness landscapes as well as reconstructing ancestral sequences.

Reviewer #1 (Public review):

Summary:

The authors aim to study mutational paths connecting WW domains with different binding specificities. Their approach combines an unsupervised sequence generative model based on RBMs with a path-sampling algorithm. The key result is that most intermediate sequences along the designed transition paths retain measurable binding activity in wet-lab assays, whereas paths containing the same mutations introduced in a randomized order are largely non-functional. This difference is attributed to epistatic interactions captured by the RBM model.

Strengths:

Exploring mutational paths in high-dimensional protein sequence space is a challenging problem. The computational framework used here is state-of-the-art and is strengthened by systematic experimental characterization of binding activity. The study is comprehensive in scope, including multiple transition paths both within and across WW specificity classes, and the integration of modeling with high-throughput experimental validation is a clear strength.

Reviewer #2 (Public review):

The manuscript is extremely clear and well written, the experimental evidence is strong and the methods are clearly described, so I do not have major issues to raise. A few minor issues are listed below. Some of these were presented in my first review but were not addressed by the authors.

(2) In Section 3.3 the authors say that direct paths connecting Class I and Class IV behave similarly to indirect paths, despite having lower scores according to the RBM. How generic is this? Does it also happen for other classes? This might be an important point to address as direct paths are easier to sample.

(3) The path shown in Fig.4 goes through a region of non-functionality around sequences 18-19. It seems that the sample path is basically exploring the functional regions for Class I and Class II/III separately trying to approach the other class, but then it can't really do the switch.

By contrast the path going from Class I to Class IV seems able to perform the functional switch in a single step (20-21) without losing too much of the function.

Perhaps the authors could better comment on this? Is this a limitation of the sampling method, or a fundamental biological fact?

(4) On page 12 it is stated that temperature was chosen to 1/3 to maximize the score. This is important and should be mentioned earlier (I didn't notice it until that point).

(5) On page 13 it is stated that "However, the scores of the ancestral sequences along the phylogenetic pathways assigned by the RBM are significantly lower than the ones of the RBM-designed sequences. This result is expected as ASR reconstruction does not take into account epistasis, differently from RBM, and we expect ASR sequences to generally be of lesser quality."

I was very surprised by this result. My own experience with ASR shows that on the contrary, sequences found by ASR (via maximum likelihood) tend to have high scores in the (R)BM and tend to be more stable than extant sequences. I attribute this to the fact that ASR typically finds a "consensus" sequence that maximizes the contribution to the score coming from the fields (the profile), which is typically dominant over the epistatic signal, resulting in a bigger score. Maybe the authors did not use maximum likelihood in the ASR? Some clarification might be useful here.

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

Summary:

The authors aim to study mutational paths connecting WW domains with different binding specificities. Their approach combines an unsupervised sequence generative model based on RBMs with a path-sampling algorithm. The key result is that most intermediate sequences along the designed transition paths retain measurable binding activity in wet-lab assays, whereas paths containing the same mutations introduced in a randomized order are largely non-functional. This difference is attributed to epistatic interactions captured by the RBM model.

Strengths:

Exploring mutational paths in high-dimensional protein sequence space is a challenging problem. The computational framework used here is state-of-the-art and is strengthened by systematic experimental characterization of binding activity. The study is comprehensive in scope, including multiple transition paths both within and across WW specificity classes, and the integration of modeling with high-throughput experimental validation is a clear strength.

Weaknesses:

A major concern is whether the stated goal of specificity switching is fully achieved. Along the sampled transition paths, most intermediate variants appear to retain specificity close to either the initial or the final class, rather than exhibiting gradually shifting specificity. For example, in Figure 4G (Class I to Class II/III), binding appears largely binary, with intermediates behaving similarly to one of the endpoints. A similar pattern is observed in Figure 3H for the Class I to Class IV transition, where binding responses are close to 0 or 1. In this sense, the specificity-switching objective is only partially realized by assigning two endpoints with different specificity. This raises a broader conceptual question: is it possible that different WW specificities evolved from a common ancestor without passing through intermediates that exhibit mixed or intermediate specificity? If so, then inferring specificity-switching pathways purely from extant natural sequences may be fundamentally challenging.

We have added a sentence in the introduction to underline these points, in particular: Is the specificity switch happening through abrupt or more gradual specificity changes? Does it correspond to a bottleneck in sequence space?

Reviewer #2 (Public review):

This is an extremely important work that shows how one can use generative models to construct specificity-switching mutational paths in complex fitness landscapes. The experimental evidence is very clear, and the theoretical tools are innovative.

The work will likely have a deep impact on future research aimed at understanding how evolution navigates fitness landscapes as well as reconstructing ancestral sequences.

The manuscript is extremely clear and well written, the experimental evidence is strong, and the methods are clearly described, so I do not have major issues to raise. A few minor issues are listed below.

(1) I consider the WW domain as an 'easy' case from the point of view of generative modelling. The domain is rather short, epistatic effects are not very strong (e.g. Boltzmann learning usually converges very quickly to a very paramagnetic state), and the resulting models are well interpretable (e.g. the hidden units of the RBM correlate well with subclasses).

This is not always (not often?) the case, however. In more complex proteins, the learning procedures can be slower and the resulting models less interpretable. Just for completeness, perhaps the authors could comment on the generality of the results and what they would expect for other systems based on their experience.

We have added a sentence in conclusion to discuss the application of our framework to more complex and longer proteins.

(2) In Section 3.3, the authors say that direct paths connecting Class I and Class IV behave similarly to indirect paths, despite having lower scores according to the RBM. How generic is this? Does it also happen for other classes? This might be an important point to address, as direct paths are easier to sample.

We have added a sentence in conclusion to emphasize the need for a more exhaustive study of non-direct vs direct paths.

(3) The path shown in Figure 4 goes through a region of non-functionality around sequences 18-19. It seems that the sample path is basically exploring the functional regions for Class I and Class II/III separately, trying to approach the other class, but then it can't really make the switch.

By contrast, the path going from Class I to Class IV seems able to perform the functional switch in a single step (20-21) without losing too much of the function.

Perhaps the authors could better comment on this? Is this a limitation of the sampling method, or a fundamental biological fact?

We now better stress and discuss the comparison between Class I to Class IV paths and Class I to Class II paths in the conclusion.

(4) On page 12, it is stated that the temperature was chosen to 1/3 to maximize the score. This is important and should be mentioned earlier (I didn't notice it until that point).

We have added a sentence in Section 3.2 on the lowering of the sampling temperature to 1/3 to obtain high-scores sequences.

(5) On page 13, it is stated that: "However, the scores of the ancestral sequences along the phylogenetic pathways assigned by the RBM are significantly lower than the ones of the RBM-designed sequences. This result is expected as ASR reconstruction does not take into account epistasis, differently from RBM, and we expect ASR sequences to generally be of lesser quality."

I was very surprised by this result. My own experience with ASR shows that, on the contrary, sequences found by ASR (via maximum likelihood) tend to have high scores in the (R)BM, and tend to be more stable than extant sequences. I attribute this to the fact that ASR typically finds a "consensus" sequence that maximizes the contribution to the score coming from the fields (the profile), which is typically dominant over the epistatic signal, resulting in a bigger score. Maybe the authors did not use maximum likelihood in the ASR? Some clarification might be useful here.

We have updated Figure 5 with shuffled and ASR sequences, and added Suppl Figure 7 to compare the score of the consensus sequences and the ones of the most ancestral nodes along the path in the ASR reconstruction with the natural scores.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

I suggest that the authors either more clearly acknowledge that smooth specificity switching is not fully demonstrated or reconsider the use of "specificity switching" in the title. An alternative framing could emphasize the study of mutational paths connecting different specificity classes, without implying a transition in specificity.

One of the main questions motivating our study was precisely to investigate where along the path and how the transition in specificity happens, i.e. through intermediate that exhibit mixed or intermediate specificity (which we had defined as ‘smooth’) or more abruptly.

For class I-IV paths, we observe a smooth specificity switch with some cross-specificity of the sequences designed in the switching-specificity region, with low but significant binding responses not only towards class I and IV ligands (the extant sequences specificity class) but also to ligand of class II. On the contrary, in the paths from class I to class II-III, we observe the abrupt switch, at the predicted specificity boundary, with sequences with a clear response to one or the other peptide. This abrupt switch requires the change of several amino acids determining the specificity class, and binding is loss when this change is incomplete.

We understand that the term “smooth” to design the cross-reactivity of the few sequences in the predicted ‘switching boundary zone’ could be misleading, so we have removed it from the abstract.

Moreover, we have added a sentence in introduction to motivate the study of where and how the specificity transition takes place, and two sentences in conclusion about the identification of where the specificity switching take place along the path, and to underline the difference between the paths from class I to class IV and the ones from class I to class II/III.

It is striking that all scrambled paths are non-functional. Given that sequences adjacent to the end states differ by only a few mutations, one might expect at least some of these variants to remain functional, as small numbers of mutations often do not abolish activity. The authors should discuss this result in greater depth and more clearly explain how it uniquely supports the role of epistatic interactions.

The sampling of the scrambled paths is not strictly random. Along the scrambled path the mutations are inserted in the reverse order; the scrambled paths are therefore chosen to maximize the sequence divergence between the unscrambled sequences (in which the mutation is predicted to be functional by the path sampling algorithm) and the one in which it is tested in the scrambled path. The fact that the background matters is a clear sign of epistasis.

We have better explained this important point in Section 3.3, where we introduce scrambled paths.

Specificity class annotations are inferred using RBM latent weights. However, as shown in Figure 2E, the boundaries between classes in latent space are not sharply defined. It would strengthen the analysis to validate these class assignments using alternative representations, such as clustering in the latent space of a VAE or embeddings from protein language models, to assess whether the inferred class boundaries are robust and consistent.

Beyond RBM weights, the boundaries between clusters have been assessed by inferring two different RBMs (which we call class specific RBM) on class specific MSA, such models validated the robustness of the boundaries. Most importantly, we have shown that the classification is consistent with experiments.

To further answer this comment, we have added Suppl. Figures 7 and 8, showing that a simple PCA approach, as well as VAE agree with our clustering.

In Figure 3C and Figure 3F, variants 18 and 19 appear to represent transition states located in regions of latent space with little or no training data. Intuitively, such variants would be expected to have a lower probability than variants 22, which lie in a high-density region of the RBM model. However, this expectation is not reflected in Figure 3C. A similar issue appears in Figure 4. The authors should carefully re-examine these results and clarify whether this behavior arises from the model, the scoring procedure, or a potential error.

We confirm that variants 18 and 19 in mutational path between classes I-IV have larger scores than the class IV wild-type domain. We have attributed the low score of this class IV wild-type (the extant sequence) to its smaller structural stability, as discussed in Sec 3.3 and with Supplementary Figure 3.

The key point here is that the scores of Natural sequences are spread, as shown with the histogram in Fig. 5 (green distribution), corresponding to more or less stable natural protein. The RBM model (with a low sampling temperature favoring higher RBM scores) is able to generate artificial sequences far away in sequence space (i.e. with high Hamming distance with respect to natural one), while keeping high scores. The functionality of designed proteins with high RBM scores has been demonstrated in previous work by some of us, e.g. [Malbranke et al PLoS Comp Bio 2026], [Huot, Monasson, Cocco Cell system 2026], [Fernandez-de-Cossio-Diaz Nature Communication 2026], building up on similar results for BM models [Russ et al Science 2020].

We have now clarified the point of the sampling at low temperature earlier in the manuscript in Section 3.2.

Reviewer #2 (Recommendations for the authors):

(1) In Figure 2 F-G, maybe a histogram of the scores of natural sequences could be added for comparison.

We have added the histogram of scores of natural sequences in figure 5, where we discuss the scores of the generated sequences for the different paths.

(2) In Section 3.4, "(Wild type 1YWI or formin binding protein)" is not clear; there might be a typo. Please check

We do refer to Formin-binding protein 1, also known as FNBP1. We have reformulated the corresponding sentence.

(3) On page 16 at the end of section 4, the authors could consider citing "Integrating experimental feedback improves generative models for biological sequences" by Calvanese et al. among the active learning methods.

Thanks for suggesting to cite this very relevant work. We have added the reference.

(4) Reference 78 is stated "in preparation (2019)", I guess this is a mistake and could be fixed.

The reference has been updated.

eLife Assessment

In this manuscript the authors established a novel three-dimensional culture system for stratified epithelia that allows epithelial cells to undergo epithelial-to-mesenchymal transition (EMT) and subsequent mesenchymal-to-epithelial transition (MET) while migrating through a membrane with 3.0-µm micropores, and, thus, provides a highly valuable tool to study EMT and possibly wound regeneration or metastasis. Furthermore, a set of experiments provides compelling data suggesting that TGF beta signaling and actin polymerization promote movement of epithelial cells into the pores, while Piezo1 and Keratin 6 prevent keratinocyte migration and EMT.

Reviewer #1 (Public review):

Summary:

The study describes the migration of epidermal keratinocytes through porous membranes and observed a unique size selection whereby only on 3-micron membrane are keratinocytes able to migrate and reform an intact epidermis. The authors propose that the model replicates three cell states of intact epidermis, EMT, and MET. They also show that this response depends on the actin cytoskeleton, keratin 6, and Piezo1, and the migration could be stimulated with TGFbeta ligands.

Strengths:

Strengths of the study include the establishment of a simple yet robust in vitro model that capture all three cell states, which could be useful for future investigation of wound healing or metastasis across a range of epithelia. There is also good quantitative characterisation of the pore size effects, providing some interesting observations about the physical regulation of keratinocyte migration, and the study provides some initial insights into the regulatory mechanisms. The images and presentation are clear.

Weaknesses:

While there are some insights into the underlying mechanisms, there could be more detailed analysis of the key signalling pathways and functional relationships between actin, keratins, Piezo, and EMT genes. Although the extent of new biological insight is somewhat limited, the study still demonstrates the utility of the model, and these points may be important questions to address in future studies.

Reviewer #2 (Public review):

Summary:

The revised manuscript by Nohara et al. presents a novel 3D assay that allows for stratification of epithelia, active EMT through small pores, and active MET. They show that 3um pores allow for keratinocytes to sample the pore through filopodia and up regulate EMT genes to transverse the pores to the other side of the membrane where EMT genes are down regulated as the cells re-establish stratified epithelia. The TGFbeta pathway and actin polymerization promotes movement of cells into the pores and Piezo1 and KRT6 actively block this movement. This work provides a novel 3D assay that is likely to become a benchmark to analyze these processes using a more complex system than other current culture-specific EMT and MET assays.

Strengths:

The strengths of the manuscript include the foundational analysis of the pathways involved in establishing the 3-compartment epithelium. The authors have incorporated live imaging, drug studies, KO analysis, and RNA-sequencing to show the relevant pathways involved. The authors were responsive to the original round of reviews and present a much-improved study with greater details on molecular markers, quantification, and clarity.

Reviewer #3 (Public review):

Summary:

The authors established an experimental system that reproduced three-dimensional triphasic epithelia, i.e., the original epithelium, its EMT, and MET. Keratinocytes (KCs), skin epithelial cells, placed on a microporous membrane migrated through 3.0-um or larger micropores. The 3.0-um-pored membrane induced an epithelial structure with three states: stratified KCs above the membrane, KCs showing EMT within the micropores, and a new stratified epithelium under the membrane. The membrane with larger micropores failed to maintain this triphasic epithelia. Live imaging revealed that KCs moved in a reciprocating manner, with actin-rich filopodia-like KC structures extending into and out of the 3.0-um micropores, while the cells migrated unidirectionally into larger micropores. KO of Piezo1 and keratin 6 increased KC entry to and exit from the 3.0-um micropores. Their results demonstrate that benign keratinocytes migrate through confined spaces in a reciprocating manner, which might help form triphasic epithelia, recapitulating wound healing processes.

Strengths:

Careful observation of the behaviour of keratinocytes on the different sized pores. CrispR-Cas9 gene editing to KO Piezo 1 and keratin 6 isoforms in HaCaT keratinocytes.

Weaknesses:

The authors have improved the observed weaknesses including better statistical analysis and more detail on replicates. They have also performed staining for laminin beta3 chain.

Author response:

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

Changes made independently of the reviewers' comments

In addition to the revisions described below, we have made the following three changes during the revision process. None of them affects the overall claims of the paper.

(1) Removal of the blebbistatin data. The blebbistatin experiments (Figure 3f and h in the original manuscript) did not reproduce in repeated trials. We have therefore removed these data, together with the related descriptions in the Results, Discussion, and Materials and Methods.

(2) Removal of the pan-KRT6 KO-2 data. The original manuscript presented two pan-KRT6 KO cell lines. We subsequently found that an insufficient number of biological replicates had been obtained for KO-2, although its results were consistent in direction with those of KO-1. We have removed the KO-2 data and renamed pan-KRT6 KO-1 as pan-KRT6 KO.

(3) Revised statistics. As described in our response to Reviewer #1's comment (2), we re-analyzed the quantitative data using a generalized linear model that accounts for experimental batch. With this analysis, the effect of ruthenium red on cell exit (Figure 4b) and the effect of one TGF-β receptor inhibitor on cell exit (Figure 3e) no longer reached statistical significance. The corresponding descriptions in the Results have been amended.

Public Reviews:

Reviewer #1 (Public review):

Summary:

The study describes the migration of epidermal keratinocytes through porous membranes and observes a unique size selection whereby only on 3-micron membrane are keratinocytes able to migrate and reform an intact epidermis. The authors propose that the model replicates three cell states of the intact epidermis, EMT, and MET. They also show that this response depends on the actin cytoskeleton and Piezo1, and the migration could be stimulated with TGF-beta ligands.

Strengths:

Strengths of the study include the establishment of a simple yet robust in vitro model that captures all three cell states, which could be useful for future investigation of wound healing or metastasis. There is also good characterisation of the pore size effects, providing some interesting observations about the physical regulation of keratinocyte migration. The images and presentation are clear.

Weaknesses:

(1) Some of the terminology would benefit from better definition or refinement. Triphasic suggests different physical behaviours (e.g. liquid-liquid phase separation) rather than cellular properties. Perhaps it would be better to refer to these as cell states or to describe the model more specifically as an invasion or EMT model. Likewise, the term 'reciprocating' implies two-way communication, but it is used to describe two-way migration or oscillating migration. Here, perhaps oscillatory would be clearer.

We appreciate the reviewer’s comment. According to the reviewer’s suggestion, we have revised the title, abstract, and main text to avoid the terms “reciprocating” and “triphasic” and have instead adopted “oscillatory” and “three-compartment,” respectively.

(2) The quantification and statistical analysis of key results could be improved. Notably, quantification of immunostaining in Figures 1 and 2 would strengthen core findings, and greater detail is needed on the sample sizes and number of experiments used for statistical analysis. These details are missing or only appear to N=1 in some places.

We have added the quantification and statistical analysis to the revised manuscript. We realized that some of the data in the original manuscript combined technical and biological replicates. Therefore, we have revised the statistical analysis and presented the sample size in each experiment in the revised manuscript.

(3) There is an attempt to analyse the underlying molecular mechanisms, but these studies lack depth and detail. For example, it is not clear how actin, keratins, and piezo1 communicate to regulate cell migration. Are they acting directly on EMT genes such as SNAI1 or through changes in cell mechanics and cell-cell adhesions? Likewise, is TGF-beta signalling active in the system (e.g. nuclear pSMAD during cell migration)? As a result, the new biological insight is somewhat limited and confirms much of what is known about these pathways in keratinocyte migration.

We have elaborated these matters in the Discussion section of the revised manuscript. Unfortunately, we were not able to specifically determine if nuclear pSMAD is present in the cells within micropores, due to technical issues described below. However, as TGF-β inhibition did not affect the entry into and the exit from the micropores (Figure 3b, e), TGF-β signaling is unlikely to be essential for this process.

Reviewer #2 (Public review):

Summary:

The manuscript by Nohara et al. presents a novel 3D assay that allows for stratification of epithelia, active EMT through small pores, and active MET. They show that 3 µm pores allow for keratinocytes to sample the pore through filopodia and upregulate EMT genes to transverse the pores to the other side of the membrane where EMT genes are downregulated as the cells re-establish stratified epithelia. The TGFbeta pathway and actin polymerization promote the movement of cells into the pores and Piezo1 and KRT6 actively block this movement. This work provides a novel 3D assay that is likely to become a benchmark to analyze these processes using a more complex system than other current culture-specific EMT and MET assays.

Strengths:

The strengths of the manuscript include the foundational analysis of the pathways involved in establishing the tri-phasic epithelium. The authors have incorporated live imaging, drug studies, KO analysis, and RNA sequencing to show the relevant pathways involved.

Weaknesses:

While the authors provide strong evidence that the tri-phasic epithelium represents the EMT process, the MET process is largely relegated to the absence of EMT genes. It would be interesting to know how the stratified MET epithelia submerged in the media is similar or different from the stratified epithelia at the air-liquid interface.

We appreciate the reviewer's comment and agree that the comparison between the MET-derived epithelium beneath the membrane and the original epithelium at the air–liquid interface is an intriguing question. Our current data provide partial insight: the epithelium beneath the membrane re-expressed E-cad (Figure 1d) and showed laminin β3 deposition comparable to that of the epithelium above the membrane (Figure 1—figure supplement 1e), indicating that it re-establishes at least some features of an epithelial tissue. However, it also retained elevated Vim and N-cad relative to the epithelium on the 0.4-µm-pored membrane (Figure 1—figure supplement 1b, c), suggesting that the MET is not complete. A systematic comparison, including barrier function and terminal differentiation markers, would require a dedicated study, which we plan to undertake.

Reviewer #3 (Public review):

Summary:

The authors established an experimental system that reproduced three-dimensional triphasic epithelia, i.e., the original epithelium, its EMT, and MET. Keratinocytes (KCs), skin epithelial cells, placed on a microporous membrane migrated through 3.0-um or larger micropores. The 3.0-um-pored membrane induced an epithelial structure with three states: stratified KCs above the membrane, KCs showing EMT within the micropores, and a new stratified epithelium under the membrane. The membrane with larger micropores failed to maintain this triphasic epithelium. Live imaging revealed that KCs moved in a reciprocating manner, with actin-rich filopodia-like KC structures extending into and out of the 3.0-um micropores, while the cells migrated unidirectionally into larger micropores. KO of Piezo1 and keratin 6 increased KC entry to and exit from the 3.0-um micropores. Their results demonstrate that benign keratinocytes migrate through confined spaces in a reciprocating manner, which might help form triphasic epithelia, recapitulating wound healing processes.

Strengths:

Careful observation of the behaviour of keratinocytes on the different-sized pores. CrispR-Cas9 gene editing to KO Piezo 1 and keratin 6 isoforms in HaCaT keratinocytes.

Weaknesses:

There is no analysis of the matrix produced by the keratinocytes on the different pore sizes as this may influence migration.

According to the reviewer’s suggestion, we have included images of laminin β3 labeling in 3D epidermis formed on 0.4-µm- or 3.0-µm-pored membranes in the revised manuscript (Figure 1—figure supplement 1e). Laminin β3, a subunit of laminin 332, was detected in the epithelium formed on the 0.4-µm-pored membrane, as well as in all three compartments of the tissue formed on the 3.0-µm-pored membrane. We did not observe an apparent difference in laminin β3 deposition between the two pore sizes, although we note that this assessment is qualitative and that a quantitative comparison of matrix production would require a separate study.

HaCaT cells are quite different from normal keratinocytes in terms of migration. Pilcher et al. PMID: 9182674

We have included this limitation in the Discussion section of the revised manuscript and cited the suggested publication (Pilcher et al. 1997).

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

(1) Some of the terminology needs better definition or refinement. Triphasic suggests different physical behaviours (e.g. liquid-liquid phase separation) rather than cellular properties. Perhaps it would be better to refer to these as cell states or to describe the model more specifically as an invasion or EMT model. Likewise, the term 'reciprocating' implies two-way communication, but it is used to describe two-way migration or oscillating migration. Here, perhaps oscillatory would be clearer.

According to the reviewer’s suggestion, we have revised the title, abstract, and main text to avoid the terms “reciprocating” and “triphasic” and have instead adopted “oscillatory” and “three-compartment,” respectively.

(2) The qualitative images in Figure 1 need better quantitative support through image analysis of the immunostaining to confirm changes in marker expression within the different regions of the epidermis. As this is a core finding of the study, it requires additional quantification beyond the qPCR results, which also do not provide spatial information.

In accordance with the reviewer's suggestion, we have added quantitative image analysis of E-cad, N-cad, Vim, and Slug in the epithelium on the 0.4-µm-pored membrane and in the compartments above and below the 3.0-µm-pored membrane (Figure 1—figure supplement 1a–d). Vim was significantly more abundant in both compartments of the 3.0-µm-pored condition than in the 0.4-µm-pored condition. N-cad showed the same trend, reaching statistical significance for the comparison between the 0.4-µm-pored membrane and the compartment below the 3.0-µm-pored membrane, and was also significantly more abundant below than above the 3.0-µm-pored membrane. Slug showed a similar trend that did not reach statistical significance, and E-cad did not differ significantly among the three regions. Unlike the qPCR results, these data provide spatial information on marker expression.

(3) More detail is needed regarding the number of independent experiments and cells analysed. This appears to be missing from both the methods and figure captions.

We apologize for the lack of detail. In the Materials and Methods, we have added a full description of the experimental design (measurement points per biological replicate, composition of each experimental batch) and of the statistical analysis, including the gamma generalized linear model with experimental batch as a categorical fixed effect, and the software used. In the figure legends, we have added the number of biological replicates, the statistical test, and the meaning of each graph element for every quantitative panel, as well as the number of replicates underlying each representative image. For Figure 2g and 2h, we have stated the number of independent experiments. We note that our quantification is based on the fraction of the imaging field occupied by GFP fluorescence rather than on individual cell counts; we therefore report the number of biological replicates and measurement points rather than the number of cells analyzed.

(4) Figure 2i,j needs quantification to support the conclusions. Were the studies in 2g,h replicated in independent experiments? Likewise, could KCs in the micropores simply appear more positive for Vimentin because they are smaller? It would help to show quantification of a marker that does not change or goes down in the channel.

In response to the reviewer's suggestion, we quantified the Vim/E-cad fluorescence intensity ratio, using E-cad as the internal reference marker (Figure 2—figure supplement 1d). The ratio was significantly lower within the micropores than above the membrane, indicating that Vim signals were not enriched within the micropores relative to E-cad. We therefore agree with the reviewer that the apparent increase in Vim positivity within the micropores in the original images was, at least in part, attributable to the smaller apparent size of the cells in this region. The quantification instead indicates that the partial EMT phenotype was already present in the cells above the membrane rather than being acquired upon entry into the micropores, which is consistent with the elevated Vim intensity both above and below the 3.0-µm-pored membrane in primary KCs (Figure 1—figure supplement 1c). We have revised the description of Figures 2i and 2j accordingly.

The experiments shown in Figures 2g and 2h were performed three times independently, and both panels summarize the pooled data from all three experiments. This is now stated in the Materials and Methods and in the figure legends.

(5) How does the oscillatory migration response depend on the length of the channel? If the channels were longer, would they not be able to exit and reform the epithelium? This would require a different experimental system but could be important for understanding the fundamental principles of the observed migratory responses.

We agree with the reviewer that the length of the channel may influence cellular behavior. In our study, we used 3.0-µm-pored membranes from Millicell (Merck) and Transwell (Corning), both of which have a membrane thickness of approximately 10 µm. Greiner also offers 3.0-µm-pored membranes with longer channels; however, the pore orientation in these products is not uniform, making them unsuitable for our experimental setup. As these are the only commercially available options accessible to us, we are currently unable to perform experiments addressing the effect of channel length. We agree that this is an important question and warrants investigation in future studies using appropriately engineered systems.

(6) The underlying mechanisms are not described in much detail and there is no linkage between the pathways and molecules investigated. How exactly do actin, keratins, and piezo1 communicate to regulate cell migration? Are they acting directly on EMT genes such as SNAI1 or through changes in cell mechanics and cell-cell adhesions? Likewise, is TGF-beta signalling active in the system (e.g. nuclear pSMAD during cell migration)?

Our data support a model in which mechanosensing and cytoskeletal remodeling, rather than transcriptional EMT programs alone, primarily regulate keratinocyte migration through confined micropores. Notably, inhibition of TGF-β signaling, a major driver of canonical EMT programs, did not impair keratinocyte entry into or exit from the micropores (Figure 3). These findings suggest that while EMT-associated pathways can modulate the process, as evidenced by the enhanced migration observed upon addition of TGF-β ligand, keratinocyte migration through micropores does not depend on classical TGF-β–driven EMT signaling. We could not directly assess nuclear pSMAD localization within the micropores because autofluorescence of micropore walls hindered the detection of nuclear signals in whole-mount imaging. Moreover, sagittal sectioning of frozen or FFPE-reconstructed 3D epidermis did not yield a sufficient number of nuclei in the micropores for assessing pSMAD positivity. Therefore, we cannot exclude context-dependent activation of TGF-β signaling. However, the lack of functional inhibition upon TGF-β receptor blockade indicates that canonical TGF-β signaling is not essential for this process. Our experiments further indicate that actin polymerization is essential for both entry into and exit from 3.0-µm micropores, suggesting that dynamic protrusive activity is a prerequisite for confined migration. Piezo1 and keratin 6 appear to function as negative modulators of this process. Piezo1, a mechanosensitive ion channel, likely influences migration by regulating cytoskeletal tension and cell shape, consistent with previous reports (Holt et al., 2021). Keratin 6, a stress-induced intermediate filament, may modulate actin organization and the mechanical properties of keratinocytes, as suggested by increased stress fiber formation in Krt6a/Krt6b-null cells (Wong & Coulombe, 2003).

We have incorporated these points into the revised Discussion accordingly.

(7) While the culture model is interesting and a simple yet useful tool to study EMT/MET, the new insight into these processes is somewhat limited and confirms much of what we already know about Piezo1, TGF-beta, and the cytoskeleton in KC migration. A clearer demonstration of new insight would help to strengthen the utility of the model.

We agree that Piezo1, TGF-β signaling, and cytoskeletal dynamics have previously been implicated in keratinocyte migration. Our intention was not to claim the identification of entirely new molecular regulators of migration. Rather, the conceptual advance of our study lies in demonstrating how confined spatial architecture alone can generate a spatially organized epithelium comprising spatially graded EMT and MET states within a single continuous tissue.

Specifically, our model reveals that keratinocytes undergoing confined migration exhibit oscillatory protrusive behavior that preserves the integrity of the overlying epithelium while enabling the formation of a new stratified epithelium beneath the membrane. To our knowledge, this dynamic integration of confined migration, partial EMT, and subsequent MET within a stratified epithelial context has not been previously demonstrated. The oscillatory behavior itself represents a distinct migratory mode compared to the predominantly unidirectional migration described in two-dimensional or Transwell assays.

In summary, the novelty of this study lies in revealing how physical confinement can organize epithelial states and migratory dynamics in a self-organizing manner. We have included these conceptual advances in the revised manuscript.

Reviewer #3 (Recommendations for the authors):

In the introduction the authors state that 3D epidermis is not useful for wound healing. There are lots of papers using 3D epidermis for wound healing/re-epithelialization experiments.

We agree with the reviewer’s point. Our original intention was to emphasize the utility of our new 3D model; however, we have moderated the tone in the Introduction to avoid underestimating the applicability of existing 3D epidermis models for wound healing and re-epithelialization studies and cited the relevant papers in the revised manuscript.

As the cultures on the different membrane pore sizes are in culture for 14 days, a substantial matrix is produced. I think it would be useful to show staining for laminin 332 at the minimum as this may regulate migration.

Production may also be influenced by the pore size/substrate.

According to the reviewer’s suggestion, we have included images of laminin β3 labeling in 3D epidermis formed on 0.4-µm- or 3.0-µm-pored membranes in the revised manuscript (Figure 1—figure supplement 1e). Laminin β3, a subunit of laminin 332, was detected in the epithelium formed on the 0.4-µm-pored membrane, as well as in all three compartments of the tissue formed on the 3.0-µm-pored membrane. We did not observe an apparent difference in laminin β3 deposition between the two pore sizes, although we note that this assessment is qualitative and that a quantitative comparison of matrix production would require a separate study.

HaCat cells are a slightly flawed keratinocyte model but I can see why you used them for gene manipulation, actin expression. Their flaws should be acknowledged in the Discussion.

We have included this limitation in the Discussion section of the revised manuscript and cited the suggested publication (Pilcher et al. 1997).

The RNA-seq dataset should be publicly available.

We have made this RNA-seq dataset available publicly (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE282553) and have presented this information in the Data availability section.

In Figure 2, i and j in the legend the authors refer to 3 and 8-micron pores, but these are not labelled in the Figure.

This was our error in mentioning 8.0-µm micropores in the Figure 2 legend. We intended to show only the 3D structure formed by the 3.0-µm-pored membrane. We have removed the reference to “8.0-µm micropores” in the revised manuscript.

伊森的眼神应该是很戒备的

Chloe甜甜地笑着

脚底的伤口渗出的暗红色鲜血斑斑点点地落在雪白的羊毛地毯。

从车外看，车里的Chloe探到车窗玻璃前，

补充一句：整个过程发生不过三秒。

这里应该是车外视角，那四个护工随后反应过来，很快朝着车门跑过去，但是在他们到达的前一秒车门已经被啪地关上了

拿着注射器的护工阴恻恻地笑着，边走过来，边说，“怎么不跑了？”

伊森痛苦地弓下了腰，用手捂着腹部。

跑到街角，停了下来

伊森抓住路过的一个热狗推车的车架，猛地用全身力气往后一退，朝着护工奔来的方向。

看到电击棒和针头后，似乎是想起了什么痛苦的回忆，爆了一句粗口。

补充：伊森边跑便气喘吁吁地说

应该是伊森一边大步跑着

这里应该补充一个细节，一个女士本来恰好挡在了男主奔跑路径的前方，男主大叫一声，闪开，女士吓了一跳

Ethan出现在画面的第一秒发出一句脏话，类似于damn，fuck

不要形容成是沾满鲜血的赤脚，改成是“一双伤痕累累的赤脚，脚底还依稀渗出了丝丝血迹”这种表述

eLife Assessment

This important study presents convincing findings on creating an exhaustive library of new enhancer-AAVs targeting astrocytes and oligodendrocytes with high potential for both basic and translational work, which will be of value to a large and growing community. The revisions appropriately addressed some of the previous concerns by updating description of glial biology in the Introduction and modifying text on experimental results and approaches. Overall, these amendments have strengthened the claims. The work will be of interest to neuroscientists working on glial cell biology.

Reviewer #1 (Public review):

[Editors' note: this version has been assessed by the Reviewing Editor without further input from the original reviewers. The authors have addressed the comments raised in the previous round of review.]

The goal of this study was to generate a library of new enhancer-driven AAVs in order to selectively and efficiently target astrocytes and oligodendrocytes in rodents. The implied criteria are that such viral vectors should have high specificity for the intended cell type and effectively express in all astrocytes/oligos in the brain or, alternatively, be specific for defined brain regions, layers, or subtypes of astrocytes/oligos. In addition, they should be compatible with intravenous retro-orbital delivery to facilitate experimentation and brain-wide targeting (i.e., show organ specificity and high efficiency in the brain). Ideally, these new AAVs would also maintain their characteristics across disease contexts and show applicability in non-human primates. Tools with such characteristics are generally lacking in studying glial cells and would be extremely useful to scale up and accelerate glial research, allowing targeting of astrocytes/oligos with distinct molecular identity and intersectional strategies.

At present, however, none of the enhancer-AAVs presented in the study seems to meet this combination of criteria, at least not with the level of stringency typically expected in the field. The main reason is that, in its current form, the study does not present one candidate AAV iteratively improved to meet all these criteria; instead, it presents a catalogue of new AAVs with various degrees of specificity, completeness, and mixed characteristics. Therefore, their utility should be interpreted cautiously. Moreover, the way specificity and completeness are intermixed in the analysis makes it difficult to evaluate the actual utility of any given AAV. The study might have been strengthened by focusing on a small set of the most promising candidates (i.e., AiE0890m_3x2C) and validating them thoroughly for expression specificity, completeness, effective cargo expression, ability to allow specific pan-astrocyte or astrocyte-subtype targeting in vivo, and preserved properties in NHPs and in disease, as this would encourage their adoption by the community. Currently, too many AAVs are assessed inconsistently against the desired criteria, with none being evaluated through and through.

The impact of the catalogue is also greatly diminished by the fact that a suite of AAVs with outstanding specificity and efficiency is already available for the study of astrocytes (e.g., 4x6T AAVs) and was not utilized as a standard to benchmark the new library, making it difficult to appreciate the relative benefits of the new AAVs. The inclusion of expression data in NHPs is very significant, but benchmarking against established AAVs would also be needed to fully appreciate their value.

Importantly, readers should also be aware that the study seems noticeably limited in its literacy with glial biology. The introduction and discussion frame the field in a way that seems outdated, creating the impression that the diverse roles of glia in health and disease have not yet been studied, which may inadvertently be perceived as dismissive and stigmatizing.

In summary, the paper introduces potentially useful viral tools and lays the foundations for future multiplexed targeting of distinct glial cell subpopulations in rodents and in non-human primates, which are extremely important directions. Some of the regionally restricted or even sparsely expressed AAVs may prove valuable in enabling subpopulation-specific targeting or molecular profiling strategies, but currently lack full benchmarking. At present, the promises over the utility of the new tools seem overstated, and the library may not yet represent an actionable resource for targeting astrocytes and oligodendrocytes.

Reviewer #2 (Public review):

Enhancer elements are regulatory DNA sequences that are capable of driving specific expression patterns. As these elements are generally short and context-independent, enhancers can be used in expression vectors (e.g., packaged in an adeno-associated virus, AAV) to limit expression to target cell populations. This approach was identified as a major strategy for cell-type-specific manipulation in the brain and has been pursued by both standard research studies as well as large-scale efforts led by the BRAIN Initiative. This manuscript describes a major effort to generate enhancer-AAVs targeting astrocytes and oligodendrocytes orchestrated by a large research team led by the Allen Institute for Brain Science. This manuscript parallels other recent publications describing sets of enhancer-AAVs, following rigorous, similar methods with relatively broad testing and application.

To identify and screen candidate enhancers, the scientists prioritized candidates via analysis of single-nucleus accessibility and methylation datasets (i.e., snATAC-seq) and tested them in mice. The scientists prioritized candidate enhancers that exhibited specificity of accessibility in the target cell type. Following selection, the scientists cloned the candidate sequences into AAV vectors with a minimal promoter and reporter gene, packaged the virus, delivered it to the mouse brain, and screened for activity based on reporter expression. Candidates that passed initial screening were further characterized via imaging and sorting, followed by single-cell RNA-seq. This process had around a 50% success rate and yielded 25 astrocyte and 21 oligodendrocyte enhancer-AAVs with the targeted cell-type-specific expression patterns.

The scientists went on to test for subtype-specific activity patterns, finding wide diversity in astrocyte activities across sub-populations and conversely, homogenous oligodendrocyte activation. They optimized a few of these via concatenating the enhancer core sequence to increase expression levels of the reporter gene and showed strong specificity and completeness of cell targeting for a set of these enhancer-AAVs. Following characterization and validation, they then deployed these enhancer-AAVs in a number of demonstration applications to show the utility for basic and translational science. All the constructs developed here are available for public use via Addgene, ensuring that these new tools can be used by other researchers.

There really are no obvious weaknesses in the work presented here, from the generation of the enhancer-AAVs to use in sophisticated validation studies. The enhancer-AAV testing is rigorous and provides critical information necessary for other scientists to select and use these constructs. The applications demonstrate the power of enhancer-AAV approaches. The toolbox presented here may not enable specific targeting of all relevant cellular subtypes or activity states for astrocytes and oligodendrocytes, and future work will be needed to fully understand the activity of the enhancers, identity of the target cell types, and context-dependent utility of these constructs. However, the set of enhancer-AAVs developed here should be transformative for researchers working on accessing and manipulating these cell types and have a major impact on the field.

Author response:

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

eLife Assessment:

This important study presents convincing findings on creating an exhaustive library of new enhancer-AAVs targeting astrocytes and oligodendrocytes with high potential for both basic and translational work, which will be of value to a large and growing community. However, the outdated description of glial biology in the Introduction, the overstated claims of utility in the Conclusion, and the loose stringency in the criteria used to assemble the library diminish the strengths of the claims. The work will be of interest to neuroscientists working on glial cell biology.

We thank eLife for the positive comments. We also are happy to provide a revised manuscript in response to the critical feedback. Revisions focus on improving the language and the descriptions of the vectors. We believe these improvements will help clarify the study and showcase the importance of astrocyte- and oligodendrocyte-specific enhancer AAVs for genetic access to these important cell types.

Public Reviews:

Reviewer #1 (Public review):

The goal of this study was to generate a library of new enhancer-driven AAVs in order to selectively and efficiently target astrocytes and oligodendrocytes in rodents. The implied criteria are that such viral vectors should have high specificity for the intended cell type and effectively express in all astrocytes/oligos in the brain or, alternatively, be specific for defined brain regions, layers, or subtypes of astrocytes/oligos. In addition, they should be compatible with intravenous retro-orbital delivery to facilitate experimentation and brainwide targeting (i.e., show organ specificity and high efficiency in the brain). Ideally, these new AAVs would also maintain their characteristics across disease contexts and show applicability in non-human primates. Tools with such characteristics are generally lacking in studying glial cells and would be extremely useful to scale up and accelerate glial research, allowing targeting of astrocytes/oligos with distinct molecular identity and intersectional strategies.

At present, however, none of the enhancer-AAVs presented in the study seems to meet this combination of criteria, at least not with the level of stringency typically expected in the field. The main reason is that, in its current form, the study does not present one candidate AAV iteratively improved to meet all these criteria; instead, it presents a catalogue of new AAVs with various degrees of specificity, completeness, and mixed characteristics. Therefore, their utility should be interpreted cautiously. Moreover, the way specificity and completeness are intermixed in the analysis makes it difficult to evaluate the actual utility of any given AAV. The study might have been strengthened by focusing on a small set of the most promising candidates (i.e., AiE0890m_3x2C) and validating them thoroughly for expression specificity, completeness, effective cargo expression, ability to allow specific pan-astrocyte or astrocyte-subtype targeting in vivo, and preserved properties in NHPs and in disease, as this would encourage their adoption by the community. Currently, too many AAVs are assessed inconsistently against the desired criteria, with none being evaluated through and through.

The impact of the catalogue is also greatly diminished by the fact that a suite of AAVs with outstanding specificity and efficiency is already available for the study of astrocytes (e.g., 4x6T AAVs) and was not utilized as a standard to benchmark the new library, making it difficult to appreciate the relative benefits of the new AAVs. The inclusion of expression data in NHPs is very significant, but benchmarking against established AAVs would also be needed to fully appreciate their value.

Importantly, readers should also be aware that the study seems noticeably limited in its literacy with glial biology. The introduction and discussion frame the field in a way that seems outdated, creating the impression that the diverse roles of glia in health and disease have not yet been studied, which may inadvertently be perceived as dismissive and stigmatizing.

In summary, the paper introduces potentially useful viral tools and lays the foundations for future multiplexed targeting of distinct glial cell subpopulations in rodents and in nonhuman primates, which are extremely important directions. Some of the regionally restricted or even sparsely expressed AAVs may prove valuable in enabling subpopulation specific targeting or molecular profiling strategies, but currently lack full benchmarking. At present, the promises over the utility of the new tools seem overstated, and the library may not yet represent an actionable resource for targeting astrocytes and oligodendrocytes.

We thank Reviewer 1 for the constructive commentary. We would like to point out that these enhancer-AAV sequences are all publicly available for research use through Addgene and have already been in use in multiple other publications. As one example, our enhancer AiE0380h was used as a recent positive control astrocyte-specific agent to help guide the discovery of enhancers that were activated in gliosis conditions (Zamboni et al., 2026 Nat Neurosci; PMID: 41331142). As another example, several of the vectors have been highly requested at Addgene including AiE0390m_3xC2 and AiE0410m to target astrocytes and oligodendrocytes.

The reviewer pointed out that we did not compare the effectiveness of our enhancer-AAV vectors to the GfaABC1D-4x6T elements described by Gleichman et al. Although it is true we did not perform head-to-head comparisons, we have characterized our vectors by the same metrics as Gleichman et al. which is summarized in Author response image 1. The results shown here indicate that our enhancer-AAV vectors are comparable to the GfaABC1D-4x6T vector characterized by Gleichman.

Author response image 1.

AiE0390m-based enhancer-AAV vectors show comparable levels of specificity and completeness to that observed with GfaABC1D as observed by Gleichmann et al.

It was not the intent of our study to quantitatively compare our vectors or studies, rather to provide independent sets of reagents to allow astrocyte and oligodendrocyte marking that will provide utility to the neuroscience community. However we have provided additional characterization that adds value to the enhancer elements we studied. We characterized our best-performing vectors by scRNA-seq, in peripheral tissue (liver), across species (mouse, rat, NHP), with a known difficult transgene (NeuroD1-mCherry), with a physiologic reporter (iACH-SnFR), and in disease context (Dravet syndrome). We also found new agents that provide astrocyte regional specificity and also those that mark oligodendrocytes. We feel that having sets of tools with differing properties will provide greater flexibility in choosing one that works for many applications. As examples, it could be desired to have higher or lower expression levels for any certain applications, or possibly different levels of completeness or sparsity. Sparse labeling is particularly valuable for evaluating morphology, while dense labeling could be advantageous to deliver a ubiquitous therapeutic. Having multiple validated tools enables this flexibility and support the whole field.

Finally we would like to highlight that the enhancer elements we describe here are fully complementary to the existing sets of glial tools, including GfaABC1D and the 4x6T elements described by Gleichman et al. Importantly, enhancer elements are fully compatible with microRNA-binding sites like 4x6T, so combinations of these elements can provide fine control over targeted cell types. Furthermore, some enhancers provide different types of responses to cell states. AiE0390m has activity that is unperturbed by Dravet syndrome epilepsy, unlike GfaABC1D which expresses more strongly and in some neurons in this disease (Figure 8). This is similar to the IREN5 element described recently which activates in gliotic astrocytes near sites of damage (Zamboni et al.). Therefore, enhancer activities will help the field target and manipulate astrocytes and other glial cells in differential pathological disease states. As a result of these extra layers of flexibility, we anticipate that these enhancers will become tools for marking and manipulating astrocytes and enhancers in unforeseen ways. To help adoption in the community, detailed characterization of these and all related Allen Institute enhancer-AAV vectors are now available online in an easy-to-use resource (Allen Institute Genetic Tools Atlas at https://brain-map.org/bkp/experiment/genetic-tools/genetic-tools-atlas).

We have also added new references and reframed the introduction and discussion of this work to provide a more current survey of the glial field.

Reviewer #2 (Public review):

Enhancer elements are regulatory DNA sequences that are capable of driving specific expression patterns. As these elements are generally short and context-independent, enhancers can be used in expression vectors (e.g., packaged in an adeno-associated virus, AAV) to limit expression to target cell populations. This approach was identified as a major strategy for cell-type-specific manipulation in the brain and has been pursued by both standard research studies as well as large-scale efforts led by the BRAIN Initiative. This manuscript describes a major effort to generate enhancer-AAVs targeting astrocytes and oligodendrocytes orchestrated by a large research team led by the Allen Institute for Brain Science. This manuscript parallels other recent publications describing sets of enhancerAAVs, following rigorous, similar methods with relatively broad testing and application.

To identify and screen candidate enhancers, the scientists prioritized candidates via analysis of single-nucleus accessibility and methylation datasets (i.e., snATAC-seq) and tested them in mice. The scientists prioritized candidate enhancers that exhibited specificity of accessibility in the target cell type. Following selection, the scientists cloned the candidate sequences into AAV vectors with a minimal promoter and reporter gene, packaged the virus, delivered it to the mouse brain, and screened for activity based on reporter expression. Candidates that passed initial screening were further characterized via imaging and sorting, followed by single-cell RNA-seq. This process had around a 50% success rate and yielded 25 astrocyte and 21 oligodendrocyte enhancer-AAVs with the targeted cell-type-specific expression patterns.

The scientists went on to test for subtype-specific activity patterns, finding wide diversity in astrocyte activities across sub-populations and conversely, homogenous oligodendrocyte activation. They optimized a few of these via concatenating the enhancer core sequence to increase expression levels of the reporter gene and showed strong specificity and completeness of cell targeting for a set of these enhancer-AAVs. Following characterization and validation, they then deployed these enhancer-AAVs in a number of demonstration applications to show the utility for basic and translational science. All the constructs developed here are available for public use via Addgene, ensuring that these new tools can be used by other researchers.

There really are no obvious weaknesses in the work presented here, from the generation of the enhancer-AAVs to use in sophisticated validation studies. The enhancer-AAV testing is rigorous and provides critical information necessary for other scientists to select and use these constructs. The applications demonstrate the power of enhancer-AAV approaches. The toolbox presented here may not enable specific targeting of all relevant cellular subtypes or activity states for astrocytes and oligodendrocytes, and future work will be needed to fully understand the activity of the enhancers, identity of the target cell types, and context-dependent utility of these constructs. However, the set of enhancer-AAVs developed here should be transformative for researchers working on accessing and manipulating these cell types and have a major impact on the field.

We thank Reviewer 2 for their positive feedback. We would like to re-emphasize that these enhancer-AAV sequences are all publicly available for research use through Addgene, with characterization data beyond what we have presented here is available publicly on Allen Institute Genetic Tools Atlas. Further these tools have already been in use in multiple other publications. We are eager to see how the research community makes use of these tools.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

My main suggestion is that the authors should try to make their library less of a catalogue and more focused on delivering one or two enhancer-AAVs that surpass existing ones, thus effectively delivering a new tool with great utility to the field. To achieve this, authors should pick their best candidate AAVs and evaluate them systemically across all the criteria, with side-by-side comparison to two benchmarks: the classic GFAP promoter AAV, and the 4x6T AAVs developed by Gleichman et al (PMID: 37973910).

Thanks for the recommendation. Figures 2P, 5K, 6, 7, and 8 show comparison of our best astrocyte enhancer elements (AiE0390m and AiE0390m_3xC2) to GFAP promoter GfaABC1D. This work was done prior to the Gleichman et al. study so GfaABC1D-4x6T was not available at the time for direct comparison. However, as shown in Author response image 1, our data with AiE0390m and AiE0390m_3xC2 are comparable to that shown for GfaABC1D4x6T. Additionally we compared our enhancer-AAV vectors to the proB12 enhancer element described by Juttner et al. (Figures 2P and 5L), and again our enhancer-AAV vectors compare similarly. Finally, we also compared our oligodendrocyte-specific enhancers to the MBP promoter and we find our enhancers strongly outperform that promoter element in terms of specificity and completeness (Figure 3P and 5M). Overall, we have compared our new enhancer elements against reagents commonly used in the field, using established criteria for benchmarking effectiveness.

Despite the effort put towards quantification and benchmarking, we believe there is significant value to having a variety of diverse and orthogonal tools in the field. Although GfaABC1D is an excellent promoter tool and maintains strong specificity under homeostatic conditions, it changes its character in different contexts. It took years and difficult experiments to realize this and caused investigators to believe astrocytes can differentiate into neurons with the right transgene. We confirm that the reality is this promoter changes activity when it expresses the NeuroD1 transgene (Fig. 6). Also, we demonstrate AiE0390m3xC2 maintains faithful astrocyte expression (Fig. 6). In a more recent example, Zamboni et al. showed that AiE0380h is downregulated in activated astrocytes. This isn’t an experiment we conducted and were not aware of that property. However, having multiple strong tools is a huge advantage when evaluating astrocytes and oligodendrocytes in different disease states and different experimental contexts. Investigators with only one or two tools available are at the mercy of the quirks of the tool. In this way, having a large collection of astrocyte and oligodendrocyte tools available is a major advantage to researchers that need a viral tool for glial expression in a new context. And importantly, as we show for hepatocytes (Fig. 7), investigators can predictions if any of our tools will change activity in different experimental contexts if epigenetic data is available. This prediction is not possible with promoters.

For instance, 0390m is described as their best candidate but its efficiency is very low (~60%, Fig.10I). The concatenated version (AiE0390m_3x2C) is greatly improved, but it was not thoroughly evaluated: no data are provided regarding how its characteristics are maintained in disease context, its expression in other organs when delivered intravenously, or how it would be classified in Fig.2. Most notably, despite being engineered for RO injection, it was tested ICV in Fig. 9, missing an opportunity to demonstrate ease of use.

As shown in Author response image 1, the efficiency (we prefer the term “completeness” since “efficiency” conflates labeling extent with expression level) we observed for AiE0390m is actually similar to or better than that observed for GfaABC1D-4x6T. AiE0390m_3x2C shows a still greater completeness of labeling, at the expense of a slightly diminished specificity with indirect reporter (Cre; Ai14). We have amended the colored bar graphs to write the observed completeness values directly in the figure for clarity.

We have not tested this tool in the context of disease or the background expression in liver since the point of the manuscript was to present a library, not one specific tool. These are important experiments for a user focused on epilepsy or gene therapy to independently evaluate.

Several sections of the text include claims or descriptions that are misleading and should be revised. This applies both to conclusions drawn from the presented data and to the framing of the field of glial biology.

(1) Overstated/misleading conclusions: Segments such as "this collection of glial enhancer-AAVs will enable characterization of astrocyte and oligodendrocyte populations and their roles across species, disease states, and behavioral epochs" are unfounded based on the current library authors present and need to be revised. The conclusion that 2120m and 2160m are labeled "mutually exclusive regions" is misleading too, and no information is available on the specificity of 2160m. Similarly, the claim that the new AAVs are more predictable "across disease states" is a stretch, considering that a single disease model was employed. These are only examples.

Thank you for the suggestions. We have amended the language in our manuscript as suggested to improve the accuracy and clarity of our claims, and limited our interpretation to one mouse model of epilepsy. AiE2160m predominantly labels subtelencephalic astrocytes (Figure 2—Figure Supplement 3) of the molecular taxonomy label “5109-AstroNT NN_3” (Figure 4D; taxonomic labels from Yao et al., 2023 PMID 38092916). However, we do strongly believe that this collection of tools, admittedly incompletely validated, will serve as an excellent starting point for investigator who need tools tailored to their biological need.

(2) Outdated description of the field of glial biology: The text contains multiple segments that are incredibly dismissive of the current state of glial research. Sentences such as "Astrocytes provide trophic support for neurons, coordinate regional wiring patterns, respond to and regulate neurotransmission", or "this suggests that the astrocytes help selectively maintain glutamate or GABA tone depending on the local neurotransmitter balance" are incredibly reductive and dissonant with the reality of what the field has achieved, both conceptually and technically, and seem shockingly siloed from the past1520 years of research. This dramatically taints the credibility of what the authors have achieved and needs to be revised.

Thank you for the constructive criticisms. Our language was intended to highlight the importance of glial cells without providing a comprehensive review. However the examples and wording did tilt toward passive roles and outdated references, whereas much recent work shows active instructive glial roles in regulating neural circuits. We have revised the language and added more recent examples/citations to reflect the chief roles that astrocytes play in the healthy brain.

(3) The framework used to assess specificity and completeness would benefit from being more systematic, clear, and aligned with standards in the field.

Thank you for the criticism. We have made our definitions of “Specificity” and “Completeness” more clear in the text. We have also added numerical text to our bar graph color presentation of completeness levels in Figures 2, 3, and 5 for easier interpretation. As shown in Author response image 1, our metrics are equivalent and comparable to those presented previously by Gleichman et al.

(4) The apparent lack of core expertise in biology is detrimental to methodology. The authors seem unaware of the extreme stringency in the field: a 'specificity' of 80% would be considered extremely poor, and 90% would be merely acceptable. The bar for what the field typically expects for astrocyte-targeting is upward of 95%, usually close to 98-99%. See PMID: 37973910 as an example.

We are happy that all of our best astrocyte-specific enhancer-AAV vectors (AiE0380h, AiE0390m, AiE0390h, AiE0387m, AiE0390m_3xC2, AiE0390h_3xC2, AiE0387m_3xC1) showed typical specificities of 98-99% or more. This was assessed by both IHC and scRNAseq. As one example, AiE0390h showed 452/454 cells positive by IHC (99.6% specific), and 86/86 sorted cells by RNA-seq (100% specific). This was data from two different animals in each assay (four total). In some assays we observed literally 100% specificity (807/807 labeled cells were astrocytes by IHC for AiE0390m). We made similar observations with the best oligodendrocyte-specific vectors (AiE0410m, AiE0641m, AiE0395h, AiE0397m, AiE0410m_3xC2). Moreover, it is not common for investigators to use transcriptomics to assess specificity in addition to histology.

(5) AAVs with specificity above 95% could thus be labeled "specific," but all others should be considered "not specific" (perhaps with a gradation "poorly specific" for 95-90% and "not specific" below 90%). Either way, the category "mixed specificity" makes little sense.

We are happy that all of our best astrocyte-specific enhancer-AAV vectors (AiE0380h, AiE0390m, AiE0387m, AiE0390m_3xC2, AiE0390h_3xC2, AiE0387m_3xC1) showed specificities of 98-99% or more, and only cells with astrocyte morphology observable throughout the brain. Other vectors were highly specific in cortex but also strongly labeled neurons in other regions; for example, AiE2118m shows strong and specific (>99%) astrocyte labeling in VISp, but changes specificity to isolated neuron populations in other regions like midbrain and medulla. Hence it has a “mixed” specificity profile. We have edited the text to clarify this grouping. Additionally, we hope that the matrix we provide in the new Figure 2—Figure Supplement 2 will aid in interpretation of regions where specificity is high and where specificity is low.

(6) The tables that the authors provide (Figure 2 and Figure 2 Supplement 1) currently mix notions of specificity with notions of completeness, making it extremely difficult to assess the utility and quality of any of the AAVs. Instead, they should be separated, with first an assessment of specific and then an assessment of completeness/regionality only for those AAVs that are fully specific. Alternatively, authors could present their results as a matrix.

We have amended the colored bar graphs to state the observed completeness levels directly in Figures 2, 3, and 5, rather than only as color scales as previously shown. We believe this improves the interpretability of the figure.

(7) A more rigorous and useful way to assess CNS-wide or regional expression would be to provide quantitative region-by-region data with color-coded z-scoring per region, combined with meaningful descriptors (e.g., "Forebrain + cortex" instead of "most brain"). This could be a matrix with regions listed at the top and AAVs vertically.

We thank the reviewer for the suggestion. We have added this suggested matrix as Figure 2—Figure Supplement 2. We hope it aids in interpretation of the data.

(8) The criteria used for the classification of AAVs seem confusing or misleading. For example, AiE0390h is described as "CNS-wide" but then highlighted for its lack of expression in the basal ganglia, making it unsuitable as a true pan-astrocyte AAV. Conversely, Ai0386m and AiE0386h, classified as "weak", show stronger expression than several of the AAVs classified under the "most CNS" category.

We thank the reviewer for the observations. Delivering AAVs IV via PHP.eB routinely target basal ganglia structures including striatum less efficiently than other parts of brain including cortex. This was quantified in our recent publication using molecular barcodes, and is true regardless of targeting neurons or oligodendrocytes or astrocytes in this region (approximately ½ log lower transduction as shown in Supplementary Figure 7a, 7c; Hunker et al., 2026 Nat. Comm. PMID 42049725). This is reflected in the current dataset of weaker expression in striatum and other related structures with each of the astrocyte or oligodendrocyte-targeting AAVs including GFAP promoter. However, some signal is present in striatal astrocytes with AiE0390h. On data review the example full-stitch images given for AiE0386m/h were not representative of average signal intensity across replicates, whereas the inset images were representative. In this resubmission we have swapped images for 386h and 386m in Figure 2—Figure Supplement 1 with more representative full-size images. We hope this has increased the interpretability of the dataset. Importantly we assessed fluorescence levels from enhancer-AAV vectors by multiple techniques: microscopy, immunohistochemistry, flow cytometry, and scRNA-seq.

(9) Additionally, a concern is that the regional specificity reported for some AAVs is extremely loose, which seems to be of limited practical use. It would be more impactful to focus on a couple of truly non-overlapping AAVs with genuine potential, and thoroughly assess whether, injected together, they could be used to express distinct cargos (effectors or reporters) into distinct populations, layers, or regions with no or minimal overlap. This could include 2120m and 2121m, which may hold promise for targeting subregions of the basal ganglia (though, because they also express outside of the BG, a follow-up experiment involving stereotaxic injection into these regions would be needed to test whether regional specificity is preserved with that route of delivery). Similarly, AiE0381h seems oddly specific to the CA2 of the hippocampus, and this could be very useful. These additional experiments could open the way to cell-type-specific combinatorial strategies, which would represent a monumental technical leap for the field.

Thank you for the insightful comment. Although we agree that the suggestions of multiplexing two enhancer-AAV vectors with different specificities has high significance, we have done similar multiplexing work and unfortunately at present it is not yet predictable which combinations will work for multiplexing. Some combinations do work and others do not. For examples see Figures 7 and S9 of Hunker et al., 2025 Neuron PMID 40403704. Because of the complication of interpreting results, we have decided not to pursue these experiments in this manuscript. We have added text to the discussion to highlight the future unrealized possibility of multiplexing enhancer-AAVs.

AiE0381h has background expression in CA2 projection neurons, which is a frequent site of off-target labeling for AAV9-derived vectors in enhancer and capsid screens. As one example Nonnenmacher et al. observed frequent CA2 neuron-enriched expression in a capsid screen (PMID: 33553485 Figure 5B). As another example we observe off-target labeling in CA2 with AiE2119m as well (Figure 2—Figure Supplement 1). It is unclear what makes CA2 neurons unique in this way, and it is unclear why it is the most prominent for AiE0381h in this set. Regardless, we have demarcated the off-target CA2 site of expression in Figure 2—Figure Supplement 1, along with other off-target sites of expression from other vectors, and noted it in the figure legend.

(10) The "sparse" AAVs (which authors labeled "scattered") should not be dismissed. It would be worth assessing whether astrocytes expressing these AAVs map onto specific molecular signatures, i.e., these AAVs target a very specific population of sparse astrocytes in various regions. This would be of substantial benefit to the field, allowing subpopulation-specific molecular characterizations (e.g., TRAP) or functional manipulations via chemogenetic actuators (e.g., DREADDS).

We thank the reviewer for this important highlight. We too are fascinated by the enhancerAAVs that show sparse/scattered labeling patterns. We have confirmed that the sparse labeling pattern is reproducible with multiple replicate animals tested per vector, and reproducible across orthologous pairs, with the best example being AiE0374m (mouse ortholog) and AiE0374h (human ortholog) both showing this sparse expression pattern. Our interest led us to conducting single cell RNA-seq profiling studies to see if we could see any clear molecular distinctions of the labeled cells (compared to other labeled astrocytes. However, we did not observe any gene expression differences using scRNA-seq when comparing the sparse AiE0374m-labeled cortical astrocytes to cortical astrocytes labeled by enhancer-AAV vectors that broadly labeled cortical astrocytes (Fig. 4A). Furthermore, tools for locating transcription factor binding motifs (MAST) did not elucidate any obvious unique transcription factor that defined this group of enhancer-AAVs (not shown). As a result the molecular interpretation of the sparse expression patterns is not yet known. We cannot say what distinguished these astrocytes from others, but the phenomenon inspires further experimentation that is beyond the scope of this study. We provide these data and vectors to the field in hopes of it enabling unique experiments to better understand this population, as well as conduct experiment where sparse astrocyte labeling is valuable. We have edited the text to call this set “Sparse” and highlight these possible use cases in the discussion.

Other concerns:

(1) Terms such as cell type vs. subtype or subpopulation are not used adequately.

(Astrocytes are a cell type, and protoplasmic astrocytes are a subtype of astrocytes.)

We are following the nomenclature of molecular taxonomies of brain cells (example references Yao et al., Nature 2023 PMID: 38092916; Siletti et al., Science PMID: 37824663; Zu et al., Nature 2023 PMID: 38092917). This nomenclature has the advantage of linking molecular information to enhancer discovery and the eventual targeting of every brain cell type with this technology.

(2) Considerations about the effect of viral titers would be important.

We deliver a constant dose of 5e11 vg per animal, irrespective of production titer.

Production titers ranged from 4.47e12 to 1.47e14 gc/mL, interquartile range 3.13e13-6.55e13 gc/mL, mean 5.10e13 gc/mL, n = 215 total viral preps. This detail has been added to the

methods.

(3) A stronger articulation to distinguish this paper from the prior publication in Cell [PMID: 40403729] would be needed.

We focus our characterization of the vectors across species, organs, disease states, and with difficult non-reporter vectors. The prior paper was focused on a larger breadth of testing with a publicly accessible database attached, whereas this paper constitutes a deeper dive into the properties glial cell targeting reagents. We have added additional text in discussion to highlight the differences between the manuscripts. Furthermore, Supplementary Table 2 shows the additional experiments added in the current study which allows deeper characterization of these glial cell-targeting collections. We strongly believe the community is best served by our deep dive into glial enhancer discovery and evaluation of these tools.

(4) The NHP data is highly significant, but here again, it is missing benchmarking against established tools such as 4x6T.

Since Gleichman did not test their element in NHP it is not validated in NHP. It may or may not work there. Our enhancer elements, by virtue of being selected for conservation of activity across mammalian species, are predicted to work across mammalian species, and many of them in fact do work across mammalian species. Besides GFAP promoter (references Heffernan et al., 2022 J. Neurosci. Meth. PMID: 35202614; Matsuzaki et al., 2024 MTMCD; PMID: 39391837) our enhancer elements are the only control elements validated for highly specific astrocyte expression in primate, and our oligodendrocytespecific element AiE0410m shows higher specificity than that observed for MBP promoter in primate previously (Matsuzaki et al., 2024 MTMCD; PMID: 39391837).

(5) Figure 5 M is never cited.

Thank you for the comment. We cite Figure 5M on line 233 of the revised manuscript.

Reviewer #2 (Recommendations for the authors):

I have no recommendations for the authors. This work is rigorous, broad in scope for testing, validation, and application, and overall an exceptional and significant piece of work.

We thank Reviewer 2 for their positive comments.

dsp

Version 5 of this preprint has been peer-reviewed and recommended by Peer Community in Infections.<br /> See the peer reviews and the recommendation.

targeting and cost effectiveness

real time bidding

analytical ease

targeting

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