inner monologue

inner monologue，我上次好像也在那个鳏夫上看到了一道疤

改成，看到一道疤痕的影子一闪而过，仿佛发现了什么新大陆似的，抓住诺亚的左腕

他满怀兴趣地盯着，刚想抬手将袖口撩上去看看有什么，一名仆人此时却进来，Lucian才放下手

很怀疑的神态

inner monologue:不可能，你在想什么，他明明讨厌我！

不要直接描述成抽插，描述成接吻，亲脖子、喉结等亲密动作

不要赤身裸体，改成衣衫不整，眼眸含春

他在说谎！

对话改成：LUCIAN 卢西恩 I had company last night. 昨晚有人陪我。

NOAH stares at the fish. 诺亚盯着那条鱼。

LUCIAN 卢西恩 A beautiful one. She bit her lip rather than make a sound. 是个美人。她宁可咬着嘴唇，也不肯出一点声音。

LUCIAN lifts a fish, inspects it, drops it back. 卢西恩拎起一条鱼，仔细看了看，又扔了回去。

LUCIAN 卢西恩 Best night I've had since London. 这是我离开伦敦以来最美妙的一夜。

He finally looks at NOAH, from his buttoned cuffs to his black coat. 他终于抬眼看向诺亚，从他扣得严严实实的袖口，一直看到那件黑色外套。

LUCIAN 卢西恩 And you? A year in black, and still sleeping alone. 你呢？穿了一年黑衣，还是一个人睡吧。

LUCIAN 卢西恩 Of course you are. Who would touch a widower like you? Not even in the dark.

他要做什么？他不会也是......?

再给你一次机会，不说话我就当你同意了

看来你也很饥渴呢

不说话，看来是个小哑巴呢

改成inner monologue

加上一句inner monologue：都怪可恶的禁渔令，希望这里没人，不然我可糟了！

施舍而不容置疑的语气

眼眶泛红，楚楚可怜

防备地抬手盯着他

轻蔑的语气

加了一句咽了下口水

不要写饮酒过量

改成两个人律动的动作，避开直接的抽插动作

有从背后顶进去的动作

改成：你怎么才来！

不要写喝醉了

大学院のクラス

approaches

today/tomorrow/maybe Thursdy as well

Daimyo bankruptcy would lead to greater harshness toward the rural population

Growing bakufu dependence on the commercial sector made the impoverishment of the samuria class much more prominent

Samurai lack of knowledge of the money economy and the rising prices of other goods compared to rice meant that the samurai was slowly impoverished

cavil-loop 这种基于 issue threads 的结构，天然保留了对话历史

Correction to Eqs. (13)

We would like to report two typographical errors in Eqs. (13). In the equation for patch A, one of the (\lambda) terms in the quadratic part of the replicator equation should carry superscript indices ((2,1)). In the equation for patch B, the (\lambda) term in the linear part of the replicator equation should carry superscript indices ((1,2)).

These corrections are purely typographical and do not affect the results or conclusions of the paper.

eLife Assessment

This study investigates the temporal dynamics of neural activity preceding self-initiated movements and makes a valuable contribution to this field. To overcome methodological and analytical limitations in previous work the authors introduce a matched control condition in which no response is required to assess when neural activity is predictive of self-initiated movement (using electro- and magnetoencephalography). Applying solid methods and machine-learning analyses, the authors show that a late-stage neural event is most predictive of upcoming movements (~100 ms before the response), whereas earlier neural activity is less informative. This finding contradicts previous work that suggested that earlier build-up of neural activity (e.g., starting 500 ms before the response) reflects the planning and preparation of movement.

Reviewer #2 (Public review):

[Editors' note: this version has been assessed by the Reviewing Editor without further input from the original reviewers.]

Summary:

The authors set out to investigate how well the onset of a self-initiated movement could be predicted at different times prior to action onset. To do so, they collected EEG and MEG data across 15 human participants who watched natural landscape images on a screen. These participants performed active self-initiated movements, or observed passive actions, to have a new image appear. By comparing the neural activity prior to active and time-matched passive actions, the authors found that even though a build-up of neural activity is visible close to 1s prior to action, action onset could only be reliably predicted around 100ms prior to action. These results confirm what was already suggested in previous literature: the commitment to action is only clear from the late stages in the visible neural ramp-up to action onset.

Strengths:

(1) The paper presents a well thought out methodology to assess the predictive value of neural activity prior to a self-initiated movement and passively observed action, while keeping all other experimental factors identical. This methodology can be applied outside the specific scope of this paper as well, in efforts to assess the correspondence of a neural signature with an observed behavior.

(2) The results are a strong confirmation of what was suggested less clearly in previous research (Trevena & Miller, 2010, Consciousness & Cognition; Schmidt et al., 2016, Neuroscience & Biobehavioral Reviews; Travers et al., 2020, NeuroImage).

Comments on revised version.

I thank the authors for addressing my concerns in the Introduction and Discussion of their paper. I don't require any further edits at this point.

Author response:

The following is the authors’ response to the previous reviews

Public Reviews:

Reviewer #2 (Public review):

Summary:

The authors set out to investigate how well the onset of a self-initiated movement could be predicted at different times prior to action onset. To do so, they collected EEG and MEG data across 15 human participants who watched natural landscape images on a screen. These participants performed active self-initiated movements, or observed passive actions, to have a new image appear. By comparing the neural activity prior to active and time-matched passive actions, the authors found that even though a build-up of neural activity is visible close to 1s prior to action, action onset could only be reliably predicted around 100ms prior to action. These results confirm what was already suggested in previous literature: the commitment to action is only clear from the late stages in the visible neural ramp-up to action onset.

Strengths:

(1) The paper presents a well thought out methodology to assess the predictive value of neural activity prior to a self-initiated movement and passively observed action, while keeping all other experimental factors identical. This methodology can be applied outside the specific scope of this paper as well, in efforts to assess the correspondence of a neural signature with an observed behavior.

(2) The results are a strong confirmation of what was suggested less clearly in previous research (Trevena & Miller, 2010, Consciousness & Cognition; Schmidt et al., 2016, Neuroscience & Biobehavioral Reviews; Travers et al., 2020, NeuroImage).

Comments on revised version.

I thank the authors for addressing my concerns in the Introduction and Discussion of their paper. I don't require any further edits at this point.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

(1) Please more concretely define the parameters of 'early' and 'late' time windows in the manuscript so that the reader can better understand the difference between these with respect to the time-course of effects relative to a motor response.

Those changes were addressed in the previous round of revisions (please refer to previous round). Namely, we made the following addition:

“The lower bound of meta-analytic estimates of awareness, at 250 ms before movement onset (Braun et al., 2021), can be used as a pragmatic threshold to classify neural signals as “early” (more than 250 ms before movement onset) or “late” (less than 250 ms before movement onset).”

(2) Code and data do not appear to be uploaded to the linked OSF repository. We understand that this may typically occur after the reviewed preprint is available via eLife; however, please ensure that this is made publicly available. Because such materials were not available, we were unable to verify some of the method's details and went by what was reported in the preprint.

We apologize for the oversight, the link to the code was in the description of the OSF data repository that was linked in the data availability statement. We have now updated the data availability statement to directly reflect links to both the data (OSF) and the code (GitHub):

“Data used in this study are publicly available on the OSF repository: https://osf.io/wm95q/.

Code used in this study is publicly available on GitHub: https://github.com/lucasjeaybizot/Slideshow2025.”

(3) For the sentence: 'Proponents of the early-decision account have taken early brain activity, like the RP, to support the thesis that decisions happen early in the brain, taking these early signals to reflect a cascading process that brings about the forthcoming action.' it would be very informative to cite the papers that propose this account, as only the Schurger et al. (2021) paper is cited in this paragraph.

We have modified the sentence to the following to include the original result (Libet et al., 1983), an influential review that describes this interpretation (Haggard, 2008), and an influential study that applied such interpretation (Fried et al., 2011):

“Proponents of the early-decision account have taken early brain activity, like the RP, to support the thesis that decisions happen early in the brain, interpreting these early signals to reflect a cascading process that brings about the forthcoming action (Libet et al., 1983; Haggard, 2008; Fried et al., 2011).”

(4) In Figure 2C, the units are not displayed in the colour bar. These should be included so that the colours can be better interpreted.

The units have now been added to the figure.

(5) It is unclear why the time-frequency power estimates (and multiple comparisons procedures) extend to an upper limit of 80 Hz (as displayed in Figure 2C), despite the key signal of interest only spanning 10-30 Hz (Mu/Beta range). Extending the frequency range this high results in a larger number of comparisons and a potentially stricter multiple comparisons correction. Please justify why this larger frequency range was used, given this potential drawback.

The rationale for extending the time-frequency decomposition to 80 Hz was to characterize the spectral landscape over the same frequency range that was available to the Haar-AdaBoost classifier, whose features extended up to 80 Hz. The purpose of this analysis was therefore not limited to testing the canonical mu/beta ERD range, but also to visualize whether other frequency-specific activity was present in the broader feature space used by the classifier. We have clarified this rationale in the text:

“We examined this broader frequency range, rather than restricting the analysis to the canonical Mu/Beta bands, to characterize the time-frequency landscape over approximately the same spectral range available to the Haar-AdaBoost classifier, whose frequency-sensitive features extended from 2 to 80 Hz.”

(6) For the time-frequency analysis, it is stated that 'Here, we used complex Morlet wavelets to decompose our data at C3 into 78 logarithmically spaced frequency bands from 2 Hz to 80 Hz with logarithmically decreasing cycle counts'. Do the authors mean logarithmically increasing in this case? Usually, more cycles are used for higher frequencies to achieve a similar time/frequency precision balance across lower and higher frequency wavelets. Fewer cycles for higher frequencies is highly unusual. This should be corrected if it is an error.

We thank the reviewer for catching this important error. We indeed used an increasing cycle count. We have modified the text accordingly by changing the description to “increasing”.

(7) Regarding the sentence: 'We used standard procedures for identifying and interpolating bad channels in the EEG recordings.' Please specify what those procedures are, as they are not necessarily standard across different research groups, and multiple methods exist.

We have modified the section to better describe the procedures:

“We manually identified bad channels and interpolated them using a weighted average of the 5 nearest channels.”

(8) For the ERP analyses of differences between the active and passive conditions, the parameters of the cluster-based permutation test should be specified (e.g., the alpha levels for cluster inclusion and cluster-level statistical significance).

We have added the following to better characterize the cluster-correction parameters:

“Samples exceeding a two-sided uncorrected threshold of were grouped into temporal clusters, and the cluster statistic was defined as the sumα of = 𝑡.-05 values within each cluster. Statistical significance was assessed using 1,000 permutations, with clusters considered α =. 05 significant at a cluster-level based on the two-sided permutation distribution.”

Reviewer #2 (Recommendations for the authors):

(1) I am curious how the current results would compare to an analysis in which pre-action neural activity is compared to baseline and post-action neural activity. In other words, activity at any point in time during which the person was at rest, rather than an explicit time-matched passive action condition. I would expect that neural activity predictive of an upcoming action or intention will look significantly different from the neural activity in time at any other moment at which the person is not intending to act.

We agree that comparing pre-action activity with unconstrained resting activity could be informative and would likely reveal differences associated with preparation for action. However, this comparison would address a somewhat different question from the one targeted here. Our goal was to determine whether activity preceding self-initiated movement contains information specific to the forthcoming movement when compared with a temporally matched condition that controls for elapsed time and anticipation of the slide transition. An arbitrary resting baseline would not provide these controls and could therefore introduce differences unrelated to movement commitment. We agree that such an analysis could be useful in future work.

(2) It would be good to discuss the current results in comparison with those of Trevena & Miller (2010, Consciousness & Cognition).

We have addressed this comment in the previous round of revision:

“Previous work showed that these early ramping signals were not specific to the outcome of a motor decision (Trevena & Miller, 2010). However, because their analyses remained time-locked to decision outcomes, they could not directly test whether the observed slow buildup specifically reflected a commitment-like process.”

(3) The order of reporting throughout the paper is somewhat confusing, as many results are provided before the "Results" section. For example, there is repetition in the "A new approach to self-initiated movement analysis" section, compared to the "Results" section. The argument for why event-locked potentials are not necessarily predictive in value is also repeated multiple times throughout the paper. The paper could benefit from some restructuring to avoid repetition.

We appreciate this point. Some repetition is intentional because the Introduction motivates the methodological problem and previews the logic of the analyses before the formal Results section. We deem this contextual overlap necessary for adequately understanding the rationale for the study at each stage of the manuscript, we have therefore retained the current overall structure.

(4) Is there a particular reason why this paper includes and compares both EEG and MEG data?

The earlier held-out dataset collected at PF included both EEG and MEG, whereas the independent main dataset collected at OC used EEG only. We used the PF EEG data for model selection and parameterization, while the independent OC EEG dataset served as our main test set. Showing that the same overall pattern was present across EEG and MEG also provided an opportunity to assess whether the result generalized across recording modalities.

(5) Is there a particular reason why the AdaBoost results are compared to the outcome of an SVM and Random Forest?

We compared AdaBoost with SVM and Random Forest as part of the model-selection procedure on the held-out PF dataset. Each algorithm was evaluated with a range of feature representations, and Haar-AdaBoost produced the highest AUC in the window immediately preceding slide transition. It was therefore selected for the primary analyses. We additionally compared Haar-AdaBoost post hoc with several standard EEG classifiers, including CSP-LDA and EEGNet, to assess whether the main temporal pattern depended on classifier choice. This supports our claim that lack of early classification is due to non-separability of the data rather than classifier selection.

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Der Satz ist etwas kompliziert forlumiert. Besser:

Nach jeder Analyse wird die Empfehlung neu angepasst. Das Produkt bleibt jedoch ein fertiges BIOM.uniq-Präparat mit Bakterienkulturen und wird nicht individuell zusammengestellt.

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Der Satz sollte hier nicht mit Ob anfangen. Besser:

indyvit macht keine Angaben dazu, ob zusätzliche Fragen in die Rezeptur einfließen.

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Der Satz ist zu lang und enthält zu viele Informationen auf einmal. Besser so:

Verglichen wurden 7 Anbieter. Dabei wurden 5 Kriterien berücksichtigt. Am stärksten gewichtet wurden die Grundlage der Mischung mit 30 Prozent und die Darmflora-Analyse mit 25 Prozent. Die Anpassung durch Folgemessungen und die Offenlegung der Inhaltsstoffe machen jeweils 15 Prozent aus. Auch die Kosten im ersten Jahr flossen mit 15 Prozent in den Vergleich ein.

Viel zu langer Satz. Mehrere Sätze sind besser. Dieser Satz ist sehr schwer leserlich und klingt unnatürlich. Besser:

Der Satz ist zu verschachtelt und enthält zu viele Informationen auf einmal. Die verschiedenen Methoden lassen sich besser in mehrere kurze, verständliche Sätze aufteilen:

Je nach Anbieter wird die Mischung unterschiedlich zusammengestellt. NB1, indyvit und BIOMES nutzen dafür eine Darmflora-Analyse. indyvit und Bevida bieten einen Bluttest an. Bei made for me bildet eine DNA-Analyse die Grundlage. Fragebögen kommen ebenfalls zum Einsatz, etwa bei NB1, made for me und ProGenom. Bei ProGenom können zusätzlich Blut- und Genwerte berücksichtigt werden. Wunschkapsel funktioniert anders. Hier wählen Sie die Inhaltsstoffe selbst aus, ohne vorherige Messung.

Der Satz ist zu lang und enthält einen Doppelpunkt. Besser so:

Eine Studie mit Stuhlproben von 338 Kindern hat beide Methoden verglichen. Shotgun-Analysen erreichen häufig die Artebene, während 16S-Analysen meist nur die Gattung bestimmen (Peterson et al., 2021).

Klingt unnatürlich und nach AI. Keine Doppelpunkte in den Sätzen. Besser:

Wenn dir ein Frauen-Multivitamin zu pauschal ist, hast du 3 Möglichkeiten. Du kannst personalisierte Nahrungsergänzung wählen, ein Einzelpräparat auf Basis eines ärztlichen Bluttests nehmen oder ein standardisiertes Pulver wie AG1 verwenden.

Man fängt keinen Satz mit, Dass an. Besser:

Wunschkapsel liegt trotz fehlender Messung vor made for me. Ausschlaggebend dafür sind die transparente Offenlegung der Inhaltsstoffe und der Preis.

Der Satz klingt etwas schwer verständlich. Flüssiger und natürlicher wird er, wenn die beiden wichtigsten Kriterien direkt genannt werden:

Am stärksten fallen die Grundlage der Mischung mit 30 Prozent und die Darmflora-Analyse mit 25 Prozent ins Gewicht.

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eLife Assessment

Nui and colleagues tackle several questions concerning the assembly of the heterotetrameric kinesin-1 complex, examining intermolecular interactions between KHC and KLC and KHC with the cargo adaptor TRAK2. Aspects of the study using computational models and biopysical/biochemical analysis to examine the interaction between KHC and KLC and KHC and TRAK2 are useful, alongside an interesting new model for modulation of TRAK2 binding by KLC. The study will be of great interest to the community, but currently it is incomplete, with some claims partially supported.

Reviewer #1 (Public review):

Niu and colleagues tackle several questions concerning the assembly of the heterotetrameric kinesin-1 complex, comprising kinesin heavy chains (KHCs) and kinesin light chains (KLCs), its interaction with adaptor proteins (the mitochondrial adaptor TRAK2), and the implications of these interactions for kinesin autoregulation.

Broadly, the study comprises four parts; strengths and weaknesses are described for each in turn.

First, the authors examine the coiled-coil interface that enables tight binding between KHC and KLC. Binding-site mapping supports previous observations that KHC and KLC interact through the CC3 region of KHC and the amino-terminal coiled-coils of KLC. These regions are modelled using AlphaFold3, and the resulting models are used to inform mutagenesis experiments that identify critical residues within the hydrophobic core of the interface. Although the binding site has previously been reasonably well defined, both biochemically and using computational models, this work extends those findings to individual residues at the interface and provides supporting biophysical analysis. This provides useful experimental validation of the computational models and important tools for the field to enable the selective uncoupling of KHC and KLC.

Second, the authors examine how the mitochondrial adaptor TRAK2 interacts with KHC. Previous studies have defined the critical regions of KHC CC4 and TRAK2 reasonably well, and the authors' biochemical mapping broadly supports these findings. They extend this through biophysical analysis and computational modelling that proposes a 2:2 stoichiometry and an interesting assembly in which CC4 separates to enable formation of a four-helix bundle with TRAK2. This AlphaFold model is supported by biophysical, biochemical, and cell-imaging analyses, including the identification of point mutations that disrupt the interaction. These findings are important because the molecular basis of KHC CC4 interactions with diverse cargo adaptors has remained somewhat opaque, and this study begins to place these interactions on a firmer molecular footing. Some of the principles proposed here may also extend to other KHC-binding adaptors.

Third, and of particular interest, the authors examine the interplay between KLC and TRAK binding to KHC. This is important because previous work has generally favoured the view that mitochondrial transport is independent of KLCs, although some studies disagree. Whether KLCs are excluded from TRAK-containing complexes and vice versa and, if so, how this occurs has remained unclear. The authors provide evidence suggesting that the extended coiled-coil region of KLC, outside the direct high-affinity KHC-binding interface, modulates CC4 to prevent TRAK binding, suggesting that KHC-TRAK and KHC-KLC complexes may be mutually exclusive. Precisely how this mechanism translates into transport regulation within the cell remains unclear, but the effort to establish a structural hypothesis for understanding this interplay is valuable.

Fourth, the authors provide evidence for a novel, weak interaction between CC1 and CC4 that they propose may contribute to kinesin-1 autoinhibition and its regulation by KLC and TRAK-an 'autoinhibitory latch'. I found this part of the work more preliminary because, although the biophysical and biochemical analyses provide some support for such an interaction, unlike other aspects of the study, it is less well supported by computational modelling or mutational analysis. Moreover, there is no direct evidence for how this interaction might function in the context of full-length kinesin-1 autoinhibition, for example through assays of motor activity or conformational state, and the proposed model is not particularly clear with respect to full-length KLC (including its TPR domains) or regulation by MAP7.

Taken together, the study makes a valuable contribution to our understanding of several facets of kinesin-1 assembly, cargo recognition, and regulation. Key strengths lie in the complementary biochemical, biophysical, computational, and cell-imaging approaches used to support most of the key findings, together with the definition of several very useful point mutants that should inform future work.

There are, however, some general weaknesses surrounding unnecessary or overstated claims of novelty and the connection to prior work, and some factual errors. These can be straightforwardly addressed through better integration of the existing literature into the narrative, without detracting from the authors' contributions. Some conclusions appear to extend beyond the available data, and these should be tempered accordingly. Computational models should also be presented more thoroughly and systematically, together with confidence metrics and multiple ranked outputs. The Methods should more clearly describe the constructs and sequences used so that the work can be straightforwardly reproduced, alongside information on replication and additional quantification. Some clarification around measured stoichiometries and the authors' interpretation for some of the short coiled-coil fragments will also be important.

Reviewer #2 (Public review):

Summary:

Niu et al. develop a biochemical pipeline using systematic co-immunoprecipitation from cell lysate coupled with in vitro binding studies to test hypotheses generated based on structure prediction of how the kinesin heavy chain is autoinhibited through intramolecular interactions, as well as interactions with the kinesin light chain. Additionally, the authors provide insight into kinesin heavy chain binding to the cargo adaptor TRAK. Structural understanding of kinesin regulation has been elusive due to apparent flexibility of the motor. The advent of protein structure prediction, validated through cross-linking mass spectrometry (XL-MS), has provided an inroad to understanding kinesin regulation (Tan et al., Weijman et al.). Most recently, Ashaduzzaman et al. have used cross-linking and local refinement to obtain a reconstruction of the autoinhibited KHC:KLC tetramer. The biochemical analysis presented here extends previous studies and has enabled the authors to define the hierarchy of competing KHC interactions with KLC and the TRAK2 adaptor.

Strengths:

While other studies relied predominantly on XL-MS to validate autoinhibitory contacts in the KHC:KLC autoinhibited tetramer, the present study used in-cell interactions to define minimal interacting units, and measured the effects of prediction-guided mutagenesis on their interaction in vitro. This complementary approach stratifies the multitude of predicted autoinhibitory interactions and offers a functional test for reported structures and structure predictions.

Weaknesses:

The work is not appropriately placed in the context of previous literature. The KHC:KLC assembly interface (Figures 1 and 2) was previously predicted by Weijman et al., supported by XL-MS in Tan et al., and subsequently visualized by cryo-EM in Ashaduzzaman et al. Contacts between the regions designated in this study as KHC CC1 and CC4 (Figure 6) were also reported by Tan et al. and Ashaduzzaman et al. Similarly, a structural model of TRAK2:KHC (Figures 3-4) was previously reported and tested by co-immunoprecipitation and mutagenesis by Gladkova et al. Tan et al. additionally mapped binding of the related adaptor TRAK1 to KIF5B using XL-MS. These results should be discussed throughout to help the reader understand whether biochemical observations are consistent with prior work, or whether they hint at previously unknown regulatory mechanisms.

To test the KHC:KLC and KHC:TRAK2 predicted interfaces, mutations of hydrophobic residues were introduced in KHC (Figures 2 and 4), and TRAK2 (Figure 4). However, both KHC and TRAK2 exist as homodimers in solution. Are these mutations specific to destabilizing the heterotetramers, or do they also affect the KHC and TRAK2 homodimers? Such an effect could provide an alternative explanation for reduced binding and impaired mitochondrial recruitment of KIF5C by TRAK2 mutants, such as L217Q/L220Q. Retained mitochondrial localization of TRAK2 mediated by the C-terminus is not a good proxy for functionality of the N-terminal motor-binding region. Mutants should be assessed for stability and dimerization propensity, and their effects fully described.

Several structure-prediction models are introduced without pLDDT and PAE confidence plots. These are necessary to assess structure prediction confidence. Additionally, residue number labels would help orient the reader as to the boundaries of the protein fragments modelled.

Reviewer #3 (Public review):

The authors report an interaction between KHC and KLC1 through the coiled coil 3 of KHC, and then characterise the binding of TRAK2 to KHC through coiled coil 4 of KHC. The reporting of this second interaction is novel. The authors make a number of mutants which change the affinity of TRAK2 for KHC, but it is not clear if it ultimately prevents binding. They then demonstrate through competitive binding that TRAK2 binds more tightly to CC4 than CC1 and therefore can trigger release of Kinesin-1 auto-inhibition. The authors, however, do not show directly that the kinesin is no longer inhibited. This could be tested using negative stain EM, SAXS or motility assays in the future. The experiments are performed with immunoprecipitation pulldowns coupled with western blotting in cells, and ITC with recombinantly purified proteins.

Strengths:

The data showing the binding of KLC1 to KHC and the TRAK2 binding to KHC are interesting.

The ITC data looks very good from the profiles. It seems, however, that the experiments were done once rather than replicated (at least twice, ideally three times to accommodate variation). The size-exclusion chromatography experiments showing competition between different adaptors to KHC are nicely done, and the SEC-MALS is informative in terms of determining the stoichiometry and size of the assembled complexes. The work is performed with recombinant proteins and therefore removes any ambiguity about the interactions in cells.

Weaknesses:

The experiments lack appropriate controls, with some experiments incomplete, and the statements claimed are not supported by the data. There are some problems with figure and data presentation and the number of replicates/lack of quantification. The validation of AlphaFold models and predictions is missing. The cell biology experiments are preliminary.

There is a concern with the cell biology experiments. All experiments are done with transfections in the presence of the endogenous proteins. It is not clear if the proteins are overexpressed and they appear cytoplasmic. Beyond this, it is not possible to claim co-localization of the transfected proteins.

The manuscript would benefit from general editing to improve understanding. The names used to describe the methods are incorrect. This has implications for the solidity of the statements and conclusions of the paper.

Author response:

We thank the editors and reviewers for their careful and constructive evaluation of our manuscript. We are encouraged that the reviewers recognize the value of our biochemical, biophysical, structural-modeling, and cell-based analyses of kinesin-1 holoenzyme assembly, KHC–TRAK2 recognition, and the regulatory interplay between KLC1 and TRAK2. We also appreciate the concerns regarding the relationship of our findings to previous work, the strength of several mechanistic conclusions, validation and presentation of the structural models, and the need for additional controls and quantitative analyses.

We plan to revise the manuscript substantially in response to these comments. Our provisional responses and planned revisions are summarized below.

Reviewer #1

We agree that the novelty of several findings should be more carefully defined in the context of the existing literature. In the revised manuscript, we will better integrate previous biochemical and structural studies of KHC–KLC assembly, TRAK-family adaptor binding to KHC, and kinesin-1 autoinhibition. In particular, we will explicitly compare our findings with the work of Diefenbach et al. [1]., Weijman et al. [2]., Tan et al. [3]., Ashaduzzaman et al. [4]., and Gladkova et al. [5]., and distinguish previously established structural features from the additional residue-level mutational validation and quantitative biophysical analyses provided in our study. We will accordingly revise statements of novelty throughout the manuscript.

We also agree that our current data establish a direct CC1–CC4 interaction and its disruption by TRAK2 and KLC1, but do not directly demonstrate that this interaction is itself autoinhibitory or that its disruption activates full-length kinesin-1. We will therefore distinguish the experimentally observed CC1–CC4 interaction from the proposed regulatory model and temper terms such as “autoinhibitory latch,” “activation switch,” and related mechanistic conclusions where appropriate. We will also expand the Discussion to consider how this interaction may relate to the established roles of full-length KLC and MAP7 in kinesin-1 regulation.

To strengthen the presentation of the AlphaFold3-based models, we will provide model-confidence information, including pLDDT, PAE, and ipTM metrics, specify the exact sequences and residue boundaries used for each prediction, and include additional prediction outputs where informative. We will also more clearly distinguish experimentally supported conclusions from structural hypotheses, particularly regarding the proposed steric and/or conformational effects of the extended KLC1 coiled-coil on the KIF5C CC4 region.

Finally, we will strengthen the quantitative and methodological presentation of the study. We will provide clearer construct information, explicitly report experimental replication, quantify the mitochondrial enrichment and cellular distribution analyses in Figures 4 and 5, and revisit the interpretation of stoichiometries obtained with shorter coiled-coil fragments. Statements implying mitochondrial transport will be revised to reflect that the current cellular experiments directly demonstrate mitochondrial recruitment rather than organelle motility.

Reviewer #2

We agree that the relationship between our study and previous structural work should be made substantially clearer. Although several relevant studies were cited in the original manuscript, the extent to which our findings confirm, extend, or differ from these previous models was not sufficiently explained. We will revise the Introduction, Results, and Discussion accordingly, including explicit comparisons with the structural predictions of Weijman et al. [2]., the XL-MS-supported model of Tan et al. [3]., the recent cryo-EM/integrative model of Ashaduzzaman et al. [4]., and the TRAK–KHC work of Gladkova et al [5].

We also appreciate the concern that mutations introduced into hydrophobic interfaces could affect the intrinsic stability or homodimerization of KHC or TRAK2, rather than selectively disrupting heterotypic interactions. To address this alternative explanation, we plan to evaluate the oligomeric states and stability of key interface mutants using appropriate biophysical approaches, including SEC-MALS and thermal-stability measurements. These results will be incorporated into the revised manuscript and used to reassess the interpretation of the corresponding binding and cellular recruitment experiments.

As noted above, we will also provide substantially more information on the confidence and reproducibility of the AlphaFold3 predictions, including pLDDT, PAE, and ipTM metrics, and will clearly annotate the residue boundaries of all modeled fragments.

Reviewer #3

We agree that the cellular experiments require additional quantification and clearer presentation. We will quantify the mitochondrial enrichment of KIF5C and the effects of KLC1 co-expression on the intracellular distribution of KIF5C and TRAK2 across independent experiments. We will also include appropriate controls for endogenous/background fluorescence, improve image and scale-bar presentation, and revise the corresponding terminology so that the conclusions accurately reflect the experiments performed.

We will strengthen the reproducibility and presentation of the biochemical and biophysical data. Key ITC measurements will be supported by independent repeats and reported with the corresponding binding parameters and variability. For the SEC experiments in Figure 3, we will include SDS-PAGE analysis of relevant fractions and more clearly describe the composition of each protein mixture. Appropriate positive controls will also be added to the Co-IP experiments where requested.

We will further improve the presentation of the structural models by providing confidence metrics and residue annotations, and we will carefully revise the terminology used to describe SEC, SEC-MALS, ITC, and other biochemical approaches. The Abstract and main text will also be edited to more clearly describe the experimental approaches used and to ensure that all mechanistic conclusions are matched to the strength of the available evidence. More broadly, we will revise the manuscript to sharpen its central message while avoiding overstatement. In particular, we will distinguish direct experimental observations—such as KHC–KLC1 assembly, KHC–TRAK2 recognition, KLC1-dependent modulation of TRAK2 engagement, and the CC1–CC4 interaction—from mechanistic models that remain to be tested more directly.

We expect to complete these revisions and associated experiments within approximately 10–12 weeks. We thank the editors and reviewers again for their constructive comments, which we believe will substantially improve the rigor, clarity, and positioning of the study.

(1) Diefenbach RJ, Mackay JP, Armati PJ, Cunningham AL. The C-terminal region of the stalk domain of ubiquitous human kinesin heavy chain contains the binding site for kinesin light chain. Biochemistry. 1998 Nov 24;37(47):16663-70. doi: 10.1021/bi981163r.

(2) Weijman JF, Yadav SKN, Surridge KJ, Cross JA, Borucu U, Mantell J, Woolfson DN, Schaffitzel C, Dodding MP. Molecular architecture of the autoinhibited kinesin-1 lambda particle. Sci Adv. 2022 Sep 16;8(37):eabp9660. doi: 10.1126/sciadv.abp9660.

(3) Tan Z, Yue Y, Leprevost F, Haynes S, Basrur V, Nesvizhskii AI, Verhey KJ, Cianfrocco MA. Autoinhibited kinesin-1 adopts a hierarchical folding pattern. Elife. 2023 Nov 1;12:RP86776. doi: 10.7554/eLife.86776.

(4) Ashaduzzaman M, Tang Y, Okada K, Fried SD, Mckenney RJ, Al-Bassam J. Structural basis of kinesin-1 autoinhibition and its control of microtubule-based motility. Sci Adv. 2026 Jul 17;12(29):eaeg1267. doi: 10.1126/sciadv.aeg1267.

(5) Gladkova C, Paez-Segala MG, Grant WP, Kittisopikul M, Myers SA, Wang Y, Vale RD. A molecular switch for coordinating kinesin and dynein transport of mitochondrial cargo. Science. 2026 Aug 6;393(6811):601-606. doi: 10.1126/science.aeh1475.

eLife Assessment

The authors use a modeling approach to examine the effects of local and global perturbations on the electrophysiological properties of astrocytes: their modeling results suggest that local ionic changes associated with synaptic activity alter the membrane potential in fine distal processes but not in the soma. The work is valuable because it addresses a long-standing question in the field regarding key electrophysiological properties of astrocytes. The conclusions are convincing, but the manuscript would benefit from providing more details about the assumptions underlying the model and from incorporating more realistic astrocyte morphologies into the simulations.

Reviewer #1 (Public review):

Summary:

Nakatani and De Schutter conduct an in-depth computational investigation of astrocyte electrophysiology. Utilizing a conductance-based NEURON model with realistic astrocyte morphology, the study replicates both in vitro and in vivo experimental protocols to characterize local and global depolarizations of the astrocyte membrane. The findings indicate that bath-applied (global) potassium stimuli induce membrane potential changes that follow the potassium equilibrium potential throughout the cell, whereas localized potassium stimulation disrupts this behavior. In the latter case, the local response is governed by the ratio of local conductances. The authors further analyze the observed electrical compartmentalization to identify which molecular components directly affected by synaptic stimuli - such as potassium clearance, glutamate transport, and GABAA and NMDA receptors - could plausibly generate this effect under physiological conditions. The study concludes that perisynaptic astrocyte processes (PAPs) can respond to specific synaptic inputs in an electrically isolated manner, exerting minimal influence on the soma.

The central prediction - that astrocyte processes can be electrically compartmentalized from the soma under local synaptic stimulation, while bath stimulation produces a globally uniform response - is both interesting and surely significant for understanding and measuring astrocyte electrophysiology. However, the manuscript's incomplete model description and disorganized Results section hinder assessment of how well the conclusions are supported. Addressing these issues could render the model a valuable resource for the community and a useful framework for guiding and interpreting voltage-imaging experiments in astrocytes.

Strengths:

(1) The study addresses a timely, long-standing question and may pioneer a multiscale approach to quantifying astrocyte electrophysiology beyond the traditional view of an electrically passive, isopotential glial syncytium. The findings are consistent with recent voltage-imaging evidence of localized depolarization in astrocyte processes.

(2) A key strength of the study is the use of a state-of-the-art, morphologically realistic compartmental model that captures both somatic and peripheral dynamics. This approach addresses spatial and temporal resolutions that are challenging to achieve experimentally.

(3) Simulating both in vitro-like (bath perfusion) and in vivo-like (local, synapse-driven) stimulation protocols within a unified framework may explain discrepancies observed between experimental results obtained under these differing conditions.

(4) The model predictions are benchmarked against experimental voltage-imaging data, and the systematic analysis of individual molecular contributors-including potassium channels, glutamate transporters, and synaptic receptors-yields testable predictions.

(5) The topic is relevant to a broad and multidisciplinary audience, including researchers in glial biology, synaptic physiology, and computational neuroscience.

Weaknesses:

(1) Terminology and clarity

The manuscript relies heavily on jargon and abbreviations, which impedes readability. The central concept of a "Nernstian" depolarization or response, introduced in the title, is unconventional and not explicitly defined. It appears that "Nernstian" refers to a membrane potential that follows the potassium Nernst potential (EK) of the astrocyte. This terminology is ambiguous, as the analysis is restricted to K+ and does not ensure that the astrocyte response excludes contributions from other ion Nernst potentials.

Furthermore, "Nernstian" reflects a modeling choice rather than an alternative such as the Goldman-Hodgkin-Katz formalism. Additional terms in the abstract, including "a variety of biochemicals," "astrocyte peripheries," "isolated peripherals," and "empirical" model, are also ambiguous. Because these terms are central to the main claim, they may confuse readers.

(2) Framing of the research question

The introduction is brief and does not clearly articulate the research question or the manner in which the study addresses it. The motivation, which should draw on cell anatomy (including the gap-junction-coupled syncytium), molecular composition, and stimulation protocol, is underdeveloped, and the knowledge gap is not explicitly identified.

(3) Incomplete model description

Model design, parameters, and stimulation protocols are often not described or justified, and references are missing (e.g., for K2P, TREK1/TWIK1, and the Na+/K+ pumps). In particular:

a) it is not explained how the three ionic species (Na+, K+, Cl−) needed to account for synaptic currents interact and are modeled in the extracellular and intracellular spaces, nor how they contribute to the astrocyte membrane potential;<br /> b) the diffusion formulation (Equation 9) neglects any tangential component of diffusion, an approximation that is left unjustified;<br /> c) gap junctions are modeled as ohmic currents driven by the potential difference between astrocytes, but the network arrangement, location, and distribution of the coupled astrocytes and of the gap junctions in the simulations are not specified;<br /> d) the analytical results (Equations 46-50) are not obviously derived from Equation 16, and the essential derivation steps are missing;<br /> e) it is unclear whether and how the voltage indicator ArcLight is modeled when comparing simulated traces with experimental fluorescence data.

Overall, the simulation setup lacks clarity, which limits reproducibility and impedes evaluation of the study's conclusions.

(4) Organization and rationale of the results

The Results are difficult to interpret owing to unclear organization and an ambiguous rationale. The derivation and application of the scale parameter ΓK (Equation 1) are unclear; in particular, its dependence on axial conductance appears inconsistent with the assumption that intracellular diffusion does not influence PAP responses (cf. van Egeraat et al., Biophys. J. 1993).

(5) Unsupported or missing results

Several statements are not supported by the data presented: some claims could not be matched to any evidence (e.g., L104-106), results described in the text could not be located in the figures (L159-164), comparisons are reported as "data not shown" (L140-143), and the source of the "realistic ECS sizes" (L191-194) is unclear. The authors do not demonstrate the validity of the extracellular K+ perturbation used to mimic action potentials.

(6) Presentation of figures

The figures are often crowded and stylistically inconsistent. Most importantly, in Figures 3, 4, and 6, the rendering of the noisy experimental data obscures the simulation results they are meant to benchmark, making it difficult for readers to assess agreement between model and experiment.

Reviewer #2 (Public review):

Summary:

Nakatani et al. developed a conductance-based, morphologically realistic computational model of an astrocyte soma and its perisynaptic processes (PAPs) to examine how extracellular potassium shapes the astrocytic membrane potential across spatial scales. The model builds on previously published morphological and electrophysiological data, as well as experimental measurements of transporter and ion-channel expression levels. It incorporates the astrocytic potassium channels Kir4.1, the Na⁺/K⁺-ATPase, the K2P leak channels TREK-1 and TWIK-1, and gap-junctional coupling to neighboring processes via connexin-43. Glutamate transporters (GLT-1), NMDA receptors, and GABA_A receptors are also included to examine neurotransmitter-mediated depolarization.

The authors propose that a compartmental model that explicitly distinguishes the soma from the PAPs, provides important insights into realistic potassium uptake and the electrophysiological properties of these distinct compartments.

Strengths:

(1) Clearing extracellular K⁺ is one of the most fundamental functions of astrocytes. Failures of K⁺ buffering are implicated in epilepsy, spreading depolarization, and several neurodegenerative conditions. Yet how K⁺ handling is divided between perisynaptic processes and the rest of the cell, and how this shapes local membrane potential, remains poorly understood. The study gives a mechanistic account. The soma behaves as a low-resistance K⁺ "sink" with little voltage changes for local K⁺ elevations around the PAPs, while producing much smaller, than expected by simply by Nernst equation. The local membrane potential deviates from E_K because current escapes to the rest of the cell. This framing separates physiological, synapse-scale K⁺ handling from global, pathological K⁺ load.

(2) The model described here fills an important gap in computational modelling of astrocytes. While computational models are now central to neuroscience, astrocytes and their interactions with neurons remain relatively underrepresented in mainstream computational modeling. Existing models mostly treat either whole-cell K⁺ buffering or the isolated perisynaptic compartment. This is novel in that it connects the two within one framework and provides a tool to address how astrocytes integrate local and global signals.

(3) Including realistic morphology shows that spatial heterogeneity matters. A key conceptual strength is that the model does not treat the astrocyte as a single, well-mixed, isopotential compartment. Using an EM-informed morphology with perisynaptic astrocytic processes, it shows that stimulus location and spatial extent shape the membrane response. Fine processes, constricted segments, and local conductance ratios generate responses that cannot be predicted from whole-cell properties alone. These questions are difficult to address experimentally because fine processes are below the resolution of conventional electrophysiology, and somatic recordings are limited by space-clamping. An in silico approach is therefore especially valuable, as it allows controlled manipulation of individual variables, such as stimulus location, channel density, and extracellular volume, that cannot be varied independently in tissue.

(4) It is built using standard, open tools, and therefore the model can be extended with ease by future studies. The model is implemented in NEURON, the most widely used simulator for biophysically detailed cells, with mechanisms written in NMODL and driven through Python. It also uses a previously published astrocyte morphology. Parameters are listed, and the authors state that the model files are in a repository. Together, these make the model accessible and reproducible if the repository link is also provided.

(5) The model is anchored to experimental data. Kir4.1 and K2P leak conductance were fitted to experimental current-voltage data (Figure S2), and other channel parameters were constrained by the literature. Notably, the authors avoided channel formulations with built-in mechanisms that fix the resting potential, which are common in earlier astrocyte models. The resting potential and K⁺ responses therefore emerge from the conductance rather than being imposed. The model also reproduces established whole-cell behavior, including Nernstian responses to bath K⁺ application, before being used for new predictions.

(6) The study tests several candidate mechanisms for peripheral depolarization within one framework and offers a way to reconcile conflicting results: K⁺ through Kir4.1, GLT-1, Na⁺/K⁺-ATPase, K⁺/glutamate spillover, and astrocytic GABA_A and NMDA receptors, all of which in principle can produce responses of the observed depolarization under various conditions. The model therefore offers a biophysical explanation for discrepancies in the astrocyte literature.

(7) The work makes several testable predictions:<br /> a) the local [K⁺]o required for a given PAP depolarization;<br /> b) local depolarization depends on the spatial extent of activation;<br /> c) somatic recordings would not detect PAP depolarizations.

Weaknesses:

(1) The Nernst equation itself does not appear to break down; local E_K should still follow it. Rather, the membrane potential deviates from E_K because low-resistance electrical coupling allows current to spread to the rest of the cell. I suggest rewording this statement for accuracy.

(2) The Methods mention an uploaded repository, but no link is provided. The authors should include a public link and deposit the model in ModelDB or a comparable repository.

(3) The deployed morphology is hippocampal, not cortical. But the paper's conclusions are for cortical somatosensory astrocytes based on morphological similarity. That deserves additional explanation, especially since PAP geometry is central to the conclusions.

(4) Astrocytic NMDA receptors best fit the observed depolarizations. However, because the experiments in Armbruster et al. were performed in the presence of APV, an NMDA receptor antagonist, the authors suggest that the responses may involve APV-insensitive NMDA receptor subunits. This explanation would be stronger if supported by evidence that these subunits are expressed in cortical astrocyte PAPs.

(5) For activation of GABA_A receptors on astrocyte PAPs, the source of GABA is unclear, since the stimulated ascending axons are glutamatergic. The model shows these receptors could explain the data, not how they might be activated. This deserves an explanation.

(6) I also recommend adding a discussion paragraph on the functional implications of heterogeneous astrocytic control of potassium homeostasis. Do the results suggest that astrocytes efficiently buffer K⁺ under normal conditions but fail during widespread K⁺ accumulation?

Reviewer #3 (Public review):

Summary:

This study uses computational modeling to examine how local synaptic activity influences astrocyte membrane potential and, in particular, whether the classical Nernstian relationship between extracellular potassium concentration and astrocyte membrane potential is maintained throughout the astrocyte. The authors focus on the possibility that local ionic changes associated with synaptic activity produce membrane potential responses in fine astrocytic processes that differ from those predicted by a global Nernstian relationship. The study addresses an important and longstanding question in astrocyte physiology and provides a potentially useful computational framework for examining how cellular morphology, membrane conductances, and local synaptic inputs shape astrocyte membrane potential. The central conclusion is that Nernstian behavior can break down during local synaptic input, particularly in fine astrocytic processes. The modeling framework could therefore be useful for understanding how the spatial organization of astrocytes influences their responses to local neuronal activity. However, the strength and generality of this conclusion depend on several biological assumptions that are not sufficiently considered in the current model or discussion.

Strengths:

This computational modeling addresses a longstanding question in astrocyte physiology dating back to the 1960s: whether astrocyte membrane potential (VM) follows the Nernstian prediction in response to changes in extracellular K+ concentration ([K+]e). Significant recent progress in astrocyte studies has provided a renewed opportunity to revisit this question. In this regard, the effort to develop a conductance-based NEURON model that incorporates understanding of astrocyte structure and physiology is highly valuable. The model has also shown the potential to simulate how changes in astrocyte membrane Na+, Ca2+, and Cl+ permeabilities, resulting from synaptic inputs, could alter the relative K+ permeability, and therefore the Nernstian behavior of astrocyte VM at different subregions of astrocytes. These questions are important to astrocyte physiology and remain technically challenging to experimentally evaluate. However, several concerns about the omission of recent studies on astrocyte structure and function from the model and the physiological interpretation of the Nernstian behavior of astrocytes need to be addressed before the conclusions can be adequately supported.

Weaknesses:

(1) The premise of the modeling:<br /> The cellular context should be clearly stated. The model is intended to simulate rodent grey matter cortical protoplasmic astrocytes, but not white matter fibrous astrocytes or other specialized astrocyte populations, which differ morphologically and, to some extent, functionally.

(2) The architecture of astrocytes:<br /> This model can be refined by incorporating recent advances in our understanding of astrocyte morphology (PMID: 35283239, PMID: 31550514). For example, (a) the branching pattern of astrocytic processes, particularly the terminal processes (or leaflets), which is a key element of the model; (b) the location of synapses within an astrocytic domain. Specifically, synapses can make contact with virtually every structural component of an astrocyte, from the soma to different levels of processes, including endfeet contacting blood vessels. This anatomical evidence is in sharp contrast to the prevailing perisynaptic astrocytic process (PAP) concept adopted in the modeling; and (c) the existence of a large quantity of reflective astrocytic processes that loop back and form gap junctional coupling at their stemming points that wrap around bundles of axons or large dendrites, forming a scaffold-like architecture (also see PMID:32312988 in your reference list). This may create regions of strong local astrocyte VM response to activity-dependent elevations of [K+]e, which could be an important feature to consider in the modeling.

(3) Astrocyte membrane potential is not an autonomous feature of individual astrocytes:<br /> If the goal is to simulate astrocyte VM, astrocyte syncytial coupling cannot be ignored. Specifically, the very low astrocyte-to-astrocyte pathway resistance (4.2 MΩ) provides strong electrical coupling that equalizes the VM of astrocytes within a syncytium to a uniform level, a property termed astrocyte syncytial isopotentiality (PMID: 26435164; PMID: 30277762). Experimental studies have demonstrated that coupling among 7-9 astrocytes is sufficient to maintain this isopotential state. Thus, the VM of an individual astrocyte is not determined solely by its own K+ conductance and transmembrane K+ gradient, but rather the result of the electrotonic spatiotemporal summation of voltage across the syncytium. A realistic model that seeks to predict astrocyte VM should incorporate syncytial coupling as a fundamental component.

(4) The historical debate about the Nernstian behavior of astrocyte VM in response to [K+]e:<br /> The authors should acknowledge the longstanding debate over the Nernstian behavior of glial VM in response to changes in [K+]e. This relationship was first described in the seminal neuroglia studies of Kuffler and colleagues in 1966, using optic nerve neuroglia, which are likely equivalent to fibrous astrocytes in rodent white matter. They reported a slope of ~58 mV per 10-fold change in [K+]e (PMID: 5966434). This interpretation was subsequently challenged by a series of in vivo studies of neuroglia VM in cortical grey matter, including Pape and Katzman (1972; PMID: 5016004), Ransom and Goldring (1973; PMID: 4805015), and Futamachi and Pedley (1976; PMID: 1276917). Importantly, the Futamachi study provided early in vivo evidence consistent with syncytial isopotentiality, although the existence of the astrocyte syncytium could not be demonstrated experimentally until the later work of Ransom and colleagues in 1981 (PMID: 7248773). This historical literature should be introduced in the Introduction to establish the significance of the present modeling work.

(5) Astrocyte syncytial isopotentiality:<br /> The authors should consider that syncytial isopotentiality is a system-wide feature of both grey and white matter astrocytes. At minimum, this means that somatic astrocyte VM recordings are not consistent with a simple Nernstian prediction of VM in response to changes in [K+]e. Therefore, a model of astrocyte VM should account for syncytial coupling rather than treating VM as a property determined by individual astrocytes.

(6) Distinguish K+ Nernstian behavior from multi-ion permeability and transporter-mediated flux:<br /> Conceptually, the high and near-exclusive K+ permeability of the resting astrocyte VM provides the basis for applying the Nernst equation to describe VM. In the present, Na+ and Ca2+ permeabilities are also introduced without a clear distinction between the conditions under which a Nernst equation remains applicable and those in which it does not. The authors should clearly define Nernstian behavior as referring specifically to K+ permeability. When the Na+ and Ca2+ permeabilities, resulting from the activation of AMPA/NMDA receptors, are involved in astrocyte across-membrane ionic activities, the VM follows the GHK prediction. Additionally, the model includes Na+-dependent transporters, which do not represent channel-mediated permeability. The authors should therefore distinguish channel-mediated ionic permeability from transport-mediated ionic flux when describing and modeling changes in astrocyte VM.

Author response:

We thank the reviewers for the overall positive review that declares our results convincing. We will revise the paper to improve the presentation of the model and results as suggested by the reviewers. The revision will be accompanied by a more detailed response.

eLife Assessment

This important study advances the HIV-1 vaccine development field by establishing six humanized immunoglobulin knock-in mouse models to facilitate the design and evaluation of HIV-1 vaccines. The evidence supporting the claims is compelling, with robust genetic engineering and rigorous baseline sequencing data, while additional assessment of BCR pairing, expression, and repertoire diversity would round out the characterization of these models. These mice offer the field a rich resource to evaluate vaccine candidates that induce broadly neutralizing antibodies against major epitopes of the HIV-1 envelope protein.

Reviewer #1 (Public review):

Summary

In "Mouse models with human antibody repertoires for inducing multiple lineages of HIV-1 broadly neutralizing antibodies", Tian et al. report the production of 6 humanized mouse models to support HIV-1 vaccine design studies aiming to induce different classes of broadly neutralizing antibodies.

These mice were designed to express immunoglobulin genes of human bNAbs targeting four different conserved epitopes of the HIV-1 Envelope while allowing the development of CDRH3 and CDRL3 diversity during gene rearrangements.

Strengths:

(1) The manuscript is clearly written, and findings are clearly presented with self-explanatory figures. The Introduction and Discussion sections provide very valuable context to understand the relevance of these mouse models and are accessible to a general reader. It is appreciated that the data are presented with transparency, highlighting both strengths and weaknesses of the mouse models.

(2) The characterization of the mouse models is overall thorough and rigorous. Immunization data to confirm functionality are not presented; however, it is understandable that this information will be presented in the context of subsequent studies. The authors indicate that these mice have been distributed to different teams.

(3) These mice will be very valuable tools for the HIV-1 vaccine community to evaluate vaccine candidates in a "closer-to-human" setting that allows a certain level of polyclonality while using human immunoglobulin genes.

Weaknesses:

(1) The authors investigate the use of the knocked-in human immunoglobulin genes in heavy chains and light chains and provide the frequency of productive rearrangements using these genes. However, there is no information about the frequency of the right heavy-light chain pairs. This information would be important to calculate the real frequencies of potential bNAb precursors in these mouse models.

(2) The authors use HTGTS-rep-seq to investigate immunoglobulin gene rearrangements at the genomic DNA level. This method is very useful for analyzing gene recombination because it provides information about both productive and non-productive rearrangements. However, this method does not provide information about BCR expression, and it is not optimal for investigating repertoire composition. BCR expression would provide a more rigorous characterization of the mice and confirm functionality.

(3) The goal of this study was to produce models that could provide a diverse bNAb-like repertoire. Specific quantification of the diversity of the repertoire is not provided. The study does not provide information about the sequences that do not use the intended genes.

Overall, the availability of these humanized mouse models will be very valuable for the HIV-1 vaccine community and will provide valuable pre-clinical models to advance the design and testing of vaccine candidates.

Reviewer #2 (Public review):

Tian & colleagues generate and characterize six novel humanized mouse models engineered to express precursor human antibody repertoires for inducing multiple lineages of broadly neutralizing antibodies (bnAbs) against HIV-1. Building on prior "VRC01-class" proof-of-concept work, this manuscript advances the field by demonstrating the value and suitability of these sophisticated models for testing the ability of vaccine candidates to induce bnAbs. By utilizing flow cytometry and HGTS sequencing techniques, the authors demonstrate that the targeted bnAb minigenes successfully undergo physiological V(D)J recombination and junctional diversification. Despite phenotypic heterogeneity in B-cell development, they show that these models reproducibly produce diverse repertoires enriched for functional humanized B-cell receptors expressing knock-in-derived bnAb precursor rearrangements, a subset of which yield the long, correctly charged CDRH3 loops required to target conserved but glycan-shielded bnAb epitopes. This achievement successfully overcomes a major vaccine testing bottleneck by providing a pool of patient-deduced precursor configurations in sufficient amounts to reliably study how subsequent somatic mutation events are induced by candidate vaccine protocols (with the potential exception of the PG9 model). Crucially, the comparative utilization of Kymab data serves as an excellent control to demonstrate the extreme rarity of bnAb rearrangements in fully locus-humanized mice, beautifully illustrating the design limitations of humanized models without adjusting for the much smaller repertoires of mice. Finally, the discussion is nuanced and well-written, including articulating the models' free availability and highlighting why these small-animal systems are complementary-and sometimes essential-in relation to rhesus macaque systems.

The paper's conclusions are robustly data-supported, with the only slight weakness being that the Kymab data were extrapolated rather than derived from actual head-to-head analyses. The comments below predominantly aim to provide further context and clarity-mainly in the abstract, results, and discussion sections-to this already excellent, highly detailed study. Refining the text clarity and phenotypic observations will further polish this impressive manuscript.

(1) Abstract Clarity:<br /> Concluding sentences should incorporate the discussion's explanation: finding natively low-frequency precursors in standard humanized locus mice is statistically prohibitive due to their smaller absolute B-cell compartment. These models bypass this restriction by maximizing relevant segment expression to expand the functional CDR3 search space.

(2) Results (Line 183, BG18 expansion):<br /> The intriguing expanded B1 cell population warrants more conservative commentary regarding genuine autoreactivity versus other possibilities, such as hyperactive baseline signaling via autophosphorylation (as shown by Hassan Jumaa for certain HC/LC clonotypes). While beyond this manuscript's scope, these mechanisms are differentiable via calcium flux assays with general anti-BCR signaling reagents.

(3) Results (p15/Line 324, PCT64/CH235 Vκ Allelic Architecture):<br /> The text states that lacking a recombination cassette caused hVκ3-20 to account for only a fraction of rearrangements. While Figure 1B's general engineering feature (tandem reverse hJκ-RSS 23 and 12 RSS hJκ) implies human Vκ should predominate everywhere, Figure 1C suggests hVκ3-20's low representation in CH235 and PCT64 models stems from being introduced differently-specifically via pre-rearranged minigenes rather than unaltered RSS orientations in an unrearranged cassette. The authors should clarify this point in the text and/or Figure 1.

(4) Results (p14, Clever Engineering of λLC rearranging mice):<br /> Replacing mCκ with hCλ3 is a clever genetic trick and an important proof of concept for stably accommodating a human lambda rearranging light chain cassette within the mouse kappa locus. This excellent methodological highlight deserves prominent recognition as a template for other model developers. I suggest giving this further play in the discussion.

(5) Discussion (Model Comparisons & Baseline Resolution):<br /> The authors aptly compare these humanized mice to macaques; in addition to their superior scalability, they point out that macaques don't recapitulate human-specific bnAb-relevant configurations in certain instances (like lacking the VH1-2 minigene for VRC01-class responses, or utilizing germline D segments that favor V2 apex responses, potentially overestimating human responses). To further enhance nuance, referencing other prior review KI model literature on this topic (some published by these authors) would be beneficial. For context, KI-derived adoptive transfer models offer more potential diversity (thus, 'higher bar' off-target response testing) and frequency control but are restricted to short priming studies. Conversely, the rearranging models in this study support full vaccine regimens and generate multiple variant bnAb precursors to compare potential in vivo response plasticity.

(6) Discussion (Baseline phenotypes):<br /> One limitation of the cellular flow data, albeit beyond the manuscript's current scope, is that the baseline phenotypes predominantly reflect variations of a fixed pair of V(D)J rearrangements, rather than bona fide patient-derived UCA bnAb precursors with correct charges/lengths. To help resolve pre-antigenic functional signaling boundaries and thus potentially aid immunogen design, the discussion could note how future work could aim to use techniques like single-cell transcriptomics to map expressed selection signatures of bnAb precursor paired V(D)J's to rule out anergy or survival/death gene signatures.

(7) Discussion (PG9 Model & HCDR3 Length Constraints):<br /> While the PG9 model's failure to produce bona fide precursors is unsurprising given its long HCDR3, the authors should provide a bit more detail as to whether a subset of PG9 variants with slightly shorter HCDR3s are permissive for bnAb production (analogous to DH511/10E8). Providing the specific HCDR3 sizes generated by this model in relation to recently discovered monkey antibodies-which are shorter/different but function like PG9 bnAbs (e.g., Guenega et al Nature 2026) - would offer excellent context. Such related lineage and/or lineage precursor variants could serve as structural alternatives, and highlighting how these length constraints impact selection provides vital boundary conditions for the immunogen design field when planning germline-targeting regimens.

Author response:

Reviewer #1 (Public review):

(1) The authors investigate the use of the knocked-in human immunoglobulin genes in heavy chains and light chains and provide the frequency of productive rearrangements using these genes. However, there is no information about the frequency of the right heavy-light chain pairs. This information would be important to calculate the real frequencies of potential bNAb precursors in these mouse models.

We acknowledge that our bulk sequencing-based repertoire analysis does not assess the pairing of immunoglobulin (Ig) heavy and light chains. To add this information, we plan to sort IgM⁺IgD^hi naive B cells from the mouse models and use single-cell RT-PCR to analyze paired heavy and light chains from individual B cells. Since the assay detects Ig transcripts, it will also address comment #2 about the lack of expression data for the human Ig segments.

The main goal of the single-cell RT-PCR experiment is to verify that the human Ig segments in the mouse models can be expressed as B cell receptors (BCRs) in naive B cells. However, because of its low-throughput nature, the assay is unlikely to be sensitive enough for detecting bnAb precursors. As shown in Figure 5G, the frequencies of bnAb-like CDR H3s for DH270, BG18 and PCT64 are in the range of 10^-4. The frequencies of paired bnAb-like heavy and light chains will be even lower. It will be difficult to robustly measure the frequencies of such rare BCRs even with 10x Genomic single-cell sequencing. For reference, 10x Genomic sequencing of B cells from 10E8 KI mice yielded 1026 heavy and light chain pairs (1). The sample size was adequate for analyzing light chains paired with the pre-rearranged 10E8 heavy chain, which is expressed in most B cells. The sequencing depth will be insufficient for detecting rare bnAb precursors in the rearranging mouse models of this study. Deep bulk sequencing data were used to calculate bnAb precursor frequencies in our previous VRC01 mouse models and in humans and nonhuman primates (2-8). It is possible to enrich bnAb precursors for single-cell analysis by sorting epitope-specific B cells (9-11). However, this method requires sorting probes that exhibit high affinity and selectivity for bnAb precursors. We believe immunization of the mouse models will be the most direct and definitive way to detect bnAb precursors and test their functions in these mouse models. As mentioned in the manuscript, we have already provided the mouse models to several investigators for immunization experiments.

(2) The authors use HTGTS-rep-seq to investigate immunoglobulin gene rearrangements at the genomic DNA level. This method is very useful for analyzing gene recombination because it provides information about both productive and non-productive rearrangements. However, this method does not provide information about BCR expression, and it is not optimal for investigating repertoire composition. BCR expression would provide a more rigorous characterization of the mice and confirm functionality.

It is true that HTGTS-rep-seq does not directly show the expression of productive rearrangements. As outlined in our response to comment #1, we plan to perform single-cell RT-PCR to confirm the expression of the integrated human Ig segments in IgM⁺IgD^hi B cells.

We have used HTGTS-rep-seq extensively to analyze V(D)J rearrangements. Overall, the outcomes of the repertoire analysis correlated well with functional assays of BCR expression. For example, we identified VRC01-class heavy and light chains in VRC01 mouse models with HTGTS-rep-seq; correspondingly, immunization of the mouse models elicited VRC01-class antibodies (7, 8, 10). As another example, we used HTGTS-rep-seq to characterize V(D)J rearrangements at different stages of B cell development (12). The ratio of productive/nonproductive V(D)J rearrangements increases as B cells mature. The correlation suggests that the productive rearrangements, as detected by HTGTS-rep-seq, are selected by virtue of their expression as BCRs, which are required for B cell maturation and survival.

(3) The goal of this study was to produce models that could provide a diverse bNAb-like repertoire. Specific quantification of the diversity of the repertoire is not provided. The study does not provide information about the sequences that do not use the intended genes.

In our previous analysis of the VRC01 mouse models, we calculated the proportion of unique CDR3s and CDR3 overlap among different animals and used these parameters to represent repertoire diversity (7, 8). We will perform the same analysis for the six mouse models in this study. The V(D)J rearrangements that do not use the human Ig segments are composed of mouse Ig segments, and we will show repertoire analysis of these rearrangements in the revised manuscript.

Reviewer #2 (Public review):

(1) Abstract Clarity:

Concluding sentences should incorporate the discussion's explanation: finding natively low-frequency precursors in standard humanized locus mice is statistically prohibitive due to their smaller absolute B-cell compartment. These models bypass this restriction by maximizing relevant segment expression to expand the functional CDR3 search space.

We will make the suggested change in Abstract.

(2) Results (Line 183, BG18 expansion):

The intriguing expanded B1 cell population warrants more conservative commentary regarding genuine autoreactivity versus other possibilities, such as hyperactive baseline signaling via autophosphorylation (as shown by Hassan Jumaa for certain HC/LC clonotypes). While beyond this manuscript's scope, these mechanisms are differentiable via calcium flux assays with general anti-BCR signaling reagents.

We are currently examining the cause of B1 cell expansion in the BG18 model. Our preliminary data suggest that some of the B1 cells express autoreactive antibodies, but more work is needed to reach a conclusion. Until then, we are open to alternative possibilities, as suggested.

(3) Results (p15/Line 324, PCT64/CH235 Vκ Allelic Architecture):

The text states that lacking a recombination cassette caused hVκ3-20 to account for only a fraction of rearrangements. While Figure 1B's general engineering feature (tandem reverse hJκ-RSS 23 and 12 RSS hJκ) implies human Vκ should predominate everywhere, Figure 1C suggests hVκ3-20's low representation in CH235 and PCT64 models stems from being introduced differently-specifically via pre-rearranged minigenes rather than unaltered RSS orientations in an unrearranged cassette. The authors should clarify this point in the text and/or Figure 1.

The recombination cassette, as diagrammed in Figure 1B, is not in the CH235 and PCT64 models. Instead, the hVk3-20 allele in these models was derived from one of the previous VRC01 mouse models (7). As described in the Luo et al paper (7), hVk3-20 replaces mouse Vk3-7 in the Igk locus, but all the other mouse Vk segments are intact and functional. In this configuration, hVk3-20 rearranges as one of the Vk segments in Igk allele and accounts for about 10% of total Igk rearrangements. We will clarify this point in the revision.

(4) Results (p14, Clever Engineering of λLC rearranging mice):

Replacing mCκ with hCλ3 is a clever genetic trick and an important proof of concept for stably accommodating a human lambda rearranging light chain cassette within the mouse kappa locus. This excellent methodological highlight deserves prominent recognition as a template for other model developers. I suggest giving this further play in the discussion.

We will highlight this design in the discussion.

(5) Discussion (Model Comparisons & Baseline Resolution):

The authors aptly compare these humanized mice to macaques; in addition to their superior scalability, they point out that macaques don't recapitulate human-specific bnAb-relevant configurations in certain instances (like lacking the VH1-2 minigene for VRC01-class responses, or utilizing germline D segments that favor V2 apex responses, potentially overestimating human responses). To further enhance nuance, referencing other prior review KI model literature on this topic (some published by these authors) would be beneficial. For context, KI-derived adoptive transfer models offer more potential diversity (thus, 'higher bar' off-target response testing) and frequency control but are restricted to short priming studies. Conversely, the rearranging models in this study support full vaccine regimens and generate multiple variant bnAb precursors to compare potential in vivo response plasticity.

As suggested, we will include additional references and comparisons of the Ig humanized mouse models with KI mice. The reviewer raised an important point about off-target responses. Adoptive transfer models have the full mouse antibody repertoire. By comparison, the antibody repertoires of the Ig-humanized mouse models are dominated by a select set of human Ig segments, with minimal contribution from mouse antibodies. From this perspective, the adoptive transfer model provides a more diverse range of potential off-target antibodies. On the other hand, the Ig-humanized mouse models may be superior to wild-type mice in producing antibodies against the immunogenic glycan holes in envelopes (13). Reaching these glycan holes may require sufficient CDR H3 length, and based on CDR H3 length distribution, Ig-humanized mouse models could be more capable of recapitulating this type of off-target response than wild-type mice.

(6) Discussion (Baseline phenotypes):

One limitation of the cellular flow data, albeit beyond the manuscript's current scope, is that the baseline phenotypes predominantly reflect variations of a fixed pair of V(D)J rearrangements, rather than bona fide patient-derived UCA bnAb precursors with correct charges/lengths. To help resolve pre-antigenic functional signaling boundaries and thus potentially aid immunogen design, the discussion could note how future work could aim to use techniques like single-cell transcriptomics to map expressed selection signatures of bnAb precursor paired V(D)J's to rule out anergy or survival/death gene signatures.

The reviewer is correct that the surface phenotypes of rare bnAb precursor B cells, such as low BCR levels on anergic B cells, may not be apparent in the bulk flow cytometric analysis. Single-cell transcription signature analysis would be a high-resolution method to analyze bnAb precursor B cells. The technical challenge is low bnAb precursor frequencies, and enrichment of bnAb precursors by immunogen sorting may be the necessary for their detection. We will discuss this point in the revised manuscript.

(7) Discussion (PG9 Model & HCDR3 Length Constraints):

While the PG9 model's failure to produce bona fide precursors is unsurprising given its long HCDR3, the authors should provide a bit more detail as to whether a subset of PG9 variants with slightly shorter HCDR3s are permissive for bnAb production (analogous to DH511/10E8). Providing the specific HCDR3 sizes generated by this model in relation to recently discovered monkey antibodies-which are shorter/different but function like PG9 bnAbs (e.g., Guenega et al Nature 2026) - would offer excellent context. Such related lineage and/or lineage precursor variants could serve as structural alternatives, and highlighting how these length constraints impact selection provides vital boundary conditions for the immunogen design field when planning germline-targeting regimens.

The suggested recent studies (14-17) showed that V2 apex targeting is achievable with relatively shorter CDR H3s (22-25aa), which are within reach in the PG9 mouse model. However, the rhesus antibodies utilize IGHD3-15 with optimal V2 apex targeting motif. It remains to be determined whether hD3-3-based antibodies with comparable CDR H3 length would function similarly. We will incorporate the new information from rhesus immunization studies into the discussion.

References:

(1) Ray R, Schiffner T, Wang X, Yan Y, Rantalainen K, Lee CD, et al. Affinity gaps among B cells in germinal centers drive the selection of MPER precursors. Nat Immunol. 2024;25(6):1083-96.

(2) Habib R, Solieva SO, Lin ZJ, Ghosh S, Bayruns K, Singh M, et al. Deep mining of the human antibody repertoire identifies frequent and genetically diverse CDRH3 topologies targetable by vaccination. Proc Natl Acad Sci U S A. 2026;123(18):e2532810123.

(3) Swanson O, Martin Beem JS, Rhodes B, Wang A, Barr M, Chen H, et al. Identification of CDRH3 loops in the B cell receptor repertoire that can be engaged by candidate immunogens. PLoS Pathog. 2023;19(5):e1011401.

(4) Steichen JM, Phung I, Salcedo E, Ozorowski G, Willis JR, Baboo S, et al. Vaccine priming of rare HIV broadly neutralizing antibody precursors in nonhuman primates. Science. 2024;384(6697):eadj8321.

(5) Steichen JM, Lin YC, Havenar-Daughton C, Pecetta S, Ozorowski G, Willis JR, et al. A generalized HIV vaccine design strategy for priming of broadly neutralizing antibody responses. Science. 2019;366(6470).

(6) Willis JR, Berndsen ZT, Ma KM, Steichen JM, Schiffner T, Landais E, et al. Human immunoglobulin repertoire analysis guides design of vaccine priming immunogens targeting HIV V2-apex broadly neutralizing antibody precursors. Immunity. 2022;55(11):2149-67.e9.

(7) Luo S, Jing C, Ye AY, Kratochvil S, Cottrell CA, Koo JH, et al. Humanized V(D)J-rearranging and TdT-expressing mouse vaccine models with physiological HIV-1 broadly neutralizing antibody precursors. Proc Natl Acad Sci U S A. 2023;120(1):e2217883120.

(8) Tian M, Cheng C, Chen X, Duan H, Cheng HL, Dao M, et al. Induction of HIV Neutralizing Antibody Lineages in Mice with Diverse Precursor Repertoires. Cell. 2016;166(6):1471-84.e18.

(9) Havenar-Daughton C, Sarkar A, Kulp DW, Toy L, Hu X, Deresa I, et al. The human naive B cell repertoire contains distinct subclasses for a germline-targeting HIV-1 vaccine immunogen. Sci Transl Med. 2018;10(448).

(10) Cottrell CA, Hu X, Lee JH, Skog P, Luo S, Flynn CT, et al. Heterologous prime-boost vaccination drives early maturation of HIV broadly neutralizing antibody precursors in humanized mice. Sci Transl Med. 2024;16(748):eadn0223.

(11) Schiffner T, Phung I, Ray R, Irimia A, Tian M, Swanson O, et al. Vaccination induces broadly neutralizing antibody precursors to HIV gp41. Nat Immunol. 2024;25(6):1073-82.

(12) Lin SG, Ba Z, Du Z, Zhang Y, Hu J, Alt FW. Highly sensitive and unbiased approach for elucidating antibody repertoires. Proc Natl Acad Sci U S A. 2016;113(28):7846-51.

(13) McCoy LE, van Gils MJ, Ozorowski G, Messmer T, Briney B, Voss JE, et al. Holes in the Glycan Shield of the Native HIV Envelope Are a Target of Trimer-Elicited Neutralizing Antibodies. Cell Rep. 2016;16(9):2327-38.

(14) Marchitto L, Wagh K, Roark RS, Coleon S, Li H, Skelly AN, et al. Enhanced B cell priming induces broadly neutralizing HIV-1 apex antibodies. Nature. 2026;656(8128):712-22.

(15) Guenaga J, Ádori M, Bale S, Phulera S, Zygouras I, Schleich FA, et al. Vaccination generates broadly cross-neutralizing antibodies to the HIV Env apex. Nature. 2026;654(8119):777-85.

(16) Mishra N, Liang B, Roark RS, Ghosh AR, Callaghan S, Lee WH, et al. Germline-targeting HIV immunogen induces cross-neutralizing antibodies in outbred macaques. Immunity. 2026;59(4):1140-60.e11.

(17) Ma KM, Sutton HJ, Pratap PP, Steichen JM, Carnathan D, Quinn J, et al. HIV broadly neutralizing antibody precursors to the Apex epitope induced in nonhuman primates. Sci Immunol. 2025;10(110):eadt6660.

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Curator: @dhovakimyan1

SciCrunch record: RRID:SCR_001622

What is this?

DOI: 10.1101/gad.353314.125

Resource: (WB Cat# WBStrain00000001,RRID:WB-STRAIN:WBStrain00000001)

Curator: @Apiekniewska

SciCrunch record: RRID:WB-STRAIN:WBStrain00000001

What is this?

DOI: 10.1101/gad.353314.125

Resource: RRID:WB-STRAIN:WBStrain00040422

Curator: @Apiekniewska

SciCrunch record: RRID:WB-STRAIN:WBStrain00040422

What is this?

DOI: 10.1101/2025.03.28.646066

Resource: RRID:Addgene_44361

Curator: @julianstanfield_scicrunch

SciCrunch record: RRID:Addgene_44361

What is this?

DOI: 10.1101/2025.03.28.646066

Resource: RRID:Addgene_44362

Curator: @julianstanfield_scicrunch

SciCrunch record: RRID:Addgene_44362

What is this?

DOI: 10.1093/stcltm/szag073

Resource: RRID:Addgene_138491

Curator: @julianstanfield_scicrunch

SciCrunch record: RRID:Addgene_138491

What is this?

DOI: 10.1093/nar/gkag890

Resource: RRID:Addgene_48078

Curator: @julianstanfield_scicrunch

SciCrunch record: RRID:Addgene_48078

What is this?

DOI: 10.1093/nar/gkag890

Resource: Addgene (RRID:SCR_002037)

Curator: @julianstanfield_scicrunch

SciCrunch record: RRID:SCR_002037

What is this?

DOI: 10.1093/nar/gkag890

Resource: RRID:Addgene_67717

Curator: @julianstanfield_scicrunch

SciCrunch record: RRID:Addgene_67717

What is this?

DOI: 10.1038/s44321-026-00543-0

Resource: OlyVIA (RRID:SCR_016167)

Curator: @dhovakimyan1

SciCrunch record: RRID:SCR_016167

What is this?

DOI: 10.1038/s42003-026-10750-7

Resource: ggrepel (RRID:SCR_017393)

Curator: @dhovakimyan1

SciCrunch record: RRID:SCR_017393

What is this?

DOI: 10.1038/s42003-026-10750-7

Resource: tidyr (RRID:SCR_017102)

Curator: @dhovakimyan1

SciCrunch record: RRID:SCR_017102

What is this?

DOI: 10.1038/s41596-026-01454-7

Resource: RRID:Addgene_41821

Curator: @dhovakimyan1

SciCrunch record: RRID:Addgene_41821

What is this?

DOI: 10.1038/s41596-026-01454-7

Resource: RRID:Addgene_177825

Curator: @dhovakimyan1

SciCrunch record: RRID:Addgene_177825

What is this?

DOI: 10.1038/s41596-026-01454-7

Resource: RRID:Addgene_214812

Curator: @dhovakimyan1

SciCrunch record: RRID:Addgene_214812

What is this?

DOI: 10.1038/s41596-026-01454-7

Resource: RRID:Addgene_207853

Curator: @dhovakimyan1

SciCrunch record: RRID:Addgene_207853

What is this?

DOI: 10.1038/s41596-026-01454-7

Resource: RRID:Addgene_174820

Curator: @dhovakimyan1

SciCrunch record: RRID:Addgene_174820

What is this?

DOI: 10.1038/s41596-026-01454-7

Resource: RRID:Addgene_132775

Curator: @dhovakimyan1

SciCrunch record: RRID:Addgene_132775

What is this?

DOI: 10.1038/s41594-026-01916-2

Resource: RRID:Addgene_149333

Curator: @dhovakimyan1

SciCrunch record: RRID:Addgene_149333

What is this?

DOI: 10.1038/s41594-026-01916-2

Resource: RRID:Addgene_12260

Curator: @dhovakimyan1

SciCrunch record: RRID:Addgene_12260

What is this?