Excellent conceptual foundation. This section is critical for shifting the mindset from theoretical sovereignty to practical application. However, to make this argument truly impactful, we should introduce and anchor the core concepts first before diving into the specific literature.

Before quoting Couture, Pohle, or Repetto, let’s add a brief introductory setup that explicitly defines:

• What "Autarky" means in the context of AI (the illusion of absolute digital isolation/self-sufficiency).
• What "Relational Sovereignty" implies (agency and negotiation within an interdependent global network).

By defining this contrast upfront, the reader will immediately understand why the proliferation of multiple "sovereigns" (Repetto 2025) and Barasa’s "continuum of strategic postures" matter. It will also create a much stronger baseline for the final argument: why full-stack self-sufficiency is unrealistic for Latin America and the Caribbean (LAC), and why the region must focus on strategic leverage instead.

"tenue" ?

Idem ?

Bibliographie ?

J'ai rédigé un peu ce paragraphe.

"encore sous-représentées et rejoindre ainsi les objectifs EDI de l'UdeM" ?

spreken echter --> spreken bij het uitwisselen van gegevens echter

verhuizing verwerkt in het BRP-systeem moet --> verhuizing, verwerkt in het BRP-systeem, moet

Ik weet niet of dat hier moet maar misschien wel goed te benoemen dat beide leveranciers (in het verleden) een belangrijke bijdrage hebben geleverd aan onze producten.

Nadeel van het tonen van zo'n lijst is dat je leveranciers vergeet waardoor je het gevaar loopt de kritiek te krijgen de ene leverancier boven de andere te stellen.. Zo mis ik o.a. Visma. Ik weet dat de leerlijnen voor intern gebruik zijn maar ze staan wel in een publieke repository en zijn dus voor iedereen te vinden. Overigens vind ik deze lijst zeker wel waarde hebben en behoud ik hem het liefst. Dus beter er voor zorgen dat we compleet zijn. Dat heeft ook wel weer het nadeel dat we er voor moeten blijven zorgen ook compleet te blijven.

In Nederland een relatief kleine groep leveranciers is --> Nederland heeft een relatief kleine groep leveranciers

Zou je hier niet ook kunnen vermelden dat gemeenten hierdoor innovatiever kunnen zijn omdat leveranciers gedwongen zijn dat te zijn om zich niet uit de markt te manouevreren.

wwarbij --> waarbij

gaan dieper --> gaan we dieper

Misschien moet je hieraan toevoegen:

• werk dat (veelal kleinere) gemeenten uitbesteden aan andere gemeenten of andersoortige organisaties.

o --> of

om --> op

eLife Assessment

This important study provides solid novel evidence for a role of ripples in the hippocampus in visual short-term memory. The work is strong in employing state-of-the-art intracranial electrophysiology in epilepsy patients with multivariate pattern classifiers in the context of an elegant experiment, but several aspects of the theoretical framing, mechanistic interpretation, and analysis strategy are incomplete.

Reviewer #1 (Public review):

Summary:

Cai et al. investigated the role of ripples in the hippocampus and coupled between the hippocampus and the neocortex in visual short-term memory (VSTM) using a similar lures match-to-sample task. The main findings are that hippocampal, but not neocortical ripples, ramp up during the maintenance period, peaking shortly before the memory response is given. This ramping-up effect was stronger for correct compared to incorrect trials. Furthermore, the authors show that stimulus category could be better decoded during coupled hippocampo-neocortical ripples compared to uncoupled ripples. These results provide compelling novel evidence for a role of ripples in supporting human visual short-term memory.

Strengths:

(1) State-of-the-art intracranial EEG in 13 patients during a well-designed visual short-term memory task, with simultaneous hippocampal and neocortical recordings.

(2) Thorough analysis pipeline with validation to detect ripple events, and distinguish them from spurious ripple activity (i.e., as induced by IEDs).

(3) Use of multivariate classifiers to resolve the neural representation of the stimuli.

Weaknesses:

It is difficult to find clear weaknesses in this paper, as the analyses are thorough, the results are clear, and the writing is excellent. However, some more sanity checks on the validity of ripples could have been conducted (i.e., making sure that ripple events have multiple peaks in the unfiltered raw signal at the ripple frequency). Also, the time window for coupled ripples appears to be a bit long, which makes it questionable to what degree these ripples are coupled (i.e., the time window is ~5 times longer than the duration of a ripple event). Lastly, the ramping-up effect could have been more clearly depicted in the figures, but that's a fairly minor point.

Reviewer #2 (Public review):

Summary:

Liu et al. record intracranial EEG from the hippocampus and lateral temporal lobe in thirteen neurosurgical patients while they perform a delayed match-to-sample visual short-term memory task. The central question is whether hippocampal sharp-wave ripples (brief high-frequency oscillations well established in the long-term memory consolidation literature) also contribute to the active maintenance of visual representations over a short delay. The authors report three main findings: hippocampal ripple rates progressively ramp up across the 7-second maintenance period, hippocampal ripples temporally co-occur with ripples in the lateral temporal lobe, and these coupled events coincide with above-chance category-level decoding of the memorized stimulus in the lateral temporal lobe. The findings are interpreted within the dynamic coding framework of working memory, which predicts discrete reactivation bursts rather than sustained firing during maintenance. The question is timely, and the use of intracranial recordings affords a level of temporal and spatial resolution unavailable to non-invasive methods.

Strengths:

The study addresses a genuinely important and underexplored question: whether a neural mechanism best characterized in the context of offline memory consolidation is also engaged during active online maintenance. The use of intracranial recordings in humans is well suited to this question, providing the millisecond temporal resolution and regional specificity needed to detect transient high-frequency events. The dissociation from long-term memory, tested by splitting remembered trials according to whether the item was later recalled in a cued-recall test, directly addresses what would otherwise be a significant confound, and the finding that ripple dynamics during maintenance are unrelated to subsequent long-term memory performance adds specificity to the interpretation. The coupled ripple analysis is methodologically grounded, and the finding that coupled but not isolated ripples coincide with elevated memory decoding is mechanistically informative. The multivariate decoding approach applied to lateral temporal lobe spectral power provides a meaningful index of memory reactivation that goes beyond simple univariate rate measures. The control analysis and the alternative ripple detection method provide useful robustness checks. The public availability of preprocessed data and analysis code on OSF is commendable.

Weaknesses:

(1) Theoretical motivation for examining ripples in visual short-term memory.

A fundamental question that the paper does not adequately address is why hippocampal ripples, a mechanism strongly associated with offline memory consolidation during sleep, where they coordinate the transfer of hippocampal representations to cortex through temporally compressed replay, should be recruited for the online maintenance of visual information over a seconds-long delay. The Introduction acknowledges this gap but does not close it. The dynamic coding framework is used to motivate the ramping-up prediction, but this framework is agnostic about the specific neural mechanism responsible for reactivation bursts. In particular, the literature cited by the authors predicts high-frequency population activity or gamma bursts, but not specifically hippocampal ripples. The reasoning that "ripples share key properties with postulated reactivation bursts" risks being circular: it amounts to saying that ripples could be the relevant mechanism because the relevant mechanism has properties that ripples also have. A stronger theoretical motivation would require either evidence that the replay or reactivation computations that ripples support during offline states are also engaged during active short-term maintenance, or a mechanistic account of how the circuit processes underlying ripple generation are recruited differently across these two contexts.

This concern is compounded by what the authors present as one of their main controls. The finding that ripple dynamics during maintenance are not associated with subsequent long-term memory performance is treated as a reassurance that the observed effects are specific to short-term memory. But if ripples are canonically a long-term memory consolidation mechanism, the observation that they are engaged by a short-term memory task while appearing disengaged from concurrent long-term memory encoding is itself a finding that demands explanation. Resolving this tension is important for the paper's contribution to be correctly interpreted by the field.

(2) Ripple detection and specificity.

Even granting that ripples could in principle contribute to short-term memory maintenance, the study does not establish that the detected events are physiological sharp-wave ripples rather than broadband high-frequency activity. The detection band (70-180 Hz) substantially overlaps with the high-gamma range, which is a well-established proxy for local neural population activity and coding, and is broader than the 80-120 Hz band used by several of the cited papers, including Vaz et al. (2019), Ngo et al. (2020), Chen et al. (2021), Staresina et al. (2023), and Kunz et al. (2024). Without demonstrating that detected events have the hallmark features of physiological sharp-wave ripples, a clear narrowband spectral peak, and characteristic waveform morphology, it is difficult to conclude that the observed effects reflect a ripple-specific mechanism rather than a more general high-frequency population activity phenomenon. The reported mean rate of 0.29 Hz is somewhat higher than rates reported in some recent work, such as Chen et al. (2021, ref 74) and Kunz et al. (2024, ref 15). It is worth noting that van Schalkwijk and Helfrich (2026, Nature Communications) demonstrated that a large proportion of awake ripple detections in the human medial temporal lobe reflect false positives arising from aperiodic 1/f noise, with task-related modulations of this noise floor producing spurious detections. The authors present an 80-120 Hz control analysis as a robustness check, but this inverts the appropriate logic: if 80-120 Hz is the more validated band, as the cited literature suggests, it should serve as the primary analysis rather than a supplementary one.

(3) Internal inconsistency with the dynamic coding framework.

The authors invoke the dynamic coding framework, which predicts that reactivation bursts should ramp up toward the end of the retention interval in the region where memory representations are actively maintained. The hippocampal ramping-up result is presented as confirming this prediction. However, the lateral temporal lobe, the region where above-chance category decoding is found and memory reactivation is attributed, shows no corresponding ramp-up. The authors acknowledge this asymmetry but do not offer a mechanistically satisfying explanation, and the suggestion that the effect might exist in unsampled subregions cannot be evaluated with the current data. This leaves the framework's core prediction unconfirmed in the region that is claimed to maintain the representations.

(4) Coupled ripples, directionality of hippocampal-lateral temporal coupling, and the ramping-up paradox.

The conclusion that coupled hippocampal-lateral temporal ripples coordinate memory reactivation creates a logical tension that the paper does not resolve. If hippocampal ripples drive lateral temporal reactivation only when co-occurring with lateral temporal ripples, and hippocampal ripples ramp up in a memory-predictive fashion, then the absence of lateral temporal ripple ramping up implies that the hippocampal ramp-up is not primarily expressed through the coupled ripple mechanism, undermining the coherence of the two main findings. The coupled ripple analysis further quantifies only temporal co-occurrence and provides no evidence about the direction of influence. Without demonstrating that hippocampal ripples systematically precede lateral temporal ripples (i.e., the expected signature of hippocampus-to-cortex information flow), the central claim that hippocampal ripples drive lateral temporal reactivation remains an interpretive assumption. Directly testing whether lateral temporal ripples specifically coupled to hippocampal ripples show a ramping temporal profile during maintenance (even if overall lateral temporal ripple rates do not) is necessary to establish whether the lateral temporal lobe engages in hippocampally-gated reactivation bursts in the manner the framework predicts. Additionally, reporting the distribution of peak lags between hippocampal and lateral temporal ripple peaks, and testing whether hippocampal ripples systematically precede lateral temporal ripples, is similarly necessary to support the directional interpretation.

(5) Trial-level analysis clarity.

The paper reports that ripples occurred in 54%, 79%, and 27% of trials during encoding, maintenance, and retrieval, respectively, but does not state whether subsequent analyses were conducted on trials thresholded by ripple occurrence. Given that occurrence rates vary substantially across stages and conditions, this inclusion criterion has implications for interpreting rate differences and should be stated explicitly.

(6) Statistical model specification.

The methods describe the ramping-up analysis using both a "logistic" link function and a "Poisson link function" in different places, with the dependent variable described inconsistently as ripple occurrence and ripple count. These are not equivalent, and the distinction matters for interpreting the reported coefficients. Additionally, the regional dissociation in Figure 3 appears to be assessed by fitting separate models to each region and comparing results informally. This does not constitute a direct test of whether slopes differ between regions and risks the well-known error of inferring a difference based on one p-value being significant while another is not. A direct region × time interaction test would more cleanly support the claimed dissociation.

Reviewer #3 (Public review):

Summary:

Liu, He, et al. present results suggesting hippocampal ripples support short-term working memory. The basic finding that hippocampal ripples increase during a 7s working memory maintenance period is intriguing and previously not shown as far as I know, but a lack of control analyses within the task, across brain regions, or as compared to alternative oscillatory signals makes the overall evidence weak. The author needs to more thoroughly evidence this signal via several analyses (suggested below) to strengthen their finding. The paper moves on to a hippocampal-cortical ripple coupling analysis that needs further methodological details and corrected statistics to make a meaningful contribution. As is, the ripple coupling results don't seem to necessarily relate to the hippocampal ripples found in the maintenance period, making the manuscript somewhat incoherent and of low impact in its current form.

Major issues:

(1) The framing sets up "visual short term memory" (VSTM) and "long term memory" (LTM) as two different things. A long line of research with humans possessing MTL/hippocampus damage shows the hippocampal memory system contributes to working memory only when the task is difficult enough to warrant its recruitment (see Hannula et al. 2006 J. of Neuroscience, Pertzov et al. 2013 Brain, or particularly Jeneson et al. 2012 Learning & Memory and J. of Neuroscience). This theory therefore, suggests that the hippocampus contributes to working memory via LTM mechanisms, as opposed to it possessing two different roles (VSTM and LTM). While the authors might disagree with this framing, at a minimum, they should describe this line of work. As is, it's difficult to know how their task fits into this literature since it's a cross between a pattern separation probe (identify repeats from lures), working memory (7 s delays), and subsequent cued associate recognition. Addressing why they used this combination of task features would help frame its place in the literature.

(2) The basic idea of looking for hippocampal ripples as a marker for working memory maintenance is new, with no prior literature (that I know of in rodents or in the handful of human intracranial ripple papers) to build on. That said, I suspect hippocampal ripples act as a proxy for hippocampal activation, providing a possible explanation for the hippocampal ripple increase shown during the Maintenance period. The effect they show is well supported by the mixed effects modeling (MEM), making it a potentially meaningful finding, but considering the novelty, it's rather important that control analyses rule out alternative possibilities. I suggest two important ones and a third related to the lack of parametric manipulations in the next paragraph. First, the authors frame the paper by suggesting hippocampal ripples share features with beta/gamma burst theories of working memory maintenance. In that case, the obvious question is why use a ripple detector instead of measuring gamma (or beta) activity as in this previous work? Some work has suggested hippocampal ripples act differently than high-frequency activity (see Sakon et al. 2024 J. of Neuroscience), so an analysis contrasting ripples and gamma seems rather important. Second, and relatedly, the authors only compare the hippocampus and lateral temporal cortex (LTC), likely because these tend to be sites with strong coverage in epilepsy cases. That's ok, but typically there is also reasonable coverage in other MTL areas like entorhinal cortex and amygdala, which would serve as important controls to show what they're measuring likely relates to sharp-wave ripples (a hippocampal phenomenon) and not something more generic like gamma or HFA (as shown in Sakon et al. 2024, Howard et al. 2003 Cerebral Cortex, Axmacher et al. 2007 reference 26, Meltzer et al. 2008 Cerebral Cortex, etc.).

(3) Related to the last point, since there are no parametric manipulations (e.g., different delay durations, different set sizes, varying lure difficulties) there's no way to assess increased hippocampal ripples with stronger loads, which would be important for determining the hippocampal dependence of their task in the first place. Do the authors have any justification for this task as an assessment of hippocampal working memory? I could imagine using a top vs. bottom tercile of lure discrimination difficulty (as assessed across all participants or control non-patients) to compare hippocampal activity. But only after the first trial, each pair is used since only then would the patient have awareness of the difficulty of the upcoming comparison. Or maybe something could be done by comparing VSTM performance by splitting patients based on how they performed at the LTM test.

(4) Also related to the VSTM vs. LTM framing, the authors use an "LTM" cued category recognition task--presumably done at the end of the repeat/lure recognition task--as a way to argue that the hippocampal ripple effects they see relate to VSTM and not LTM. The LTM task is disappointingly underdescribed, where even in the methods (lines 588-592) I cannot figure out when this task was probed, how many trials were done in comparison to the VSTM task, etc. Considering they use the LTM task to support their VSTM interpretation, it's rather crucial to understand precisely what they did. As is, the comparison they do present relies on a statistical error, where they compare p-values (n.b. https://www.nature.com/articles/nn.2886) instead of performing a direct interaction test (lines 177-180). Specifically, if they want to say their signal relates more to VSTM subsequent memory rather than LTM subsequent memory, they need to run a model of the form: ripple_rates ~ remembered + test_type + remembered*test_type (where test_type is either their VSTM or LTM task).

(5) As noted, the increase in hippocampal ripples during maintenance seems substantial, and the MEM confirms a significant increase over time. That said, the presentation of the data is atypical, with an example raster from one channel followed by average time courses of ALL participants below it. Why not show full raster plots for all participants? Ripples are so sparse that all the data in the task can be visualized in a single raster easily. A swarm plot indicating inter-patient variability in the maintenance signal also seems crucial. As is, there is no way to assess how much of the signal depends on a small subset of channels or patients.

(6) To compare ripple rates across task phases, they average over the bounds of each phase (lines 657-660) and input these into their MEMs. This approach makes sense for quantifying what we see in the ripple plots (Figure 2), except for Encoding, where they average over the entire 3 s window, even though there is clear tuning only from ~0-1 s. Using the tuned region and not the entire window is standard and would be more appropriate for the comparisons to maintenance, retrieval, etc (e.g., line 147-148 doesn't check out when looking at the figure), otherwise you are averaging over a seeming ripple inhibition from 1-2 s. They perform a cluster-based permutation test as is, so that a window or something a bit wider would be appropriate.

(7) The authors pivot to a hippocampal-cortical ripple coupling analysis to build the argument that the hippocampal ripples shown in Figure 2 support memory maintenance in the cortex. They use a window of -500 to 500 ms from hippocampal ripples to assess coupling. This is quite wide, since it doesn't seem plausible that a cortical ripple 500 ms from a hippocampal ripple means they synchronize. They cite two papers to justify the analysis, both of which use {plus minus}500 ms windows, but for spindle-ripple coupling, not ripple-ripple, so are miscited. Later in the paper, they switch to {plus minus}50 ms for another coupling analysis, raising the question of why they used {plus minus}500 ms in the previous analysis to begin with. If they want to claim cortical ripples are tuned by hippocampal ripples all the way up to 500 ms away, they should show the rasters (as in Figure 4a) and timecourse ripple rates, but going beyond {plus minus}500 ms to show that ripples in the {plus minus}50-500 ms range are above, say 500-1000 ms to justify their window selection. I will point out that there IS previous work that used {plus minus}500 ms to measure cortical-cortical ripple coupling (Dickey et al 2022 PNAS, which should be cited regardless, as I believe the first hippocampal-cortical ripple paper showing memory effects), although the figures in that paper suggest anything beyond {plus minus}250 ms returns to baseline (see Figure 2A-B).

(8) Lines 239 to 243 comparing p-values instead of an interaction test.

(9) I don't understand what "Further analysis based on the identified cluster" means (line 271). I see in Figure 5c that their broadband classifier identified a window of optimal decoding, but did they use only activity in this cluster to train the subsequent classifier (Figure 5d)? If so, this is not described in the methods. And if it is done that way, I don't think the logic makes sense. As mentioned in comment 6, the ripples during encoding tune to 0-1s after image presentation. So it doesn't make sense to use a 1.85-2.25 s window for ripple-locked decoding-they should just be using the 0-1 s window (or whatever their cluster-based permutation test shows in Figure 2b). Otherwise, it would appear they are studying two different phenomena.

(10) As is, the results in Figure 5d need to be redone. First, the results described on lines 271-275 once again suffer from comparing p-values. They need to run an interaction model if they want to claim Maintenance shows stronger ripple-locked decoding than Encoding (it almost certainly will not, since Encoding appears to show some evidence of decoding (p=0.118)). Second, even if they do change the framing to say Encoding and Maintenance show significant decoding, is it meaningful if Retrieval fails to? If you cannot decode the same information at the time of retrieval as is theoretically being held in working memory during the delay, the coupled ripple reactivation story wouldn't appear to make sense. They do show significant Retrieval decoding in Figure 5a-b, but since I don't really understand how they settled on the "identified cluster" in Figure 5c, I'm not sure what to make of the difference between these decoders.

(11) Finally, as mentioned in the summary, the analyses in Figures 2-3 seem disjointed from those in Figures 4-5. Part of this has to do with the switch to a broadband classifier, then a switch back to coupled ripples, and then, as I already mentioned, decoding results with time windows that don't align with the hippocampal ripple effects they showed earlier. Further, since the main point of Figures 2-3 is to establish a ramp in hippocampal ripples across maintenance, shouldn't they be trying to show how the decoding changes over the course of the Maintenance period? It would also help the interpretation of Figure 5 to see how the coupled ripples change over time in Figure 4 (as they showed them in Figure 2).

Minor issues:

(1) Instead of citing a software package like Emmeans, the statistical test being performed should be explained.

(2) Decoding % accuracy in the heatmaps in Figure 5 and supplementary would be more intuitive, particularly since Figure 5b uses accuracy anyway.

(3) Figure 2b is misleading with an unnecessary change in the y-axis for retrieval.

(4) In Figure 2d, a significant cluster is mentioned, but not drawn onto the figure as in Figure 2b.

eLife Assessment

This valuable study combines sub-millimeter 7T fMRI, EEG, representational similarity analysis, and deep neural network modeling to investigate layer-specific spatiotemporal dynamics underlying human object processing in early visual cortex and lateral occipital cortex; the authors report temporally distinct signatures in superficial layers of LOC that are interpreted as reflecting sequential feedforward and feedback processing during visual recognition. The multimodal methodological approach and empirical dataset are substantial and will be of broad interest to researchers in visual neuroscience, layer-fMRI methodology, and computational vision. However, the evidence supporting the central interpretation of interareal feedback remains incomplete, as the observed dynamics could also be explained by alternative mechanisms such as within-area recurrent processing, and there are additional concerns regarding several methodological and modeling choices underlying claims about increasing representational complexity at later time points. Overall, the study provides solid evidence for layer- and time-specific neural dynamics during object processing, while the interpretation of these signals as feedback-related remains provisional.

Reviewer #1 (Public review):

Summary:

This study combines representational similarity analysis (RSA) with 7T layer-specific fMRI and EEG to examine how neural representations in specific cortical layers of EVC and LOC correspond to the temporal dynamics of visual processing. The authors interpret these correspondences as reflecting feedforward and feedback processes, based on their relative timing and their similarity to representations in different layers of a deep neural network (DNN).

Strengths:

The combination of RSA with laminar fMRI is a promising approach for dissociating the functional roles and dynamics of different cortical layers within the same functional region, and it holds considerable potential for elucidating computational mechanisms both within and between levels of the visual hierarchy. However, several issues should be addressed before the authors' conclusions can be fully supported.

Weaknesses:

(1) The authors report that the representation in the LOC superficial layer resembles EEG-derived neural representations at ~400 ms post-stimulus, and that this similarity is best explained by representations in the higher layers of the DNN. From these two observations, they conclude that activity in the LOC superficial layer is driven by feedback signals. However, neither line of evidence directly dissociates feedforward from feedback contributions.

Specifically, late-stage representations in LOC could instead reflect the outcome of local recurrent computation, given that the superficial layer also serves as an output layer of the local cortical circuit. Moreover, the correlation with the DNN peaks at higher layers rather than being dominated by them, and feature tuning in higher DNN layers does not necessarily map onto higher-order cortical regions such as PFC.

While a feedback contribution to the LOC superficial layer is consistent with theoretical predictions and known cortical anatomy, the current evidence is indirect. I would recommend that the authors either tone down this conclusion or, at a minimum, explicitly clarify the strength and limitations of the evidence in the Discussion.

(2) I could not find information regarding the fMRI slice orientation or whether temporal regions beyond LOC were covered. The reported FOV (192 × 192 mm) seems quite large if only EVC and LOC were targeted. Did the authors acquire data from other object-selective regions in the temporal cortex, and if so, did they analyze these?

It would strengthen the feedback interpretation considerably if the RDM of the LOC superficial layer could be shown to resemble RDMs from more anterior temporal regions, which would be consistent with feedback originating from higher-order object-processing areas.

(3) Related to the previous point, LOC is a relatively large region, and based on the figures, it appears that the LOC ROI may contain two subregions. It would be helpful for the authors to show the location and extent of the LOC ROI in example participants.

If the ROI does indeed span two subregions, do these subregions share the same laminar profile and temporal dynamics?

(4) The authors report no feedback-related information in EVC, which contrasts with a number of prior fMRI studies that have demonstrated object-related feedback signals in EVC. One plausible explanation for this discrepancy is task relevance: in the present study, participants performed only a fixation color-change task, whereas in previous work they were required to attend to object features or identity (e.g., Morgan et al., 2019, J Neurosci; Kok et al., 2016, Curr Biol; Mohsenzadeh et al., 2018, eLife; Hou et al., 2026, eLife). Task demands on object processing may substantially modulate the strength of feedback signals to EVC, and this possibility warrants discussion.

(5) A substantial body of work has used specialized paradigms to dissociate feedforward and feedback signals in EVC (e.g., Williams et al., 2008, Nat Neurosci; Fan et al., 2016, PNAS; Hou et al., 2026, eLife). These studies are directly relevant to the current work but are not cited.

(6) Multidimensional scaling (MDS) visualizations of the RDMs (as in, e.g., Mohsenzadeh et al., 2018) are not included in the manuscript. These visualizations are important for interpreting the representational format across different layers of LOC and EVC, and I would encourage the authors to include them.

Reviewer #2 (Public review):

Summary:

Carricarte and colleagues set out to identify and functionally characterize feedforward (FF) and feedback (FB) information flow during object perception in humans, a question that has been difficult to address non-invasively because FF and FB signals overlap rapidly in time and across regions. The authors capitalize on the canonical cortical microcircuit-FF terminations primarily in middle layers, FB terminations primarily in superficial and deep layers, to spatially separate these signals using sub-millimeter (0.9 mm isotropic) GE-BOLD fMRI at 7T in early visual cortex (EVC) and lateral occipital complex (LOC). They combine these layer-resolved fMRI patterns with millisecond-resolution EEG (from a previously published dataset using the same 24 images) via representational similarity analysis-based EEG-fMRI fusion, and use a Vision Transformer (DeiT) trained on ImageNet to characterize the feature complexity of the resulting spatiotemporal signatures.

The authors first review their approach at the macroscale, replicating the expected EVC-then-LOC temporal hierarchy and the EVC-low/LOC-high feature complexity gradient. They then apply the same framework at the mesoscale of cortical layers, reporting: (a) early middle-layer signals in both EVC (~100 ms) and LOC (~160 ms) consistent with FF processing, (b) a later superficial-layer signal in LOC (~400 ms) interpreted as FB; (c) a layer-uniform feature-complexity profile in EVC (peaking at low-mid DNN layers across all depths); and (d) a feature-complexity dissociation in LOC, where middle-layer signals correspond to mid-to-high DNN layers and superficial-layer signals to high DNN layers. They argue that this complexity shift, combined with the timing difference, indicates interareal FB into LOC.

Strengths:

(1) The combination of layer-fMRI at 7T, EEG, and DNN-based representational analysis is well motivated through RSA. Each modality compensates for a known limitation of the others (fMRI: poor temporal resolution; EEG: poor spatial resolution; DNN: surrogate for representational format), and the RSA framework provides a principled common currency. Relatedly, the two-step macroscale-then-mesoscale design, in which the macroscale fusion replicates established findings before the same approach is applied at the layer level, is a sound and welcome scientific strategy that strengthens confidence in the combined-modality inferences.

(2) The authors include multiple complementary controls: partialing out lower layers to mitigate vascular draining, voxel-count matching across layers, an alternative DNN (AlexNet), an alternative time-window definition based on between-layer differences, and time-resolved commonality analyses. The convergence across these analyses is reassuring.

(3) Methodological transparency: The authors are forthright about partial-volume effects, foveal-confluence aggregation, and the indirect nature of the temporal estimates derived from EEG-fMRI fusion.

Weaknesses:

The central interpretive claim-that the late (~400 ms), superficial-layer LOC signal indexes interareal feedback that increases representational complexity-is intriguing, but in my view it is not yet fully supported by the evidence presented based on the following context.

(1) Eye movements as a possible confound for late signals. Stimuli were presented for 1 second, and fixation was enforced only behaviorally via a color-change task on a central cross. No eye-tracking is reported for either the fMRI or EEG datasets. While this approach is not uncommon, the absence of gaze monitoring introduces ambiguity when the goal is to decouple feedforward and feedback contributions at fine temporal resolution in EEG recordings. Under these conditions, multiple image-driven saccades within a trial are plausible, and saccade patterns are likely to be systematically image-specific, given the small (n = 24) and heterogeneous naturalistic stimulus set. Critically, the temporal window over which RDM correlations are interpreted as feedback coincides with the period during which observers typically make 2-4 fixations (average fixation durations of ~250-330 ms; Rayner, 1998; Henderson, 2003), meaning the late EEG-fMRI fusion peaks fall in a window where image-locked saccadic activity and successive foveation-driven feedforward responses would be expected to accumulate. Late peaks could therefore reflect cumulative feedforward responses across successive foveations rather than top-down feedback. The manuscript would be strengthened by providing eye-tracking data (if available), control analyses leveraging post-hoc indicators, or a discussion citing prior evidence that EEG/fMRI response profiles in this paradigm are robust to such eye movements.

(2) Decoding accuracy along the visual hierarchy raises questions about whether LOC is adequately engaged. Pairwise decoding accuracy is substantially higher in EVC than in LOC (Figure 1D), and the noise ceiling for LOC RDMs is markedly lower than for EVC across all layers (Supplementary Figure 4D-F). This pattern inverts the canonical hierarchical gradient of progressively stronger object decoding along the ventral visual stream, as well as the analogous gradient observed in DNN late layers that underlies the commonality analyses. As written, it is unclear how the manuscript reconciles this with its emphasis on LOC's role in higher-order, feedback-modulated representations with greater tolerance or increased complexity--unless decoding accuracies should be understood as image-level discrimination rather than at the level of object-category discrimination. A parsimonious alternative is that the 24-image set is too small or too coarse to reveal category-level representations in LOC robustly, such that LOC RDMs may be driven by lower-level or background/contextual variance and noise. This concern has direct bearing on the mesoscale commonality analyses supporting the "feedback transmits high-complexity features" conclusion. I would encourage the authors to (a) report split-half reliability of LOC RDMs alongside the commonality analyses, and either (b) acknowledge that the feature-complexity inferences are conditional on LOC RDMs faithfully capturing object structure rather than residual contextual/low-level variance, or (c) discuss how replication with a richer stimulus set might bear on the feedback-content interpretation.

(3) The interareal feedback interpretation could be more robustly defended against intra-areal alternatives. In EVC, the authors carefully consider non-feedback explanations for layer-specific dynamics, including lateral connections modulating gain and superficial GE-BOLD bias, and conclude these are sufficient. The same skepticism is not extended to LOC, where the corresponding superficial-layer signal is interpreted as interareal feedback, with speculative sourcing to DLPFC. Slow (unmyelinated) horizontal/lateral propagation in superficial cortical layers (e.g., Davis et al., 2024) can, in principle, produce delayed superficial-layer signals on the timescale observed here without any interareal contribution. This asymmetry is compounded by the treatment of the absence of sustained EVC activity following the middle-layer peak, which is dismissed as a "limitation of the spatial and temporal sensitivity of our measurements" (lines 388-390). If feedback to EVC truly cannot be resolved with this method, the corresponding feedback claim in LOC-imaged with the same protocol warrants comparable caution. The manuscript would benefit from either presenting positive evidence that distinguishes interareal feedback from intra-areal recurrence (e.g., frequency-band signatures, source-resolved EEG, or coupling with frontal regions), or qualifying the conclusion to "delayed superficial-layer activity consistent with either interareal feedback or intra-areal recurrence."

(4) The predictive coding framing is invoked but not well-grounded. The Discussion (lines 349-357) includes a theoretical implication of predictive coding. Predictive coding makes content-specific claims-feedback carries predictions, feedforward carries error signals relative to those predictions, and dissociating these requires manipulations of expectation, congruence, or predictability, none of which are present in the current design. The observed layer-wise timing differences do not bear evidence for rejecting non-predictive accounts. I would suggest either removing this framing or explicitly noting that the present data neither support nor refute predictive coding.

Reviewer #3 (Public review):

Summary.

Carricarte and colleagues use 0.9mm 7T fMRI in EVC and LOC, fused with previously collected EEG using the same stimulus set, in order to dissect feedforward and feedback contributions to human object processing through their layer-specific termination patterns. They report a feedforward signal in middle layers of EVC (~100ms) and LOC (~160ms), and a later signal in superficial LOC (~400ms) that they interpret as interareal feedback. Using commonality analysis with a Vision Transformer, they argue that this late signal carries higher-complexity features than the earlier signal, and conclude that feedback actively increases representational complexity in LOC.

Strengths.

The empirical work is methodologically ambitious. Sub-millimeter 7T coverage of both EVC and LOC, combined with layer-resolved EEG-fMRI fusion, represents a substantial technical achievement. The authors first reproduce established macroscale EEG-fMRI fusion patterns at 7T before extending the approach to the layer level. The figures throughout are beautifully designed and convey complex analyses with clarity. The empirical core of the paper - that LOC contains layer-distinct dynamics at distinct times, with the late signal carrying representational structure that differs in some way from the early signal - is supported by the data, though with caveats imposed by the LOC noise ceiling.

Weaknesses.

The authors' interpretation of these data (interareal feedback that reflects feature-complexity, related to the functional role of these signals) is not adequately supported and requires either reframing or substantial additional evidence.

Feedback vs. recurrence. The late superficial-LOC signal is interpreted as interareal feedback, but the data are equally consistent with within-area recurrence, lateral connections, or sustained feedforward dynamics. A reader expecting evidence of higher-area signals returning to early-time middle layers - a signature of interareal feedback - finds none in either region.

"Functional role" overclaim. The paper repeatedly claims to characterize the "functional role" of feedforward and feedback, but contains no behavioral linkage, no perturbation, and no analysis relating signals to perceptual outcomes; the fMRI task is explicitly orthogonal to object processing. What is demonstrated is spatiotemporal dynamics and representational format - both valuable, neither equivalent to functional role.

DNN analysis. The DNN analyses use several non-standard modeling choices that introduce more uncertainty than clarity. In the main analyses, the authors only use four sampling points from a single model (DeiT-small): transformer blocks 1, 7, and 12, plus the classification head. Then, the authors make their headline claims about complexity by comparing block 12 and the classification head; within the model, this is a distinction between an embedding layer and a supervised category readout, not a feature-complexity gradient. As such, the author's interpretation conflates semantic layers with representational "complexity." A more convincing use of this modeling strategy would be to demonstrate these effects in multiple models that might disentangle these factors-e.g., supervised (ResNet/ViT), self-supervised (DINOv2), and vision-language (CLIP) models-then to visualize these brain-model relationships across all layers. Alternatively, there are many suitable model-free analyses that could demonstrate the unique representational information within LOC without introducing any model-related concerns.

Reliability of LOC layer-resolved RDMs. The lower-bound noise ceiling for LOC mesoscale RDMs is approximately 0.05 across layers, with deep-LOC reliability essentially at zero. The central layer-resolved dissociation rests on RDMs that individual subjects barely reproduce; consequently, the deep LOC layer is dropped from the commonality analysis (Figure 4C shows only middle/superficial layers, while Figure 4B shows all three for EVC) because the data cannot support it. This is not damning, but it is consequential, and not sufficiently addressed in the manuscript.

eLife Assessment

This foundational and valuable study expands our understanding of circadian clock work in non-model taxa in wider environmental niches, using solid methods for protein and RNA detection to describe the expression pattern of PDH, cry2, and per in the central nervous system of Euphausia superba. While the anatomical annotation is extensive, support for the identification of the clock network is incomplete.

Reviewer #1 (Public review):

Summary:

Hüppe and colleagues characterized the network of neurons in the central nervous system of Antarctic krill that contained pigment-dispersing hormone (PDH), an important output factor in the circadian clock of insects. These neurons in the brain are putative clock neurons since a subset also expressed the clock genes period and cryptochrome 2. As one of the ocean's major contributors to biomass, krill is an ecologically important marine species that experiences challenging daily and seasonal environmental fluctuations in its high-latitude habitat. A comprehensive study of krill's internal clock may help to understand the extent of its resilience to the rapidly changing climate.

The authors used antibody staining against PDH across the whole central nervous system and additional in situ hybridization for cry2 and per mRNA, with a focus on the supraesophageal ganglion. There, they identified the major neuropils in the eye stalks and central brain of Antarctic krill. The resulting staining pattern aligns with the identified circadian clock network in insects and PDH-expressing networks in other crustaceans, making these neurons highly likely candidates for krill clock neurons.

Strengths:

(1) This study provides the first clues about the circadian clock architecture in a non-model organism in chronobiology, Antarctic krill, with a clear 3D reconstruction of the putative clock network.

(2) The authors effectively place their results within the extensive body of literature on arthropod circadian clock networks to argue that the neurons they describe are likely the circadian clock in krill.

Weaknesses:

(1) The data presented here are not sufficient to support the claim that the described network is the circadian clock because functional evidence is missing.

(2) Additionally, the study falls short of identifying any elements of the positive limb of the canonical circadian clock transcriptional-translational feedback loop, e.g., clk or cyc, in the PDH-expressing neurons.

(3) No sample sizes are reported, making it difficult for readers to assess the generalizability of the presented data.

Reviewer #2 (Public review):

Summary:

This study advances our understanding of the neuronal basis of the circadian clock in pancrustaceans. It extends our knowledge on the pigment-dispersing hormone system and provides links to information on the expression of core clock components, cryptochrome 2, and period. The data are sound and well-documented.

Comments:

The neuronal components of the arthropod circadian clock system have been analysed extensively in insects. Much less information on this system is available on malacostraca crustacea crustaceans. However, considering that malacostracan crustaceans and insects go back to a common pancrustacean ancestor and considering that we know that the brain architecture in these two groups shares many commonalities (see, e. g., extensive reviews by N. J. Strausfeld), we have to expect that crustaceans and insects share many of the characteristics of the circadian system. This is the case, e. g., for the network of pigment-dispersing hormone-positive neurons. The authors cite these studies, although late in the paper (discussion, line 339ff), and I suggest to move this info into the introduction: "339 ff: The arborization pattern of the PDH-network has been described in various malacostracan crustaceans, including Carcinus maenas (Alexander et al., 2020; Mangerich & Keller, 1988; Mangerich et al., 1987), Cancer productus (Hsu et al., 2008), Orconectes limosus (de Kleijn et al., 1993; Mangerich & Keller, 1988; Mangerich et al., 1987), Homarus americanus (Harzsch etal., 2009), Cherax destructor, Procambarus clarkii (Sullivan et al., 2009), and Procambarus virginalis (Luna et al., 2010)."

The strength of this paper is that it extends our knowledge on the PDH system and brings together neuroanatomical information on PDH-positive neurons with information on the expression of core clock components, cryptochrome 2, and period. That way, it advances our understanding of the neuronal basis of the circadian clock in pancrustaceans. The data are sound and well documented, and the authors are to be applauded for the superb dissection presented in Figure 1.

Below, please find some essential suggestions on how to further improve the paper.

(1) Framing of the study:

I know that krill is a key element of the Southern Ocean's food webs, but my sense is that discussing the current findings in a context of resilience of this species to global ocean change means largely overselling this study:

- Lines 47, 48: "and the resilience of this key species in a rapidly changing Southern Ocean."

- Lines 70 ff: "Hence, understanding the mechanisms of adaptation, including biological clocks, is crucial for predicting how species, populations, and whole ecosystems will respond to climate change."

- 154 ff: "The Southern Ocean environment experiences rapid change (Abram et al., 2025; Meredith et al., 2019; Thomalla et al., 2023). To assess krill's resilience to environmental changes, understanding the mechanisms that govern daily and seasonal timing in krill is essential."

- 325 ff: "The rhythmic adaptation of krill to its high-latitude environment is key to its success in the Southern Ocean, which in turn represents a cornerstone for the well-being of the whole krill centred ecosystem. To predict krill's resilience to rapid environmental changes, it is essential to understand the mechanisms that govern daily and seasonal timing in krill."

- 597 ff: "A detailed mechanistic understanding of the flexibility of clock-based processes is therefore essential to predict krill resilience in a changing Southern Ocean."

My understanding is that duration of day length is one of the most predictable environmental drivers, and - despite the seasonal changes of day length - nevertheless a very stable one compared to fluctuations of environmental drivers such as temperature or salinity (see, e.g. this recent review on environmental driver fluctuations on nervous system functioning in crustaceans: Stein W, Harzsch S (2021) The Neurobiology of Ocean Change - insights from decapod crustaceans. Zoology: 125887. https://www.sciencedirect.com/science/article/pii/S094420062030146X).

I do not see how global ocean change may significantly change day length, and what this study has to do with understanding this species' resilience against ocean change. I suggest that you explain in more detail why the light day length will change in the future or strongly tone this aspect. Statements such as Line 76 ff: "Due to their disproportionate importance for ecosystem function, understanding the resilience of ecological key species is essential in assessing the fate of ecosystems in the future." are completely out of focus here and, again, trying to oversell the current study.

(2) Uncited essential studies of crustacean neuroanatomy, missing connection to contemporary crustacean neurobiology:

- Line 157: "despite the ecological importance of E. superba, only very little is known about its neurobiology".

- Line 329: "However, so far, little was known about the neurobiology of krill in general."

I agree that this species' brain is understudied, but this makes it even more important to cite the little information that IS available. Please consider this essential reading for any crustacean neurobiologist: "Sandeman, D.C., Scholtz, G., Sandeman, R.E., 1993. Brain evolution in decapod crustacea. J. Exp. Zool. 265, 112-133." to find information on the basic brain anatomy in E. superba.

The manuscript in many places seems to reinvent the wheel and raises the impression that our knowledge of crustacean brain morphology is close to zero. The authors in places seem to operate in a vacuum, and I find it disturbing that in a study on the crustacean brain, very few references are provided to studies on crustacean brain anatomy, such as the following essential book chapter: "Schmidt, M., 2016. Malacostraca. In: Schmidt-Rhaesa, A., Harzsch, S., Purschke, G. (Eds.), Structure & Evolution of Invertebrate Nervous Systems. Oxford University Press, Oxford, pp. 529-582. https://www.researchgate.net/publication/315366157"

In terms of brain anatomy, I would like to know if the authors have a hypothesis on whether and how their target species' brain structure may be similar or different to the brains of other "shrimps" as described, e. g., in the following studies. If so, please elaborate in the introduction:

Krieger J, Hörnig MK, Sandeman RE, Sandeman DC, Harzsch S (2020), Masters of communication: The brain of the banded cleaner shrimp Stenopus hispidus (Olivier, 1811) with an emphasis on sensory processing areas. Journal of Comparative Neurology 528(9): 1561-1587.

Meth R, Wittfoth C, Harzsch S (2017) Brain architecture of the Pacific White Shrimp Penaeus vannamei Boone, 1931 (Malacostraca, Dendrobranchiata): correspondence of brain structure and sensory input? Cell and Tissue Research 369(2): 255-271.

(3) Lacking rigor and command of crustacean brain nomenclature

I suggest that for their brain nomenclature, the authors should rigorously stick to that laid out by Sandeman et al. 1992 (not yet cited in the ms): Sandeman, D.C., Sandeman, R.E., Derby, C.D., Schmidt, M., 1992. Morphology of the brain of crayfish, crabs, and spiny lobsters: a common nomenclature for homologous structures. Biol. Bull. 183, 304-326.

More specifically, in lines 41, 163, 199, 204, 207, and throughout the paper, the authors use the terms "Optic lobes" or "optic lobe neuropils". To the best of my knowledge, "optic lobe" is not a term used in crustacean neuroanatomy at all (as opposed to insects). Lamina, medulla, and lobula are collectively referred to as "visual neuropils" (see Krieger, J., Hörnig, M. K., Sandeman, R. E., Sandeman, D. C., & Harzsch, S. (2020). Masters of communication: The brain of the banded cleaner shrimp Stenopus hispidus (Olivier, 1811) with an emphasis on sensory processing areas. Journal of Comparative Neurology, 528(9), 1561-1587. https://doi.org/10.1002/CNE.24831). The medulla terminalis and mushroom bodies are referred to as "lateral protocerebrum". All afore-mentioned neuropils are summarized as "eyestalk neuropils" (compare nomenclature in Schmidt 2016 as referenced above).

Line 170, 172, 175 ff, and Figure 1. "abdomen", "abdominal ganglia": Contra the book chapter by Siegel 2016 "Introducing Antarctic Krill Euphausia superba Dana, 1850", his Fig. 1.2, the "tail" of crustaceans in most books on crustacean anatomy is not called "abdomen" but instead "pleon"; hence the name "pleopods" for the appendages of the pleon (instead of "abdomipods"). What is more, I suggest using the terms "pleon ganglia" instead of "abdominal ganglia", following the terminology suggested in "Harzsch S, Sandeman D, Chaigneau J (2012) Morphology and development of the central nervous system. In: Forest J and von Vaupel Klein JC (Eds.). Treatise on Zoology - Anatomy, Taxonomy, Biology. The Crustacea Vol. 3. Brill, Leiden pp. 9-236."

Line 174: "thoracic ganglia". In Figure 1, there is a labelling mistake as these ganglia are named "thoracaic ganglia".

Line 176, and throughout the paper: "supraesophageal ganglion". Following the standard nomenclature for crustaceans (see, e. g., Schmidt, M., 2016. Malacostraca. In: Schmidt-Rhaesa, A., Harzsch, S., Purschke, G. (Eds.), Structure & Evolution of Invertebrate Nervous Systems. Oxford University Press, Oxford, pp. 529-582. https://www.researchgate.net/publication/315366157", this structure (as in insects) is typically called a "brain". For terminology, also consult the following nomenclature paper: "Richter, S., Loesel, R., Purschke, G., Schmidt-Rhaesa, A., Scholtz, G., Stach, T., Vogt, L., Wanninger, A., Brenneis, G., Döring, C., Faller, S., Fritsch, M., Grobe, P., Heuer, C. M., Kaul, S., Møller, O. S., Müller, C. H. G., Rieger, V., Rothe, B. H., Stegner, M., Harzsch, S. (2010). Invertebrate neurophylogeny: Suggested terms and definitions for a neuroanatomical glossary. Frontiers in Zoology, 7. https://doi.org/10.1186/1742-9994-7-29".

Line 212, and throughout the paper - hemielliposoid body: please refer to Harzsch Krieger 2011 and the numerous references to studies by Strausfeld cited therein in crustaceans. Strausfeld has provided compelling evidence that the crustacean hemiellipsoid body is equivalent to the insect mushroom body, so this term should be replaced. Harzsch, S., & Krieger, J. (2021). Genealogical relationships of mushroom bodies, hemiellipsoid bodies, and their afferent pathways in the brains of Pancrustacea: Recent progress and open questions. Arthropod Structure & Development, 65, 101100. HYPERLINK "https://doi.org/10.1016/J.ASD.2021.101100" https://doi.org/10.1016/J.ASD.2021.101100.

Legend, figure 2, and others, and throughout the paper: "The olfactory neuropiles comprise the lateral antennal neuropile (LAN, ochre), the olfactory lobes (OL, yellow), and the antennal neuropile (AnN, green)." This is a strange terminological mix that you should urgently revise according to the standard terminology by Sandeman et al. 1992 (as referenced above). The LAN is the lateral antenna 1 neuropil. The AnN is the antenna 2 neuropil. The AnN is NOT deutocerebral but tritocerebral.

Author response:

Reviewer #1 (Public review):

Summary:

Hüppe and colleagues characterized the network of neurons in the central nervous system of Antarctic krill that contained pigment-dispersing hormone (PDH), an important output factor in the circadian clock of insects. These neurons in the brain are putative clock neurons since a subset also expressed the clock genes period and cryptochrome 2. As one of the ocean's major contributors to biomass, krill is an ecologically important marine species that experiences challenging daily and seasonal environmental fluctuations in its high-latitude habitat. A comprehensive study of krill's internal clock may help to understand the extent of its resilience to the rapidly changing climate.

The authors used antibody staining against PDH across the whole central nervous system and additional in situ hybridization for cry2 and per mRNA, with a focus on the supraesophageal ganglion. There, they identified the major neuropils in the eye stalks and central brain of Antarctic krill. The resulting staining pattern aligns with the identified circadian clock network in insects and PDH-expressing networks in other crustaceans, making these neurons highly likely candidates for krill clock neurons.

Strengths:

(1) This study provides the first clues about the circadian clock architecture in a non-model organism in chronobiology, Antarctic krill, with a clear 3D reconstruction of the putative clock network.

(2) The authors effectively place their results within the extensive body of literature on arthropod circadian clock networks to argue that the neurons they describe are likely the circadian clock in krill.

Weaknesses:  

(1) The data presented here are not sufficient to support the claim that the described network is the circadian clock because functional evidence is missing.

(2) Additionally, the study falls short of identifying any elements of the positive limb of the canonical circadian clock transcriptional-translational feedback loop, e.g., clk or cyc, in the PDH-expressing neurons.

(3) No sample sizes are reported, making it difficult for readers to assess the generalizability of the presented data.

We thank the reviewer for recognizing the contribution of this study to advancing our understanding of clock systems in non-traditional model organisms. We acknowledge that definitive functional evidence would require the generation of null mutants of core clock components, which is currently not feasible in this species. In a revised version, we will adjust our claims to more precisely reflect the evidence presented and include sample sizes to allow the reader to better assess the representativeness of the results.

Reviewer #2 (Public review):

Summary:

This study advances our understanding of the neuronal basis of the circadian clock in pancrustaceans. It extends our knowledge on the pigment-dispersing hormone system and provides links to information on the expression of core clock components, cryptochrome 2, and period. The data are sound and well-documented.

Comments:

The neuronal components of the arthropod circadian clock system have been analysed extensively in insects. Much less information on this system is available on malacostraca crustacea crustaceans. However, considering that malacostracan crustaceans and insects go back to a common pancrustacean ancestor and considering that we know that the brain architecture in these two groups shares many commonalities (see, e. g., extensive reviews by N. J. Strausfeld), we have to expect that crustaceans and insects share many of the characteristics of the circadian system. This is the case, e. g., for the network of pigment-dispersing hormone-positive neurons. The authors cite these studies, although late in the paper (discussion, line 339ff), and I suggest to move this info into the introduction: "339 ff: The arborization pattern of the PDH-network has been described in various malacostracan crustaceans, including Carcinus maenas (Alexander et al., 2020; Mangerich & Keller, 1988; Mangerich et al., 1987), Cancer productus (Hsu et al., 2008), Orconectes limosus (de Kleijn et al., 1993; Mangerich & Keller, 1988; Mangerich et al., 1987), Homarus americanus (Harzsch etal., 2009), Cherax destructor, Procambarus clarkii (Sullivan et al., 2009), and Procambarus virginalis (Luna et al., 2010)."

The strength of this paper is that it extends our knowledge on the PDH system and brings together neuroanatomical information on PDH-positive neurons with information on the expression of core clock components, cryptochrome 2, and period. That way, it advances our understanding of the neuronal basis of the circadian clock in pancrustaceans. The data are sound and well documented, and the authors are to be applauded for the superb dissection presented in Figure 1.

Below, please find some essential suggestions on how to further improve the paper.

(1) Framing of the study:

I know that krill is a key element of the Southern Ocean's food webs, but my sense is that discussing the current findings in a context of resilience of this species to global ocean change means largely overselling this study:

Lines 47, 48: "and the resilience of this key species in a rapidly changing Southern Ocean."

Lines 70 ff: "Hence, understanding the mechanisms of adaptation, including biological clocks, is crucial for predicting how species, populations, and whole ecosystems will respond to climate change."

154 ff: "The Southern Ocean environment experiences rapid change (Abram et al., 2025; Meredith et al., 2019; Thomalla et al., 2023). To assess krill's resilience to environmental changes, understanding the mechanisms that govern daily and seasonal timing in krill is essential."

325 ff: "The rhythmic adaptation of krill to its high-latitude environment is key to its success in the Southern Ocean, which in turn represents a cornerstone for the well-being of the whole krill centred ecosystem. To predict krill's resilience to rapid environmental changes, it is essential to understand the mechanisms that govern daily and seasonal timing in krill."

597 ff: "A detailed mechanistic understanding of the flexibility of clock-based processes is therefore essential to predict krill resilience in a changing Southern Ocean."

My understanding is that duration of day length is one of the most predictable environmental drivers, and - despite the seasonal changes of day length - nevertheless a very stable one compared to fluctuations of environmental drivers such as temperature or salinity (see, e.g. this recent review on environmental driver fluctuations on nervous system functioning in crustaceans: Stein W, Harzsch S (2021) The Neurobiology of Ocean Change - insights from decapod crustaceans. Zoology: 125887. https://www.sciencedirect.com/science/article/pii/S094420062030146X).

I do not see how global ocean change may significantly change day length, and what this study has to do with understanding this species' resilience against ocean change. I suggest that you explain in more detail why the light day length will change in the future or strongly tone this aspect. Statements such as Line 76 ff: "Due to their disproportionate importance for ecosystem function, understanding the resilience of ecological key species is essential in assessing the fate of ecosystems in the future." are completely out of focus here and, again, trying to oversell the current study.

(2) Uncited essential studies of crustacean neuroanatomy, missing connection to contemporary crustacean neurobiology:

Line 157: "despite the ecological importance of E. superba, only very little is known about its neurobiology".

Line 329: "However, so far, little was known about the neurobiology of krill in general."

I agree that this species' brain is understudied, but this makes it even more important to cite the little information that IS available. Please consider this essential reading for any crustacean neurobiologist: "Sandeman, D.C., Scholtz, G., Sandeman, R.E., 1993. Brain evolution in decapod crustacea. J. Exp. Zool. 265, 112-133." to find information on the basic brain anatomy in E. superba.

The manuscript in many places seems to reinvent the wheel and raises the impression that our knowledge of crustacean brain morphology is close to zero. The authors in places seem to operate in a vacuum, and I find it disturbing that in a study on the crustacean brain, very few references are provided to studies on crustacean brain anatomy, such as the following essential book chapter: "Schmidt, M., 2016. Malacostraca. In: Schmidt-Rhaesa, A., Harzsch, S., Purschke, G. (Eds.), Structure & Evolution of Invertebrate Nervous Systems. Oxford University Press, Oxford, pp. 529-582. https://www.researchgate.net/publication/315366157"

In terms of brain anatomy, I would like to know if the authors have a hypothesis on whether and how their target species' brain structure may be similar or different to the brains of other "shrimps" as described, e. g., in the following studies. If so, please elaborate in the introduction:

Krieger J, Hörnig MK, Sandeman RE, Sandeman DC, Harzsch S (2020), Masters of communication: The brain of the banded cleaner shrimp Stenopus hispidus (Olivier, 1811) with an emphasis on sensory processing areas. Journal of Comparative Neurology 528(9): 1561-1587.

Meth R, Wittfoth C, Harzsch S (2017) Brain architecture of the Pacific White Shrimp Penaeus vannamei Boone, 1931 (Malacostraca, Dendrobranchiata): correspondence of brain structure and sensory input? Cell and Tissue Research 369(2): 255-271.

(3) Lacking rigor and command of crustacean brain nomenclature

I suggest that for their brain nomenclature, the authors should rigorously stick to that laid out by Sandeman et al. 1992 (not yet cited in the ms): Sandeman, D.C., Sandeman, R.E., Derby, C.D., Schmidt, M., 1992. Morphology of the brain of crayfish, crabs, and spiny lobsters: a common nomenclature for homologous structures. Biol. Bull. 183, 304-326.

More specifically, in lines 41, 163, 199, 204, 207, and throughout the paper, the authors use the terms "Optic lobes" or "optic lobe neuropils". To the best of my knowledge, "optic lobe" is not a term used in crustacean neuroanatomy at all (as opposed to insects). Lamina, medulla, and lobula are collectively referred to as "visual neuropils" (see Krieger, J., Hörnig, M. K., Sandeman, R. E., Sandeman, D. C., & Harzsch, S. (2020). Masters of communication: The brain of the banded cleaner shrimp Stenopus hispidus (Olivier, 1811) with an emphasis on sensory processing areas. Journal of Comparative Neurology, 528(9), 1561-1587. https://doi.org/10.1002/CNE.24831). The medulla terminalis and mushroom bodies are referred to as "lateral protocerebrum". All afore-mentioned neuropils are summarized as "eyestalk neuropils" (compare nomenclature in Schmidt 2016 as referenced above).

Line 170, 172, 175 ff, and Figure 1. "abdomen", "abdominal ganglia": Contra the book chapter by Siegel 2016 "Introducing Antarctic Krill Euphausia superba Dana, 1850", his Fig. 1.2, the "tail" of crustaceans in most books on crustacean anatomy is not called "abdomen" but instead "pleon"; hence the name "pleopods" for the appendages of the pleon (instead of "abdomipods"). What is more, I suggest using the terms "pleon ganglia" instead of "abdominal ganglia", following the terminology suggested in "Harzsch S, Sandeman D, Chaigneau J (2012) Morphology and development of the central nervous system. In: Forest J and von Vaupel Klein JC (Eds.). Treatise on Zoology - Anatomy, Taxonomy, Biology. The Crustacea Vol. 3. Brill, Leiden pp. 9-236."

Line 174: "thoracic ganglia". In Figure 1, there is a labelling mistake as these ganglia are named "thoracaic ganglia".

Line 176, and throughout the paper: "supraesophageal ganglion". Following the standard nomenclature for crustaceans (see, e. g., Schmidt, M., 2016. Malacostraca. In: Schmidt-Rhaesa, A., Harzsch, S., Purschke, G. (Eds.), Structure & Evolution of Invertebrate Nervous Systems. Oxford University Press, Oxford, pp. 529-582. https://www.researchgate.net/publication/315366157", this structure (as in insects) is typically called a "brain". For terminology, also consult the following nomenclature paper: "Richter, S., Loesel, R., Purschke, G., Schmidt-Rhaesa, A., Scholtz, G., Stach, T., Vogt, L., Wanninger, A., Brenneis, G., Döring, C., Faller, S., Fritsch, M., Grobe, P., Heuer, C. M., Kaul, S., Møller, O. S., Müller, C. H. G., Rieger, V., Rothe, B. H., Stegner, M., Harzsch, S. (2010). Invertebrate neurophylogeny: Suggested terms and definitions for a neuroanatomical glossary. Frontiers in Zoology, 7. https://doi.org/10.1186/1742-9994-7-29".

Line 212, and throughout the paper - hemielliposoid body: please refer to Harzsch Krieger 2011 and the numerous references to studies by Strausfeld cited therein in crustaceans. Strausfeld has provided compelling evidence that the crustacean hemiellipsoid body is equivalent to the insect mushroom body, so this term should be replaced. Harzsch, S., & Krieger, J. (2021). Genealogical relationships of mushroom bodies, hemiellipsoid bodies, and their afferent pathways in the brains of Pancrustacea: Recent progress and open questions. Arthropod Structure & Development, 65, 101100. HYPERLINK "https://doi.org/10.1016/J.ASD.2021.101100" https://doi.org/10.1016/J.ASD.2021.101100.

Legend, figure 2, and others, and throughout the paper: "The olfactory neuropiles comprise the lateral antennal neuropile (LAN, ochre), the olfactory lobes (OL, yellow), and the antennal neuropile (AnN, green)." This is a strange terminological mix that you should urgently revise according to the standard terminology by Sandeman et al. 1992 (as referenced above). The LAN is the lateral antenna 1 neuropil. The AnN is the antenna 2 neuropil. The AnN is NOT deutocerebral but tritocerebral.  

We thank the reviewer for acknowledging this paper's contribution to our understanding of the neuronal basis of the circadian clock in Pancrustaceans, as well as for the positive evaluation of the data documentation and presentation.

We would like to clarify that we are aware of the existing body of literature on crustacean neuroanatomy and did not intend to present our data as a first in this field. This study intersects multiple communities (e.g., chronobiology, crustacean neurobiology, krill ecology), and the current focus of the manuscript arose from an attempt to make the paper as accessible to these communities as possible. We acknowledge, however, that the current version falls short in its engagement with the existing literature on crustacean brain anatomy. We therefore thank the reviewer for the input on crustacean neuroanatomy and its nomenclature, which will help us improve the manuscript in these respects. In a revised version, we plan to adjust the framing of the study to more precisely reflect the data presented. This will include better situating the present findings within the existing literature on crustacean neuroanatomy and its specific nomenclature, while toning down the emphasis on ecological importance and implications.

Reviewer #3 (Public review):

Summary:  

A solid and very descriptive study of gene expression of three factors in krill, PDH, per, and cry2 that are important for circadian rhythms in insects. The results reveal optic areas in which PDH colocalises with each or per and cry2, and central brain areas where it does not. The authors speculate on the functional implications of their results for biological rhythms.  

Comments:

This manuscript describes a detailed anatomical study of the brain of krill in a circadian gene expression context. The results are well described, and the work is well done considering the obvious technical/practical difficulties of working with this species. Having stated that, the authors in their Methods write that the animals, after being caught, were placed in constant darkness. Is there any idea at all of when in ZT these brains were processed? Are the representations of gene expression taken at random around the clock? Perhaps the authors might make this explicit somewhere in the ms as it is an important point.

The manuscript focuses mostly on PDH and its overlap or not with per or cry2. I found Figures 5 and 6 particularly confusing. The panels show PDH colocalising (or not-filled or unfilled arrows) with cry2 or with per. What they do not show (to me) is that per and cry2 colocalise. Now, of course, they probably do, but Figure 5 does not show this - or am I misinterpreting it? In Figure 6 again, I cannot see any panels with per and cry2 overlaid. Seems different sections were used for each probe? Is that what 'Areas with high per/ cry2-expression are marked by white arrowheads' means? I see that lines 493 and 494 confirm my suspicions that per/cry were not shown to be colocalised. Perhaps the authors could make this clearer up front than halfway through the Discussion, and clarify this in their legends, which are a little misleading in this respect?

We thank the reviewer for his positive evaluation of our work, acknowledging the difficulty when working with this organism, and for the constructive comments. In a revised version of the manuscript, we will clarify the sampling time in the Methods. We will also state upfront — and in the figure legends — that per and cry2 were assessed on separate sections and their direct co-localization was therefore not demonstrated. However, as both components were independently shown to co-localize with PDH, their spatial overlap is nevertheless suggested by the shared co-localization with PDH. We will make this reasoning explicit earlier in the manuscript to avoid any misleading implications.

eLife Assessment

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

Reviewer #1 (Public review):

Summary:

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

Strengths:

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

Weaknesses:

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

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

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

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

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.

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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.

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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.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

Learn more at Review Commons

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.

Heftige Nutzung von HP5 10/10

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monotony of life

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Inversion of enlightenment understanding of time

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

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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

who gave it? if name available, add it

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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

Resource: University of Wisconsin Madison Genome Editing and Animal Models Core Facility (RRID:SCR_021070)

Curator: @dhovakimyan1

SciCrunch record: RRID:SCR_021070

What is this?

DOI: 10.64898/2026.06.05.730521

Resource: University of Wisconsin Madison Genome Editing and Animal Models Core Facility (RRID:SCR_021070)

Curator: @scibot

SciCrunch record: RRID:SCR_021070

What is this?

DOI: 10.64898/2026.06.04.730003

Resource: (IMSR Cat# JAX_007914,RRID:IMSR_JAX:007914)

Curator: @dhovakimyan1

SciCrunch record: RRID:IMSR_JAX:007914

What is this?

DOI: 10.64898/2026.06.04.730003

Resource: RRID:IMSR_JAX:039524

Curator: @dhovakimyan1

SciCrunch record: RRID:IMSR_JAX:039524

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DOI: 10.64898/2026.06.04.730003

Resource: RRID:Addgene_114472

Curator: @dhovakimyan1

SciCrunch record: RRID:Addgene_114472

What is this?

DOI: 10.64898/2026.06.04.730003

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

Curator: @dhovakimyan1

SciCrunch record: RRID:IMSR_JAX:021881

What is this?

DOI: 10.64898/2026.06.04.729515

Resource: RRID:Addgene_199715

Curator: @dhovakimyan1

SciCrunch record: RRID:Addgene_199715

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DOI: 10.64898/2026.06.04.729515

Resource: RRID:Addgene_8454

Curator: @dhovakimyan1

SciCrunch record: RRID:Addgene_8454

What is this?

DOI: 10.64898/2026.06.03.729416

Resource: RRID:Addgene_104454

Curator: @dhovakimyan1

SciCrunch record: RRID:Addgene_104454

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DOI: 10.64898/2026.06.03.729416

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

Curator: @dhovakimyan1

SciCrunch record: RRID:CVCL_0023

What is this?

DOI: 10.64898/2026.06.03.729374

Resource: RRID:Addgene_207697

Curator: @dhovakimyan1

SciCrunch record: RRID:Addgene_207697

What is this?

DOI: 10.64898/2026.06.03.729374

Resource: RRID:Addgene_127875

Curator: @dhovakimyan1

SciCrunch record: RRID:Addgene_127875

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DOI: 10.64898/2026.06.02.729594

Resource: RRID:IMSR_JAX:000664

Curator: @dhovakimyan1

SciCrunch record: RRID:IMSR_JAX:000664

What is this?

DOI: 10.64898/2026.06.02.729594

Resource: (RCB Cat# RCB2767, RRID:CVCL_0594)

Curator: @dhovakimyan1

SciCrunch record: RRID:CVCL_0594

What is this?

DOI: 10.64898/2026.06.02.729594

Resource: (JCRB Cat# IFO50011, RRID:CVCL_0058)

Curator: @dhovakimyan1

SciCrunch record: RRID:CVCL_0058

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DOI: 10.64898/2026.06.01.725956

Resource: (BioLegend Cat# 100528, RRID:AB_312729)

Curator: @dhovakimyan1

SciCrunch record: RRID:AB_312729

What is this?

DOI: 10.64898/2026.05.23.26353575

Resource: (Abcam Cat# ab5392, RRID:AB_2138153)

Curator: @dhovakimyan1

SciCrunch record: RRID:AB_2138153

What is this?

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

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DOI: 10.3390/ijms27115097

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

Curator: @dhovakimyan1

SciCrunch record: RRID:CVCL_0470

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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

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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

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DOI: 10.1371/journal.pgen.1012107

Resource: (RRID:CVCL_ZD33)

Curator: @evieth

SciCrunch record: RRID:CVCL_ZD33

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Resource: (RRID:CVCL_S891)

Curator: @evieth

SciCrunch record: RRID:CVCL_S891

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DOI: 10.1038/s41388-026-03843-4

Resource: (RRID:CVCL_0063)

Curator: @dhovakimyan1

SciCrunch record: RRID:CVCL_0063

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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

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DOI: 10.1038/s41388-026-03840-7

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

Curator: @dhovakimyan1

SciCrunch record: RRID:CVCL_3509

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DOI: 10.1038/s41388-026-03836-3

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

Curator: @dhovakimyan1

SciCrunch record: RRID:SCR_018063

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DOI: 10.1038/s41388-026-03836-3

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

Curator: @dhovakimyan1

SciCrunch record: RRID:AB_2810983

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DOI: 10.1038/s41388-026-03836-3

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

Curator: @dhovakimyan1

SciCrunch record: RRID:AB_2940948

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DOI: 10.1016/j.spinee.2026.06.009

Resource: RRID:SCR_027821

Curator: @nmaralla

SciCrunch record: RRID:SCR_027821

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DOI: 10.1016/j.neuroscience.2026.06.002

Resource: ImageJ (RRID:SCR_003070)

Curator: @nmaralla

SciCrunch record: RRID:SCR_003070

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DOI: 10.1016/j.neuron.2026.05.015

Resource: ImageJ (RRID:SCR_003070)

Curator: @nmaralla

SciCrunch record: RRID:SCR_003070

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DOI: 10.1016/j.chembiol.2026.05.005

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

Curator: @nmaralla

SciCrunch record: RRID:CVCL_0393

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DOI: 10.1016/j.chembiol.2026.05.005

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

Curator: @nmaralla

SciCrunch record: RRID:CVCL_0022

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DOI: 10.1016/j.celrep.2026.117510

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

Curator: @nmaralla

SciCrunch record: RRID:CVCL_0367

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DOI: 10.1016/j.celrep.2026.117510

Resource: (RRID:CVCL_0063)

Curator: @nmaralla

SciCrunch record: RRID:CVCL_0063

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DOI: 10.1016/j.celrep.2026.117510

Resource: RRID:Addgene_12259

Curator: @nmaralla

SciCrunch record: RRID:Addgene_12259

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DOI: 10.1016/j.cell.2026.05.025

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

Curator: @nmaralla

SciCrunch record: RRID:AB_10679889

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DOI: 10.1016/j.cell.2026.04.047

Resource: RRID:Addgene_158233

Curator: @nmaralla

SciCrunch record: RRID:Addgene_158233

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Resource: RRID:AB_2881544

Curator: @dhovakimyan1

SciCrunch record: RRID:AB_2881544

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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

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DOI: 10.7554/eLife.97632

Resource: GraphPad Prism (RRID:SCR_002798)

Curator: @scibot

SciCrunch record: RRID:SCR_002798

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DOI: 10.7554/eLife.97632

Resource: ImageJ (RRID:SCR_003070)

Curator: @scibot

SciCrunch record: RRID:SCR_003070

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DOI: 10.64898/2026.06.04.730249

Resource: Allen Mouse Brain Atlas (RRID:SCR_002978)

Curator: @scibot

SciCrunch record: RRID:SCR_002978

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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

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DOI: 10.64898/2026.06.04.730242

Resource: RRID:Addgene_12260

Curator: @scibot

SciCrunch record: RRID:Addgene_12260

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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

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DOI: 10.64898/2026.06.04.730242

Resource: RRID:Addgene_62196

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SciCrunch record: RRID:Addgene_62196

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DOI: 10.64898/2026.06.04.730242

Resource: RRID:Addgene_59026

Curator: @scibot

SciCrunch record: RRID:Addgene_59026

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DOI: 10.64898/2026.06.04.730242

Resource: RRID:Addgene_59025

Curator: @scibot

SciCrunch record: RRID:Addgene_59025

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DOI: 10.64898/2026.06.04.730242

Resource: (RRID:CVCL_0063)

Curator: @scibot

SciCrunch record: RRID:CVCL_0063

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DOI: 10.64898/2026.06.04.730242

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

Curator: @scibot

SciCrunch record: RRID:AB_1550038

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DOI: 10.64898/2026.06.04.730242

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

Curator: @scibot

SciCrunch record: RRID:AB_1089246

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DOI: 10.64898/2026.06.04.730242

Resource: (RRID:CVCL_9773)

Curator: @scibot

SciCrunch record: RRID:CVCL_9773

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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

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DOI: 10.64898/2026.06.04.730242

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

Curator: @scibot

SciCrunch record: RRID:AB_228341

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DOI: 10.64898/2026.06.04.730242

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

Curator: @scibot

SciCrunch record: RRID:AB_2716006

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DOI: 10.64898/2026.06.04.730242

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

Curator: @scibot

SciCrunch record: RRID:AB_228307

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DOI: 10.64898/2026.06.04.730242

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

Curator: @scibot

SciCrunch record: RRID:AB_2801452

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DOI: 10.64898/2026.06.04.730242

Resource: (RRID:CVCL_9S58)

Curator: @scibot

SciCrunch record: RRID:CVCL_9S58

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DOI: 10.64898/2026.06.04.730242

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

Curator: @scibot

SciCrunch record: RRID:CVCL_Y803

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DOI: 10.64898/2026.06.04.730242

Resource: RRID:AB_2744331

Curator: @scibot

SciCrunch record: RRID:AB_2744331

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DOI: 10.64898/2026.06.04.730242

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

Curator: @scibot

SciCrunch record: RRID:AB_314615

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DOI: 10.64898/2026.06.04.730242

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

Curator: @scibot

SciCrunch record: RRID:AB_11148943

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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

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DOI: 10.64898/2026.06.04.730242

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

Curator: @scibot

SciCrunch record: RRID:AB_2799122

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DOI: 10.64898/2026.06.04.730242

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

Curator: @scibot

SciCrunch record: RRID:AB_2252226

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DOI: 10.64898/2026.06.04.730242

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

Curator: @scibot

SciCrunch record: RRID:AB_2751644

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DOI: 10.64898/2026.06.04.730242

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

Curator: @scibot

SciCrunch record: RRID:AB_314874

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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

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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

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DOI: 10.64898/2026.06.04.730242

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

Curator: @scibot

SciCrunch record: RRID:AB_2923010

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DOI: 10.64898/2026.06.04.730242

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

Curator: @scibot

SciCrunch record: RRID:AB_2120992

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DOI: 10.64898/2026.06.04.730242

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

Curator: @scibot

SciCrunch record: RRID:AB_10984611

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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

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DOI: 10.64898/2026.06.04.730242

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

Curator: @scibot

SciCrunch record: RRID:AB_528421

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