RvH: - Dit heet in de ITS “regels voor het leveren van goederen”. Is er een reden om dit anders te noemen? - We lijken ons te beperken tot Laden en lossen. Goed om eens door te spreken. Er zijn veel andere typen nodig, elders in de RTTI, om het levervoertuig überhaupt bij een laad- en losplek te laten komen (Toegangsbeperkingen, zones, etc)

RvH: - Gaan de afslag ge- en verboden meenemen in toegangsbeperkingen? o Eventueel per voertuigtype?

RvH: - Dit heet in de ITS “permanente toegangsbeperkingen”. Is er een reden om dit anders te noemen?

kijk in George! voor benaming van kenmerken.

In de Nederlandse mobiliteitsinformatie heeft George meestal betrekking op de NDW-applicatie GEOgrafische Registratie van GEgevens. Het is een beheertoepassing waarmee wegbeheerders gegevens in het Nationaal Wegenbestand (NWB) kunnen onderhouden. Dus als iemand binnen de Nederlandse mobiliteitswereld zegt: "dat staat in George", bedoelt hij meestal de NDW-registratie van wegkenmerken en weggeometrie van het NWB

van Dirk Simon, wat is een beperking precies? Voor bijzonder transport is er vaak iets meer ruimte.

van Bas. Lees de rvv: https://wetten.overheid.nl/BWBR0004825/ en kijk hoe dat hierop projecteert

Projecteer de matrix (van Bas) hierop. Deze matrix geeft de afhankelijkheids- of kruisrelaties weer tussen de verschillende beperkingen. Bijvoorbeeld, een toegangsvoorwaarde voor een tussen kan een snelheidsbeperking zijn, of een senlheidbeperking kan gelden voor specifieke beperking op basis van GLBH.

Regimes ontbreken in het geheel. Dit zijn condities die in de regel met onderborden worden weergegegevn. bijvoorbeeld zonaal, weersafhankelijkheid, tijdsafhankelijkheid.

hoe zit dat met inhaalverbod weergegeven door tekens op de weg? Bijv doorgetrokken streep. Ik denk dat dit net als verkeersborden geen informatie is die uitgewisseld wordt. Dwz de inhaalvertbod informatie wordt wel uitgewisseld maar niet het verkeersteken.

zwaar vrachtverkeer moet 'vrachtverkeer' zijn.

een weglink bestaat uit rijbanen. Een rijbaan uit rijstroken.

klopt dit wel?Zijn de klassen gerelateerd aan specifieke toegangsinformatie

valt een bestelwagen hier ook onder?

hoort hier niet informatie mbt voertuigklasse bij?

waarom zijn de enumeraties niet klikbaar?

Dit diagram moet vollediger. Mist o.a. verkeersteken

Verkeersteken en verkeersbord kan weg. Er wordt geen informatie uitgewisseld over de locatie van verkeersborden in de context van verkeersregeling

gewicht/aslast maximaal gewicht is geen voertuigkenmerk, voertuigklasse heeft er ook niets mee te maken.

Voorstel om naam van deze klasse terug te brengen naar G/L/B/H

bij snelheidsbeperking: richting moet kenmerkrichting zijn: Richting waarvoor een wegkenmerk geldt

waar is de Permanente Toegangsbeperking?

any sentence that describes explicit design implications

This is exciting work, and I speak for multiple researchers in asking the authors to commit to sharing the published human disorderome phage libraries it's based on.

This is not a trivial request: Dr. Ivarsson has long refused requests to share published libraries. Among other things this makes it challenging to assess reproducibility of findings.

This comment does not bear on the important work the authors report, for which they should be congratulated.

TIENE UNA RESOLUCION DE 100 m

Dos bandas espectrales:

Banda 10 TIRS 1 (10,6 - 11,19 µm) 100 m

Banda 11 TIRS 2 (11,5 - 12,51 µm) 100 m

tienen una resolución de 1 km en el nadir

MWIR: Mid-Wave Infrared (Infrarrojo de Onda Media).

TIR: Thermal Infrared (Infrarrojo Térmico).

What does "FN" mean? Is it a meaningful response ?

this seems unaligned withthe bars/medians stated!

this doesn't seem to match the bars == the bars seem tp show around 20 -- adjust the scaling carefully and check vertical alignment!!

dates cut off here. find a way they show. Anonymous --> Anon. -- other stuff in tooltips?

Give the date updated ans and the number of responses at the top

Interesting take, plotting cultural attitudes of models alongside those of countries. The dimensions survival-self-expression and secular-traditional are a bit odd, apparently stemming from the World Values Survey. What would you get if you plot this stuff on the 6 dimensions of Hofstede [[Cultures and Organizations by Geert Hofstede]] 1980s, which shows a more nuanced picture that isn't as strongly geo-graphically oriented as these two axis.

We should label 'what kind of research training' here. I'm not sure the right term ... social science, quantitative modeling of cost/benefit forecasting, etc., economics, statistics, etc.

"I'd genuinely like" sounds like AI slop

link/tooltip evidence

The Fermi estimation session thing is a bit of an overstatement -- something we're considering running soon

add hyperlinks to these, and tooltip explanations

The proposal is more than just intensive research training. The idea is that it's outside of a university institution and provides more direct hands-on experience and one-to-one or small group feedback, sort of replacing grades, degrees, and accreditation it's proven work and personal recommendations.

This takes up a bit too much space on the page.

This looks too much like a marketing slogan. It's more of a question as to whether this is worthwhile and, if so, what direction to take it in.

consider a log scale or a 'discontinuity here -- with Foi Gras all the others appear minute

give some column headers instead

"my" not "our" -- and make this a tootip

(and already collected 10+)

I want to check this -- make it clear that this synthesis is your (Claude Opus atm) analysis.

I still don't want to suggest that I am 'reading this' in a way suggesting a particular conclusion!

make middle column narrower. Also less whitespace on the right within each column

Example hypothes.is comment. Flag @daaronr on any other comments to be sure I am notified

label this better -- what actually are the columns/outcomes here? And maybe put in whole shrimp and shrimp paste too

my intuitions -- at least in early stages, even if they see it as 'real meat' the chicken, fish, etc. imitating products may still be seen as distinct and not compete only with the product theory imitate

was it calibrated on this though?

clarify what this means

rephrase -- it's not necessarily 'half' -- there are other issues too

tooltip what this means

language should be more tentative -- this is an AI judgement; it's not entirely credible to 'judge ourselves'

add to header -- "annotation tool"

what is "R" again?

--> His work could be seen as a 'missing piece' for our analysis.

['the piece' suggests there is only one missing piece']

too strong -- we do not "conclude" anything

Explain more carefully ... we are not just a 'model' but also a calculator allowing you to provide different assumptions, and a template/example for future modelers

make wider, use the space generated

could be connected

--> The models focus on different aspects -- Production cost (TEAs) and Demand-side

Könnte man das theoretisch nicht auch direkt auf dem Chripstack Server im Codec Teil machen?

Unsere Stütze im Studium!

Danke Justus

PLAYER INFO: You walk down the road. You notice that through the mists and cloud the orb of the weak sun is now at an angle passed the mid point indicating the afternoon has come. As you walk the main road you notice there are more people about. They are all smiling but the smile does not reach their eyes. Many of them offer you the greeting Danika told you "All will be well" but little else in the way of dialogue. You ask for directions a couple of times from people. They provide short but accurate answers, one even says looking at Varric, looks like you need his services about a week ago and laughs and walks briskly off.

After passing the Town Square the directions bring you to a rather uninviting shop, two stories tall with a sign shaped like a coffin above the front door. All of the window shutters are closed up tight, and a deathly silence surrounds the establishment.

Review of Gaut et al.

Gaut et al. claim that their synthetic cell, “SpudCell,” demonstrates “a complete cell cycle” with “genome replication, growth, resource acquisition via feeding, and genetically encoded division,” and that the system has a 90 kb genome encoding resource uptake, transcription, translation, growth, genome replication, and division. If supported, this would be a major milestone for bottom-up synthetic cell biology.

After reading the paper, I think the technical work is real and interesting, but the headline claim is overstated. A more defensible summary would be: the authors have built a chemically defined liposome system that can couple cell-free gene expression, φ29-dependent plasmid amplification, genetically controlled feeder-liposome fusion, externally imposed or externally triggered division, and selection among prebuilt variants across serial cycles. That is already a substantial achievement. The problem is that the manuscript and surrounding public framing repeatedly imply a level of autonomy and cell-like reproduction that the experiments do not yet demonstrate.

My detailed concerns are below.

1. “Complete cell cycle” is used too loosely

My main concern is the use of the phrase “complete cell cycle.” The multigenerational experiments in Figure 3 are not driven by genetically encoded division machinery. They consist of incubation with feeder liposomes followed by mechanical extrusion.

The genetically encoded division experiments appear later and are much less autonomous. The cells express tagged αHL, but division is induced by externally added streptavidin plus linker chemistry. In the combined growth-and-division experiment, cells are immobilized on beads, fed with Ni-NTA feeder liposomes, and division is then triggered by adding biotin-FLAG antibody linker and streptavidin.

Therefore, the paper does not yet show the clean cycle implied by the title-level framing: repeated autonomous genetically encoded growth → genome replication → division → inheritance → repeat. Instead, it shows different modules distributed across different experimental formats.

1. “Feeding” is largely cytoplasmic resupply

In the actual cell-cycle experiment, feeder liposomes contain RNA polymerase, φ29, the PURE system, and small molecules, but no DNA. Thus, “feeding” is not metabolism in the biological sense. It is targeted fusion with biochemical refill vesicles.

This is a clever engineering solution, but it also means the 90 kb genome is not close to encoding a minimal living cell. Many of the hardest functions are supplied as purified materials, including ribosomes and translation components. The supplementary discussion acknowledges this limitation directly.

1. The minimal-genome framing is weakened by an internal inconsistency

The Results section first states that the genome is encoded across seven plasmids. Shortly afterward, the cell-cycle section says that the 90 kb genome is divided into eight plasmids. Figure 3 and the single-cell analysis then return to a seven-plasmid genome.

This may be a simple manuscript error, but it is a significant one in a paper whose central framing depends on a defined minimal genome.

1. Genome replication is plasmid amplification, not a regulated cell-cycle program

As far as I can tell, the genome replication assay is primarily φ29 rolling-circle amplification of multiple plasmids. That is useful and technically important, but it is not equivalent to a regulated cellular genome-replication program.

The inheritance data are also a major weakness. Only 30% of analyzed cells contain all seven plasmids after five generations. That is difficult to reconcile with a robust reproductive cell cycle. At minimum, the authors should separate “bulk detection of all plasmids in a daughter fraction” from “individual daughter cells inherit a complete genome.”

1. The selection experiment is interesting but should not be overinterpreted

The selection experiments are among the stronger parts of the paper. T7Max αHL increases fusion, and T7Max compartments become more abundant under serial growth/division. The paper reports that GFP-marked weak-promoter cells drop from 50% to 34%, while GFP-marked T7Max cells rise to 58%; sequencing also shows T7Max increasing, including from 10% to 38% after five generations.

However, the mutation was introduced artificially and did not arise spontaneously. The supplement acknowledges this point directly: the system demonstrates selection among engineered variants, but not Darwinian evolution in the stronger sense. True Darwinian evolution would require mutations arising within the synthetic cells and spreading because of their effects on fitness.

1. The public framing is ahead of the evidence

I am also concerned by the public framing around this preprint. The University headline describes the work as the “world’s first synthetic cell with a complete life cycle,” and the release says the system was built from non-living chemical components, replicates a biological cell’s life cycle, and may eventually transform medicine, materials, industrial chemicals, and manufacturing.

That is a lot of public-facing certainty for a non-peer-reviewed preprint whose own supplement says major work remains before robust independent life and evolution. SpudCell uses E. coli ribosomes, runs only a limited number of generations before machinery degrades, has incomplete genome inheritance, requires repeated feeder liposome addition, and needs external streptavidin/linker proteins for division.

Summary

This paper reports a substantial technical advance in bottom-up synthetic-cell engineering. But the current framing blurs the difference between a chemically assisted liposome workflow and a self-maintaining synthetic cell with a complete autonomous life cycle. The work would be much stronger if the authors narrowed the claim, clearly separated the experimental modules, and avoided language that implies robust synthetic life before the system demonstrates autonomous, multigenerational growth, genome replication, division, and inheritance in the same lineage.

eLife Assessment

This important study provides a detailed characterization of individual sarcomeres' contractility and of their synchrony in spontaneously beating cardiomyocytes derived from human induced pluripotent stem cells. The combination of high-resolution tracking, statistical analysis and mesoscopic modeling leads to compelling evidence that sarcomeres operate as dynamically unstable units, leading to stochastic heterogeneities in their contraction-elongation cycles depending on substrate stiffness. The work will be relevant to scientists interested in muscle biophysics, nonlinear dynamics and synchronization phenomena in biological systems.

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, the authors present comprehensive experimental observations and a theoretical framework to explain the heterogeneous behaviour of sarcomeres in cardiomyocytes. They show that a stochastic component exists in their contractile activity, which may act as a feedback mechanism regulating physiological function.

Strengths:

Experiments and data analysis are robust and valid. The rigorous statistical analysis and unbiased methods enable the authors to draw well-supported conclusions that go beyond the existing literature. Their outcomes inform about cellular activity at the individual level and the authors explain how the transient dynamics of single sarcomeres are governed by a force-velocity relationship and lead to the complex contractile patterns. The similarity of the results to the study cited in [24] demonstrates the validity of the in vitro setup for answering these questions and the feasibility of such in-vitro systems to extend our knowledge of out-of-equilibrium dynamics in cardiac cells.

Very interesting the suggestion that the interplay between intrinsic fluctuations and the dynamic instability are part of a feedback mechanism for maintaining structural and functional homeostasis.

The addition of the theoretical model and the new text of the manuscript improves the clarity of the study.

Reviewer #2 (Public review):

Summary:

Sarcomeres, the contractile units of skeletal and cardiac muscle, contract in a concerted fashion to power myofibril and thus muscle fiber contraction.

Muscle fiber contraction depends on the stiffness of the elastic substrate of the cell, yet it is not known how this dependence emerges from the collective dynamics of sarcomeres. Here, the authors analyze contraction time series of individual sarcomeres using live imaging of fluorescently labeled cardiomyocytes cultured on elastic substrates of different stiffness. They find that a reduced collective contractility of muscle fibers on unphysiologically stiff substrates is partially explained by a lack of synchronization in the contraction of individual sarcomeres.

This lack of synchronization is at least partially stochastic, consistent with the notion of a tug-of-war between sarcomeres on stiff sarcomeres. A particular irregularity of sarcomere contraction cycles is 'popping', the extension of sarcomers beyond their rest length. The statistics of 'popping' suggest that this is a purely random process.

Strengths:

This study thus marks an important shift of perspective from whole-cell analysis towards an understanding the collective dynamics of coupled stochastic sarcomeres.

Reviewer #3 (Public review):

The manuscript of Haertter and coworkers studied the variation of the length of a single sarcomere and the response of microfibrils made by sarcomeres of cardiomyocytes on soft gel substrates of varying stiffness.

The measurements at the level of a single sarcomere are an important new result of this manuscript. They are done by combining the labeling of the sarcomeres z line using genetic manipulation and a sophisticated tracking program using machine learning. This single sarcomere analysis shows strong heterogeneities of the sarcomeres that can show fast oscillations not synchronized with the average behavior of the cell and what the authors call popping events which are large amplitude oscillations. Another important result is the fact that cardiomyocyte contractility decreases with the substrate stiffness, although the properties of single sarcomeres do not seem to depend on substrate stiffness.

The authors suggest that the cardiomyocyte cell behavior is dominated by sarcomere heterogeneity. They show that the heterogeneity between sarcomere is stochastic and that the contribution of static heterogeneity (such as composition differences between sarcomeres) is small.

Strengths:

All the results are, to my knowledge, new and original. The authors also made a theoretical model where each sarcomere is described by a Langevin equation based on a non-linear coupling between force and velocity of the sarcomeres. This model accounts well for the experimental results including the observation of what the authors call popping events.

Author response:

The following is the authors’ response to the previous reviews

Public Reviews:

Reviewer #1 (Public review):

Summary:

In this manuscript, the authors present comprehensive experimental observations and a theoretical framework to explain the heterogeneous behaviour of sarcomeres in cardiomyocytes. They show that a stochastic component exists in their contractile activity, which may act as a feedback mechanism regulating physiological function.

Strengths:

Experiments and data analysis are robust and valid. The rigorous statistical analysis and unbiased methods enable the authors to draw well-supported conclusions that go beyond the existing literature. Their outcomes inform about cellular activity at the individual level and the authors explain how the transient dynamics of single sarcomeres are governed by a force-velocity relationship and lead to the complex contractile patterns. The similarity of the results to the study cited in [24] demonstrates the validity of the in vitro setup for answering these questions and the feasibility of such in-vitro systems to extend our knowledge of out-of-equilibrium dynamics in cardiac cells.

Very interesting the suggestion that the interplay between intrinsic fluctuations and the dynamic instability are part of a feedback mechanism for maintaining structural and functional homeostasis.

The addition of the theoretical model and the new text of the manuscript improves the clarity of the study.

Reviewer #2 (Public review):

Summary:

Sarcomeres, the contractile units of skeletal and cardiac muscle, contract in a concerted fashion to power myofibril and thus muscle fiber contraction.

Muscle fiber contraction depends on the stiffness of the elastic substrate of the cell, yet it is not known how this dependence emerges from the collective dynamics of sarcomeres. Here, the authors analyze contraction time series of individual sarcomeres using live imaging of fluorescently labeled cardiomyocytes cultured on elastic substrates of different stiffness. They find that a reduced collective contractility of muscle fibers on unphysiologically stiff substrates is partially explained by a lack of synchronization in the contraction of individual sarcomeres.

This lack of synchronization is at least partially stochastic, consistent with the notion of a tug-of-war between sarcomeres on stiff sarcomeres. A particular irregularity of sarcomere contraction cycles is 'popping', the extension of sarcomers beyond their rest length. The statistics of 'popping' suggest that this is a purely random process.

Strengths:

This study thus marks an important shift of perspective from whole-cell analysis towards an understanding the collective dynamics of coupled, stochastic sarcomeres.

Reviewer #3 (Public review):

The manuscript of Haertter and coworkers studied the variation of the length of a single sarcomere and the response of microfibrils made by sarcomeres of cardiomyocytes on soft gel substrates of varying stiffness.

The measurements at the level of a single sarcomere are an important new result of this manuscript. They are done by combining the labeling of the sarcomeres z line using genetic manipulation and a sophisticated tracking program using machine learning. This single sarcomere analysis shows strong heterogeneities of the sarcomeres that can show fast oscillations not synchronized with the average behavior of the cell and what the authors call popping eveents which are large amplitude oscillations. Another important result is the fact that cardiomyocyte contractility decreases with the substrate stiffness, although the properties of single sarcomeres do not seem to depend on substrate stiffness.

The authors suggest that the cardiomyocyte cell behavior is dominated by sarcomere heterogeneity. They show that the heterogeneity between sarcomere is stochastic and that the contribution of static heterogeneity (such as composition differences between sarcomeres) is small.

Strengths:

All the results are, to my knowledge, new and original. The authors also made a theoretical model where each sarcomere is described by a Langevin equation based on a non-linear coupling between force and velocity of the sarcomeres. This model accounts well for the experimental results including the observation of what the authors call popping events.

We thank you and the reviewers for the positive evaluation of our revised manuscript.

Recommendations for the authors:

Reviewer #2 (Recommendations for the authors):

(1) Origin of the 3-Hz oscillation and required model extension. These oscillations are reproduced by our model, and their origin is already discussed in the manuscript (see lines 403–406).

(2) Inclusion of all 5085 LOIs vs. the selected 2321. We have expanded the explanation of the LOI selection criteria in the manuscript and clarified that the main conclusions are not sensitive to this choice (lines 161-166)

(3) Fig. 3G caption — popping rate. The caption has been updated to clarify the units and normalization. 

(4) Fig. 4G — "Length x" vs. ΔL. Notation corrected for consistency.

(5) Fig. 4G — gray data points. Confirmed: these represent the mean, and the caption has been updated accordingly.

(6) Relation of k_l to the true substrate stiffness. We have added the following clarification: "The model evaluation compared the distributions of sarcomere length changes and velocities from simulations with representative experimental LOIs from substrates (5, 15, and 85 kPa, mapped to k_l = 0.5, 1.5 and 8.5 in our 1-D model; k_l is unitless, so only the ratios between values are meaningful — rescaling k_l leaves model output unchanged under correspondingly rescaled parameters) covering the full range of mechanical loads." (lines 365-369)

(7) Could a simpler model fit the data? The cubic polynomial in Eq. (3) was deliberately chosen as a generalist ansatz rather than imposed: its coefficients were obtained by data-driven inference via Differential Evolution, and if lower-order terms within this family had sufficed, the higher-order coefficients would have been driven toward zero. The inferred nonmonotonic force–velocity relation has two extrema separated by an unstable negative-slope branch, which sets a lower bound on the polynomial order — a linear F–v is monotonic and a quadratic admits only a single extremum, so cubic is the minimum polynomial order capable of producing the observed shape. Furthermore, the qualitative phenomena we report — popping events, dynamic instability, and stochastic heterogeneity — cannot arise from any monotonic force–velocity relation, as discussed in the section on the non-monotonic instability. With 10 parameters covering complex contractile dynamics at the individual sarcomere and myofibril level across different substrate stiffnesses, the present model is parsimonious within the family of polynomial force–velocity ansätze; we have not exhaustively searched alternative non-polynomial functional families, but any such alternative would still need to reproduce the same non-monotonic shape that the data require.

(8) Lines 497–507 in the Discussion. On reflection, we feel these lines provide useful context for the broader interpretation and would prefer to retain them.

(9) Line 331 — motivation of Eq. (3). We have added citations to prior work motivating this form of the equation for the broader readership.

(10) Line 427 — "scaled". Corrected.

Reviewer #3 (Recommendations for the authors):

We thank the reviewer for the recommendation of a theoretical appendix. The full model code, with the formulation and implementation documented in detail, is publicly available in our GitHub repository accompanying the paper, which we believe provides a complete reference for readers wishing to explore the model further. We therefore feel an additional appendix is not necessary within the scope of this revision.

eLife Assessment

The authors developed a new Agbl5 KO allele, extending the deletion to the N-terminus of CCP5 to explore its function in mouse ependymal cells and trachea. They show that the KO mice exhibit severe hydrocephalus due to mislocated basal bodies and impaired ciliary beating. The findings are valuable with implications in the subfield of cell biology. The evidence is solid in that the methods, data and analyses largely support the claims with only a few remaining weaknesses.

Reviewer #1 (Public review):

Summary:

Dad et al. explored the roles of cytosolic carboxypeptidase 5（CCP5）in the development of ependymal multicilia in the brain. CCP family are erasers of polyglutamylation of ciliary-axoneme microtubules. The authors generated a new mutant mouse of Agbl5 gene, which encodes CCP5, with deletion of its N-terminus and partial carboxypeptidase (CP) domain (named AGBL5M1/M1).

Strengths:

The mutant mice revealed lethal hydrocephalus due to degeneration of ependymal multicilia. Interestingly, this is in contrast with the phenotype of Agbl5 mutants with disruption solely in the CP domain of CCP5 (named AGBL5M2/M2) that did not develop hydrocephalus despite increased glutamylation levels in ependymal cilia as observed for AGBL5M1/M1 mutants. The study has been well-performed and the findings suggest a unique function of the N-domain of CCP5 in ependymal multicilia stability.

Weaknesses:

The content of this article is relatively descriptive and lacks molecular insights, regarding the function of the CCP5 N-domain.

Comments on revised version.

The authors have appropriately revised the manuscript in response to most of my comments.

Reviewer #2 (Public review):

Summary:

This study analyzed consequences of Agbl5 mutation on ependymal cells development and function. Authors first characterize their mutant mouse line reporting a reduced lifespan and severe hydrocephalus. Next, they report defect in ependymal cell cilia number and motility. They provide evidence for impaired basal bodies organisation, cilia glutamylation.

Strengths:

Description of a mutant mouse which implicate Cytosolic Carboxypeptidase 5 (the product of Agbl5 gene) for proper ependymal cells.

Weaknesses:

Description of phenotype are incomplete:

Previous comment: Microtubules are involved in the local organization of ciliary basal bodies (see Werner et al., Vladar et al.,2011; Boutin et al., 2014). It would be interesting that the author checks whether the subapical network of microtubule is glutamylated or not during ependymal cells differentiation and how this network is affected in their mutants.

Although authors now provide images of glutamylation in figure S8 their conclusion claiming that GT335 signal is increased in cilia of Agbl5M1/M1 mutant is not supported convincingly by those pictures. Quantification would be needed.

Reviewer #3 (Public review):

Summary:

The authors developed a new Agbl5 KO allele by extending the deletion to the N-terminus of CCP5 to investigate its function in mouse ependymal cells and trachea.

Strengths:

They show that the KO mice exhibit severe hydrocephalus due to disorganized and mislocated basal bodies. Additionally, they present evidence of both impaired beating coordination and a reduction in ciliary beating.

The manuscript is well-written, and the experiments are convincing.

Comments on revised version.

The authors have taken all of my comments into account and have revised their manuscript to my satisfaction.

Author response:

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

We thank the Editors for the positive assessment on our manuscript. We also thank the Reviewers for their positive remarks and constructive comments. Based on the Reviewers’ feedback, we have conducted additional experiments and provided supporting data to address Reviewers’ comments. Particularly, we provided quantitative measurement for rotational polarity of ependymal cells in Agbl5^M1/M1 mutants and assessed the microtubule polarization. We quantified the intensity of apical actin network in ependymal cells to strength the role of CCP5 in organizing actin network. Using scanning electron microscopy, we demonstrated the affected polarity of trachea multicilia in Agbl5^M1/M1. We co-immunostained ependymal cilia with GT335 and acetylated tubulin to address the effects on their length in cilia in the mutant. We assessed the presence and length of primary cilia in ependymal cell progenitors to identify their potential contribution to the defective polarity in Agbl5^M1/M1 ependymal cells. We feel that these revisions have much strengthened this MS.

Public Reviews:

Reviewer #1 (Public review):

Summary:

Dad et al. explored the roles of cytosolic carboxypeptidase 5（CCP5）in the development of ependymal multicilia in the brain. CCP family are erasers of polyglutamylation of ciliary-axoneme microtubules. The authors generated a new mutant mouse of Agbl5 gene, which encodes CCP5, with deletion of its N-terminus and partial carboxypeptidase (CP) domain (named AGBL5M1/M1).

Strengths:

The mutant mice revealed lethal hydrocephalus due to degeneration of ependymal multicilia. Interestingly, this is in contrast with the phenotype of Agbl5 mutants with disruption solely in the CP domain of CCP5 (named AGBL5M2/M2) that did not develop hydrocephalus despite increased glutamylation levels in ependymal cilia as observed for AGBL5M1/M1 mutants. The study has been well-performed and the findings suggest a unique function of the N-domain of CCP5 in ependymal multicilia stability.

Weaknesses:

The content of this article is relatively descriptive and lacks molecular insights.

We thank the Reviewer’s positive comments. To address the molecular insights of the dysregulated planar cell polarity (PCP) in Agbl5^M1/M1 ependyma, we have conducted additional experiments to assess the microtubule polarization in ependymal cells (Figure 7O-P). We quantified the intensity of actin networks around BB patches to better understand how it is affected in the ependyma of the mutants and contributes to the dispersion of BBs (Figure 4M-N), (Please see Recommendations for the authors).

We also assessed trachea multicilia in Agbl5^M1/M1 mutants using SEM and found that the polarity of trachea multicilia was affected as well (Figure S2).

Reviewer #2 (Public review):

Summary:

This study analyzed the consequences of Agbl5 mutation on ependymal cell development and function. The authors first characterize their mutant mouse line reporting a reduced lifespand and severe hydrocephalus. Next, they report a defect in ependymal cell cilia number and motility. They provide evidence for impaired basal body organisation and cilia glutamylation.

Strengths:

Description of a mutant mouse which implicates Cytosolic Carboxypeptidase 5 (the product of Agbl5 gene) for proper ependymal cells.

Weaknesses:

Description of phenotype is incomplete:

We thank the Reviewer’s constructive comments. We have performed additional quantitative analysis of the phenotypes in Agbl5^M1/M1 that we feel strengthen this study.

Figure 3G - the sequence from the movie is not really informative. Providing beating frequencies as quantification of the data would be more informative.

We have provided the beating frequency as well as the mean vector length of cilia beating directions (that reflects the coordination of cilia) in Figure 3H and 3I respectively in the revised manuscript.

Figure 3 - the quantification of actin network would strengthen the message.

We agree with the Reviewers. We have quantified the total intensity of actin around BBs and the actin intensity normalized to signals of the BB marker (CEP164). The data have been provided in Figure 4M and 4N respectively. The quantitative analysis showed that both the total intensity of apical actin network and the intensity of F-actin per BB are reduced in Agbl5^M1/M1 ependymal cells compared to that in wild-type mice, suggesting that CCP5 is involved in organizing actin network around BB. This analysis certainly improves the clarity of this message.

Lines 219 -220 - the authors conclude «Taken together, in Agbl5M1/M1 ependymal cells, the expression of genes promoting multiciliogenesis were not impaired but certain proteins associated with differentiated ependymal cells are not properly expressed». However, they do not assess gene but protein expression (IF). In addition, their quantification shows differences in the number of FoxJ1 positive cells which indeed is an impaired expression.

We will clarify this statement and emphasize the number of FoxJ1-positive cells.

Microtubules are involved in the local organization of ciliary basal bodies (see Werner et al., Vladar et al.,2011; Boutin et al., 2014). It would be interesting for the authors to check whether the subapical network of microtubules is glutamylated or not during ependymal cell differentiation and how this network is affected in their mutants.

We thank the Reviewer’s constructive comments. We conducted an immunostaining on whole-mount lateral walls of lateral ventricles for GT335 and Centrin1, the position of the latter being used to localize the subapical layer. While the GT335 signal in multicilia is increased in Agbl5^M1/M1 ependyma (Figure S8E), its signals underneath BBs are not much different between the mutant and wild-type (Please see Figure S8C, D, G, H).

Showing the data mentioned in the discussion on Cep110 would be a nice addition to the paper.

These data have been provided in Supplementary Figure S9.

Line 354: "The latter serves as a component of tissue polarity that is required for asymmetric PCP protein localization in each cell (Boutin et al., 2014; Vladar et al., 2012)." The cited reference did not demonstrate that this microtubule network is required for asymmetric PCP localization.

We thank the Reviewer for critical reading. The cited reference (Bountin et al., 2014) has been removed.

Reviewer #3 (Public review):

Summary:

The authors developed a new Agbl5 KO allele, extending the deletion to the N-terminus of CCP5 to explore its function in mouse ependymal cells.

Strengths:

They show that the KO mice exhibit severe hydrocephalus due to disorganized and mislocated basal bodies. Additionally, they present evidence of both impaired beating coordination and a reduction in ciliary beating.

Weaknesses:

The manuscript is well-written but lacks specific interpretations of the results presented. Further experiments are needed to be fully convincing.

We thank the Reviewer’s comments. We have performed further analysis and conducted additional experiments to strengthen this study.

(1) We have quantified the intensity of actin staining around BB patches and its intensity relative to the number of BBs to assess to which extent the actin networks in Agbl5^M1/M1 ependymal cells are affected (please refer to the above response to the comments of Reviewer 2#). The results were shown in Figure 4M-N.

(2) We Co-stained tdTomato with an ependymal cell-specific markers to strengthen the expression of Agbl5 in ependymal cells (please see Figure 6C-E).

(3) We have conducted co-immunostaining of GT335 and Ac-Tub and compared the length of their signals in ependymal multicilia between WT and Agbl5^M1/M1 mice (please see Figure 6O, P, R, S).

(4) We quantified the area of ependymal cells in the wild-type and Agbl5^M1/M1 mice. Indeed, the area of ependymal cells is increased in the mutants. However, the primary cilia are present in the ependymal cell progenitors of Agbl5^M1/M1 mice and have similar length with that in the wild-type (Please see Figure 7M, N and our response to this point below).

(5) We performed additional analysis to address the affected rotational polarity in the Agbl5^M1/M1 mutant mice (please see Figure 3I, Figure 7E).

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

(1) The authors showed that the actin networks were severely affected, leading to impaired stability of basal bodies and that the intensity and length of acetylated tubulin signal in the multicilia were dramatically reduced in AGBL5M1/M1mutant mice (Figures 3 and 5). Data also suggested the dysregulation of planar cell polarity. Are expression and localization of other planar cell polarity proteins such as tyrosinated tubulin and Fzd6 affected in mutant mice?

We thank the Reviewer’s recommendations. We have assessed the expression of tyrosinated tubulins and found they are similarly polarized in ependymal cells from wild-type and Agbl5^M1/M1 mice. The results are presented in Figure 7O, P in the revised MS. We also tried to assess the expression of Fzd6. However, with the antibody we tested, Fzd6 signals were not convincing. Therefore, we prefer to not showing the results and drawing a conclusion on it.

(2) The phenotype of multiciliated cells in tracheas should also be examined in mutant mice. It is important to elucidate whether AGBL5 commonly functions in multiciliated cells of other organs.

We thank the Reviewer’s suggestion. We have assessed the multicilia in the tracheas of P30 mice using scanning electron microscopy. Indeed, unlike the multicilia in wild-type mice that orientate to the same direction, those in the tracheas of Agbl5^M1/M1 mice often radiate to different directions in individual cells (Figure S2). Therefore, Agbl5 appears commonly involved in the alignment of multicilia.

(3) According to Figure 1B, AGBL5 is highly expressed in the brain. Which cells in the brain express it besides ependymal cells?

Based on the localization of tdTomato tracer engineered in Agbl5 mutant alleles (Figure 5B), Agbl5 is broadly expressed in the brain, including most if not all neurons, but its expression is much weaker in the subventricular zone (Please see Figure 5B). We clarified this in the revised MS.

(4) From a mechanistic point of view, it is necessary to identify binding proteins with the N-domain of AGBL5 and perform functional analyses.

We agree with the Reviewer. We feel that identification of the binding partners of CCP5 N-domain and functional analysis may be more suitable to go along with other mechanistic analysis on the function of CCP5 in ependymal cell polarities in our future study.

Reviewer #2 (Recommendations for the authors):

(1) Movie 3: The authors could comment on beating direction that seems impaired at the cell scale here, analysis of rotational polarity would be a plus.

We thank the reviewer’s recommendation. We have analyzed the beating directions of cilia in individual cells and presented their consistency in each cell using mean vector length. These results indeed demonstrated defective rotational polarity in the cell level in Agbl5^M1/M1 mice (please refer to Figure 3I). We also analyzed the beating directions of ependymal multicilia in earlier stage in tissue level (Figure 7E). The mean vector length of cilia beating direction in Agbl5^M1/M1 mice is significantly reduced compared to that in wild-type, suggesting an aberrant rotational polarity in the tissue level in the mutant (Figure 7E).

(2) Line 166 : ref to Werner et al., 2011 is not correct (no ependymal cells in that paper).

We thank the reviewer’s critical reading. This reference has been removed.

(3) Figure S4: B and D look similar picture to me same for C and F.

We apologize for using the wrong images in this Figure. It has been corrected (Revised Figure S5).

(4) Line 328: "Therefore, CCP5 apparently contributes to the establishment of both translational and tissue polarities in ependymal cells." Should be rephrased since translational polarity is also a tissue-level parameter which is the coordinated positioning of the ciliary patch. Cf Mirzadeh et al., 2010; Boutin et al., 2014.

We thank the Reviewer’s comments. The sentence has been rephrased. This concept has been clarified where else needed in the revised manuscript. 

(5) Line 348: "Planar cell polarity (PCP) pathway is essential for the establishment of rotational and tissue polarities in ependymal cells" Rotational polarity also has a tissular component (ie coordination of beating direction across tissue which is reflected by coordination of basal body polarities across tissue).

We thank the Reviewer’s comments. We have clarified this point in the revised MS.

(6) Incomplete bibliography citation (ie Walentek et al. without date).

We thank the Reviewer’s critical reading. This bibliography citation has been fixed.

Reviewer #3 (Recommendations for the authors):

(1) Figure 3: The authors assert that the mutant's apical actin networks are significantly disrupted. However, the cell shown in Figure 3Q-R exhibits less compact centrioles than the controls, which could account for the reduction in phalloidin staining. Because centriole dispersion is variable in the mutant, quantifying actin staining in representative cells would be necessary to support such a statement.

We thank the Reviewer’s comments. To address this concern, we have quantified the total intensity of actin network around BBs as well as the intensity of F-actin signals normalized to the level of immunosignals of BBs ((revised Figure 4M, N) please also refer to our response to Reviewer 1#). The results indicated the intensity of actin signal per BB is reduced in the mutant compared to that of wild-type mice. We feel that this analysis strengthened our statement.

(2) Figures S3 and 4A-B show that the authors examine tdT expression to show that Agbl5 is expressed in ependymal cells but not in the SVZ. However, the tdT signal intensity is very low, and cells are very dense in this brain region. Double staining with specific markers of ependymal and/or SVZ cells would help convince readers that tdT is not expressed in SVZ cells.

We agree with the Reviewer that the intensity of tdT signal is low, but broadly detectable in brain. Compared with its expression in ependymal cells, that in SVZ is much lower if any (Figure 4B’). To further confirm the identity of tdT-positive cells along the surface of ventricles, we have co-stained the brain sections of Agbl5^WT/M1 mice for tdT and S100b, a marker of mature ependymal cells (Figure 5C-E). The signal of tdt is colocalized with that of S100b and is much lower in cell layers next to S100b-positive cells.

(3) Figure 4C-D and S4: The authors demonstrate that the number of FoxJ1+ cells per section increases at P7 (4C-E), while the number of S100β+ cells per mm decreases. Quantifications should be carried out in a similar manner to ensure comparability (number of positive cells per mm). Additionally, it remains unclear how to interpret these results, as S100β and FoxJ1 are two markers of differentiated cells, yet they exhibit opposite trends compared to controls. Is this a direct or indirect effect of Agbl5 mutation? The increase in the number of FoxJ1+ cells is particularly surprising given that the number of GT335 multicilia per mm remains unchanged (Figure 5).

We agree with the Reviewer that quantifications should be carried out in a similar manner. In the revised MS, the quantification of Foxj1-positive cells is presented in number per mm (Figure 5I). To be noted, the expression of Foxj1 was assessed at P7 when ependymal cells are differentiating. while the expression of S100β was assessed at P17 when ependymal cells are supposed to be fully mature. Although S100b is used as a marker of mature ependymal cells, given its unclear function, we removed the results of S100b-positiving cell counting to avoid confusion in the revised manuscript.

(4) Figure 5: In this figure, the authors analyze the labeling obtained with GT335, Acetylated Tubulin, and Arl13b antibodies. They show that the area of the cilium labeled by GT335 has increased, while the area labeled by the Acetylated Tubulin antibody has decreased in the knockout (KO) compared to the control. However, the length of the cilia observed through labeling with the Arl13b antibody remains unchanged. These observations are intriguing, but the low-magnification images in Figure 4 do not allow for the differences in ciliary axoneme labeling to be seen. Double GT335/AcTub labeling and higher magnifications are necessary for improved visualization of the differences in labeling along the axonemes.

We thank the Reviewer comments. We have co-stained the cilia with GT335 and Ac-Tub antibodies, re-quantified cilia length labeled with respective antibodies and provided high magnification images. Please see the revised Figure 6O,P,R,S.

(5) Figure 6: An analysis of ciliary beats using a high-speed camera shows no difference in ciliary beat frequency between the control and KO groups. At least, 3 animals should be analyzed. According to Figure 5, these findings indicate that the decrease in ciliary acetylation and the increase in ciliary glutamylation do not affect the beat frequency; instead, they disrupt the orientation of the beats. While these results are intriguing, they require further confirmation. Analyzing ciliary beats with a high-speed camera is informative, but at least three animals per genotype should be examined to ensure rigor. Furthermore, if the coordination of ciliary beats is impaired within the cells, this should be validated by double-labeling centrioles and basal feet to demonstrate that the orientation of cilia within the cells is abnormal.

We thank the Reviewer’s comments. Sections shown in Figure 5 (currently Figure 6) are from P7 mice, while the ciliary beating analysis shown in Figure 6 (currently Figure 7) is from P15 mice. As the PTM changes in cilia were also observed in Agbl5^M2/M2, we don’t think this is the cause that disrupts the orientation of the beats. The rotational polarity of Agbl5^M1/M1 ependymal cells is affected. Please refer to the analysis in Figure 3I and Figure 7E in the revised manuscript.

(6) Figure 6F-G: β-Catenin labeling reveals cells of varying sizes in the KO. This phenotype is typical of ciliary mutants that lack primary cilia (Mirzadeh et al., 2010). Hence, it is essential to examine the mutation's impact on the presence, length, and positioning of the primary cilium in ependymal cell progenitors.

We thank the Reviewer’s constructive comments. We assessed the area of ependymal cells labeled with β-Catenin. Indeed, the ependymal cells in the mutant showed larger area than that of wild-type. The ratio of the area of BB patch over that of cell surface is reduced (please see Figure 7O, P in the revised manuscript). However, primary cilia are present in ependymal cell progenitors in the mutant and exhibit comparable length with those in the wild-type (Figure S8). Due to some technique problems, we were unable to get convincing results from whole-mount ventricle walls for the primary cilium positioning at this time. We speculate that the localization of certain sensory proteins in primary cilia or the positioning of primary cilia might be affected in Agbl5^M1/M1 mice. We discussed this possibility and will certainly systemically assess this intriguing aspect in our future investigation.

(7) Given the regular beating frequency in the KO at P15, how do the authors explain the complete absence of ciliary beating in the adult? How many animals were analyzed? One would expect ciliary beating to remain unaffected as it was at P15 unless the cilia structure was specifically altered at the adult stage. Is that the case?

We thank the Reviewer’s critical questions. We do think that the ciliary structure of Agbl5^M1/M1 ependymal cells is likely altered during aging. Given that only Agbl5^M1/M1 but not Agbl5^M2/M2 mice develop hydrocephalus, we speculate the N-domain of CCP5 may contribute to the integrity of ependymal multicilia. We have added this in the Discussion section. For each genotype, 2 mice were analyzed.

(8) Line 264 of the manuscript: replace intercellular with intracellular.

It has been revised.

(9) Indicate the number of animals analyzed in each experiment

It has been included in figure legends.

eLife Assessment

This paper addresses a valuable research question on the modest heritability of the brain's response to movie watching, and how heritability varies under different parameters such as regional spatial hyperalignment and BOLD frequency bands. The topic of this paper is of interest to fMRI methodological experts, and potentially to a broader cognitive neuroscience audience, and those with an interest in understanding the heritable sources of individual differences in brain function. Although some of the conclusions could be strengthened by future cross validation studies in independent and larger family-based samples, and through complementary twin/family and SNP-based models, taken altogether, the analyses and results provide convincing evidence for the overall conclusions.

Reviewer #1 (Public review):

Summary:

Gruskin and colleagues use twin data from a movie-watching fMRI paradigm to show how genetic control of cortical function intersects with the processing of naturalistic audiovisual stimuli. They use hyperalignment to dissect heritability into the components that can be explained local differences in cortical-functional topography and those that cannot. They show that heritability is strongest at slower-evolving neural time scales, and more evident in functional connectivity estimates than in response time series.

Strengths:

This is a very thorough paper that tackles this question from several different angles. I very much appreciate the use of hyperalignment to factor our topographic differences and found the relationship between heritability and neural time scales very interesting. The writing is clear and the results are compelling. In general, I don't have many complaints after a couple reads through the manuscript; most of my comments below are relatively minor suggestions and points of clarification.

Weaknesses:

The only "weaknesses" I identified were some points where I think the methods, interpretation, or visualization could be clarified:

On page 16, you compare heritability in functional connectivity (FC) and response time series and find that the heritability effect is larger in FC. In general, I agree with your diagnosis that this is in large part due to the fact that FC captures the covariance structure across parcels, whereas response time series only diverge in terms of univariate time-point-by-time-point differences. Another important factor here is that (within-subject) FC can be driven by intrinsic fluctuations that occur with idiosyncratic timing across subjects and are unrelated to the stimulus (whereas time-locked metrics like ISC and time-series differences cannot, by definition). This makes me wonder how this connectivity result would change if you used intersubject functional connectivity (ISFC) analysis to specifically isolate the stimulus-driven components of functional connectivity (Simony et al., 2016). This, to me, would provide a closer comparison to the ISC and response time series results, and could allow the authors to quantify how much of the heritability in FC is intrinsic versus stimulus-driven. I'm not asking that the authors actually perform this analysis, as I don't think it's critical for the message of the manuscript-but it could be an interesting future direction. As the authors discuss on page 17, I also suspect there's something fundamentally shared between response time series and connectivity as they relate to functional topography (Busch et al., 2021) that drives part of the heritability effect.

The observation that regions with intermediate ISC have the largest differences between MZ, DZ, and UR is very interesting, but it's kind of hard to see in Figure 1B. Is there any other way to plot this that might make the effect more obvious? For example, I could imagine three scatter plots where the x- and y-axes are, e.g., MZ ISC and UR ISC, and each data point is a parcel. In this kind of plot, I would expect to see the middle values lifted visibly off the diagonal/unity line toward MZ. You could even color the data points according to networks like in Figure 3C. (You also might not need to scale the ISC axis all the way to r = 1, which would make the differences more visible.)

On page 9, if I understand correctly, you regress the vector of ISC values across parcels out of the vector of heritability values across parcels and then plot the residual heritability values. Do you center the heritability values (or include some kind of intercept) in the process? I'm trying to understand why the heritability values go from all positive (Figure 2A) to roughly balanced between positive and negative (Figure 2B). Important question for me: How should we interpret negative values in this plot? Can you explain this explicitly in the text? (I also wonder if there's a more intuitive way to control for ISC. For example, instead of regressing out ISC at the parcel/map level, could you go into a single parcel and then regress the subject-level pairwise ISC values out when computing the heritability score?)

On page 4 (line 155), you say "we shuffled dyad labels"-is this equivalent to shuffling rows and columns of the pairwise subject-by-subject matrix combined across groups? I'm trying to make sure your approach here is consistent with recommendations by Chen et al., 2016. Is this the same kind of shuffling used for the kinship matrix mentioned at line 189?

I found panel A in Figure 4 to be a little bit misleading because your parcel-wise approach to hyperalignment won't actually resolve topographic idiosyncrasies across a large cortical distance like what's depicted in the illustration (at the scale of the parcels you're performing hyperalignment within). Maybe just move the green and purple brain areas a bit closer to each other so they could feasibly be "aligned" within a large parcel. Worth keeping in mind when writing that hyperalignment is also not actually going to yield a one-to-one mapping of functionally homologous voxels across individuals: it's effectively going to model any given voxel time series as a linear combination of time series across other voxels in the parcel.

References:

Busch, E. L., Slipski, L., Feilong, M., Guntupalli, J. S., di Oleggio Castello, M. V., Huckins, J. F., Nastase, S. A., Gobbini, M. I., Wager, T. D., & Haxby, J. V. (2021). Hybrid hyperalignment: a single high-dimensional model of shared information embedded in cortical patterns of response and functional connectivity. NeuroImage, 233, 117975. https://doi.org/10.1016/j.neuroimage.2021.117975

Chen, G., Shin, Y. W., Taylor, P. A., Glen, D. R., Reynolds, R. C., Israel, R. B., & Cox, R. W. (2016). Untangling the relatedness among correlations, part I: nonparametric approaches to inter-subject correlation analysis at the group level. NeuroImage, 142, 248-259. https://doi.org/10.1016/j.neuroimage.2016.05.023

Simony, E., Honey, C. J., Chen, J., Lositsky, O., Yeshurun, Y., Wiesel, A., & Hasson, U. (2016). Dynamic reconfiguration of the default mode network during narrative comprehension. Nature Communications, 7, 12141. https://doi.org/10.1038/ncomms12141

Comments on revised version.

The authors have adequately addressed my previous comments. This is a strong contribution: the methods are sophisticated, the statistical treatment is rigorous, and the results are quite interesting/compelling. I'm happy to endorse the revised manuscript as a finalized version.

Just to confirm: The subjects watched all different movies across the two days, right? For a moment I was wondering "are Day 1 and Day 2 repetitions of the same movies?" Given that Day 1 and Day 2 are an organizational feature of several figures, it might be worth making this very explicit in the Methods and reminding the reader in the Results section.

Reviewer #3 (Public review):

Strengths:

It's sort of novel to study the heritability of movie-watching fMRI data. The methodology the authors used in the paper is also supportive of their findings. Figures are nicely organized and plotted. They finally found that sensory processing in the human brain is under genetic control over stable aspects of brain function (here referring to neural timescale and resting state connectivity).

Weaknesses:

What I am worried about most is the sample size and interpretation of heritability.

(1) Figure 1. I assumed that the authors just calculated the ISC within each group (MZ, DZ, and UR). Of course, you can get different variations between each group. Therefore, there is heritability. Why not calculate ISC across the whole sample, then separate MZ, DZ, and UR?

(2) Heritability scores in the paper are sort of small. If the sample size is small, please consider p-values, which will tell more about the trustworthiness of your heritability.

(3) I don't understand the high-frequency signals in fMRI data. It's always regarded as noise, the band 1 here in particular.

(4) The statement "we show that the heritability of brain activity patterns can be partially explained by the heritability of the neural timescale" should come from Figure 5. However, after controlling for NT, the heritability decreased max. 0.025 in temporal areas. I am not sure this change supports the statement. If the visual cortex is outlined, and combining ISC changes in the visual cortex, I think this would somehow be answered. Instead of delta h2, adding a new model h2 would be obvious to the readers.

(5) Figures 7 and 8, when getting the difference of heritability, please also consider the standard errors of the heritability estimates. Then you can compare across networks/regions.

(6) I think movie VS resting state is a really important result in this paper. However, there is almost no discussion. Discussing this part would be more beneficial for understanding the genetic control over the neuron arousal and excitation circuits.

Comments on revised version.

The whole manuscript has been improved a lot, and the concerns have been clarified.

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

Summary:

Gruskin and colleagues use twin data from a movie-watching fMRI paradigm to show how genetic control of cortical function intersects with the processing of naturalistic audiovisual stimuli. They use hyperalignment to dissect heritability into the components that can be explained by local differences in cortical-functional topography and those that cannot. They show that heritability is strongest at slower-evolving neural time scales and is more evident in functional connectivity estimates than in response time series.

Strengths:

This is a very thorough paper that tackles this question from several different angles. I very much appreciate the use of hyperalignment to factor out topographic differences, and I found the relationship between heritability and neural time scales very interesting. The writing is clear, and the results are compelling.

We thank Reviewer 1 for their kind words and enthusiastic support of our manuscript.

Weaknesses:

The only "weaknesses" I identified were some points where I think the methods, interpretation, or visualization could be clarified.

(1) On page 16, the authors compare heritability in functional connectivity (FC) and response time series, and find that the heritability effect is larger in FC. In general, I agree with your diagnosis that this is in large part due to the fact that FC captures the covariance structure across parcels, whereas response time series only diverge in terms of univariate time-point-by-time-point differences. Another important factor here is that (within-subject) FC can be driven by intrinsic fluctuations that occur with idiosyncratic timing across subjects and are unrelated to the stimulus (whereas time-locked metrics like ISC and timeseries differences cannot, by definition). This makes me wonder how this connectivity result would change if the authors used inter-subject functional connectivity (ISFC) analysis to specifically isolate the stimulus-driven components of functional connectivity (Simony et al., 2016). This, to me, would provide a closer comparison to the ISC and response time series results, and could allow the authors to quantify how much of the heritability in FC is intrinsic versus stimulus-driven. I'm not asking that the authors actually perform this analysis, as I don't think it's critical for the message of the manuscript, but it could be an interesting future direction. As the authors discuss on page 17, I also suspect there's something fundamentally shared between response time series and connectivity as they relate to functional topography (Busch et al., 2021) that drives part of the heritability effect.

We agree that investigating the heritability of ISFC (or stimulus-driven functional connectivity) would make for a very interesting future direction. Ultimately, we chose to analyze FC (vs. ISFC) profiles to allow for direct comparison with the sizable existing literature on the heritability of FC (such as in our Movie vs. Rest FC analysis) and decided to refrain from analyzing ISFC data in order to keep the present manuscript focused. ISFC analysis of this dataset will be a focus of future work.

(2) The observation that regions with intermediate ISC have the largest differences between MZ, DZ, and UR is very interesting, but it's kind of hard to see in Figure 1B. Is there any other way to plot this that might make the effect more obvious? For example, I could imagine three scatter plots where the x- and y-axes are, e.g., MZ ISC and UR ISC, and each data point is a parcel. In this kind of plot, I would expect to see the middle values lifted visibly off the diagonal/unity line toward MZ. The authors could even color the data points according to networks, like in Figure 3C. (They also might not need to scale the ISC axis all the way to r = 1, which would make the differences more visible.)

We thank R1 for this helpful suggestion- we originally set the y-axis limits to r = 1 in order to facilitate comparison between ISC (Fig. 1B) and FC profile (Fig. 6B) similarity, but we agree that this renders the group differences harder to discern and have updated the plot accordingly (along with thicker lines to enhance readability). We prefer to keep the line plots in the main body as they allow for direct comparison of all three groups on the same plot, but we have included the scatter plot version in Fig. S2 for those who are interested.

(3) On page 9, if I understand correctly, the authors regress the vector of ISC values across parcels out of the vector of heritability values across parcels, and then plot the residual heritability values. Do they center the heritability values (or include some kind of intercept) in the process? I'm trying to understand why the heritability values go from all positive (Figure 2A) to roughly balanced between positive and negative (Figure 2B). Important question for me: How should we interpret negative values in this plot? Can the authors explain this explicitly in the text? (I also wonder if there's a more intuitive way to control for ISC. For example, instead of regressing out ISC at the parcel/map level, could they go into a single parcel and then regress the subject-level pairwise ISC values out when computing the heritability score?).

We indeed included an intercept in this model using MATLAB’s fitlm function. This means that the model estimates the best-fitting line of the following form: heritability_i=β0+β1ISC_i +ε_i. We agree that the interpretation of these ε_i values and alternative approaches to controlling for ISC should be clarified. As such, we have added the following passages to the text:

Methods: “Because the heritability of ISC is constrained by the degree of synchronization in a given area, we also sought to identify areas in which BOLD time courses were more/less heritable than would be expected based on ISC alone by fitting a linear model of the form heritability_i=β0+β1ISC_i+ε_i and plotting the residuals. Regarding alternative approaches to controlling for ISC, although the heritability model introduced by Ge et al. allows for the inclusion of covariates defined at the subject level (e.g., age), it does not allow for covariates that are defined at the dyad level (e.g., pairwise ISC).”

Results: “Here, negative values in the residual map indicate parcels where heritability is lower than expected based on ISC, while positive values indicate higher-than expected heritability.”

(4) On page 4 (line 155), the authors say "we shuffled dyad labels"- is this equivalent to shuffling rows and columns of the pairwise subject-by-subject matrix combined across groups? I'm trying to make sure their approach here is consistent with recommendations by Chen et al., 2016. Is this the same kind of shuffling used for the kinship matrix mentioned in line 189?

Briefly, shuffling the kinship matrix involved permuting the rows and columns of the matrix in the same manner (also known as the quadratic assignment procedure), whereas shuffling the dyad labels involved random permutations of the three group labels (MZ, DZ, unrelated), which could not be done through matrix operations as the age- and gender matching precluded the use of a complete similarity matrix. However, given concerns raised by Reviewer 2, we have removed our significance claims from this (and similar) sections, which we discuss in more detail in response to Reviewer 2’s weakness A.

(5) I found panel A in Figure 4 to be a little bit misleading because their parcel-wise approach to hyperalignment won't actually resolve topographic idiosyncrasies across a large cortical distance like what's depicted in the illustration (at the scale of the parcels they are performing hyperalignment within). Maybe just move the green and purple brain areas a bit closer to each other so they could feasibly be "aligned" within a large parcel. Worth keeping in mind when writing that hyperalignment is also not actually going to yield a one-to-one mapping of functionally homologous voxels across individuals: it's effectively going to model any given voxel time series as a linear combination of time series across other voxels in the parcel.

We agree that our efforts to present a simplified depiction of hyperalignment may mislead less familiar readers and have amended Fig. 4A according to this suggestion. We have also added text to the methods section (below) to clarify that the outputs of hyperalignment are time series that reflect linear combinations of other voxels’ time series from that parcel.

“This approach independently transforms each subject's data within discrete anatomical parcels into the common space, yielding functionally aligned vertex time series that are calculated as weighted linear combinations of the original time series from all other vertices within that same parcel for that subject.”

(6) I believe the subjects watched all different movies across the two days, however, for a moment I was wondering "are Day 1 and Day 2 repetitions of the same movies?" Given that Day 1 and Day 2 are an organizational feature of several figures, it might be worth making this very explicit in the Methods and reminding the reader in the Results section.

We agree that this would be helpful and have added the following text to the relevant sections:

“All clips were only viewed once by each subject, with the exception of the brief montage which was included at the end of each of the four runs for test-retest purposes.”

“To characterize the heritability of brain responses to complex stimuli, we used 7T fMRI data from 178 HCP Young Adult subjects acquired across two days (using two largely non-overlapping sets of movie stimuli, see Methods)…”

References:

Busch, E. L., Slipski, L., Feilong, M., Guntupalli, J. S., di Oleggio Castello, M. V., Huckins, J. F., Nastase, S. A., Gobbini, M. I., Wager, T. D., & Haxby, J. V. (2021). Hybrid hyperalignment: a single high-dimensional model of shared information embedded in cortical patterns of response and functional connectivity. NeuroImage, 233, 117975. https://doi.org/10.1016/j.neuroimage.2021.117975

Chen, G., Shin, Y. W., Taylor, P. A., Glen, D. R., Reynolds, R. C., Israel, R. B., & Cox, R. W. (2016). Untangling the relatedness among correlations, part I: nonparametric approaches to inter-subject correlation analysis at the group level. NeuroImage, 142, 248259. https://doi.org/10.1016/j.neuroimage.2016.05.023

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Reviewer #2 (Public review):

Summary:

The authors attempt to estimate the heritability of brain activity evoked from a naturalistic fMRI paradigm. No new data were collected; the authors analyzed the publicly available and well-known data from the Human Connectome Project. The paper has 3 main pieces, as described in the Abstract:

(1) Heritability of movie-evoked brain activity and connectivity patterns across the cortex.

(2) Decomposition of this heritability into genetic similarity in "where" vs. "how" sensory information is processed.

(3) Heritability of brain activity patterns, as partially explained by the heritability of neural timescales.

Strengths:

The authors investigate a very relevant topic that concerns how heritable patterns of brain activity among individuals subjected to the same kind of naturalistic stimulation are. Notably, the authors complement their analysis of movie-watching data with resting-state data.

Weaknesses:

The paper has numerous problems, most of which stem from the statistical analyses. I also note the lack of mapping between the subsections within the Methods section and the subsections within the Results section. We can only assess results after understanding and confirming the methods are valid; here, however, Methods and Results, as written, are not aligned, so we can't always be sure which results are coming from which analysis.

(A) Intersubject correlation (ISC) (section that starts from line 143): "We used nonparametric permutation testing to quantify average differences in ISC for each parcel in the Schaefer 400 atlas for each day of data collection across three groups: MZ dyads, DZ dyads, and unrelated (UR) dyads, where all UR dyads were matched for gender and age in years." ... "some participants contributed to ISC values for multiple dyads (thus violating independence assumptions)"

This is an indirect attempt to demonstrate heritability. And it's also incorrect since, as the authors themselves point out, some subjects contribute to more than one dyad.

Permutation tests don't quantify "average differences", they provide a measure of evidence about whether differences observed are sufficient to reject a hypothesis of no difference.

Matching subjects is also incorrect as it artificially alters the sample; covarying for age and sex, as done in standard analyses of heritability, would have been appropriate.

It isn't clear why the authors went through the trouble of implementing their own nonparametric test if HCP recommends using PALM, which already contains the validated and documented methods for permutation tests developed precisely for HCP data.

The results from this analysis, in their current form, are likely incorrect.

We appreciate that permutation tests do not quantify average differences and intended to write “We used non-parametric permutation testing to quantify [the significance of] average differences…”. Our intention with this analysis was not to demonstrate heritability, but rather to quantify group differences in ISC in a manner that is interpretable for readers who are unfamiliar with h² (e.g., “identical twins’ BOLD time courses were 59% more similar than those from pairs of unrelated individuals”) and motivate the formal heritability analysis used later in the paper. Indeed, all of the heritability analyses in this paper leveraged a validated multidimensional heritability method first introduced by Ge et al. (2016) and used by many other investigators since then. Furthermore, we covaried for age and sex at the subject level in all our heritability analyses, and always tested the significance of these heritability values using a validated permutation procedure (the quadratic assignment procedure; Hubert & Schultz, 1976) that respects the non-independence of dyadic data.

Regarding the shuffling procedure used for Figure 1, while PALM is the standard for univariate, subject-level GLMs in the HCP pipeline and can accommodate nested designs (i.e., subjects within families), it is not designed to handle the unique relational dependencies of dyadic ISC analysis (i.e., the same subject contributing to multiple dyads). Although the element-wise resampling approach was the most appropriate approach available, it is known to inflate the false positive rate (Chen et al., 2016; doi:10.1016/j.neuroimage.2016.05.023); given that this analysis was simply meant to motivate our later hypothesis testing heritability analyses, we have removed significance claims from this section of the manuscript. Still, we emphasize that this has no bearing on the validity of our conclusions which were supported by our formal heritability analyses; throughout our paper we have correctly used the appropriate methods to back the stated claims.

(B) Functional connectivity (FC) (section that starts from line 159): Here the authors compute two 400x400 FC matrix for each subject, one for rest, one for movie-watching, then correlate the correlations within each dyad, then compared the average correlation of correlations for MZ, DZ, and UR. In addition to the same problems as the previous analysis, here it is not clear what is meant by "averaging correlations [...] within a network combination". What is a "network combination"? Further, to average correlations, they need to be r-to-z transformed first. As with the above, the results from this analysis in its current form are likely incorrect.

We regret that R2 had difficulty understanding our analysis and have added the following text to the relevant Methods section to clarify our approach:

“For example, there are 16 parcels in the Kong et al. Auditory network and 17 parcels in the Language network, so the FC profile for a given subject’s Auditory-Language network combination consists of the (16 * 17 =) 272 correlation coefficients between all unique pairs of one parcel from each network.”

As we stated in the previous Methods paragraph, “All Pearson r values in this and all other analyses were Fisher z-transformed before averaging (and converted back to Pearson r for visualization)”. Thus, contrary to the reviewer’s assertion, these analyses were performed correctly. Once again, we emphasize that this analysis was not intended to demonstrate heritability, but rather to describe group differences in FC in familiar units.

(C) ISC and FC profile heritability analyses (section that starts from line 175): Here, the authors use first a valid method remarkably similar to the old Haseman-Elston approach to compute heritability, complemented by a permutation test. That is fine. But then they proceed with two novel, ill-described, and likely invalid methods to (1) "compare the heritability of movie and rest FC profiles" and (2) to "determine the sample size necessary for stable multidimensional heritability results". For (1), they permute, seemingly under the alternative, rest and movie-watching timeseries, and (2), by dropping subjects and estimating changes in the distribution.

The (1) might be correct, but there are items that are not clearly described, so the reader cannot be sure of what was done. What are the "153 unique network combinations"? Why do the authors separate by day here, whereas the previous analyses concatenated both days? Were the correlations r-to-z transformed before averaging?

The (2) is also not well described, and in any case, power can be computed analytically; it isn't clear why the authors needed to resort to this ad hoc approach, the validity of which is unknown. If the issue is the possibility that the multidimensional phenotypic correlation matrix is rank-deficient, it suffices that there are more independent measurements per subject than the number of subjects.

Regarding (1), we have clarified in section 2.6 that the 153 unique network combinations reflect each unique pair of 17 Kong networks. All of our analyses, including this one, were performed separately for each day of data collection, as we state throughout the paper and visualize in our figures (although we acknowledge that, on some occasions, we [conservatively] performed FDR-correction on a combined set of p-values, as discussed in our response to K). Given that the null hypothesis for this analysis is that rest FC and movie FC are equally heritable, we are not sure why permuting rest and movie FC matrices would be invalid. All Pearson r values were z-transformed before averaging, as we stated in our paper.

Regarding (2), we included this analysis in response to editorial concerns that our heritability analyses were not sufficiently powered, and we chose this approach because it serves as a simple way to demonstrate the stability of our results at various sample sizes whose validity is self-evident. Furthermore, this sort of subsampling approach has been used many times before in our field (e.g., Marek et al., 2022) and others (e.g., Manyara et al., 2024) to demonstrate the sample-size dependence and stability of statistical effects. We have added text explaining this to the relevant Methods section (2.6).

(D) Frequency-dependent ISC heritability analysis (from line 216): Here, the authors decompose the timeseries into frequency bands, then repeat earlier analyses, thus bringing here the same earlier problems and questions of non-exchangability in the permutations given the dyads pattern, r-z transforms, and sex/age covariates.

We did not use dyadic permutation testing for any of the frequency-dependent ISC analyses; rather, we used the jackknife SEMs to compare heritability across frequency bands and have added an explicit description of this to section 2.7. We have addressed the r-z transform and covariate concerns in previous comments.

(E) FC strength heritability analysis (from line 236): Here, the authors use the univariate FC to compute heritability using valid and well-established methods as implemented in SOLAR. There is no "linkage" being done here (thus, the statement in line 238 is incorrect in this application. SOLAR already produces SEs, so it's unclear why the authors went out of their way to obtain jackknife estimates. If the issue is non-normality, I note that the assumption of normality is present already at the stage in which parameters themselves are estimated, not just the standard errors; for non-normal data, a rank-based inversenormal transformation could have been used. Moreover, typically, r-to-z transformed values tend to be fairly normally distributed. So, while the heritabilities might be correct, the standard errors may not be (the authors don't demonstrate that their jackknife SE estimator is valid). The comparison of h2 between dyads raises the same questions about permutations, age/sex covariates, and r-z transforms as above.

We used jackknife SEs for these analyses to maintain consistency with the multidimensional heritability package used here, which only outputs jackknife SEs. We note that this jackknife approach (and the corresponding multidimensional heritability analysis) was detailed in prior work (Anderson et al., 2021), and that the leave-one-family-out jackknife has a long history of being used to estimate SEs in heritability studies, especially when working with smaller samples (Knapp et al., 1989). We are also not sure what “the comparison of h2 between dyads” means- heritability cannot be compared “between” dyads; rather, it is defined across dyads.

(F) Hyperalignment (from line 245): It isn't clear at this point in the manuscript in what way hyperalignment would help to decompose heritability in "where vs. how" (from the Abstract). That information and references are only described much later, from around line 459. The description itself provides no references, and one cannot even try to reproduce what is described here in the Methods section. Regardless, it isn't entirely clear why this analysis was done: by matching functional areas, all heritabilities are going to be reduced because there will be less variance between subjects. Perhaps studying the parameters that drive the alignment (akin to what is done in tensor-based and deformation-based morphometry) could have been more informative. Plus, the alignment process itself may introduce errors, which could also reduce heritability. This could be an alternative explanation for the reduced heritability after hyperalignment and should be discussed. An investigation of hyperaligment parameters, their heritability, and their co-heritability with the BOLD-phenotypes can inform on this.

To help set up our hyperalignment analyses, we have added text to the introduction explaining how hyperalignment would help to decompose heritability. The description in the Methods section included a reference to Bazeille et al., 2021, in which the hyperalignment method used here is discussed in detail. Still, we have added citations to additional papers (also cited in the Bazeille et al. paper, and elsewhere in our paper) in case that might be helpful. We note that it is not the case that all heritabilities were reduced by hyperalignment- as can be seen in Figs. 4D, 8A, and S15, hyperalignment did increase heritability in some voxels and network combinations. This would be expected under the alternative (albeit unlikely) hypothesis that functional topographies are not heritable, such that topographic variation between related individuals would obscure similarities in their (heritable) topography-independent brain responses. Recognizing that this alternative is unlikely, we believe the main novelty of this analysis comes from the magnitude of the hyperalignment effect (up to 40% of brain-wide heritability) and its spatial pattern (e.g., larger heritability decreases in visual vs. auditory cortex, the opposite of our NT result).

We agree that we would see lower post-hyperalignment heritability if the alignment process itself introduced errors/noise, but this would be deeply surprising as hyperalignment increases ISC by design (and errors/noise could only decrease ISC). To demonstrate this, we have added Figure S7 which shows that (as expected) ISC across all voxels and subject pairs increases after hyperalignment (and that this increase is larger when hyperalignment is performed in larger parcels). Given that hyperalignment increased ISC, and that it is blind to twin status, we are unsure how it could have introduced errors that would have confounded this result.

(G) Relationships between parcel area and heritability (from line 270): As under F), how much the results are distorted likely depends on the accuracy of the alignment, and the error variance (vs heritable variance) introduced by this.

We agree that alignment accuracy could potentially impact parcel-level differences in how much heritability changes following hyperalignment, and we included the frequency dependent h²_residuals (controlling for differences in ISC) in Fig. 3 for this reason, as more accurate hyperalignment should result in greater increases in ISC, raising the heritability ceiling. We note that we observe similar relationships between parcel rank and frequency dependent changes in these residualized maps, suggesting that our parcel-level differences are not simply the result of better alignment in more sensory parcels.

(H) Neural timescale analyses (from line 280): Here, a valid phenotype (NT) is assessed with statistical methods with the same limitations as those previously (exchangability of dyads, age/sex covariates, and r-z transforms). NT values are combined across space and used as covariates in "some multivariate analyses". As a reader, I really wanted to see the results related to NT, something as simple as its heritability, but these aren't clearly shown, only differences between types of dyads.

We have addressed the exchangeability, covariates, and r-z transform comments above (in A). As we explained for our FC strength analyses, we are underpowered to evaluate the heritability of unidimensional traits (like the heritability of NT magnitude), and the heritability of a closely-related measure (BOLD turnover magnitude) has already been established in a larger sample of HCP subjects (https://doi.org/10.1152/jn.00402.2022). Still, we agree that more results related to the heritability of NTs would be of interest to our readers. As such, we have added an analysis in section 3.4 quantifying the heritability of multivariate NT topographies and used SOLAR to quantify the heritability of NT magnitudes, with the disclaimer that this and similar analyses are underpowered (hence the large difference in day 1 and day 2 heritability effect sizes). We also removed significance claims for the dyadic NT similarity analysis.

(I) Significance testing for autocorrelated brain maps and FC matrices (from line 310): Here, the authors suddenly bring up something entirely different: reliability of heritability maps, and then never return to the topic of reliability again. As a reader, I find this confusing. In any case, analyses with BrainSMASH with well-behaved, normally distributed data are ok. Whether their data is well behaved or whether they ensured that the data would be well behaved so that BrainSMASH is valid is not described. As to why Spearman correlations are needed here, Mantel tests, or whether the 1000 "surrogate" maps are valid realizations of the data under the null, remains undemonstrated.

We brought up reliability in this section because we show the reliability of our results across the two days of data collection several times in the paper. R2 is correct to point out that BrainSMASH was validated using normally distributed brain maps, and although some of our brain maps contain normally distributed values, others are right skewed (due largely to the fact that many voxels/parcels exhibit low ISC while visual/auditory areas have very high ISC). In preparing our original manuscript, we visualized BrainSMASH’s variogram outputs for one of the most skewed inputs (vertex-wise BOLD time course heritability) and found that the autocorrelation structures of the empirical and null maps were well-matched. We did not include this in the original manuscript as it is not commonplace in the field to report the variograms, see Author response image 1. Furthermore, our use of Spearman (vs. Pearson) correlations renders these distributional differences less relevant, as the Spearman correlation transforms all inputs to a uniform distribution. To empirically check that these distributional differences do not bias our results, we retested the significance of all brain map associations using the spin test (10.1016/j.neuroimage.2018.05.070), an alternative method that does not assume normally distributed inputs, and obtained identical p-values for all analyses (P<.001 in all cases).

Author response image 1.

(J) Global signal was removed, and the authors do not acknowledge that this could be a limitation in their analyses, nor offer a side analysis in which the global signal is preserved.

Although we agree that GSR is a contentious preprocessing step for certain analyses, it has explicitly been shown to increase ISC signal-to-noise without compromising FC fingerprints (Graff et al., 10.1016/j.dcn.2022.101087), and it is uncommon to perform ISC analyses with and without GSR. Still, we have added additional text to our Methods section explaining our rationale for using GSR and that this could affect our results. We also re-ran our main analysis (BOLD time course heritability) with and without GSR and found that GSR had little impact on our results; we have included this in our manuscript as Fig. S4.

Specifically, we see that GSR resulted in a slight increase in heritability (average Day 1 h² with/without GSR = .064/.060; Day 2: .068/.061) and almost no effect on the spatial pattern of our results (With GSR/without GSR Spearman ρ = .99, P_brainSMASH < .001 on both Day 1 and Day 2).

(K) FDR is used to control the error rate, but in many cases, as it's applied to multiple sets of p-values, the amount of false discoveries is only controlled across all tests, but not within each set. The number of errors within any set remains unknown.

We agree that the FDR usage in our original manuscript was inconsistent, in that for two analyses we FDR-corrected p-values from the two days of data collection together (instead of correcting p-values from each day separately and reporting voxels/parcels/etc. that were significant at q<.05 on both days, as in the rest of our analyses). We note that both approaches are more conservative than reporting significant results at q<.05 separately; regardless, to maintain consistency we have updated all analyses such that FDR correction is always performed separately for each day of data collection.

(L) Generally, when studying the heritability of a trait, the trait must be defined first. Here, multiple traits are investigated, but are never rigorously defined. Worse, the trait being analyzed changes at every turn.

Here, we analyze the heritability of movie-evoked BOLD time courses (Figures 1-5) as well as FC profiles (Figures 6-8). We defined FC profiles in our Introduction as an individual’s pattern of pairwise FC strengths (and further detailed how we quantified FC profiles in the relevant Methods section), and believe that “BOLD time course” is a well understood phrase in the field and does not need to be further defined. We also used hyperalignment to decompose the heritability of these traits into topography-dependent and independent portions, and (new to this version) also explicitly quantify the heritability of neural timescales, which we defined as the AUC of the ACF until the first negative ACF value in both the relevant Results and Methods sections.

To make this clearer, we have modified the last paragraph of our Introduction to begin with:

In the present work, we address these questions by analyzing 7T fMRI recordings of a twin sample acquired by the Human Connectome Project (Van Essen et al., 2013) to quantify the heritability of two distinct high-dimensional traits—stimulus-evoked BOLD time courses and functional connectivity profiles—across the cortex.

Reviewer #3 (Public review):

Strengths:

It's sort of novel to study the heritability of movie-watching fMRI data. The methodology the authors used in the paper is also supportive of their findings. Figures are nicely organized and plotted. They finally found that sensory processing in the human brain is under genetic control over stable aspects of brain function (here referring to neural timescale and resting state connectivity).

Weaknesses:

What I am worried about most is the sample size and interpretation of heritability.

(1) Figure 1. I assumed that the authors just calculated the ISC within each group (MZ, DZ, and UR). Of course, you can get different variations between each group. Therefore, there is heritability. Why not calculate ISC across the whole sample, then separate MZ, DZ, and UR?

We believe that this question is getting at the difference between pairwise ISC (i.e., correlating one BOLD time course from one subject with that from another subject) and leave-one-subject-out ISC (i.e., correlating one BOLD time course from one subject with the corresponding average time course across all other subjects). We chose to use the pairwise ISC method because it allows us to capitalize on the information contained in the n² pairwise ISC matrix (whereas the other approach averages out meaningful information to yield a n¹ ISC matrix) and leverage a more sophisticated multidimensional heritability approach. Also, the leave-one-subject-out approach introduces additional issues re: handling family-level data (e.g., should we include a subject’s twin in the leave-one-subject-out average? If so, how should we handle subjects who don’t have a twin in the dataset, as averaging data from different numbers of subjects will lead to different ISC magnitudes? etc.).

(2) Heritability scores in the paper are sort of small. If the sample size is small, please consider p-values, which will tell more about the trustworthiness of your heritability.

We report p-values for heritability throughout our paper (e.g., stating that BOLD time courses are significantly heritable in 99% of parcels in Figure 2), and we believe that the reliability of our spatial maps across days of data collection (also quantified with p-values) further demonstrates the trustworthiness of our results. Finally, as we demonstrate in Figure S5, our sample size is more than sufficient to reliably detect small effects.

(3) I don't understand the high-frequency signals in fMRI data. It's always regarded as noise, the band 1 here in particular.

In addition to driving shared neuronal responses (which are captured in BOLD signal oscillations <.1 Hz or so), movies also elicit shared cardiac, respiratory, and motion responses across participants at higher frequencies. Although we used a relatively conservative denoising approach here, we believe some of these non-neuronal signals are still present in our data; alternatively, it is also possible that these signals reflect “fast” BOLD responses at >.15 Hz (as discussed in 10.1016/j.neuroimage.2021.118658). In any case, the fact that information in this frequency band is considerably less heritable than information in slower frequency bands supports the idea that this band is noisier and suggests that our heritability results are driven by canonical neuronal activity-related BOLD signals.

(4) The statement "we show that the heritability of brain activity patterns can be partially explained by the heritability of the neural timescale" should come from Figure 5. However, after controlling for NT, the heritability decreased max. 0.025 in temporal areas. I am not sure this change supports the statement. If the visual cortex is outlined, and combining ISC changes in the visual cortex, I think this would somehow be answered. Instead of delta h2, adding a new model h2 would be obvious to the readers.

Although the decrease of 0.025 is small, we note that this constitutes around ~50% of BOLD time course heritability in some voxels (seen in comparison to Fig. 4C), and the spatial pattern of this result is quite consistent across days of data collection, indicating its reliability. Furthermore, the whole-brain distributions of results shown in Fig. 5B are clearly skewed towards negative values, indicating that controlling for NT partially reduces (or “explains”) BOLD time course heritability. Still, we agree that showing raw h² values in addition to the difference maps would be helpful for some readers and have added a corresponding supplementary figure (S12) which shows these.

(5) Figures 7 and 8, when getting the difference of heritability, please also consider the standard errors of the heritability estimates. Then you can compare across networks/regions.

We did consider adding standard errors for these heritability estimates, but found that visualizing standard errors for each of the 153 unique network combinations in our heatmaps rendered the visualizations difficult to parse, and given that our hypotheses concerned global (e.g., hyperaligned vs. MSM-aligned) or network-level (e.g., sensory vs. associative) patterns, we focused on calculating standard errors/p-values for these analyses (although we note that dyad-level standard errors can be found in Fig. 6B, where they are clearly marginal compared to the group effects).

(6) I think movie VS resting state is a really important result in this paper. However, there is almost no discussion. Discussing this part would be more beneficial for understanding the genetic control over the neuron arousal and excitation circuits.

We agree that this result was relatively under-explored in our Discussion section and have added additional text (lines 851-855) to connect this result to recent work on arousal-dependent uniqueness of FC.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

(1) Do the authors have any ideas why we see this hotspot of heritability in pMTG/LOTC? It really jumps out in Figure 1A and Figure 2. The more posterior sensory MT+ area seems to drop when regressing out ISC in Figure 2B, but this pMTG area stays hot. Is there anything special about this kind of multimodal biological motion/action observation / social perception area (Pitcher & Ungerleider, 2021)? I don't think this is necessary to discuss in the manuscript, but I'm curious if the authors have any speculation.

We are not certain as to why BOLD time courses in this parcel are particularly heritable- although this area is associated with biological motion, that particular function tends to be more right lateralized, and here we see nominally higher heritability in the left hemisphere. Per a Neurosynth review (and consistent with the left lateralization), we believe this may have more to do with speech processing, but a more definitive answer will require further investigation.

(2) Page 3, line 127: "More information on these clips"-it might be worth saying a little bit more here just to make sure people understand that these are audiovisual clips, they include language, they're long enough to convey meaningful social and narrative information, etc.

We agree and have added additional details on the clip composition to the relevant methods paragraph.

(3) Figure 1 caption: can you add a sentence reminding readers what's going on with Day 1 and Day 2?

We thank R1 for this suggestion and have added a sentence to this effect at this location.

(4) Page 9, line 379: "although these more associative parcels do not encode a substantial amount of stimulus-specific information"-is this really true? I suspect these association areas still have decent ISCs, even if there are many processing stages downstream of the raw stimulus.

Although these parcels are not the most synchronized by the stimulus, we agree that it is unfair (and vague) to say that they do not encode a substantial amount of stimulus-specific information. We have edited this sentence to make a more specific claim and highlight the relatively lower ISC in these parcels vs. more unimodal sensory areas.

(5) Page 9, line 417: Can you unpack a bit more what you mean by "supra-BOLD frequency band"?

Here, we refer to the fact that BOLD signals resulting from neuronal firing events have frequencies below ~.15 Hz (Josephs and Henson, 1999). We have added additional text and the Josephs and Henson citation to this line to further unpack this point.

(6) Page 18, line 695: This discussion of how attention and gaze might partly shape response time series reminded me of recent work by Borovska & de Haas (2024)-might be worth citing.

We are grateful to R1 for alerting us to this very relevant work and have included a reference to it in our discussion.

(7) Page 19, line 755: I'm not sure I'd describe the hyperalignment results here as a "deleterious effects [on] heritability"-my reading was that hyperalignment allows you to say something more specific about heritability of function by allowing you to effectively factor out heritability effects that reduce to individual differences cortical topography; this seems like a good thing!

We agree that “deleterious” was a poor word choice given its negative connotation, and have edited this sentence to read:

“With this in mind, future studies investigating genetic correlations between brain function and behavioral variables may benefit from hyperalignment, as it can factor out individual-specific cortical topography and thus yield more precise estimates of functional heritability.”

(8) I would love to see a ventral view in some of these plots! Not asking you to recreate the figures, but the ventral temporal cortex is an area of interest for many folks in the movie fMRI space (e.g., Haxby et al., 2011).

We agree that ventral views would be of interest to some readers and have added the corresponding maps for our main results in supplementary figures S3 and S9.

References:

Borovska, P., & de Haas, B. (2024). Individual gaze shapes diverging neural representations. Proceedings of the National Academy of Sciences, 121(36), e2405602121. https://doi.org/10.1073/pnas.2405602121

Haxby, J. V., Guntupalli, J. S., Connolly, A. C., Halchenko, Y. O., Conroy, B. R., Gobbini, M. I., Hanke, M., & Ramadge, P. J. (2011). A common, high-dimensional model of the representational space in human ventral temporal cortex. Neuron, 72(2), 404416. https://doi.org/10.1016/j.neuron.2011.08.026

Pitcher, D., & Ungerleider, L. G. (2021). Evidence for a third visual pathway specialized for social perception. Trends in Cognitive Sciences, 25(2), 100-110. https://doi.org/10.1016/j.tics.2020.11.006

Reviewer #2 (Recommendations for the authors):

(1) To address the common core analytical problems listed under A), B), C), D), E), and basically throughout the methods:

(a) Conduct permutations with exchangability restrictions to account for the pattern of dyad-relationships as e.g. implemented in PALM.

(b) Control for age and sex covariates as covariates (e.g. as in SOLAR), rather than by matching.

(c) Perform r-to-z transforms when conducting further analyses on correlations that assume normality.

(d) For all analyses that assume normal distributions, e.g. in SOLAR and BrainSMASH, check that this is the case.

We have explained how PALM is not suited for the study of effects that are defined at the dyad level (A), that we controlled for age and sex covariates in all our formal heritability analyses in our original submission (B), that we always performed r-to-z transforms when indicated in our original submission (C), and that our spatial permutation results don’t hinge on distributional differences (D).

(2) Replace SEs derived from kacknife approach with those from SOLAR, or provide a comparison and motivation and/or demonstrate that SEs are correct.

A more thorough explanation of the block jackknife procedure can be found in prior work introducing the multidimensional heritability method used here (Anderson et al., 2021).

(3) Given problem (F & G):

(a) Consider studying the parameters that drive the hyperalignment. They can be included as covariates in heritability analyses, and/or their heritability is of interest to understand the reasons for the heritability reduction post-hyperaligment.

We agree that this would be interesting but the specific parameters that drive hyperalignment are beyond the scope of this study.

(b) Include the alternative explanation of hyperalignment-induced noise in the discussion.

We have added a figure showing that hyperalignment does not increase noise in ISC and explained here why “hyperalignment-induced noise” does not constitute a reasonable alternative explanation for our results.

(4) Add heritability results for NT phenotypes.

We have added heritability analyses for NT topography and (global) NT magnitude, as detailed above.

(5) Motivate global signal removal, and acknowledge this process typically alters results substantially.

We have added an explanation of our rationale for using GSR and shown in this response that it does not in fact substantially alter the results.

(6) Rephrase and/or clarify the following:

(a) "permutations quantify average differences" (under A).

(b) "network combinations" and related analyses (under B & C).

(c) why some analyses are separated per visit/day and others not (C).

(d) methods and reasons for sample size estimation (C).

We have rephrased or clarified all of the above.

Reviewer #3 (Recommendations for the authors):

(1) Participants should be recleared. I know HCP 7T data has 184 subjects. How can the authors have 176 twins and 690 unrelated subjects?

As we reported in our Methods section, 178 subjects had complete movie-watching datasets, and 176 subjects had complete movie-watching and resting-state datasets. Of the 178 subjects with complete movie-watching data, we identified 690 age- and sex-matched dyads.

(2) Figure 1. I don't find Figure S1A in Figure S1.

We thank R3 for catching this error- we have amended this reference to read Fig. S1.

(3) I could also suggest putting Figure 1 and Figure 2 together.

We thank R3 for this suggestion- ultimately, we prefer to keep these figures separate to reinforce the difference between our dyadic similarity and formal heritability analyses.

eLife Assessment

This study presents important findings by identifying small molecules that can stabilize and refold missense-mutated VHL tumor suppressor protein, offering a potential therapeutic approach for clear cell renal cell carcinoma. The computational design approach is well-executed, but the evidence is incomplete due to insufficient demonstration that HIF2 downregulation occurs through on-target VHL rescue rather than off-target effects. Additional experiments with appropriate controls are needed to establish the specificity of the mechanism.

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 some of comments raised in the previous round of review and have opted to proceed to a Version of Record without additional review.]

Summary:

This is an excellent and strong paper. The authors not only show the mechanisms of action of destabilizing mutations in VHL, but notably, they also go on to computationally design and experimentally test an inhibitor that restores wild-type pVHL function, offering starting points for a new class of kidney cancer drugs. The approach that the authors take here can be used to target destabilizing mutations in repressor proteins, common in diseases, including cancer.

Strengths:

This paper is the culmination of an extraordinary amount of work, over years, including method development and testing by a broad range of tools and experiments. It is thorough and comprehensive. It is also well-written and easy to follow.

Reviewer #2 (Public review):

Summary:

Inactivating VHL mutations are common in clear cell renal cell carcinoma, and about half of those mutations unfold/destabilize the protein rather than directly interfering with critical protein-protein interactions. The authors identify a compound that can stabilize/refold mutant VHL and seemingly restore its ability to downregulate its major downstream targets.

Strengths:

The authors use a clever combination of virtual and cell-based screens, followed by suitable biophysical and cell-based validation assays, to arrive at a VHL refolder. This compound is suboptimal from an ADME point of view, but could be a starting point for further medicinal chemistry optimization. Success would have implications for other diseases linked to similar loss-of-function mutations.

Weaknesses:

In going from CP4 to CP4.29 the authors screened based on downregulation of HIF. This is logical but also introduces the danger of identifying chemicals that can downregulate HIF in an "off-target" manner i.e. non-specifically. It therefore essential to clearly show that CP4.29 downregulates steady-state levels of HIF and HIF target genes in cells with suitable (hydrophobic core) VHL mutants but not in isogenic cells lacking VHL.

Author response:

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

We are most grateful to both reviewers for providing valuable feedback on our manuscript.

Reviewer 1 had solely favorable comments, with no suggestions for revision.

Reviewer 2 pointed out that experiment evaluating the effect of CP4 on pVHL half-life (originally included as Figure 3c) was difficult to evaluate because of CP4’s effect on pVHL abundance prior to cycloheximide treatment. We agree with this assessment, and we opted to remove this experiment from the revised manuscript since it was not central to our overarching conclusions.

Reviewer 2 also pointed out that experiment evaluating the effect of CP4.29 on HIF-2α half-life (originally included as Figure 4g) was not very compelling. We agree with this assessment, and we opted to remove this experiment from the revised manuscript since it was not central to our overarching conclusions.

We agree with Reviewer 2’s suggestion that additional experiments could further solidify that C4.29 downregulates HIF2 in a purely “on-target” manner, however we prefer to reserve such studies for the future.

Reviewer 2 also made several valuable suggestions for the text itself (awkward wordings / citations / clearer figure legends). We appreciate this feedback and have updated the text accordingly.

niafacv

eLife Assessment

This important study advances our understanding of the biomechanics of seed processing in birds by providing a comprehensive 3D kinematic analysis of coordinated bill and tongue movements across two species with contrasting biting forces. The evidence is convincing, combining high-speed XROMM with Bayesian statistical modeling in a rigorous and technically innovative framework that advances the understanding of avian feeding kinematics. Strengthening the statistical validation of qualitative claims, particularly for tongue-seed velocity relationships, and improving the accessibility of the probabilistic modeling framework would further solidify the conclusions.

Reviewer #1 (Public review):

Summary:

The authors quantified and compared the 3D kinematics of bill and tongue movements between two seed-eating bird species: one that specializes on soft seeds, and one that is more adapted to feeding on hard seeds. Their goal was to determine specifically what the role of the tongue was for processing (e.g., dehusking) seeds, and to understand how differences in biting strength between species affect other aspects of seed processing. The authors provided intricate (visual) details of seed processing movements, and showed how coordination between the tongue and cranial kinesis (i.e., mobility of the upper bill relative to the cranium) is both critically important for properly positioning seeds to enhance feeding efficiency. Many studies have detailed how seed-eating birds process seeds, but this study has elevated those to a new level of quantification and visualization for readers to fully experience firsthand. Furthermore, the authors established that the force-velocity trade-off that has been observed between bill functions (e.g., feeding and singing) is largely driven by the contractile properties of the muscles. The conclusions are well supported by the results, and the authors placed the results more broadly into the context of manual grasping, making the argument that these birds achieve high levels of dexterity with far fewer degrees of freedom, which could have potential biomimetic applications.

Strengths:

This study builds upon - and advances - our understanding of the feeding mechanics of seed-eating birds using cutting-edge 3-dimensional modeling and kinematics. Their quantitative analyses of upper and lower bill, tongue, and seed displacements are complemented by elegant visualizations of seed processing in each species. Their comprehensive Bayesian modeling statistical framework tackles the issue of small sample sizes (i.e., few subjects) with volumes of data for each (i.e., lots of sequential kinematic variables) that plague comparative biomechanics studies, principally because (a) it is difficult to gather these high resolution XROMM and muscle contractile data on more than just a few subjects, and (b) these data streams are inherently very large, as they are gathered at high frame and sampling rates. Furthermore, I believe their approach to statistically testing for differences between species sets a new standard for our field that could (perhaps should?) be implemented in other similar types of studies. Another strength is in how the results were packaged: each subsection indicated how the objectives were addressed, and there were concluding statements trailing each subsection that helped deliver the key takeaways.

Weaknesses:

A potential weakness is one that the authors themselves mentioned, regarding the body (and skull) size differences between species. Because gape size limits bite force, and given the force-velocity tradeoff in muscle function, there could be limitations on the rapid manipulation of relatively large seeds for similar reasons in the smaller finches. I see that the small finches appear to overcompensate in their beak rotations, but it's not clear how those compensatory movements might affect their seed processing kinematics with their preferred seed sizes. This does not nullify the authors' conclusions, but the results for the smaller finches might not be entirely representative of seed processing mechanics in smaller species.

Reviewer #2 (Public review):

Summary:

This study investigates coordinated beak-tongue movements in seed manipulation, biting, and dehusking in songbirds. A comparative analysis of the seed-eating process in two songbird species with different biting forces, the domestic canary and Java sparrow, was conducted using high-speed XROMM with anatomical marker tracking and quantitative behavioral analysis. The authors have done a great job analyzing upper and lower beak rotation and translation, seed orientation and movement speed, and tongue kinematics.

Strengths:

The methodological approach of using high-speed (500 fps) X-ray reconstruction for 3D kinematic tracking in small animals is novel and powerful. It enables high temporal resolution tracking of orofacial movements and could potentially inspire future orofacial research in mammals, including mice and marmosets. Moreover, this study encompasses a wide range of anatomical components involved in seed manipulation behavior, including the upper and lower beak, the tongue, and jaw muscles. The behavioral quantification of these components is solid. The findings that both the upper and lower beaks contribute to seed processing, that the lower beak exhibits greater up-and-down and left-to-right flexibility than the upper beak during seed processing, and that the tongue plays an important role in transporting seeds into the mouth are all solid conclusions consistent with observations of bird feeding behavior. Nevertheless, it is valuable to confirm and quantitatively characterize these observations experimentally. The videos are excellent and very informative.

Weaknesses:

(1) The paper often resorts to qualitative descriptions (e.g., "a high positive correlation of tongue velocity and seed velocity", "Compared to positioning, the measured velocities of both seed and tongue were much lower") instead of providing exact quantitative measurements or statistical results. The authors stated that temporal autocorrelation biases standard statistical analyses (lines 205-210), but this rationale does not justify the absence of statistical validation. Suggestion: use appropriate methods for time-series data, such as a permutation test, to test the significance of correlations between variables and avoid false positives.

(2) (Minor) The marker-tracking image shown in Figure 1B could benefit from the inclusion of a higher-contrast, zoomed-in frame of the head showing the metal markers without the red tracking points, alongside the same frame with the red tracking points overlaid, to provide readers with a clearer view of the X-ray image and the methodology and its precision.

(3) (Minor: possibly soften the mechanistic claim). The proposed mechanism of lingual papillae on the tongue surface may aid food manipulation and food movement towards the posterior region of the mouth is interesting, yet the evidence describing their morphology is not strong enough to support the claim about their functional roles. Furthermore, the claim that papillae orientation affects food transport in lines 294-296 lacks supporting experimental evidence. In addition, the roles of extrinsic and intrinsic tongue muscles in controlling dexterous tongue shape changes and movements are not discussed.

Author response:

We would like to express our gratitude for the thorough evaluation of our manuscript by the editors and reviewers. We are grateful for the overall positive assessment. The suggestions for improvement are reasonable, and we are certain that addressing these points will improve the clarity, accessibility, and scientific integrity of the study. Thus, we plan to conduct a revision of the manuscript, addressing all the points raised. The most important planned adjustments are outlined below.

(1) Improving the accessibility of the probabilistic modeling framework

Reviewer 1 kindly stated that our Bayesian modeling framework for testing for species differences 'sets a new standard for our field.' As a new standard, however, the method should be explained in a more accessible way. Hence, we plan to provide additional explanations for the statistical workflow, e.g., by providing comprehensible visuals, to make the workflow easier to understand and easier to apply.

(2) Statistical validation of qualitative claims

We acknowledge that a statistical validation of qualitative claims regarding the relationship between seed and tongue movements and between upper and lower beak movements would considerably strengthen the validity of our findings. We thank Reviewer 2 for bringing permutation tests to our attention for quantifying the correlation between time series. Since permutation tests involving index-shuffling of one of the data sets are generally not valid for time-series data [1, 2], we'll consider a variant of a trial-swapping permutation test, such as a permute-match test [3]. Alternatively, the truncated time shift (TTS) test [2] might be an option, as also this method is valid for auto-correlated time series data. At this point, we can't tell yet which method we'll use for the revised manuscript. We need more time to assess the requirements of each method and evaluate which test is most appropriate to answer our specific research questions and best fits our kind of data.

(3) Adjustments in the discussion

Following the suggestion by Reviewer 1, we'll refine our discussion on the effects of skull size differences, putting more emphasis on the implications of potential effects for feeding kinematics in small species.

Furthermore, as suggested by Reviewer 2, we'll soften our discussion on potential functions of lingual papillae in seed processing, as the current literature lacks experimental evidence for the claimed mechanistic roles.

References

(1) Yuan, A. E., & Shou, W. (2022). Data-driven causal analysis of observational biological time series. Elife, 11, e72518.

(2) Yuan, A. E., & Shou, W. (2024). A rigorous and versatile statistical test for correlations between stationary time series. PLoS biology, 22(8), e3002758.

(3) Yuan, A. E., & Shou, W. (2025). Permute-match tests: Detecting significant correlations between time series despite nonstationarity and limited replicates. eLife, 14.

DOI: 10.1016/j.intimp.2026.117071

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Resource: (Millipore Cat# 05-804, RRID:AB_310014)

Curator: @scibot

SciCrunch record: RRID:AB_310014

What is this?

DOI: 10.1016/j.isci.2026.115819

Resource: (Millipore Cat# 05-803, RRID:AB_310013)

Curator: @scibot

SciCrunch record: RRID:AB_310013

What is this?

DOI: 10.1016/j.isci.2026.115819

Resource: (Millipore Cat# ABN454, RRID:AB_2888681)

Curator: @scibot

SciCrunch record: RRID:AB_2888681

What is this?

DOI: 10.1016/j.isci.2026.115819

Resource: (Millipore Cat# AB5733, RRID:AB_11212377)

Curator: @scibot

SciCrunch record: RRID:AB_11212377

What is this?

DOI: 10.1016/j.isci.2026.115819

Resource: (Synaptic Systems Cat# 188 011, RRID:AB_2147096)

Curator: @scibot

SciCrunch record: RRID:AB_2147096

What is this?

DOI: 10.1016/j.isci.2026.115819

Resource: (Abcam Cat# ab151559, RRID:AB_2893278)

Curator: @scibot

SciCrunch record: RRID:AB_2893278

What is this?

DOI: 10.1016/j.isci.2026.115817

Resource: (KCB Cat# KCB 200970YJ, RRID:CVCL_0336)

Curator: @scibot

SciCrunch record: RRID:CVCL_0336

What is this?

DOI: 10.1016/j.isci.2026.115817

Resource: RRID:CVCL_3891

Curator: @scibot

SciCrunch record: RRID:CVCL_3891

What is this?

DOI: 10.1016/j.isci.2026.115817

Resource: RRID:AB_778139

Curator: @scibot

SciCrunch record: RRID:AB_778139

What is this?

DOI: 10.1016/j.isci.2026.115817

Resource: (Cell Signaling Technology Cat# 9101, RRID:AB_331646)

Curator: @scibot

SciCrunch record: RRID:AB_331646

What is this?

DOI: 10.1016/j.isci.2026.115817

Resource: (Cell Signaling Technology Cat# 9102, RRID:AB_330744)

Curator: @scibot

SciCrunch record: RRID:AB_330744

What is this?

DOI: 10.1016/j.isci.2026.115817

Resource: (IMSR Cat# JAX_000664,RRID:IMSR_JAX:000664)

Curator: @scibot

SciCrunch record: RRID:IMSR_JAX:000664

What is this?

DOI: 10.1016/j.isci.2026.115817

Resource: (Cell Signaling Technology Cat# 9221, RRID:AB_2561045)

Curator: @scibot

SciCrunch record: RRID:AB_2561045

What is this?

DOI: 10.1016/j.isci.2026.115817

Resource: RRID:AB_10829092

Curator: @scibot

SciCrunch record: RRID:AB_10829092

What is this?

DOI: 10.1016/j.isci.2026.115817

Resource: RRID:AB_726493

Curator: @scibot

SciCrunch record: RRID:AB_726493

What is this?

DOI: 10.1016/j.isci.2026.115817

Resource: (Cell Signaling Technology Cat# 9198, RRID:AB_2561044)

Curator: @scibot

SciCrunch record: RRID:AB_2561044

What is this?

DOI: 10.1016/j.isci.2026.115817

Resource: RRID:AB_399306

Curator: @scibot

SciCrunch record: RRID:AB_399306

What is this?

DOI: 10.1016/j.isci.2026.115817

Resource: (Abcam Cat# ab110413, RRID:AB_2629281)

Curator: @scibot

SciCrunch record: RRID:AB_2629281

What is this?

DOI: 10.1016/j.isci.2026.115817

Resource: (Cell Signaling Technology Cat# 14312, RRID:AB_2798446)

Curator: @scibot

SciCrunch record: RRID:AB_2798446

What is this?

DOI: 10.1016/j.isci.2026.115716

Resource: (Cell Signaling Technology Cat# 3671, RRID:AB_330248)

Curator: @scibot

SciCrunch record: RRID:AB_330248

What is this?

DOI: 10.1016/j.isci.2026.115716

Resource: (Thermo Fisher Scientific Cat# A-11012, RRID:AB_2534079)

Curator: @scibot

SciCrunch record: RRID:AB_2534079

What is this?

DOI: 10.1016/j.isci.2026.115716

Resource: (Cell Signaling Technology Cat# 3629, RRID:AB_2249358)

Curator: @scibot

SciCrunch record: RRID:AB_2249358

What is this?

DOI: 10.1016/j.isci.2026.115716

Resource: (Cell Signaling Technology Cat# 2118, RRID:AB_561053)

Curator: @scibot

SciCrunch record: RRID:AB_561053

What is this?

DOI: 10.1016/j.isci.2026.115716

Resource: (Cell Signaling Technology Cat# 13166, RRID:AB_2798136)

Curator: @scibot

SciCrunch record: RRID:AB_2798136

What is this?

DOI: 10.1016/j.isci.2026.115716

Resource: (Cell Signaling Technology Cat# 93065, RRID:AB_2800199)

Curator: @scibot

SciCrunch record: RRID:AB_2800199

What is this?

DOI: 10.1016/j.isci.2026.115716

Resource: (Thermo Fisher Scientific Cat# A-11001, RRID:AB_2534069)

Curator: @scibot

SciCrunch record: RRID:AB_2534069

What is this?

DOI: 10.1016/j.isci.2026.115716

Resource: (Cell Signaling Technology Cat# 2500, RRID:AB_10839118)

Curator: @scibot

SciCrunch record: RRID:AB_10839118

What is this?

DOI: 10.1016/j.isci.2026.115716

Resource: (Cell Signaling Technology Cat# 6943, RRID:AB_10013641)

Curator: @scibot

SciCrunch record: RRID:AB_10013641

What is this?

DOI: 10.1016/j.isci.2026.115716

Resource: (Cell Signaling Technology Cat# 2541, RRID:AB_2174466)

Curator: @scibot

SciCrunch record: RRID:AB_2174466

What is this?

DOI: 10.1016/j.isci.2026.115716

Resource: (Cell Signaling Technology Cat# 3528, RRID:AB_2160882)

Curator: @scibot

SciCrunch record: RRID:AB_2160882

What is this?

DOI: 10.1016/j.isci.2026.115716

Resource: (Sigma-Aldrich Cat# V9131, RRID:AB_477629)

Curator: @scibot

SciCrunch record: RRID:AB_477629

What is this?

DOI: 10.1016/j.isci.2026.115716

Resource: RRID:AB_2291558

Curator: @scibot

SciCrunch record: RRID:AB_2291558

What is this?

DOI: 10.1016/j.isci.2026.115716

Resource: (BD Biosciences Cat# 610467, RRID:AB_2128060)

Curator: @scibot

SciCrunch record: RRID:AB_2128060

What is this?

DOI: 10.1016/j.isci.2026.115716

Resource: (Cell Signaling Technology Cat# 8556, RRID:AB_10891442)

Curator: @scibot

SciCrunch record: RRID:AB_10891442

What is this?

DOI: 10.1016/j.isci.2026.115716

Resource: (Jackson ImmunoResearch Labs Cat# 111-035-144, RRID:AB_2307391)

Curator: @scibot

SciCrunch record: RRID:AB_2307391

What is this?

DOI: 10.1016/j.isci.2026.115716

Resource: (Cell Signaling Technology Cat# 4848, RRID:AB_10694415)

Curator: @scibot

SciCrunch record: RRID:AB_10694415

What is this?

DOI: 10.1016/j.isci.2026.115716

Resource: (Jackson ImmunoResearch Labs Cat# 115-035-068, RRID:AB_2338505)

Curator: @scibot

SciCrunch record: RRID:AB_2338505

What is this?

DOI: 10.1016/j.isci.2026.115716

Resource: RRID:AB_3698765

Curator: @scibot

SciCrunch record: RRID:AB_3698765

What is this?

DOI: 10.1016/j.isci.2026.115716

Resource: (Sigma-Aldrich Cat# A5979, RRID:AB_476749)

Curator: @scibot

SciCrunch record: RRID:AB_476749

What is this?