“破”了一个“太”大的“洞”，引发了“洪”水，淹了老鼠“巢”！

汉江 ➡️ 最长支流（生理常识） 极简秒记： 跑的距离最“长”，流的“汗”（汉）肯定最多！ (考场回想：看到“最长”，本能想到长跑 ➡️ 长跑会怎样？会疯狂流汗！ ➡️ 汗 = 汉江。) 2. 嘉陵江 ➡️ 流域面积最大（数学/买房常识） 极简秒记： 嫌房子“面积”不够大？直接在数字后面“加零”（嘉陵）！ (考场回想：看到“面积最大”，脑子里直接蹦出算面积的画面 ➡️ 怎么让面积变大？在后面疯狂“加零”！ ➡️ 加零 = 嘉陵江。) 3. 岷江 ➡️ 流量最大（搞笑动作反差） 极简秒记： 本来只想“抿”（岷）一小口，结果水“流量”太大，直接喷了一脸！ (考场回想：看到“流量最大”，脑子里直接浮现喝水被呛到的搞笑画面 ➡️ 为什么流量大？因为你本来只想“抿”一口！ ➡️ 抿 = 岷江。)

出“大”“太”阳了，肯定“无”（巫）“雪”！

昆仑”山“齐连”在一起，直接把路“横断”了！

直着往前“纵”跑得最快，像螃蟹一样“横”着走肯定慢！

风雅

Example comment

Who does define the profiles?

meaning: everything but

test

Thank you for doing this work and creating this resource for the community! I agree that it fills a critical gap in the field, especially for the "biology" part of "astrobiology". I had a few different comments and questions.

1. In Table 2, the SO42- ion's chosen concentration is lower than the lower bound. Was this for a reason (I may have missed it)?

2. On phosphate, sounds like this was a tricky measurement all around. The validation argument seems possibly circular to me? IC has the co-elution issue with sulfate, ClO₄⁻ measures low, and the fallback is that the cation balance requires a corresponding anion contribution. But charge balance was also the design criterion listed earlier (or partly, so you could achieve the target of pH 7.7), so I'm not sure it can serve as the independent check. Have you considered another method to constrain total P? ICP-OES might be able to, and you already use it; caveat is that I haven't done ICP-OES so this is a guess based on literature.

3. Upping from 1X to 10X moves every ion at once. What separates the 30 mM perchlorate tolerance specifically from tolerance of ionic strength or osmotic load? Did you consider a perchlorate titration at fixed 1× DMM to isolate that stressor, ideally against an osmolality-matched chloride arm?

4. Did you consider an EDTA-free variant? At 16.5 mM in the 1000× stock it's sort of comparable to the summed chelatable metals. I wondered how much of the Figure 3A comparison is total-vs-total across studies with possibly different chelation backgrounds?

The Court disposes of group definition in a single sentence, adopting a wholly self-identification-based description: the Rohingya are "the group that is commonly called the Rohingya, self-identifies as such and claims a long-standing connection to Rakhine State." This is doing enormous latent work for a case about genocide, where Article II requires the intent to destroy a "national, ethnical, racial or religious group... as such." The jurisprudence on group definition under the Genocide Convention has oscillated between a subjective test (self- and other-identification, as in Akayesu, ICTR) and a more objective, stigmatization-based test (as the ICTY later preferred in Krstić and Jelisić, asking whether the perpetrator treated the victims as a distinct group, regardless of any formally verifiable ethnic marker). Myanmar's own domestic position — that the Rohingya are not a recognized ethnic group under its 1982 Citizenship Law and are properly termed "Bengali" — makes this a live methodological choice, not a formality. By adopting self-identification without engaging either test explicitly, the Court effectively pre-empts a threshold merits question (does a protected group exist at all) at the preliminary-objections stage, without saying so. My own view: this is probably the right practical result — a respondent should not be able to deny genocide jurisdiction merely by denying the existence of the group it is accused of destroying — but the Court reaches it by citation to its own provisional-measures order rather than by articulating why self-identification is the legally correct standard. That silent borrowing means the merits chamber inherits an unreasoned definitional premise it will eventually have to either defend on its own terms or quietly build on further.

ahha

rather than measuring some aggregate value of potential energy, we measure it per unit charge, to provide a standard measurement at any given point in a system.

并行归约是指：在并行计算中，将分布在 N 个不同处理单元（线程/核心） 上的大量数据，通过某个满足结合律（Associative）和交换律（Commutative）的二元运算符（如 +、*、max、min），“多对一”地合并成最终的一个（或一小批）结果的过程。

串行思维：for i in range(N): sum += arr[i]（需要 N 步，依次等待）。

并行思维：N 个人各拿一个数，两两配对相加，不断合并，直到剩下最后一个人拿着总和。 2^k=N,经过k轮运算，每轮处理一半的数据，处理完N个样本。复杂度为O(log(N))

并行归约的本质是利用树形拓扑将对一维数组的累加复杂度从 O(N) 降到 O(log N)

要求掌握这里结论的推导过程

推导logsumupexp和logsoftmax的区别 logsum就是原softmax的分母部分

zj=zj-m+m, LSE=logsum(e^(zj-m+m)) LSE=loge^msum(e^(zj-m)) = m +logsum(e^(zj-m))

Biggest obstacle! I think our lives are so busy that eating like Europeans would be a huge improvement for me personally!

I think this as a tool would be very helpful, but alone may make me want more delicious food experiences than before, I am not convinced.

I have heard alot about midful eating lately, it is difficult to be that in the moment.

eLife Assessment

This valuable study characterizes how antibody responses converge on similar functional solutions despite diverse genetic backgrounds, providing a resource that is of importance in understanding immune responses and informing vaccine research. The evidence is solid, with extensive and well-executed analyses supporting the primary findings, although broader conclusions regarding vaccine design would benefit from more cautious interpretation and fuller discussion of the study's limitations. The work will be of interest to researchers studying antibody responses, viral evolution, and vaccine development.

Reviewer #1 (Public review):

Summary:

Based on previous work showing that viral evolution follows reproducible patterns in diverse animals, the authors sought to examine whether the antibody response operates under similar constraints. By analyzing over 17,000 B cells isolated from 6 monkeys at 3 different time points, the authors convincingly show that the immune response does follow specific patterns of responses to different classes of epitopes based on the infecting virus. Moreover, each of these clusters has characteristic (cross-) binding and neutralization properties. Importantly, these classes are independent of the underlying immunogenetics, which (as expected) vary significantly between monkeys. This last point is particularly relevant for vaccine design, as it means that immunogens may not need to be as narrowly focused on specific germline genes as previously thought.

Strengths:

The large number of B cells cultured for this study is a particular strength, as is the fact that they were isolated in an antigen-unbiased fashion. The experiments are well-designed and comprehensive.

Weaknesses:

The genetic element is a relatively minor component overall and more qualitative than quantitative. It would be nice to investigate other properties of the repertoire like CDRH3 length and possible public clones, as well.

Reviewer #2 (Public review):

Summary:

Song et al. comprehensively analyzed the SHIV-infected macaque B cell repertoires and commonalities among their antibody responses, despite their diverse genetic background. They suggest these studies would inform HIV-1 vaccine design.

Strengths:

This study is well-designed and used proper analysis methods, and the figures are clear and effectively presented.

Weaknesses:

However, it tends to overstate its novelty and significance, emphasizing points that are relatively obvious (e.g., different classes of antibodies can recognize a common epitope) and appears to have been overwritten and unnecessarily fancy ("conceptually analogous to ecomorph evolution", "epitopic convergence"). Moreover, some limitations of the rhesus macaque model and the differences between bnAbs and nAbs should be discussed. That said, the underlying data are solid and important in their detail, and the manuscript will be a useful resource for HIV-1 vaccine and pathogen studies.

eLife Assessment

This is an important study reporting a new phenotype for a gene cluster that has previously been associated with the responses of the Gram-negative opportunistic pathogen Pseudomonas aeruginosa to flow fluid. Expression of the froABCD gene cluster is induced by HOCl in vitro and by activated immune cells, which produce these types of reactive chlorine species and the evidence presented by the authors is in many places convincing. Overall, the authors have been responsive to the previous review, although the exact mechanism of fro-induction by HOCl remains unclear. The high cysteine- and methionine content of the anti-sigma factor FroI hints at a direct oxidative modification of this protein during activation of the operon and a corneal infection model shows that fro is upregulated in P. aeruginosa 20 h after infection, but the evidence that HOCl is the causative agent of fro upregulation under these conditions is at present circumstantial. This study is of interest to infection biologists interested in mechanisms of bacterial pathogenicity.

Reviewer #1 (Public review):

Summary:

Foik et al. report that hypochlorous acid, a reactive chlorine species generated during host defense, activates the transcription of the froABCD in P. aeruginosa. This gene cluster had previously been associated with a potential role during flow of fluids and appears to be regulated by the sigma factor FroR and its anti-sigma factor FroI. In the present study, the authors show that froABCD is expressed both in neutrophils and macrophages, which they claim is likely a result of HOCl but not H2O2 production. Fro expression is also induced in a murine model of corneal infection, which is characterized by immune cells invasion. Expression of the fro system can be quenched by several antioxidants, such as methionine, cysteine, and others. FroR-deficient cells that lack froABCD expression during HOCl stress, appear more sensitive to the oxidant.

Strengths:

The authors provide a number of data supporting their claim that transcription of the froABCD system is induced by reactive chlorine species. This was shown by RNAseq, qRT-PCR, and through microscopy using a transcriptional reporter fusion. Likewise, elevated expression of froABCD was shown in vitro and in vivo, excluding potential in vitro artifacts. The manuscript, while mostly descriptive, is easy to follow and the data were presented clearly and convincingly. The authors have also been responsive to concerns from the previous review.

Weaknesses:

(1) Line 10: "HOCl preferentially oxidizes....". Please consider modifying the language to: "the second-order rate constant of HOCl is significantly higher with Met/Cys compared to other aa."

(2) I am not sure I completely understand Fig 1B. Is the promoter right upstream of yfp or is yfp located downstream of froA? If the latter is the case, wouldn't this be a translational fusion?

(3) My previous comment regarding why fro expression is higher during phagocytosis in macrophages compared to neutrophils has been somewhat (albeit not convincingly), addressed by the authors in the response to the reviewer, but this discussion should be part of the manuscript as the macrophage data were shown.

(4) Line 122: The statement "The degree of fro inhibition by 4-ABAH...." is incorrect unless the authors can provide experimental evidence. Fro expression is not upregulated because MPO is inhibited by 4-ABAH, which results in less hOCL production.

(5) Can Supp Fig. 1 be quantified in a similar way it was done for HOCl to allow for a better comparison if HOCl or flow is the more potent inducer?

(6) Overall, the fro expression (YFP/mCherry) seems highly variable for treatment with HOCl (Fig. 2C: ~65; 2D: ~20; why is fro expression 3x lower?

(7) The authors should provide evidence that N-chlorotaurine can activate fro expression also. They said they weren't able to obtain chlorinated taurine, but this is quite simple to produce: PMCID: PMC1219228

(8) Fig. 4 supplement 1: Please provide concentrations for the oxidants used in these experiments.

(9) Lines 251/252: change to: upregulation of instead of in

(10) Chaperones and other heat-shock genes are more upregulated in ∆froR, indicating elevated HOCl-mediated oxidative damage, which supports their findings.

(11) Complementation of ∆froR is missing

(12) Line 198: The growth experiment at 4 uM shows differences between WT and mutant, but at 2 uM cells showed already low fro expression due to cell death (which has not been proven by CFU counts). This discrepancy should at least be discussed.

(13) The critical in vitro experiment is missing: does purified FroI get oxidized by HOCl and dissociated from FroR?

(14) Lines: 350-355: The claim that the fro system is the first-line defense is unproven.

Reviewer #2 (Public review):

Summary:

Foik et al. studied the regulation of the fro operon in response to HOCl, an oxidant derived from immune cells, especially neutrophils. They use a transcriptional fusion of YFP to the froA promotor in an mCherry expressing P. aeruginosa strain to determine fro-induction under the microscope. They use this system to study fro expression in medium, in the presence of neutrophils and macrophages, neutrophil-conditioned medium, and several chemical stimuli, including NaCl, HOCl, hydrogen peroxide, nitric acid, hydrochloric acid, and sodium hydroxide. They also use a corneal infection model to demonstrate that froA is upregulated in P. aeruginosa 20 h post infection and perform transcriptional analyses in WT and a froR mutant in response to HOCl.

Strengths:

Their data clearly shows that HOCl is a strong inducer of the fro Operon. Addition of HOCl-quenching chemicals together with HOCl abrogates the response. They also show that a froR mutant is more susceptible to HOCl than WT. Their transcriptomic data reveals genes under control of the FroR/FroI sigma factor/anti sigma factor system.

Weaknesses:

Although the presented evidence is mostly solid, some of their findings need to be evaluated more carefully; explaining the rationale behind some of the experiments might enhance the article; and some of the models proposed by the authors seem far-fetched, as outlined below:

Unexpected outcomes and open questions for future research:

(1) As outlined above, HOCl seems to be the main inducer of the fro operon. Interestingly, during interaction with immune cells, macrophages and neutrophils seem to induce a reporter gene under fro control in a similar manner, although macrophages are generally thought to produce less HOCl, when compared to neutrophils. May be this view needs to be revised, or another reactive species, produced by macrophages, can activate the fro operon as well.

(2) HOCl is typically unstable in the presence of biomolecules. Nevertheless, medium conditioned by activated neutrophils is a strong inducer of the fro operon. The medium used by the authors for this experiment contains taurine, and, as the authors acknowledge, this taurine will likely react with HOCl to form the more stable taurine N-chloramine. Similarly, the MinA bacterial medium used to treat P. aeruginosa with HOCl directly also contains ammonium ions at mM concentrations, which could potentially react with HOCl to form monochloramine. It could be speculated that taurine N-chloramine and other chloramines are as effective as HOCl in activating the fro-operon.

(3) The fro operon was originally described to be activated by shear stress ("flow-regulated operon"). How shear stress and HOCl-stress are related, or if fro activation by both stimuli is a coincidence, remains unclear. The authors propose a model, in which flow transports oxidizing molecules, which ultimately activate the fro operon. However, the initial work by Sanfilippo et al. (2019, Nat Microbiol) used plain LB medium in a fluidic chamber to induce the shear stress, which should be free of oxidants, and certainly of HOCl.

Comments on revised version:

The authors have addressed my concerns appropriately.

Author response:

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

We greatly appreciate the efforts of the reviewers, which have provided insightful and helpful comments to improve the manuscript. The feedback touches upon a number of topics, focusing on clarification or justification of experimental techniques and on understanding the mechanism by which P. aeruginosa detects HOCl. All reviewers raised the issue of how HOCl activates fro expression, including whether free or protein-bound methionine, cysteine, or other HOCl byproducts induce this expression. For the upcoming revision, we plan to perform experiments that address this issue and will discuss potential mechanistic models in light of the new data. In addition, we plan to perform additional experiments to address a reviewer’s concerns regarding the dependence of the fro response on HOCl production by neutrophils. The revision will correct imprecise statements pointed out by reviewers, and address all remaining issues requiring clarification or further discussion, including the range of HOCl sensitivity, relationship between HOCl and flow sensitivity, and justification for testing the fro response to nitric acid.

We have completed a number of experiments and responded thoroughly to reviewer comments below. We thank the reviewers again for their details comments, which suggested additional experiments and interpretations that have resulted in significant additional insight into the potential mechanism of HOCl sensing and its relevance with neutrophils.

Reviewer #1 (Public review):

Summary:

Foik et al. report that hypochlorous acid, a reactive chlorine species generated during host defense, activates the transcription of the froABCD in P. aeruginosa. This gene cluster had previously been associated with a potential role during the flow of fluids and appears to be regulated by the sigma factor FroR and its antisigma factor FroI. In the present study, the authors show that froABCD is expressed both in neutrophils and macrophages, which they claim is likely a result of HOCl but not H2O2 production. Fro expression is also induced in a murine model of corneal infection, which is characterized by immune cell invasion. Expression of the fro system can be quenched by several antioxidants, such as methionine, cysteine, and others. FroR-deficient cells that lack froABCD expression during HOCl stress appear more sensitive to the oxidant.

Strengths:

The authors provide a number of data supporting their claim that transcription of the froABCD system is induced by reactive chlorine species. This was shown by RNAseq, qRT-PCR, and through microscopy using a transcriptional reporter fusion. Likewise, elevated expression of froABCD was shown in vitro and in vivo, excluding potential in vitro artifacts. The manuscript, while mostly descriptive, is easy to follow, and the data were presented clearly.

We greatly appreciate the efforts of the reviewer and thank them for their succinct summary of the manuscript.

Weaknesses:

(1) Lines 60-62: Some of the authors' conclusions are not supported by the data and thus appear unfounded. One example: "we determine that fro upregulation.....These data suggest a novel mechanism..." Their data do not show that MSR upregulation is a direct effect of FroABCD. Instead, it could be possible that the FroR sigma factor also controls the expression of msr genes, which would be independent of froABCD.

We thank the reviewer for pointing out this important distinction. We have clarified in lines 63-65 in the clean version of the revision that MSR upregulation depends on FroR rather than FroABCD.

(2) The authors show increased fro transcription both in neutrophils and macrophages; however, the two types of immune cells differ quite dramatically with respect to myeloperoxidase activation and HOCl production.

Neither has this been discussed nor considered here.

We agree that the distinction between the cell types is important and have added a brief description of the differences in respiratory bursts and ROS production between the two cell types and our justification for focusing on neutrophils in lines 102-103. We think it’s very interesting that Fro appears to be activated by macrophages, which are not associated with HOCl production on their own. We think that it would be interesting to identify what is inducing Fro in macrophages in future work.

(3) With respect to the activation of fro expression upon challenge with conditioned media from stimulated neutrophils, does the conditioned media contain detectable amounts of HOCl? Do chloramines, which are byproducts of HOCl oxidation with amines, also stimulate expression?

This is an excellent question that addresses which molecules Fro is responding to from neutrophils. We have performed additional experiments that confirm that PMA-stimulated neutrophils produce HOCl (Fig. S2) through the use of a commercial hypochlorite sensor assay, which claims high specificity for detecting HOCl. We further confirmed that this production is inhibited by pretreatment with the MPO-specific inhibitor 4ABAH. These data support the interpretation that the Fro response to stimulated neutrophils requires MPO activity, of which the major product is HOCl. We have described this in lines 117-127.

Our data does not exclude the possibility that other MPO products could activate Fro expression. We were unable to obtain a reliable source of the major secondary MPO product, taurine chloramine, for our experiments, unfortunately. We believe that understanding the potential for secondary products to activate the response is an important and interesting question that can be explored in a future study. We have added a discussion of this in lines 367-376.

(4) A better control to prove that this fro expression is indeed induced by HOCl in activated neutrophils would be to conduct the experiments in the presence of a myeloperoxidase inhibitor.

We thank the reviewer for raising this point. We have performed the suggested set of experiments and found that indeed, the pre-treatment of neutrophils with MPO inhibitor 4-ABAH prior to PMA stimulation suppresses the activation of fro (Fig. 2D and Figure 2-figure supplement 1-2). The results are discussed in lines 117-127.

(5) The work was conducted with two different P. aeruginosa strains (i.e. AL143 and PAO1F). None of the figure legends provides details on which strain was used. For instance, in line 111, the authors refer to Figure S1B for data that I thought were done with PAO1F, while in 154, data were presented in the context of the infection model, which was conducted with the other strain.

We thank the reviewer for pointing this issue out. We have ensured that strain names appear in all the revised figure legends. To clarify, only mouse experiments and a related RT-qPCR assay used strain PAO1F due to prior IACUC approval of this strain and its use in previous publications.

(6) It would be good if immune cell recruitment at 2hrs and 20hrs PI could be quantified.

We previously quantified neutrophil recruitment at the site of corneal abrasion at 24 hours using the same conditions and strains (Ratitong, B. et al., J. Immun, 2022). While we do not have immune cell recruitment data for the 2 hr and 20 hr time points, the previous data show significant neutrophil recruitment near the latter time point, which is consistent with the interpretation that fro expression is activated by stimulated neutrophils. We have discussed this in lines 212-216.

(7) The conclusions of Figure 4 are, in my opinion, weak (line 187-188; "It is possible that ....."). These antioxidants likely quench the low amounts of NaOCl directly. This would significantly reduce the NaOCl concentrations to a level that no longer activates expression of fro. There is no direct evidence provided that oxidized methionine induces fro expression. Do the authors postulate that this is free methionine, or could methionine and/or cysteine oxidation in FroR increase the binding affinity of the sigma factor to the promoter? Another possibility is that NaOCl deactivates the anti-sigma factor. None of these scenarios has been considered here.

We acknowledge that our model of HOCl sensing was unclear and thank the reviewer for their insight. This critique is echoed by reviewer #2 in comment 3 as well. We recently found that the FroI anti-sigma factor has the highest concentration of methionine and cysteine residues of all known P. aeruginosa anti-sigma factors (Appendix 2—Table 1). Given that FroR and FroI form an extracytoplasmic function sigma – anti-sigma pair, which are associated with transducing extracellular signals to the cytoplasm, we have proposed an alternative model in which HOCl or secondary RCS molecules are detected through their oxidation of cysteine and methionine residues in FroI. This is discussed in lines 271-279 and lines 363-367.

(8) Line 184: The reaction constants of HOCl with Cys and Met are similar.

We thank the reviewer for pointing out this important clarification. We have revised the sentence to accurately reflect this in lines 243-244.

(9) Treatment with 16 uM NaOCl caused a growth arrest of ~15 hrs in the WT (Figure 5A), whereas no growth at all was recorded with 7.5 uM in Figure 3A.

We thank the reviewer for catching this. We have determined that the concentration of NaOCl in the reagent used for this particular experiment was lower than expected, thus requiring a higher concentration to achieve growth inhibition. We have repeated the experiment with new reagent and find that the results (now Figure 6A) are similar to the previous experiment but at a lower concentration of 4 micromolar, consistent with the concentration found to be sub-inhibitory in Figure 3A.

(10) The concentration range of NaOCl causing fro expression is extremely narrow, while oxidative burst rapidly generates HOCl at much higher concentrations. This should be discussed in more detail.

We appreciate the reviewer’s comment, which is related to reviewer #2’s comment #9. We have clarified the reported production rates of HOCl, which far surpass the bacterial MIC. After greater consideration, we believe secondary HOCl products including taurine chloramine could have a more significant role in vivo. While this molecule is less potent than HOCl, it is longer-lived, retains bactericidal activity, and retains the ability to oxidize methionine. We have discussed this in lines 377-405.

Reviewer #1 (Recommendations for the authors):

(1) Some statements in the text don't match the data shown in the Figures. For instance:

(a) Figure 2B shows ~65-fold fro expression, but the text states: "...increased expression of fro expression by 30fold..."(line 88).

The YFP/mCherry value of PMA-stimulated is 67.4 in the Figure (now Figure 2C). The fold-change is computed relative to unstimulated conditioned medium (third column, which has a value of 2.2), which is a 30-fold change. We have added a citation in the main text to the Source Data, which provides these raw values, to help clarify the computation for readers, and added in the legend that the value for unstimulated is greater than 1.

(b) Figure 3B shows ~35-fold fro expression at 1 uM NaOCl, but the text states: "...NaOCl increased fro expression by up to 74-fold..."(line 110).

We have clarified that the increase is relative to untreated, added that untreated value is below 1 in the caption, and provided a citation in the main text to the Source Data, which contains the raw values. The change is measured relative to untreated, for which the YFP/mCherry value is 0.48. The value at 1 uM is 35.7, giving a 74-fold change.

(2) Line 229: While the ∆froR strain was sensitive to HOCl, the strain was tolerant". Please revise.

We have corrected this typo (now lines 328- 329). We meant to convey that growth was not entirely inhibited in the froR strain.

Reviewer #2 (Public review):

Summary:

Foik et al. studied the regulation of the fro operon in response to HOCl, an oxidant derived from immune cells, especially neutrophils. They use a transcriptional fusion of YFP to the froA promoter in an mCherry-expressing P. aeruginosa strain to determine fro-induction under the microscope. They use this system to study fro expression in medium, in the presence of neutrophils and macrophages, neutrophil-conditioned medium, and several chemical stimuli, including NaCl, HOCl, hydrogen peroxide, nitric acid, hydrochloric acid, and sodium hydroxide. They also use a corneal infection model to demonstrate that froA is upregulated in P. aeruginosa 20 h post-infection and perform transcriptional analyses in WT and a froR mutant in response to HOCl.

Strengths:

Their data clearly shows that HOCl is a strong inducer of the fro Operon. The addition of HOClquenching chemicals together with HOCl abrogates the response. They also show that a froR mutant is more susceptible to HOCl than WT. Their transcriptomic data reveal genes under control of the FroR/FroI sigma factor/anti sigma factor system.

Weaknesses:

Although the presented evidence is mostly solid, some of their findings need to be evaluated more carefully; explaining the rationale behind some of the experiments might enhance the article, and some of the models proposed by the authors seem far-fetched, as outlined below:

We greatly appreciate the reviewer’s efforts and thank them for highlighting strengths and areas for improvement.

(1) In line 76 the authors claim "Relative to P. aeruginosa that were incubated in host cell-free media, P. aeruginosa in close proximity to human neutrophils or that were engulfed in mouse macrophages appeared to increase fro expression (Fig. 1C)". Counting bacterial cells in Figure 1C shows that 1 in 17 bacteria (5.8%) induce the froA-promotor in media in the absence of immune cells, while 4 in 72 bacteria (only 5.5%) do the same in the presence of neutrophils. Contrary to the authors' claims, it appears that P. aeruginosa actually decreases fro-expression in close proximity to neutrophils. There is a slight increase in fro-expression in bacteria co-incubated with macrophages (3 in 21, or 14.3%). A more rigorous statistical analysis might substantiate the authors' claim, but, as is, the claim "neutrophils increase fro expression" is untenable.

We believe the images alone do not give an adequate representation of the data and have quantified a larger portion of the data, which has been added as Figure 1D. The quantification supports the original claim that fro expression is increased during co-incubation with macrophages and neutrophils. Since there was not sufficient statistical sampling to distinguish engulfed P. aeruginosa from free ones, this part of the claim has been removed from the text (updated in lines 80-84).

(2) The authors should explain the rationale behind some of the chemicals used. Why did they use nitric acid? Especially at these high concentrations, a strong acid such as nitric acid might have a significant influence on the medium pH. I understand that the medium is phosphate-buffered, but 25 mM nitric acid in an unbuffered medium would shift the pH well below 2. Similar considerations apply to hydrochloric acid and sodium hydroxide.

We thank the reviewers for pointing out the need for this clarification. We have updated Figure 3D with a lower concentration of NaOH at 1 uM, which is the same concentration as NaOCl that activates fro expression. Due to the high buffering capacity of our medium, a high concentration of 6 mM NaOH was needed to induce a discernible change in pH and this high concentration of NaOH had no obvious effect on growth. Neither 1 uM nor 6 mM NaOH produced a change in fro expression, consistent with our previous findings that the effect is not due to sodium ions or higher pH. These updated findings are described in lines 181-190.

Since the effect of chloride is already controlled for using NaCl and the concentration of HCl used was not sufficient to cause a significant change in pH in the buffered medium, we have removed the HCl group from the data.

We have clarified that nitric acid was used because it is a strong oxidizer that is not found in neutrophils and that concentrations used were near the minimal inhibitory concentrations (lines 145-147 and lines 191-198). We acknowledge that the growth inhibition from HNO₃ could be due to pH or oxidation. However, since no change in fro expression was observed at concentrations approaching the inhibitory concentration, we did not address the potential effects of low pH from nitric acid on fro expression.

(3) In line 187, the authors state that "It is possible that oxidized methionine increases fro expression" and they suggest a model to that effect in Figure 5D. It is unclear why the authors singled out methionine sulfoxide, since a number of other things get oxidized by HOCl. In line 184, the authors state, in the same vein, that "HOCl oxidizes methionine residues 100-fold more rapidly than other cellular components". The authors should state which other cellular compounds they are referring to. Certainly not cysteine and other thiols, which react equally fast and are highly abundant in the cell: P. aeruginosa contains 340 µM GSH, 140 µM CoA-SH (https://doi.org/10.1074/jbc.RA119.009934) plus free cysteine and cysteines in proteins (based on codon usage, 1.34% of amino acids in proteins are cysteine, while methionine is only slightly more present at 2.10%, although a number of starting methionines are removed from mature proteins).

We acknowledge that our HOCl sensing model had been vague and unclear and thank the reviewer for their insight. This critique is echoed by reviewer #1 in comment 7 as well.

Our initial suggestion that methionine sulfoxide was sensed was motivated by the observation that methionine sulfoxide reductases are upregulated by HOCl. However, we have revised this based on feedback from reviewers and further consideration of chlorine redox chemistry. Interestingly, we found that the FroI anti-sigma factor has the highest concentration of methionine and cysteine residues of all the P. aeruginosa anti-sigma factors (Appendix 2—table 1). Given that FroR and FroI form an extracytoplasmic function sigma – anti-sigma pair, which are associated with transducing extracellular signals to the cytoplasm, we propose a model in which HOCl or secondary RCS molecules are detected by their oxidation of cysteine and methionine residues in FroI. This is discussed in lines 271-278 and lines 362-366.

(4) Overall (and this is probably not addressable with the authors' data), some very interesting questions remain unanswered: what is the molecular mechanism of fro-induction? How is the FroR/FroI system modulated by HOCl? Does the system sense free or protein-bound methionine-sulfoxide? Are certain methionine residues in these proteins directly oxidized by HOCl? Many "HOCl-sensing" proteins are also modified at cysteine residues or amino groups; could those play a role? And lastly: what is the connection between shear/fluid flow and HOCl, or are these totally separate mechanisms of fro-induction?

We thank the reviewer for raising these excellent mechanistic questions. Issues relating to HOCl sensing are addressed in the preceding comment.

Regarding the connection to shear sensing, Padron et al., 2023 found that the detection of flow in P. aeruginosa can be attributed to chemical transport, in particular to H₂O₂ that was present in growth media. Based on the same principle, we expect Fro to be upregulated in flow at much lower concentrations than those observed in stationary fluids. The activation of Fro and the effects of HOCl would thus be expected to be flow-sensitive. We have commented on this important factor in the discussion in lines 406-417.

Reviewer #2 (Recommendations for the authors):

(1) To address 1, the authors could evaluate the microscopic images in the same manner in which they evaluated the other microscopic images, as, for example, presented in Figure 2B or Figure S1B.

See response to Weakness point (1).

(2) To address 2, please explain the choice of the chemicals (why nitric acid?), but also provide the pH of the media with those high concentrations of strong acids and bases, and interpret them in light of the permissible pH range for P. aeruginosa growth. More sensible controls might be a lower NaOH concentration in the range that would be reached through the amount of NaOH in the NaOCl stock at the highest NaOCl concentrations used. As for the acids, I don't see a reason to use these acids at these high concentrations. Please explain.

See response to Weakness point (2).

(3) To address 3: The authors could specify their methionine-sulfoxide model a bit more, so that testable hypotheses can be developed. If the authors think the FroR/FroI system senses free methionine sulfoxide, they or others could add methionine sulfoxide to the medium and check induction. If they think specific methionine residues in these proteins are oxidized, they could provide evolutionary evidence of conserved methionine residues. Or, based on a structure or structural prediction, they (or others) could mutate methionine residues, e.g., at the protein's surface or potential protein/protein-interaction sites and assess the effect on HOCl-based activation. Or they could consider other amino acids known to be highly reactive towards HOCl and mutate those in a future study.

See response to Weakness point (3).

Further comments:

(4) The headline of the figure legend of Figure S1 seems incomplete. Please mention the flow experiments shown in Figure S1A.

We have updated the title of this figure, which now appears in the eLife format as Figure 1 – figure supplement 1.

(5) What is the difference between the data presented in Figure 4C (bars "UTR" and "NaOCl") and the same bars in Figure S2A? Is this redundant or a re-plot?

In this revision, Figure 4C has become Figure 5B and Figure S2A is now Figure 5—figure supplement 1. Only the 1 uM NaOCl condition is replotted. We have described this in the legend for Figure 5—figure supplement 1.

(6) Line 228: hpd is more likely a gene of the aromatic amino acid catabolism.

We thank the reviewer for pointing this out. We have removed the ‘branched’ descriptor in this sentence, now in lines 325-326.

(7) Line 229: "While the ΔfroR strain was sensitive to HOCl, the strain was tolerant (Figure 5A)". Please clarify. Which strain was tolerant? WT?

We have corrected this typo (now line 328-329). We meant to convey that growth was not entirely inhibited in the froR strain.

(8) Line 247: "Activated neutrophils produce HOCl concentrations as high as 50 µM [24]." This "50 µM" number is often quoted; the citation trail typically leads to Weiss et al. 1982 (https://doi.org/10.1172/JCI110652). However, a more factually correct statement based on that paper would be "2 x 10^6 neutrophils, activated with 30 ng/mL PMA at 37C in 1 mL of Dulbecco's buffer can produce around 50 nmol HOCl per hour". In the particular reference 24, Dybpukt et al used a methodology similar to Weiss et al., and here around 50nmol were produced by the same number of cells in 30 min in response to 100 ng/mL PMA. Please clarify accordingly.

We thank the reviewer for bringing this to our attention. We have altered the language to indicate that the production rate is for this specific set of parameters. Related to this, Reviewer #1 Comment #10 requested a more detailed discussion of the significance of the Fro response, since it is at much lower HOCl concentration than produced by neutrophils. We have discussed this in lines 377-405.

(9) Line 275: "which is strain PA14 strain". Please clarify.

We have fixed this error and entered it into the Key Resource Table.

(10) Line 311: "Cultures containing densities below 10 P. aeruginosa per frame were concentrated using a syringe filter with 0.2 or 0.8 µm pore sizes (Millipore, Burlington, MA)." Isn't that a bit concerning when testing the induction of an operon that is supposedly activated by shear through fluid flow? Did the authors convince themselves that this procedure does not induce fro?

We do not expect the filtering procedure to cause changes in gene expression because cells are imaged immediately after filtering. Nonetheless, we performed additional experiments (Fig 3F and 2D) entirely without concentrating cells and found that YFP/mCherry levels were consistent with previous data in Figure 3. This rationale has been added to the Methods section under the “Fluorescence and Phase Contrast Microscopy” section.

eLife Assessment

This study provides important insights into the neural mechanisms linking sleep and long-term memory consolidation. By combining behavioural, genetic, imaging, and connectomic approaches in Drosophila, it identifies a target neural circuit that will be of broad interest to researchers studying sleep, memory, and neural circuits. The evidence supporting the involvement of the identified circuit in the regulation of sleep and memory is solid and represents a substantial advance in the field. Nevertheless, there is limited evidence to support the mechanistic claim that this circuit directly links sleep and memory consolidation within the available data, and some results should therefore be interpreted with appropriate caution.

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 considered and discussed the comments raised in the previous round of review.]

Summary:

The authors aim to use state-of-the art behaviour, imaging and connectome techniques to identify the neural interaction between sleep and long-term memory consolidation in the PAM-DPM circuits, a well-known dopaminergic pathway within Drosophila Mushroom Body.

Strengths:

The investigation follows a logical strategy to collect huge dataset of sleep, appetitive memory and live imaging. The authors identified and showed that activation of a PAM subset: alpha-1 reduces sleep quality and memory consolidation in a starvation dependant manner. The author also convincingly demonstrated the corresponding neuronal responses of DPM neurons following PAM alpha-1 activation, and the positive role of DPM neural activity in sleep and memory consolidation. Moreover, the new data provide TRIC-LUC provided better temporal resolution of neural activity correlates for PAMalpha1-DPM inhibition. Importantly, the author demonstrated that memory loss derived from PAM alpha 1 activation can be partly restored by ectopic sleep enhancement via feeding THIP at the memory consolidation period after training.

Weaknesses:

Although the revised version carries arguments to satisfy the reviewers' concern, the writing is now less cohesive. Crucially an explanation however remains required for the following experimental contradiction: the central observation of the study indicates that PAM alpha1 activation cause DPM inhibition which disrupt sleep and memory consolidation. Therefore, one would expect a reduced PAMalpha1 and increased DPM activities after memory training, but the authors found the opposite is true from now enhanced TRIC-LUC dataset. The authors indicate this data reinforce the inhibitory nature of PAM-alph1-DPM, but it does not explain why such a reduced DPM activity is observed after training.

Reviewer #2 (Public review):

Summary:

Sleep plays a critical role in memory consolidation, but the neural mechanisms underlying this relationship remain incompletely understood. The authors examined a specific subset of PAM dopaminergic neurons, PAM-α1, and DPM neurons in Drosophila. These neurons have previously been implicated in memory, and DPM neurons have also been linked to sleep. The study explores whether this circuit provides a mechanistic link between sleep and memory consolidation.

Strengths:

The authors report several novel findings. Brief activation or inhibition of PAM-α1 neurons, or brief inhibition of DPM neurons during the first few hours after training, impairs 24-hour LTM. Notably, these brief manipulations disrupt sleep for many hours afterward, particularly during the night. The authors further show that perturbation of PAM-α1 and DPM neurons impairs sleep and appetitive memory consolidation under starvation conditions, and that pharmacological sleep induction during the night rescues the LTM defects. Together, these findings suggest that PAM-α1 and DPM neurons are involved in sleep regulation and LTM consolidation under starvation. These are important observations that advance our understanding of the circuits regulating sleep and memory consolidation.

Weaknesses:

Some claims require additional evidence or clarification.

(1) Previous studies linking impaired memory to reduced sleep have primarily examined conditions involving severe sleep deprivation. In contrast, this manuscript argues that relatively modest decreases in total sleep, accompanied by sleep fragmentation, are sufficient to impair memory consolidation. It remains unclear whether sleep fragmentation of this magnitude is itself critical for LTM consolidation. An independent method for inducing comparably mild sleep loss and fragmentation would be needed to directly test this interpretation.

(2) It is unclear why both activation and inactivation of PAM-α1 neurons produce similar effects on sleep and memory. In addition, MB299B-labeled neurons exert stronger effects on memory than MB043B-labeled neurons, whereas MB043B-labeled neurons have stronger effects on sleep. If sleep disruption is the primary driver of impaired memory consolidation, a stronger correspondence between the sleep and memory phenotypes might be expected. The authors speculate that MB043B may affect sleep through non-PAM neurons, but without identifying the relevant neurons, this remains speculative.

(3) The complex schematic model (Fig. 12), with parallel circuits and unidentified neuronal groups, underscores the difficulty of interpreting the current data. In the "less activity" arm of the model, distinct circuits are proposed to regulate sleep and LTM, respectively, and DPM neurons are not included. This makes it difficult to reconcile the model with the central claim that the PAM-α1-to-DPM microcircuit links sleep and LTM consolidation.

(4) The TRIC-LUC reporter system is not ideal for resolving dynamic changes in neuronal activity. Activity-dependent Ca²⁺ signaling must first reconstitute the TRIC transcriptional system, which then drives luciferase transcription, translation, and accumulation. The original characterization of TRIC indicates that TRIC signals accumulate and decay over several hours. Thus, the kinetics of the TRIC-LUC reporter should be interpreted cautiously, particularly when inferring transient or precisely timed changes in neuronal activity.

(5) Including data from training under fed conditions would provide a more complete understanding of state-dependent neural activity and would help distinguish starvation-specific effects from more general circuit mechanisms.

Reviewer #3 (Public review):

Summary:

Understanding the neural circuits that link sleep and memory remains a fundamental challenge in neuroscience. In this study, Lin Yan and colleagues investigate how dopamine signaling in Drosophila regulates long-term memory (LTM) formation in the context of sleep. They identify a specific microcircuit between protocerebral anterior medial dopamine neurons (PAM-DANs) and dorsal paired medial (GABAergic DPM) neurons that modulates memory consolidation. Their findings suggest that disrupting the basal activity of PAM-α1 neurons during early consolidation impairs LTM, with particularly pronounced effects under starvation conditions. Notably, sleep fragmentation caused by this disruption can be pharmacologically rescued, restoring LTM. These results provide compelling evidence how dopamine signaling plays a crucial role in linking sleep and memory, offering new insights into the underlying mechanisms.

Strength:

This study presents a well-executed investigation into sleep-memory interactions, utilizing a combination of connectomics, behavioral assays, functional imaging, and pharmacological manipulations. The authors convincingly demonstrate that the PAM-α1 and DPM circuit interact, highlighting a potential mechanism by which sleep influences memory consolidation. The anatomical and functional dissection of this circuit is of high interest to the field, and the study's integration of sleep and memory processes contributes significantly to our understanding of the role of dopamine in cognitive functions. Additional experiments investigating the contribution of MBON-α1 to the circuit, connectomic analysis together with a dissection of dopamine receptor function further strengthen the proposed circuit motif and its biological relevance.

Weaknesses:

While the study is well designed, presents compelling findings and has been further strengthened by additional experiments, some aspects remain unclear. The role of DPM neurons in memory consolidation seems not yet fully resolved, as different genetic approaches yield variable results. Furthermore, some manipulations impair memory without affecting sleep fragmentation - or vice versa, suggesting that the observed memory deficits cannot be explained solely by impaired sleep-dependent consolidation. It would also have been interesting to discuss potential mechanisms by which dopamine receptor-mediated cAMP signaling could lead to a reduction in Ca²⁺ signals. I am confident that these questions can be addressed in future studies.

Conclusion:

Overall, this study provides valuable new insights into how sleep and dopaminergic circuits interact to regulate memory consolidation in Drosophila and may reveal general principles underlying the neural regulation of memory.

Author response:

The following is the authors’ response to the previous reviews

eLife Assessment

This study provides important insights into the neural mechanisms linking sleep and long-term memory consolidation. By combining behavioural, genetic, imaging, and connectomic approaches in Drosophila, it identifies a target neural circuit that will be of broad interest to researchers studying sleep, memory, and neural circuits. The evidence supporting the involvement of the identified circuit in the regulation of sleep and memory is solid and represents a substantial advance in the field. Nevertheless, there is limited evidence to support the mechanistic claim that this circuit directly links sleep and memory consolidation within the available data, and some results should therefore be interpreted with appropriate caution.

We appreciate the reviewer’s careful evaluation of our manuscript, and we agree that (as with any experimental study) there is still a lot to do to fully understand the mechanisms underlying the linkage between sleep and memory consolidation.

Public Reviews:

Reviewer #1 (Public review):

Summary:

The authors aim to use state-of-the art behaviour, imaging and connectome techniques to identify the neural interaction between sleep and long-term memory consolidation in the PAM-DPM circuits, a well-known dopaminergic pathway within Drosophila Mushroom Body.

Strengths:

The investigation follows a logical strategy to collect huge dataset of sleep, appetitive memory and live imaging. The authors identified and showed that activation of a PAM subset: alpha-1 reduces sleep quality and memory consolidation in a starvation dependent manner. The author also convincingly demonstrated the corresponding neuronal responses of DPM neurons following PAM alpha-1 activation, and the positive role of DPM neural activity in sleep and memory consolidation. Moreover, the new data provide TRIC-LUC provided better temporal resolution of neural activity correlates for PAMalpha1-DPM inhibition. Importantly, the author demonstrated that memory loss derived from PAM alpha 1 activation can be partly restored by ectopic sleep enhancement via feeding THIP at the memory consolidation period after training.

Weaknesses:

Although the revised version carries arguments to satisfy the reviewers' concern, the writing is now less cohesive. Crucially an explanation however remains required for the following experimental contradiction: the central observation of the study indicates that PAM alpha1 activation cause DPM inhibition which disrupt sleep and memory consolidation. Therefore, one would expect a reduced PAMalpha1 and increased DPM activities after memory training, but the authors found the opposite is true from now enhanced TRIC-LUC dataset. The authors indicate this data reinforce the inhibitory nature of PAM-alph1-DPM, but it does not explain why such a reduced DPM activity is observed after training.

We thank the reviewer for their point of view. We have faithfully reported all experimental observations acquired from our enhanced TRIC-LUC dataset as objectively as possible. We note that the reviewer postulates a particular expected activity shift (reduced PAM-α1 activity and elevated DPM activity after memory training), yet it is not clear why. As our data show, this is a highly connected microcircuit and it is not easy to predict how it might change. Our data show that there is change and dismissing our empirically measured results purely based on a theoretical expectation is not justified. The brain operates as an intricately interconnected network; neural activity dynamics cannot always be simply inferred from static circuit polarity. Progress in deciphering neural circuit function relies on iterative rounds of experimental testing. Like most neuroscience investigations, the present study cannot resolve every open question, and we explicitly acknowledge several unresolved directions worthy of future exploration in the Discussion section.

Reviewer #2 (Public review):

Summary:

Sleep plays a critical role in memory consolidation, but the neural mechanisms underlying this relationship remain incompletely understood. The authors examined a specific subset of PAM dopaminergic neurons, PAM-α1, and DPM neurons in Drosophila. These neurons have previously been implicated in memory, and DPM neurons have also been linked to sleep. The study explores whether this circuit provides a mechanistic link between sleep and memory consolidation.

Strengths:

The authors report several novel findings. Brief activation or inhibition of PAM-α1 neurons, or brief inhibition of DPM neurons during the first few hours after training, impairs 24-hour LTM. Notably, these brief manipulations disrupt sleep for many hours afterward, particularly during the night. The authors further show that perturbation of PAM-α1 and DPM neurons impairs sleep and appetitive memory consolidation under starvation conditions, and that pharmacological sleep induction during the night rescues the LTM defects. Together, these findings suggest that PAM-α1 and DPM neurons are involved in sleep regulation and LTM consolidation under starvation. These are important observations that advance our understanding of the circuits regulating sleep and memory consolidation.

Weaknesses:

Some claims require additional evidence or clarification.

(1) Previous studies linking impaired memory to reduced sleep have primarily examined conditions involving severe sleep deprivation. In contrast, this manuscript argues that relatively modest decreases in total sleep, accompanied by sleep fragmentation, are sufficient to impair memory consolidation. It remains unclear whether sleep fragmentation of this magnitude is itself critical for LTM consolidation. An independent method for inducing comparably mild sleep loss and fragmentation would be needed to directly test this interpretation.

We appreciate the reviewer’s suggestion. While alternative assays for inducing sleep loss or sleep fragmentation are indeed available, this line of investigation lies beyond the core scope of the present study. We will certainly take this valuable suggestion into consideration for the future studies.

Regarding the question of whether sleep fragmentation of this magnitude per se is critical for long-term memory consolidation, we would like to highlight relevant published evidence. Prior work in rodents and human (Bonnet and Arand, 2003; Van Someren et al., 2015; Ramesh et al., 2012; Baud et al., 2014) and our earlier study (Liu et al., 2019) have demonstrated that alterations in sleep architecture, independent of changes in total sleep amount, can affect multiple physiological processes, including memory. Given this existing supporting evidence, we respectfully argue that this does not constitute a weakness of the present manuscript.

(2) It is unclear why both activation and inactivation of PAM-α1 neurons produce similar effects on sleep and memory. In addition, MB299B-labeled neurons exert stronger effects on memory than MB043B-labeled neurons, whereas MB043B-labeled neurons have stronger effects on sleep. If sleep disruption is the primary driver of impaired memory consolidation, a stronger correspondence between the sleep and memory phenotypes might be expected. The authors speculate that MB043B may affect sleep through non-PAM neurons, but without identifying the relevant neurons, this remains speculative.

The concern raised by the reviewer that certain interpretations remain speculative represents a common situation in most published research. This interesting direction warrants further investigation in future work, but falls outside the scope of the present study. In the revised manuscript, we have elaborated on the differences observed between these two GAL4 drivers. We have also conducted additional experiments to investigate a well-characterized memory-related recurrent loop of PAM-α1 neurons in sleep regulation. We respectfully note that no single study can comprehensively address all outstanding questions.

(3) The complex schematic model (Fig. 12), with parallel circuits and unidentified neuronal groups, underscores the difficulty of interpreting the current data. In the "less activity" arm of the model, distinct circuits are proposed to regulate sleep and LTM, respectively, and DPM neurons are not included. This makes it difficult to reconcile the model with the central claim that the PAM-α1-to-DPM microcircuit links sleep and LTM consolidation.

We appreciate this careful comment on our schematic model in Figure 12. This diagram aims to summarize the key findings obtained in the present study while also explicitly laying out unresolved questions that await future investigation. In our view, including open, outstanding questions in the working model does not undermine the interpretation of our existing experimental results. In the revised manuscript, we have modified the corresponding text to clarify this point and distinguish firmly between conclusions supported by our data and tentative components requiring follow-up validation.

(4) The TRIC-LUC reporter system is not ideal for resolving dynamic changes in neuronal activity. Activity-dependent Ca²⁺ signaling must first reconstitute the TRIC transcriptional system, which then drives luciferase transcription, translation, and accumulation. The original characterization of TRIC indicates that TRIC signals accumulate and decay over several hours. Thus, the kinetics of the TRIC-LUC reporter should be interpreted cautiously, particularly when inferring transient or precisely timed changes in neuronal activity.

We fully acknowledge the inherent limitations of the TRIC-LUC reporter system, as pointed out by the reviewer. Every experimental tool comes with characteristic strengths and drawbacks. Although TRIC-LUC suffers from temporal delays, our experiment does not aim to capture acute, immediate effects; instead, it examines long-term dynamics of neuronal activity. To date, within Drosophila neurobiology, no superior technique is available for non-invasive long-term monitoring of neuronal activity in freely behaving flies. We share the hope that new tools capable of reporting neuronal activity in real time will be developed and applied, which will facilitate deeper mechanistic understanding of neuronal dynamics.

(5) Including data from training under fed conditions would provide a more complete understanding of state-dependent neural activity and would help distinguish starvation-specific effects from more general circuit mechanisms.

We appreciate this suggestion. First, our memory paradigm relies on reward-based associative learning, and starvation is required for flies to express robust memory, so to do this would require a completely new experimental set up. Second, our core findings demonstrate that transient perturbations of this neuronal circuit trigger sleep disturbances and memory deficits specifically under starvation conditions. Therefore, measurements of neural activity under fed conditions are not directly relevant to the central conclusions of the present study. We agree that related experiments on other behaviors under fed states constitute an interesting direction and could be pursued in future investigations.

Reviewer #3 (Public review):

Summary:

Understanding the neural circuits that link sleep and memory remains a fundamental challenge in neuroscience. In this study, Lin Yan and colleagues investigate how dopamine signaling in Drosophila regulates long-term memory (LTM) formation in the context of sleep. They identify a specific microcircuit between protocerebral anterior medial dopamine neurons (PAM-DANs) and dorsal paired medial (GABAergic DPM) neurons that modulates memory consolidation. Their findings suggest that disrupting the basal activity of PAM-α1 neurons during early consolidation impairs LTM, with particularly pronounced effects under starvation conditions. Notably, sleep fragmentation caused by this disruption can be pharmacologically rescued, restoring LTM. These results provide compelling evidence how dopamine signaling plays a crucial role in linking sleep and memory, offering new insights into the underlying mechanisms.

Strength:

This study presents a well-executed investigation into sleep-memory interactions, utilizing a combination of connectomics, behavioral assays, functional imaging, and pharmacological manipulations. The authors convincingly demonstrate that the PAM-α1 and DPM circuit interact, highlighting a potential mechanism by which sleep influences memory consolidation. The anatomical and functional dissection of this circuit is of high interest to the field, and the study's integration of sleep and memory processes contributes significantly to our understanding of the role of dopamine in cognitive functions. Additional experiments investigating the contribution of MBON-α1 to the circuit, connectomic analysis together with a dissection of dopamine receptor function further strengthen the proposed circuit motif and its biological relevance.

Weaknesses:

While the study is well designed, presents compelling findings and has been further strengthened by additional experiments, some aspects remain unclear. The role of DPM neurons in memory consolidation seems not yet fully resolved, as different genetic approaches yield variable results. Furthermore, some manipulations impair memory without affecting sleep fragmentation - or vice versa, suggesting that the observed memory deficits cannot be explained solely by impaired sleep-dependent consolidation. It would also have been interesting to discuss potential mechanisms by which dopamine receptor-mediated cAMP signaling could lead to a reduction in Ca²⁺ signals. I am confident that these questions can be addressed in future studies.

We greatly appreciate the reviewer’s positive evaluation of our work and the thoughtful suggestions regarding future directions. As acknowledged in the manuscript, our study centers on identifying a shared circuit that coregulates sleep and memory processes. We have performed preliminary investigations of downstream circuitry, and our results indeed support the idea that sleep and memory can be modulated independently. Importantly, we have avoided drawing definitive conclusions that memory deficits arise purely from impaired sleep-dependent consolidation. Instead, we emphasize the existence of a common circuit mechanism governing both processes.

We also thank the reviewer for drawing attention to dopamine receptor-mediated cAMP signaling and Ca²⁺dynamics. This observation constitutes an additional finding that requires more extensive mechanistic follow-up. Given the scope of the current work, we have not dedicated a separate discussion section to dissecting this pathway. This promising line of inquiry will be pursued in our future research.

Conclusion:

Overall, this study provides valuable new insights into how sleep and dopaminergic circuits interact to regulate memory consolidation in Drosophila and may reveal general principles underlying the neural regulation of memory.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

No further issue, apart from the reverse figure 13 are not found in the main text as indicated.

We thank the reviewer for this careful check. We have performed a full-text search for Figure 13 throughout the revised manuscript and found no relevant citation. We have also carefully cross-checked all figure numbering and confirm that all figure labels are accurate in the current version.

Reviewer #2 (Recommendations for the authors):

In Fig. 8B, some individual GCaMP measurements show values below −100% ΔF/F₀. Under the stated definition, ΔF/F = (Fn - F0) / F0, values below −100% would require Fn to be negative. Since raw fluorescence intensity cannot be negative, values below −100% require further explanation. The authors should clarify whether Fn represents raw fluorescence or processed fluorescence, and whether the plotted traces underwent any normalization, subtraction, detrending, or transformation beyond the stated formula.

We greatly appreciate the reviewer’s rigorous scrutiny of our data and the valuable question raised. Our fluorescence signals were calculated using the standard formula ΔF/F = (Fn − F0)/F0, where Fn = F_ROI − F_background, and all calculations were implemented accordingly. We have carefully revisited all raw imaging datasets and identified the source of the issue. During initial data processing, we retained all acquired recordings without excluding samples exhibiting focal plane drift. This drift occasionally yielded negative values for Fn (F_ROI − F_background). Beyond the five DPM cell bodies from four brains in Figure 8B highlighted by the reviewer, we further detected six additional DPM cell bodies from four brains in Figure 2A affected by the same artifact. We have now excluded these drifting preparations, regenerated all corresponding plots, and updated the statistical analyses in the revised manuscript. For transparency, we upload both the raw and processed datasets as supplementary materials to clarify this point.

eLife Assessment

This important study introduces NoSeMaze, a semi-naturalistic platform for continuous, high-dimensional tracking of social and cognitive behaviors in group-housed mice, and uses it to show that individual social rank is stable across changing social contexts. By integrating automated dominance measures, proactive social behaviors, and reinforcement-learning-based profiles, the authors demonstrate a novel framework for examining how stable individual differences shape social structure. The findings provide compelling evidence that dominance traits are stable across changing social contexts and largely independent of non-social cognitive performance, supporting the view that social rank reflects an intrinsic dimension of individuality however, the broader functional significance of dominance in this paradigm remains somewhat ambiguous, including the extent to which the measured behaviors capture how dominance operates in more naturalistic social settings. This work will be of broad relevance for behavioral neuroscience and social behavior research.

Reviewer #2 (Public review):

Summary:

This manuscript presents the "NoSeMaze", a novel automated platform for studying social behavior and cognitive performance in group-housed male mice. The authors report that mice form robust, transitive dominance hierarchies in this environment and that individual social rank remains largely stable across multiple group compositions. They further demonstrate that social dominance and aggressive behaviors, like chasing, are partially dissociable and that dominance traits are independent of non-social cognitive performance. The study includes a genetic manipulation of oxytocin receptor expression in the anterior olfactory nucleus, which showed only transient effects on social rank.

Strengths:

(1) Innovative Methodology:<br /> The NoSeMaze platform is a technically elegant and conceptually well-integrated system that enables fully automated, long-term monitoring of both social and cognitive behaviors in large groups of group-housed mice. It combines tube-test-like dominance contests, voluntary chase-escape interactions, and an embedded operant olfactory discrimination task within a single, ethologically relevant environment. This modular design allows for high-throughput, minimally invasive behavioral assessment without the need for repeated handling or artificial isolation.

(2) Experimental Scale and Rigor:<br /> The study includes 79 male mice and over 4,000 mouse-days of observation across multiple group reshufflings. The use of RFID-based identification, automated data logging, and longitudinal design enables robust quantification of individual trait stability and group-level social structure.

(3) Multidimensional Behavioral Profiling:<br /> The integration of social (tube dominance, proactive chasing), physical (body weight), and cognitive (olfactory learning task) measures offers a rich, multi-dimensional profile of each individual mouse. The authors' finding that social dominance traits and non-social cognitive performance are largely uncorrelated reinforces emerging models of orthogonal behavioral trait axes or "animal personalities".

(4) Clarity and Data Analysis:<br /> The analytical framework is well-suited to the study's complexity, with appropriate use of dominance metrics, mixed-effects models, and permutation tests. The analyses are clearly explained, statistically rigorous, and supported by transparent supplementary materials.

Weaknesses:

(1) Scope Limitations (Sex):<br /> The study is limited to male mice, which represents a common but problematic bias.

(2) Ambiguity of Dominance as a Construct:<br /> While the study robustly quantifies social rank and hierarchy structure, the broader functional meaning of "dominance" remains unclear.

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

Summary:

The goal of the study was to address the question of the degree to which social position in a group is a stable trait that persists across conditions. Reinwald et al. use a custom-built cage system with automated tracking and continuous testing for social dominance that does not require intervention by the experimenter. Remixing of individuals from different groups revealed that social position was rather stable and not really predictable from other measures that were taken. The authors conclude that social position is multifaceted but dependent on characteristics like personality traits.

Strengths:

(1) Reductionistic, highly controlled setting that allows for the control of many confounding variables.

(2) Very interesting and important question.

(3) Confirms the emergence of inter-individual behavior-driven differences in inbred mice in a shared environment.

(4) Innovative paradigm and experimental setup.

(5) Fresh perspective on an old question that makes the best use of modern technology.

(6) Intelligent use of behavioral and cognitive covariables to generate a non-social context.

(7) Bold and almost provocative conclusion, inviting discussion and further elaboration.

We thank Reviewer #1 for this constructive and balanced evaluation of our work, and for highlighting both the conceptual importance of the question and the strengths of our automated, highly controlled approach.

Weaknesses:

(1) Reductionistic, highly controlled setting that blends out much of the complexity of social behavior in a community.

Our goal in developing the NoSeMaze was to provide an enriched yet standardized environment that allows animals to interact freely in a complex setting where arena geometry and access contingencies are held constant across groups. As the Reviewer also notes as a strength, this highly controlled setting with minimal experimenter interference enables us to minimize confounds and provides reproducibility across rounds and groups. We now clarify this explicitly and frame the design as a trade-off between ecological complexity and experimental controllability.

The NoSeMaze is designed as an open system that can incorporate additional configurations. In this first study with the system, we intentionally used a design that focused on single-sex groups without mating, no intruders or external threats, stable environmental conditions, and adult mice. This allowed us to establish social behavior under one defined condition. From here, future studies can add certain levels of ecological and social complexity to progressively understand their impact on specific behaviors and group dynamics.

Accordingly, we have revised the manuscript to clarify the scope and the role of social and environmental conditions to the here observed phenomena. We also describe how future studies with systematically modified conditions can be used to understand how they change behaviors. We further replaced potentially over-broad terms (e.g., “naturalistic,” “real-world”) with more precise wording throughout.

We modified the following sections:

Abstract

We removed “… within naturalistic mouse groups.” (ll. 52-54) and changed the sentence to “The approach thus enables longitudinal modeling of individuality and social position as key resilience factors.”

We changed “… in naturalistic groups.” (l. 38) to “… in larger male mouse groups.” and “… from naturalistic tube competitions …” (ll. 41-42) to “… from incidental competitions in the integrated tube tests …”. We also deleted “naturalistic” in l. 50.

Discussion (ll. 691-701)

“…The NoSeMaze aims to increase environmental complexity and group dynamics while retaining experimental control, enabling longitudinal high-dimensional phenotyping of individuals. At the same time, it is a controlled laboratory group-housing habitat optimized to capture a subset of the determinants of social complexity present in natural communities. The strength of this design lies in the continuous, observer-independent observation of complex behaviors in defined environmental and social contexts. Accordingly, we interpret our findings as applying to the social contexts and environmental conditions tested here. Building on this, its modular design allows for introducing additional environmental and social factors like stressors, mating behavior, or resource competition in the future to understand their respective impact in modifying social behaviors.”

Conclusion

We changed “… complexity of real-world behavior …” (ll. 729-730) to “… complexity of group behavior in semi-naturalistic conditions ...”

(2) The motivation to enter the test tube is not "trait" (or at least not solely a trait) but the basic need to reach food and water; chasing behavior would be less dependent on this stimulus.

Tube traversals may reflect different motivations, including routine movement between compartments to access food, the water lickport, the open arena, or the housing area. Nevertheless, we do not interpret tube-entry motivation itself as a ‘trait’. Importantly, our hierarchy readout does not quantify which animals enter the tubes, nor is it confounded by tube-entry frequency itself (cf. ll. 527-532). Rather, it captures the consistent outcomes of incidental dyadic competitions once two animals meet in the tube (push vs. retreat), aggregated across many interactions. Thus, the stable signal we report lies in repeatable competition outcomes, not in traversal propensity. Consistent with this interpretation, the overall number of tube competition events was not associated with social rank, arguing against systematic competition avoidance by low-ranking animals. In contrast, chasing is a voluntarily initiated, asymmetric interaction between an initiator and a recipient and therefore adds a social-action component beyond incidental access-linked encounters, providing a complementary readout of social behavior.

We also noted that the term ‘trait’ may not be the optimal description in this context and replaced it throughout the ms. with more precise wording, including “internalized social rank” and “propensity to chase”.

Results

We added the following paragraph (ll. 524-532):

“Tube crossings in the NoSeMaze are motivated by the intent to eat, drink, sleep, or socialize. Accordingly, competitions within the tube arise by chance when two animals enter from opposite sides at the same time. Social rank therefore captures the consistent outcomes of repeated incidental competitions (push versus retreat). This measure is not confounded by differences in tube engagement, as social rank was neither associated with participation in tube competitions (ρ = 0.11, p = 0.139; Fig. 6C, Supplementary Fig. S12C) nor with the overall number of tube detections when controlling for chasing (Spearman’s partial correlation between detection count and z-scored David’s score, corrected for the fraction of active chases: ρ = 0.022, p = 0.758).”

Discussion

We changed the following paragraphs:

Line 608-611

“Importantly, participation frequency and differences in tube entry time did not confound the resulting social ranks, underscoring the robustness of the automated incidental rank assessment.”

Lines 653-662

“These dynamic aspects of chasing, including its asymmetric initiator–recipient structure and proactive engagement, differ from the nature of tube competitions, which are incidental encounters. Together, tube-derived social rank and chasing describe complementary dimensions of social position, and, alongside other features such as clique formation [36], contribute to describe facets of a broader multidimensional social behavior. Within this complex environment, chasing emerges as a flexible behavioral propensity that is dissociable from formal social rank. Specifically, in the NoSeMaze, chasing contributes dynamically to the maintenance, negotiation, or clarification of social hierarchy structure.”

(3) Dominance is only one aspect of sociality, social structure is reduced to rank. The information that might lie in the chasing behavior is not optimally used to explain social behavior beyond the rank measure.

In this manuscript, we focused on the relationship between social rank derived from incidental tube competitions and chasing. We agree that social rank derived from tube competitions captures only one aspect of social structure. We have now clarified this point more explicitly in the revised manuscript. Here, our specific aim was to determine how chasing relates to competition-derived social rank and whether it provides information beyond rank in these mouse groups.

We also refer to another manuscript dedicated to additional aspects of social structure captured in the video data, including approach and interaction behavior as well as social clique formation. There, these measures are again examined in relation to chasing and social rank. We also plan future studies leveraging chasing in the NoSeMaze as a readout for neurophysiological investigations.

In the present study, social rank is based on dominance and subordination in incidental competitions in the integrated tube test. We treat chasing as a distinct, volitional social dimension rather than redundant “rank information”. Importantly, our chasing analyses add beyond social rank information in three ways: (1) structural asymmetry (initiator vs recipient roles) not captured by symmetric social rank measures; (2) elite-centric reciprocal dynamics rather than broad top-down enforcement; and (3) context dependence, with social rank–chasing coupling strengthening when group transitivity is lower. We revised the Discussion/Conclusion accordingly and additionally note that other aspects of social organization (e.g., affiliative bonding and higher-order network structure such as clique/rich-club organization; Nelias et al., 2025) require complementary measures (e.g., video-derived interaction networks) and are not the scope of the present study.

To avoid ambiguity, we also added the following operational definitions in the Introduction and Results:

Introduction

Lines 90-94

“In this study, we use social hierarchy to denote the group-level structure inferred from incidental competitions in the integrated tube tests, and social rank for an individual’s level within that hierarchy. Social position serves as an umbrella term for social rank and chasing behaviors.”

Results

Line 306-308

“Here, social hierarchy refers to the group-level structure reconstructed from incidental competitions in the integrated tube tests, social rank to an individual’s position within that structure, and social position to social rank together with chasing.”

We additionally clarified the distinction between chasing and social rank in the following sections.

Discussion (ll. 632-664)

“In more constrained or despotic conditions, chasing can serve as a unidirectional, dominance-related behavior directed at subordinates [9,43,44]. The larger groups observed in the NoSeMaze reveal a more nuanced role for chasing behavior. Chasing levels are individually stable, but the expression and meaning of chasing are context-sensitive. Chasing was neither broadly distributed nor consistently directed down the social hierarchy. Instead, it was initiated by a small subset of individuals – primarily those occupying high competition-based social ranks – and frequently occurred reciprocally within this group, suggesting intra-elite social dynamics rather than broad dominance enforcement. Rather than solely serving to impose social hierarchy, chasing appeared to function as a means through which individuals with high social rank monitor, negotiate, and maintain their relative standing within the top tier. Notably, the identity of frequent chasers remained stable over time and persisted across changing group compositions, indicating that the propensity to initiate chases reflects a consistent individual-level tendency rather than a purely situational response.

However, its coupling to social rank depended on the group’s hierarchy structure. In groups with less clearly defined social hierarchies (i.e., lower transitivity), active chases aligned more strongly with social rank, suggesting that mice in less structured groups rely more on proactive signaling to clarify social rank. Indeed, the top-ranked mice in these groups exhibited relatively high levels of active chases. This context-sensitivity highlights chasing’s dual role: it serves as a tool for negotiating social rank among mice at the upper end of the hierarchy, and additionally functions to establish or reinforce hierarchical clarity when social structures are ambiguous. These dynamic aspects of chasing, including its asymmetric initiator–recipient structure and proactive engagement, differ from the nature of tube competitions, which are incidental encounters. Together, tube-derived social rank and chasing describe complementary dimensions of social position, and, alongside other features such as clique formation [36], contribute to describe facets of a broader multidimensional social behavior. Within this complex environment, chasing emerges as a flexible behavioral propensity that is dissociable from formal social rank. Specifically, in the NoSeMaze, chasing contributes dynamically to the maintenance, negotiation, or clarification of social hierarchy structure. These findings reveal a novel aspect of social dynamics: chasing is not merely a dominance display but a flexible context-dependent mechanism shaped by both individual disposition and group-level social structure.”

Conclusion (ll. 718-723)

“Crucially, social position is not fully described by a single behavioral dimension. We focused here on two separable dimensions: competition-based social rank and proactive chasing. Future work should integrate additional dimensions of social organization, such as affiliative bonding and higher-order network measures [36], to capture additional aspects of social organization. Chasing behavior played a dual role, reflecting a stable individual propensity while also adapting to group-level structure.”

(4) Focus on rank bears the risk of overgeneralization for readers not familiar with the context.

As already discussed above (see also point 2 and 3), we have sharpened the framing to reduce the risk of overgeneralization. Throughout the manuscript, we now refer to tube-derived social rank explicitly as a dominance-subordination-related axis of social organization rather than a comprehensive measure of “sociality”, and we also avoid the term “traits”, but prefer the use of “internalized social rank” or “propensity to chase” when discussing stability across rounds and contexts. Also, we removed “personality-like” as the scope of the study is to set specific behaviors in relation and not to enter the field of mouse personality classification.

We clarified the definition of social rank in this manuscript in the Introduction (ll. 90-94) and the beginning of the Results (ll. 306-308, see also point 3).

Abstract

Lines 41-46

“… Across more than 4,000 mouse-days, hierarchies derived from incidental competitions in the integrated tube tests were non-despotic, transitive, and stable even when group compositions changed. This stability supports an internalized component of competition-based social rank. Chasing was also stable across contexts. Notably, chasing was concentrated among high-ranking individuals, consistent with ongoing negotiation of social rank among individuals at the upper end of the hierarchy.”

Lines 50-54

“In summary, high-dimensional tracking with the NoSeMaze reveals that social position in mice is multifaceted and shaped by stable dimensions of individual behavior that persist across changing social contexts. The approach thus enables longitudinal modeling of individuality and social position as key resilience factors.”

Discussion (ll. 622-626)

“This temporal and contextual stability supports interpreting social rank as a stable, internalized characteristic of individuals. In this sense, ‘internalized’ refers to stability across repeated rounds and reshuffled groups in this paradigm and to relatively stable tube-competition outcomes.”

Changes in the Conclusion starting l. 709 as highlighted in answer to the above point 3.

(5) Conclusion only valid for the reductionistic setting, in which environment, social and non-social changes only within narrow limits, and in which the mouse population does not face challenges

Our conclusions are bounded by the conditions tested, namely an enriched but stable environment, controlled group composition and remixing, and the absence of explicit ecological challenges such as resource scarcity or predators. As described above in the answer to point 1, we now state explicitly that our conclusions apply to the social-context variation tested here (controlled group reshuffling) under otherwise stable conditions, and we frame resource-competition and environmental-stressor manipulations as future variation to test of how this manipulation affects the here described behaviors.

We accordingly changed the Discussion as already highlighted in point 1 above and added ll. 693-704.

(6) Animals are not naive at the beginning of the experiment, but are already several weeks old.

Animals entered the study as adults and were continuously group-housed (3-5 mice/cage) before entering the NoSeMaze. To reduce effects of initial apparatus novelty, mice underwent two NoSeMaze habituation sessions prior to data collection (each several hours). We now clarify these points explicitly in the Methods and scope the inference accordingly. One of our future steps is to extend this framework to earlier developmental stages (e.g., adolescence/weaning) to capture full lifespan trajectories. This however first required establishing the approach in adult mice under controlled conditions as done in the present study.

Methods (ll. 739-747)

“A total of 79 adult male homozygous OXTRfl/fl mice (B6.129(SJL)-Oxtrtm1.1Wsy/J, RRID: IMSR_JAX:008471, Jackson Laboratory) backcrossed > F10 to C57BL/6J background (Charles River, Sulzfeld) were used for the experiments. Animals entered the NoSeMaze as adults (see Supplementary Table S2 for ages at NoSeMaze entry across rounds) and had been continuously group-housed after weaning (3–5 mice/cage), i.e., they were not developmentally or socially naïve at study onset. Of the 79 mice, 26 mice were injected six weeks before the start of the experiment with an AAV expressing Cre recombinase (rAAV1/2-CBA-Cre) into the AON pars centralis to induce bilateral OXTR deletion (OXTRΔAON). The remaining 53 animals received an AAV expressing only dTomato (rAAV1/2-CBA-dTomato). …”

Discussion (ll. 629-631)

“… A future direction is to extend this framework to earlier developmental stages to understand which early experiences shape later trajectories of social position.”

In summary, this is a wonderful study, but not one that is easy to interpret. The bold conclusion is valid only within the constraints of the study, but nevertheless points in an important direction. The paradigm is clever and could be used for many interesting follow-ups.

To define social position as a personality trait will elicit strong opposition and much debate; the nuances of the paper might be lost on many readers and call for the (re)-consideration of many concepts that are touched. I find this attitude a strength of the paper, but the approach bears the risk of misunderstanding.

We thank the Reviewer for the helpful comments. As detailed above, we tightened terminology and framing throughout the revised manuscript by defining stability explicitly in terms of repeatability across rounds and reshuffled groups within this paradigm, and described tube-derived social rank as one dimension of social behavior. We hope these edits preserve the conceptual message while reducing the risk of misunderstanding.

Reviewer #2 (Public review):

Summary:

This manuscript presents the "NoSeMaze", a novel automated platform for studying social behavior and cognitive performance in group-housed male mice. The authors report that mice form robust, transitive dominance hierarchies in this environment and that individual social rank remains largely stable across multiple group compositions. They further demonstrate that social dominance and aggressive behaviors, like chasing, are partially dissociable and that dominance traits are independent of non-social cognitive performance. The study includes a genetic manipulation of oxytocin receptor expression in the anterior olfactory nucleus, which showed only transient effects on social rank.

Strengths:

(1) Innovative Methodology:

The NoSeMaze platform is a technically elegant and conceptually well-integrated system that enables fully automated, long-term monitoring of both social and cognitive behaviors in large groups of group-housed mice. It combines tube-test-like dominance contests, voluntary chase-escape interactions, and an embedded operant olfactory discrimination task within a single, ethologically relevant environment. This modular design allows for high-throughput, minimally invasive behavioral assessment without the need for repeated handling or artificial isolation.

(2) Experimental Scale and Rigor:

The study includes 79 male mice and over 4,000 mouse-days of observation across multiple group reshufflings. The use of RFID-based identification, automated data logging, and longitudinal design enables robust quantification of individual trait stability and group-level social structure.

(3) Multidimensional Behavioral Profiling:

The integration of social (tube dominance, proactive chasing), physical (body weight), and cognitive (olfactory learning task) measures offers a rich, multi-dimensional profile of each individual mouse. The authors' finding that social dominance traits and non-social cognitive performance are largely uncorrelated reinforces emerging models of orthogonal behavioral trait axes or "animal personalities".

(4) Clarity and Data Analysis:

The analytical framework is well-suited to the study's complexity, with appropriate use of dominance metrics, mixed-effects models, and permutation tests. The analyses are clearly explained, statistically rigorous, and supported by transparent supplementary materials.

We thank the Reviewer for their evaluation of our manuscript. We appreciate their recognition of the NoSeMaze as an innovative and conceptually integrated platform, the scale and longitudinal rigor of the dataset, and the clarity and appropriateness of the analytical framework.

Weaknesses:

(1) Conceptual Novelty and Prior Work:

While the study is carefully executed and methodologically innovative, several of its core findings reaffirm concepts already established in the literature. The emergence of stable, transitive social hierarchies, the persistence of individual differences in social behavior, and the presence of non-despotic social structures have all been previously reported in mice, including under semi-naturalistic conditions (e.g., Fan et al., 2019; Forkosh et al., 2019). Although this work extends those findings with greater behavioral resolution and scale, the manuscript would benefit from a clearer articulation of what is genuinely novel at the conceptual level, beyond the technological advance.

We agree with the Reviewer that transitive dominance hierarchies and stable inter-individual differences have been demonstrated previously in mice, including in semi-naturalistic settings. Our intent was therefore to address two more specific conceptual questions that prior work typically has not tested in an integrated way:

(1) Whether an individual’s social position generalizes across distinct social contexts created by systematic changes in group composition rather than reflecting stability that is only observable within a fixed group;

(2) How the two frequently employed social dominance metrics tube competition-based social rank and chasing relate to each other in unperturbed larger groups.

Specifically, the conceptual advance is enabled by (A) continuous, handling-free estimation of competition-based social rank from incidental tube contests over weeks, (B) a repeated group-reshuffling (“accelerated longitudinal”) design that explicitly tests whether an individual’s social rank generalizes across distinct social contexts, and (C) parallel, continuous measurement of chasing and non-social reinforcement-learning behaviors in the same individuals and environment.

This integration also lets us dissociate dominance-related social dimensions (competition-based social rank vs chasing) and test their relation to individual styles in non-social reinforcement-learning. Importantly, these questions are addressed in larger intact societies (9–10 mice), where hierarchy structure is shaped by more complex network-level dynamics than in smaller groups.

We revised the Introduction and Discussion to foreground these conceptual points and to position them more explicitly in the context of prior works.

Introduction

We adapted the following section (ll. 76-98) and integrated the suggested references:

“… In mice, small groups tend to form highly despotic hierarchies [24-26], whereas larger groups exhibit more complex structures [27]. These patterns suggest a strong influence of emergent group-level dynamics on social structure [12,27]. Yet, animals do not enter social groups as blank slates [28-30]; stable latent factors in the individual may also contribute to hierarchy formation [13]. Thus, it remains unclear to what extent an individual’s social position is internalized and persists across different social contexts [31], or instead is primarily an emergent property of group-level dynamics. Disentangling these possibilities requires experimental conditions that allow unperturbed, continuous tracking of all individuals in sufficiently large groups, together with systematic changes of group composition to modulate social context.

While stable hierarchies and behavioral identity domains have been described previously in semi-naturalistic settings [9-13], many studies quantify these features either within fixed group compositions or in separate assays. Here, we therefore use systematic group reshuffling in 10-member societies to directly test whether individual differences in social rank and chasing persist across distinct social groups. In this study, we use social hierarchy to denote the group-level structure inferred from incidental competitions in the integrated tube tests, and social rank for an individual’s level within that hierarchy. Social position serves as an umbrella term for social rank and chasing behaviors. By continuously measuring social rank, chasing, and reinforcement-learning behavior in the same individuals, we further test how chasing and social rank are related to each other and how they relate to individual styles in non-social reinforcement learning.

To enable these tests, we developed the Non-invasive Sensor-rich Maze (NoSeMaze).”

Discussion

We added a brief framing statement at the beginning of the Discussion (ll. 577-583) to clarify the study’s conceptual novelty:

“Ecologically enriched, yet experimentally controlled assessments allow us to study behavioral individuality and social structure in group-living animals over extended timescales. Here, we show that individual mice carry stable, individual-specific, and multi-faceted profiles of social position and cognitive styles across changing group contexts. Our approach goes beyond prior work by testing cross-context stability under repeated, systematic group reshuffling in larger mouse societies, while measuring competition outcomes, chasing, and reinforcement-learning behavior in parallel. …”

(2) Role of OXTR Deletion:

The inclusion of the OXTR manipulation feels somewhat disconnected from the manuscript's central aims. The effects were minimal and transient, and the authors defer full interpretation to a separate study.

We appreciate the Reviewer’s point and agree that the OXTR^ΔAON manipulation can appear secondary to the manuscript’s central aims. We included OXTR^ΔAON because oxytocin-dependent social recognition memory was hypothesized originally to impact potentially also learning an individual’s position in social hierarchy networks. Even though the effects were small and transient, we nevertheless believe it is relevant to report them, also in relation to the more profound effects of the genetic manipulation reported in a related manuscript (Nelias et al., bioRxiv 2025, 10.1101/2025.08.26.672298). Therefore, we explicitly account for genotype as a covariate in the analyses such that the main conclusions do not depend on this manipulation.

To improve coherence, we have added one sentence in the Introduction motivating why OXTR^ΔAON was included, and finally more clearly signposted that deeper mechanistic interpretation is beyond the scope of the present manuscript.

Introduction (ll. 115-120)

We added one short sentence introducing the rationale behind the perturbation:

“… and (3) determine whether social rank, chasing, and non-social reward-seeking behaviors represent stable individual characteristics or dynamic features across time and changing group composition. As a secondary analysis, motivated by oxytocin’s established role in social recognition memory [32,33], we also tested whether OXTR deletion in the anterior olfactory nucleus produces detectable shifts in rank dynamics.”

Results

Lines 161-164

“… This manipulation was included as a secondary biological perturbation. The primary analyses and conclusions focus on the platform and cross-context stability, and genotype is treated as a covariate unless stated otherwise. …”

Lines 451-454

“… As a secondary analysis, we tested whether OXTR^ΔAON, which impairs de novo social recognition memory required for social clique formation in this cohort [36], also affects the measures reported here. Consistent with largely internalized features, OXTR^ΔAON produced only transient effects. …”

Discussion (ll. 594-605)

“This study focused primarily on the relation of social rank and chasing. We however also considered their relation to additional variables including the loss of oxytocin receptors in the olfactory cortex in the adult (OXTR^ΔAON), involved in de novo social recognition learning. The propensity to chase was largely unaffected by OXTR^ΔAON. Mice carrying OXTR^ΔAON displayed a transient reduction in social rank during the first week that normalized thereafter. This transient effect contrasts to the persistent impairment by OXTR^ΔAON in forming higher-order social bonds that enable membership in stable cliques, as identified by video tracking of self-paced interactions in the same cohort [36]. Together, these findings suggest that OXT-dependent olfactory learning is critical for the formation of social context-dependent higher-order bonds, but plays a limited role in shaping hierarchy-related behaviors.”

(3) Scope Limitations (Sex and Age):

The study is limited to male mice, and although this is acknowledged, the title and overall framing imply broader generalizability. This sex-specific focus represents a common but problematic bias. Additionally, results from the older mouse cohort are under-discussed; if age had no effect, this should be explicitly stated.

We thank the Reviewer for this relevant point. The study is limited to male mice. We therefore revised the title and the abstract and strengthened the limitations to make the sex-specific scope explicit.

We additionally note ongoing work extending the same framework to female groups, where we find similar hierarchy structure and cross-context stability in the NoSeMaze in preliminary unpublished data.

Regarding age, our design included two separate cohorts of different adult age ranges (young and older adult animals), but age was not the primary experimental factor. As detailed in our response to Reviewer #3 (point 1), we now quantify age structure explicitly and test age effects using a decomposition that separates between-group age differences from within-group age variation (mean age per group and each animal’s deviation from that mean). We also recomputed stability estimates with age-adjusted ICC models, and the resulting ICCs are highly similar to the original estimates (cf. new Supplementary Table S4), indicating that the reported metrics’ stability is not driven by age differences across groups.

Title

We change the title from “Individual differences drive social hierarchies in mouse societies” to “Individual differences drive social hierarchies in male mouse societies”

Abstract

We also added male in the abstract (ll. 37-38):

“The interaction of these behaviors in the shaping of social position in larger male mouse groups remains largely unknown.”

Discussion (ll. 613-617)

“… While this study focused on male mice, in which social hierarchies are best established [9], future work is needed to explore sex-specific expressions of social structure and their neurobiological underpinnings in female groups. We therefore restrict our interpretation to male mice. Critically, male social ranks were robustly maintained within the same group over time. …”

For a more detailed integration of age in the Methods, Results, and Discussion, we kindly refer to the reply to Reviewer #3, point 1.

(4) Ambiguity of Dominance as a Construct:

While the study robustly quantifies social rank and hierarchy structure, the broader functional meaning of "dominance" remains unclear. As in prior work (e.g., Varholick et al., 2019), dominance rank here shows only weak associations with physical attributes (e.g., body weight), cognitive strategy, or neuromodulatory manipulation (OXTR deletion). This recurring pattern, where rank metrics are reliably established yet poorly predictive of other behavioral or biological traits, raises important questions about what such measures actually capture. In particular, it challenges the assumption that outcomes in paradigms like the tube test or chase frequency necessarily reflect dominance per se, rather than other constructs.

We thank the Reviewer for this clarifying point. We agree that stable social rank metrics do not necessarily imply a complete or unitary measure of “dominance.” In the revised manuscript, we therefore clarified that, in this study, social rank is operationalized as consistent competitive outcomes in incidental tube-test encounters in the NoSeMaze (see also Reviewer #1, points 2-4).

Our data indicate that this competition-based social rank is related to, but not identical with, other social behaviors such as chasing. Likewise, body weight significantly contributes to social rank, but explains only part of the variance. We therefore do not interpret weak or partial associations with other variables as invalidating the social rank measure. Rather, we interpret them as indicating that social position is multidimensional and only partially captured by any single assay.

In independent subsequent studies that are currently in preparation or revision, we observed that heterogeneity in the neurobiology and response to challenges was best predicted by the competition-based social rank, also compared to the other behaviors assessed here. While these observations are beyond the scope of the present manuscript, they support our view that incidental tube competition and the resulting dominance-subordination structure may provide a biologically informative measure of one important dimension of social position. At the same time, the observation here and in many previous studies that factors such as body weight explain only a limited portion of the variance remains important for our understanding of these constructs.

We have revised the manuscript accordingly to make this distinction more explicit. In particular, we now define more precisely the terms social hierarchy, social rank, and social position in the context of this manuscript (see also reply to Reviewer #1, point 3; ll. 90-94 in the Introduction and ll. 306-308 in the Results), and we use these terms more consistently throughout. We also revised the text to avoid overstating the meaning of “dominance” where the data support a more specific interpretation.

Finally, we now emphasize more clearly both the strength and the limitation of the present approach. The NoSeMaze allows these relationships to be assessed continuously in a minimally perturbed group-housing ecology, laying ground for future incorporation of further variables and dimensions to capture how they shape social organization.

Specifically, we added text in the Discussion and Conclusion to clarify that competition-based social rank and proactive chasing represent separable dimensions related to dominance and subordination, but do not exhaust sociality or individuality, and that future work should integrate additional measures such as affiliative behavior and higher-order network structure.

Discussion

Lines 624-6631:

“… In this sense, ‘internalized’ refers to stability across repeated rounds and reshuffled groups in this paradigm and to relatively stable tube-competition outcomes. The tube-derived social rank describes here a dimension of individual social behavior. The capacity to quantify stable individual differences across changing social contexts highlights the value of the NoSeMaze for lifespan-oriented studies of behavioral individuality. A future direction is to extend this framework to earlier developmental stages to understand which early experiences shape later trajectories of social position.”

Conclusion

Lines 718-722:

“Crucially, social position is not fully described by a single behavioral dimension. We focused here on two separable dimensions: competition-based social rank and proactive chasing. Future work should integrate additional dimensions of social organization, such as affiliative bonding and higher-order network measures [36], to capture additional aspects of social organization.”

Reviewer #3 (Public review):

Reinwald et al. present the NoSeMaze, a semi-natural behavioral system designed to track social behaviors alongside reinforcement-learning in large groups of mice. Accumulating more than 4,000 days of behavioral monitoring, the authors demonstrate that social rank (determined by tube competitions) is a stable trait across shuffled cohorts and correlated with active chasing behaviors. The system also provides a solid platform for long-term measurements of reinforcement learning, including flexibility, response adaptation, and impulsiveness. Yet, the authors show that social ranking and chasing are mostly independent of these cognitive traits, and both seem mostly independent of oxytocin signaling in the AON.

Strengths:

(1) The neuroethological approach for automated tracking of several mice under semi-natural conditions is still rare in social behavioral research and should be encouraged.

(2) The assessment of dominance by two independent measures, i.e., spontaneous tube competitions and proactive chasing, is innovative and valuable.

(3) The integration of a long-term reinforcement-learning module into the semi-natural system provides novel opportunities to combine cognitive traits into social personality assessments.

(4) The open-source system provides a valuable resource for the scientific community.

Limitations:

(1) Apparent ambiguity and inconsistency in age structure and cohort participation across rounds, raising concerns about uncontrolled confounds.

(2) Chasing behavior appears more stable than tube-test competitions (Figure 4D vs. Figure 3D), which challenges the authors' decision to treat tube competitions as the primary basis for hierarchy determination.

We thank the Reviewer for the evaluation of our work. We have addressed the limitations raised below with additional analyses, clarifications, and corresponding manuscript revisions.

Major concerns:

(1) Unclear and inconsistent handling of age groups and repeated sampling. The manuscript repeatedly refers to "younger" and "older" adults, but it is unclear whether age was ever controlled for or included in models. Some mice completed only one round, others 2-5 rounds, without explanation of the criteria or balancing.

We thank the Reviewer for this clarifying comment. We now explicitly quantify participation in the different rounds in new Supplementary Table S3. Importantly, our primary stability analyses are implemented using variance-component mixed models (REML) that naturally handle unbalanced repeated-measures data. This is explained in more detail in the Methods section (ll. 1003-1111), where we added a statement that the LMEs are well suited for unbalanced repetitions.

To further address unbalanced participation, we additionally performed a conservative sensitivity analysis restricted to a balanced subset, including only sessions 1 and 2 and only mice with observations in both sessions. For the key social measures, ICC estimates were highly similar in the full dataset and in the balanced subset (e.g., z-scored competition David’s score, ICC across cohorts: 0.55 without age adjustment vs. 0.56 in the balanced first-two-session subset; active chasing: 0.74 vs. 0.72; being chased: 0.61 vs. 0.60; Supplementary Table S4), indicating that unbalanced participation did not inflate the stability estimates.

We also addressed age structure explicitly. Although age was not the primary experimental factor, the inclusion of two age cohorts allowed us to assess whether the observed behaviors and their interrelations were robust across most of the adult lifespan (cf. new Figure 1). We therefore decomposed age into a between-group component (mean age per group) and a within-group component (each animal’s deviation from its group mean) and included these terms in the relevant LME models. Tube-based dominance rank (David’s score, z-scored) showed no age effect (p_{age, cond.} = 0.63), whereas chasing metrics showed modest age associations (cf. new Supplementary Table S5). This however only indicates that chasing was associated with age to some degree. More importantly, recomputing all stability estimates using age-adjusted ICC models yielded nearly identical ICCs for the core social measures (new Supplementary Table S4), indicating that the reported stability was not affected by age differences across groups.

In addition, we revised the study design schematic (new Fig. 1; cf. Reviewer #1, Recommendations for the authors) to depict the separate age cohorts and the reshuffling procedure more clearly.

We adapted the following sections accordingly.

Methods

Lines 980-985

“… Most mice (n = 68) participated in at least two NoSeMaze rounds with reshuffled group members. Supplementary Table S2 summarizes the number of rounds per mouse and missing data due to technical problems. We examined the stability of social and reward-seeking metrics by correlating values from the first and second round (Spearman’s correlation, cf. Fig. 5). These round-1-to-round-2 correlations use one paired observation per mouse and are therefore not inflated by mice contributing >2 rounds.”

Lines 991-993

“The ICC treated mouse identity as a random intercept and NoSeMaze group (i.e., round-specific social group) as a random effect, with repetition included as fixed effect (i.e., stability across groups while holding repetition means constant).”

Lines 1003-1011

“Variance components for ICC estimation were obtained from LMEs fit by restricted maximum likelihood (REML), which yields less biased variance-component estimates and is well suited for unbalanced repeated-measures designs (i.e., different numbers of rounds per mouse). To assess potential confounding by age structure, age was decomposed into a between-group component (group-mean age) and a within-group component (each animal’s deviation from its group mean) and included as covariates. ICCs were recomputed in age-adjusted models (see Supplementary Table S4). Finally, we performed a conservative sensitivity analysis restricted to the first two sessions per mouse and to mice with observations in both sessions (“balanced first2”), to additionally account for unbalanced participation structure.”

Results

Lines 167-173

“… The study population comprised two age cohorts: younger (16-30 weeks) and older adults (55-97 weeks) (Fig. 1B). The two age cohorts were run as separate experimental series, and group reshuffling was performed within each cohort (Fig. 1C, see Supplementary Table S2). Mice lived in groups of 9-10 for multiple rounds in the NoSeMaze, with different group members in each round (Fig. 1D, see Supplementary Table S1). This allowed us to test which individual behaviors were stable across different group compositions. …”

Lines 441-450

“Because the number of rounds in the NoSeMaze was unbalanced between animals (Supplementary Table S3) and age varied across groups (Supplementary Table S1-2), we also performed robustness checks. Stability estimates changed only minimally when recomputed in age-adjusted ICC models (between-group mean age and within-group age deviation; see Methods) and when restricting analyses to a balanced first-two-session subset (sessions 1–2 only; mice with both sessions) (Supplementary Table S4). Mixed models indicated that some chasing and reinforcement-learning measures showed modest age- and/or session-related shifts in absolute levels (Supplementary Table S5), but importantly, these did not affect the observed stability patterns.”

(2) Stability of chasing appears stronger than the stability of tube competitions. Figure 4D shows highly consistent chasing behavior across weeks, while Figure 3D shows weaker and more variable correlations for tube-based David scores. This is also evident from Figure 5A-B,D. Thus, it appears that chasing, which serves to quantify dominance in similar semi-natural setups, may be a more reliable and behaviorally meaningful measure of dominance than the incidental tube competitions.

Indeed, active chasing showed higher cross-round correlations than tube-derived social rank (e.g., R1–R2 Spearman ρ = 0.75 vs 0.57; ICC_{across cohort} 0.74 vs 0.55, see Fig. 5 and new Supplementary Table S4). Importantly, this does not contradict the central finding that tube-derived social rank is stable across time and across remixed groups. Rather, it may highlight that chasing and social rank capture different aspects of dominance-subordination-related behavior with different statistical properties. Chasing reflects an individual’s propensity to actively initiate interactions (a strongly expressed, asymmetric behavior), which can be highly consistent across contexts. By contrast, social rank is a relational measure inferred from symmetric dyadic win–loss outcomes based on incidental competitions in the integrated tube tests and can vary with the specific set of competitors and interaction opportunities in each reshuffled NoSeMaze group, while still remaining substantially stable overall.

Accordingly, we do not interpret the higher repeatability of chasing as evidence that it is the “better” hierarchy measure. Tube competitions yield symmetric dyadic outcomes that directly support formal hierarchy reconstruction (David’s score/Elo, transitivity, steepness) and show convergent validity with traditional tube testing. Chasing, in contrast, is asymmetric and volitional and, in our data, is concentrated in the upper social ranks and modulated by group-level social hierarchy structure, consistent with rank negotiation/signaling rather than a mechanism that assigns a full ordering to all individuals. These different properties lead to very different “win-lose” relationships when comparing tube competition events to chasing events that we specifically illustrated in Supplementary Fig. S13. We therefore revised the Results and Discussion to frame tube-derived social rank and chasing as complementary social dimensions.

Results (ll. 402-413)

“… Specifically, stability was high for the tube competition-based David’s score (Fig. 5A, ρ = 0.57, p < 0.001), as well as for the fraction of active chases (Fig. 5B, ρ = 0.75, p < 0.001) and of times being chased (Fig. 5C, ρ = 0.54, p < 0.001). Notably, the fraction of active chases showed slightly higher across-round stability than tube-derived David’s score and the fraction of being chased. This is in line with active chases capturing an individual propensity to initiate this behavior, whereas competition-based social rank is a relational measure that is also influenced by the set of competitors and interaction opportunities in each reshuffled group. Nonetheless also social rank and being chased were overall stable. Across the full series, ICCs for these social measures were also in the good–excellent range, indicating high across-round stability (Fig. 5D, ICC = 0.548 to 0.890).”

Discussion

Line 653-664 (see also Reviewer #1, point 2)

“These dynamic aspects of chasing, including its asymmetric initiator–recipient structure and proactive engagement, differ from the nature of tube competitions, which are incidental encounters. Together, tube-derived social rank and chasing describe complementary dimensions of social position, and, alongside other features such as clique formation [36], contribute to describe facets of a broader multidimensional social behavior. Within this complex environment, chasing emerges as a flexible behavioral propensity that is dissociable from formal social rank. Specifically, in the NoSeMaze, chasing contributes dynamically to the maintenance, negotiation, or clarification of social hierarchy structure.”

(3) Unbalanced participation across rounds compromises stability analyses. Stability analyses (e.g., ICCs, round-to-round correlations) assume comparable sampling across individuals. However, some mice contribute 1 round, others 2, 3, 4, and even 5 rounds. This imbalance may inflate stability estimates or confound group reshuffling effects, and the rationale for variable participation is not explained.

We thank the Reviewer for raising this point. Indeed, participation was unbalanced across rounds (see the same Reviewer #3, Major concerns 1 and new Supplementary Table S3), mainly due to missing data from occasional technical failures of the RFID detectors or the reinforcement learning water port during acquisition in some groups (for details, see Supplementary Table S2). We added this rationale behind variable participation to our Methods section (for details, see Major concern 1, ll. 981-982, “Supplementary Table S2 summarizes the number of rounds per mouse and missing data due to technical problems.”)

We now quantify round participation (new Supplementary Table S3) and directly address potential bias from unequal sampling in two ways. First, the round-1-to-round-2 (R1–R2) stability correlations use one paired observation per mouse and therefore are not inflated by mice contributing more than two rounds. Second, ICCs were estimated from REML variance-component mixed models that account for unbalanced repeated-measures by design and use all available observations. To further rule out inflation from unequal sampling or non-random missingness, we additionally report a conservative sensitivity ICC restricted to a balanced subset including only each mouse’s first two observed sessions and only mice with both sessions (“first2-balanced”). Full-sample ICCs and first2-balanced ICCs were highly similar (new Supplementary Table S4), indicating that participation imbalance did not affect the stability estimates. For details on the changes made in the manuscript, see Reviewer #3, Major concern 1.

Recommendations for the authors: 

Editor's notes:

Should you choose to revise your manuscript, if you have not already done so, please include full statistical reporting including exact p-values wherever possible alongside the summary statistics (test statistic and df) and, where appropriate, 95% confidence intervals. These should be reported for all key questions and not only when the p-value is less than 0.05 in the main manuscript.

Readers would also benefit from noting that the mice were male in the abstract.

We have revised the manuscript accordingly and now report exact p-values values wherever possible for the key results in the main text. Because full statistical reporting for every analysis in the main text would substantially interrupt readability, we provide the complete statistical details in a Supplementary Excel File (Supplementary Material – Systematic Statistical Reporting), including sample sizes, degrees of freedom, the number and type of permutation tests, exact p-values, and 95% confidence intervals. We also provide Extended Data Sheets for all linear-mixed effects models that account for covariates such as age and round of participation in the NoSeMaze (cf. Reviewer #3, Major Concern (1) for the additional analyses). To guide readers to these resources, we now explicitly refer to the Supplementary Material – Systematic Statistical Reporting at several points in the manuscript:

Results

Lines 271-274

“Detailed statistical reporting for all analyses, including n, degrees of freedom, exact p-values, and 95% confidence intervals, is provided in the Supplementary Material - Systematic Statistical Reporting. Extended Data includes additional linear mixed-effects models controlling for potential confounding variables.”

Lines 430-431

“All statistical details are provided in the Supplementary Material – Systematic Statistical Reporting.”

Additional references to the supplementary statistical reporting were inserted at lines 252-254, 562-563, and 567-568.

Methods

Lines 962-965

“Full details on the statistical tests, including n, degrees of freedom, exact p-values, and 95% confidence intervals, are provided in the Supplementary Material – Systematic Statistical Reporting, as well as in the Extended Data for the LMEs accounting for different covariates.”

We also revised the abstract and the title to explicitly state that the mice were male.

Manuscript changes:

Results, figure legends, and supplementary tables expanded to include full statistical reporting; abstract revised to specify male mice.

Reviewer #1 (Recommendations for the authors):

I would recommend being much more explicit about the reductionistic nature of the study and how the limitations are turned here to an advantage, while at the same time acknowledging the challenges of extrapolating beyond these boundaries. Most importantly, dominance should be positioned more clearly and cautiously within a framework of social behavior (and social structure) in general. The authors include cognitive tests, etc., to generate context, but this context is dependent on the same circumstances that possibly contribute to the social structure. The information that lies in the chasing behavior as an additional measured variable might be used better to provide more context.

We thank the Reviewer for these recommendations. We have better clarified the framing to make explicit that the NoSeMaze is a controlled laboratory group-housing system. The specific conditions are now discussed in more detail in relation the observed social behaviors. We describe the tube-derived social rank as one dimension of social organization, and proactive chasing as another one. This study presents a first necessary step to understand their shared and distinguishing features. We also elaborated the Discussion to better clarify the trade-off between ecological complexity and experimental control, and to highlight that the value of the NoSeMaze for continuous, observer-independent phenotyping under standardized conditions. We now clearly state the importance to vary conditions in the system to see how the social behaviors and also their relation to non-social features changes depending on context conditions. For detailed changes, see our responses to Reviewer #1, points (1), (3), (4), and (5), and Reviewer #2, Weakness (4).

Manuscript changes:

Abstract, Introduction, Discussion, and Conclusion revised to clarify scope, construct interpretation, and the complementary roles of competition-based social rank and chasing.

The precision of the description of the experimental design should be improved. When were the cohorts mixed, or did they stay separate? Which animals were old, which were young? This remained a bit confusing.

We revised the presentation of the study design accordingly. Specifically, we clarified that the younger and older adult cohorts were run as separate experimental series and that group reshuffling occurred within, but not across, these cohorts. We also revised the study schematic in Fig. 1 and the corresponding description in the manuscript to more explicitly depict cohort structure, timing of NoSeMaze rounds, and between-round reshuffling (details are provided in the Supplementary Tables 1-3). For new age-related analyses and additional robustness checks, see our response to Reviewer #3, Major Concern (1).

Manuscript changes:

Figure 1 and its legend were revised to depict cohort structure, NoSeMaze rounds, and between-round reshuffling more explicitly. In addition, the Results subsection “Ecological longitudinal assessment in the NoSeMaze” were updated to clarify that the younger and older adult cohorts were run separately, that reshuffling occurred within but not across cohorts, and which animals belonged to each age-defined cohort. We also now cross-reference Supplementary Table S2 for round-specific age information and cohort composition.

Results

Lines 167-170

“The study population comprised two age cohorts: younger (16-30 weeks) and older adults (55-97 weeks) (Fig. 1B). The two age cohorts were run as separate experimental series, and group reshuffling was performed within each cohort (see Supplementary Table S2).”

It is also not fully clear across which groups stability measures were obtained: are these across all groups or within the subgroups with a given characteristic?

We thank the Reviewer for highlighting this point. We now state explicitly that stability measures were computed across all eligible rounds, using mixed-effects models that account for repeated observations of the same mouse and for round-specific group membership. We also added a conservative sensitivity analysis restricted to a balanced first-two-session subset. Full and balanced-subsample stability estimates were highly similar, indicating that the main conclusions are robust to the participation structure. For details, see our response to Reviewer #3, Major Concerns (1) and (3).

Manuscript changes:

Methods and Results revised to clarify the level of analysis and model structure, as well as new supplementary robustness tables (Supplementary Tables 3-5) added.

Minor point: In Figure 3, 18 groups are mentioned, but 19 are shown.

We thank the Reviewer for this clarification. The apparent discrepancy arose because the group labels in Figure 3 follow the numbering of all experimental groups, whereas only groups with available tube-competition data are shown in this panel. Accordingly, group 16 is absent because no tube data were available for that group (see Supplementary Table S2), and groups 20 and 21 are likewise not included for the same reason. We have revised the figure legend and corresponding text to make this explicit and to avoid the impression of a numbering inconsistency.

“Fig. 3: Social rank derived from incidental competitions in the integrated tube tests of the NoSeMaze.”

“C, Box plots of metrics characterizing social hierarchy for 18 groups, including transitivity, steepness, stability, and uncertainty-by-repeatability (for details, see ‘Source Data’). Group labels correspond to original experimental group IDs. Only groups with available tube-competition data are shown. Therefore, numbering is non-consecutive (e.g., groups 16, 20, and 21 are absent; see Supplementary Table S2).”

Reviewer #2 (Recommendations for the authors):

(1) To better distinguish this study from previous literature, we recommend incorporating a more focused discussion (or adding to the intro) of how the findings advance our understanding of social hierarchy beyond prior works.

We have sharpened the conceptual framing in both the Introduction and Discussion. In particular, we now distinguish more explicitly between the well-established observation that hierarchies form and remain stable within fixed semi-naturalistic groups, and the more specific question addressed here: whether an individual’s social position generalizes across changing social contexts created by repeated group reshuffling. We added additional references and also emphasize that the present study integrates continuous measurements of competition-based social rank, chasing, and reinforcement-learning features in the same individuals and environment. For details, see our response to Reviewer #2, Weakness (1).

Manuscript changes:

Introduction and Discussion revised to foreground conceptual novelty relative to prior semi-naturalistic work.

(2) Given the weak associations between dominance rank and other traits such as body weight, cognitive performance, and oxytocin receptor manipulation, we suggest further clarifying what is being captured by these measures.

We agree and have clarified this point throughout the manuscript. We now define tube-derived social rank explicitly as an operational measure based on repeated competitive outcomes from incidental dyadic tube tests, highlighting it as one dimension of social behavior.

We also make clearer that proactive chasing is not redundant with rank, but instead captures a distinct, partly dissociable dominance-related interaction mode. For details, see our response to Reviewer #2, Weakness (4).

We understand the question on the meaning of social rank as the correlations to body weight and cognitive performance are only punctual. We would like to mention here already that the competition-based social rank turns out to be a strong predictor of individual reactivity in a series of challenges. These works are in currently in preparation for publication and will make the relevance of these measures more clear. Related to this, we find in these studies that chasing and competition-based social rank predict different behavioral and neuronal aspects of individual reactivity.

Manuscript changes:

Abstract, Results, Discussion, and Conclusion revised to clarify construct interpretation and multidimensionality.

(3) We recommend modifying the title and abstract to more clearly reflect the male-only design of the study. In addition, please indicate whether any age-related differences were observed. If age had no measurable effect, this should be stated explicitly to justify the combination of age groups.

We revised the title and abstract to make the male-only design explicit. We also now report age-related analyses directly in the manuscript. Briefly, age was modeled by separating between-group age structure from within-group age variation. Some measures showed modest age associations in mean level, but most importantly, age-adjusted stability estimates for the core social metrics were highly similar to the original estimates, indicating that the reported stability is not driven by age differences across groups. For details, see our response to Reviewer #3, Major Concern (1).

Manuscript changes:

Title and abstract revised. Methods, Results, and supplementary robustness analyses expanded to report age effects explicitly.

eLife Assessment

This important study provides evidence that locus coeruleus activity is coordinated with heart rate during sleep, confirming previous work in mice and humans, with a possible role for sleep-dependent memory consolidation. The claims are supported by convincing evidence. This work will be of interest to neuroscientists focusing on sleep, memory, and autonomic functions.

Reviewer #2 (Public review):

Summary:

This convincing study builds on previously published findings in both mice and humans to advance quantitative insights into the coupling between noradrenergic activity fluctuations during mouse NREM sleep and heart rate fluctuations. The work reaffirms the presence of coordinated infraslow fluctuations in sigma power and heart rate during NREM sleep and that this coordination is enabled by noradrenaline-releasing neurons in the locus coeruleus. Also supporting previously published work in mice and humans, the authors describe a link between the strength of these infraslow fluctuations and memory consolidation in mice and humans.

Strengths:

A major finding of this study is the mechanistic insight it provides into the regulation of the previously understudied very-low-frequency (0-0.15 Hz) component of heart rate variability, and the demonstration, through elegant optogenetic bidirectional interference, that infraslow noradrenergic fluctuations are an underlying driving force. This finding will promote recognition of heart rate variability in sleeping mice as a read-out of neuronal activity patterns that control autonomic balance.

Another strength of the study is its translational part, whereby the sigma power-heart rate coupling in mouse is used to identify a previously unrecognized correlation between such coupling and memory consolidation in humans. This widens the applicability of heart rate variability measures, highlighting their use as biomarkers for noradrenergic fluctuations and associated sleep-dependent memory consolidation.

Weaknesses:

The study impresses by the thorough parallel analysis of both mouse and human correlational data between electrophysiological and fluorescent activity measures of the sleeping brain. Further work will be needed to disentangle the mechanisms by which heart rate is regulated, notably the contribution of parasympathetic and sympathetic nervous systems, to establish the very low frequency heart rate variability in mice as a novel biomarker for noradrenergic dynamics in the sleeping brain.

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

Summary:

This study examined whether infraslow fluctuations in noradrenaline and in heart rate are coupled and how they are affected by sleep transitions. The authors used the fluorescent NA biosensor GRAB-NE2m in the medial prefrontal cortex of mice to record extracellular NA while also recording EEG and EMG during sleep-wake episodes. They also analyzed previously published human data to reproduce relationships they found between sigma power and RR intervals in mice.

Strengths:

This is an impressive study with significant strengths, as it involves a rich set of data that includes not only observations of associations between heart rate and noradrenergic dynamics but also optogenetic manipulation of the locus coeruleus. Human data is presented to show parallels in the association between sigma power during sleep and phasic heart-rate bursts.

We thank Reviewer #1 for their thoughtful, detailed, and constructive evaluation of our manuscript. We appreciate their recognition of the strengths of the study, particularly the integration of noradrenergic recordings, optogenetic manipulation, and cross-species analyses. We are especially grateful for the reviewer’s careful attention to clarity, experimental interpretation, and control comparisons. The comments have helped us sharpen the framing of our hypotheses, clarify causal claims, improve statistical reporting, and better explain our closed-loop approach and heart rate analyses. We have addressed each point in detail below and believe that the revisions substantially strengthen the manuscript.

Weaknesses:

(1) Language could be clearer and more precise. As detailed below, in both the introduction and the discussion, the way the hypotheses and study objectives are described could use some revision to be more precise and accurate.

Thank you for this helpful comment. We have sharpened the description of the study objectives, hypotheses, and interpretation of the findings to better distinguish between what was directly tested, what was inferred, and what remains speculative. We revised the language throughout these sections to improve clarity, accuracy, and overall readability

(1A) In the introduction on p. 4: The overarching question is framed as "could the peripheral autonomous systems be a read-out of the central LC-NE system and thus be a biomarker of memory consolidation and LC dysfunction?" This gives the impression that the LC function would be the main influence on peripheral autonomous systems. There are, of course, many influences on peripheral autonomous systems, so it would be advisable for the authors to be more specific here about what signal(s) in particular would be predicted to be sensitive markers of LC function.

Thank you for this important point. We agree that heart rate reflects the integrated output of multiple autonomic mechanisms and should not be interpreted as being exclusively driven by LC activity. Cardiac dynamics arise from the balance between sympathetic and parasympathetic influences, which themselves are regulated by several central and peripheral systems. In addition, recent work shows that the infraslow oscillations observed during NREM sleep are not restricted to norepinephrine alone but also involve other neuromodulatory systems, including acetylcholine and serotonin (e.g., Teng et al., PNAS 2025, Kjaerby et al., iScience, 2026). Our intention was therefore not to imply that the LC is the sole driver of peripheral autonomic dynamics. We have revised our overarching questions to make them more specific: Please see new text below:

(Introduction, page 4/5). “The sympathetic and parasympathetic autonomic nervous system are involved in HRV, which is conventionally analyzed across three primary frequency bands: high frequency (HF) HRV, low frequency (LF) HRV, and very low frequency (VLF) HRV (Berntson et al., 1997). Due to the frequency overlap with VLF HRV, we wondered if central infraslow NE dynamics could be linked to this poorly understood HRV indicator. Furthermore, are infraslow NE fluctuations directly reflected by HRV under different physiological states or does LC–HR coupling scale differently with LC output? Specifically, if infraslow NE oscillations display faster frequencies - as occurs during sleep fragmentation - will cardiac dynamics exhibit corresponding changes? Conversely, given that stronger infraslow NE dynamics correlate with memory consolidation through their regulation of sleep spindles, could the peripheral autonomic signatures provide an accessible cross-species biomarker of spindle-dependent memory consolidation? Addressing these questions could help bridge mechanistic insights into LC-mediated sleep regulation with established HRV metrics used in human physiology.”

(1B) In the discussion on p. 12: "In this study, we leveraged real-time measurements of mPFC NE levels and HR measurements from EMG recordings in mice to investigate the causal link between the two variables with high temporal resolution in freely moving sleeping mice, with similar inspection in humans." To test the causal link between mPFC NA levels and HR measures, the study would manipulate NA levels just in the mPFC and not elsewhere in the brain. However, in this study, the manipulation occurred in the LC, and so there would be broad cortical changes in NA levels. Thus, it could be that LC activity causes HR changes via a non-PFC pathway.

We thank the reviewer for this important comment. Indeed, mPFC NE is merely a readout of LC activations and we expect that NE in other brain regions would show the same patterns. Indeed, mPFC NE is not expected to provide any causal link to heart rate. We have revised added a sentence to the results section and also changed the initial summary part of the discussion to reflect this better.

(Results, page 6). “mPFC was selected as a representative cortical readout of LC-mediated norepinephrine dynamics, as infraslow NE fluctuations are coordinated across widespread brain regions.”

(Discussion, page 14/15). “Variability in HR is a non-invasive biomarker of autonomic nervous system function and is frequently disrupted in ageing and Alzheimer’s disease. Here, by combining real-time measurements of mPFC NE dynamics with simultaneous HR recordings in freely sleeping mice, we demonstrate that HR closely tracks the infraslow phasic activity of the LC–NE system.”

(2) Comparisons with the control condition need further development.

(2A) While the authors did include a key YFP control condition, in the main text no direct statistical comparison between the closed-loop optogenetic stimulation (ChR2) condition and the YFP control condition was reported. (It was reported in Supplementary Figure 2c-d.) Instead, in the main text, the authors only reported that the effects of stimulation were significant in the closed-loop condition and not in the control. However, that is not the same as demonstrating that the two conditions significantly differed from each other, and it is the direct test that is important for the conclusions, so it seems important to include this result in the main presentation.

We thank the reviewer for this important point and agree that direct statistical comparisons between ChR2 and YFP conditions are important for interpretation. These comparisons were performed and are shown in Supplementary Figure 2c–d, but we acknowledge that this was not sufficiently emphasized in the main text. We are now more clearly referring to this comparison in Result section:

(Results, page 9). “The magnitude of pre-stimulation NE descent and post-stimulation NE ascent was reduced as the thresholds increased, indicating less pronounced NE dynamics as LC stimulation became more frequent (Fig. 2e, for direct comparison with YFP control, see Suppl. Fig. 2c-d).”

Our rationale for prioritizing the within-animal threshold comparisons in the main figure was that the central experimental question concerned how progressive shifts in infraslow NE oscillatory frequency influence the NE–HR relationship. Because variability in viral expression levels (both NE sensor expression and LC opsin expression) introduces substantial between-animal variability, we considered within-animal comparisons across threshold conditions to provide the most informative representation of how changes in LC-driven NE dynamics alter cardiac responses. That said, we agree that highlighting the direct ChR2 versus YFP comparison is important for the overall interpretation. As mentioned, the reference to Suppl. Fig. 2c– d, where the between-group analyses are visualized are now clearly referred to.

(2B) In addition, the authors should address the issue that the pre-stimulation NE was consistently significantly lower in the YFP condition than in the ChR2 condition (see Supplementary Figure 2c), which is a potential confound.

We thank the reviewer for bringing up this important point. We agree that differences in stimulation timing between ChR2 and YFP animals could complicate interpretation in a closed-loop design and appreciate the opportunity to clarify this aspect of the experiment.

In the ChR2 condition, animals were exposed to repeated optogenetic LC activation designed to mimic progressively faster infraslow NE dynamics. Such repeated stimulation is expected to produce a gradual elevation in tonic NE levels across the recording session, which explains the higher pre-stimulation baseline relative to YFP controls. We acknowledge that elevated tonic NE levels could introduce additional physiological effects. For example, higher NE tone would be expected to increase α2mediated autoinhibitory feedback on LC neurons and presynaptic NE release. Within the LC itself, we expect that optogenetic stimulation would largely override such effects due to the strong Na+-mediated depolarization induced by ChR2 activation. However, NE release in downstream regions such as the mPFC may be influenced to some extent by elevated tonic noradrenergic tone. Importantly, such feedback mechanisms would likely also occur under physiological conditions characterized by elevated LC activity, such as stress or sleep fragmentation. Because the goal of our stimulation paradigm was to model progressively faster infraslow NE dynamics under physiologically relevant conditions, we believe this feature of the manipulation may in fact increase the translational relevance of the model. We specifically address this elevation in Figure 3, where we show that very rapid stimulation regimes are accompanied by signs of compensatory cardiovascular regulation, likely reflecting baroreceptor-mediated responses to sustained increases in heart rate.

We agree that the precise contribution of elevated tonic NE to the overall manipulation cannot be fully disentangled in the present study. We therefore avoid overinterpreting these effects and have instead added text to the manuscript acknowledging this consideration without extensive speculation.

(Results, page 9) “Since pre-stimulation NE baseline levels progressively became higher in the ChR2 condition compared with YFP controls (Fig. 2c+e, Supplementary Fig. 3a-f), it demonstrates that they arise from stimulation-dependent modulation of noradrenergic tone rather than nonspecific signal drift. As a result, the pre-stimulation state at higher thresholds differed between ChR2 and control conditions, which should be considered when interpreting the immediate effects of laser stimulation across thresholds.”

(2C) Direct comparison of the strengths of correlations shown in Figure 2h vs. Supplementary Figure 2f should be included. Currently, we see relatively weak correlations in both ChR2 and YFP conditions, and it is not clear if the relationships differ in the control. It seems they are still present in the control condition but weaker which would contradict the apparently broad claim on p. 7 that "No such effects were present in the control condition" (it is not entirely clear whether this claim refers to all effects discussed in the figure or just a subset - this language should be clarified).

We thank the reviewer for this important comment and agree that the original wording could be interpreted as implying a complete absence of an NE–RR relationship in the YFP condition. To address this concern, we directly compared the strength of the NE– RR relationship between ChR2 and YFP animals. Please see Supplementary Figure 3h.

Using a linear mixed-effects model that accounted for repeated measurements within animals, we found that the slope of the NE–RR relationship was significantly steeper in ChR2 animals than in YFP controls (all thresholds: slope difference = 1.915, p < 0.0001; thresholds −15, −10, and −5 only: slope difference = 2.367, p < 0.0001). Consistent with this result, comparison of Pearson correlations using Fisher's r-to-z transformation also indicated significantly stronger coupling in ChR2 animals than in YFP controls (see Statistics in the Supplementary File).

These analyses demonstrate that an inverse relationship between NE and RR is present under physiological conditions in YFP animals, but that optogenetic LC activation substantially strengthens this coupling. We have revised the corresponding text to clarify this:

(Results, Page 9): “An inverse NE–RR relationship was present in both ChR2 (Fig. 2h) and YFP (Suppl. Fig. 3f) groups but was significantly stronger in ChR2 animals than in YFP controls (Suppl. Fig. 3h).”

(2D) Did the YFP controls vs. ChR2 animals show any differences in the number of NA states that triggered stimulation in the closed-loop system? With ChR2 animals, stimulation changes NA, which could change future triggering. In YFP animals, nothing changes NA (other than natural fluctuations), so the dynamics of stimulation timing could diverge between groups in a way that complicates interpretation. Specifically, if ChR2 stimulation raises NA and prevents future threshold crossings, ChR2 animals may end up receiving fewer subsequent stimulations than YFP animals (or a different temporal clustering). If the number or pattern of stimulation differed in two groups, it would be important to have a yoked control where matched animals get the same stimulation pattern but not triggered by their own NA.

We thank the reviewer for this important point. We agree that differences in stimulation timing between ChR2 and YFP animals could complicate interpretation in a closed-loop design.

Importantly, stimulation triggering was based on relative declines in NE fluorescence calculated against a rolling 2-minute baseline, rather than absolute NE levels. Thus, as tonic NE levels gradually increased in ChR2 animals, the threshold adapted accordingly, reducing the likelihood that elevated baseline NE alone would prevent future triggering. Instead, stimulation continued to occur when NE declined relative to the recent baseline, thereby preserving the infraslow closed-loop structure.

The number of stimulation events across thresholds is already reported in the manuscript (Methods, p. 30 and corresponding figure legends), but we have now indicated more clearly in the result section where to find the information:

(Results, Page 9). “Mean traces of NE and RR were aligned to LC stimulation onset (Fig. 2c-d, for number of laser stimulations see Fig. 2 legend or Methods).”

(Methods, Page 31). “For the LC activation-related analysis, 108 events were found for Threshold -15 (11 of these being YFP), 260 events for Threshold -10 (55 of these YFP), 777 events for Threshold -5 (296 of these YFP), 1,444 events for Threshold 0 (510 of these YFP), and 1,148 events for Threshold 5 (377 of these YFP) across ten animals (four being YFP).”

While the total number of events was lower in YFP animals, this is expected in part due to the smaller group size (4 YFP vs. 6 ChR2 animals included in this analysis). Furthermore, because ChR2 stimulation increased NE levels by design, more pronounced subsequent declines in NE may have modestly facilitated additional threshold crossings.

Nevertheless, the overall temporal structure of the stimulation paradigm remained comparable across groups, and YFP animals underwent the same closed-loop stimulation protocol. Our primary comparison was mainly based on shifts in within-animal NE oscillatory frequency and how this change would impact the connection to HR. Thus, we believe the present control condition appropriately addresses the central question of whether optogenetic LC activation are able to conduct a continuum of NE oscillations.

(3) Some more discussion/explanation of the rationale for the closed-loop approach and how it influences how we should interpret the results could be useful. For instance, currently, it is not clear whether LC stimulation needs to be timed after an NA dip to yield the effects seen.

We thank the reviewer for pointing this out. The rationale for the closed-loop LC stimulation approach was to test whether the relationship between infraslow LC–NE dynamics and heart rate is maintained only under physiological infraslow conditions or whether it breaks down when the rhythm becomes progressively faster, as occurs during sleep fragmentation and other high-arousal states. Specifically, we asked whether heart rate continues to track LC–NE fluctuations as the infraslow rhythm shifts toward higher frequencies, thereby assessing its utility as a potential biomarker of disrupted restorative sleep.

To address this while preserving the intrinsic temporal structure of infraslow LC activity, we implemented a closed-loop strategy in which stimulations were triggered following defined declines in the NE signal, using a rolling preceding 2-minute window as baseline. This allowed LC activation to occur during the descending phase of the endogenous infraslow cycle, maintaining its physiological phase structure while systematically increasing its effective frequency. By progressively relaxing the decline threshold, stimulations were triggered earlier in the cycle, thereby compressing the infraslow period in a controlled manner.

Importantly, the intention was not to test whether LC stimulation specifically needs to occur after an NE dip to elicit the observed effects. Rather, triggering stimulation during the decay phase provided a way to accelerate the infraslow rhythm without disrupting sleep through indiscriminate stimulation. This enabled us to examine whether the coupling between LC–NE dynamics and heart rate remains stable under increasingly rapid infraslow regimes. Our results indicate that this relationship weakens at higher infraslow stimulation frequencies, suggesting that heart rate reliably reflects physiological LC–NE oscillations but becomes less tightly coupled when the rhythm is compressed beyond its normal range.

We have clarified this rationale in the revised manuscript by adding the below section in the result section.

(Result, page 8/9). “This approach enabled controlled compression of the infraslow NE cycle by triggering LC activation during the descending phase of the endogenous NE signal, thereby increasing the effective oscillatory frequency while preserving the temporal structure of physiological LC–NE dynamics. This strategy allowed us to test whether heart-rate responses continue to track LC-driven NE fluctuations as the infraslow rhythm becomes progressively faster.”

(4) The section on heart rate decelerations is hard to follow. In particular, I was not sure how to interpret Figure 3f-j. For Figure 3f, what does the middle line represent? The laser onset or the max RR value after laser onset? What is the baseline that is used to correct the values to obtain amplitudes? If it is the whole period before the maximal RR value or the laser onset, wouldn't baseline values differ significantly across conditions and so potentially account for differences seen between conditions in the reported HR decelerations? Larger HR decelerations may be seen in conditions with higher HR simply as a regression to the mean phenomenon.

We thank the reviewer for this feedback and agree that additional clarification of Figure 3f–j is warranted.

For Figure 3f, the central line represents the peak RR value (maximal heart-rate deceleration) identified within the 2–7 s window following laser onset, rather than the laser onset itself. We realize this was not sufficiently clear and have revised the figure and corresponding Results text to clarify this point.

Regarding baseline correction, RR amplitudes were calculated as the difference between the RR at peak deceleration and the mean RR during the 8–10 s period preceding the RR peak, as described in the manuscript. Thus, the baseline was defined locally for each event and was not based on the entire pre-laser period or stimulation onset. We chose this approach to account for shifts in baseline heart rate across conditions and to capture the relative magnitude of the deceleration response rather than absolute RR values.

We appreciate the reviewer’s point regarding potential regression-to-the-mean effects, particularly in conditions with higher baseline heart rates. This is an important consideration. However, because the amplitude measure was baseline-corrected on an event-by-event basis, we believe the reported differences are unlikely to be explained solely by higher pre-stimulation heart rate. At the same time, our findings clearly show that elevated baseline heart rate influence the dynamic range of deceleration responses. Our findings show that under physiological conditions with elevated HR, larger heart-rate fluctuations would also contribute to increased HRV, which is often interpreted positively, despite potentially reflecting fragmented or dysregulated sleep states in this context.

To improve readability, we have revised the figure and associated text.

(Results, page 10). “To quantify the HR decelerations that happened after LC activation, we took the maximal RR value (so slowest HR) 2-7 s after LC stimulation and baseline corrected the value to the mean RR during the 8–10 s period preceding the RR peak to obtain their amplitude.”

(5) The findings regarding LC suppression could be further clarified.

(5A) Page 8: "observed a response in NE decline" - please be more precise. Did NE decline more or less?

We thank the reviewer for this suggestion and agree that the original wording was imprecise. To clarify the direction and nature of the response, we have revised the text to state:

(Results, page 11). “...we observed a gradual NE decline sustained throughout the laser period that was not observed in the YFP condition…”

(5B) It would be helpful to also show the correlation between NE and RR in the control (YFP) condition and whether there were any differences between YFP and Arch conditions (Figure 4e).

We thank the reviewer for this suggestion. We have now added the corresponding YFP correlation to Figure 4e. In the YFP group, the relationship between NE and RR showed a similar negative trend but did not reach statistical significance (p = 0.053). To directly assess whether the NE–RR relationship differed between Arch and YFP animals, we performed both a linear mixed-effects analysis and a Fisher r-to-z comparison.

Neither analysis revealed a significant difference between groups. The linear mixed-effects model showed no significant Group × NE interaction (slope difference = 0.994, p = 0.51), indicating that the NE–RR coupling was not altered by LC suppression. Similarly, Fisher's r-to-z comparison found no significant difference between the correlations (p = 0.42).

We believe this result is consistent with the relatively modest nature of the LC suppression paradigm. While Arch stimulation produced a clear reduction in NE levels, it did not induce a large shift in the overall NE–RR relationship. Instead, the data suggest that heart-rate responses remain coupled to noradrenergic fluctuations under both physiological conditions and during mild LC suppression. We have added the YFP data and clarified this interpretation in the revised manuscript.

(Results, page 12): “A similar negative relationship was observed in YFP controls (Fig. 4e), and the strength of the NE–RR association did not differ significantly between Arch and YFP animals (Suppl. File, Statistics), suggesting that LC suppression did not substantially alter the underlying coupling between these measures.”

(5C) This sentence took me multiple readings to understand - it would be helpful to rewrite to make it clearer: "indicating that, while HR generally did not respond strongly to LC suppression, the variability in RR responses was dependent on NE changes to the suppression (Figure 4e)."

We agree with the reviewer that this phrasing is hard to understand and we have optimized for better clarity. Please see new version below:

(Results, page 11/12). “Notably, despite the absence of a robust group-level HR effect, NE and RR responses remained negatively correlated across trials (Fig. 4e), indicating that HR dynamics continued to track the magnitude of noradrenergic suppression at the individual-response level”.

(5D) The two colors in Figure 4 are similar and hard to distinguish.

We agree that the colors are hard to separate and have altered them to make them easier to separate.

(5E) The correlations shown in Figure 4j seem to be driven by just two of the cases. Are the effects significant when outliers are removed?

We thank the reviewer for raising this point. To assess whether the observed correlation in Figure 4j was disproportionately driven by a small number of data points, we performed a formal outlier analysis using the ROUT method (Q = 1%). This analysis did not identify any statistical outliers in the dataset. Therefore, we did not have an objective basis for excluding any observations from the analysis.

(5F) Page 10: Were there any differences in memory performance between the Arch and YFP conditions?

We thank the reviewer for this question. The memory experiments were based on a previously published dataset (Kjaerby, Andersen et al., 2022), in which the primary objective was to assess the effect of LC suppression on sleep spindle dynamics and memory consolidation. In the present study, we performed an additional analysis by extracting heart-rate (RR) measures from these recordings to evaluate whether cardiac responses could serve as a biomarker of LC-mediated noradrenergic regulation.

However, due to technical limitations (EMG recording often suffers from noise) in extracting reliable RR signals from all animals in this dataset, the number of subjects available for this secondary analysis was reduced. All these considerations are described in Methods/Mice. As a result, we were not sufficiently powered to perform a direct statistical comparison of memory performance between Arch and YFP groups based on RR measures alone. Instead, we examined whether RR responses to LC suppression predicted behavioral performance across animals. When pooling Arch and YFP conditions, we observed a correlation between the magnitude of the RR response and subsequent memory performance, suggesting that heart-rate dynamics reflect noradrenergic modulation relevant for memory consolidation.

To avoid overinterpretation, we have therefore limited our conclusions to reporting this association rather than making direct group-level comparisons between Arch and YFP animals, and we have clarified this point in the revised manuscript.

(Results, page 13): “Interestingly, across pooled Arch and YFP animals, larger RR increases following LC suppression were associated with better subsequent memory performance. Additionally, RR and NE responses to LC suppression were negatively correlated indicating that animals showing stronger NE reductions also exhibited larger RR changes. Together, these findings suggest that heart-rate dynamics covary with noradrenergic responses during sleep and may reflect physiological processes relevant for sleep-dependent memory consolidation.”

(5G) Page 10: "We found a correlation between RR responses to LC suppression and sigma power, suggesting that a stronger HR reduction response is linked to higher spindle power." It should be noted in the text that the correlation was not specific to sigma (it was also seen for theta and beta, Figure 4i).

We agree with reviewer that this should be highlighted. We have changed the sentence:

(Results, page 12). “Furthermore, we found a correlation between RR responses to LC suppression and sigma power, suggesting that a stronger HR reduction response is linked to higher spindle power; similar correlations were also observed in the theta and beta frequency ranges (Fig. 4h-i).”

(6) It is not clear which of the sigma power and RR interval findings do/do not exactly line up between the mice and humans. It could be helpful to have a table comparing them. For instance, was the finding in humans that pre-HRB sigma power was positively associated with slowing in heart rate after the HRB also seen in mice? Was there evidence in mice (as seen in the human sample) that sleep-dependent memory improvement was associated with pre-HRB sigma power?

We thank the reviewer for this thoughtful comment and agree that the cross-species comparisons could be communicated more clearly. Our intention was not to imply exact one-to-one correspondence between all mouse and human findings, but rather to examine whether central–autonomic coupling surrounding phasic heart-rate events shows conserved features across species while acknowledging species-specific physiological differences.

Importantly, the mouse and human analyses were designed to address related but not identical questions. In mice, we leveraged optogenetic LC suppression to probe a more causal relationship between noradrenergic activity, heart-rate slowing, spindle-related dynamics, and memory consolidation. Previous work using this dataset demonstrated that 2-minute LC suppression robustly enhances spindle density and that spindle enhancement correlates with improved memory performance. In the present study, we therefore asked whether heart-rate slowing covaries with this LC-mediated spindle/memory relationship, supporting HR as a potential biomarker of these restorative processes.

By contrast, causal manipulation of LC activity is not feasible in humans. Instead, we focused on naturally occurring HRBs as putative downstream signatures of phasic LC– NE activity, motivated by our mouse findings that NE increases precede HR accelerations. We observed conserved coupling between sigma activity and HR dynamics across species, although the temporal profile differed, with sigma activity occurring closer to the HRB in humans than in mice. These temporal differences may reflect species-specific differences in cardiac and sleep physiology.

Regarding the reviewer’s specific questions, the positive relationship between pre-HRB sigma power and post-HRB heart-rate slowing was tested in humans, where this metric showed the strongest relationship to behavioral outcome. We did not directly test the same measure in mice because, unlike humans, HR recovery following HRBs did not show a pronounced baseline shift (Fig. 5b), limiting the interpretability of this comparison. Similarly, we did not directly correlate pre-HRB sigma power with memory performance in mice, as the more causal LC suppression paradigm already demonstrated a spindle–memory relationship in this species and was the focus of our mechanistic analysis.

To reduce confusion, we have revised the Results conclusion to make a clearer overview:

(Results, page 14): “In conclusion, mice and humans displayed evidence of conserved autonomic-central coupling, reflected in coordinated HR and sigma power dynamics surrounding phasic cardiac events.

However, the temporal relationship between these events differed across species, likely reflecting differences in sleep and cardiovascular physiology. In mice, causal manipulation of LC activity demonstrated that heart-rate dynamics covary with LC-mediated noradrenergic and spindle-related processes linked to memory consolidation. In humans, sigma power preceding HR bursts was associated with both post-HRB heart-rate slowing and sleep-dependent memory improvement, suggesting that autonomic–central coupling surrounding HR events may provide a translational marker of restorative sleep processes (Fig. 5n).”

(7) Page 18: It is not clear if the sex of mice was balanced across controls and optogenetics groups.

We thank the reviewer for this important comment and agree that the sex distribution should be reported more clearly. These experiments relied on the availability of animals from the heterozygous TH-Cre transgenic line, and given the relatively small cohort sizes, perfect balancing across sex and experimental groups was not always feasible.

For the LC activation experiments, the sex distribution was: YFP: 2 male / 2 female; ChR2: 4 male / 2 female. For the LC suppression experiments, the distribution was: YFP: 4 female; Arch: 3 female / 1 male.

Although the groups were not perfectly sex balanced, we had no strong reason to expect robust sex-dependent differences in the physiological effects of these optogenetic manipulations, particularly given the relatively strong and acute nature of the intervention. At the same time, we acknowledge that the present study was not powered to assess sex as a biological variable, and subtle sex-dependent effects therefore cannot be excluded. To improve transparency, we have now clarified the sex distribution in the Methods/Mice section.

Reviewer #2 (Public review):

Summary:

The major part of this study reproduces previously published findings in both mice and humans and provides incremental analyses on these findings. In essence, the work reaffirms the presence of coordinated infraslow fluctuations in sigma power and heart rate during NREM sleep. It further confirms previous findings that coordination depends on noradrenaline-releasing neurons in the locus coeruleus. Also supporting previously published work in mice and humans, the authors describe a link between the strength of these infraslow fluctuations and memory consolidation in mice and humans.

Strengths:

The authors successfully replicate key previously reported phenomena across both mice and humans. Confirmatory studies and demonstrations of reproducibility are essential for progress in neuroscience. To maximize their value, such studies should clearly acknowledge their confirmatory nature and carefully situate what, in their view, are novel results, going beyond existing literature.

Weaknesses:

The authors' interpretation of their data needs to be revised. Many of their claims regarding the mechanistic basis of their findings and the predictive value of their correlative datasets are not supported by the available evidence.

In the present manuscript, several citations of literature on the work they reproduce lack precision or completeness, which reduces transparency and obscures how the reported findings relate to previously established results.

We thank Reviewer 2 for the thoughtful comment regarding positioning of our findings relative to the literature, and caution in mechanistic interpretation. In response, we have revised the Introduction, Results, and Discussion to more clearly acknowledge foundational studies in this area and to better clarify how the present work extends beyond them.

We agree that prior work has demonstrated infraslow coupling between sigma activity, norepinephrine (NE) dynamics, and heart rate (HR), and has established a role for the locus coeruleus (LC) in coordinating these oscillations. However, cardiac measures in these studies were typically treated as secondary observations rather than as primary experimental targets. A central goal of the present study was therefore to provide a systematic and mechanistically grounded characterization of NE-mediated HR dynamics during sleep across multiple timescales, including infraslow oscillations, sleep–wake transitions, and causal manipulations of LC activity.

Importantly, we also aimed to relate infraslow HR fluctuations to the very-low-frequency (VLF) component of heart rate variability (HRV), which remains comparatively under-characterized and mechanistically unresolved in the clinical HRV literature. By linking LC activity, NE dynamics, and HR fluctuations across behavioral states, our findings provide a biologically grounded framework that may help explain this component of HRV.

A second major objective of the study was translational. Because direct LC recordings are not feasible in humans, we asked whether cardiac dynamics alone could reflect the infraslow, memory-consolidating potential of sleep and thus serve as a noninvasive biomarker. By directly manipulating LC activity and demonstrating corresponding changes in HR dynamics, our results strengthen the mechanistic rationale for using HRV—particularly its VLF component—as an accessible proxy of LC-dependent sleep physiology.

We therefore respectfully disagree with the suggestion that the present study does not provide novel insight. Rather, the revised manuscript now more clearly emphasizes that our contribution lies in (i) systematically characterizing NE-dependent HR dynamics across sleep states, (ii) linking these dynamics to the poorly understood VLF component of HRV, and (iii) establishing a causal and translational framework for using cardiac measures as markers of LC-mediated sleep processes.

We hope the reviewer finds that the revised Introduction and Discussion better highlight both the existing literature and the specific advances provided by the present work.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

I have been convinced by discussions about the replicability crisis that it should be a standard practice to share data upon publication in a publicly accessible online repository such as Open Science Framework or OpenNEURO. Making data available can increase the impact of the research study. Simply stating "data available upon request" as done in the current draft is not sufficient as, unfortunately, when data are not shared upon publication in a public repository, it can be impossible to gain access by request from the researchers - the majority of requests from other researchers to obtain data are not complied with (e.g., Vanpaemel, Vermorgen, Deriemaecker, & Storms, 2015; Wicherts, Bakker, & Molenaar, 2011).

We fully agree with the reviewer about the importance of data sharing for transparency, reproducibility, and maximizing the impact of research. Consistent with these principles, we will make the human dataset publicly available on the Open Science Framework upon publication and have added the link to this repository in the manuscript under Data Availability (https://osf.io/g6emj/). With respect to the mouse dataset, we respectfully note that this dataset is currently the subject of multiple planned and ongoing analyses that extend beyond the scope of the present manuscript. Releasing these data publicly at this stage could compromise these efforts and lead to potential misinterpretation prior to completion of the full analytic pipeline. For this reason, we believe that it would not be appropriate to share the mouse data in a public repository at this time. However, we remain committed to transparency and will make the mouse data available upon reasonable request during this period, with the intention of publicly releasing the dataset once the planned analyses are complete.

(1) p. 4: There is some orphan text at the top of the page ("marker of Alzheimer's disease. Furthermore, maintaining LC neural density prevents neurodegeneration (13). Given the reported reduction in HRV in aging and Alzheimer's disease, suppressed").

Thank you. We have removed the orphan text.

(2) p. 22: What does NFR refer to?

Novel-to-familiar ratio. We have removed the abbreviation from the main text. It is now only used in the figure.

(3) I might have missed this information, but it was not clear to me how the epochs to be analyzed were selected, and for the mice, how much wake vs. sleep they included.

We thank the reviewer for this comment and apologize that the epoch selection criteria were not sufficiently clear. They were mentioned under Methods. For the optogenetic analyses in mice, epochs were selected based on NREM sleep including microarousals to ensure that physiological responses were evaluated within stable sleep conditions while allowing for natural brief interruptions.

For the LC activation (ChR2) experiments, stimulation epochs were included only if NREMinclMA began at least 30 s before laser onset and continued for at least 30 s after laser onset. For the LC suppression (Arch) experiments, the criterion was similarly ≥30 s of NREMinclMA prior to laser onset, but extending ≥60 s following laser onset to accommodate the longer suppression response profile.

Thus, analyses were intentionally restricted to sleep periods, and wakefulness was not included except where it emerged naturally as an outcome of the manipulation or transition under investigation. We have now clarified these inclusion criteria in the Methods/ Event marker selection section to improve transparency.

(4) In the Supplement, Figure 3 appears before Figure 2.

Thanks. We have corrected it.

Signed, Mara Mather

Reviewer #2 (Recommendations for the authors):

The authors' interpretation of their data needs to be revised. Many of their claims regarding the mechanistic basis of their findings and the predictive value of their correlative datasets are not supported by the available evidence.

There are three major directions in which this study would need to be revised.

Part 1 - Literature citations of both mouse and human literature need to be revised, and citations placed in a manner that accurately reflects what has been previously done. In detail:

(1.1) The statement on p. 4 regarding "the extent to which phasic infraslow NE fluctuations ...is not well understood" disregards a previous publication in which closed-loop optogenetic stimulation of LC was already shown to regulate HR variations on the infraslow time scale (10.1016/j.cub.2021.09.041).

We thank the reviewer for this important point. The study by Osorio-Forero et al. (2021) was already cited as ref. 4 in our original manuscript and in the discussion, we specifically highlighted this study: ‘These findings build on Osorio-Forero et al. (35) as well as other studies’; however, we agree that our wording did not sufficiently emphasize its key finding that closed-loop optogenetic manipulation of LC activity can coordinate infraslow heart-rate fluctuations and spindle clustering during NREM sleep.

We have revised the relevant paragraph in the introduction to explicitly acknowledge that ref. 4 demonstrated causal coordination between LC activity, sleep spindle dynamics, and heart-rate fluctuations. We have also refined our statement of the knowledge gap to clarify which novel questions our study addresses. We believe these revisions more accurately position our work within the existing literature and clearly distinguish our contributions from prior studies.

We have updated the introduction in several places to address these comments:

(Introduction, page 4). “It has previously been reported that HR fluctuates at similar infraslow frequencies as NE fluctuations and sigma power in mice (Lecci et al., 2017; Osorio-Forero et al., 2021) and that phasic HR fluctuations correlate with infraslow changes in pupil diameter (Carro-Domínguez et al., 2025), a proxy for changes in NE levels (Murphy et al., 2014; Reimer et al., 2016). Importantly, optogenetic manipulation of the LC has demonstrated that infraslow LC activity coordinates sleep spindle clustering and heart rate fluctuations during NREM sleep (Osorio-Forero et al., 2021). Together, these findings support a functional coupling between the central LC–NE system and peripheral cardiac dynamics, further supported by findings that HR increases accompany MAs during NREM sleep (Carro-Domínguez et al., 2025;

Osorio-Forero et al., 2025).”

(Introduction, page 4/5). “Due to the frequency overlap with VLF HRV, we wondered if central infraslow NE dynamics could be linked to this poorly understood HRV indicator. Furthermore, are infraslow NE fluctuations directly reflected by heart-rate variability under different physiological states or does LC–HR coupling scales differently with LC output? Specifically, if infraslow NE oscillations display faster frequencies - as occurs during sleep fragmentation - will cardiac dynamics exhibit corresponding changes? Conversely, given that stronger infraslow NE dynamics correlate with memory consolidation through their regulation of sleep spindles, could the peripheral autonomic signatures provide an accessible cross-species biomarker of spindle-dependent memory consolidation? Addressing these questions could help bridge mechanistic insights into LC-mediated sleep regulation with established HRV metrics used in human physiology.”

(1.2) In this same published paper, optogenetic stimulation of LC was already used to "determine the causal relationship..." (p.6). However, the authors do not cite these data.

We thank the reviewer for this comment. As noted in our response to Comment 1.1, ref. 4 was already cited in the Introduction, and we have now revised that section to more explicitly emphasize this paper. In addition, we have modified the wording in the Results section.

(Results, page 8). “After finding the inverse correlation between NE and RR in natural sleep transitions, we next sought to further characterize the causal influence of LC activity on HR dynamics, using a closed-loop optogenetic approach to modulate NE oscillatory frequency during NREM sleep.”

(1.3) The authors' speculation about LC-induced sympathetic and parasympathetic actions is premature: this study does not provide pharmacological experiments in this direction. However, two published studies implied a parasympathetic mechanism linked to infraslow fluctuations of LC activity (10.1016/j.cub.2021.09.041, 10.1016/j.cub.2017.12.049). These findings should be appropriately cited. While HR decelerations may reflect compensatory autonomic responses, there is no direct evidence in the present study that these effects are sympathetically mediated. An alternative, and equally plausible, interpretation is enhanced parasympathetic activity. This distinction is particularly important given that the observed increases in mean HR during LC stimulation cannot distinguish between reduced parasympathetic activity and increased sympathetic drive.

We thank the reviewer for raising this important point and agree that the current study does not provide direct mechanistic evidence to disentangle sympathetic versus parasympathetic contributions to LC-mediated heart rate regulation. This was not the intention of our study; rather, the relevant discussion section was meant to provide mechanistic interpretations and hypotheses based on the observed physiology. To avoid overstating our conclusions, we have revised the wording to more clearly emphasize the speculative nature of these interpretations. Furthermore, we have added the suggested references demonstrating that muscarinic blockade reduces heart rate and pupil fluctuations during sleep, which support the possibility of a parasympathetic contribution to infraslow LC-related dynamics.

(Discussion, page 16/17). “Prior findings demonstrate that LC is linked to the autonomic nervous system. Stimulation of LC projections decrease parasympathetic cardiac vagal activity (Wang et al., 2014) and also influences sympathetic output through direct projections to the preganglionic cells in the sympathetic nervous system (Karemaker, 2017; Nygren and Olson, 1977; Samuels and Szabadi, 2008). While the mechanisms generating VLF HRV are not well defined (Armour, 2003; Shaffer et al., 2014; Wang et al., 2014), there is a clear parasympathetic component (Taylor et al., 1998). This combined with the ability of pharmacological blockage of the parasympathetic system to block infraslow oscillations of HR (Osorio-Forero et al., 2021) and pupil diameter during sleep (Yüzgeç et al., 2018), led us to expect a slowing of HR during LC suppression due to parasympathetic disinhibition.”

(1.4) It is not clear why prefrontal NE signals were associated with HR fluctuations. Literature evidence indicates other brain areas that are functionally more directly linked to autonomous fluctuations.

We thank the reviewer for raising this point. We do not speculate that mPFC NE activity is causally linked to heart rate fluctuations or that the mPFC directly mediates the observed autonomic dynamics. Instead, mPFC NE signaling was used as an experimentally accessible readout of LC activity. Importantly, accumulating evidence suggests that infraslow NE oscillations are globally coordinated phenomena that are expressed across multiple brain regions during sleep, making mPFC NE a valid proxy for LC-driven neuromodulatory state dynamics. We have clarified this in the result section:

(Results, page 6). “mPFC was selected as a cortical readout of LC-mediated norepinephrine dynamics, as infraslow NE fluctuations are coordinated across widespread brain regions.”

(1.5.a) The lack of effect of Arch-inhibition of LC on infraslow NE signals is concerning. Prior work showed that bilateral LC inhibition does affect NE signals and also infraslow sigma power fluctuations (10.1016/j.cub.2021.09.041, 10.1038/s41593-024-01822-0). This discrepancy should be explicitly acknowledged and discussed on p.9.

We thank the reviewer for highlighting this important point. To clarify, we did observe a robust effect of LC inhibition on noradrenergic signaling, with clear suppression of the NE signal following Arch-mediated LC inhibition (Fig. 4c), aligned to laser onset. In addition, LC suppression increased neuronal synchronization, including enhanced sigma power relative to YFP controls (Fig. 4h), consistent with previous reports showing that reduced LC activity promotes synchronized sleep-related oscillations. Thus, we do not interpret our findings as indicating an absence of LC suppression effects.

The apparent discrepancy relates specifically to the absence of a statistically significant group-level shift in infraslow NE or HRV power during NREM sleep, rather than the efficacy of the manipulation itself. We note that our inhibition paradigm was intentionally mild, consisting of repeated 2-minute suppression periods separated by 4-minute intervals, and was designed to introduce subtle shifts within physiological ranges rather than globally reorganize infraslow sleep structure. Accordingly, we consider the immediate NE and heart-rate responses to LC inhibition to be the most sensitive physiological readouts of LC-mediated regulation in this context.

We also note that the studies cited by the reviewer used different suppression paradigms, including more frequent manipulations. Thus, while our suppression scheme did not result in detectable infraslow reorganization, we do not believe they reflect an ineffective LC suppression as we demonstrated a clear NE reduction in response to time-locked LC suppression.

We have added a sentence to the result section to explain the lack of effect infraslow power:

(Results, page 12). “This likely reflects the relatively mild and intermittent LC suppression paradigm, which was designed to remain within physiological ranges and therefore did not globally reorganize infraslow sleep dynamics.”

(1.5.b) Additionally, the authors state in the Discussion that they "find no consistent modulation in HR during LC suppression, suggesting that the LC-HR connection is more strongly associated with sympathetic activity rather than parasympathetic inhibition". However, the results primarily demonstrate an absence of modulation in mean HR, while preserving a significant relationship between RR intervals and stimulation. This indicates a modulation of heart rate variability, even in the absence of changes in average HR. Notably, such variability-related effects may fall outside the VLF range and could instead involve higher-frequency components.

We thank the reviewer for this important clarification. We agree that our original wording may have conflated the absence of modulation in mean HR with the absence of autonomic modulation more generally. To address this point, we revised the Discussion to emphasize that LC activity may influence VLF HRV through sympathetic activation and/or indirect modulation of cardiac vagal activity, even in the absence of robust changes in average HR. We have softened our previous interpretation that the LC-HR relationship is primarily sympathetic in nature and instead discuss a more nuanced interaction between sympathetic and parasympathetic influences on HRV dynamics.

(Discussion, page 16/17). “Prior findings demonstrate that LC is linked to the autonomic nervous system. Stimulation of LC projections decrease parasympathetic cardiac vagal activity (Wang et al., 2014) direct projections to the preganglionic cells in the sympathetic nervous system (Karemaker, 2017; Nygren and Olson, 1977; Samuels and Szabadi, 2008). While the mechanisms generating VLF HRV are not well defined (Armour, 2003; Shaffer et al., 2014; Wang et al., 2014) there is a clear parasympathetic component (Taylor et al., 1998). This combined with the ability of pharmacological blockage of the parasympathetic system to block infraslow oscillations of HR (Osorio-Forero et al., 2021) and pupil diameter during sleep (Yüzgeç et al., 2018) led us to expect a slowing of HR during LC suppression due to parasympathetic disinhibition. Interestingly, we found no consistent modulation in HR during LC suppression, suggesting that the LC-HR connection may also somehow be driven by sympathetic outflow. Previous research had indicated that LF power may also represent sympathetic activity, but this interpretation has been challenged due to the mixed contribution of both autonomic branches (Houle and Billman, 1999; Japundzic et al., 1990; Reyes et al., 2013). LF and HF ratio (LF/HF) were traditionally thought to reflect balance between sympathetic and parasympathetic activities (i.e., the sympatho-vagal balance), though currently considered as an oversimplification of non-linear integration of autonomic signals (Billman, 2013; Pagani et al., 1986). Our findings implicate the VLF may offer a precise marker for central arousal states, given its overlaps with infraslow phasic fluctuations of LC-NE levels. Although parasympathetic activity appears important for the expression of VLF oscillations, growing evidence suggests that VLF dynamics reflect broader interactions between the heart and autonomic nervous system rather than simple sympathetic or parasympathetic control alone (Armour, 2003; Shaffer et al., 2014). Within this framework, infraslow LC–NE dynamics may represent one central contributor to these slow cardiac fluctuations during sleep either directly through sympathetic activation or indirectly by inhibition of cardiac vagal activity.”

(1.6) The relationship between sigma power fluctuations and HR is different in humans than in mice. This has been shown before (10.1126/sciadv.1602026, 10.1038/s41593025-02159-y). This work should be mentioned on p. 11.

We already acknowledge prior studies demonstrating species differences in the relationship between sigma power fluctuations and heart rate in the discussion section. However, to accommodate the reviewer’s comment and improve clarity for the reader, we have now also added the suggested references to the Results section, where the relationship between sigma power fluctuations and HR is first discussed.

(Results, page 14). “These temporal differences may reflect species-specific physiology differences in cardiac timescales, which has also been previously reported (Bergel et al., 2025; Carro-Domínguez et al., 2025; Lecci et al., 2017).”

(1.7) Correlations between the strength of infraslow sigma power fluctuations and memory consolidation have been published and should be discussed (10.1126/sciadv.1602026). It is surprising to see that correlations with learning in humans are done using pre-HRB sigma peaks rather than heart rate. This is a measure that is very close to the one used by Lecci et al.; this similarity should be clearly acknowledged. The way the data are currently presented limits this manuscript's novelty, also in its translational aspect.

We agree that the work by Lecci et al. (2017) established an important relationship between the association of infraslow sigma power fluctuations and memory consolidation, which is highly relevant to our findings.

Importantly, the underlying infraslow fluctuations in neuromodulatory tone are increasingly recognized as key regulators of sleep spindle dynamics (sigma power), including from our own previous work demonstrating that direct manipulation of locus coeruleus–norepinephrine infraslow rhythms alters spindle organization and sleep continuity. Thus, our findings are conceptually aligned with prior studies linking sigma fluctuations to memory consolidation.

However, we would like to clarify an important distinction in our translational approach. While Figure 4 demonstrates that heart rate dynamics during sleep can predict memory performance in mice, Figure 5 was designed to address the translational potential of these findings in humans, where direct neuromodulatory readouts are not readily accessible. Here, we deliberately focused on heart rate bursts and their associated sleep dynamics as a clinically tractable physiological measure.

We acknowledge that the pre-HRB sigma increase may appear conceptually similar to the measure used by Lecci et al.; however, our approach is not equivalent. Rather than selecting spindle or sigma peaks themselves, we aligned analyses to heart rate accelerations and examined the robust upregulation of sigma activity preceding these events. In this framework, sigma activity serves as a physiological readout linked to autonomic dynamics, rather than being the primary anchor of analysis. We chose this measure because heart rate bursts are influenced by multiple physiological factors, and the associated sigma dynamics provided the clearest and most robust relationship with memory outcomes in the human dataset.

We have revised the Discussion to more clearly acknowledge the similarity to prior work. We believe our findings extend prior observations by providing evidence that sleep-related heart rate fluctuations may serve as a non-invasive readout of the memory-preserving function of sleep.

(Discussion, page 18). “Previous work in humans demonstrated that the strength of infraslow sigma oscillations correlates with sleep-dependent memory consolidation in humans (Lecci et al., 2017).”

(1.8) Conclusions as to whether sigma fluctuations might be slightly slower and less powerful in mice are not justified. More work is required to determine which infraslow manifestations are most useful for cross-species comparisons. Moreover, little is currently known about LC activity in human sleep. A careful look into how pupil diameter correlates with sigma power should provide clues for further discussion (see Carro-Dominguez et al). A detailed study of infraslow fluctuations in human sleep should also be discussed https://doi.org/10.1101/2024.11.06.620875.

We thank the reviewer for this thoughtful comment. We agree that our original phrasing suggesting that sigma dynamics in mice may be “slower and less powerful” than in humans was overly interpretive. We have removed this sentence from the Results section. In addition, we have expanded the Discussion to more thoroughly integrate recent human literature on infraslow sleep dynamics.

(Discussion, page 19). “Importantly, infraslow fluctuations of sigma power in human sleep have received growing attention. Recent work demonstrates that the infraslow fluctuation of sigma power segments N2 sleep into functional phases associated with arousal and memory-related sleep markers (Dimitriades et al., 2024). Complementary findings using pupillometry show that pupil diameter fluctuates on similar infraslow timescales during NREM sleep and is inversely related to spindle clustering, providing indirect evidence that arousal-related noradrenergic dynamics shape human sleep microstructure (Carro-Domínguez et al., 2025). However, LC activity during human sleep remains inferred rather than directly measured, and systematic perturbation studies linking LC output to spindle–autonomic coupling in humans are currently lacking. Together, these observations underscore both the promise and the current limitations of cross-species comparisons of infraslow sleep dynamics.”

(1.9) Citation of literature should be as explicit as possible. Referring to "many studies rely on plasma levels..." while including some that actually did real-time fiber photometric measures is misleading.

We thank the reviewer for this suggestion and agree with the reviewer about the importance of accurately representing prior studies. We have now updated the discussion to clarify this.

(Discussion, page 15). “Many studies have linked HR to NE (Fawaz and Simaan, 1963; Sundaram et al., 1991; Tanoue et al., 2022; Watson et al., 1979). Many rely on plasma levels of NE, which, while linked to central NE (Gurguis and Uhde, 1998), has low temporal resolution, making causal interpretations harder. In recent years, the use of biosensors and fibre photometry allows for very reliable estimate of the temporal dynamics of NE changes making association to HR more precise (Osorio-Forero et al., 2021).”

(1.10) Regarding the discussion on the baroreflex: The emphasis is placed predominantly on sympathetically mediated effects. However, the description of the baroreflex loop is incomplete, as it overlooks the substantial contribution of parasympathetic modulation. In particular, heart rate adjustments within the baroreflex are primarily mediated by parasympathetic mechanisms.

We agree with reviewer that this important notion should be added. We have rephrased the discussion as below:

(Discussion, page 20). “These neurons suppress the activity of the rostral ventrolateral medulla, ultimately resulting in reflex parasympathetic activation with sympathetic inhibition lowering the HR (Aicher et al., 2000; Lanfranchi and Somers, 2002).”

(1.11) Regarding the interpretation of HRV analysis, the discussion places disproportionate weight on sympathetic modulation in the interpretation of HRV metrics. This framing is inconsistent with recent conceptual clarifications, including a recent Nature Reviews Cardiology article by Menuet et al. (10.1038/s41569-02501160-z), which cautions against simplistic low-frequency/high-frequency (LF/HF) interpretations of autonomic balance.

We thank the reviewer for this thoughtful comment. We agree that HRV frequency bands should not be interpreted as exclusive markers of specific autonomic branches. In the original manuscript, we cited Billman (2013), which challenges the validity of LF/HF as a measure of sympatho-vagal balance, to acknowledge these conceptual limitations. However, we recognize that some of our phrasing, particularly in the section discussing compensatory HR decelerations, may have implied branch-specific dominance.

We have updated the Discussion section as follows:

(Discussion, page 17). “Interestingly, we found no consistent modulation in HR during LC suppression, suggesting that the LC-HR connection may also somehow be driven by sympathetic outflow. Previous research had indicated that LF power may also represent sympathetic activity, but this interpretation has been challenged due to the mixed contribution of both autonomic branches (Houle and Billman, 1999; Japundzic et al., 1990; Reyes et al., 2013). LF and HF ratio (LF/HF) were traditionally thought to reflect balance between sympathetic and parasympathetic activities (i.e., the sympatho-vagal balance), though currently considered as an oversimplification of nonlinear integration of autonomic signals (Billman, 2013; Pagani et al., 1986). Our findings implicate the VLF may offer a precise marker for central arousal states, given its overlaps with infraslow phasic fluctuations of LC-NE levels. Although parasympathetic activity appears important for the expression of VLF oscillations, growing evidence suggests that VLF dynamics reflect broader interactions between the heart and autonomic nervous system rather than simple sympathetic or parasympathetic control alone (Armour, 2003; Shaffer et al., 2014). Within this framework, infraslow LC–NE dynamics may represent one central contributor to these slow cardiac fluctuations during sleep either directly through sympathetic activation or indirectly by inhibition of cardiac vagal activity.”

We have also replaced the title of the Discussion section title “Locus-coeruleus-mediated sympathetic control drives compensatory heart rate decelerations” with “Locus-coeruleus activation drives compensatory heart rate decelerations via autonomic feedback mechanisms”

(1.12) In particular, the manuscript attributes VLF power primarily to sympathetic activity, despite evidence that very-low-frequency RR-interval oscillations are strongly dependent on parasympathetic integrity. Notably, parasympathetic blockade has been shown to nearly abolish VLF oscillations in humans (Taylor et al., Circulation, 1998; doi:10.1161/01.CIR.98.6.547).

We thank the reviewer for highlighting this important point. It was not our intention to imply that VLF HRV is driven primarily by sympathetic activity or to disregard the important role of parasympathetic integrity in shaping VLF oscillations. Our intention was to present the multifactorial and incompletely resolved nature of the VLF component; however, if our wording can be interpreted otherwise, we agree that clarification is warranted.

In response, we have revised the manuscript to better reflect the current understanding of VLF physiology. Specifically, we now make clearer that, while VLF HRV remains less mechanistically defined than HF and LF HRV, substantial evidence supports a strong parasympathetic contribution to the expression of VLF oscillations. We have incorporated the reviewer-suggested references within this comment and others and clarified this point throughout the revised manuscript.

(Discussion, page 16). “While the mechanisms generating VLF HRV are not well defined (Armour, 2003; Shaffer et al., 2014; Wang et al., 2014) there is a clear parasympathetic component (Taylor et al., 1998).”

(Discussion, page 17). “Although parasympathetic activity appears important for the expression of VLF oscillations, growing evidence suggests that VLF dynamics reflect broader interactions between the heart and autonomic nervous system rather than simple sympathetic or parasympathetic control alone (Armour, 2003; Shaffer et al., 2014).”

(1.13) Moreover, based on work by Armour (2003) and Kember et al. (2000, 2001), the VLF rhythm is thought to emerge from stimulation of afferent sensory neurons within the heart, further arguing against a purely sympathetic interpretation. Together, these findings indicate that the discussion overemphasizes sympathetic mechanisms and underrepresents the contribution of parasympathetic and afferent cardiac pathways to HRV, particularly in the VLF range.

We thank the reviewer for this important point. Our response to this comment is largely aligned with our response to comment (1.12). In the revised Discussion, we now more clearly acknowledge that VLF oscillations likely arise from more complex cardioautonomic interactions than simple sympathetic and parasympathetic innervation alone. At the same time, we have intentionally avoided an extensive discussion of these mechanisms, as our experimental design does not directly address these pathways.

Part 2 - There are a number of conceptual issues that need more careful elaboration:

(2.1) A highly problematic point throughout this study is the choice of AUCs, notably of NE signals and RR intervals, rather than the signal amplitudes. It confuses the correlations to events of different durations, such as MAs and wakefulness. It is not possible to draw conclusions of the kind "cardiac rhythm are tightly coupled with the infraslow phasic NE ..." because such statements ignore that AUCs conflate amplitude and duration of a signal.

We thank the reviewer for this comment and agree that the distinction between amplitude- and duration-related signal features is important. We deliberately chose AUC measures because our intention was to capture the overall physiological response over time, including both the magnitude and temporal evolution of the signal, rather than relying solely on a single peak value. In this context, AUC provides information about the shape and sustained nature of NE and RR changes within a defined time window, which we considered particularly relevant for temporally dynamic responses.

At the same time, we appreciate the reviewer’s concern that AUC may conflate response amplitude and duration, particularly when comparing events of different lengths such as microarousals and wakefulness. To minimize this issue, the AUC windows were intentionally kept relatively short and fixed, thereby limiting the influence of prolonged wake episodes or differences in transition duration on the measure. Thus, our intention was not to quantify the total duration of awakenings, but rather the immediate physiological response profile surrounding the event.

To address this concern more directly, we compared amplitude- and AUC-based measures across all mice. The results are now shown in Suppl. Figure 1g. We found a strong correspondence between amplitude and AUC measurements for NE signals, indicating that the observed relationships are not dependent on the choice of metric. A similar, albeit weaker, relationship was observed for RR responses. This likely reflects physiological constraints on heart-rate dynamics, where the initial heart-rate acceleration is relatively similar across vigilance-state transitions (Figure 1e), while the duration of the response differs substantially. As a result, amplitude measures may underestimate differences between transitions, whereas AUC better captures the extent of the cardiac response. Importantly, direct comparison of NE and RR amplitudes still revealed a significant relationship, supporting the overall conclusion that cardiac and noradrenergic responses are coupled. However, this relationship was weaker than that observed using AUC measures, suggesting that incorporating temporal aspects of the response captures additional biologically relevant information.

In addition to the figure, we have revised the Results section to include these considerations:

(Results, page 7): “These comparisons were performed using area under the curve (AUC) estimates of NE and R-R responses. Importantly, comparing AUC and peak amplitude measures showed a similar overall relationship, and the coupling between NE and RR remained significant when only peak amplitudes were considered (Suppl. Fig. 1g), indicating that the findings are not solely driven by the response duration aspect of AUC. The somewhat stronger relationship observed with AUC-based measures may reflect rapid saturation of heart-rate responses across vigilance-state transitions, making response persistence an informative component of the physiological signal.”

(2.2) A next problematic aspect of the study is the analysis of noradrenergic signals and HR at transitions (e.g., from NREM sleep to sleep wakefulness or to microarousals). The abstract does not mention these data, leaving open how they fit into the paper's message. There are also two problems with it: a) Noradrenaline levels increase with wakefulness, as do many other neuromodulators. This is not novel. Furthermore, why use an AUC measure for 0-25 s when MAs last only 5 or 15 s? b) biosensor signal comparisons are difficult to make for state transitions, because blood flow changes and modifies the fluorescent signal.

We thank the reviewer for these comments and appreciate the opportunity to clarify the rationale and interpretation of these analyses.

Regarding the inclusion of vigilance-state transitions, our intention was not to claim novelty in the observation that NE levels increase during wakefulness. Rather, these analyses were included to provide an additional physiological context in which to examine the coupling between NE and heart-rate dynamics. Specifically, the transition analyses allowed us to determine whether graded changes in NE across sleep-to-wake transitions were mirrored by corresponding RR changes (Fig. 1d–e) and whether these responses covaried (Fig. 1f), thereby strengthening the overall conclusion that cardiac dynamics track noradrenergic signaling across naturally occurring sleep-state fluctuations.

Regarding the use of AUC measures, we deliberately chose this metric because it captures the overall physiological response over a defined time window, including both magnitude and temporal evolution, rather than relying solely on a peak value. In this context, AUC was intended to reflect differences in the overall response profile, for example that NE and RR responses during microarousals may return more rapidly toward baseline than during sustained wakefulness. To minimize the influence of differing event durations, the analysis window was kept fixed (0–25 s) across all transition types. Importantly, we directly compared AUC- and amplitude-based measures and found that they produced largely similar relationships (Suppl. Fig. 1g). The coupling between NE and RR responses remained significant when only peak amplitudes were considered, indicating that the observed relationship is not solely driven by response duration. However, the relationship was somewhat stronger with AUC-based measures, likely because heart-rate responses rapidly saturate across vigilance-state transitions, making response persistence an informative component of the physiological signal. As mentioned in the previous comment, we have added a Figure and new result text to highlight this.

Regarding the concern about blood-flow related artifacts in fluorescent biosensor signals, we agree that hemodynamic contamination is an important consideration for neuromodulator recordings and applies broadly to biosensor-based measurements, including analyses of infraslow fluctuations. To minimize this issue, ΔF/F calculations were performed using the isosbestic control channel, which serves to correct for movement- and hemodynamic-related signal fluctuations. Furthermore, hemodynamic artifacts typically occur rapidly at state transitions, whereas GRAB-NE signals display slower dynamics. If uncorrected hemodynamic contamination strongly influenced the signal, we would expect abrupt signal distortions tightly aligned to arousal onset, which was not evident in our recordings. While we cannot completely exclude residual hemodynamic influences, we do not believe they account for the graded NE responses observed across vigilance-state transitions or the corresponding relationship with RR dynamics.

(2.3) Wordings such as 'extent of arousal' to compare MAs and wakefulness are problematic. Microarousals and wakefulness are qualitatively different behaviorally, physiologically, and in terms of neuromodulatory conditions

We thank the reviewer for the helpful comment. Our intention was not to imply that microarousals and wakefulness are qualitatively identical states differing only in magnitude. Rather, we used arousal in the broader neurophysiological sense, referring to the degree of activation of central arousal systems, with wakefulness representing part of this continuum. However, we recognize that in the sleep field, arousal is often used more specifically to describe brief EEG desynchronization events and that we furthermore use micro-arousals as a broad term for these sleep arousals, which may make our wording confusing. To avoid ambiguity, we have revised the manuscript to replace this terminology with vigilance state, sleep–wake state, or state transitions, depending on the context.

(2.4) To interpret linear correlations between datasets, even for the ones with Rsquare values < 0.5, as 'predictive' represents an overextended interpretation that is not supported by available evidence. This concern is aggravated due to the use of AUCs that confound amplitudes and time courses. This is in particular the case for Figure 5d, 5k, or 5m…

We thank the reviewer for this important comment. We agree that the term predictive may overstate the interpretation of these correlations, particularly given the modest R² values in some analyses. Our intention was to highlight an association between heartrate and NE-related measures rather than imply strong predictive performance or causality. We have therefore revised the wording throughout the manuscript to avoid predictive language and instead refer to these relationships as associations or correlations.

Regarding the use of AUC, we selected summary metrics based on the physiological characteristics of the signal of interest. In cases where responses were characterized primarily by rapid shifts to a new level, amplitude measures were used. In contrast, AUC was chosen when both the magnitude and duration of the response were considered physiologically relevant.

Part 3. A substantial number of experimental and analytical points require clarification. Here is a list of a few examples; many observations noted here apply equally to other figure panels.

(3.1) Many figure panels leave it open about whether averages or representative data are shown, how many animals are included, and what kind of measures are plotted.

We thank the reviewer for this comment and agree that clarity in figure presentation is important. In response, we have carefully revised the figure legends throughout the manuscript to more explicitly state whether data shown are representative examples or group averages, clarify the number of animals included in each analysis, and specify the measures being plotted. We have also added “data are shown as mean ± SEM” where this information was previously not explicitly stated and clarified when n refers to the number of animals. We believe the revised figure legends now provide clearer guidance for interpretation, and further statistical details are available in the accompanying statistics table. It should also be noted that number of animals used for all experiments can be found in the Method section ‘Mice’.

(3.2) In case experiments were done in a paired manner (e.g., the LC stimulations), individual data points should be shown connected for the different conditions.

We thank the reviewer for this suggestion. While we agree that connecting individual data points is valuable for paired experimental designs, this visualization is not appropriate for the LC stimulation analyses presented here. Specifically, each condition reflects pooled stimulation events selected across animals rather than a single summary value per animal that can be directly matched across columns. As such, individual points in one condition do not map one-to-one onto points in the next condition, making connected visualizations potentially misleading.

Importantly, although the data are presented as pooled event-level measures, the paired structure of the experiment was accounted for in the statistical analyses, such that repeated measurements within animals and the paired nature of the design were included in the relevant comparisons.

(3.3) Numerous analyses involve heart rate measures from the neck EMG during wakefulness. However, it is not specified how, in this case, RR peaks could be detected within the high-activity EMG.

We thank the reviewer for this question. The procedure for heart-rate detection during wakefulness is described in detail in the Heart rate detection Methods section. Briefly, RR intervals were extracted from preprocessed neck EMG recordings using a previously validated approach for mouse sleep studies. To minimize contamination from movement-related EMG activity during wakefulness, R-peaks were not detected within periods extending 100 ms before and 250 ms after detected movement, as these segments were considered too noisy for reliable peak detection. Heart-rate estimates during these excluded periods were subsequently interpolated using surrounding valid RR intervals to preserve temporal continuity and enable analysis of HR dynamics before and after movement episodes.

(3.4) Figure 1b: PSD for RR intervals. The supplementary figure says that 11-minutelong NREMS or 5-minute-long NREMS periods were used. These are very rare events in mice, for which the average bout duration is around 2 min and the cycle length is 10 minutes. The methods do not explain how these bouts were chosen, how many of them were included, and why shorter bouts were not analyzed. Single cases or means?

We thank the reviewer for this important point and agree that additional clarification was warranted. The selection of long NREM (including microarousals) periods was motivated by methodological considerations related to spectral analysis of very slow oscillations rather than by an assumption that these bout lengths are representative of average NREM duration in mice. Because our analysis focused on very-low-frequency dynamics (~1 cycle every 50 s), sufficiently long continuous recordings are required to reliably estimate power at these frequencies and avoid fragmentation-related edge effects that disproportionately affect shorter bouts.

For this reason, shorter NREM episodes were excluded from the PSD analysis, as they do not provide sufficient duration to robustly capture slow-frequency components. We initially compared PSD estimates using NREM periods of at least 11 min (allowing ~10 VLF cycles) and 5 min duration to assess whether the shorter 5 min recordings introduced bias (Supplementary Fig. 1f). While longer periods resulted in higher overall power estimates, the frequency distribution remained highly similar between conditions. We therefore selected 300 s (5 min) as the inclusion criterion for the remainder of the study, as periods exceeding 10 min are uncommon in mice and would substantially limit analyses across experimental paradigms.

To further reduce bias related to bout duration, PSD estimates were weighted by NREM episode length, as longer bouts showed systematic effects on power estimates. For Figure 1, the analysis included 144 NREM-with-MA bouts across 7 animals, and data shown represent group means rather than single examples.

This description is also included in the Methods section/Data Analysis.

(3.5) Figure panel 1c,d: In Panel c, what is plotted?

As stated in the figure legend, it is the cross-correlation between NE and R-R. We have added more description in the figure legend.

A cross-correlation between two signals? If yes, how were these signals chosen per transition? Looks rather like they plot some time course across a transition. What is time point 0?

We thank the reviewer for pointing out that this analysis was insufficiently explained. The analysis shown represents a cross-correlation between the NE and RR signals, performed to assess their temporal relationship across different vigilance-state transitions. Specifically, for each transition type, NE and RR signals were extracted within the corresponding time windows and cross-correlated to determine the strength and timing of their interaction.

In this context, time point 0 (lag = 0) represents perfect temporal alignment between the two signals. Positive or negative lags indicate whether changes in one signal systematically precede or follow changes in the other. Due to methodological differences between the rapid electrical heart signal and the slower fluorescent NE signal, we intentionally avoided overinterpreting fine temporal lead–lag relationships. Rather, the aim of this analysis was to characterize the overall interaction between the two signals across transitions.

Because the relationship between NE and RR was predominantly inverse, negative cross-correlation values indicate that increases in NE are associated with decreases in RR (i.e., faster heart rate), and vice versa. Thus, this analysis served primarily to confirm and extend our other findings by quantifying the interaction between NE and heart-rate dynamics across vigilance-state transitions.

We have revised the descriptive sentence in the Results section to improve clarity and have added a more detailed description of the signals included directly in the figure panel.

(Results, page 7). “Here, cross-correlation analysis revealed a predominantly negative relationship between NE and RR across vigilance-state transitions, indicating that increases in NE were associated with reductions in RR (i.e., faster heart rate; Fig. 1c).”

(Figure 1 legend). “Cross correlation (how strongly and at what temporal offset the two signals covary) between NE and RR during transitions”.

In Panel d, what is measured here? Is the time point of the dotted line a NA trough or the moment of a transition? Show the data with connected lines. Heart rate calculation during wakefulness?

We thank the reviewer for these questions and apologize that this was not sufficiently clear. In panel d, the dotted line indicates the NE trough, not the moment of a vigilance state transition, as specified in both the figure and figure legend. The analysis is aligned to detected NE troughs during sleep and examines the subsequent physiological dynamics, including transitions into wakefulness.

We have updated the sentence in the result section to make it a bit more clear:

(Results, page 7): “To explore the NE-RR relationship across sleep-wake transitions, we examined four progressive sleep-to-wake transitions using the preceding NE trough as the time stamp (time 0):…”

Regarding the suggestion to connect data points, as noted in an earlier response, these analyses are based on event detection, where multiple events contribute from each animal. Thus, each condition represents pooled events across animals rather than a single matched value per animal, making connected-line visualizations inappropriate and potentially misleading. Importantly, the paired structure of the experimental design was accounted for in the statistical analyses.

Heart-rate detection during wakefulness was usually not possible due to movement artefacts in the EMG. In the detection, we excluded periods with movement. Our EMG-based detection approach is described in the Heart rate detection Methods section. Briefly, movement-contaminated periods were excluded from R-peak detection (100 ms before and 250 ms after detected movement), and RR intervals were subsequently interpolated using surrounding valid values to preserve temporal continuity. Because analyses were aligned to NE troughs occurring during sleep, we limited the temporal window to avoid excessive contamination from movement-related noise associated with subsequent wakefulness. We have slightly revised the text to make these points clearer.

Panel C is a cross correlation between NE and RR from the same traces included in the mean traces. x=0 is the NE through like the other figures.

Panel D is the mean traces of NE during transitions with the dotted line at x=0 being the NE though

Yes, but x = 0 for the cross-correlation is not the NE trough. It says something about how aligned NE and R-R are in time (see response further up in (3.5)).

(3.6) The sigma band should ideally be chosen between 10-15 Hz for better consistency with the literature.

We thank the reviewer for this suggestion and agree that consistency in the definition of frequency bands is important for comparison across studies. The sigma range used in the present manuscript was selected to match our previous publications and analyses, thereby allowing direct comparison with our earlier findings on LC-mediated regulation of sleep spindles and infraslow sleep dynamics. Maintaining the same band definition also ensured consistency across the datasets analyzed in this study.

We acknowledge that having a shared definition of sigma power would improve alignment and facilitate comparisons across laboratories. At present, there remains some variability in the exact frequency boundaries used for spindle and sigma analyses across studies and species, although there are ongoing efforts within the sleep field to improve standardization. Importantly, we do not expect that modest adjustments of the sigma-band boundaries would materially affect the conclusions of the present study, as the spindle-related activity of interest lies well within the selected frequency range and the observed effects are broad rather than restricted to a narrow frequency bin.

Moving forward, we aim to follow emerging consensus recommendations where appropriate. To clarify this point for readers, we have added a statement in the Methods section explaining that the sigma band was chosen to maintain consistency with our previous publications.

(Methods: EEG power, page 29). “The sigma band was defined as 8 - 15 Hz to maintain consistency with our previous publications. Modest differences in sigma-band boundaries are not expected to affect the main conclusions.”

(3.7) What are 'extreme LC stimulation frequencies'. The only information available is that stimulations were done for 2s at 20 Hz.

We thank the reviewer for pointing out that this wording was unclear. By “extreme LC stimulation frequencies”, we did not refer to the within-stimulation pulse frequency (which remained constant at 20 Hz for 2 s across all conditions). Rather, we referred to the effective frequency of LC activation at the infraslow timescale, which was progressively increased through the closed-loop stimulation paradigm.

Specifically, stimulations were triggered when NE levels crossed increasingly permissive thresholds during the descending phase of the endogenous NE signal. As thresholds increased over time (from −15 ΔF/F (%) to +5 ΔF/F (%)), stimulations occurred progressively earlier in the infraslow cycle, thereby compressing the oscillatory period and increasing the effective frequency of LC recruitment while preserving the endogenous temporal structure of NE dynamics.

Thus, “extreme stimulation frequencies” refers to the highest rate of repeated LC activations achieved through the closed-loop paradigm, where stimulations became increasingly frequent at the infraslow level rather than changes in the 20 Hz pulse train itself. To avoid confusion, we have revised the wording throughout the manuscript to refer more explicitly to faster infraslow LC activation frequencies or increased infraslow stimulation frequency.

We have added more information in the result section to highlight this better.

(Results, page 8). “We employed a closed-loop paradigm, where LC stimulations (2 s 20 Hz (10 ms) blue laser pulses with a light intensity of 5 mW) were triggered when NE levels fell below increasing thresholds (-15, -10, -5, 0 and 5 ΔF/F (%), Fig. 2a-b, Methods). This approach enabled controlled compression of the infraslow NE cycle by triggering LC activation during the descending phase of the endogenous NE signal, thereby increasing the effective oscillatory frequency while preserving the temporal structure of physiological LC–NE dynamics. This strategy allowed us to test whether heart-rate responses continue to track LC-driven NE fluctuations as the infraslow rhythm becomes progressively faster.”

(3.8) Could the discrepancy between panels 4c, left and right, be due to limited sample size? The whole figure lacks indications of sample numbers, making interpretation difficult.

We thank the reviewer for this comment. We assume the reviewer is referring to the apparent discrepancy between the NE response and heart-rate response following LC suppression (Fig. 4c–d), where LC inhibition induced a clear reduction in NE levels, whereas mean heart-rate responses were less pronounced.

The figure legends state sample sizes and event numbers. Specifically, for these analyses, n = 8 animals (4 Arch, 4 YFP) were included, comprising 48 Arch events and 38 YFP events, our interpretation is that this discrepancy reflects a biological observation rather than a failed manipulation. Specifically, LC suppression robustly reduced NE levels, confirming the effectiveness of the optogenetic intervention, whereas HR did not exhibit a similarly consistent group-level response. However, as highlighted by the correlation analyses, variability in RR responses remained associated with the magnitude of NE suppression, suggesting that heart-rate dynamics still reflected noradrenergic modulation at the individual-response level despite the absence of a strong mean effect.

(3.9) It would be great if Figure 4 j could be more explicitly illustrated. For example, behavioral traces that lead to higher NFR and corresponding changes in RR AUC should be shown for animals with large and small effect sizes. The sample size seems excessively low. Can this explain the difference in slopes compared to Figure 4e, right panel?

We thank the reviewer for this suggestion. To improve the interpretation of Figure 4j, we have now added representative examples illustrating animals with high and low memory performance and their respective RR responses following LC suppression.

We agree that the sample size for this analysis is limited. As noted in the Methods, Figure 4j is based on a secondary analysis of a previously published dataset (Kjaerby, Andersen et al., 2022), where heart-rate measures were retrospectively extracted from EMG recordings. Due to noise-related limitations in RR detection, reliable cardiac measures could not be obtained from all animals, reducing the number of subjects available for this analysis. For this reason, we have deliberately avoided direct statistical comparisons between Arch and YFP animals and instead limited our conclusions to the observed association between RR responses and memory performance across animals.

Regarding the difference in slope compared with Figure 4e, the two analyses are based on different levels of aggregation and address different questions. Figure 4e examines the relationship between NE and RR responses across individual LC suppression events, resulting in multiple observations per animal. In contrast, Figure 4j uses a single mean RR response and a single behavioral outcome per animal. Furthermore, Arch and YFP animals were pooled in Figure 4j to maximize statistical power and because the dataset was not sufficiently powered for direct group comparisons. Consequently, the slopes are not expected to be directly comparable between the two figures.

(3.10) It would be important to show anatomical validation of viral expression in THcre animals and optic fiber positioning.

We thank the reviewer for this comment. Anatomical validation of viral expression in TH-Cre animals and optic fibre placement was performed for these experiments and has been reported previously in Kjaerby et al. Nature Neuroscience paper, from which this dataset was derived. Specifically, viral targeting and fibre positioning were histologically verified as part of the original experimental validation. This is mentioned in the Method section ‘Surgery’: Viral expression and injection sites were validated through immunostaining of perfused brain slices from the experimental animals (see Kjaerby et al. (3) for more information).

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Update: This manuscript has successfully completed peer review and is now officially published as the Version of Record in the World Journal of Urology (Springer Nature).

Please cite and refer to the final published article using the official DOI: https://doi.org/10.1007/s00345-026-06640-6

eLife Assessment

This important study reveals distinct representations of task-related information in the dendrites and somata of cortical neurons during sensorimotor learning and behavioral adaptation. The evidence is compelling, combining imaging of dendritic and somatic activity during behavior to demonstrate compartment-specific encoding of sensory cues, motor actions, and corrective signals. The work will be of broad interest to neuroscientists studying dendritic computation, motor learning, and the cellular mechanisms underlying adaptive behavior.

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:

In this manuscript, Scheib et al. identify distinct calcium dynamics in the somata and tuft dendrites of layer 5 pyramidal cells in mice performing a licking task. Animals are trained to lick water ports on the left or right following an acoustic cue, and can adjust their targeting when the ports are displaced. For tongue premotor cortical neurons projecting to the ventromedial thalamus, calcium transients in tuft dendrites are tightly locked to the direction-instructive cue, while somatic calcium signals are more broadly dispersed and more frequently synchronized with tongue motion and port contact. Finally, when the targets are shifted, tufts exhibit a sparse but large corrective signal on an improperly-targeted first lick, and the changes in population activity in the tufts and somata differ after adaptation to the new port locations.

Strengths:

In my opinion, this is a very strong manuscript which reports several novel and significant observations, contains high-quality data and (for the most part) reasonable analyses, and is clear and well-written. Most prior studies of cortical sensorimotor processing have measured the output of neurons using extracellular recording - an approach which obscures potentially important signaling differences between neuronal compartments. This study leverages cutting-edge imaging techniques in mice to document large, time-dependent differences between calcium signals at cortical somata and tuft dendrites. This phenomenon could have major implications at the cellular level for synaptic plasticity, and at the systems and behavioral levels for motor adaptation.

Weaknesses:

At a conceptual level, the authors may wish to elaborate a bit on what sensorimotor computation they think the circuit is implementing, and how their results help explain this implementation. Several possibilities are raised: tuft activation could "prime" the pyramidal cells in advance of movement initiation (line 319ff), or could track errors to engage plasticity (line 351ff) and solve the credit assignment problem (line 362ff). It might be helpful to make one of these proposals more concrete with a computational model, but this is not strictly necessary. [The authors explain that they will address this with modeling work in subsequent research.]

Reviewer #2 (Public review):

Summary:

The authors set out to compare functional encoding in the tuft dendrites and somata of a specific cortical cell type during motor planning and learning.

Strengths:

The investigation of a specific projection type (L5 ET) is a strength that aids reproducibility and interpretation. The elegant approach to increasing the depth of field of dendritic imaging is another strength. The data analyses are largely clear in their methods, scope, and interpretation. The writing is extremely clear and appropriately referenced, with an excellent Introduction, in particular.

Weaknesses:

This work is largely observational, describing signals that might reflect computational transformations and/or instruct plasticity, but those possibilities have not yet been deeply investigated. The manuscript does a good job of laying out these as future directions.

Reviewer #3 (Public review):

Summary:

This article by Scheib et al. investigates how layer 5 extratelencephalic (ET) neurons in the frontal cortex encode sensorimotor information during motor learning, focusing on differences between their apical tuft dendrites and somas. The authors alternated recordings among these ET neuronal compartments in the mouse anterior lateral motor cortex (ALM) during a cued directional licking task with a target port shift. They found that while tuft dendrites predominantly encode sensory cues, with a subset selectively active during corrective actions, somatic activity was more strongly associated with action timing. Additionally, learning induced divergent plasticity: tuft dendrites increased their selectivity but decreased response gain, maintaining stable net selectivity, whereas somas showed increased net selectivity early in learning. Together, these findings reveal distinct sensorimotor representations and learning-related plasticity in dendritic and somatic compartments, providing insight into how compartment-specific activity in the frontal cortex may contribute to motor skill acquisition.

Strengths:

The authors developed an innovative imaging approach and a comprehensive data analysis pipeline to address a knowledge gap in the literature. By alternating imaging of dendritic tufts and somas in the same animals, they compare compartment-specific activity during motor learning and identify distinct encoding of task variables and learning-related plasticity across these compartments. Interestingly, a subset of dendritic tufts shows activity associated with corrective actions. The findings are discussed in the context of current theories of dendritic computation, credit assignment, and motor learning, providing a useful foundation for future mechanistic studies.

Weaknesses:

No major weaknesses were identified.

Author Response:

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

eLife Assessment

This important study reveals distinct representations of task-related information in the dendrites and somata of cortical neurons during sensorimotor learning and behavioral adaptation. The evidence is compelling, combining simultaneous imaging of dendritic and somatic activity during behavior to demonstrate compartment-specific encoding of sensory cues, motor actions, and corrective signals. The work will be of broad interest to neuroscientists studying dendritic computation, motor learning, and the cellular mechanisms underlying adaptive behavior.

Thank you for this excellent summary. We recommend one change: removing the word “simultaneous”. It could perhaps be replaced with “concurrent” or simply omitted. Tuft dendrites and somata were imaged on alternating days, and most readers will probably interpret “simultaneous” as implying a faster, interleaved sampling rate.

Public Reviews:

Reviewer #1 (Public review):

Summary:

In this manuscript, Scheib et al. identify distinct calcium dynamics in the somata and tuft dendrites of layer 5 pyramidal cells in mice performing a licking task. Animals are trained to lick water ports on the left or right following an acoustic cue, and can adjust their targeting when the ports are displaced. For tongue premotor cortical neurons projecting to the ventromedial thalamus, calcium transients in tuft dendrites are tightly locked to the direction-instructive cue, while somatic calcium signals are more broadly dispersed and more frequently synchronized with tongue motion and port contact. Finally, when the targets are shifted, tufts exhibit a sparse but large corrective signal on an improperly-targeted first lick, and the changes in population activity in the tufts and somata differ after adaptation to the new port locations.

Strengths:

(1.1) In my opinion, this is a very strong manuscript which reports several novel and significant observations, contains high-quality data and (for the most part) reasonable analyses, and is clear and well-written. Most prior studies of cortical sensorimotor processing have measured the output of neurons using extracellular recording - an approach which obscures potentially important signaling differences between neuronal compartments. This study leverages cutting-edge imaging techniques in mice to document large, time-dependent differences between calcium signals at cortical somata and tuft dendrites. This phenomenon could have major implications at the cellular level for synaptic plasticity, and at the systems and behavioral levels for motor adaptation. As described below, I have only one major technical concern (which should be addressable with additional analysis), along with several relatively minor suggestions for improving the manuscript.

We thank the reviewer for their insightful summary of the significance of the differences that we identified in the task-related activity of tuft dendrites and somata.

Weaknesses:

(1.2) At a conceptual level, the authors may wish to elaborate a bit on what sensorimotor computation they think the circuit is implementing, and how their results help explain this implementation. Several possibilities are raised: tuft activation could "prime" the pyramidal cells in advance of movement initiation (line 319ff), or could track errors to engage plasticity (line 351ff) and solve the credit assignment problem (line 362ff). It might be helpful to make one of these proposals more concrete with a computational model, but this is not strictly necessary.

We thank the reviewer for this feedback. We absolutely agree that detailed computational models of each proposed computation will be very valuable and constitute an important follow-up to this work. We hope to collaborate with theorists to take that next step. Each possible computation noted by the reviewer reflects distinct differences that we observed in the task-related activity of tuft dendrites and somata. They are not mutually exclusive hypotheses to explain the same phenomenon. As such, we think they are best addressed independently in future modeling work. By making the data and a concise description of the main findings available immediately, we hope to allow computational experts in each of these areas to take advantage of the results of this study without delay.

(1.3) My only major technical concern relates to the analyses in Figures 4F-H, 5G-I, and 6H-K (c.f. equations 2-5). Typically, one identifies population-level factors by projecting neural activity onto fixed dimensions of interest; this makes it possible to see how activity evolves over time along interpretable coordinates. Here, however, the coding directions are redefined at each time point, so the "choice" activity at time t is actually a different signal from the "choice" activity at t+1. This procedure is a bit like comparing the activity of one neuron at one time point with the activity of a different neuron at a later time point. It also makes the physiological interpretation more complicated: if the dimensions are fixed, one can see how a downstream neuron could "read out" the signal by computing a weighted sum of the activity of upstream neurons, but it is harder to see how this could happen if the weights are always rotating.

We thank the reviewer for raising this point. We agree that our use of projections along coding directions (CDs) defined at each time point is a less conventional use of coding directions, although nearly identical calculations have been previously used to assess population-level selectivity and code stability in this task (Chen et al., 2017; Yang et al., 2022). As noted in the article, given low numbers of error trials and high trial-to-trial variability, we found that estimating the selectivity of individual ROIs for these task-dimensions was not robust and was subject to overfitting. Cross-validated projections at each timepoint provided a far more robust measure of population selectivity. Furthermore, we were able to orthogonalize stimulus, choice and outcome CDs to better identify distinct encoding of each task-variable. Finally, because the primary goal of the study was to identify any differences between tuft dendrite and somatic encoding, we think that calculating the population selectivity at each timepoint gives readers a less biased view of the selectivity of the two compartments, whereas calculating a CD over a single arbitrary time window could conflate differences in dynamics with differences in selectivity.

We agree that calculating the CD at each timepoint makes it hard to see where the code is stable and where it is rotating, and thus how a downstream neuron might “read out” the signal. To provide this information, we have added new panels to the supplement showing the correlation of CDs across time (Figure 4 - figure supplement 1B,D). We also now provide this information for CR-CA in Figure 5—figure supplement 2A (the plots previously presented in 2A were the correlations of CR with CA, rather than CR-CA; an error that has been fixed). The following changes were also made to the Results section to clarify this issue:

“From the linear model, we calculated coding directions (CDs) at each timepoint that maximally separated Stimulus, Choice, and Outcome activity (Figure 4F; Figure 4—figure supplement 1A) and estimated the direction and selectivity along each dimension across time (Figure 4—figure supplement 1B-E; see Methods). Allowing CDs to rotate in time (see Figure 4—figure supplement 1B,D), although unconventional, ensured that comparisons of population selectivity across the two compartments were not biased by the selection of an arbitrary CD time window.”

We also identified a mistake in the description of CD orthogonalization in the Methods, which has been corrected as follows:

“For each timepoint, each selectivity CD was then orthogonalized with respect to the other two selectivity CDs by a QR decomposition in which that selectivity CD was last in the order.”

(1.4) A few comments on the behavioral task and results. After the port shift, the error rate is quite high, and doesn't diminish much between the early and late epochs (approximately 42% and 38% error rate, respectively; Figure 1I). That is, mice do not seem to fully master the task. Clearly, animals do alter their aim, but even this does not seem to change much between early and late periods (Figure 1J). I recommend that the authors show the behavioral data at a finer level of granularity (e.g., by plotting the change in exit trajectory on all individual trials across sessions, with a loess fit) to allow an assessment of the adaptation rate and when adaptation saturates. It would also be more conventional to refer to the behavioral changes as "motor adaptation," instead of "skill learning." (The latter would be appropriate if the port offset were randomized across trials, and animals received two separate cues for direction and offset, but I suspect this task would be too difficult for mice to learn.)

We agree with the reviewer that by the end of the late period, performance on the right side (Figure 1I) has still not returned to pre-shift levels. This may reflect mice not fully mastering the task, as the reviewer suggests, or it may reflect that after the shift, the right port is substantially more difficult to reach than the left port. Unfortunately, because of high animal-to-animal and lick-to-lick variability, plotting the post-shift lick angle at a finer level of granularity is not statistically informative.

With regard to the nature of the learning in our task, we selected “skill learning” as the best description of the motor learning task based on distinctions between adaptation and the learning of motor skills by Krakauer et al., 2019 and Heald et al., 2021. Conceptually, the difference is whether an existing motor controller memory is simply updated with new parameters, or whether the motor context has changed sufficiently that a distinct motor controller memory (which can still use parts of previous memories) is formed. In our task, after the port shift the left port forms an obstacle to reaching the right port. This obstacle was simply not present before the shift. Before the shift, ports were approximately equidistant from the mouth and easily avoided given the port separation and tongue width. Thus, avoiding an obstacle would presumably not be part of the initial motor controller memory and a distinct memory would need to be constructed.

We agree with the reviewer, however, that given that we do not have fine-timescale dynamics of behavioral changes in response to the shift, and did not conduct other experiments (such as returning the ports to their original location) that would typically be conducted to identify “adaptation-like” or “skill learning-like” dynamics, we cannot empirically distinguish between the two. We now clarify in “Study limitations” that we call the studied behavior “skill learning” based on the nature of the task, but that our behavioral analysis cannot distinguish between adaptation and skill learning:

“We refer to the behavioral paradigm as motor “skill learning” strictly based on the nature of the task. After the shift, mice must avoid a new obstacle close to the mouth (i.e., the left port), which we assume requires the formation of a distinct motor controller memory and therefore would be considered skill learning (Krakauer et al., 2019). However, we did not confirm that the mice exhibited specific behavioral characteristics of skill learning and it is possible that other kinds of motor learning (e.g., motor adaptation) were dominant.”

(1.5) This is perhaps a semantic point, but it might not be entirely accurate to refer to the activity evoked by the directional cue as "sensory." Typically, a "sensory" response should encode some feature of a stimulus - in this case, the frequency of a tone. Here, it seems likely that the cue-aligned activity reflects the instructed lick direction, rather than the auditory information per se. (Presumably, these premotor neurons do not have well-behaved auditory tuning curves.) By comparison, in macaques performing center-out reach tasks, activity in dorsal premotor cortex rapidly ramps up following a visual cue instructing the direction of an upcoming reach, but one usually wouldn't refer to this activity as "visual" or "sensory" (though this is sometimes done). I suggest the authors either use "Instruction" or similar (e.g., in Figure 4F), or clarify in the text whether they think the activity is a genuine auditory response or something else.

We understand how this could cause confusion. “Sensory” was meant to denote the nature of the differences in external events between the trial types used to calculate selectivity, not to imply that the activity was necessarily selective for detailed features of the cues outside the context of the task. Previous work in ALM cortex has labeled this selectivity direction as “stimulus” (Yang et al., 2022; Chen et al., 2024) to better emphasize that it is simply defined by the external cue. Where appropriate, we have revised the article to use “stimulus” or “instructional cues” in place of “sensory” for clarity and to better conform with convention.

Reviewer #2 (Public review):

Summary:

The authors set out to compare functional encoding in the tuft dendrites and somata of a specific cortical cell type during motor planning and learning.

Strengths:

(2.1) The investigation of a specific projection type (L5 ET) is a strength that aids reproducibility and interpretation. The elegant approach to increasing the depth of field of dendritic imaging is another strength. The data analyses are largely clear in their methods, scope, and interpretation. The writing is extremely clear and appropriately referenced, with an excellent Introduction, in particular.

We thank the reviewer for their appreciation of the study design, imaging methods, and scholarship of the article.

Weaknesses:

(2.2) It is not obvious whether the selected labeling strategy avoids labeling Layer 6 CT neurons, which would contaminate dendritic recordings. The images provided suggest enrichment in L5, but a discussion of this important potential caveat is warranted, especially since within-cell comparisons of apical dendrites to somata were not performed.

We thank the reviewer for emphasizing the need to discuss this potential issue. For the following reasons, it is likely that the vast majority of dendrites we imaged in layer 1 originated from layer 5 ET neurons. First, as the reviewer notes, the provided images suggest enrichment in layer 5. This enrichment likely reflects the fact that most L6 CT neurons in motor and premotor cortex send denser projections to other thalamic nuclei than to VM thalamus (Winnebust et al., 2019, Cell), where we targeted our retrograde-Cre injections. Second, L6 CT neurons are predominantly untufted (Ledergerber and Larkum, 2010, J. Neurosci.), including in motor and premotor cortex (Peng et al., 2021, Nature; Ichikawa, 2025, Front. Neuroanat.). A recently identified subclass of L6 CT neurons in secondary motor cortex has dense projections to VM thalamus, but this class also appears to extend minimal dendrites into L1 (Li et al., 2024, bioRxiv). Nonetheless, we did not label post-hoc tissue collected from imaged mice with markers of precise laminar boundaries, and thus cannot definitively rule out the possibility that dendrites from a subclass of L6 CT neurons with tuft dendrites were also imaged. We have added the following paragraph to the “Study limitations” section to make readers aware of these issues:

“L5 ET neurons in premotor cortex elaborate extensive tuft dendrites in L1, whereas Layer 6 (L6) corticothalamic (CT) neurons are predominantly untufted (Jiang et al., 2020; Peng et al., 2021). Thus, although we cannot rule out the possibility that dendrites from a subclass of L6 CT neurons were also sampled, it is likely that the vast majority of dendrites we recorded in L1 originated from L5 ET neurons.”

(2.3) The application of DeepInterpolation to dendritic data appears to be novel, and little detail or vetting is provided. The reader is left guessing: Was the model retrained or fine-tuned on dendritic data? How does the denoising affect the resulting segmentation and activity traces? Is denoising necessary for this workflow?

We thank the reviewer for requesting this useful additional information.

In all cases, the model was retrained for each dendritic or somatic imaging session. Denoising improved segmentation consistency, as measured by comparing segmentations of individual sessions from the same animal. This is now specified in the Methods as follows:

“The DeepInterpolation model was trained on each imaging session prior to denoising of that session. Denoising prior to NMF-based segmentation resulted in more robust and consistent dendrite segmentation than NMF-based segmentation without prior denoising (0.79 +/- 0.01 ⍴ vs. 0.46 +/- 0.01 ⍴; mean of the max Spearman correlation of components across sessions; random subsample of N = 3 mice, 15 sessions, 400 components).”

With regard to how denoising impacts activity traces, examples were shown in Figure 2I, K. To provide more quantitative information to the reader, we calculated estimates of the power and reliability of the spectral content of dendrite activity traces extracted with or without denoising. These data are now shown in the new panel, Figure 2 - figure supplement 2G. The power spectral density of the denoised activity and the estimated reliable power spectral density of the raw traces match up to approximately 2.6 Hz (Figure 2 - figure supplement 2G), which is not far from the bandwidth of GCaMP8m, given its estimated combined rise and decay (Figure 2 - figure supplement 3B, C). Some frequencies beyond this point have been suppressed beyond what would be expected due to photon shot noise (as estimated by the replicate coherence-weighted PSD, or “recoverable” PSD). Further characterization of the precise nature of the suppressed high-frequency information – which could be suppressed artifacts (e.g., fast brain motion) or lost signal detail (i.e., GCaMP8m rise kinetics) – is beyond the scope of this paper.

Details of the PSD calculations have been added to the Methods, and the following statement has been added to the Results: “Power spectral density of the denoised traces and the coherence-weighted power spectral density of the raw traces match up to approximately 2.6 Hz (Figure 2 - figure supplement 2G; Methods), which is not far from the bandwidth of GCaMP8m, given its estimated combined rise and decay (Figure 2 - figure supplement 3B, C).”

(2.4) The activity patterns of the recorded cells appear to lack the characteristic ramping during the delay epoch previously reported in both calcium imaging and electrophysiology studies. Given that a major contribution to the significance of the work is to constrain models of ALM function, a discussion of how the data aligns with previous measurements in the same circuit would improve the work.

Preparatory selectivity and ramping activity can be seen in Figure 3H, Figure 6I, and Figure 5 – figure supplement 1B. We note that in ALM cortex, the ramping mode explains a minority of the total variance (~17%, Yang et al., 2022), but it can appear particularly prominent in projections along certain fixed CDs.

(2.5) It would be very informative to compare differences in signals between dendrites and somata of the same cells. Consistently tracing dendrites to their respective somata would assuage worries of potential contamination from dendrites of deeper cells and enable more direct comparisons of signal transformations between dendrites and somata. It would be good to understand the relationship between dendritic calcium signals and backpropagating action potentials in this task. The authors detect less frequent calcium events in tufts versus somata; is this due to selective backpropagation of action potentials? The dynamics of this process were recently investigated by Adam Cohen's group in vivo and in vitro, and measurements in the present settings could be compared to such work.

We agree with the reviewer that being able to compare differences in signals between the dendrites and somata of the same cells would be very valuable. However, reliable tracing of tuft dendrites to somata from in vivo 2P anatomical imaging requires extremely sparse labeling, such that very few neurons are recorded per animal (Kerlin et al., 2019, eLife; Otor et al., 2022, Science). As stated in the “Study limitations” section of the Discussion, we suspected (correctly) that some task-related selectivity (i.e., selectivity for corrective action) would be sparsely represented in the dendrites, and thus adopted a labeling and image processing strategy that allowed us to record from many dendrites per animal. This strategy necessarily comes at the expense of generating a labeling density that precludes reliable tracing of tuft dendrites to their respective somata based on 2P morphology alone. As discussed in our response to reviewer comment 2.2 and a new paragraph of “Study limitations,” substantial contamination of the dendrite recordings by dendrites of L6 CT neurons is highly unlikely. Future studies could use simultaneous functional imaging across large volumes combined with activity-based segmentation or post-hoc high-resolution imaging of tissue sections registered to in vivo 2P imaging to accomplish both high-throughput dendritic imaging and reliable tracing.

We thank the reviewer for pointing out that we could discuss selective backpropagation as a potential mechanism more explicitly. Our results are consistent with previous studies of L5 tufts in vivo (Francioni et al., 2019, eLife), including in ALM cortex (Maristany de las Casas et al., 2026, Science), that reported that rates of multi-branch calcium transients in the tuft dendrites of L5 neurons are lower than somatic spike rates. As discussed in “Study limitations,” there is not a clear approach in our data to determine the precise nature of the events underlying the calcium transients we measured in the tuft dendrites. Selective backpropagation of action potentials is certainly one possibility and we agree that recent research from Dr. Adam Cohen’s group should be discussed. We have added the following to the Discussion:

“Based on previous calcium imaging of L5 tufts in ALM cortex of mice engaged in similar tasks (Kerlin et al., 2019; Maristany De Las Casas et al., 2026), we suspect that most of the activity we measured was coincident with global tuft or hemi-tree events, as well as somatic spiking. Recent in vivo voltage imaging in the hippocampus has also indicated that most spikes in distal dendrites start as bAPs that have been selectively amplified (Wu et al., 2026; Lee et al., 2026).”

(2.6) The Coding Direction analyses presented in this work, while consistent with previous literature on population codes in ALM, are at odds with the nature of the measurements here. The changes in representation that occur between the dendrites and soma of an individual cell are probably best thought of in terms of the dynamics of signals themselves within individual neurons, rather than in the information encoded across a population.

We thank the reviewer for giving us the opportunity to clarify this issue. As noted in the article, given low numbers of error trials and high trial-to-trial variability, we found that estimating the selectivity of individual ROIs for these task dimensions was not robust and was subject to overfitting. Cross-validated projections at each timepoint provided a far more robust measure of population selectivity. Furthermore, we were able to orthogonalize stimulus, choice and outcome CDs to better identify distinct encoding of each task variable in the population activity. Thus, the analyses are not at odds with the nature of the measurements in the study.

Nevertheless, it is true that by recalculating the CD at each timepoint, our selectivity projections do not provide the same information as conventional projections along a fixed CD, which can indicate where the selectivity code is stable and where it is changing. To provide this information we have added new panels to the supplement showing the correlation of selectivity CDs across time (Figure 4 - figure supplement 1B, D).

(2.7) This work is largely observational, describing signals that might reflect computational transformations and/or instruct plasticity, but those possibilities have not yet been deeply investigated. The manuscript does a good job of laying out these as future directions.

We agree with the reviewer. As noted by the reviewer in comment (2.1), we combined a number of approaches in an innovative manner to explore how tuft dendrite activity differs from somatic activity at the population level during motor learning. These measurements provide the necessary foundation for future mechanistic studies and we think it is appropriate to share them at this stage of investigation and in the format of this article.

Reviewer #3 (Public review):

Summary:

This article by Scheib et al. investigates how layer 5 extratelencephalic (ET) neurons in the frontal cortex encode sensorimotor information during motor learning, focusing on differences between their apical tuft dendrites and somas. The authors alternated recordings among these ET neuronal compartments in the mouse anterior lateral motor cortex (ALM) during a cued directional licking task with a target port shift. They found that while tuft dendrites predominantly encode sensory cues, with a subset selectively active during corrective actions, somatic activity was more strongly associated with action timing. Additionally, learning induced divergent plasticity: tuft dendrites increased their selectivity but decreased response gain, maintaining stable net selectivity, whereas somas showed increased net selectivity early in learning. Together, these findings reveal distinct sensorimotor representations and learning-related plasticity in dendritic and somatic compartments, providing insight into how compartment-specific activity in the frontal cortex may contribute to motor skill acquisition.

Strengths:

The authors developed an innovative imaging approach and a comprehensive data analysis pipeline to address a knowledge gap in the literature. By alternating imaging of dendritic tufts and somas in the same animals, they compare compartment-specific activity during motor learning and identify distinct encoding of task variables and learning-related plasticity across these compartments. Interestingly, a subset of dendritic tufts shows activity associated with corrective actions. The findings are discussed in the context of current theories of dendritic computation, credit assignment, and motor learning, providing a useful foundation for future mechanistic studies.

We thank the reviewer for highlighting interesting findings in the paper and their assessment that it provides a “useful foundation for future mechanistic studies”.

Weaknesses:

No major weaknesses were identified.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

A very minor suggestion: it would be useful to mention the model organism in the abstract or title.

(1.6) Thank you for catching this. We have added the model organism to the abstract as follows:

“Using longitudinal two-photon calcium imaging, we investigated sensorimotor encoding in the apical tuft dendrites and somata of L5 extratelencephalic (ET) neurons in the frontal cortex of mice during learning of a discrete change to a cued dexterous action.”

Reviewer #3 (Recommendations for the authors):

Major:

(3.1) Lines 197-199: It is unclear why the authors conclude that somas have stronger representations of choice and task outcome. In Figure 4G, there is no significant difference between dendrites and somas for Choice or Outcome coding selectivity. The differences in the Sensory/Choice and Sensory/Outcome ratios shown in Figure 4H,I are likely explained by stronger Sensory selectivity in dendrites (Fig 4g), rather than by stronger Choice or Outcome encoding in somas.

We agree with the reviewer’s interpretation of the data. The statement at 197 - 199 was meant to reflect relative selectivity, but it was imprecise. We have replaced that sentence with the following, more precise sentence:

“Somatic activity also encoded these features, but the representation of the stimulus was weaker – and the representation of action timing was stronger – than in the tuft dendrites.”

(3.2) Figure 4B: The authors realign FL-associated IRFs to GO-cue timing using the mean FL latency for each trial type and animal. Because FL timing is jittered across trials and may differ between CL and CR trials, this could smear the realigned traces and complicate the interpretation of contact-associated activity. The authors should consider using trial-by-trial FL timing for realignment or quantify the impact of FL-timing variability on the resulting traces.

We aligned average GO- and contact-IRFs in Figure 4B so that comparisons of their magnitudes could be drawn from the same time window.

With regard to jitter across trials, we think the reviewer may have misinterpreted how the mean IRFs in Figure 4B are calculated. The contact-IRF, by its nature, is calculated once per animal and trial type with respect to FL timing and shifted once based on mean FL latency. There is no smearing due to trial-to-trial FL timing.

With regard to systematic differences in FL timing across animals and CR vs. CL, the reviewer is correct that this could – in theory – smear the realigned mean contact-IRF shown in Figure 4B. However, differences in mean FL latency across animals and trial-types are small compared with the long-timescale contact-IRFs. Thus, the non-realigned (i.e., always FL-aligned) mean contact-IRF looks nearly identical to Figure 4B just globally offset in time, as shown in Author response image 1:

Author response image 1.

Since this is nearly identical to data already presented in Figure 4B, we do not think it is necessary to include it in the revised article. However, we have added the following to the Methods:

“Population averages of contact-IRFs that were not shifted prior to averaging were nearly identical (excluding the overall temporal shift; data not shown), indicating that pooling of mean IRFs across animals and trial types produces minimal smearing of the final population IRF.”

(3.3) Figure 5A: Are CA trials specific to motor learning, or do they reflect a corrective lick toward the alternative port after an unrewarded lick? An analysis of the second lick on left-error trials or pre-shift right-error trials could help distinguish whether correction licking reflects a general decision change after failed reward, or a motor-command correction specific to post-shift motor learning. The authors should also report the prevalence of CA versus AP trials and clarify whether these trial types are behaviorally distinct.

We thank the reviewer for highlighting the need to emphasize that CA trials reflect a distinct behavior related to reaching the displaced port.

By definition, CA trials started as Motor Error trials and thus reflected a corrective lick toward the same port after an unrewarded lick. Almost all first contact licks on Motor Error trials were well outside the distribution of correct left licks both pre- and post-shift (Figure 1 - figure supplement 1B,D), consistent with the interpretation of this first lick as directed toward the right port. Thus, we see no evidence suggesting that CA trials involve a decision change. CA trials are exceedingly rare pre-shift, because Motor Error trials are rare pre-shift (Figure 1I, only ~5% of all right trials).

With regard to other error types before the shift, most expert-trained mice did not immediately sample the other port with a second lick after an unrewarded lick. They usually either stopped licking immediately or licked the unrewarded port multiple times before switching ports. When port switches occurred pre-shift, timing was highly variable across mice and trials. Even on rewarded trials, some mice would “check” the unrewarded port after consuming the reward, as can be seen in Figure 3I, J. All of these behaviors are clearly distinct from the stereotyped second lick that occurred on CA trials after the shift. We agree that the prevalence of CA and AP trials, as well as the prevalence of immediate port alternation, should be reported, and we have added that information to the article as follows:

“On Correction Attempted (CA) trials, the first lick made contact with the incorrect port, and the mouse chose to direct a second lick toward the correct port (Figure 5A; prevalence: 54% of motor error trials). We interpreted these licks as a corrective action, because the tongue exit angle shifted further toward the correct target (Figure 5B). Abandoned Port (AP) trials were the same as CA trials, except the mouse either did not make a second attempt or the second lick was directed toward the incorrect port (Figure 5A; prevalence: 46% of motor error trials).”

(3.4) The classification of pre-shift errors into motor and decision errors is not clear. If error-trial exit angles follow a unimodal distribution (Figure 1- Figure Supplement 1C), then the distinction between motor and decision errors may not be behaviorally well separated. The authors should explain how these categories are validated and whether conclusions depending on this classification are robust to alternative definitions.

We do not conclude that motor errors and decision errors are distinguishable pre-shift. Pre-shift licks were classified into motor error and decision error categories only to demonstrate that the boundary we established for classifying post-shift licks classifies extremely few (~5%, Figure 1I) pre-shift licks as motor errors. No conclusions were drawn from comparisons between pre-shift licks classified as decision errors and those classified as motor errors. The categorization is defined by the distribution of exit angles pre-shift and validated by the bimodal distribution of exit angles on error trials post-shift. To improve clarity regarding our classification of pre-shift errors, we have added the following to the Results:

“Exit angles after the shift exhibited a bimodal distribution across error trials (Figure 1G,H; Figure 1—figure supplement 1C,D), supporting this distinction in error type. The frequency of licks classified as motor errors on right-cued trials increased significantly after the shift (median pre-shift 0.06, median post-shift 0.42, p < 0.001; Figure 1I; Figure 1—figure supplement 1C,D), reflecting the new challenge of avoiding the left lickport. In contrast to after the shift, exit angles on error trials before the shift were unimodal (Figure 1—figure supplement 1C). These errors were classified based on the fixed CB in order to demonstrate that very few pre-shift licks qualify as motor errors (Figure 1I), and not to suggest that tongue trajectories before the shift are behaviorally well-separated.”

(3.5) Figure 1- Figure Supplement 1D, post-shift decision errors: Are these truly decision errors? The lick angles appear similar to those observed before the shift, suggesting that these trials may reflect execution of a "default" or "uncertain" lick trajectory rather than an incorrect choice under the new contingency.

The post-shift exit angles on right-cued decision error trials (Figure1 - figure supplement 1D, grey) are similar to the lick angles on correct left-cued trials pre-shift (Figure 1 - figure supplement 1A, red) and clearly different from the correct right-cued trials pre-shift (Figure 1 figure supplement 1A, blue). Thus, to the extent that the animal’s intention can be measured from lick trajectory, it was targeting the incorrect (left) port. It is also true that it may still target the previous location of the left port (a “default” left trajectory), but because the decision error makes precise targeting irrelevant to the task outcome (it is easy to reach the left port after the shift), we do not designate it as a joint decision error and motor error. As to whether the deliberative process leading to this action is somehow cognitively distinct from other behaviors typically labeled as decision errors or incorrect choices, we cannot say.

Minor:

(3.6) Vocabulary consistency: soma vs somata.

When data are shown for, or derived from, multiple somata, we use “somata”. When data are shown for an individual soma (such as in a panel with data from a single example soma), we use “soma.” We could not find any use of “somas,” which would indeed be inconsistent.

(3.7) Figure 1C: I am not sure why the lick trajectories do not depict the tongue exiting the mouse. What time window is shown? Why does it look like the trajectories are shifted to the left?

We thank the reviewer for identifying this issue. The definition of the location labeled “mouth” was accidentally omitted. The lick trajectories in Figure 1C do depict the tongue tip once it became visible to the cameras. Jaw opening and shifting partly determined the location where the tongue became visible in the videography. These movements varied from mouse to mouse and trial to trial, so exit angle was measured from the approximate midpoint between the temporomandibular joints, which is the grey point in 1C. We have fixed the captions and Methods to precisely define this location. With regard to the appearance of a slight leftward shift in the trajectories, this reflects how the tongue exits the mouth and how the tongue tip curves downward as the tongue approaches the port.

(3.8) Figure 1- Figure supplement 1: it could ease the comparisons to report population statistics, such as median, from panel A to panel B and D, population statistics from B to D.

Thank you. We have added these statistics to the Figure 1 - figure supplement 1 caption.

(3.9) Choice boundary (CB) should be defined in line 100, not 110.

Thank you. We have fixed this.

(3.10) Line 109: claim not supported by referenced figure (Figure 1 - Figure Supplement 1). Lick angle histogram to the right port, pre-shift does not overlap substantially with lick angle to the left port, post-shift.

We thank the reviewer for the opportunity to clarify this. We agree that Figure 1 - figure supplement 1 is not sufficient to support the claim. First, we want to make clear that Figure 1 - figure supplement 1 does not contradict the claim. The new location of the left port can obstruct the tongue during right-cued licks, regardless of the distributions of left licks pre- or post-shift. Second, to confirm that the new location of the left port would obstruct a substantial fraction of pre-shift right-cued lick trajectories, we measured the minimum distance between tongue trajectories and the post-shift location of the left port. Of pre-shift right-cued exit trajectories, 30 +/- 5% came within 1.25 mm – half of the combined tongue width (1.5 mm) and port width (1 mm) – of the port center.

To make this claim more precise, we have changed the statement as follows:

“Thus, on right-cued trials, mice continuing to follow the pre-shift motor plan would be biased to more frequently contact the new left port location (Figure 1E,F; 30 +/- 5% of pre-shift trajectories came within a tongue-width of the new location) and receive punishment (i.e., timeout).

(3.11) Line 113: claim not supported by referenced figure. Figure 1G does not display error trials.

We have changed the line to refer to “both correct and error trials”, such that reference to Figure 1G is also appropriate.

(3.12) Figure 2 - Figure Supplementary 3 & method: how is noise estimated?

Thank you. The following has been added to the Methods:

“For Figure 2 - figure supplement 3, noise was estimated as the square-root of the geometric mean of the Welch power spectrum in a high-frequency band (0.25–0.5 times the frame rate; Giovannucci et al., 2019).”

(3.13) Figure 2D: Was imaging during the shift epoch always performed in dendrites? If so, could the imaging schedule bias comparisons between dendritic and somatic activity during learning, especially given that mice show behavioral learning between early and late post-shift sessions (Figure 1J)?

No, imaging during the shift was not always performed in the dendrites. The following has been added to the Methods to make clear that the post-shift data reflect dendritic and somatic imaging conducted on the day of the shift with roughly similar frequency:

“For Figure 5 and Figure 6, which make comparisons between dendritic and somatic activity during the post-shift period, 67% of animals providing somatic data (4 of 6 mice) underwent somatic imaging on the day of the shift and 80% of mice providing dendritic data (8 of 10 mice) underwent dendritic imaging on the day of the shift.”

(3.14) Lines 163-164, "we observed that the onset of tuft activity was consistently time-locked to the GO cue (vertical green line; Figure 3B). This was in contrast to somatic activity, which had more variable timing (Figure 3E)." The authors cite panels B and E in support of this point, but these appear to be example ROIs. It would be helpful to clarify how representative these examples are, since the corresponding population summaries in panels G and H do not make the effect immediately apparent.

These examples are representative, as supported by the population summary of activity time-locked to the GO-cue versus port contact in Figure 4B.

(3.15) Figure 4B: It could be useful to add the lick traces here as well. To allow the reader to have an idea of contact timing with respect to the Go cue and compare the sustain response with the licking pattern.

We understand how this could be helpful. However, since these exact traces are already present in Figure 3I,J, we think that adding them to Figure 4 is unnecessary and would add complexity to an already very busy figure.

(3.16) Figure 5E: Why are the imaging sessions labeled 0 and +1 rather than 0 and +2? Are the dendritic and somatic imaging not alternated?

Yes, imaging was not alternated for 3 of the 22 mice. We have clarified this in the Methods, as follows:

“Somatic and dendritic imaging sessions alternated every other day (19 of 22 mice), except for 3 mice in which only one compartment was imaged daily (dendrite-only: 2 mice, soma-only: 1 mouse). The exceptions were due to brain curvature or the angle of the coverslip with respect to the brain, such that only one compartment could be imaged and the other compartment was underneath skull regrowth or dural thickening that made high-quality imaging impossible.”

(3.17) Figure 6C, legend: I suppose the authors meant "remapping", not "Post-shit SI distribution" for the description of the right column.

Thank you. We have fixed this label.

make this: Weil dein Darm Nährstoffe individuell verarbeitet, ist eine Standarddosis für dich nicht immer optimal.

make this: Eine Standarddosis orientiert sich am Durchschnitt. Wer den eigenen Ausgangspunkt kennt und mit Ballaststoffen, Vielfalt und Fermentiertem nachsteuert, arbeitet näher an dem, was der eigene Darm wirklich braucht.

make this: ist

make this: Dein Darm und die Dosis sind wichtig

make this: Das funktioniert bei jedem Menschen etwas anders.

make this: .

make this: in deinem Körper ankommt

make this: Eine Mikrobiom-Analyse

delete

make this: Nahrungsergänzung

make this: Grundsätzlich hast du in Sachen Ernährung und Vitamine vieles selbst in der Hand:

make this: oder

make this: Doch je

make this: Der praktische Schluss ist nicht, dass die Vitamine, die du bisher nimmst, ohne Test gar nichts bringen.

make this: Die Standarddosis eines Präparat ist nicht auf deinen Darm abgestimmt.

delete

delete

make this: Ein gutes

make this: Anbieter von Ernährungsergänzung

make this: setzen deshalb an deinem individuellen Mikrobiom an, statt an einem Durchschnittsprofil.

make this: Viele dieser Befunde liefern mechanistische Plausibilität aus dem Laborstudien, keinen direkten Nachweis, dass eine bestimmte Bakterienzusammensetzung deinen Vitamin-D-Spiegel hebt. Im Kern ist belegt: Deine Darmbakterien greifen an mehreren Stellschrauben der Nährstoffaufnahme mit ein und sie tun das bei jedem Menschen etwas anders.

make this: Belastbare Zahlen aus Studien mit Menschen sind hier allerdings dünn.

delete

make this: von

make this: Die Untersuchung wurde an einer kleinen und speziellen Gruppe durchgeführt. Sie ist ein Beleg dafür, dass das Mikrobiom die Reaktion auf ein Präparat vorhersagt, erlaubt aber keine allgemeine Dosierungsregel.

make this: mit 24 Personen sprachen 79 Prozent

make this: ableiten. Sie fragen danach, wie aktiv deine Bakterien sind, statt nur aufzulisten, welche vorhanden sind

make this: Nährstoff-Empfehlungen

make this: deinem eigenen Mikrobiom

make this: Ernährungsstudie

make this: Ganz klar ist die Befundlage aber nicht

make this: Deine Darmbakterien vergären Ballaststoffe, die dein Dünndarm nicht verdaut, zu kurzkettigen Fettsäuren: Acetat, Propionat und Butyrat.

make this: deckt ihn aber nicht

make this: zur Deckung deines Vitaminbedarfs bei

make this: bestimmt

delete

make this: leben

make this: welche Bakterien

make this: Die Bakterien im Dickdarm gelten inzwischen als eigene, wenn auch kleine Vitaminquelle – neben dem, was du isst

make this: kann

make this: Ist dein Darm nur Empfänger oder produziert er selbst Vitamine?

make this: aus der Analyse deines Mikrobioms

make this: eine

make this: Wer wissen will, wie der eigene Darm funktioniert, kann das inzwischen messen lassen.

make this: ist eine pauschale Dosis eines Präparats für dich immer nur eine grobe Schätzung dessen, was du brauchst.

make this: Vitamintablette

make this: nehmen

The evidence for what? Need to clarify. Also don't use "mechanistisch". Also: if the studies were just observational and found a correlation, how do they explain the functioning at the same time?

make this: stärker auf den eigenen Darm abstimmen.

make this: Die Standarddosis eines Vitaminpräparats orientiert sich immer nur am Bevölkerungsdurchschnitt.

make this: Zwischen Tablette und Blutbahn sitzt dein Darmmikrobiom – und das ist bei jedem Menschen etwas anders.

make this: davon dein Körper wirklich verwertet

eLife Assessment

This study provides a useful analysis of the effects of arginine vasopressin (AVP) on islet cells in pancreatic tissue slices, using sophisticated spatiotemporal calcium recordings to show that AVP affects α and β cells differently depending on glucose concentration. The calcium imaging, analytical approaches, and V1b receptor-targeted peptide ligands are strengths of the work. However, the reviewers were concerned that the proposed mechanistic model is not sufficiently supported by the data. Characterisation of β-cell responses remains incomplete, and potential off-target effects and limited receptor specificity raise alternative explanations, including indirect effects mediated through α cells. The study would have been strengthened by signalling pathway analyses, genetic validation (e.g. β-cell-specific V1bR deletion), or selective V1b receptor silencing. The RNAscope data included in the revision indicate broader expression patterns but do not clearly establish receptor localisation within specific endocrine populations.

Reviewer #1 (Public review):

Summary:

The paper investigates how AVP modulates pancreatic alpha and beta cell activity using acute mouse pancreatic tissue slices, calcium imaging, hormone secretion assays, RNAscope, and newly synthesized receptor-selective ligands. The Authors report that AVP regulates islet cell activity in a glucose- and state-dependent manner, with maximal effects occurring within physiological AVP concentrations and a bell-shaped concentration-response profile. They conclude that V1b receptors are the principal mediators of these effects and propose that IP3 receptor-dependent signaling underlies the observed nonlinear responses.

Strengths:

The use of fresh pancreatic tissue slices preserves islet architecture and cell-cell interactions, providing a physiologically relevant experimental model compared with isolated islets or immortalized cell lines.

The combination of live calcium imaging, hormone secretion measurements, RNAscope, and pharmacological characterization of newly synthesized receptor-selective ligands represents a technically comprehensive experimental approach that addresses AVP signaling from multiple complementary perspectives.

Weaknesses:

(1) The central mechanistic model of the manuscript is not supported by the experimental data. Although the Authors repeatedly attribute the observed bell-shaped responses to IP3 Receptor activation and inactivation, no direct mechanistic evidence is provided to implicate IP3 receptors. Experiments assessing IP3 receptor function using genetic manipulation and direct measurements of IP3 signaling are necessary before such mechanistic conclusions can be drawn.

(2) The Authors should directly demonstrate V1b receptor expression in β cells using complementary approaches, since the RNAscope data indicate broader expression but do not convincingly establish receptor localization within specific endocrine populations.

(3) In my opinion, the central conclusion that V1b receptors are the predominant mediators of the observed effects is insufficiently supported because definitive loss-of-function experiments are lacking. Genetic deletion or selective silencing of V1b receptors should be provided to validate the proposed mechanism.

(4) The heterogeneous responses observed among islets substantially weaken the proposed mechanistic model. Data should be provided to identify the determinants responsible for activation, absence of response, or inhibition in individual islets.

(5) Please explain why the marked changes in alpha-cell calcium activity were not accompanied by corresponding alterations in glucagon secretion. This apparent discrepancy requires additional experimental evidence.

(6) The Authors need to provide stronger evidence linking the observed calcium dynamics with insulin secretion, since calcium measurements alone cannot establish the proposed functional consequences.

(7) Proper assays should be provided to assess whether the newly synthesized ligands exhibit comparable selectivity and efficacy at murine receptors rather than relying primarily on pharmacological characterization performed using human receptor-expressing cell lines.

(8) The proposed absence of V1a receptor involvement is based primarily on pharmacological inhibition. Independent experimental approaches should be provided to exclude a contribution of this receptor subtype.

(9) They must provide additional quantitative analyses demonstrating that the reported bell-shaped concentration-response relationship is robust across individual experiments rather than reflecting substantial biological variability.

(10) The Authors should include experiments evaluating endogenous AVP signaling under more physiological conditions instead of relying predominantly on exogenous agonist administration.

(11) I believe the role of forskolin deserves further clarification because many conclusions were obtained under cAMP-permissive conditions that may substantially influence AVP responses. Additional experiments without pharmacological cAMP stimulation should be presented.

(12) Please clarify how beta cells and alpha cells were identified exclusively from functional activity patterns during calcium imaging and provide independent validation of cell identity within the analyzed recordings.

(13) In my opinion, the manuscript relies heavily on changes in intracellular calcium activity as a surrogate for endocrine function, whereas the secretion data do not consistently support the proposed functional conclusions. Additional evidence is needed to establish a direct relationship between the observed calcium dynamics and hormone release.

(14) The Authors should better reconcile their findings with previous reports showing minimal or absent AVP receptor expression in β cells and explain how the current data resolve these discrepancies rather than adding another possible interpretation.

Reviewer #2 (Public review):

Summary:

In this paper Drs. Kercmar, Murko and Bombek make a series of observations related to the role of AVP in pancreatic islets. They use the pancreatic slice preparation that their group is well known for. The observations on the slide physiology are technically impressive. However, I am not convinced by the conclusions of this manuscript for a number of reasons. At the core of my concern is perhaps that this manuscript appears to be motivated to resolve 'controversies' surrounding the actions of AVP on insulin and glucagon secretion. This manuscript adds more observations, but these do not move the field forward in improving or solidifying our mechanistic understanding of AVP actions on islets. A major claim in this manuscript is the beta cell expression of the V1b Receptor for AVP, but the evidence presented in this paper fall short of supporting this claim. Observations on the activation of calcium in alpha cells via V1b receptor align with prior observations to this effect and can explain the effects of beta cell calcium and insulin secretion better than an explanation where beta cells express functional V1BR, for which direct evidence is lacking.

I have focused my main concerns below. I hope the authors will consider these suggestions carefully - please be assured that they were made with the intent to support the authors and increase the impact of this work.

Strengths:

The main strength of this paper is the technical sophistication of the approach and the analysis and representation of the calcium traces from alpha and beta cells.

Weaknesses:

(1) There are excellent data that indicate that the actions of AVP are mediated via V1bR on alpha cells and that V1bR is 1) not expressed by beta cells and 2) does not activate beta cell calcium at all at 10 nM - which is the same concentration used in this paper (Figure 4G) for peak alpha cell Ca2+ activation (see https://doi.org/10.1016/j.cmet.2017.03.017; cited as ref 30 in the current manuscript). Any published stimulatory actions of AVP on insulin secretion can be explained by the potentiating effects of glucagon, released in response to AVP stimulation of alpha cells.

(2) The RNAscope data offered in the revision as a second line of evidence for the expression of the V1bR in beta cells do not convince. I applaud the authors for trying as these are hard experiments to do well, as evidenced from the Gcg RNAscope signal that is not at all concentrated in the islet periphery, and in fact both color puncta occur outside of the islet at similar density. Absent a convincing concentration of Gcg signal (which is a very abundant transcript in alpha cells), it is hard to depend on these results. They certainly do not substitute experiments to determine cell autonomous activation of isolated beta cells by AVP. Claim of beta cell expression of V1br, require a more direct demonstration by staining (if appropriate antibodies exist), by beta cell-specific deletion of V1br, or by documenting the direct calcium activation in isolated beta cells in the absence of alpha cells. This should include a demonstration of Gaq-dependence in isolated beta cells.

(3) We know from bulk RNAseq data on purified alpha, beta, and delta cells from both the Huising and Gribble groups that there is no expression of V2a. I will point you to the data from the Huising lab website published almost a decade ago (http://dx.doi.org/10.1016/j.molmet.2016.04.007) - which is publicly available and can be used to generate figures (https://huisinglab.com/data-ghrelin-ucsc/index.html). They indicate the absence of expression of not only AVP2 receptors anywhere in the islet - but the lack of expression of V1bra, V1brb, and Oxtr in beta cells. These AVP/OXT receptor expression data are largely and helpfully confirmed by the efforts in this paper that involved the generation of the V1aR agonist and V2R antagonist.

(4) Importantly, the lack of V1br from beta cells does not invalidate observations that AVP affects calcium in beta cells, but it does indicate that these effects are mediated 1) indirectly, downstream of alpha cell V1br or 2) via an unknown off-target mechanism (less likely). The different peak efficacies in Figure 4G would also suggest they are not mediated by the same receptor. The recent work by Huixia Ren and colleagues (PMID: 41916313) that demonstrates that glucagon accelerates the frequency of beta cell calcium is in line with such a scenario.

(5) The use of forskolin across almost all traces complicates the interpretation of the results. The design does not account for the elevation of cAMP in alpha cells and subsequent release of glucagon - particularly upon co-stimulation with AVP which permits glucagon release by activating a calcium response in alpha cells. This glucagon then could activate beta cells. If resolving the mechanism of action is the goal, often less is more. The activation of Gaq-mediated calcium is not cAMP dependent (although the downstream hormone secretion clearly often is). As was shown, AVP does not activate calcium in beta cells in the absence of cAMP. The experiments should have been completed in the absence of cAMP/forskolin, which would likely have had different outcomes on the beta cell responses and to the hormone secretion.

(6) It is motivated by a desire to 'study the AVP dependence of both alpha and beta cells at the same time'. As best as I can determine, the design choice to conduct most studies under sustained forskolin stimulation is related to the permissive actions of AVP on hormone secretion in response to cAMP-generating stimuli. The permissive actions by AVP that are cited are on hormone secretion - which in many cell types requires activation of both calcium and cAMP signaling. Whether the activation of V1br and subsequent calcium responsive is permitted by cAMP is unclear. I believe the argument the authors are making here is that the activation of beta cell calcium by AVP is permitted by forskolin. i.e. the cAMP stimulated by it in beta cells.

(7) Figure 9 suggests a pharmacological activation of beta cell V1bR in the low pM range. How do the authors reconcile this compare with the apparent absence of an effect of AVP stimulation at low pM to low nM doses in beta cells (Figure 5A). I note that there are changes over time with sustained beta cell stimulation with 8 mM glucose, but these changes are relatively subtle, gradual and quite likely represent the progression of calcium behaviors that would have occurred under sustained glucose irrespective of these very low AVP concentrations. I will note that the Kd of the V1bR for AVP is around 1 nM, with tracer displacement starting around 100 pM according to the data in figure 6B, which is hard to reconcile with changes in beta cell calcium by AVP doses that start 10-100-fold lower than this dose at 1 and 10 pM (Figure 9).

Reviewer #3 (Public review):

Summary:

This work aims to better understand the role of arginine vasopressin (AVP) in the control of islet hormone secretion. This builds on previous literature in this area reporting on the actions of AVP to stimulate islet hormones. The gap in literature being addressed by these studies is primarily focused on the glucose-dependency of AVP on both insulin and glucagon secretion. A secondary objective is to explore the role of individual receptors with the use of newly generated peptides and existing tools. The methods include the use of Ca2+ imaging in pancreas slices from mice, with additional outcomes including insulin secretion in some areas. The conclusions presented are that AVP acts through V1b receptors in both alpha- and beta-cells, that this activity occurs in the high cAMP environment, and is glucose dependent.

Strengths:

The area of research is emerging with plenty of room for new contributions. The concept of AVP stimulating islet hormone secretion is important and deserving of further insight. The use of pancreas tissue to image primary cells makes the experiments physiologically relevant. The advancement of novel tools in this area should be helpful to other groups investigating the actions of AVP.

Comments on revised version:

Overall, the authors have modified their conclusions to more accurately capture the results of this manuscript. They also add the significant limitations outlined in the review process to the Discussion. With the addition of new data, however, a few concerns have emerged.

(1) The rational for showing somatostatin staining in Figure 1 a is unclear. It also does not appear to be the same region of interest as in panel B.

(2) It is difficult to assess the success of the RNAscope with the representative image used in Figure 1b. It is surprising how low the Gcg signal is in this image, suggesting some optimization is required. Additionally, how many mice were used (biological replicates) and how many V1b receptor+ and Gcg+ cells per mouse were quantified for the RNAscope images? This must be indicated in the methods section and should be sufficiently powered to make a conclusion.

(3) While the addition of insulin and glucagon secretions with AVP ramp provide a functional output to the calcium imaging, it is unclear why the measurements are sometimes log10 transformed (Figure 5 K and L) but not always (Figure 5E). It is difficult to interpret negative glucagon values. What is the functional output of the dose-dependent calcium response to AVP in alpha cells if it is not glucagon?

(4) Finally, the highlights section has not been refined to the revised interpretations of the manuscript.

Author response:

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

eLife Assessment

This study presents a useful finding on the effects of arginine vasopressin (AVP) on islet cells in pancreatic tissue slices, using technically sophisticated spatio-temporal calcium recordings to confirm that AVP influences α and β cells differently depending on glucose concentrations. While the study's methods - particularly the calcium imaging techniques and peptide ligand design targeting V1b receptors - are strong, the reviewers were concerned about several aspects of the experimental design. However, the results on βcell responses are incomplete and insufficient to support the manuscript's claims, especially due to the high variability of islet responses and lack of mechanistic and functional (hormone release) data. There are also concerns about the possibility of off-target effects and incomplete receptor specificity, noting that the study would have been significantly strengthened by inclusion of signaling pathway interrogation, hormone output assays, genetic validation (e.g., β cell-specific deletion of V1br), and receptor localization, although the work will still be of interest to researchers studying islet physiology in the context of health and diabetes.

We sincerely thank the reviewers and editors for their thorough evaluation of our manuscript and their recognition of its technical strengths, including the advanced spatio-temporal calcium imaging and the rational design of selective V1b receptor ligands. We appreciate their acknowledgement of the study’s relevance for understanding AVP effects in a physiologically intact islet context and their positive assessment of our methodological rigour and innovation. The reviewers’ constructive feedback has helped us clarify the boundaries and intent of our study, which focuses on the glucose- and context-dependent modulation of α- and β-cell activity, rather than exhaustive molecular dissection.

While the reviewers rightly emphasize the importance of receptor specificity and downstream signaling validation, we respectfully suggest that some of their concerns may reflect a lingering bias toward reductionist frameworks. Our interpretation is rooted in the emerging understanding that β-cell behaviour is largely defined by dynamic intercellular interactions within the islet collective, rather than by static gene expression or receptor localization alone (Jin et al., 2025; Korošak et al., 2021; Rutter et al., 2024). Recent studies have demonstrated that roles such as “leader” or “hub” β cells are transient and emergent, governed more by timing, environment, and local network structure than by fixed molecular identity (Postic et al., 2023; Gosak et al., 2018).

This has profound implications for how we interpret cell responsiveness to agents like AVP: what appears as biological variability may in fact reflect context-sensitive transitions within a non-linear, self-organizing system (Stožer et al., 2021). Hence, we chose to focus on functional collective dynamics using intact pancreatic slices, rather than isolated cell models which fail to preserve the essential network architecture of islets. Although the addition of genetic models or isolated receptor measurements would strengthen receptor-specific conclusions, we argue that such approaches alone cannot resolve the physiological complexity of a system where function arises from cell–cell communication and spatiotemporal context.

Indeed, the lack of direct correlation between receptor transcript abundance and functional outcomes has been noted in prior studies, reinforcing the view that function cannot be strictly predicted by molecular presence (Rutter et al., 2024). As articulated in our manuscript, the islet behaves as a sensory collective (Fancher & Mugler, 2017), where emergent patterns— not static cell identity—determine behaviour. This perspective aligns with broader shifts in biology away from strict genetic determinism toward causal emergence and collective agency (Ball, 2023; Levin, 2021).

We therefore believe our study contributes not only new pharmacological insights but also a conceptual reframing of how AVP responses should be interpreted in a complex organ like the pancreas. We have added new data addressing reviewer suggestions—such as glucagon secretion assays, clarifications on the role of forskolin, and an analysis of event timing—that further support our conclusions. We also expanded the discussion on how islet variability is functionally meaningful, not just noise, and explained why β-cell responses to AVP must be interpreted within this probabilistic framework.

We agree that future work should include receptor-specific knockouts and more direct signaling pathway assays, but these would need to be designed with careful consideration of the islet’s dynamic topology and the emergent nature of β-cell roles. In this light, we see our study not as the final word, but as a necessary systems-level foundation for more targeted interventions. We thank the reviewers again for their careful critiques and hope that our response clarifies both the rationale and scope of our work. Our revisions aim to enhance the paper’s clarity while maintaining its commitment to an integrative, physiology-rooted approach.

We thank the reviewers and editors for their thoughtful and constructive assessment of our work. We are especially grateful for their recognition of the study’s technical strengths, including the use of spatio-temporal calcium imaging in intact pancreatic tissue and the strategic development of receptor-selective peptide ligands. We also appreciate their acknowledgement that our study contributes to the understanding of glucose-dependent AVP effects in islet physiology. The reviewers’ concerns regarding variability, receptor specificity, and functional validation helped us further clarify the scope and context of our study.

We respectfully submit that some reservations stem from a reductionist framing that may not fully account for the collective behaviour of islets. As we and others have shown, β-cell function arises from emergent, self-organizing network dynamics, not just from static gene expression or receptor abundance (Jin et al., 2025; Korošak et al., 2021; Postic et al., 2023). In this view, pharmacological heterogeneity across islets is not simply noise or experimental inconsistency, but a signature of dynamic attractor states within the islet network (Stožer et al., 2021). Because an islet functions as a coupled system, most response variability originates from its emergent collective behavior, which eclipses variability in receptor expression or metabolic state.

For this reason, even single-islet receptor quantification or ATP measurements would provide limited explanatory power: it is the state of the network—not absolute receptor levels—that determines whether a perturbation elicits activation or inhibition. As we illustrate in our graphical abstract, a single islet tested repeatedly under identical glucose conditions can yield divergent responses, simply because it occupies different dynamic states. These findings are in line with systems biology and network science approaches, which have revealed that cell function, especially in the β-cell collective, cannot be fully understood through reductionist parameters alone (Gosak et al., 2018; Ball, 2023).

We have included glucagon secretion assays and new analyses to address key reviewer suggestions. Still, we chose not to pursue extensive knockouts or cAMP imaging, as these would require a different experimental scope and could risk disrupting the very dynamics we aim to understand. Likewise, while direct measurements of V1bR or IP3R expression would add molecular detail, they are not definitive without network context. The bell-shaped AVP dose-response curve and its explanation through IP3R inactivation are supported by prior studies; we invoke this mechanism not speculatively, but because it provides the most parsimonious explanation for the glucose-dependent shift in β-cell responsiveness.

We also clarify that our study does not aim to resolve every mechanistic detail, but rather to offer a systems-level insight into how AVP modulates islet dynamics across varying glucose and cAMP contexts. The implications extend beyond AVP pharmacology, suggesting that perturbations to β-cell function must be understood within a probabilistic, state-dependent framework (Fancher & Mugler, 2017). This resonates with emerging concepts in cell physiology that emphasize causal emergence and local agency over static molecular determinism (Levin, 2021; Rutter et al., 2024).

In summary, we see our work as part of a necessary shift in perspective—from linear receptor-function models to context-sensitive dynamic systems. We are grateful for the opportunity to revise our manuscript in response to insightful feedback and hope our clarifications and new data will strengthen its impact for the islet research community.

Public Reviews:

Reviewer #1 (Public review):

Summary:

The authors confirmed earlier findings that AVP influences α and β cells differently, depending on glucose concentrations. At substimulatory glucose levels, AVP combined with forskolin - an activator of cAMP -did not significantly stimulate β cells, although it did activate α cells. Once glucose was raised to stimulatory levels, β cells became active, and α cell activity declined, indicating glucose's suppressive effect on α cells and permissive effect on β cells. Under physiological glucose levels (8-9 mM), forskolin enhanced β-cell calcium oscillations, and AVP further modulated this activity. However, AVP's effect on β cells was variable across islets and did not significantly alter AUC measurements (a combined indicator of oscillation frequency and duration). In α cells, forskolin and AVP led to increased activity even at high glucose levels, suggesting that α cells remain responsive despite expected suppression by insulin and glucose.

Experiments with physiological concentrations of epinephrine suggest that AVP does not operate via Gs-coupled V2 receptors in β cells, as AVP could not counteract epinephrine's inhibitory effects. Instead, epinephrine reduced β cell activity while increasing α cell activity through different G-proteincoupled mechanisms. These results emphasize that AVP can potentiate αcell activation and has a nuanced, context-dependent effect on β cells.

The most robust activation of both α and β cells by AVP occurred within its physiological osmo-regulatory range (~10-100 pM), confirming that AVP exerts bell-shaped concentration-dependent effects on β cells. At low concentrations, AVP increased β cell calcium oscillation frequency and reduced "halfwidths"; high concentrations eventually suppressed β cell activity, mimicking the muscarinic signaling. In α cells, higher AVP concentrations were required for peak activation, which was not blunted by receptor inactivation within physiological ranges.

Attempting to further dissect the role of specific AVP receptors, the authors designed and tested peptide ligands selective for V1b receptors. These included a selective V1b agonist; a V1b agonist with antagonist properties at V1a and oxytocin receptors; and a selective V1a antagonist. In pancreatic slices, these peptides seem to replicate AVP's effects on Ca²⁺ signaling, although responses were highly variable, with some islets showing increased activity and others no change or suppression. The variability was partly attributed to islet-specific baseline activity, and the authors conclude that AVP and V1b receptor agonists can modulate β cell activity in a statedependent manner, stimulating insulin secretion in quiescent cells and inhibiting it in already active cells.

We applaud the reviewer to capture the essence of work in their introduction.

Strengths:

Overall, the study is technically advanced and provides useful pharmacological tools. However, the conclusions are limited by a lack of direct mechanistic and functional data. Addressing these gaps through a combination of signaling pathway interrogation, functional hormone output, genetic validation, and receptor localization would strengthen the conclusions and reduce the current (interpretive) ambiguity.

Thank you!

Weaknesses:

(1) The study is entirely based on pharmacological tools. Without genetic models, off-target effects or incomplete specificity of the peptides cannot be fully ruled out.

We partially agree with this comment and acknowledge that genetic models would provide a valuable complementary approach to address possible off-target effects or incomplete peptide specificity. However, genetic models also have important limitations, particularly when the aim is to resolve subtle, population-level physiological differences in beta cell activity. We therefore used pharmacological tools at different concentrations to test whether the observed effects were concentration-dependent and consistent with the expected receptor-mediated actions. An advantage of the pancreatic slice preparation is that it preserves much of the native tissue environment and allows pharmacological manipulation within concentration ranges closer to in vivo efficacy, thereby reducing the likelihood of nonspecific effects. To compensate for the lack of genetic models, we now emphasize the collective activity analysis as an additional strength of the study and have clarified this limitation in the revised manuscript.

(2) Despite multiple claims about β cell activation or inhibition, the functional output - insulin secretion - is weakly assessed, and only in limited conditions. This aspect makes it very hard to correlate calcium dynamics with physiological outcomes.

We agree that the functional output needed stronger support and have therefore expanded the hormone secretion experiments. While the effects of AVP and its analogues were tested during a stable plateau phase in the Ca²⁺ imaging experiments, this phase provides only a narrow dynamic range for insulin release measurements in mouse slices. We therefore added a sequence of stimulations on the same slices, using 8 mM glucose and 500 nM forskolin, with glucose lowered to a non-stimulatory range between different AVP concentrations. These new experiments better define how AVP-dependent changes in Ca²⁺ dynamics translate into insulin secretion under conditions with a broader secretory dynamic range. The new insulin and glucagon secretion data have now been added to the manuscript as Figure 5, and the text has been revised accordingly.

(3) Insulin and glucagon secretion assays should be provided; the authors should measure hormone release in parallel with Ca2+ imaging, using perifusion assays, especially during AVP ramp and peptide ligand applications.

We added insulin and glucagon secretion assays for AVP ramp to Figure 5.

Additionally, there is no standardization of the metabolic state of islets. The authors should consider measuring islet NAD(P)H autofluorescence or mitochondrial potential (e.g., using TMRE) to control for metabolic variability that may affect responsiveness.

We agree that standardization of the metabolic state of the islets would further strengthen the interpretation of the responsiveness data. We attempted to address this experimentally, but the results were inconclusive and therefore not included in the manuscript. Based on our previous unpublished observations, NAD(P)H levels appear to be significantly higher and less variable in islets within tissue slices than in isolated islets, suggesting that the slice preparation may better preserve the native metabolic state. However, we acknowledge that this remains an important limitation and we now indicate that in the manuscript. Additional experiments will be required to establish a robust and standardized approach, for example by combining NAD(P)H autofluorescence and/or mitochondrial potential measurements with Ca²⁺ imaging.

(4) There is a high degree of variability in response to AVP and V1b agonists across islets (activation, no effect, inhibition). Surprisingly, the authors do not fully explore the cause of this heterogeneity (whether it is due to receptor expression differences, metabolic state, experimental variability, or other conditions).

This is a well-taken point and has indeed been one of the major bottlenecks in interpreting the results of this study. We agree that the variability in responses to AVP and V1b agonists may reflect several factors, including receptor expression, metabolic state, experimental conditions, and differences in the functional state of individual islets. However, our data also suggest that the beta cell population within an islet should be considered as a dynamic, non-linear system, in which even small differences in initial conditions or collective state can result in qualitatively different outcomes, including activation, no apparent effect, or inhibition. In this framework, the response to AVP is not determined by receptor expression alone, but by the current physiological context of the islet network. This is also why we believe that pharmacological tests are most informative when interpreted within a defined functional state rather than as isolated receptor-specific readouts. As indicated in the graphical abstract, apparently similar islets may occupy different dynamic states and therefore respond differently to the same Gq/PLC/IP3R stimulus. We have now expanded the discussion to make this interpretation more explicit and to acknowledge that receptor expression, metabolic variability, and experimental factors remain possible contributors that will require further targeted studies.

The following text has been added to expand the discussion:

“The heterogeneous responses observed across different islets, where some showed increased activity while others showed no detectable change or inhibition, could intuitively be attributed to variability in V1b receptor expression or signaling capacity among β cells. Such an explanation would be consistent with differences in receptor density, coupling efficiency to Gq proteins, or downstream signaling components such as PLC or IP₃ receptors. However, our data suggest that receptor-level variability alone is unlikely to fully explain the observed response spectrum, and that the current functional state of the islet collective must also be considered. The islet behaves as a non-linear dynamic system in which the same molecular perturbation can produce different functional outcomes depending on the current state of the β-cell collective. In such systems, cells or cell populations do not occupy a single deterministic activity state, but rather move within a landscape of possible states, with perturbations shifting the probability distribution of transitions between them. This concept is well established in dynamical systems approaches to biological cell-state transitions, where attractor landscapes, noise, and signaling inputs determine the probability of moving between alternative functional states rather than enforcing a single fixed output.

In this framework, AVP and V1b receptor-selective agonists may reshape the probability landscape of β-cell activity. Depending on the initial metabolic, electrical, and Ca²⁺-handling state of the islet, the same stimulus may increase oscillation frequency, produce little detectable effect, or shift the system toward reduced activity or functional inactivation. This interpretation is also consistent with studies of pancreatic islet dynamics showing that βcell Ca²⁺ activity emerges from coupled electrical, metabolic, and network interactions rather than from the properties of individual cells alone. Thus, molecular variability in V1b receptor expression or signaling capacity may contribute to the heterogeneous responses, but it is unlikely to determine them without considering the collective dynamic state of the islet.”

(5) There is no validation of V1b receptor expression at the protein or mRNA level in α or β cells using in situ hybridization, immunohistochemistry, or spatial transcriptomics.

We agree with the reviewer that spatial validation of V1b receptor expression is important for interpreting the cellular targets of AVP signaling in the islet. We have therefore added RNAscope in situ hybridization data to the revised manuscript to assess V1b receptor mRNA expression within the pancreas and islet. These new data show a broader expression pattern of V1b receptor transcripts within the islet than originally assumed, suggesting that AVP signaling may not be restricted to a single endocrine cell population. At the same time, the RNAscope analysis confirms previous reports of higher AVP receptor expression in glucagon-positive alpha cells. We have added these results to Figure 1 and revised the corresponding Results and Discussion sections to clarify that the observed functional responses may reflect both direct effects on beta cells and indirect intra-islet effects mediated through alpha-cell signaling.

(6) AVP effects are described in terms of permissive or antagonistic effects on cAMP (especially in relation to epinephrine), but direct measurements of cAMP in α and β cells are not shown, weakening these conclusions. The authors should use Epac-based cAMP FRET sensors in α and β cells to monitor the interaction between AVP, forskolin, and epinephrine more conclusively.

We agree that direct measurements of cAMP dynamics in alpha and beta cells would provide a more conclusive assessment of the interaction between AVP, forskolin, and epinephrine signaling. We attempted to address this experimentally; however, within the time domain of the Ca²⁺ oscillations analyzed here, the temporal resolution and robustness of currently available cAMP readouts were not sufficient to resolve these interactions reliably. Even at slower time scales, cAMP sensor signals can be difficult to interpret quantitatively and may be overinterpreted if not tightly linked to the functional readout. We have therefore moderated the wording of the manuscript and now describe the proposed permissive or antagonistic interaction between AVP/V1b and cAMP-dependent signaling as an interpretation supported by the pharmacological Ca²⁺ response patterns, rather than as a directly demonstrated cAMP mechanism. We now explicitly acknowledge in the limitations that most experiments were performed under cAMP-permissive conditions, which increases sensitivity for detecting AVP-dependent modulation but complicates the separation of direct beta cell effects from intra-islet interactions. Future studies using optimized cell-type-specific Epac-based sensors will be required to resolve this interaction.

(7) Single-islet transcriptomics or proteomics (also to clarify variability) should be provided to analyze receptor expression variability across islets to correlate with response phenotypes (activation vs inhibition). Alternatively, the authors could perform calcium imaging with simultaneous insulin granule tracking or ATP levels to assess islet functional states.

We agree that single-islet transcriptomics, proteomics, or simultaneous metabolic readouts could provide useful complementary information, particularly for describing molecular variability across islets. However, we do not think that differences in receptor expression or ATP levels alone are sufficient to explain the diversity of response phenotypes observed here. Our interpretation is that the beta cell population behaves as a collective dynamic system, in which the same input can lead to different outcomes depending on the current state of the network and its local physiological context. In such a system, AVP/V1b signaling does not necessarily impose a single deterministic response, but changes the probability distribution of accessible states, including activation, inhibition, or no detectable response. Theoretically and partially confirmed by the preliminary data, even the same islet exposed repeatedly under apparently identical conditions could be expected to display different responses if it occupies a different position within this dynamic state space at the time of stimulation. This concept is summarized in the graphical abstract and is central to our interpretation of the pharmacological data. We have therefore clarified in the Discussion that receptor expression, ATP levels, and other molecular parameters may modulate the response landscape, but are unlikely to fully define the observed functional phenotype without considering the collective dynamics of the islet.

Added to Discussion section: “In this framework, AVP and V1b receptorselective agonists may reshape the probability landscape of β-cell activity. Depending on the initial metabolic, electrical, and Ca²⁺-handling state of the islet, the same stimulus may increase oscillation frequency, produce little detectable effect, or shift the system toward reduced activity or functional inactivation. This interpretation is also consistent with studies of pancreatic islet dynamics showing that β cell Ca²⁺ activity emerges from coupled electrical, metabolic, and network interactions rather than from the properties of individual cells alone (63). Thus, molecular variability in V1b receptor expression or signaling capacity may contribute to the heterogeneous responses, but it is unlikely to determine them without considering the collective dynamic state of the islet.”

(8) While the study implies AVP acts through V1b receptors on β cells, the signaling downstream (e.g., PLC activation, IP3R isoforms involved) is simply inferred but not directly shown.

We agree that downstream signaling was not directly resolved at the level of PLC activation or specific IP3R isoforms. However, we did not infer Gq/PLC/IP3R involvement solely from AVP pharmacology, but used ACh as an independent Gq-coupled receptor reference stimulus in the same pancreatic slice preparation. With ACh concentration ramps, we could reproduce both activation and inactivation patterns observed with AVP/V1b stimulation, supporting the interpretation that these responses arise from modulation of the Gq-dependent Ca²⁺ signaling axis.

In addition, in prelilminary expriments we could observe that inhibition of Gq activity with YM254890, as well as interference with IP3R-dependent signaling using Xestospongin C, diminished the response, although not completely. This incomplete suppression is important, because it suggests that beta cell Ca²⁺ homeostasis and collective islet activity are not controlled by a single linear pathway, but by partially redundant and context-dependent mechanisms. We have therefore revised the manuscript to state more cautiously that our data support the involvement of Gq/PLC/IP3R-dependent signaling, while acknowledging that direct measurements of PLC activity and IP3R isoform-specific contributions remain outside the scope of the present study.

(9) The interpretation that IP3R inactivation (mentioned in the title!) underlies the bell-shaped AVP effect is just hypothetical, without direct measurements. Assays in β (and/or α)-cell-specific V1b KO mice and IP3R KO mice must be provided to support these speculations.

We agree that the involvement of IP3R-dependent signaling should be stated with appropriate caution. However, the concept of IP3R inactivation as a mechanism contributing to bell-shaped Gq-dependent Ca²⁺ responses is not purely hypothetical, since IP3R inactivation has been directly demonstrated in previous studies and provides a parsimonious explanation for the shift from activation to suppression at higher AVP concentrations. In the present study, this interpretation is further supported by the glucose dependence of the AVP concentration-response relationship, where different stimulatory glucose conditions shift the apparent efficacy peak.

We also agree that cell-specific V1b receptor and IP3R knockout experiments would be valuable future approaches. In this respect, we have obtained preliminary results from a small sample of IP3R triple-knockout mice, which cannot yet be fully included because they are part of an ongoing collaboration. In these experiments, supraphysiological AVP concentrations did not produce the IP3R-like beta-cell response pattern observed in controls, namely reduced halfwidth and increased frequency, whereas alpha cell stimulation was preserved similarly to WT slices.

At the same time, we believe that definitive knockout experiments must be carefully designed, because the beta cell population behaves as a dynamic collective system in which the response to AVP depends on the current functional state of the islet, glucose context, and intercellular coupling. We therefore now present IP3R inactivation as a strongly supported mechanistic interpretation rather than as a directly proven mechanism in this study, and we explicitly acknowledge that cell-specific V1b and IP3R genetic models will be required to fully resolve this pathway.

Reviewer #2 (Public review):

Summary:

In this paper, Drs. Kercmar, Murko, and Bombek make a series of observations related to the role of AVP in pancreatic islets. They use the pancreatic slice preparation that their group is well known for. The observations on the slide physiology are technically impressive. However, I am not convinced by the conclusions of this manuscript for a number of reasons. At the core of my concern is perhaps that this manuscript appears to be motivated to resolve 'controversies' surrounding the actions of AVP on insulin and glucagon secretion. This manuscript adds more observations, but these do not move the field forward in improving or solidifying our mechanistic understanding of AVP actions on islets. A major claim in this manuscript is the beta cell expression of the V1b Receptor for AVP, but the evidence presented in this paper falls short of supporting this claim.

Observations on the activation of calcium in alpha cells via V1b receptor align with prior observations of this effect.

I have focused my main concerns below. I hope the authors will consider these suggestions carefully - please be assured that they were made with the intent to support the authors and increase the impact of this work.

We thank the reviewer for their detailed input and support to increase the impact of our work and our understanding of important cellular processes overall. We have considered their suggestions carefully to further expand the strenghts of our approach and analysis.

Strengths:

The main strength of this paper is the technical sophistication of the approach and the analysis and representation of the calcium traces from alpha and beta cells.

Thank you!

Weaknesses:

(1) The introduction is long and summarizes a substantive body of literature on AVP actions on insulin secretion in vivo. There are a number of possible explanations for these observations that do not directly target islet cells. If the goal is to resolve the mechanistic basis of AVP action on alpha and beta cells, the more limited number of papers that describe direct islet effects is more helpful. There are excellent data that indicate that the actions of AVP are mediated via V1bR on alpha cells and that V1bR is a) not expressed by beta cells and b) does not activate beta cell calcium at all at 10 nM - which is the same concentration used in this paper (Figure 4G) for peak alpha cell Ca2+ activation (see https://doi.org/10.1016/j.cmet.2017.03.017; cited as ref 30 in the current manuscript).

We thank the reviewer for this important comment and agree that the literature on AVP actions in vivo is complex, with several possible sites of action outside the islet. We have therefore revised the Introduction to make the rationale more focused and to better separate systemic effects of AVP from studies addressing direct actions on pancreatic islet cells. At the same time, we chose not to restrict the Introduction only to the alpha cell V1bR literature, because one of the aims of the manuscript is precisely to address why AVP effects on insulin secretion have remained difficult to interpret across experimental contexts.

Our results fully confirm a central aspect of the study cited by the reviewer, namely that V1bR activation robustly stimulates alpha cell Ca²⁺ activity under non-stimulatory glucose conditions, and that 10 nM AVP does not produce a uniform activation of beta cell Ca²⁺ activity. In fact, in a substantial fraction of beta cell populations, 10 nM AVP failed to activate oscillations, consistent with the view that alpha cells are the more sensitive and more direct cellular target of AVP/V1bR signaling. However, we do not think that the available transcriptomic evidence is sufficient to categorically exclude V1bR expression or functional relevance in beta cells. Re-analysis of the published dataset, together with more recent datasets and our newly added RNAscope data, supports a higher relative expression of V1bR transcripts in alpha than in beta cells, but does not justify treating beta (or non-alpha) cell expression as absent.

We have therefore revised the manuscript to avoid overstating beta cell V1bR expression as a major isolated claim. Instead, we now present the data as evidence that AVP/V1bR signaling acts most prominently through alpha cells, while beta cell responses emerge in a concentration-, glucose-, and statedependent manner within the intact islet. This interpretation is consistent with the reviewer’s concern that 10 nM AVP preferentially activates alpha cells, but it also accommodates our observation that beta cell collective activity can be modulated under defined pharmacological and metabolic conditions. We believe that this is an important distinction, because the absence of a uniform beta cell Ca²⁺ activation at one AVP concentration does not exclude beta cell modulation by AVP/V1bR signaling within the intact islet network. The Introduction and Discussion have been revised accordingly to clarify that our study does not simply challenge the alpha cell V1bR model, but expands it by examining how AVP-dependent alpha cell activation, possibly lower beta-cell receptor expression, and collective beta cell dynamics interact in the native pancreatic slice preparation.

(2) We know from bulk RNAseq data on purified alpha, beta, and delta cells from both the Huising and Gribble groups that there is no expression of V2a. I will point you to the data from the Huising lab website published almost a decade ago (http://dx.doi.org/10.1016/j.molmet.2016.04.007) - which is publicly available and can be used to generate figures (https://huisinglab.com/dataghrelin-ucsc/index.html). They indicate the absence of expression of not only AVP2 receptors anywhere in the islet, but also the lack of expression of V1bra, V1brb, and Oxtr in beta cells. Instead of the detailed list of expression of these 4 receptors elsewhere in the body, it would be more directly relevant to set up their pancreatic slice experiments to summarize the known expression in pancreatic islets that is publicly available. It would also have helped ground the efforts that involved the generation of the V1aR agonist and V2R antagonist, which confirm these known AVP/OXT receptor expression patterns.

We thank the reviewer for pointing us more directly to the publicly available islet expression datasets. We agree that the expression of AVP/OXT receptors in purified alpha, beta, and delta cells provides an important reference frame for interpreting our pharmacological data, and we have revised the manuscript to summarize these islet-specific datasets more directly rather than emphasizing receptor expression in other organs. These data support the absence or very low expression of V2 receptors in islet endocrine cells and confirm that V1b receptor expression is substantially enriched in alpha cells compared with beta cells.

At the same time, as outlined in our response above, we do not think that the currently available transcriptomic datasets are sufficient to categorically exclude low-level V1bR transcript expression or functional relevance in beta cells within the intact islet. For this reason, we added independent RNAscope validation to assess V1bR transcripts in the pancreatic slice preparation. These data confirm stronger V1bR expression in glucagon-positive alpha cells, while also showing a broader expression pattern within the islet and pancreas.

We have also revised the rationale for the pharmacological experiments using V1aR- and V2R-directed tools. We now present these experiments not as evidence for unexpected receptor expression, but as functional controls that are consistent with the known AVP/OXT receptor expression patterns in pancreatic islets. This better aligns the manuscript with the existing transcriptomic literature while preserving the main physiological question of the study: how AVP/V1bR-dependent signaling reshapes alpha-cell activity and beta-cell collective dynamics in intact pancreatic tissue.

(3) Importantly, the lack of V1br from beta cells does not invalidate observations that AVP affects calcium in beta cells, but it does indicate that these effects are mediated a) indirectly, downstream of alpha cell V1br or b) via an unknown off-target mechanism (less likely). The different peak efficacies in Figure 4G would also suggest that they are not mediated by the same receptor.

We agree with the reviewer that the absence or very low abundance of V1bR transcripts in beta cells in published transcriptomic datasets would not invalidate the observation that AVP modulates beta-cell Ca²⁺ activity. It does, however, raise the important question of whether this modulation is mediated indirectly through alpha-cell V1bR activation, through V1bR expression in beta cells that is difficult to resolve transcriptomically, or through another mechanism. To address this more directly, we have now added RNAscope data, which confirm relatively stronger V1bR transcript enrichment in glucagon-positive alpha cells, but also show a broader V1bR transcript signal within the islet and pancreas. Thus, while our data support alpha cells as the dominant V1bR-positive endocrine population, they do not support a strict absence of V1bR-associated signaling capacity in the beta cell compartment.

We also agree that different peak efficacies in alpha and beta cells could be interpreted as evidence for distinct receptors or indirect mechanisms. However, we favor a different interpretation: the apparent efficacy of AVP depends strongly on the physiological state in which the cells are tested. This is particularly evident in beta cells, where the AVP efficacy peak shifts with glucose concentration, suggesting that the beta-cell response is shaped by the metabolic and Ca²⁺-handling context rather than by receptor occupancy alone. In this framework, the same V1bR/Gq-dependent input can generate different downstream Ca²⁺ outcomes in alpha and beta cells because the two cell types operate in different dynamic regimes.

We have therefore revised the manuscript to acknowledge this dilemma more explicitly. We now state that beta cell effects of AVP could include indirect alpha cell-dependent components, but given the magnitude and statedependence of the beta cell Ca²⁺ response it is unlikely to be driven by alpha cell activation. Instead, our preferred interpretation is that AVP/V1bR signaling acts within the intact islet as a context-dependent perturbation of the collective beta cell Ca²⁺ system, with IP3R-dependent mechanisms being modulated by glucose-dependent changes in beta cell excitability and intracellular Ca²⁺ handling.

(4) The rationale for the use of forskolin across almost all traces is unclear. It is motivated by a desire to 'study the AVP dependence of both alpha and beta cells at the same time'. As best as I can determine, the design choice to conduct all studies under sustained forskolin stimulation is related to the permissive actions of AVP on hormone secretion in response to cAMPgenerating stimuli. The permissive actions by AVP that are cited are on hormone secretion, which in many cell types requires activation of both calcium and cAMP signaling. Whether the activation of V1br and subsequent calcium response is permitted by cAMP is unclear. I believe the argument the authors are making here is that the activation of beta cell calcium by AVP is permitted by forskolin. i.e., the cAMP stimulated by it in beta cells. However, the design does not account for the elevation of cAMP in alpha cells and subsequent release of glucagon, particularly upon co-stimulation with AVP, which permits glucagon release by activating a calcium response in alpha cells. This glucagon could then activate beta cells. If resolving the mechanism of action is the goal, often less is more. The activation of Gaq-mediated calcium is not cAMP dependent (although the downstream hormone secretion clearly often is). As was shown, AVP does not activate calcium in beta cells in the absence of cAMP. The experiments in Figures 1, 2, and 4 should have been completed in the absence of cAMP first.

We agree with the reviewer that the use of forskolin needs to be explained more clearly, and we have revised the manuscript accordingly. Our rationale was based on the established permissive role of cAMP in AVP-dependent endocrine responses, but we acknowledge that this does not necessarily imply that the upstream V1bR/Gq-mediated Ca²⁺ response itself is cAMP-dependent. The reviewer is also correct that forskolin elevates cAMP broadly and therefore may affect both alpha and beta cells, including the possibility that AVP-enhanced alpha cell activation and glucagon release secondarily influence beta cell activity.

In fact, our initial experiments were performed without forskolin and revealed an important difficulty: stimulatory glucose alone can increase cAMP levels to a variable extent, as also supported by our previous work on epinephrine signaling, thereby shifting the apparent peak efficacy of AVP stimulation. Thus, forskolin was originally used to reduce this variability and create a more defined cAMP-permissive background in which alpha and beta cell responses could be compared in the same slice. However, we agree that this design works against isolation of beta cell-autonomous AVP effects.

Within a scope of another study we have done an independent series of more focused experiments using GLP-1 receptor stimulation, which preferentially increases cAMP signaling in beta cells compared with the broad cAMP elevation produced by forskolin. We have clarified that the modulation of the AVP-dependent pathway by GLP-1 and related ligands at largely supports beta cell-autonomous AVP effects. It is part of ongoing work and will be reported independently, because a full mechanistic dissection of cAMP–AVP interactions goes far beyond the scope of the present study.

(5) It is unexpected that epinephrine in Figure 2 does not activate the alpha cell calcium? A recent paper from the same group (Sluga et al) shows robust calcium activation in alpha cells in a similar prep by 1 nM epinephrine, which is similar to the dose used here.

We thank the reviewer for pointing this out, but we would like to clarify that epinephrine did significantly activate alpha-cell Ca²⁺ activity in our experiments, as shown in Fig. 3F. This result is consistent with our previous study by Sluga et al., where low nanomolar epinephrine robustly activated alpha cell Ca²⁺ signals in the pancreatic slice preparation. The main point of the present comparison was therefore not that epinephrine is inactive in alpha cells, but that AVP produces a substantially stronger and reproducible alpha cell Ca²⁺ response under comparable experimental conditions. This is also consistent with the data of van der Meulen et al., supporting the view that AVP/V1bR signaling is a particularly potent activator of alpha cell activity. We have revised the text to make this comparison clearer and to avoid the impression that epinephrine failed to activate alpha cells in our preparation.

(6) Figure 8 suggests a pharmacological activation of beta cell V1bR in the low pM range. How do the authors reconcile this comparison with the apparent absence of an effect of AVP stimulation at low pM to low nM doses in beta cells (Figure 4A)? I note that there are changes over time with sustained beta cell stimulation with 8 mM glucose, but these changes are relatively subtle, gradual, and quite likely represent the progression of calcium behaviors that would have occurred under sustained glucose, irrespective of these very low AVP concentrations. I will note that the Kd of the V1bR for AVP is around 1 nM, with tracer displacement starting around 100 pM according to the data in figure 5B, which is hard to reconcile with changes in beta cell calcium by AVP doses that start 10-100-fold lower than this dose at 1 and 10 pM (Figure 8).

We agree that the interpretation of low-pM AVP effects requires caution, particularly when compared with reported V1bR binding affinities. The apparent discrepancy between Fig. 4A and Fig. 8 most likely reflects differences in experimental design, stimulation context, and readout sensitivity. In Fig. 4A, we assessed acute AVP effects under conditions in which beta cell Ca²⁺ responses are relatively threshold-dependent and where low AVP concentrations produced little or no activation. In contrast, Fig. 8 analyzes prolonged beta cell population dynamics during sustained stimulation with 8 mM glucose, a physiological stimulatory context in which even weak modulatory inputs may become detectable at the level of collective Ca²⁺ activity.

Importantly, the strongest and statistically significant effect was observed at 100 pM AVP, while lower pM concentrations showed only a trend. We therefore do not interpret the low-pM range as evidence for robust direct pharmacological activation of beta cell V1bR. Rather, these data suggest that AVP may exert permissive or modulatory effects within an already active beta cell network, where glucose-dependent excitability, receptor-effector coupling, and Ca²⁺ amplification mechanisms can enhance the apparent efficacy of weak inputs. This interpretation is consistent with the known permissive role of AVP in endocrine responses, where AVP may not act as a primary activator alone but can increase the efficacy of other physiological stimuli.

We have also clarified that sustained 8 mM glucose alone does not account for these effects, since Suppl. Fig. 1 shows no comparable time-dependent progression of Ca²⁺ behavior under sustained glucose stimulation alone. Thus, we now present the low-concentration AVP effects as subtle, contextdependent modulation within the physiological stimulatory range, rather than as evidence for direct beta cell activation at concentrations below the expected receptor affinity range.

Reviewer #3 (Public review):

Summary:

This work aims to better understand the role of arginine vasopressin (AVP) in the control of islet hormone secretion. This builds on previous literature in this area reporting on the actions of AVP to stimulate islet hormones. The gap in literature being addressed by these studies is primarily focused on the glucose-dependency of AVP on both insulin and glucagon secretion. A secondary objective is to explore the role of individual receptors with the use of newly generated peptides and existing tools. The methods include the use of Ca2+ imaging in pancreas slices from mice, with additional outcomes including insulin secretion in some areas. The conclusions presented are that AVP acts through V1b receptors in both alpha- and beta-cells, that this activity occurs in the high cAMP environment, and is glucose dependent.

Strengths:

The area of research is emerging with plenty of room for new contributions. The concept of AVP stimulating islet hormone secretion is important and deserving of further insight. The use of pancreas tissue to image primary cells makes the experiments physiologically relevant. The advancement of novel tools in this area should be helpful to other groups investigating the actions of AVP.

We would like to thank the reviewer for recognizing the potential of our emerging area of research.

Weaknesses:

The conclusions are only modestly supported by the data and lack experimental depth and rigor. The rationale for only conducting studies at high cAMP conditions is not entirely clear and limits the conclusions that can be made. The use of Ca2+ is helpful, but it is a surrogate for hormone secretion. Additional measurements of hormone secretion are needed to enhance the robustness of these conclusions. Consideration of paracrine effects between alpha- and beta-cells is only superficially made and is likely essential in the context of the experimental design. For instance, there is clear literature that alpha-cells secrete several factors that work in paracrine interactions on beta-cells and autocrine actions back on alpha-cells. Conducting these studies in a high cAMP context only completely overlooks these interactions, skewing the interpretations made by the investigators. Finally, the clarity of the experiments and results could be significantly enhanced.

We thank the reviewer for this balanced assessment and for emphasizing several issues that are central to the interpretation of our study. We agree and now explicitly state in the Limitations, that Ca²⁺ oscillations are a surrogate readout for hormone secretion and that currently used stimulation protocols are not optimized to directly quantify the relationship between Ca²⁺ dynamics and secretory output. To address this limitation, we have now expanded the functional part of the study by adding complete insulin and glucagon secretion measurements during AVP concentration ramps. These new data provide a stronger functional framework for interpreting the Ca²⁺ imaging results, while also clarifying that Ca²⁺ activity and secretion cannot be assumed to correlate linearly under all stimulation protocols.

We have also revised the rationale for the high-cAMP experimental condition. The original aim was to reduce variability arising from glucose-dependent endogenous cAMP signaling and to study alpha and beta cell responses in a common permissive background. However, we agree that broad forskolin stimulation complicates the interpretation of cell-autonomous versus paracrine mechanisms. Independent experiments within a scope of another study demonstrate that using GLP-1 co-stimulation, which provides a more beta-cell-oriented cAMP-permissive condition and supports the interpretation that AVP can modulate beta-cell collective activity in a manner that is not solely secondary to alpha-cell activation.

We fully agree that paracrine interactions within the islet are physiologically important and must be considered, particularly in intact pancreatic slices. Nevertheless, the rapid onset of the AVP effects observed in beta cell Ca²⁺ activity argues against a mechanism mediated predominantly by slower indirect paracrine loops. High AVP concentrations, as shown in Fig. 5, significantly shorten the intervals between Ca²⁺ events in both alpha and beta cells, but that the activity of the two cell populations remains largely noncoordinated. This temporal dissociation does not exclude paracrine modulation altogether, but it argues against a simple alpha-cell-driven explanation for the beta-cell response.

We have revised the manuscript to state these points more clearly and to moderate conclusions where the data support modulation rather than definitive cell-autonomous receptor action. We believe that the added secretion experiments and alpha/beta event-timing analysis substantially strengthen the physiological interpretation of the study, and we thank the reviewer for raising these issues.

Recommendations for the authors:

Reviewer #2 (Recommendations for the authors):

(1) The paragraph discussing the benefits of slice physiology over islets is not reflective of how most - if not all of your colleagues who do islet experiments conduct these. Many labs have reported for years high-quality GSIS experiments, synchronous calcium responses, and a plethora of studies detailing the mechanism of hormone and neurotransmitter actions using islet models, and have done so well. Slice physiology is a unique and helpful model that can have advantages over other models. This particular reviewer uses both models in their lab and each has benefits and - inevitably - drawbacks. Many of the possible drawbacks cited for islet studies apply equally to slices, including the possibility of altered gene expression, lack of innervation, and circulation. Added drawbacks are the exposure to higher levels of pancreatic enzymes from the slice, which require co-culture with enzyme inhibitors.

We agree with the reviewer and have revised these limitations accordingly. Our intention was not to imply that isolated islet preparations are generally inferior, since they have provided a highly productive and rigorous experimental platform for GSIS, synchronized Ca²⁺ dynamics, and mechanistic studies of hormonal and neurotransmitter regulation with standardized protocols with all their positive and negative sides. We modified the presentation of pancreatic slices as a complementary model with specific advantages, particularly preservation of local tissue architecture, while also acknowledging their limitations. The revised text therefore avoids a comparative hierarchy between slices and isolated islets and instead emphasizes that both models have distinct strengths and drawbacks depending on the experimental question.

(2) If you want to demonstrate direct actions on beta cells, deconstructing the islet would be a better way to go. Less complicated, not more. Dissociated beta cells, instead of slices, were used just to prove or disprove the hypothesis of direct beta cell effects of AVP.

We agree that dissociated beta cells can be a useful reductionist model to test whether AVP is capable of acting directly on individual beta cells. However, this approach would also remove the collective beta-cell activity that is central to the physiological question addressed in the present study. Since our data indicate that AVP effects emerge within the intact islet as rapid and extensive changes in coordinated Ca²⁺ dynamics, dissociation would not necessarily provide a more informative model for understanding these responses. The fast onset and magnitude of the beta cell response argue against a predominantly indirect non-autonomous mechanism, and this interpretation is further supported by the GLP-1 co-stimulation experiments, which are more consistent with beta cell-autonomous modulation. We have therefore clarified in the revised manuscript that dissociated-cell experiments would be valuable for a narrowly defined receptor-cell autonomy question, but would not resolve the collective islet dynamics that are the focus of this work.

(3) If you want to sustain the claim of beta cell expression of V1br, you would have to demonstrate this far more directly by staining (if appropriate antibodies exist), by beta cell-specific deletion of V1br, or by highly selective, well-validated pharmacology. This should include a demonstration of Gaqdependence in isolated beta cells.

We have added RNAscope in situ hybridization data to the revised manuscript to assess V1b receptor mRNA expression within the pancreas and islet. These new data show a broader expression pattern of V1b receptor transcripts within the islet than originally assumed, suggesting that AVP signaling may not be restricted to a single endocrine cell population. At the same time, the RNAscope analysis confirms previous reports of higher AVP receptor expression in glucagon-positive alpha cells. We have added these results to Figure 1 and revised the corresponding Results and Discussion sections to clarify that the observed functional responses may reflect both direct effects on beta cells and indirect intra-islet effects mediated through alpha-cell signaling.

Minor

(1) O'Carroll et al. should be cited in the context of islet permissive actions of AVP/cAMP. PMID: 18434353, although that paper offers no evidence that the AVP-dependent potentiation of insulin release is mediated directly by beta cells. It does confirm dependence on PKC.

We agree and have added O’Carroll et al. in the revised manuscript in the context of AVP/cAMP-dependent permissive actions on islet hormone secretion. We therefore use it as support for the broader concept of AVP-dependent amplification of secretion in a permissive signaling context, rather than as direct evidence for beta-cell-autonomous V1bR signaling.

(2) Figure 2 E-H, glucose concentration mislabeled.

We thank the reviewer for pointing this out. The glucose concentration label has been clarified: panels E–H show pooled data from separate experiments performed at 8 mM glucose, whereas panel D shows a representative experiment performed at 9 mM glucose.

(3) The insulin secretion in 4E is difficult to interpret without a low-glucose control. If this is hard to do in a slice preparation, a separate static islet secretion experiment would help here. The possibility that the inhibition of insulin secretion traces back to the activation of delta cells by AVP could be considered - I struggle to come up with a plausible mechanistic explanation why AVP (which activates calcium in alpha and in beta cells in the presence of 8 mM G plus forskolin according to your data) would inhibit insulin secretion.

We agree that the original insulin secretion experiment was difficult to interpret without a clearer low-glucose reference condition. To address this, we have added new insulin release experiments in which glucose was lowered to a non-stimulatory range between AVP concentrations, followed by sequential stimulation with 8 mM glucose and 500 nM forskolin in the same slices. These new data provide a broader dynamic range for assessing insulin secretion and allow a more direct comparison between AVP-dependent Ca²⁺ modulation and secretory output.

We also agree that AVP-dependent inhibition of insulin secretion requires careful interpretation. One possible explanation is not simply activation of delta cells, but a failure of the beta cell collective to maintain coordinated activity at very high AVP concentrations. In the Ca²⁺ imaging data, high AVP concentrations increase activity in many beta cells, but numerous cells within the islet fail to keep pace with the collective oscillatory rhythm, leading to fragmented and less synchronized population activity. Thus, despite increased frequency of Ca²⁺ oscillations in the islet, the integrated beta cell output may become less efficient for insulin secretion. We have added this interpretation to the revised manuscript and now discuss delta cell activation as a possible contributing mechanism, but not as the primary explanation supported by our current data.

The article is now published in STAR Protocols. doi: 10.1016/j.xpro.2026.104704.

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