Reviewer #2 (Public review):

Summary:

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

Strengths:

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

Weaknesses:

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

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

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

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

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

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

Reviewer #3 (Public review):

Summary:

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

Strengths:

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

Weaknesses:

No major weaknesses were identified.

プロンプトのgit管理を実施する。

As of June 2026 - Claude 4.5 Opus (high reasoning) is up to 76.80% (results from 2026-02-17)

eLife Assessment

This valuable study examines whether reduced cooperation is driven by betrayal aversion beyond nonsocial loss aversion, using matched social and nonsocial risky decision-making tasks combined with computational modeling and EEG. The authors provide solid empirical evidence that social risk is processed differently from matched nonsocial risk, offering a meaningful contribution to the study of cooperation and decision-making under uncertainty. However, further justification of the computational modeling approach would strengthen some of the conclusions. This work will be of interest to researchers studying social decision-making, cooperation, trust, and the neural and computational mechanisms underlying risk and betrayal aversion.

Reviewer #1 (Public review):

Summary:

The non-social task was a classic risky decision-making task with a binary choice between an option with a sure gain and a risky option with a probabilistic gain or loss. In the social task, the sure option was an individual gain (as in the non-social option) and the probabilities in the risky option, which were shown to participants, were framed as probabilities of other previous participants (i.e., "partners") to cooperate or not; a probabilistic gain (when the partner cooperated) also led to a gain of the partner, while a probabilistic loss meant that the partner would receive the amount lost by the participant. This loss was framed as "betrayal." The authors show differences in how probabilities and amounts (of gains/losses) affected choices, RTs, and ERPs (P3 and LPP).

Strengths:

Since participants faced decisions with the same individual payoffs in a non-social and a social condition, this setup made it possible to use identical standard analyses for choices, RTs, and ERPS as well as (almost) identical economic models for the two conditions.

Weaknesses:

(1) The task does not include many components that are usually considered central for cooperation or "betrayal" and this is not discussed appropriately. At the same time, the "emotional aspects" of the operationalized "betrayal" are not directly assessed.

a) The standard economic game for cooperation is the prisoner's dilemma, in which participants make independent choices at the same time without getting any explicit information on the cooperation probability of their partner before they make their decisions. Furthermore, most of the time the interactions are repeated. Actually, the trust game as one other frequently used economic game, also includes a back and forth of transfers between the partners. So, here, I am not so convinced by the operationalization of a low cooperation probability, which is shown before the decision, as "betrayal." The authors should motivate and explain their rationale more clearly in reference to such other tasks.

b) The setup of the task, especially the fake interaction with the fake partners, should be made clearer in the main text (before reporting the results). I would argue for including the task picture in the main text.

c) In general, I am in favour of taking participants' choice behaviour as the main outcome measure. But given the strong implications of "emotional costs" made by the authors, I would have expected some ratings of "betrayal" on a trial-by-trial basis. I would at least include this as a shortcoming.

d) Also, given the framing of the study, I would have expected some exploratory analyses regarding individual differences with respect to, e.g., social value orientation, etc. I would at least include this as an outlook.

(2) The standard statistical analyses could be improved.

a) It is good that the authors have rather long sections using standard regression analyses. But they are a bit lengthy, and the modelling should be more prominent.

b) In a couple of places, the authors say something like "this is significant, but that is not." Here, it has been made very clear that the interaction term needs to be looked at. As far as I can see, this has not always been done.

c) For this binary choice, the difference in expected value (EV) between the sure and the risky options is one crucial comparison. But the authors never take that into account. This difference does not depend on the amount, which the authors dub "principal." That is, the sure option simply has an EV of x, i.e., the amount. The risky option has the EV = p2x + (1-p)0.5x, with p being the probability of gain/cooperation. That is, the two options have the same EV at p=1/3, independent of x. This should be made clear.

d) Relatedly, RTs should depend on the differences in EV (and not so much on p or on x per se). This can be seen by the more or less quadratic relationship between p and RTs (Fig 1A), with a peak around a p of 1/3.

e) RTs are often log-transformed. It should be briefly mentioned why this was not done here.

(3) The modelling evidence is relatively weak. This is my main point.

a) (Cumulative) prospect theory should be introduced.

b) The models seem overly complicated with many free parameters. I would have expected some simpler versions and more comparisons between models that differ in just one parameter.

- e.g., it is really nice that the authors used a probability weighting function. BTW: Please describe this more clearly in the introduction and in the results. But for this limited range of probabilities, this might be too much.

- e.g., why directly assume two different exponents in the utility function for gains and losses, and in addition a loss aversion parameter lambda? Only lambda would be a better starting point here.

c) The differences in AIC (Figure 2A) seem rather minuscule, and the distribution of winning models is not very peaked. I am not convinced that Model 3 is the winning model.

d) Crucially, and related to the previous points, judging from Fig 2C, the "betrayal" parameter kappa seems to be zero for about half of the participants. The authors should look into this.

- Would a model just like model 3 but without kappa (i.e., kappa set to zero) perform better? Is this just model 2?

- How is kappa set in the non-social condition?

- This massive skew, to say the least, is never discussed.

- A correlation is definitely not warranted.

(4) The ERP results seem to me rather superficial. But I am not an EEG expert.

a) The authors do not seem to look at the outcome phase, which could be interesting for differences in reward/loss processing in the two task versions.

b) Again, differences in EV seem to be more important from a conceptual point than probabilities or amounts; see my comment 2d.

c) Also, the authors report ERPs for the two task types separately but do not seem to run proper comparisons between them, see my comment 2b.

(5) Preregistration: It should be made very clear early on that this study was not preregistered.

(6) Quality checks: The authors should check if some participants are outliers in terms of the number of missed trials, always choosing the same option, etc. It is notoriously difficult to find good post hoc reasons for excluding participants (one reason why replications and preregistrations are important). In any case, the data quality should be checked and described a bit more.

Reviewer #2 (Public review):

Summary:

This paper investigates risk and cooperation decisions by integrating computational modeling with event-related potential (ERP) measures. Participants completed two tasks involving financial risk and cooperation under possible betrayal. The comparison between social and non-social decision-making is interesting and potentially valuable. However, the conceptual framing, theoretical grounding, and modeling rationale require substantial clarification.

Strengths:

(1) The paper introduces comparable tasks to probe social vs. non-social decision making.

(2) The authors use a model to identify a psychological distinction and test its validity using neural data.

Weaknesses:

(1) Conceptual framing and theoretical clarity

The primary theoretical contribution of the paper is currently unclear. Specifically, it is not clear what key difference the authors hypothesize between risk and cooperation conditions. This distinction should be grounded in prior literature.

The manuscript states: "Indeed, mutual cooperation maximizes social welfare, whereas betrayal benefits the trustee but comes at the trustor's expense in the Trust Game (Joyce et al., 1995)." However, the authors do not discuss the substantial literature on the Trust Game, which is used here but not explicitly acknowledged.

• The original Trust Game framework and behavior in one-shot settings (e.g., Berg et al., 1995).

• The persistence of cooperation even when defection is economically optimal (e.g., Berg et al., 1995; Fehr & Fischbacher, 2003).

• The influence of trustworthiness of the partner on cooperation decisions has been previously studied (Ma et al., 2022).

• Differences between social and non-social decision-making contexts have also been reported with matched tasks (Liu et al., 2024).

(2) Distinction between constructs (risk, loss aversion, betrayal aversion)

The introduction introduces multiple related constructs-risk aversion, loss aversion, and betrayal aversion-but does not clearly differentiate them. A theoretically grounded distinction is needed.

In particular:

• The manuscript introduces multiple related constructs, or maybe the terms are used interchangeably? The distinction between risk aversion, loss aversion, defection aversion, and betrayal aversion should be clearly defined.

• Betrayal aversion versus loss aversion is introduced but not clearly differentiated. Importantly, it should be clarified that this distinction is not experimentally manipulated but instead inferred through computational modeling. This point is currently not made explicit, which leads to confusion in the introduction

• The computational model should be introduced clearly in the introduction. Without explaining how these constructs are operationalized in the model, the framework is difficult to follow.<br /> The statement "In the risk task, losses were solely impersonal" is also unclear. It seems the authors may mean "personal or non-social" rather than "impersonal" as rewards are always personally relevant.

(3) Hypotheses and preregistration

The manuscript would benefit from more theoretical rationale for hypotheses. For example:

• What is the basis for hypothesizing that financial loss aversion and betrayal aversion independently affect cooperation choices?

• Why should these constructs be separable and modeled independently?

• Additionally, the absence of preregistration is a limitation that should be acknowledged even more.

• Given the flexibility of the modeling approach and number of parameters, this is particularly important.

• For instance, the rationale for focusing on decision times is also not clearly explained and should be better motivated.

(4) Computational modeling

There are several concerns regarding the modeling approach:

• The choice of model comparison metric should be justified. Why is AIC used rather than BIC, which penalizes model complexity more strongly? This is particularly relevant given the inclusion of additional parameters to capture processes not directly measured by the task.

• Full model recovery analyses are missing. A full model recovery is necessary to demonstrate that competing models produce distinguishable behavioral patterns. This needs to be shown in order to justify the specificity of the winning model

• How correlated are the parameters across participants, particularly loss and betrayal parameters?

• More broadly, it is unclear how well loss aversion and betrayal aversion can be differentiated based on behavior alone. If these constructs are separable, they should predict distinct aspects of behavior.

(5) ERP analyses

The ERP results (e.g., P300 and LPP) seem to suggest that betrayal aversion is relevant in both time periods and similarly.

• Do neural signals differentially reflect betrayal aversion versus loss aversion earlier and later on?

• Are there significant interaction effects between betrayal and loss aversion for each ERP component?

Reviewer #3 (Public review):

Summary:

In this study, the authors aim to address two questions. First, do people avoid cooperation primarily because of betrayal aversion beyond loss aversion? Second, can the effects of betrayal aversion and loss aversion be dissociated at the behavioral and neural levels? To address these questions, the authors compared individuals' choices of taking risks in a nonsocial risk task with those in a social cooperation task, with the two tasks matched in success probability and principal amount. They fitted computational models that include betrayal-aversion and loss-aversion terms and related the model parameters to ERP measures. Based on these analyses, the authors concluded that betrayal aversion has a stronger effect on cooperation than loss aversion and that betrayal is encoded earlier than loss in the brain. This is an important research question, and the attempt to combine computational modeling with ERP analysis is valuable. However, the current data analyses may not be able to support all the conclusions the authors made. For instance, the claims concerning the dissociation between betrayal aversion and loss aversion are not yet sufficiently supported by the evidence.

Strengths:

(1) The research question is theoretically important. Distinguishing betrayal aversion from loss aversion is important for research on trust, cooperation, and risky decision-making.

(2) The approach of integrating behavioral measures, self-report ratings, computational modeling, and ERP data is valuable and gives the study significance.

(3) The behavioral findings are broadly consistent. Participants reported stronger emotional responses in the cooperation task and were less willing to accept risk in the cooperation condition. These findings are generally in line with previous work on betrayal aversion and provide a reasonable manipulation check for the contrast between social and nonsocial risk.

Weaknesses:

(1) The manuscript states that the two tasks are matched in probability and principal amount, but the cooperation task additionally introduces partner outcomes, betrayal, and prosocial components. The Methods section states that, in the cooperation task, if both players cooperate, the principal is doubled and then split equally; if the partner betrays, half of the participant's principal is transferred to the partner. The model also includes an expected-other-reward term, namely, V_other=ω[p⋅2X+(1-p)⋅1.5X]. This raises an interpretive concern: if the two tasks differ not only in whether the source of uncertainty is social, but also in partner outcome, intentionality, and potential inequity structure, then the fitted "betrayal aversion" parameter may in fact reflect multiple motives rather than betrayal aversion alone. In the current experimental design, the "betrayal aversion" parameter may not be uniquely interpretable as a pure betrayal-specific construct, and the current evidence is insufficient to support such a specific interpretation.

(2) Participants were informed that the cooperation probabilities were derived from previous real participants, whereas in fact these probabilities were randomly generated. In addition, six participants explicitly expressed doubts about the authenticity of the social interaction, yet the authors retained these participants with only the brief statement that this "did not affect the results." For such a critical manipulation, this explanation is too brief. I recommend that the authors report robustness analyses excluding skeptical participants. Since six participants reportedly doubted the authenticity of the social interaction, and some participants also performed poorly on the catch trials, it would be important to show whether the main behavioral, modeling, and ERP findings remain after excluding these participants. This is especially important because the manuscript's central interpretation depends on the assumption that the cooperation task was genuinely experienced as social.

(3) The descriptions of the sample size are inconsistent across sections. The Participants section states that, after excluding one participant for misunderstanding the instructions, the final sample consisted of 49 participants; however, the behavioral results section later states that only 42 participants were included in the final analyses due to recording problems. This discrepancy is important because readers need to know clearly which sample was used for the behavioral analyses, which for the model fitting, and which for the ERP analyses; whether these analyses were conducted on the same participants; and whether the exclusion criteria were consistent across analyses. The manuscript needs a more transparent description of sample size and exclusion criteria.

(4) The authors need to do more thorough analyses to validate their models. In addition to AIC and parameter recovery, I would encourage the authors to include other model comparison metrics where possible, such as BIC and exceedance probability, as well as model-recovery analyses. The authors should also do model-based simulation analyses to show that the winning model can capture the contextual effects observed in real data.

(5) The authors should explain the rationales for the choice of ERP time windows and component selection in more detail. The current ERP analyses are time-locked to principal onset, and P3/LPP are extracted from fixed time windows. The authors should explain why this is the most appropriate time-locking point for examining betrayal- and loss-related computations, and why alternative time-locking points, such as probability-cue onset or other key task events, were not used. More importantly, the time windows of P3 and LPP are defined arbitrarily in the current analyses. The authors need to apply a more principled approach to define ERP components. It looks like the P3 and LPP are from the same ERP component in Figure 3.

(6) The manuscript has several internal inconsistencies in terminology, figure references, and result descriptions. These issues weaken the clarity of the arguments and reduce the readability of the manuscript.

(7) The authors partially achieved their aims. The study does provide evidence that social risk and nonsocial risk are not treated equivalently, and it also offers a computational framework that is informative for the field. This is an important topic, and the overall approach is promising.

Author response:

We agree that the manuscript would benefit from a more clearly articulated conceptual framing, stronger model validation, more explicit statistical and ERP comparisons, and improved transparency regarding task design, sample inclusion, and preregistration. In the revised manuscript, we plan to address these points through substantial revision of the Introduction and Discussion, along with additional robustness and validation analyses, and more cautious interpretation of the main findings.

Reviewer #1 raised important points about the framing of the cooperation task, the interpretation of betrayal, the standard statistical analyses, the modelling, and the ERP analyses. In response, we plan to clarify that the present task captures betrayal-related social risk or anticipated partner defection, rather than betrayal in its full interpersonal and emotional sense, and to better motivate this operationalization with reference to the betrayal-aversion and trust-game literature. We will moderate our claims regarding “emotional costs,” incorporate a more explicit task overview and accompanying schematic into the main text, and frame individual differences as a key avenue for future research. In addition, we will streamline the standard behavioral analyses, make the expected-value structure of the task explicit, add EV-based analyses of choice and reaction time, strengthen the ERP analyses, clarify that the study was not preregistered, and provide a complete report of data-quality checks. For the modelling section, a central revision will be to simplify the model structure and refit the models using a Bayesian hierarchical approach.

Reviewer #2 emphasized the need for stronger theoretical framing and more specific distinctions between related constructs. In the revised manuscript, we will substantially revise the Introduction to better situate the present task in relation to the Trust Game literature and prior work comparing social and non-social decision-making under matched payoff structures. We will also define risk aversion, loss aversion, anticipated partner defection, and betrayal-related aversion more explicitly, and clarify that the distinction between betrayal-related aversion and loss aversion is inferred through computational modelling rather than directly manipulated as separate experimental factors. We also plan to introduce the computational model earlier in the manuscript, clarify how the key constructs are operationalized, replace unclear wording such as “impersonal losses,” strengthen the rationale for our hypotheses, and acknowledge the lack of preregistration more clearly.

Reviewer #3 highlighted the need to align our conclusions more closely with the current evidence. In the revised manuscript, we will moderate the interpretation of the betrayal-related parameter, acknowledging that the cooperation task differs from the non-social risk task not only in social versus non-social uncertainty, but also in partner outcome, intentionality, and potential inequity structure. We therefore plan to avoid treating this parameter as a pure betrayal-specific construct and to describe it more cautiously as capturing betrayal-related social risk or aversion to anticipated partner defection. We also plan to report robustness analyses excluding participants who expressed doubts about the social interaction, as well as participants with poor catch-trial performance or otherwise low-quality data, and to clarify the sample sizes and exclusion criteria used for behavioral, modelling, and ERP analyses. Finally, we will strengthen model validation and ERP reporting, including broader validation analyses and more cautious interpretation if the evidence for temporal dissociation between betrayal-related aversion and loss aversion proves weaker than currently stated.

Across these revisions, we also intend to simplify the model structure and use Bayesian hierarchical fitting to strengthen model validation, while avoiding overly strong claims if the additional analyses provide only modest support for a single preferred model.

use this instead: Fabian Lindhofen Fabian Lindhofen ist Autor bei Crafins Studio und schreibt über Wirtschafts- und Tech-Themen. Standort Berlin.

make this: übernommen und gewann so zusätzlich an finanzieller Stabilität und Produktentwicklungsressourcen.

what are you trying to say here? is the idea that they have to be added becuase there are only 7 channels? if so, clarify this

check if this is really considered high. otherwise change to: Relativ hohe Kundenzufriedenheit

make this: versucht nicht

add name if available

drop this

in the two other articles, the reader is not directly addressed. Is it on purpose that he is here? Otherwise drop this everywhere in the article and change the sentences accordingly

make this: Eine Multichannel-Warenwirtschaft ersetzt nicht IHr Shopsystem, sondern sitzt dahinter und verbindet Shopify, Shopware oder WooCommerce mit allen weiteren Kanälen.

Struttura sintesi ecc

Forse non è che tutte le forme di ipercolesterolemia familiare sono dovute a PCSK9, ma solamente alcune varianti, come detto qua

In graphical abstract c'è anche riferimento a cancro, potrebbe valere la pena leggere l'articolo

Potrebbe essere interessante citare pcsk9 come coinvolta in tutti questi processi oltre a soffermarsi su colesterolo e cancro

Credo che questo articolo stia dicendo "dato che ci stanno 2 forme funzionanti di PCSK9, vanno targettate entrambe". Potrebbe essere utile se si parla di PCSK9 struttura sintesi ecc ecc. Nell'articolo dei prof lo usano solo per dire che una riduzione del 32% dei LDLR dopo somministrazione di PCSK9 è in "linea" con altri studi (tra cui questo)

Cet été j'écris un paragraphe sur les liens entre : - éducation fondée sur asymétrie et autorité statutaire et contradiction avec visée émancipation dans l'apprentissage du cst Et je rajoute des éléments sur : - limites de l'injonction à libérer sa parole notamment à partir de : https://theses.hal.science/tel-04391413 https://theses.hal.science/tel-00974344v1

Fonte principale per questo argomento, usato come principale anche dal paper prof

@RolandGroen , het plaatje mixt nu de Task-status en de Patient lifecycle status. De Task.status canceled wordt gebruikt maar valt niet op want in status staat "Actief". Net zo goed de Task.status completed valt niet op omdat in de status staat "Deleted". Ik zou fan zijn van dat alle blokjes de Task.status te geven.

Note: This response was posted by the corresponding author to Review Commons. The content has not been altered except for formatting.

Learn more at Review Commons

Reply to the reviewers

Revision Plan

1. General Statements

We thank the reviewers for their positive and constructive assessment of the manuscript. We are encouraged that all three reviewers recognise the value of coelsch as an open-source framework for haplotyping and crossover detection from single-cell gamete sequencing data, and that they view the study as a useful contribution to the fields of recombination and genetic research. We are particularly grateful that Reviewer 1 described the manuscript as an "interesting and important study" and a "genuinely useful methodological framework that fills a real gap in the recombination biology toolkit", while Reviewer 2 highlighted its "strong innovation, complete technical pipeline, and significant biological implications" and considered it an "important technical breakthrough". We also appreciate Reviewer 3's assessment that the study provides "timely guidance for experimental design", that the results are "important for guiding plant single-cell research" in general, and that the work "has the potential to attract a broad readership".

In our view, the main contribution of the manuscript is the development of a platform-agnostic method for recovering haplotypes and crossover events from single-cell sequencing data. This addresses an important practical gap: single-cell gamete sequencing has strong potential for high-throughput haplotyping and recombination mapping, but its broader use requires tools that can accommodate the very different coverage structures produced by different sequencing modalities and platforms. coelsch was designed to meet this need.

The experimental datasets in the manuscript serve two purposes. First, they demonstrate that coelsch can be applied across multiple single-cell modalities and platforms, including scRNA, scATAC and scWGA sequencing from 10x Genomics, BD, and Takara platforms. Second, they illustrate the kinds of biological and practical questions that can be addressed with single-cell gamete sequencing, including crossover detection in meiotic mutants and large-scale analysis of natural variation in recombination.

While all reviewers strongly supported the publication of the work, they also raised important points about specific aspects, including technical variation and reproducibility, the rationale for using 10x scRNA to generate the diversity panel dataset, and the effects of coverage on crossover localisation, amongst others. We agree that addressing these points will make the manuscript clearer and more useful to readers. Our planned revisions therefore aim to strengthen the experimental and computational support for the framework, clarify the interpretation of the modality comparisons, and provide additional guidance for researchers who may wish to apply coelsch or related single-cell sequencing approaches in future studies.

2. Description of the planned revisions

2.1. Additional technical replicates and clearer treatment of batch/sample-handling effects

Reviewers 1, 2 and 3 all noted that the comparison of different platforms and modalities is based on limited replication, with different nuclei isolation and processing strategies used for different technologies. Reviewer 3 requested a fully controlled benchmark in which the same nuclei preparation is split across all tested platforms. We agree that this would be the ideal design for a dedicated head-to-head benchmarking study. However, the primary aim of the manuscript is to demonstrate the applicability of coelsch across different single-cell sequencing data types, rather than to provide a definitive benchmark of the intrinsic performance of each modality and platform.

In addition, a fully matched and replicated cross-platform experiment for all technologies is not feasible. Isolated nuclei deteriorate rapidly after preparation and must be processed promptly for single-cell library construction; this makes it impractical to distribute the same preparation across multiple time- and labour-intensive workflows. However, this design is feasible for 10x scRNA-seq and 10x scATAC-seq. To address this point directly, we will therefore generate two matched technical replicates each of 10x scRNA-seq and 10x scATAC-seq from nuclei isolated in the same sorting run.

We will also improve our library-level QC summary tables. We will report, where available, the number of nuclei used for loading, recovered barcodes, barcodes retained after QC, inferred high-quality nuclei and artefacts, informative fragments per nucleus, genomic bin coverage, and final nuclei used for crossover calling. This will make the effects of loading, capture efficiency, QC filtering, and modality-specific data loss more transparent.

In the revised text, we will distinguish more clearly between modality-specific effects and possible batch/sample-preparation effects. Where the current manuscript implies that differences are intrinsic properties of sequencing platforms, we will soften the interpretation unless supported by the new replicate data, reproducibility analyses, or well-supported properties that have been reported previously in literature.

2.2. Rationale for using 10x scRNA-seq in the natural variation panel

Reviewers 1 and 3 asked why the natural variation panel was analysed using 10x scRNA-seq, given that Takara scWGA produced higher per-cell crossover localisation accuracy in the modality comparison. We will revise the manuscript to explain this experimental decision more clearly.

The natural variation panel was designed as a high-throughput experiment requiring sufficient numbers of usable nuclei from many pooled F₁ hybrids. In our hands, 10x scRNA-seq has generally produced the largest number of usable nuclei barcodes and the lowest proportion of artefacts. This makes 10x scRNA-seq well suited to experiments where many nuclei are required per genotype. By contrast, applying Takara scWGA to a pooled panel of this scale would be expected to recover only tens of usable nuclei per F₁ hybrid, which would be insufficient for robust recombination-rate or landscape estimation.

We will add this explanation to the relevant Results section and clarify that the choice of 10x scRNA-seq reflects a trade-off between per-cell crossover resolution and the number of informative nuclei recovered per genotype. We will also add genotype-level summaries for the pooled natural variation experiment, including assigned nuclei per genotype and genotype-specific genomic coverage of informative fragments.

2.3. Reproducibility of recombination landscapes across replicates and modalities

Reviewer 1 requested recombination landscape plots for all tested modalities, and several comments raised the need to show within-modality reproducibility. We will add recombination landscape plots for wild-type Col-0 × Ler libraries across the tested modalities, including the newly generated replicate 10x scATAC and scRNA libraries.

We will assess reproducibility using comparisons of unsmoothed, non-overlapping windowed recombination-rate estimates, both within and between modalities. These will be quantified using bootstrapped estimates of spearman rank correlation coefficient, and visualised using scatterplots and/or recombination landscapes.

2.4. Sequencing depth, coverage, and crossover localisation resolution

Reviewers 1 and 3 requested clearer quantitative reporting of crossover resolution and a stronger analysis of depth effects. We will revise the manuscript to report practical crossover localisation resolution for each modality, including median and interquartile localisation error or interval size in genomic units.

We will expand the simulation analyses to compare false-positive and negative rates and localisation accuracy across modalities, including telomere-proximal error profiles for scWGA and scATAC as well as 10x RNA data. We will perform downsampling analyses to assess how crossover detection accuracy changes as a function of informative-fragment depth. Where feasible, we will compare depth-matched subsets across modalities to distinguish effects of sequencing depth from modality-specific coverage structure.

These analyses will be used to clarify the extent to which each modality is suitable for different applications, such as broad landscape estimation, crossover counting, or fine localisation.

2.5. Artefact detection, high doublet rates, and representativeness after filtering

All three reviewers raised concerns about the high proportion of barcodes excluded by the filtering procedure, particularly in the Takara scWGA dataset. In hindsight, we believe part of this concern stems from the poor choice of terminology ("doublets") we used to describe these excluded barcodes.

While true doublets (i.e. two nuclei entering a single droplet or nanowell) are one likely source of such signals, the filtering procedure more broadly identifies artefactual barcodes that do not exhibit a clear single-gamete haplotype structure. These barcodes may arise from a variety of sources, including doublets, multiplets, high levels of ambient DNA or RNA, or empty droplets containing only ambient material. Although visual examination can be used to make predictions about the source of these artefacts, our detection method does not attempt to distinguish between them, and artefacts in different modalities may stem from different sources in varying proportions. We will therefore revise the terminology throughout the manuscript to clarify that these represent a broader class of low-confidence or noise barcodes, rather than confirmed doublets.

For the Takara scWGA data, we will revise the manuscript to discuss the discrepancy between the CellSelect well classifications (which uses proprietary software to label doublets) and the final artefact predictions from coelsch. We can only speculate as to why CellSelect failed to detect many apparent doublet and multiplet artefacts in this experiment, but we agree with the reviewer that the most likely explanation is the small size of Arabidopsis pollen nuclei relative to the expectations of the imaging and classification procedure. To support this interpretation, we will add supplementary analysis comparing the CellSelect images from individual nanowells with the final doublet predictions inferred from scWGA data. This will allow readers to see examples of wells classified as acceptable by CellSelect but subsequently inferred to contain artefacts based on their haplotype structure.

We will also add sensitivity analyses showing how key results change under different artefact-filtering thresholds. These analyses will include crossover count distributions, recombination landscape estimates, and modality-level comparisons. We will examine the extreme upper tail of crossover counts observed in 10x scATAC-seq and assess whether these barcodes are artefacts that have escaped detection.

Finally, we will assess whether retained singlets are representative of the input data with respect to informative-fragment counts, coverage, and inferred crossover patterns. This will address the concern that filtering could preferentially remove nuclei with particular recombination profiles.

2.6. Biases arising from pollen nuclear biology

Reviewer 2 raised an issue concerning the biases arising from the two different nuclei types present in mature trinuclear Arabidopsis pollen, and reviewer 3 endorsed this point. While we do not agree with the reviewer that scRNA and scATAC cannot capture sperm nuclei due to their condensed nature (see Parker et al. 2025 PLoS Biology for evidence against this claim), it is true that technical variation in nuclei isolation and sorting may affect the relative representation of nuclei types - usually, however, resulting in the underrepresentation of vegetative nuclei (Parker et al. 2025). We will add text addressing this point to the manuscript.

It is also true that differences in expressed genes between vegetative and sperm nuclei, which have very different transcriptomic profiles, will affect the distribution of informative reads for crossover analysis in scRNA data, and therefore may also have an impact on the recovered recombination landscapes (despite that the underlying landscapes are biologically identical). We will address this in the manuscript by adding recombination landscape plots and reproducibility scatterplots (as described in point 2.3) comparing sperm and vegetative nuclei from scRNA-seq to the manuscript.

2.7. Robustness of the pipeline and parameter choices

Reviewer 3 raised the concern that quantitative conclusions depend on a single pipeline with fixed parameter choices. We will address this by adding a parameter-sensitivity analysis for the main computational steps. Specifically, we will test the robustness of crossover calling on simulated data to changes in bin size and rHMM parameters, showing how these affect sensitivity to noise and agreement of predictions with ground truth data.

2.8. Natural variation analysis: genotype-specific coverage and terminal crossover enrichment

Reviewers 1, 2 and 3 raised concerns about whether natural variation in crossover rate and terminality could be influenced by genotype-specific coverage, marker density, pooling imbalance, or dropout. We will add a more detailed description of how pollen from different F₁ hybrids was pooled and how genotype assignment was performed. We will report genotype-level recovery statistics, including the six hybrids excluded from downstream analysis, and discuss how imbalances may arise, e.g. through biological variation in pollen count and fertility, biases in nuclei isolation or sequencing, and biases in genotyping and informative fragments.

Reviewer 1 specifically asked whether the lower terminal crossover index observed in Cvi-0 crosses compared with Col-0 crosses could reflect systematic differences in informative-fragment distributions rather than true biological differences in crossover localisation. We will address this by using the genotype-specific informative-fragment distributions observed in the diversity-panel scRNA-seq dataset to simulate crossover datasets with known ground truth. This will allow us to test whether differences in marker variant or expressed-gene distributions causing variation in informative-fragment distribution could systematically bias terminal crossover detection in Cvi-0 crosses relative to Col-0 crosses.

If feasible within the revision timeframe, we will also perform an orthogonal validation experiment for a selected comparison showing a clear difference in crossover terminality, such as Col-0 × Sah-0 and Cvi-0 × Sah-0. This would use progeny sequencing of backcross populations to estimate recombination landscapes independently of single-cell scRNA-seq, providing a direct test of whether the inferred terminality difference is supported by conventional recombination mapping. If this experiment cannot be completed within the revision timeframe, we will clearly state this limitation and base the revised interpretation on the simulation analyses described above.

2.9. Broader applicability and practical guidance for users

Reviewer 1 requested more discussion of applicability beyond Arabidopsis and to outcrossing or polyploid species. We will expand the Discussion to address the requirements and limitations of applying coelsch in other systems.

2.10. Minor figure, reference, and presentation revisions

We will address the remaining minor comments, including adding missing axis labels and checking duplicated references.

3. Description of the revisions that have already been incorporated in the transferred manuscript

No revisions have yet been incorporated in the transferred manuscript.

4. Description of analyses that authors prefer not to carry out

4.1. Full new benchmark across all modalities from the same nuclei preparation.

As acknowledged in section 2.1, we agree with Reviewer 3 that a fully controlled benchmark in which the same isolated nuclei preparation is split across all tested platforms would be the ideal experimental design for separating intrinsic modality- or platform-specific effects from sample-handling and batch effects. However, this is not feasible for all technologies within the scope of this revision, because isolated nuclei degrade quickly, the single-cell sequencing methods are time- and labour-intensive, and the relevant platforms are not all available to us in the same location.

We will therefore not perform a complete new cross-platform benchmark across all modalities. Instead, we will address this issue in the parts of the experiment where a matched design is feasible: we will generate two additional matched technical replicates each for 10x scRNA-seq and 10x scATAC-seq from nuclei isolated in the same sorting run. We will also revise the manuscript to more clearly acknowledge the limitations imposed by the lack of a fully matched cross-platform design and to ensure that our conclusions are interpreted in that context.

4.2. Profiling the natural variation panel with a second modality

Reviewer 1 suggested profiling at least a subset of the diversity panel with an additional single-cell modality. We agree that this would be useful, but we do not currently plan to generate a second-modality dataset for the natural variation panel. We would like to point out that this dataset introduces 34 genetic maps in a single sequencing experiment, which is not easily repeated.

The natural variation experiment was designed as a high-throughput survey across many F₁ hybrids, and repeating even a subset with scWGA or scATAC would require substantial additional sample preparation and sequencing. Instead, we will strengthen the justification for the use of 10x scRNA-seq by adding genotype-level coverage summaries and simulations to show which conclusions are well supported at the observed data density.

4.3. Orthogonal progeny sequencing from the exact same F₁ plants

Reviewer 3 suggested that progeny sequencing from the same F₁ plants used for single-cell assays would provide a direct ground truth. This experiment would require additional crosses, progeny generation, and matched single-cell and progeny sequencing, which would not be justified by the insights that this effort delivers: While progeny sequencing can provide an independent validation dataset, we do not agree that it would constitute a substantially better ground truth than the simulations used here. Simulations provide a known ground truth for every individual barcode, whereas progeny sequencing cannot, for the obvious reason that pollen grains are destroyed during single-cell sequencing and therefore cannot be used to generate offspring. In addition, progeny-derived recombination landscapes are not a perfect ground truth at the population level, since segregation distortion and post-meiotic selection can alter the observed distribution of recombination events relative to those present in the original pollen population.

4.4. Formal benchmarking of ____coelsch____ as a structural-variant detection method

Reviewer 2 asked whether large structural variants were identified in other accessions besides Zin-9, and what sensitivity and specificity can be expected from recombination coldspot-based structural-variant detection. We agree that this is an interesting question, given that the Zin-9 inversion was identified through its strong effect on recombination. However, we do not plan to develop or benchmark coelsch as a comprehensive structural-variant detection method as part of this revision.

The Zin-9 event was identified by visual inspection of the recombination maps, where it appeared as an unusually large and conspicuous recombination coldspot. We did not develop a systematic structural-variant calling procedure, as we do not view recombination suppression alone as a sufficiently specific signal for structural-variant detection. Coldspots can arise for many reasons, including centromere proximity or local recombination modifiers. Therefore, although large rearrangements such as inversions or translocations may sometimes be detectable through their effects on recombination, coelsch should not be considered as a general-purpose structural-variant caller.

In the revised manuscript, we will clarify this limitation and avoid implying that recombination coldspot analysis provides comprehensive structural-variant discovery. We will report that we did not observe other genotype-specific coldspots of comparable scale to the Zin-9 event among the other analysed accessions, although smaller coldspots such as one corresponding to the previously reported 2.2Mb inversion on Chromosome 1 of N13 were identifiable. We will not provide formal estimates of sensitivity and specificity for structural-variant detection, as this would require independent benchmark datasets or dedicated simulations that are beyond the scope of the present study.

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Referee #3

Evidence, reproducibility and clarity

In this study, Parker et al. benchmark three single-cell sequencing modalities (scRNA-seq, scATAC-seq, and scWGA) in Arabidopsis gametes and deliver an open-source, end-to-end framework for data processing that enables high-throughput crossover mapping across hybrids. By systematically comparing these modalities, the work quantifies trade-offs in throughput, genomic coverage, and crossover detection sensitivity, offering timely guidance for experimental design in plant systems where single-cell genomics is still emerging and platform benchmarks are very limited. The pipelines are further supported by the discovery of a previously unrecognized ~10 Mb pericentric inversion in the Zin-9 accession. The experimental design is technically interesting, and the results are important for guiding plant single-cell research. The work has the potential to attract a broad readership. However, several aspects of the experimental design, validation strategy, and parameter robustness require further clarification and, where possible, additional analyses.

Major comments

1. The modality comparison is based on one scRNA-seq library and two libraries each for scATAC-seq and scWGA. While the limited replication is acknowledged in the Discussion, the authors also report unexpected and run-specific observations (e.g. unusually high doublet rates in the 10x scRNA-seq library; "unexpected" doublet behavior in scWGA), making it difficult to separate platform-intrinsic properties from sample preparation and run-to-run variation. Differences in nuclei isolation buffers, purification strategies (e.g. density gradients, FACS, centrifugation), and potentially loaded nuclei numbers between platforms (which have not been specified in detail) further confound modality-level conclusions. For example, total usable barcodes vary drastically between the samples (e.g. 15k/20k/33k for 10x scRNA-seq, only 3.8k for BD even though it has the same capture capacity as 10X). Do these differences reflect different capture efficiencies between the platforms, or variation in nuclei quality/quantity, or modality-specific limitations in QC thresholds? It would strengthen the study to provide, for each library, the number of nuclei prior to loading and before/after QC, and to add independent biological replicates under modality-appropriate, optimized handling, ideally including a design where the same nuclei pool is split across all three modalities.
2. All quantitative inferences rely on one custom analysis pipeline with multiple interdependent steps and fixed parameter choices (e.g. bin size, HMM transition structure, smoothing settings, background subtraction, doublet filters). The lack of benchmarking against independent crossover callers, or of systematic parameter sweeps, leaves it unclear how robust key patterns are to alternative analytical choices. It would substantially increase confidence to assess sensitivity of the main conclusions to key parameters (for example varying bin size, rigid chain length/transition penalties, enabling/disabling background subtraction and doublet filtering), and/or compare coelsch to other HMM-based crossover callers such as sgcocaller/comapr on at least a subset of the data.
3. Accuracy is evaluated by comparisons to prior backcross/progeny datasets generated in different conditions, and by simulations calibrated to those references. While this is informative, systematic biases shared between the new pipeline and the reference datasets could remain undetected. Internal, orthogonal validation (e.g. progeny sequencing performed on the same F₁ plants used for single-cell assays) would provide a more direct ground truth and avoid potential circularity in bias assessment.
4. The benchmark does not evaluate the impact of sequencing depth across modalities, which could influence the variation in per-barcode fragment counts and genomic bin coverage between scRNA-seq, scATAC-seq, and scWGA. Down-sampling aligned reads or informative fragments to fixed per-barcode targets (e.g. 250, 500, 1000 informative fragments) within each modality would clarify how much of the observed performance gap is attributable to depth rather than modality-specific biology or library structure. Constructing depth-matched subsets between scWGA and scATAC/scRNA datasets would help to test whether the breadth vs. depth trade-offs persist when sequencing resources are equalized.
5. In the pooled 34-hybrid single-nucleus RNA-seq dataset, it would be very informative to present detection sensitivity and resolution across genotypes (e.g. captured nuclei, distributions of informative fragments, covered bins, and expected localization error by genotype). Genotypes will differ in expression patterns, which will alter the number and distribution of informative fragments per nucleus, and thus ultimately influence inferred recombination rates and crossover terminality. Furthermore, the background subtraction filter relies on genotype-level background models. Given that all genotypes were pooled prior to nuclei isolation, can the authors show that estimated ambient/background profiles are comparable across genotypes?

Minor comments

1. The manuscript currently attributes more uneven coverage in scRNA-seq primarily to expression-biased sampling of heterozygous sites. Would the choice of using nuclei, rather than whole cells which would also allow the capture of cytosolic RNA, for the scRNA-seq be an additional reason for lower total number and genomic dispersion of informative fragments?
2. The sentence "This allows informed experimental and analytical choices ..." could be accompanied with a compact infographic or table (for example as an extension of Fig. 1B) summarizing key trade-offs and recommended use-cases for each modality (throughput, per-cell resolution, coverage breadth, susceptibility to doublets/ambient RNA, recommended applications).
3. Related to the point above, the choice to profile the F₁ hybrids using the 10x scRNA-seq modality is understandable from a throughput perspective, but the results presented in Fig. 1 and Table 1 suggest scWGA offers higher crossover accuracy, scATAC superior genomic breadth, compared to 10x scRNA-seq which in addition also showed a high doublet rate. Expanding the rationale for prioritizing scRNA-seq here (e.g. cost, compatibility with downstream expression analyses, or technical constraints for scWGA/scATAC at this scale) would clarify the experimental logic for the reader.

Referee cross-commenting

I strongly agree with the points raised by Reviewers #1 and #2. In particular, including additional replicates (ideally derived from the same pollen pool, processed identically and run across all modalities) would provide robustness to the benchmark. However, repeating these experiments, re-running the benchmark, and updating the interpretation would require substantial additional time, likely exceeding the suggested 1-3 month revision timeframe proposed by the other reviewers. Additional clarification of the analysis and representation of requested details (e.g. the recombination landscape plots (Reviewer #1), clarification of balanced pollen representation from each F₁ during pooling (Reviewers #2 and #3), and evaluation of how varying filtering strategies (e.g. doublet detection thresholds) affect the observed recombination patterns (Reviewers #2 and #3)) would also improve evaluation and transparency of the study. From a technical perspective major point 3 raised by Reviewer #2 (including information on the intrinsic biological characteristics of the material in the modality performance analysis) would provide substantially important context for users and improve interpretation of the benchmark.

Significance

Previous studies have successfully applied single-cell whole-genome amplification and linked-read sequencing to individual gametes to measure recombination rates and distributions, demonstrating the feasibility of this high-throughput alternative to progeny sequencing. This study extends that concept by delivering open-source pipelines for multiple single-cell modalities and by directly comparing the performance of scRNA-seq, scATAC-seq, and scWGA for mapping meiotic recombination in Arabidopsis gametes, offering both a practical resource and a performance evaluation for plant single-cell genomics.

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Referee #2

Evidence, reproducibility and clarity

This manuscript presents coelsch, a cross-platform computational framework for single-cell gamete recombination analysis. It systematically benchmarks the performance of four mainstream single-cell sequencing modalities in meiotic crossover detection, successfully applies the method to a natural variation panel of Arabidopsis thaliana, and identifies the largest natural inversion reported in this species to date. This work demonstrates strong innovation, a complete technical pipeline, and significant biological implications. I would like to recommend revision. My concerns are listed below for the authors' consideration and revision.

Major concerns

1. Biological Replicates and Batch Effect Control The number of biological replicates per sequencing modality is limited (2 libraries for 10x scATAC and Takara scWGA, 1 library each for 10x scRNA and BD scRNA), and experiments for different modalities were performed in separate batches. Have the authors evaluated the impact of inter-batch technical variation on recombination rate estimates? In particular, for platforms with drastically different doublet rates (e.g., 49.7% for 10x scRNA vs. 26.3% for BD scRNA), how did the authors distinguish or avoid inherent platform differences from batch effects?

The natural variation analysis used a pooled library strategy for 40 F₁ hybrids without biological replicates. How did the authors ensure balanced pollen representation of each F₁ during pooling? For the 6 F₁ hybrids excluded due to insufficient data, was this due to initial pooling bias or sequencing capture preference? Could this introduce systematic bias into the natural variation analysis results? 2. Consistency of Pollen Nuclei Isolation Methods Different nuclei isolation protocols were used for each sequencing modality: Percoll density gradient centrifugation for 10x scATAC, no Percoll purification for Takara scWGA, and flow cytometry sorting combined with 10x/BD scRNA. Have the authors assessed how these different isolation methods affect nuclei integrity, viability, and capture bias for pollen nuclei? For example, could flow cytometry sorting selectively exclude nuclei of specific sizes or densities, thereby compromising the representativeness of recombination rate estimates? 3.Systematic impact of the inherent structure of pollen on different sequencing modalities Mature Arabidopsis thaliana pollen has a canonical trinucleate structure, consisting of one transcriptionally hyperactive vegetative nucleus and two sperm nuclei with highly condensed chromatin and almost complete transcriptional silencing. While all three nuclei share identical genome sequences, they exhibit fundamental differences in chromatin state and molecular features, which will have profoundly distinct effects on different sequencing modalities-an issue not addressed or controlled for in this study.

Differential technical capture bias: scRNA-seq and scATAC-seq rely on mRNA and accessible chromatin signals, respectively, and thus theoretically can only capture valid data from vegetative nuclei; sperm nuclei will be filtered out during quality control due to insufficient signal. In contrast, scWGA is based on whole-genome DNA amplification, independent of transcriptional activity or chromatin state, and can capture both vegetative and sperm nuclei. Have the authors validated the actual nuclear type composition in datasets from each modality through experiments (e.g., nuclear size sorting, DAPI staining quantification, immunofluorescence labeling)? Could this systematic difference in nuclear type composition compromise the fairness of performance comparisons between modalities? The uneven coverage of scRNA/scATAC is primarily determined by gene expression levels and chromatin accessibility (e.g., high coverage at highly expressed genes, extremely low coverage at heterochromatic regions such as centromeres), whereas coverage bias in scWGA mainly stems from technical preferences of whole-genome amplification. When comparing the resolution and accuracy of recombination detection across modalities, did the authors clarify the contributions of "intrinsic biological characteristics of nuclear types" from "technical characteristics of the sequencing technologies themselves"? 4. Accuracy and Validation of Doublet Detection Method This study reports exceptionally high doublet rates (~49% for 10x scATAC, ~70% for Takara scWGA), and there is a significant discrepancy with the results from Takara's official CellSelect software (80% of wells labeled "Good" by CellSelect were classified as doublets by coelsch). Have the authors validated the false positive and false negative rates of coelsch's doublet detection method through independent experiments (e.g., mixing pollen of known genotypes, manual microscopic validation of selected wells)? Such a high doublet filtering rate leads to a drastic reduction in the number of effective cells (e.g., only 628 singlets remained from a total of 2081 barcodes in the two Takara scWGA libraries). Have the authors assessed the representativeness of the remaining cells after filtering? In particular, for low-coverage scRNA data, could filtering result in the loss of cells with specific recombination patterns? 5. Depth and Breadth of Natural Variation Analysis This study finds significant differences in recombination rate and terminal crossover enrichment among different natural accessions, with Cvi-0 hybrids exhibiting higher overall recombination rates but lower terminal recombination rates. Have the authors further explored the genetic basis underlying these differences? Besides the 10 Mb inversion in Zin-9, did the authors identify similar large structural variations in other natural accessions? What is the sensitivity and specificity of the recombination coldspot-based method for detecting structural variation? For example, what is the minimum size of inversions or translocations that can be reliably detected?

Minor concerns

• The mutants used in this study (zyp1, figl1, recq4ab, etc.) were generated by crossing mutant lines in the Col-0 background with corresponding mutant lines in the Ler background, resulting in heterozygous F₁ backgrounds. For example, the zyp1 mutant used Col-0 background zyp1-1 and Ler background zyp1-6. Could this heterozygous mutant background affect the accurate measurement of meiotic processes and recombination rates? Have the authors considered validation using F₁ populations from homozygous mutant lines?
• The Takara scWGA dataset for wild-type Col-0 × Ler contains only 224 high-quality nuclei, while mutant sample sizes range from tens to hundreds. Is this sample size sufficient for fine-scale analysis of recombination rate distributions, especially for the detection of low-frequency recombination events? There are also a few minor issues regarding the references-some appear to be duplicates, such as references 11 and 31, which seem to be the same in both the published version and the bioRxiv preprint. Please double check. Additionally, have the authors considered the cost implications of these single-cell-based technologies, as well as their previously published linked-read sequencing approach?

Overall, this manuscript represents an important technical breakthrough in the field of meiotic recombination research, providing a unified computational framework for large-scale, cross-platform single-cell gamete recombination analysis. The above questions mainly focus on the rigor of experimental design (especially the omission of the unique biological issue of pollen trinucleate structure), the depth of computational method validation, and the expansion of biological findings, and do not affect the core conclusions of the manuscript. I suggest that the authors address these questions and provide clear responses in the revised manuscript. If these issues are properly resolved, this work will provide a powerful tool for investigating the genetic and molecular mechanisms of plant meiotic recombination.

Referee cross-commenting

I agree with Reviewers 1 and 3. Addressing most of the points we raised would bring this manuscript to publication standard.

Significance

This study develops a unified computational framework for meiotic crossover (CO) mapping using single‑cell sequencing of Arabidopsis pollen, benchmarks four single‑cell modalities, and identifies natural recombination variation and a large novel pericentric inversion. Overall, the work is technically sound, biologically meaningful, and fills a key gap in scalable gamete‑based recombination profiling.

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Referee #1

Evidence, reproducibility and clarity

Summary:

Parker et al. present coelsch and coelsch_mapping_pipeline, two open-source tools for platform-agnostic haplotyping and crossover detection from single-cell sequencing data, benchmarked across four modalities: 10x scATAC, 10x scRNA, BD scRNA, and Takara scWGA. The study applies these tools to Arabidopsis thaliana F₁ pollen to recover known recombination frequencies, characterise the effects of coverage sparsity via simulation, and profile natural variation in crossover rate and distribution across 34 F₁ hybrids from 22 diverse accessions. As a by-product of the recombination maps, the authors identify a previously unrecognised ~10 Mb pericentric inversion in the accession Zin-9 - the largest natural inversion described to date in A. thaliana.

This is an interesting and important study and is suitable in scope and rigour for publication in a Review Commons affiliate journal. By combining computational and experimental framework, the authors address a genuine methodological gap: while single-cell gamete sequencing is a powerful approach for recombination mapping, the consequences of choosing among available sequencing modalities have not been systematically evaluated. The tools are open-source, data are deposited, and the biological conclusions are well-grounded. Importantly, the limitations of the tools are also mentioned, which is appreciated. Therefore, this manuscript presents a genuinely useful methodological framework that fills a real gap in the recombination biology toolkit. The biological discovery (Zin-9 inversion) adds independent value. However, several analytical choices require better justification, some results sections are under interpreted, and a number of presentation issues should be addressed before acceptance.

Major comments:

1. Mismatch between best-performing modality and diversity panel application

The most critical concern is a logical inconsistency in the experimental design. The authors demonstrate convincingly that Takara scWGA achieves higher per-cell resolution and more accurate crossover detection than the droplet-based RNA methods. Yet the diversity panel - the study's key biological application - is analysed exclusively using 10x scRNA. No comparison with other modalities is provided for the panel, and no external recombination data for these accessions are included for validation. The authors should either: (i) include at least a subset of accessions profiled by an additional modality; or (ii) provide a more thorough quantitative justification for why 10x scRNA throughput outweighs the loss of resolution in this specific context, showing that cross-accession comparisons remain interpretable at scRNA coverage levels. 2. Could variation in crossover terminality result from analysis artefacts?

The authors demonstrate consistently higher rates of terminal crossovers in Col hybrids than in Cvi hybrids, 'implying genetic background modulation of crossover localisation'. However, their simulation analysis also demonstrates that telomere proximal crossovers are disproportionally missed in 10x RNA data. Therefore, could the Col vs. Cvi terminality differences result from a greater/lower occurrence of false negatives in different genotypes using this approach, rather than bona fide differences in CO number (caused by e.g. differences in telomere proximal marker density in Col vs. Cvi)? If so, this should be explicitly mentioned.<br /> 3. Doublet rates in Takara scWGA are unexplained

The Takara iCELL8 platform implements microscopy-based automated well selection to prevent doublets, yet coelsch identifies a ~70% doublet rate in these libraries. This is mentioned briefly but not adequately explained in the main text. The authors should provide a more thorough explanation for why the CellSelect imaging software fails to exclude pollen nuclei doublets (likely due to small nuclear size), and they should discuss what this implies for the utility of this platform for future experiments. This is important practical information for readers considering the Takara workflow. 4. Recombination landscape figures are incomplete

Figure 2C shows recombination landscapes only for mutant genotypes profiled by Takara scWGA. Equivalent per-chromosome landscape plots should be provided for all modalities tested on wild-type Col-0 × Ler material. This is essential to visually communicate the coverage-driven differences in landscape resolution that the authors describe, and to verify that 10x scATAC and scRNA recover similar gross distributions despite lower per-cell depth. 5. Extreme crossovers number in 10x scATAC are not discussed

The violin plots in Figure 2A show that 10x scATAC produces a wider upper tail of estimated crossover numbers than other modalities, with some barcodes exceeding 20 crossovers per nucleus - values far above the biological expectation for Arabidopsis. This is not acknowledged or explained. Is this an artefact of the high doublet contamination in this dataset (even after filtering), or a property of the HMM applied to fragmented ATAC data? An explicit discussion or supplementary analysis is required. 6. Resolution of crossover detection is undereported

Figure 3C shows boxplots of crossover localisation error across modalities, but this analysis is not discussed quantitatively in the main text. Readers need to understand the practical resolution (in kb) achievable by each modality in terms of crossover interval size. This is particularly important because the paper claims applicability for genetic mapping experiments, where localisation precision directly determines utility. 7. Telomeric false-negative rate in scWGA is not reported

The simulation analysis of false negatives near telomeres (Figure 3B) is presented only for 10x RNA data. Given that the authors use Takara scWGA for mutant genotyping and claim higher sensitivity, it is critical to also show the telomeric false-negative profile for scWGA. The current text implies that scWGA should avoid this problem, but this is not demonstrated. 8. Comparison between libraries from the same modality is absent

Two independent 10x scATAC and two Takara scWGA libraries were generated, but no within-modality reproducibility analysis of crossover rates or landscapes is presented. Crossover rates and landscape correlations between technical replicates should be shown to establish that the observed modality-level differences are not driven by library-preparation variability. 9. Applicability to non-Arabidopsis and heterozygous species

The Discussion notes that the approach relies on isogenic founder crosses and high-quality parental assemblies but does not explore the practical barriers to applying coelsch in outcrossing or polyploid species. Given the broad framing of the title ('platform-agnostic'), the authors should discuss what adaptations would be needed for crop species or other organisms where chromosome-scale haplotype-resolved assemblies are not available.

Minor comments:

1. Figure 5B - Please add axis labels in Mb.
2. Figure 2A - library replicates: The two 10x scATAC libraries are not differentiated in Figure 2A. Showing them separately (or indicating per-library medians) would improve transparency.
3. Droplet vs. plate combination: The Discussion does not address whether complementary modalities could be combined (e.g., using droplet-based data for landscape estimation and scWGA for localisation refinement within the same experiment). A brief discussion of this possibility would strengthen the practical utility of the framework.

Referee cross-commenting

All points raised by reviewers 2 & 3 seem reasonable and would substantially improve the quality of the manuscript

Significance

General assessment: The paper from Parker et al., provides the first systematic evaluation of single-cell sequencing modalities for recombination mapping in Arabidopsis and presents new bioinformatic tools for analysing recombination in single-cell data. The novel utility of the approach is demonstrated for assessing recombination rate across a wide variety of Arabidopsis hybrids. Different platforms provide different benefits/limitations and these are well presented. However, the manuscript would benefit from a more thorough presentation of all the different analyses that were performed.

Advance: Most recombination mapping studies in Arabidopsis utilise progeny sequencing. Here, the authors present an alternative approach, using single-cell gamete sequencing which will more easily facilitate recombination mapping in large populations, which will be particularly useful for future studies investigating the influence of natural variation on recombination rate and location. The advance is mostly technical, but the study also generates novel biological observations about chromosome structural rearrangements in Arabidopsis.

Audience: The study is likely to be of main interest to individuals studying recombination in plants (particularly using bioinformatic approaches and analysing the influence of natural variation). However, researchers with an interest in single-cell sequencing and broader genomics will also be an audience for this paper.

Describe your expertise:

I am a researcher in plant meiotic recombination and I am well placed to assess the general importance and impact of the study within the context of the field. However, I would not consider myself a specific expert in bioinformatics.

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monotony of life

the word choice of breeding is interesting and worth noting, it's not merely producing or growing but creating in a carnal, bodily way

Inversion of enlightenment understanding of time

L'articolo analizza se togliere PCSK9 e quindi avere poche LDL ha effetti avversi. Non li ha.

is this not what humans do? hebbian plasticity?

i thought that the interesting part of human vs deep learning nets was that NNs went in BOTH directions (backprop + feed forward) whereas humans only went in one direction

https://1drv.ms/i/c/4286f33e519f4694/IQBlaFlBKoh0R7GmuKspNvC4AZ2g8EmaJGX065kYJZNQ8yU?e=G9TVdL

integration based only on magnitude

integration of both magnitude and phase

angular distance from the peak centre(boresight) to the first null

add name if available

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make this: Diese Übersicht zeigt, welche Plattformen für welche typischen Anforderungen besonders geeignet sind. In Folge werden Plattformen im Detail vorgestellt.

I agree with Elias: it's confusing that the headline and this sentence state that 5 systems will be compared but then 6 systems are named. In the following it's 5 again.

Follow one logic for the entire article.

eLife Assessment

This important study demonstrates that extrachromosomal circular DNA and chromatin-associated proteins are components of stress granules. The data from a range of cellular and microscopy approaches are convincing, but the main conclusions would be further strengthened by demonstrating functional relevance and by extending the analysis to additional cell types. This paper will be of broad interest to cell biologists and those studying stress granule formation.

Reviewer #1 (Public review):

Summary:

In this manuscript, Demeshkina and Ferré-D'Amaré showed that extrachromosomal circular DNA (eccDNA) and chromatin-associated proteins are present in stress granules, based on proteomic and sequencing analyses. Using HCR-FISH combined with imaging, the authors showed the colocalization of eccDNA with stress granule proteins. Furthermore, they found that CRISPR machinery targeting the eccDNA component of stress granules disrupts stress granule assembly, and that this effect is largely independent of Cas9 endonuclease activity. Notably, expression of cytoplasmic chromatin factors restores stress granule formation in the presence of CRISPR machinery in yeasts. This also rescues the growth defect caused by hypoxic stress, which correlates with impaired stress granule formation. Together, this manuscript provides insight into the presence of eccDNA in cytoplasmic membraneless organelles, specifically stress granules, and suggests a functional role for eccDNA within these structures under stress conditions.

Strengths:

The authors used a panel of ribonucleases to demonstrate that stress granule cores isolated from yeast and HEK293 cells are resistant to plasmid-safe DNase, an enzyme that does not degrade circular double-stranded DNA. To further support the presence of extrachromosomal circular DNA (eccDNA) in stress granules, they performed Circle-Seq on stress granule cores. The gel electrophoresis and sequencing experiments complement each other well, providing consistent evidence for eccDNA within these granules. Overall, this study provides insight into potential cytoplasmic roles for eccDNA, an area that remains largely unexplored.

Weaknesses:

(1) Figure 1F suggests that stress granule cores are susceptible to DNase I but not to plasmid-safe DNase (psDNase). However, its smearing pattern in the psDNase condition appears similar to that in the DNase I treatment shown in Figure 1E, although psDNase produces more discrete bands. The authors should comment on these differences between Figures 1E and 1F, or consider revising Figure 1F to improve consistency with Figures 1E and 1D.

(2) The authors should clearly define "colocalization". Does it refer to complete spatial overlap between two signals (i.e., VCP and T30), or partial overlap (i.e., AHNAK DNA and G3BP)? Figure 3 and the associated text are descriptive. Quantitative analysis would strengthen the conclusions. For example, the authors could analyze the fraction of molecules localized to stress granules or provide Pearson's correlation coefficient or similar measurements.

(3) The authors used a CRISPR-based approach to target the Ty1 LTR retrotransposon, an abundant stress granule eccDNA, and they observed a loss of stress granule formation. However, this phenotype may be specific to Ty1 eccDNA rather than representative of all eccDNA species present in granules. In particular, the title "Cytoplasmic circular DNA is a key constituent of stress granules" implies a broader role. To support this claim, the authors should consider approaches that more globally deplete eccDNA rather than targeting a single eccDNA.

(4) The authors should provide additional experimental evidence to support the claim that eccDNA is packaged in a chromatin-like state. The rescue of stress granule formation by ectopic expression of modified chromatin-associated proteins (CHD1NES and GCN5NES) following CRISPR treatment does not necessarily demonstrate that eccDNA is packaged like chromatin under basal conditions.

Reviewer #2 (Public review):

Summary:

The authors report the presence of extrachromosomal circular DNAs (eccDNAs) within the core of stress granules purified from both yeast and mammalian cells.

Strengths:

This study is important for understanding the molecular mechanisms underlying stress granules containing eccDNAs and is likely to have a major impact on future research. A major strength of the study is the extensive experimental validation performed in yeast cells. In particular, cytoplasmic CRISPR-mediated targeting of eccDNAs suppresses stress granule formation and impairs recovery from hypoxic stress in yeast cells.

Weaknesses:

The conclusions would be further strengthened by validating the functional findings in an additional model system, such as mammalian cells.

Comments:

(1) Section: "Stress granule cores contain eccDNA"

a) The presence of eccDNAs would be more convincingly demonstrated using an orthogonal validation approach, such as DNA FISH targeting MYC and Centromere 8 (CEN8) on metaphase spreads from HEK293T cells (as performed in PMID: 34819668).

b) The study would also benefit from assessing the presence of eccDNAs in the extracellular medium. For example, DNA could be extracted from conditioned media and analyzed by PCR using primers spanning eccDNA breakpoint junctions (as performed in PMID: 40074906; PMID: 36123406).

(2) Section: "eccDNA-CRISPR abrogates stress granules"

These findings should be further validated under additional stress conditions, such as drug-induced stress (like methotrexate) or nutrient deprivation in the cell medium.<br /> In addition, the same set of experiments should be performed in HEK293T cells to support the broader relevance of the observations.

Author response:

Public Reviews:

Reviewer #1 (Public review):

Summary:

In this manuscript, Demeshkina and Ferré-D'Amaré showed that extrachromosomal circular DNA (eccDNA) and chromatin-associated proteins are present in stress granules, based on proteomic and sequencing analyses. Using HCR-FISH combined with imaging, the authors showed the colocalization of eccDNA with stress granule proteins. Furthermore, they found that CRISPR machinery targeting the eccDNA component of stress granules disrupts stress granule assembly, and that this effect is largely independent of Cas9 endonuclease activity. Notably, expression of cytoplasmic chromatin factors restores stress granule formation in the presence of CRISPR machinery in yeasts. This also rescues the growth defect caused by hypoxic stress, which correlates with impaired stress granule formation. Together, this manuscript provides insight into the presence of eccDNA in cytoplasmic membraneless organelles, specifically stress granules, and suggests a functional role for eccDNA within these structures under stress conditions.

Strengths:

The authors used a panel of ribonucleases to demonstrate that stress granule cores isolated from yeast and HEK293 cells are resistant to plasmid-safe DNase, an enzyme that does not degrade circular double-stranded DNA. To further support the presence of extrachromosomal circular DNA (eccDNA) in stress granules, they performed Circle-Seq on stress granule cores. The gel electrophoresis and sequencing experiments complement each other well, providing consistent evidence for eccDNA within these granules. Overall, this study provides insight into potential cytoplasmic roles for eccDNA, an area that remains largely unexplored.

Weaknesses:

(1) Figure 1F suggests that stress granule cores are susceptible to DNase I but not to plasmid-safe DNase (psDNase). However, its smearing pattern in the psDNase condition appears similar to that in the DNase I treatment shown in Figure 1E, although psDNase produces more discrete bands. The authors should comment on these differences between Figures 1E and 1F, or consider revising Figure 1F to improve consistency with Figures 1E and 1D.

We suggest that the appropriate comparisons are between the DNase I and psDNase treatments within each figure panel, and not between panels (e.g., Figures 1E vs. 1F). The electrophoretic gels in the different panels were run for different lengths of time, and therefore the comparison between gels would be spurious. In Figure 1E, electrophoresis after DNase I treatment results in a characteristic smear, while after psDNase treatment yields discrete bands (lanes 2–3 vs. 4–5). Electrophoretic conditions for this figure were optimized to minimize diffusion and allow quantitative evaluation. The electrophoresis shown in Figure 1F, which compares yeast and mammalian stress granule core nucleic acids, was run for a longer period — as evidenced by the greater migration distance from the loading wells — yet still clearly shows the same qualitative difference between DNase I (smear, lane 3) and psDNase (discrete bands, lanes 1–2) treatments for the yeast samples. The apparent discrepancy noted by the referee therefore simply reflects the difference in electrophoretic conditions between the gels shown in the two separate figure panels.

(2) The authors should clearly define "colocalization". Does it refer to complete spatial overlap between two signals (i.e., VCP and T30), or partial overlap (i.e., AHNAK DNA and G3BP)? Figure 3 and the associated text are descriptive. Quantitative analysis would strengthen the conclusions. For example, the authors could analyze the fraction of molecules localized to stress granules or provide Pearson's correlation coefficient or similar measurements.

In our considered opinion, categorizing colocalization as either "partial" or "complete" implies a level of molecular precision that is physically unattainable at the resolution limits of any current light microscopy modality, and would therefore be misleading. Our approach employs super-resolution confocal laser scanning microscopy (Airyscan) with hybridization chain reaction fluorescence in situ hybridization (HCR-FISH) or with immunofluorescence. The detection method used offers higher spatial resolution and signal-to-noise ratio than single-point detector/physical pinhole confocal (or widefield epifluorescence) microscopy used in most prior stress granule studies. Despite these enhancements, the system retains inherent diffraction-imposed limits: a lateral (XY) resolution of ~130 nm and an axial (Z) resolution of ~350–400 nm, defining the minimum separable distance between two fluorescent signals. Structures smaller than these thresholds remain unresolved within a single point spread function (PSF) maximum – a volume sufficiently large to simultaneously accommodate multiple stress granule cores or tens of thousands of individual proteins (such as G3BP) and dozens of nucleic acid molecules several thousand nucleotides in length. Consequently, any detected fluorescence signal may represent the superimposition of a large and indeterminate number of individual molecules or particles. True molecular interaction analysis remains for future studies using technologies with angstrom resolution (e.g., cryo-electron tomography, cryo-EM, X-ray crystallography, smFRET, EPR, NMR, etc.). Metrics such as Pearson's correlation coefficient report solely on the degree of signal overlap at the PSF scale (hundreds of nanometers) and would not provide any insight beyond what is already conveyed by our data.

(3) The authors used a CRISPR-based approach to target the Ty1 LTR retrotransposon, an abundant stress granule eccDNA, and they observed a loss of stress granule formation. However, this phenotype may be specific to Ty1 eccDNA rather than representative of all eccDNA species present in granules. In particular, the title "Cytoplasmic circular DNA is a key constituent of stress granules" implies a broader role. To support this claim, the authors should consider approaches that more globally deplete eccDNA rather than targeting a single eccDNA.

We respectfully disagree with the referee that further depletion of eccDNA would alter our conclusions. A central finding of our study is that stress granules can be abrogated cytoplasmically by co-expressing a Cas9 endonuclease, active or inactivated by point mutations (D10A /H840A), and a gRNA (which is itself a fusion of the crRNA and trcrRNA, natively separate RNAs in the source bacterium). We show in Figure 4 that when the gRNA targets the Ty1 sequences, endonucleolytically active holoenzyme co-expression in the cytoplasm results in loss of the corresponding eccDNAs, as assayed by sequencing of the relevant cytoplasmic fractions. Critically, when a catalytically inactive Cas9 protein (dCas9) is co-expressed with the gRNA instead of the wild-type endonuclease, depletion of the eccDNAs containing Ty1 sequences no longer takes place (Figures 4D and 4E), but stress granule formation is still abrogated (Figure 4C).

In our manuscript, we indicated (as "data not shown”) that co-expression with Cas9 of a gRNA "targeting" a sequence that is absent from the S. cerevisiae genome still results in abrogation of stress granule formation. These data are shown in Author response image 1. The gRNA is targeted to the sequence 5’-agaatcgatgcattt, which is absent in the genome of the yeast strain used.

Author response image 1.

It follows from our experiments that stress granule abrogation (1) is not a result of the catalytically active Cas9 endonuclease; (2) is not a result of the presence of a gRNA-directed but catalytically inactive Cas9 holoenzyme, but (3) is the result of the presence of a CRISPR holoenzyme (as defined above) in the cytoplasm.

To reiterate, abrogation of stress granules occurs when a Cas9-gRNA complex is present in the cytoplasm, regardless of whether the nuclease activity exists, or the gRNA targets a sequence that is present in the genome. Importantly, the holoenzyme is required for this phenomenon: presence of the endonuclease or the gRNA alone does not abrogate stress granule formation (Figures S5).

It is because of this unexpected observation that we next hypothesized that activities of the Cas9-gRNA complex other than sequence-specific gRNA-targeted endonucleolytic activity is driving the suppression of stress granule formation. The best documented such activity is DNA sequence sampling (1-dimensional diffusion). We think that 1-dimensional diffusion of the Cas9-gRNA holoenzyme is displacing from the cytoplasmic eccDNA interactors whose association with the DNA is required to drive stress granule assembly. The fact that the stress-granule suppressive effect of cytoplasmic Cas9-gRNA expression can itself be suppressed by two completely unrelated proteins whose only shared feature is action on chromatin (CHD1 and GCN5) strongly supports this hypothesis (Figures 4G, 4H and S6; also response to point 4, below), in addition to confirming that cytoplasmic eccDNA is packaged by histones in a conformation that CHD1 and GCN5 can both recognize.

(4) The authors should provide additional experimental evidence to support the claim that eccDNA is packaged in a chromatin-like state. The rescue of stress granule formation by ectopic expression of modified chromatin-associated proteins (CHD1NES and GCN5NES) following CRISPR treatment does not necessarily demonstrate that eccDNA is packaged like chromatin under basal conditions.

We would like to reiterate the temporal order in our experimental design (detailed in full in Methods and summarized in Results). Cas9_NES-gRNA and CHD1_NES (or GCN5_NES) were expressed simultaneously (not sequentially) in the cytoplasm. This was intentional, so as to give each player ample opportunity to engage its preferred substrate under non-stress conditions, prior to the brief oxidative stress. The referee appears to believe that cytoplasmic eccDNA was pre-exposed to Cas9_NES-gRNA, and then the bound endonuclease challenged with chromatin-modifying enzymes.

Our experimental design accounts for the contrasting substrate specificities of CRISPR and chromatin-modifying enzymes. Cas9-gRNA (holoenzyme) binds to nucleosome-free DNA with sub-nanomolar dissociation constant (Kd 0.1–1 nM) but its association with chromatinized DNA is impeded 5- to 100-fold (Isaac et al., 2016; Yarrington et al., 2018; Strohkendl et al., 2021). In contrast, whereas CHD1 binding to DNA is strictly nucleosome-dependent — its chromodomains actively block engagement with protein-free DNA (Hauk et al., 2010), and its productive binding (Kd 10–200 nM) relies on obligate multivalent contacts with the histone octamer, H4 tail, and wrapped DNA (Farnung et al., 2017; Sundaramoorthy et al., 2018).

Our observation that stress granule formation was unperturbed following oxidative stress is most parsimoniously interpreted as CHD1_NES outcompeting the CRISPR machinery for cytoplasmic binding to eccDNA by virtue of the latter existing in a histone-bound state that is recognized as chromatin by CHD1 –simultaneously favoring CHD1_NES engagement and impeding Cas9 access. Thus, our experiment in effect employs stress granule formation as a readout for differential binding to chromatin or chromatin-like eccDNA.

Farnung, L., Vos, S.M., Wigge, C., and Cramer, P. (2017). Nucleosome-Chd1 structure and implications for chromatin remodelling. Nature, 550(7677), 539–542.

Hauk, G., McKnight, J.N., Nodelman, I.M., and Bharat, T.A.M. (2010). The chromodomains of the Chd1 chromatin remodeler regulate DNA access to the ATPase motor. Mol Cell, 39(5), 711–723.

Isaac, R.S., Jiang, F., Doudna, J.A., Lim, W.A., Narlikar, G.J., and Bhatt, D.L. (2016). Nucleosome breathing and remodeling constrain CRISPR-Cas9 function. Nature Struct Mol Biol, 23(12), 1097–1103.

Strohkendl, I., Saifuddin, F.A., Gibson, B.A., Bhatt, D.L., Russell, R., and Bharat, T.A.M. (2021). Inhibition of CRISPR-Cas9 by bacteriophage-encoded proteins. Mol Cell, 81(8), 1665–1679.

Sundaramoorthy, R., Hughes, A.L., Singh, V., Wiechens, N., Ryan, D.P., El-Mkami, H., Petoukhov, M., Svergun, D.I., Treutlein, B., Sproll, P., and Owen-Hughes, T. (2018). Structural reorganization of the chromatin remodeling enzyme Chd1 upon engagement with nucleosomes. eLife, 7, e35720.

Yarrington, R.M., Verma, S., Schwartz, S., Trautman, J.K., and Carroll, D. (2018). Nucleosomes inhibit target cleavage by CRISPR-Cas9 in vivo.PNAS, 115(38), 9450–9455.

Reviewer #2 (Public review):

Summary:

The authors report the presence of extrachromosomal circular DNAs (eccDNAs) within the core of stress granules purified from both yeast and mammalian cells.

Strengths:

This study is important for understanding the molecular mechanisms underlying stress granules containing eccDNAs and is likely to have a major impact on future research. A major strength of the study is the extensive experimental validation performed in yeast cells. In particular, cytoplasmic CRISPR-mediated targeting of eccDNAs suppresses stress granule formation and impairs recovery from hypoxic stress in yeast cells.

Weaknesses:

The conclusions would be further strengthened by validating the functional findings in an additional model system, such as mammalian cells.

Comments:

(1) Section: "Stress granule cores contain eccDNA"

(a) The presence of eccDNAs would be more convincingly demonstrated using an orthogonal validation approach, such as DNA FISH targeting MYC and Centromere 8 (CEN8) on metaphase spreads from HEK293T cells (as performed in PMID: 34819668).

The relationship between eccDNA dynamics and stress granule assembly across distinct cell cycle phases remains an important and poorly explored question. To our knowledge, no published data currently describe how stress response mechanisms are regulated during mitotic division, particularly in metaphase. Our identification of eccDNA as a component of stress granule cores can provide a first tractable framework to investigate this relationship. However, a systematic and in-depth characterization of this phenomenon warrants a dedicated future investigation.

(b) The study would also benefit from assessing the presence of eccDNAs in the extracellular medium. For example, DNA could be extracted from conditioned media and analyzed by PCR using primers spanning eccDNA breakpoint junctions (as performed in PMID: 40074906; PMID: 36123406).

We agree with the referee that eccDNA biology represents a fascinating and rapidly evolving area of research, particularly given the emerging role of eccDNA in oncogenesis. In this context, our identification of eccDNA as a core structural component of stress granules opens a novel avenue for exploring the connection between stress-dependent translational regulation and disease-associated eccDNA dynamics. While we acknowledge the importance of this direction, a rigorous investigation of this relationship requires extensive multifaceted experimentation that falls beyond the scope of the current study.

(2) Section: "eccDNA-CRISPR abrogates stress granules"

These findings should be further validated under additional stress conditions, such as drug-induced stress (like methotrexate) or nutrient deprivation in the cell medium. In addition, the same set of experiments should be performed in HEK293T cells to support the broader relevance of the observations.

We agree with the referee that the composition and dynamics of stress granules arising from different stressors is an important endeavor. However, given the range of stressors documented to result in stress granule formation, those studies fall well beyond the scope of this manuscript. We will note however that the presence of eccDNA in stress granules of yeast and human cells is strong evidence for conservation of function(s). We think that exploration of the role of eccDNA in stress granule formation across the kingdoms of life (stress granules were first observed in heat-shocked tomato plants), cell cycle stages, stressors, etc. will be important research programs for the future.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

(1) Figures 3D and 3I: The use of magenta and red makes it difficult to distinguish between the two labeled signals. Consider using more contrasting colors to improve visual clarity.

We appreciate the comment regarding color choices in the figures. In our view, magenta and red are sufficiently distinguishable as nucleic acid labels, particularly when combined with the green signal representing G3BP in these panels.

(2) Figures 3F and 3G: Do the authors have an explanation for why AHNAK or MAPT DNA (white) does not colocalize with the anti-DNA immunofluorescence signal?

Immunofluorescence (IF) is standard for detecting protein antigens but has limitations when the target is a non-protein molecule such as DNA, owing to its compacted chromatinized state. Anti-DNA antibodies can miss a significant fraction of their targets because the DNA backbone remains largely inaccessible, a limitation that DNA-FISH overcomes by directly hybridizing probes to denatured DNA sequences with high specificity. The fixation step required for both IF and FISH imaging can introduce additional steric barriers that disproportionately restrict antibody access compared to small nucleic acid probes. Even under optimized conditions, the IF signal with anti-DNA antibodies is inherently reflective of a subset of the total cellular DNA content.

(3) Adding a subtitle on page 12 ("The abundant histones in purified stress granule...") would improve the overall structure and readability of the manuscript.

We think that an additional subtitle would not substantially improve the readability of what is, admittedly, a very dense manuscript that employs a diversity of experimental approaches.

(4) It would strengthen the analysis if statistical significance were included for the different time points in Figure 5C.

We appreciate the reviewer’s suggestion. Figure 5C shows the largest difference at 40–45 hours after stress recovery, which is statistically significant between Cas9NES-gRNA (or dCas9NES-gRNA) and Cas9NES or gRNA only (two-tailed Student’s t-test, *, p ≤ 0.05). All primary experimental data are publicly available (FigShare) so further analyses can be performed by interested future parties.

Axiology exists under terms such as ethics or positionality. How we protect the people we are studying or acknowledging how our personal backgrounds and biases affect our work.

Some researchers may assume that because they are being objective, their personal feelings don't matter to the data.However, values are always present below the surface, whether you admit it or not.

For example: why choose to do research on this specific topic instead of something else?

Axiology is the study of values.

It can be split into ethics (what is right and wrong) and aesthetics (what is beautiful or tasteful).

By selecting specific methods in research (surveys, interviews...) you are already making a claim about how knowledge is created.

Research adds knowledge and believing that truth exists objectively will lead your epistemology to include statistics or experiments VS believing truth to be subjective where your epistemology will lead you to conduct interviews and review stories.

Epistemology is the study of knowledge. It asks how we know what we know and what actually counts as knowledge.

To sum up:

You do need to clearly define the specific terms, concepts, and boundaries of your chosen field (Domain) and how they connect to other fields (Interface) before you can start your research.

When delving into the approach of ontology it can be broken down into three levels:

1) Philosophical ontology: It asks the general big-picture questions like what exists? what is real?

2) Domain ontology: It is related to specific fields of study (ex: education, biology...) Instead of delving into meanings of existence, researchers in specific fields agree on the reality of certain concepts and agree they're worth studying within their disciplines.

3) Interface ontology: It is when disciplines intersect within a single study. It is a shared set of definitions so that people from different fields can understand each other.

Ruolo funzionale PCSK9

Forse qualche accenno a cancro

TERAPIE

ajoutée

make this: Ein Lieferschwellen-Modul überwacht die EU-Umsatzgrenze und bildet die OSS-Regeln ab, Mehrwährung und ein Übersetzungs-Modul sind vorhanden. Mehr als 200 Integrationen sowie die No-Code-Middleware Xentral Connect binden Shops, Marktplätze und Logistik an.

delete "die"

Make this: Der Schritt über die Landesgrenze scheitert im E-Commerce selten am Produkt, sondern an der Operative.

article explaining for three AI tasks, image labeling, image captioning and speech transcription, how to do them locally in browser (w a local webserver). The speech transcription used whisper as local model, I prefer Nvidia Parakeet for its multilingual capabilities. But the setup is interesting. It realistically describes on-device speeds (on M2 a 2 to 5x transcription vs real time. But you can deploy these as webworkers nicely it seems

via [[Stephen Downes p]]

[[Peter Rukavina p]] on making your rss reader a way to write the web too. Somehow the webmention pings did not arrive. Added them. He does this on top of Readwise

This Obsidian Dataview snippet lists backlinks that are not also outgoing links from a note. Suggested as useful in building a MOC. Not sure about that though, in my case I link to [[MOC Olifantenpaadjes lens view op Notions 20240930211903]] not to include it, but to reference it.

DOI: 10.64898/2026.06.05.730521

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DOI: 10.64898/2026.06.05.730521

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DOI: 10.64898/2026.06.04.730003

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DOI: 10.64898/2026.06.04.730003

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DOI: 10.64898/2026.06.04.730003

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DOI: 10.64898/2026.06.04.730003

Resource: (IMSR Cat# JAX_021881,RRID:IMSR_JAX:021881)

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DOI: 10.64898/2026.06.04.730003

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DOI: 10.64898/2026.06.04.729515

Resource: RRID:Addgene_199715

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DOI: 10.64898/2026.06.04.729515

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DOI: 10.64898/2026.06.04.729515

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DOI: 10.64898/2026.06.04.729515

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DOI: 10.64898/2026.06.04.729515

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DOI: 10.64898/2026.06.03.729416

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DOI: 10.64898/2026.06.03.729416

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DOI: 10.64898/2026.06.03.729416

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DOI: 10.64898/2026.06.03.729416

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DOI: 10.3390/ph19060899

Resource: (CCLV Cat# CCLV-RIE 1035, RRID:CVCL_0023)

Curator: @dhovakimyan1

SciCrunch record: RRID:CVCL_0023

What is this?

DOI: 10.3390/ph19060899

Resource: (RRID:CVCL_0291)

Curator: @dhovakimyan1

SciCrunch record: RRID:CVCL_0291

What is this?

DOI: 10.3390/ph19060899

Resource: (KCLB Cat# 88065, RRID:CVCL_0027)

Curator: @dhovakimyan1

SciCrunch record: RRID:CVCL_0027

What is this?

DOI: 10.3390/ijms27115097

Resource: (TKG Cat# TKG 0509, RRID:CVCL_0470)

Curator: @dhovakimyan1

SciCrunch record: RRID:CVCL_0470

What is this?

DOI: 10.3390/cells15111035

Resource: (IMSR Cat# JAX_000651,RRID:IMSR_JAX:000651)

Curator: @dhovakimyan1

SciCrunch record: RRID:IMSR_JAX:000651

What is this?

DOI: 10.3390/cells15111035

Resource: RRID:CVCL_5370

Curator: @dhovakimyan1

SciCrunch record: RRID:CVCL_5370

What is this?

DOI: 10.3390/cells15111035

Resource: University of Virginia School of Medicine Flow Cytometry Core Facility (RRID:SCR_017829)

Curator: @dhovakimyan1

SciCrunch record: RRID:SCR_017829

What is this?

DOI: 10.3390/cells15111035

Resource: (ATCC Cat# CRL-2539, RRID:CVCL_0125)

Curator: @dhovakimyan1

SciCrunch record: RRID:CVCL_0125

What is this?

DOI: 10.1371/journal.pgen.1012107

Resource: (RRID:CVCL_ZD33)

Curator: @evieth

SciCrunch record: RRID:CVCL_ZD33

What is this?

DOI: 10.1371/journal.pgen.1012107

Resource: (RRID:CVCL_S891)

Curator: @evieth

SciCrunch record: RRID:CVCL_S891

What is this?

DOI: 10.1038/s41388-026-03843-4

Resource: (RRID:CVCL_0063)

Curator: @dhovakimyan1

SciCrunch record: RRID:CVCL_0063

What is this?

DOI: 10.1038/s41388-026-03843-4

Resource: (RRID:CVCL_4784)

Curator: @dhovakimyan1

SciCrunch record: RRID:CVCL_4784

What is this?

DOI: 10.1038/s41388-026-03843-4

Resource: (ECACC Cat# 90112714, RRID:CVCL_0035)

Curator: @dhovakimyan1

SciCrunch record: RRID:CVCL_0035

What is this?

DOI: 10.1038/s41388-026-03840-7

Resource: (BCRC Cat# 60067, RRID:CVCL_3509)

Curator: @dhovakimyan1

SciCrunch record: RRID:CVCL_3509

What is this?

DOI: 10.1038/s41388-026-03836-3

Resource: Zeiss LSM 710 Confocal Inverted Microscope (RRID:SCR_018063)

Curator: @dhovakimyan1

SciCrunch record: RRID:SCR_018063

What is this?

DOI: 10.1038/s41388-026-03836-3

Resource: (Proteintech Cat# SA00013-1, RRID:AB_2810983)

Curator: @dhovakimyan1

SciCrunch record: RRID:AB_2810983

What is this?

DOI: 10.1038/s41388-026-03836-3

Resource: (Sangon Biotech Cat# D110087, RRID:AB_2940948)

Curator: @dhovakimyan1

SciCrunch record: RRID:AB_2940948

What is this?

DOI: 10.1016/j.spinee.2026.06.009

Resource: RRID:SCR_027821

Curator: @nmaralla

SciCrunch record: RRID:SCR_027821

What is this?

DOI: 10.1016/j.neuroscience.2026.06.002

Resource: ImageJ (RRID:SCR_003070)

Curator: @nmaralla

SciCrunch record: RRID:SCR_003070

What is this?

DOI: 10.1016/j.neuron.2026.05.015

Resource: ImageJ (RRID:SCR_003070)

Curator: @nmaralla

SciCrunch record: RRID:SCR_003070

What is this?

DOI: 10.1016/j.chembiol.2026.05.005

Resource: (ATCC Cat# CRL-2611, RRID:CVCL_0393)

Curator: @nmaralla

SciCrunch record: RRID:CVCL_0393

What is this?

DOI: 10.1016/j.chembiol.2026.05.005

Resource: (NIH-ARP Cat# 2188-324, RRID:CVCL_0022)

Curator: @nmaralla

SciCrunch record: RRID:CVCL_0022

What is this?

DOI: 10.1016/j.celrep.2026.117510

Resource: (NIH-ARP Cat# 177-450, RRID:CVCL_0367)

Curator: @nmaralla

SciCrunch record: RRID:CVCL_0367

What is this?

DOI: 10.1016/j.celrep.2026.117510

Resource: (RRID:CVCL_0063)

Curator: @nmaralla

SciCrunch record: RRID:CVCL_0063

What is this?

DOI: 10.1016/j.celrep.2026.117510

Resource: RRID:Addgene_12259

Curator: @nmaralla

SciCrunch record: RRID:Addgene_12259

What is this?

DOI: 10.1016/j.cell.2026.05.025

Resource: (Abcam Cat# ab96089, RRID:AB_10679889)

Curator: @nmaralla

SciCrunch record: RRID:AB_10679889

What is this?

DOI: 10.1016/j.cell.2026.04.047

Resource: RRID:Addgene_158233

Curator: @nmaralla

SciCrunch record: RRID:Addgene_158233

What is this?

DOI: 10.1007/s11011-026-01883-z

Resource: RRID:AB_2881544

Curator: @dhovakimyan1

SciCrunch record: RRID:AB_2881544

What is this?

DOI: 10.1007/s11011-026-01883-z

Resource: (Abcam Cat# ab133462, RRID:AB_2801653)

Curator: @dhovakimyan1

SciCrunch record: RRID:AB_2801653

What is this?

DOI: 10.1007/s11011-026-01883-z

Resource: (Proteintech Cat# 20139-1-AP, RRID:AB_2878645)

Curator: @dhovakimyan1

SciCrunch record: RRID:AB_2878645

What is this?

DOI: 10.1007/s11011-026-01883-z

Resource: RRID:AB_2935426

Curator: @dhovakimyan1

SciCrunch record: RRID:AB_2935426

What is this?

DOI: 10.7554/eLife.97632

Resource: GraphPad Prism (RRID:SCR_002798)

Curator: @scibot

SciCrunch record: RRID:SCR_002798

What is this?

DOI: 10.7554/eLife.97632

Resource: ImageJ (RRID:SCR_003070)

Curator: @scibot

SciCrunch record: RRID:SCR_003070

What is this?

DOI: 10.64898/2026.06.04.730249

Resource: Allen Mouse Brain Atlas (RRID:SCR_002978)

Curator: @scibot

SciCrunch record: RRID:SCR_002978

What is this?

DOI: 10.64898/2026.06.04.730242

Resource: (RRID:CVCL_9771)

Curator: @scibot

SciCrunch record: RRID:CVCL_9771

What is this?

DOI: 10.64898/2026.06.04.730242

Resource: (Agilent Cat# M0725, RRID:AB_10013485)

Curator: @scibot

SciCrunch record: RRID:AB_10013485

What is this?

DOI: 10.64898/2026.06.04.730242

Resource: RRID:Addgene_12260

Curator: @scibot

SciCrunch record: RRID:Addgene_12260

What is this?

DOI: 10.64898/2026.06.04.730242

Resource: RRID:Addgene_12259

Curator: @scibot

SciCrunch record: RRID:Addgene_12259

What is this?

DOI: 10.64898/2026.06.04.730242

Resource: RRID:Addgene_62197

Curator: @scibot

SciCrunch record: RRID:Addgene_62197

What is this?

DOI: 10.64898/2026.06.04.730242

Resource: RRID:Addgene_62196

Curator: @scibot

SciCrunch record: RRID:Addgene_62196

What is this?

DOI: 10.64898/2026.06.04.730242

Resource: RRID:Addgene_59026

Curator: @scibot

SciCrunch record: RRID:Addgene_59026

What is this?

DOI: 10.64898/2026.06.04.730242

Resource: RRID:Addgene_59025

Curator: @scibot

SciCrunch record: RRID:Addgene_59025

What is this?

DOI: 10.64898/2026.06.04.730242

Resource: (RRID:CVCL_0063)

Curator: @scibot

SciCrunch record: RRID:CVCL_0063

What is this?

DOI: 10.64898/2026.06.04.730242

Resource: (Cell Signaling Technology Cat# 3900, RRID:AB_1550038)

Curator: @scibot

SciCrunch record: RRID:AB_1550038

What is this?

DOI: 10.64898/2026.06.04.730242

Resource: (BioLegend Cat# 330707, RRID:AB_1089246)

Curator: @scibot

SciCrunch record: RRID:AB_1089246

What is this?

DOI: 10.64898/2026.06.04.730242

Resource: (RRID:CVCL_9773)

Curator: @scibot

SciCrunch record: RRID:CVCL_9773

What is this?

DOI: 10.64898/2026.06.04.730242

Resource: (BioLegend Cat# 330613, RRID:AB_2295395)

Curator: @scibot

SciCrunch record: RRID:AB_2295395

What is this?

DOI: 10.64898/2026.06.04.730242

Resource: (BioLegend Cat# 341603 (also 341604), RRID:AB_1595552)

Curator: @scibot

SciCrunch record: RRID:AB_1595552

What is this?

DOI: 10.64898/2026.06.04.730242

Resource: (Thermo Fisher Scientific Cat# 31460, RRID:AB_228341)

Curator: @scibot

SciCrunch record: RRID:AB_228341

What is this?

DOI: 10.64898/2026.06.04.730242

Resource: (BioLegend Cat# 392103, RRID:AB_2716006)

Curator: @scibot

SciCrunch record: RRID:AB_2716006

What is this?

DOI: 10.64898/2026.06.04.730242

Resource: (Thermo Fisher Scientific Cat# 31430, RRID:AB_228307)

Curator: @scibot

SciCrunch record: RRID:AB_228307

What is this?

DOI: 10.64898/2026.06.04.730242

Resource: (BioLegend Cat# 401401, RRID:AB_2801452)

Curator: @scibot

SciCrunch record: RRID:AB_2801452

What is this?

DOI: 10.64898/2026.06.04.730242

Resource: (RRID:CVCL_9S58)

Curator: @scibot

SciCrunch record: RRID:CVCL_9S58

What is this?

DOI: 10.64898/2026.06.04.730242

Resource: (Coriell Cat# GM25256, RRID:CVCL_Y803)

Curator: @scibot

SciCrunch record: RRID:CVCL_Y803

What is this?

DOI: 10.64898/2026.06.04.730242

Resource: RRID:AB_2744331

Curator: @scibot

SciCrunch record: RRID:AB_2744331

What is this?

DOI: 10.64898/2026.06.04.730242

Resource: (BioLegend Cat# 306509, RRID:AB_314615)

Curator: @scibot

SciCrunch record: RRID:AB_314615

What is this?

DOI: 10.64898/2026.06.04.730242

Resource: (BioLegend Cat# 352905, RRID:AB_11148943)

Curator: @scibot

SciCrunch record: RRID:AB_11148943

What is this?

DOI: 10.64898/2026.06.04.730242

Resource: RRID:AB_2194073

Curator: @scibot

SciCrunch record: RRID:AB_2194073

What is this?

DOI: 10.64898/2026.06.04.730242

Resource: (BioLegend Cat# 304047, RRID:AB_2563128)

Curator: @scibot

SciCrunch record: RRID:AB_2563128

What is this?

DOI: 10.64898/2026.06.04.730242

Resource: (Cell Signaling Technology Cat# 37849, RRID:AB_2799122)

Curator: @scibot

SciCrunch record: RRID:AB_2799122

What is this?

DOI: 10.64898/2026.06.04.730242

Resource: (BioLegend Cat# 323505, RRID:AB_2252226)

Curator: @scibot

SciCrunch record: RRID:AB_2252226

What is this?

DOI: 10.64898/2026.06.04.730242

Resource: (Miltenyi Biotec Cat# 130-119-155, RRID:AB_2751644)

Curator: @scibot

SciCrunch record: RRID:AB_2751644

What is this?

DOI: 10.64898/2026.06.04.730242

Resource: (BioLegend Cat# 311405, RRID:AB_314874)

Curator: @scibot

SciCrunch record: RRID:AB_314874

What is this?

DOI: 10.64898/2026.06.04.730242

Resource: RRID:AB_10853004

Curator: @scibot

SciCrunch record: RRID:AB_10853004

What is this?

DOI: 10.64898/2026.06.04.730242

Resource: (BioLegend Cat# 353111, RRID:AB_10917389)

Curator: @scibot

SciCrunch record: RRID:AB_10917389

What is this?

DOI: 10.64898/2026.06.04.730242

Resource: (BioLegend Cat# 350807, RRID:AB_10983063)

Curator: @scibot

SciCrunch record: RRID:AB_10983063

What is this?

DOI: 10.64898/2026.06.04.730242

Resource: (BioLegend Cat# 303709, RRID:AB_2129464)

Curator: @scibot

SciCrunch record: RRID:AB_2129464

What is this?

DOI: 10.64898/2026.06.04.730242

Resource: RRID:AB_3662040

Curator: @scibot

SciCrunch record: RRID:AB_3662040

What is this?

DOI: 10.64898/2026.06.04.730242

Resource: (BioLegend Cat# 103023, RRID:AB_493686)

Curator: @scibot

SciCrunch record: RRID:AB_493686

What is this?

DOI: 10.64898/2026.06.04.730242

Resource: (BioLegend Cat# 313611, RRID:AB_893374)

Curator: @scibot

SciCrunch record: RRID:AB_893374

What is this?

DOI: 10.64898/2026.06.04.730242

Resource: (Synaptic Systems Cat# 160 308, RRID:AB_2923010)

Curator: @scibot

SciCrunch record: RRID:AB_2923010

What is this?

DOI: 10.64898/2026.06.04.730242

Resource: (Synaptic Systems Cat# 160 011, RRID:AB_2120992)

Curator: @scibot

SciCrunch record: RRID:AB_2120992

What is this?

DOI: 10.64898/2026.06.04.730242

Resource: (Thermo Fisher Scientific Cat# MA5-13398, RRID:AB_10984611)

Curator: @scibot

SciCrunch record: RRID:AB_10984611

What is this?

DOI: 10.64898/2026.06.04.730242

Resource: (Enzo Life Sciences Cat# ADI-VAM-PS003, RRID:AB_10618753)

Curator: @scibot

SciCrunch record: RRID:AB_10618753

What is this?

DOI: 10.64898/2026.06.04.730242

Resource: (DSHB Cat# AON-1, RRID:AB_528421)

Curator: @scibot

SciCrunch record: RRID:AB_528421

What is this?

DOI: 10.64898/2026.06.04.730242

Resource: RRID:AB_3676756

Curator: @scibot

SciCrunch record: RRID:AB_3676756

What is this?

DOI: 10.64898/2026.06.04.730242

Resource: (Cell Signaling Technology Cat# 5297, RRID:AB_2616578)

Curator: @scibot

SciCrunch record: RRID:AB_2616578

What is this?

DOI: 10.64898/2026.06.04.730242

Resource: (Abcam Cat# ab16659, RRID:AB_443419)

Curator: @scibot

SciCrunch record: RRID:AB_443419

What is this?

DOI: 10.64898/2026.06.04.730242

Resource: (Abcam Cat# ab177463, RRID:AB_2811300)

Curator: @scibot

SciCrunch record: RRID:AB_2811300

What is this?

DOI: 10.64898/2026.06.04.730242

Resource: (Thermo Fisher Scientific Cat# ICN55507, RRID:AB_2334513)

Curator: @scibot

SciCrunch record: RRID:AB_2334513

What is this?

DOI: 10.64898/2026.06.04.730242

Resource: (Abcam Cat# ab52607, RRID:AB_867704)

Curator: @scibot

SciCrunch record: RRID:AB_867704

What is this?

DOI: 10.64898/2026.06.04.730242

Resource: (Thermo Fisher Scientific Cat# R960-25, RRID:AB_2556564)

Curator: @scibot

SciCrunch record: RRID:AB_2556564

What is this?

DOI: 10.64898/2026.06.04.730242

Resource: RRID:AB_11180939

Curator: @scibot

SciCrunch record: RRID:AB_11180939

What is this?

DOI: 10.64898/2026.06.04.730242

Resource: (BioLegend Cat# 303007, RRID:AB_314323)

Curator: @scibot

SciCrunch record: RRID:AB_314323

What is this?

DOI: 10.64898/2026.06.04.730242

Resource: (BioLegend Cat# 327913, RRID:AB_2876633)

Curator: @scibot

SciCrunch record: RRID:AB_2876633

What is this?

DOI: 10.64898/2026.06.04.730242

Resource: (Thermo Fisher Scientific Cat# MA1-046, RRID:AB_2092361)

Curator: @scibot

SciCrunch record: RRID:AB_2092361

What is this?

DOI: 10.64898/2026.06.04.730242

Resource: (BioLegend Cat# 345203, RRID:AB_2129167)

Curator: @scibot

SciCrunch record: RRID:AB_2129167

What is this?

DOI: 10.64898/2026.06.04.730242

Resource: (Atlas Antibodies Cat# AMAb90795, RRID:AB_2665670)

Curator: @scibot

SciCrunch record: RRID:AB_2665670

What is this?

DOI: 10.64898/2026.06.04.730242

Resource: (Agilent Cat# Z0334, RRID:AB_10013382)

Curator: @scibot

SciCrunch record: RRID:AB_10013382

What is this?

DOI: 10.64898/2026.06.04.730242

Resource: (Sigma-Aldrich Cat# S2532, RRID:AB_477499)

Curator: @scibot

SciCrunch record: RRID:AB_477499

What is this?

DOI: 10.64898/2026.06.04.730219

Resource: University of Florida ICBR NextGen DNA Sequencing Core Facility (RRID:SCR_019152)

Curator: @scibot

SciCrunch record: RRID:SCR_019152

What is this?

DOI: 10.64898/2026.06.04.730191

Resource: RRID:Addgene_184905

Curator: @scibot

SciCrunch record: RRID:Addgene_184905

What is this?

DOI: 10.64898/2026.06.04.730191

Resource: RRID:Addgene_72833

Curator: @scibot

SciCrunch record: RRID:Addgene_72833

What is this?

DOI: 10.64898/2026.06.04.730191

Resource: RRID:Addgene_52343

Curator: @scibot

SciCrunch record: RRID:Addgene_52343

What is this?

DOI: 10.64898/2026.06.04.730191

Resource: RRID:Addgene_207548

Curator: @scibot

SciCrunch record: RRID:Addgene_207548

What is this?

DOI: 10.64898/2026.06.04.730191

Resource: RRID:Addgene_207559

Curator: @scibot

SciCrunch record: RRID:Addgene_207559

What is this?