make this: Ein Virom- oder Pilz-Screening weist NB1 nicht aus. Die

delete

make this: Diese wird

make this: .

delete

make this: Einblick in den eigenen Darm

make this: So kannst du nachvollziehen

make this: .

make this: mit

make this: PDF-Auswertung

at least something worth reading

mllm arch, image-> patchify -> qformer -> condition llm.

eLife Assessment

This useful paper describes a software tool, "DrosoMating", which allows automated, high-throughput quantification of 6 common metrics of courtship and mating behaviors in Drosophila melanogaster. The validity of the tool is convincingly shown to perform as well as expert human assessments, across a wide range of conditions. While it is not suitable yet for posed based classification of individual actions, and its utility with respect to new deep learning-based tools such as DeepLabCut, SLEAP have yet to be assessed, the efficient coarse-grained detection of behavioral states in low resolution videos offered by DrosoMating represents a very helpful addition to analytical tools available for assessing courtship behaviour in Drosophila.

Reviewer #1 (Public review):

Summary:

The study of Drosophila mating behaviors has offered a powerful entry point for understanding how complex innate behaviors are instantiated in the brain. The effectiveness of this behavioral model stems from how readily quantifiable many components of the courtship ritual are, facilitating the fine-scale correlations between the behaviors and the circuits that underpin their implementation. Detailed quantification, however, can be both time consuming and error prone, particularly when scored manually. Song et al. have sought to address this challenge by developing DrosoMating, software that facilitates the automated and high-throughput quantification of 6 common metrics of courtship and mating behaviors. Compared to a human observer, DrosoMating matches courtship scoring with high fidelity. Further, the authors demonstrate that the software effectively detects previously described variations in courtship resulting from genetic background or social conditioning. Finally, they validate its utility in assaying the consequences of neural manipulations by silencing Kenyon cells involved in memory formation in the context of courtship conditioning.

Strengths:

(1) The authors demonstrate that for three key courtship/mating metrics, DrosoMating performs virtually indistinguishably from a human observer, with differences consistently within 10 seconds and no statistically significant differences detected. This demonstrates the software's usefulness as a tool for reducing bias and scoring time for analyses involving these metrics.

(2) The authors validate the tool across multiple genetic backgrounds and experimental manipulations to confirm its ability to detect known influences on male mating behavior.

(3) The authors present a simple, modular chamber design that is integrated with DrosoMating and allows for high throughput experimentation, capable of simultaneously analyzing up to 144 fly pairs across all chambers.

Weaknesses:

(1) DrosoMating appears to be an effective tool for the quantification of key courtship and mating metrics, but similar tools for automated analysis already exist. The authors present a compelling use case for DrosoMating, where it has particular advantages over tools like FlyTracker and Ctrax for high-throughput analysis. This comparative analysis, however, leaves out modern pose-estimation methods (SLEAP, DeepLabCut), better able to tolerate low contrast and occlusion. It therefore remains unclear what specific advantages it might offer over current machine learning approaches.

(2) The courtship behaviors of Drosophila males represent a series of complex behaviors that unfold dynamically in response to female signals. While metrics like courtship latency, courtship index, and mating duration are useful, they compress the complexity of actions that occur throughout the mating ritual. The authors suggest DrosoMating's modular architecture facilitates integration with behavioral classifiers like JAABA. Such integration could substantially expand the utility of this tool for the broader Drosophila neuroscience community, but in its current form its applications are confined to summary timing metrics.

(3) Validation is limited to multiple D. melanogaster strains. Cross-species studies of mating behavior diversity are increasingly common, so demonstrating the tool's accuracy across species would strengthen claims about its broader applicability.

Reviewer #2 (Public review):

This manuscript introduces DrosoMating, an integrated hardware-software pipeline designed to automate quantification of Drosophila courtship and mating behavior. The authors aim to provide a low-cost, scalable alternative to existing behavioral tracking systems, focusing on extracting key temporal metrics including courtship index, copulation latency, and mating duration from high-throughput video recordings.

A major strength of the work is the clear emphasis on experimental scalability and practical usability. The system is designed for multi-chamber recording and performs robustly under low-quality imaging conditions where conventional pose-tracking pipelines often fail. The revised manuscript substantially improves its scientific positioning through the inclusion of systematic benchmarking against widely used tools (Ctrax and FlyTracker), demonstrating that both fail to complete end-to-end analysis under these recording conditions: Ctrax due to segmentation instability and trajectory fragmentation, FlyTracker due to runtime errors during feature computation. A comparative table (Table 1) summarizes key features across tools, and the authors appropriately qualify that these limitations are specific to the low-quality video conditions tested and should not be interpreted as general shortcomings of those tools. The addition of individual-level behavioral ethograms (Figure S3) further strengthens the evidence by allowing direct assessment of the system's temporal resolution at the single-fly level.

The authors also appropriately address statistical concerns raised in review. The re-analysis using ANOVA frameworks - one-way ANOVA with Tukey's HSD for multi-strain comparisons, two-way ANOVA with Sidak's correction for genotype × training interactions - improves the rigor of the behavioral comparisons and supports the revised conclusions regarding strain and learning effects. The addition of locomotor control analyses (Figure S4) further clarifies interpretation of mutant phenotypes by partially disentangling motor from courtship-specific effects, with the revised text appropriately acknowledging contributions from both general hypoactivity and sensory impairments.

A key limitation remains the conceptual scope of the system. DrosoMating is optimized for state-based temporal segmentation rather than fine-grained behavioral annotation or posture-level decomposition. While the authors now clearly acknowledge this and position the tool appropriately, it inherently restricts its use cases compared to modern pose-estimation and classifier-based frameworks. Additionally, the benchmarking comparison is necessarily asymmetric: DrosoMating is tested on low-quality videos where it excels by design, while Ctrax and FlyTracker are evaluated under conditions outside their intended operating range. A comparison under more favorable conditions for the tracking-based tools, or an evaluation of whether modest improvements in video quality would bring conventional pipelines within functional range, would further contextualize the practical boundary between approaches. The comparison also does not include modern deep-learning-based tools (e.g., DeepLabCut, SLEAP), which may be more robust to low-contrast conditions than classical segmentation-based pipelines. These points do not diminish the demonstrated utility of DrosoMating for its intended niche but would help users make more informed decisions about tool selection.

Overall, the revised manuscript presents a well-validated and clearly positioned contribution. It defines the niche in which DrosoMating provides substantial practical value while appropriately delimiting its limitations relative to more general behavioral analysis frameworks.

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

Summary:

The study of Drosophila mating behaviors has offered a powerful entry point for understanding how complex innate behaviors are instantiated in the brain. The effectiveness of this behavioral model stems from how readily quantifiable many components of the courtship ritual are, facilitating the fine-scale correlations between the behaviors and the circuits that underpin their implementation. Detailed quantification, however, can be both time-consuming and error-prone, particularly when scored manually. Song et al. have sought to address this challenge by developing DrosoMating, software that facilitates the automated and high-throughput quantification of 6 common metrics of courtship and mating behaviors. Compared to a human observer, DrosoMating matches courtship scoring with high fidelity. Further, the authors demonstrate that the software effectively detects previously described variations in courtship resulting from genetic background or social conditioning. Finally, they validate its utility in assaying the consequences of neural manipulations by silencing Kenyon cells involved in memory formation in the context of courtship conditioning.

Strengths:

(1) The authors demonstrate that for three key courtship/mating metrics, DrosoMating performs virtually indistinguishably from a human observer, with differences consistently within 10 seconds and no statistically significant differences detected. This demonstrates the software's usefulness as a tool for reducing bias and scoring time for analyses involving these metrics.

(2) The authors validate the tool across multiple genetic backgrounds and experimental manipulations to confirm its ability to detect known influences on male mating behavior.

(3) The authors present a simple, modular chamber design that is integrated with DrosoMating and allows for high-throughput experimentation, capable of simultaneously analyzing up to 144 fly pairs across all chambers.

Weaknesses:

(1) DrosoMating appears to be an effective tool for the high-throughput quantification of key courtship and mating metrics, but a number of similar tools for automated analysis already exist. FlyTracker (CalTech), for instance, is a widely used software that offers a similar machine vision approach to quantifying a variety of courtship metrics. It would be valuable to understand how DrosoMating compares to such approaches and what specific advantages it might offer in terms of accuracy, ease of use, and sensitivity to experimental conditions.

(2) The courtship behaviors of Drosophila males represent a series of complex behaviors that unfold dynamically in response to female signals (Coen et al., 2014; Ning et al., 2022; Roemschied et al., 2023). While metrics like courtship latency, courtship index, and copulation duration are useful summary statistics, they compress the complexity of actions that occur throughout the mating ritual. The manuscript would be strengthened by a discussion of the potential for DrosoMating to capture more of the moment-to-moment behaviors that constitute courtship. Even without modifying the software, it would be useful to see how the data can be used in combination with machine learning classifiers like JAABA to better segment the behavioral composition of courtship and mating across genotypes and experimental manipulations. Such integration could substantially expand the utility of this tool for the broader Drosophila neuroscience community.

(3) While testing the software's capacity to function across strains is useful, it does not address the "universality" of this method. Cross-species studies of mating behavior diversity are becoming increasingly common, and it would be beneficial to know if this tool can maintain its accuracy in Drosophila species with a greater range of morphological and behavioral variation. Demonstrating the software's performance across species would strengthen claims about its broader applicability.

Reviewer #2 (Public review):

This paper introduces "DrosoMating," an integrated hardware and software solution for automating the analysis of male Drosophila courtship. The authors aim to provide a low-cost, accessible alternative to expensive ethological rigs by utilizing a custom acrylic chamber and smartphone-based recording. The system focuses on quantifying key temporal metrics-Courtship Index (CI), Copulation Latency (CL), and Mating Duration (MD)-and is applied to behavioral paradigms involving memory mutants (orb2, rut).

The development of open-source behavioral tools is a significant contribution to neuroethology, and the authors successfully demonstrate a system that simplifies the setup for large-scale screens. A major strength of the work is the specific focus on automating Copulation Latency and Mating Duration, metrics that are often labor-intensive to score manually.

However, there are several limitations in the current analysis and validation that affect the strength of the conclusions:

First, the statistical rigor requires substantial improvement. The analysis of multi-group experiments (e.g., comparing four distinct strains or factorial designs with genotype and training) currently relies on multiple independent Student's t-tests. This approach is statistically invalid for these experimental designs as it inflates the family-wise Type I error rate. To support the claims of strain-specific differences or learning deficits, the data must be analyzed using Analysis of Variance (ANOVA) to properly account for multiple comparisons and to explicitly test for interaction effects between genotype and training conditions.

Second, the biological validation using $w^{1118}$ and $y^1$ mutants entails a potential confound. The authors attribute the low Courtship Index in these strains to courtship-specific deficits. However, both strains are known to exhibit general locomotor sluggishness (due to visual or pigmentation/behavioral defects). Since "following" behavior is likely a component of the Courtship Index, a reduction in this metric could reflect a general motor deficit rather than a specific lack of reproductive motivation. Without controlling for general locomotion, the interpretation of these behavioral phenotypes remains ambiguous.

Third, the benchmarking of the system is currently limited to comparisons against manual scoring. Given that the field has largely adopted sophisticated open-source tracking tools (e.g., Ctrax, FlyTracker, JAABA), the utility of DrosoMating would be better contextualized by comparing its performance - in terms of accuracy, speed, or identity maintenance - against these existing automated standards, rather than solely against human observation.

Finally, the visual presentation of the data hinders the assessment of the system's temporal precision. While the system is designed to capture time-resolved metrics, the results are presented primarily as aggregate bar plots. The absence of behavioral ethograms or raster plots makes it difficult to verify the software's ability to accurately detect specific transitions, such as the exact onset of copulation.

We sincerely thank the reviewers for their constructive and detailed feedback, which has substantially improved the clarity and rigor of our work. Below is a summary of the major revisions.

(1) Comparison with existing tools. We added a new main figure (Figure 5) and Table 1 systematically benchmarking DrosoMating against Ctrax and FlyTracker on identical low-quality, high-throughput mating videos. Both conventional tools failed under our recording conditions: Ctrax showed severe segmentation instability and fragmented trajectories, while FlyTracker frequently crashed during feature computation. These results demonstrate that DrosoMating's state-detection approach bypasses the pose-tracking limitations that impair established pipelines. We also revised the Discussion to clarify that DrosoMating is specialized for robust extraction of mating timing metrics from low-quality videos, not a replacement for general-purpose tracking tools.

(2) Statistical analysis. Following Reviewer #2's recommendation, we re-analyzed Figure 3 using one-way ANOVA with Tukey's HSD for strain comparisons, and Figure 4 using two-way ANOVA with Sidak's post-hoc test for learning assays, including the critical Genotype by Training interaction term.

(3) New supplementary data. We added Figure S4 showing basal locomotor velocity of single-housed males across all four strains to decouple motor defects from courtship deficits in w1118 and y1 mutants. We also added Figure S3 with behavioral ethograms for individual flies to visually demonstrate the system's temporal resolution and detection accuracy.

(4) Additional improvements. We standardized statistical reporting across all figure legends, explicitly defined the segmentation threshold parameter s in the Methods, consistently used "Mating Duration (MD)" throughout, standardized MB247-GAL4 labeling, clarified that occluded frames are retained for mating-state detection via merged-contour analysis, and corrected minor errors including duplicate references and unnecessary quotation marks.

We believe these revisions substantially strengthen the manuscript and clearly position DrosoMating within the existing ecosystem of behavioral analysis tools.

We remain grateful for the valuable feedback from both reviewers and the editorial team, and we hope the revised version meets the standards for publication in eLife.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

(1) It's difficult to assess the utility of this tool in relation to the variety of alternative methods available for the automated scoring of courtship behavior, some of which offer more granular behavioral data than what DrosoMating has been presented to produce. A direct comparison to other approaches (e.g., FlyTracker, DeepLabCut, SLEAP) would be highly valuable for understanding what use cases DrosoMating is best suited for. Ideally, this analysis would include (i) a comparison of the accuracy of scoring courtship metrics and constituent behaviors, (ii) an assessment of the sensitivity of the various software to different experimental conditions, such as lighting and alternative chamber designs, and (iii) testing across Drosophila species, which could help highlight the particular strengths of DrosoMating over more established methods. At a minimum, a table comparing key features, requirements, and capabilities would help position DrosoMating within the existing ecosystem of tools.

We sincerely appreciate the reviewer’s insightful comments on benchmarking DrosoMating against existing tools for automated courtship behavior analysis. We fully agree that direct comparisons are essential to clarify the unique advantages and ideal use cases of DrosoMating. We have extensively revised the manuscript by adding comparative experiments, a new figure（Figure 5) and comparative table (Table1), and expanded descriptions, as detailed below.

(1) Direct comparison with conventional tracking tools

We systematically tested Ctrax and FlyTracker on the same low-quality, high-throughput mating videos used for DrosoMating. The corresponding results are presented in Figure 5.

Ctrax showed severe segmentation instability, including over-segmentation, under-segmentation, and fragmented trajectories, and failed to stably detect two flies.

FlyTracker was able to generate background models and showed partially improved segmentation after threshold adjustment, but frequently crashed during feature computation and could not complete the end-to-end analysis pipeline.

These results confirm that tracking-based tools are vulnerable to low contrast, chamber artifacts, and prolonged male–female overlap, whereas DrosoMating bypasses pose tracking and directly detects mating states.

The revised manuscript text is as follows (line 265-342):

"DrosoMating is more compatible with low-quality mating videos than conventional tracking-based pipelines.

To evaluate whether conventional fly-tracking pipelines could be used as alternative tools for extracting mating-duration metrics, we tested Ctrax and FlyTracker on representative low-quality single-chamber mating videos recorded under our standard high-throughput conditions (Fig. 5). Ctrax is designed to estimate the position and orientation of multiple walking flies while maintaining individual identities over time (Branson et al., 2009) (https://ctrax.sourceforge.net/), whereas FlyTracker aims to track fly pose, including position, orientation, body size, wing and leg positions, and to generate trajectory- and feature-based outputs for downstream behavior analysis (Eyjolfsdottir et al., 2014). Because these programs were not readily compatible with our full high-throughput behavioral recording setup, we first cropped the original videos and tested single-chamber videos containing one male and one female fly.

Using Ctrax, we observed that target detection was highly variable even within single-chamber videos. In representative frames, Ctrax could occasionally identify the two flies correctly (Fig. 5A). However, imperfect segmentation was frequently observed. In some frames, parts of the fly body were detected as additional targets, resulting in over-segmentation and an apparent increase in the number of detected flies (Fig. 5B, C). Conversely, when the male and female were close to each other or physically overlapped, the two animals were sometimes detected as a single target (Fig. 5D). These examples indicate that Ctrax detection was sensitive to the low contrast and overlapping fly bodies present in our mating videos. We then adjusted the Ctrax detection threshold to improve segmentation quality. Although threshold optimization improved detection in some frames, abnormal detections remained evident across randomly sampled frames (Fig. 5E). For example, some frames still showed incorrect target numbers, and a severe segmentation failure was observed in the lower-right example, where the detected objects did not correspond to two clearly separable flies. Thus, even after parameter optimization, Ctrax did not consistently maintain the expected two-target detection state in single-chamber mating videos.

The instability of Ctrax detection was also reflected in the trajectory output. In the first 500 frames, the generated trajectories were fragmented into multiple colored track segments rather than two continuous trajectories corresponding to the male and female (Fig. 5F). In addition, some trajectories extended outside the chamber boundary, indicating tracking errors and identity instability. Consistently, frame-by-frame quantification of detected target number showed that the detected object count did not remain stable at the expected value of two flies per chamber (Fig. 5G). Because Ctrax failed to maintain stable two-fly detection and continuous trajectories under these video conditions, its output could not be reliably used for downstream extraction of copulation latency or mating duration.

We next tested FlyTracker on the same type of cropped single-chamber mating videos. FlyTracker was developed to track multiple flies by estimating body position, orientation, size, wing and leg positions, and by maintaining fly identities across video frames; it also outputs per-frame features such as velocity, facing angle, and wing-angle-related measurements for downstream behavioral analysis (Eyjolfsdottir et al., 2014). In our videos, FlyTracker was able to generate a background model, indicating that the program could recognize the overall imaging field and chamber background (Fig. 5H). However, during calibration and segmentation, the default threshold setting produced inconsistent detection results (Fig. 5I). Although some frames were segmented relatively well under the default threshold (Fig. 5J), these successful examples were not representative of the overall tracking process, and the program frequently terminated with runtime errors during tracking or downstream feature computation. To improve detection stability, we lowered the segmentation threshold. Under this adjusted setting, FlyTracker produced more complete fly masks in representative frames (Fig. 5K), and the diagnostic output showed that two flies were detected in many sampled frames (Fig. 5L). Nevertheless, detection remained unstable in some sampled frames, including frames in which zero or one fly was detected despite the expected two flies per chamber (Fig. 5L). The full pipeline ultimately failed during feature computation, producing a runtime error before complete tracking and feature outputs could be generated (Fig. 5M). Therefore, even after threshold adjustment, FlyTracker could not provide a stable end-to-end workflow for extracting mating-duration metrics from these low-quality mating videos.

This failure mode is relevant because FlyTracker depends on stable segmentation, identity maintenance, and per-frame feature extraction. In our assay videos, low contrast, chamber-edge artifacts, and prolonged male–female overlap during copulation interfered with these requirements. As a result, FlyTracker could occasionally identify the flies in individual frames, but it did not reliably complete the full analysis pipeline required for downstream behavioral quantification. This limitation is especially important for workflows such as JAABA, which use manually labeled examples to train behavior classifiers but still depend on upstream tracking-derived features. Thus, for our low-quality high-throughput mating recordings, FlyTracker-based analysis was substantially less practical than DrosoMating, which directly outputs mating-related timing metrics without requiring continuous high-fidelity two-fly pose tracking."

(2) Evaluation of accuracy, robustness, and cross-species potential

We addressed the three key points requested by the reviewer:

(i) Accuracy: DrosoMating achieved 98–99% agreement with manual scoring. Under our experimental conditions, neither Ctrax nor FlyTracker completed an end-to-end workflow capable of reliably extracting copulation latency and mating duration. Ctrax produced fragmented trajectories with unstable target counts, and FlyTracker terminated with runtime errors during feature computation.

(ii) Robustness: DrosoMating is highly robust to low-contrast lighting and common behavioral chamber setups, whereas conventional tools require high-quality videos and fail under fly occlusion.

(iii) Cross-species testing: We did not perform cross-species validation in this revision. DrosoMating relies on mating state detection rather than species-specific morphology, suggesting potential transferability, although this remains to be experimentally validated. We have therefore revised the text to avoid claiming demonstrated cross-species performance and now describe this as a potential future application.

The revised manuscript text is as follows (line 572-576):

"Cross-species testing was not performed in this study. Although DrosoMating's state-detection approach is morphology-agnostic and therefore theoretically applicable across Drosophila species, we have revised the text to avoid claiming demonstrated cross-species performance. Formal validation across diverse species remains a promising future direction."

We added Table1 to compare key features across DrosoMating, Ctrax, and FlyTracker, including low-quality video compatibility, multi-chamber support, robustness to fly overlap, direct output of mating timing metrics, accuracy.

(3) Clarification of ideal use cases

We revised the Discussion to emphasize that DrosoMating is not intended to replace general-purpose pose or tracking tools (e.g., FlyTracker, Ctrax) that provide fine-grained behavioral data.

Instead, it offers a specialized, robust, and high-throughput workflow for scenarios requiring efficient extraction of mating timing metrics from low-quality videos, where conventional pipelines often fail.

These revisions greatly improve the clarity and positioning of DrosoMating. We thank the reviewer for this valuable suggestion.

The revised manuscript text is as follows (line 578-620):

“Comparative Evaluation with Existing Courtship Analysis Tools

A major advantage of DrosoMating is its compatibility with low-quality, high-throughput mating videos. Conventional tracking-based tools such as Ctrax and FlyTracker are powerful for trajectory- and pose-based behavioral analysis, but they generally require stable object segmentation, identity maintenance, and reliable feature extraction across frames. Ctrax was designed to estimate the positions and orientations of multiple walking flies while maintaining their identities, whereas FlyTracker aims to track detailed fly pose and generate per-frame behavioral features. Under our recording conditions, these requirements were difficult to satisfy because the videos were low contrast and the male and female frequently overlapped during copulation.

In our tests, both tools showed limited compatibility with these videos. Even after cropping to single-chamber videos and adjusting detection parameters, Ctrax produced unstable target numbers, fragmented trajectories, and tracking errors. FlyTracker could generate a background model and occasionally segment flies successfully, but detection remained unstable and the full pipeline failed during feature computation. These issues are particularly relevant for copulation latency and mating duration analysis: during copulation, the male and female remain physically coupled for a long period, which makes identity-based tracking difficult. For these timing metrics, it is more important to robustly detect the onset and offset of the mating state than to reconstruct detailed individual trajectories. DrosoMating was purpose-built to address these specific challenges. It operates reliably on lower-quality video streams, requires no complex pre-processing or manual ROI definition, and is optimized for high-throughput multi-chamber analysis. While it does not offer the same level of pose or kinematic detail as other tools, it provides a unique solution for laboratories seeking a simple, fast, and robust pipeline to quantify core reproductive timing metrics—copulation latency (CL), courtship index (CI), and mating duration (MD)—without the overhead of more complex systems (Table 1).

Although we directly tested only Ctrax and FlyTracker, this limitation may also affect workflows that depend on upstream tracking-derived features. For example, JAABA uses tracking-derived features to train behavior classifiers, and DANCE, a recent Drosophila aggression and courtship pipeline, uses JAABA-based classifiers and lists FlyTracker and JAABA as required software. Thus, our conclusion is not that these tools are generally unsuitable for Drosophila behavior analysis, but that DrosoMating provides a more practical workflow for low-quality, high-throughput videos focused specifically on mating timing.

While DrosoMating currently prioritizes mating timing metrics over discrete behavioral classification, its modular architecture provides a foundation for future integration with behavior classifiers such as JAABA. Laboratories requiring granular behavioral elements—such as wing extension or circling—would benefit from an extended pipeline that exports per-frame kinematic features for downstream classifier training. Validating this integration represents a promising future direction to broaden the tool’s utility beyond core reproductive timing assays.”

We have also added this clarification to the Table 1 legend to avoid overgeneralizing the limitations of Ctrax and FlyTracker (line 786-790):

"DrosoMating was designed to extract mating-related timing metrics from high-throughput videos without requiring continuous two-fly identity tracking. Ctrax and FlyTracker were tested on cropped single-chamber videos from the same recording setup. Their limitations described here refer specifically to these low-quality mating videos and should not be interpreted as general limitations of the tools."

(2) The coarse nature of the metrics captured by DrosoMating may limit the usefulness of this tool for many researchers. Consider integrating DrosoMating with one or more behavioral classifiers (e.g., JAABA) and validating its performance to increase its utility across a wider range of uses. Demonstrating the feasibility of this would substantially increase the tool's appeal to those who need access to more discrete behavioral elements.

We thank the reviewer for this constructive suggestion. We agree that DrosoMating is currently optimized for rapid, high-throughput quantification of core mating timing metrics (CL, CI, and MD) rather than discrete behavioral classification (e.g., wing extension, circling, or aggressive postures). This reflects a deliberate design trade-off: by prioritizing robust state detection over detailed pose tracking, DrosoMating achieves reliable performance on low-quality videos where conventional identity-based pipelines fail.

We appreciate the reviewer’s vision for expanding the tool’s utility. While DrosoMating does not presently generate the per-frame kinematic features (position, orientation, wing angles, etc.) required as input for JAABA classifiers, its modular architecture and underlying video-processing framework provide a foundation for future integration. In the revised Discussion, we have clarified that extending DrosoMating to export trajectory-derived features compatible with downstream classifiers such as JAABA represents a promising future direction—one that would broaden its applicability to laboratories requiring granular behavioral elements, without necessitating a complete overhaul of the existing pipeline.

We hope this clarification addresses the reviewer’s concern and accurately reflects both the current capabilities and future potential of the tool.

The revised manuscript text is as follows (line 614-620):

"While DrosoMating currently prioritizes mating timing metrics over discrete behavioral classification, its modular architecture provides a foundation for future integration with behavior classifiers such as JAABA. Laboratories requiring granular behavioral elements—such as wing extension or circling—would benefit from an extended pipeline that exports per-frame kinematic features for downstream classifier training. Validating this integration represents a promising future direction to broaden the tool’s utility beyond core reproductive timing assays."

(3) Please clarify how DrosoMating handles fly identification during mating? I would think mounted flies would be occluded, and if these frames are excluded from analysis, it would be expected to skew mating duration scores. It would be helpful if the authors could discuss these details.

Occluded frames are excluded from CI calculation because individual courtship actions cannot be reliably assigned during prolonged overlap. However, these frames are not discarded from mating-duration analysis. Instead, prolonged merged contours are used as evidence for copulation-state detection, and the MD timer continues until physical separation:

(1) When two flies are separate, the system detects two contours; when they overlap during mounting, it detects one merged contour. Our code processes both cases, so occluded frames are retained, not skipped.

(2) To distinguish a single fly from two overlapping flies, we analyze the shape of the merged contour. Two overlapping flies produce a characteristically different aspect ratio (elongated shape) compared to one fly. This geometric cue is fed into the state classifier to label the frame as "mating."

(3) Because these frames are classified as mating rather than excluded, the mating duration timer runs continuously through the occlusion period. Thus, mating duration is not artificially shortened.

The high agreement between DrosoMating and manual scoring for mating duration (within 10 s, no significant difference) confirms that this approach does not introduce bias.

We revised the manuscript as follow (line 191-193):

"Occluded frames are excluded from CI calculation but retained for mating-state detection via merged-contour analysis, ensuring continuous MD measurement through copulation."

(4) Please indicate the statistical tests used in Figures 3, 4, and S1. What methods were used to address multiple hypothesis testing?

We thank the reviewer for this important comment. For comparisons between two groups, two-sided Student’s t-tests were used. For comparisons among three or more groups, one-way ANOVA followed by post-hoc tests were applied. For two-factor experimental designs, two-way ANOVA was used. We have clearly stated these statistical tests in the Statistical Analysis section and have added this information to the figure legends for Figures 3, 4, and S1 in the revised manuscript.

The revised "Statistical Analysis" section is as follows (line 512-524):

"To ensure robust statistical analysis, each experimental group included at least 100 male flies (naïve, sexually experienced, or singly reared). Internal controls were incorporated into every experiment as recommended by Bretman et al. (2011) (Bretman et al., 2011). Normality of the mating duration data was confirmed using the Kolmogorov-Smirnov test (p>0.05). For group comparisons, two-sided Student’s t-tests were applied to calculate significance levels (****p<0.0001, ***p<0.001, **p<0.01, * p<0.05), Comparisons among three or more groups were performed using one-way ANOVA with Tukey’s HSD post-hoc tests. Two-factor experimental comparisons were performed using two-way ANOVA followed by Sidak’s post-hoc test. while estimation statistics (Claridge-Chang and Assam, 2016) were used to visualize effect sizes, mean differences, and precision, avoiding reliance solely on null hypothesis testing. All analyses, including data plotting, were performed using GraphPad Prism software."

(5) Line 43: "High resolution video tracking enables...".

We thank the reviewer for noting this imprecise expression. We have revised the statement to accurately describe that our method uses image-based video analysis to identify courtship and copulation events and quantify their temporal parameters. The description has been corrected in the revised manuscript line 44-46:

"Our image-based video analysis enables precise identification of courtship and copulation events, as well as quantification of their timing and duration under controlled conditions. "

(6) Line 51: remove quotation mark.

We thank the reviewer for the careful correction. The unnecessary quotation mark at Line 51 has been removed in the revised manuscript.

(7) Line 107: Chen et al, 2024 is listed twice in the references.

We thank the reviewer for the careful correction. The duplicate reference of Chen et al., 2024 has been removed from the reference list in the revised manuscript.

(8) Line 242: remove quotation mark.

We thank the reviewer for the careful correction. The unnecessary quotation mark at Line 242 has been removed in the revised manuscript.

Reviewer #2 (Recommendations for the authors):

(1) Please re-analyze the data in Figures 3 and 4 using ANOVA followed by appropriate post-hoc tests (e.g., Tukey's HSD). Specifically, use a One-way ANOVA for strain comparisons in Figure 3 and a Two-way ANOVA for the learning assays in Figure 4. The interaction term (Genotype $\times$ Training) is critical for demonstrating specific learning deficits. Update the "Statistical Analysis" section and figure legends accordingly.

We appreciate the reviewer’s recommendation. We have re-analyzed the data in Figures 3 and 4 using the suggested ANOVA approaches: one-way ANOVA with Tukey’s HSD for strain comparisons (Figure 3), and two-way ANOVA (including the Genotype × Training interaction term) with Sidak's post-hoc test for learning assays (Figure 4). The updated statistical methods are now described in the Statistical Analysis section and figure legends.

The revised "Statistical Analysis" section is as follows (line 512-524):

"To ensure robust statistical analysis, each experimental group included at least 100 male flies (naïve, sexually experienced, or singly reared). Internal controls were incorporated into every experiment as recommended by Bretman et al. (2011) (Bretman et al., 2011). Normality of the mating duration data was confirmed using the Kolmogorov-Smirnov test (p>0.05). For group comparisons, two-sided Student’s t-tests were applied to calculate significance levels (****p<0.0001, ***p<0.001, **p<0.01, * p<0.05), Comparisons among three or more groups were performed using one-way ANOVA with Tukey’s HSD post-hoc tests. Two-factor experimental comparisons were performed using two-way ANOVA followed by Sidak’s post-hoc test. while estimation statistics (Claridge-Chang and Assam, 2016) were used to visualize effect sizes, mean differences, and precision, avoiding reliance solely on null hypothesis testing. All analyses, including data plotting, were performed using GraphPad Prism software."

(2) To decouple motor defects from courtship deficits in $w^{1118}$ and $y^1$ mutants, please use your tracking data to calculate and present a "General Locomotion" metric (e.g., average velocity or total distance traveled in the absence of a female).

We thank the reviewer for this suggestion. We have now added Figure S4 showing basal locomotor velocity of single-housed males across all four strains. As expected, w^1118 and y^1 mutants move more slowly than wild-type controls.

These data reveal that both motor and sensory factors contribute to the observed courtship phenotypes. Reduced basal locomotion likely limits the males' ability to approach and follow females. However, this generalized hypoactivity is compounded by strain-specific sensory deficits: w¹¹¹⁸ males suffer visual impairment that compromises female detection (Krstic et al., 2013), while y¹ males display altered cuticular hydrocarbons that disrupt pheromonal communication (Drapeau et al., 2006). These sensory defects impair courtship initiation and female recognition independent of locomotor capacity. Thus, the reduced CI, CL, and MD in these mutants reflect the combined effects of slower movement and courtship-specific sensory impairments, rather than motor defects alone.

We have revised the manuscript to incorporate the velocity data and clarify this interpretation (line 219-226):

"Notably, reduced basal locomotor activity in w¹¹¹⁸ and y¹ mutants has been well documented in previous studies, independent of courtship behavior (Drapeau et al., 2006; Krstic et al., 2013). Consistent with these reports, our tracking data show that single-housed w¹¹¹⁸ and y¹ males exhibit lower average velocity than Canton-S and Oregon-R controls (Fig. S4A). These general locomotor differences are insufficient to fully explain the observed courtship and mating timing phenotypes, indicating that additional courtship‑related processes contribute to the observed behavioral differences."

(3) Please expand the discussion or provide a small comparative dataset contrasting DrosoMating with established tools like JAABA. Explain the specific advantages of your pipeline (e.g., cost, simplicity, focus on CL/MD) to justify its adoption over these alternatives.

We sincerely appreciate the reviewer’s insightful comments on benchmarking DrosoMating against existing tools for automated courtship behavior analysis. We fully agree that direct comparisons are essential to clarify the unique advantages and ideal use cases of DrosoMating. We have extensively revised the manuscript by adding comparative experiments, a new figure (Figure 5) and comparative table (Table 1), and expanded descriptions, as detailed below.

(1) Direct comparison with conventional tracking tools

We systematically tested Ctrax and FlyTracker on the same low-quality, high-throughput mating videos used for DrosoMating. The corresponding results are presented in Figure 5.

Ctrax showed severe segmentation instability, including over-segmentation, under-segmentation, and fragmented trajectories, and failed to stably detect two flies.

FlyTracker was able to generate background models and showed partially improved segmentation after threshold adjustment, but frequently crashed during feature computation and could not complete the end-to-end analysis pipeline.

These results confirm that tracking-based tools are vulnerable to low contrast, chamber artifacts, and prolonged male–female overlap, whereas DrosoMating bypasses pose tracking and directly detects mating states.

The revised manuscript text is as follows (line 265-342):

" DrosoMating is more compatible with low-quality mating videos than conventional tracking-based pipelines

To evaluate whether conventional fly-tracking pipelines could be used as alternative tools for extracting mating-duration metrics, we tested Ctrax and FlyTracker on representative low-quality single-chamber mating videos recorded under our standard high-throughput conditions (Fig. 5). Ctrax is designed to estimate the position and orientation of multiple walking flies while maintaining individual identities over time (Branson et al., 2009) (https://ctrax.sourceforge.net/), whereas FlyTracker aims to track fly pose, including position, orientation, body size, wing and leg positions, and to generate trajectory- and feature-based outputs for downstream behavior analysis (Eyjolfsdottir et al., 2014). Because these programs were not readily compatible with our full high-throughput behavioral recording setup, we first cropped the original videos and tested single-chamber videos containing one male and one female fly.

Using Ctrax, we observed that target detection was highly variable even within single-chamber videos. In representative frames, Ctrax could occasionally identify the two flies correctly (Fig. 5A). However, imperfect segmentation was frequently observed. In some frames, parts of the fly body were detected as additional targets, resulting in over-segmentation and an apparent increase in the number of detected flies (Fig. 5B, C). Conversely, when the male and female were close to each other or physically overlapped, the two animals were sometimes detected as a single target (Fig. 5D). These examples indicate that Ctrax detection was sensitive to the low contrast and overlapping fly bodies present in our mating videos. We then adjusted the Ctrax detection threshold to improve segmentation quality. Although threshold optimization improved detection in some frames, abnormal detections remained evident across randomly sampled frames (Fig. 5E). For example, some frames still showed incorrect target numbers, and a severe segmentation failure was observed in the lower-right example, where the detected objects did not correspond to two clearly separable flies. Thus, even after parameter optimization, Ctrax did not consistently maintain the expected two-target detection state in single-chamber mating videos.

The instability of Ctrax detection was also reflected in the trajectory output. In the first 500 frames, the generated trajectories were fragmented into multiple colored track segments rather than two continuous trajectories corresponding to the male and female (Fig. 5F). In addition, some trajectories extended outside the chamber boundary, indicating tracking errors and identity instability. Consistently, frame-by-frame quantification of detected target number showed that the detected object count did not remain stable at the expected value of two flies per chamber (Fig. 5G). Because Ctrax failed to maintain stable two-fly detection and continuous trajectories under these video conditions, its output could not be reliably used for downstream extraction of copulation latency or mating duration.

We next tested FlyTracker on the same type of cropped single-chamber mating videos. FlyTracker was developed to track multiple flies by estimating body position, orientation, size, wing and leg positions, and by maintaining fly identities across video frames; it also outputs per-frame features such as velocity, facing angle, and wing-angle-related measurements for downstream behavioral analysis (Eyjolfsdottir et al., 2014). In our videos, FlyTracker was able to generate a background model, indicating that the program could recognize the overall imaging field and chamber background (Fig. 5H). However, during calibration and segmentation, the default threshold setting produced inconsistent detection results (Fig. 5I). Although some frames were segmented relatively well under the default threshold (Fig. 5J), these successful examples were not representative of the overall tracking process, and the program frequently terminated with runtime errors during tracking or downstream feature computation. To improve detection stability, we lowered the segmentation threshold. Under this adjusted setting, FlyTracker produced more complete fly masks in representative frames (Fig. 5K), and the diagnostic output showed that two flies were detected in many sampled frames (Fig. 5L). Nevertheless, detection remained unstable in some sampled frames, including frames in which zero or one fly was detected despite the expected two flies per chamber (Fig. 5L). The full pipeline ultimately failed during feature computation, producing a runtime error before complete tracking and feature outputs could be generated (Fig. 5M). Therefore, even after threshold adjustment, FlyTracker could not provide a stable end-to-end workflow for extracting mating-duration metrics from these low-quality mating videos.

This failure mode is relevant because FlyTracker depends on stable segmentation, identity maintenance, and per-frame feature extraction. In our assay videos, low contrast, chamber-edge artifacts, and prolonged male–female overlap during copulation interfered with these requirements. As a result, FlyTracker could occasionally identify the flies in individual frames, but it did not reliably complete the full analysis pipeline required for downstream behavioral quantification. This limitation is especially important for workflows such as JAABA, which use manually labeled examples to train behavior classifiers but still depend on upstream tracking-derived features. Thus, for our low-quality high-throughput mating recordings, FlyTracker-based analysis was substantially less practical than DrosoMating, which directly outputs mating-related timing metrics without requiring continuous high-fidelity two-fly pose tracking."

(2) Clarification of ideal use cases

We revised the Discussion to emphasize that DrosoMating is not intended to replace general-purpose pose or tracking tools (e.g., FlyTracker, Ctrax) that provide fine-grained behavioral data.

Instead, it offers a specialized, robust, and high-throughput workflow for scenarios requiring efficient extraction of mating timing metrics from low-quality videos, where conventional pipelines often fail.

These revisions greatly improve the clarity and positioning of DrosoMating. We thank the reviewer for this valuable suggestion.

The revised manuscript text is as follows (line 578-620):

“Comparative Evaluation with Existing Courtship Analysis Tools

A major advantage of DrosoMating is its compatibility with low-quality, high-throughput mating videos. Conventional tracking-based tools such as Ctrax and FlyTracker are powerful for trajectory- and pose-based behavioral analysis, but they generally require stable object segmentation, identity maintenance, and reliable feature extraction across frames. Ctrax was designed to estimate the positions and orientations of multiple walking flies while maintaining their identities, whereas FlyTracker aims to track detailed fly pose and generate per-frame behavioural features. Under our recording conditions, these requirements were difficult to satisfy because the videos were low contrast and the male and female frequently overlapped during copulation.

In our tests, both tools showed limited compatibility with these videos. Even after cropping to single-chamber videos and adjusting detection parameters, Ctrax produced unstable target numbers, fragmented trajectories, and tracking errors. FlyTracker could generate a background model and occasionally segment flies successfully, but detection remained unstable and the full pipeline failed during feature computation. These issues are particularly relevant for copulation latency and mating duration analysis: during copulation, the male and female remain physically coupled for a long period, which makes identity-based tracking difficult. For these timing metrics, it is more important to robustly detect the onset and offset of the mating state than to reconstruct detailed individual trajectories. DrosoMating was purpose-built to address these specific challenges. It operates reliably on lower-quality video streams, requires no complex pre-processing or manual ROI definition, and is optimized for high-throughput multi-chamber analysis. While it does not offer the same level of pose or kinematic detail as other tools, it provides a unique solution for laboratories seeking a simple, fast, and robust pipeline to quantify core reproductive timing metrics—copulation latency (CL), courtship index (CI), and mating duration (MD)—without the overhead of more complex systems (Table 1).

Although we directly tested only Ctrax and FlyTracker, this limitation may also affect workflows that depend on upstream tracking-derived features. For example, JAABA uses tracking-derived features to train behavior classifiers, and DANCE, a recent Drosophila aggression and courtship pipeline, uses JAABA-based classifiers and lists FlyTracker and JAABA as required software. Thus, our conclusion is not that these tools are generally unsuitable for Drosophila behavior analysis, but that DrosoMating provides a more practical workflow for low-quality, high-throughput videos focused specifically on mating timing.

While DrosoMating currently prioritizes mating timing metrics over discrete behavioral classification, its modular architecture provides a foundation for future integration with behavior classifiers such as JAABA. Laboratories requiring granular behavioral elements—such as wing extension or circling—would benefit from an extended pipeline that exports per-frame kinematic features for downstream classifier training. Validating this integration represents a promising future direction to broaden the tool’s utility beyond core reproductive timing assays.”

We have added this clarification to the Table 1 legend to avoid overgeneralizing the limitations of Ctrax and FlyTracker (line 786-790):

"DrosoMating was designed to extract mating-related timing metrics from high-throughput videos without requiring continuous two-fly identity tracking. Ctrax and FlyTracker were tested on cropped single-chamber videos from the same recording setup. Their limitations described here refer specifically to these low-quality mating videos and should not be interpreted as general limitations of the tools."

(4) Complement the aggregate bar plots with behavioral ethograms or raster plots for representative individual flies. Color-code these plots for specific states (resting, following, courting, copulating) to visually demonstrate the system's temporal resolution and detection accuracy.

We thank the reviewer for this valuable suggestion. To address this comment, we have added a new supplementary figure (Figure S3) that presents behavioral ethograms for individual flies, directly complementing the aggregate bar plots in the main text. The figure displays the full temporal progression of courtship and copulation behaviors for all wells that exhibited successful mating. As requested, behaviors are color-coded (orange: courting, red: copulating) to clearly delineate different states. These ethograms visually demonstrate the system’s ability to resolve behavioral transitions with high temporal precision, confirming the accuracy of our automated detection of courtship initiation, duration, and copulation events. This addition provides critical individual-level validation that supports the aggregate statistical results presented in the main text.

(5) Standardize the use of estimation statistics (DBMs). If used in Figure 3, they should also be applied to Figure 4, with appropriate statistical comparisons between groups.

We thank the reviewer for this suggestion. We have standardized the statistical reporting format across all figure legends, with each legend explicitly stating the test used, the post hoc method (where applicable), and significance thresholds.

Our approach is as follows:

Fig. 2 and Fig.3D-I (two-group comparison, manual vs. automated scoring): DBM + Student's t-test.

Fig. 3A-C and Fig. S1B-C, E-F (multi-group comparison, 4 strains or 2 rearing conditions): One-way ANOVA + Tukey's HSD.

Fig. 4B-D and F (two-factor design, genotype × training): Two-way ANOVA + Sidak's post hoc.

We have also added a sentence to the Methods clarifying that estimation statistics (DBM) are used for single two-group contrasts, while ANOVA-based approaches are used for multi-group or multi-factor designs. The statistical methods are now consistently documented in both the Methods section and the corresponding figure legends. We hope this clarification addresses the reviewer's concern.

The revised "Statistical Analysis" section is as follows (line 512-524):

"To ensure robust statistical analysis, each experimental group included at least 100 male flies (naïve, sexually experienced, or singly reared). Internal controls were incorporated into every experiment as recommended by Bretman et al. (2011) (Bretman et al., 2011). Normality of the mating duration data was confirmed using the Kolmogorov-Smirnov test (p>0.05). For group comparisons, two-sided Student’s t-tests were applied to calculate significance levels (****p<0.0001, ***p<0.001, **p<0.01, * p<0.05), Comparisons among three or more groups were performed using one-way ANOVA with Tukey’s HSD post-hoc tests. Two-factor experimental comparisons were performed using two-way ANOVA followed by Sidak’s post-hoc test. while estimation statistics (Claridge-Chang and Assam, 2016) were used to visualize effect sizes, mean differences, and precision, avoiding reliance solely on null hypothesis testing. All analyses, including data plotting, were performed using GraphPad Prism software."

(6) Fix inconsistent labeling (e.g., "MB-247" vs. "MB247") and redundant axis labels.

Thank you for pointing this out. We have now standardized all labels to "MB247-GAL4" throughout the text and figures.

We have also removed redundant axis labels from multi-panel figures. And redundant DBM and metric labels have been streamlined in Figures 2 and 4. All statistical tests and metric definitions are now fully described in the corresponding figure legends rather than being repeated on each sub-panel.

(7) Significantly increase the size of data panels to make individual data points and error bars legible.

Thank you for this suggestion. We have standardized the figure formatting and simplified the panels by removing redundant axis labels and consolidating descriptive details into the figure legends. We believe the current panel sizes, combined with these clarifications, provide sufficient legibility for both data points and error bars in the final high-resolution PDF.

Minor Corrections:

(1) Abstract: Rephrase "lack of certain timing-related behavioral repertoires" (Line 108) to "lack of precise quantification for temporal parameters of post-copulatory behavior."

Thank you for the suggested rephrasing. We have updated the sentence accordingly.

(2) Define the physical parameter "s" (e.g., is it a pixel threshold?) to ensure reproducibility.

Thank you for this important suggestion. We have now explicitly defined the parameter s in the Methods section. Briefly, s is the grayscale intensity threshold (0–255, 8-bit) used for binary segmentation of flies from the background. It is automatically calculated as the maximum grayscale value of three user-selected reference flies plus an offset of 28, and can be manually adjusted to accommodate varying illumination conditions.

The revised manuscript text is as follows (line 499-503):

“Note on the s value: The parameter s represents the grayscale intensity threshold (range: 0–255 for 8-bit images) used for binary segmentation of flies from the background. It is automatically calculated as the maximum grayscale value at the three reference fly positions plus an offset of 28, and can be manually adjusted to accommodate varying illumination conditions.”

(3) Figure 1 Legend: Clarify the "clockwise selection" of pillars and their relation to well numbering.

Thank you for this suggestion. We have revised the Figure 1 legend to clarify that:

(1) The four pillars are selected in clockwise order starting from the top-left corner to define the chamber corners for perspective transformation (homography), which corrects for camera angle and standardizes the field of view.

(2) Well numbering is independent of pillar selection order. After automated perspective correction, wells are numbered sequentially in a left-to-right, top-to-bottom order (1–36) based on the standardized chamber layout.

The updated Figure 1 legend now reads:

"Columns (pillars) should be selected in clockwise order starting from the top-left corner to define the chamber boundaries for perspective transformation (Fig. 1A, lower). Well numbering (1–36) follows a left-to-right, top-to-bottom sequence after perspective correction and is independent of pillar selection order."

(4) Add the citation for Eastwood and Burnet (1977) regarding Courtship Latency.

Thank you for pointing this out. We have added the citation Eastwood and Burnet (1977) to the Introduction where Courtship Latency is first defined

(5) Select one term ("Mating Duration" or "Copulation Duration") and use it consistently throughout the text and figures.

Thank you for this suggestion. We have now standardized the terminology throughout the manuscript and figures. "Mating Duration" (MD) is used consistently in all instances where "Copulation Duration" previously appeared. The abbreviation MD has been retained for consistency with existing figure labels

References

Branson, K., Robie, A.A., Bender, J., Perona, P., & Dickinson, M.H. (2009). High-throughput ethomics in large groups of Drosophila. Nature Methods, 6(6), 451–458.

Claridge-Chang, A., & Assam, P.N. (2016). Estimation statistics should replace significance testing. Nature Methods, 13(2), 108–109.

Drapeau, M.D., Cyran, S.A., Viering, M.M., Geyer, P.K., & Long, A.D. (2006). A cis-regulatory sequence within the yellow locus of Drosophila melanogaster required for normal male mating success. Genetics, 172(2), 1009–1030.

Eastwood, L., & Burnet, B. (1977). Courtship latency in male Drosophila melanogaster. Behavior Genetics, 7(3), 359–372.

Eyjolfsdottir, E., Branson, S., Burgos-Artizzu, X.P., Hoopfer, E.D., Schor, J., Anderson, D.J., & Perona, P. (2014). Detecting social actions of fruit flies. Lecture Notes in Computer Science, 8692, 772–787.

Gil-Martí, B., Barredo, C.G., Pina-Flores, S., Poza-Rodriguez, A., Treves, G., Rodriguez-Navas, C., Camacho, L., Pérez-Serna, A., Jimenez, I., Brazales, L., Fernandez, J., & Martin, F.A. (2023). A simplified courtship conditioning protocol to test learning and memory in Drosophila. STAR Protocols, 4(1), 101572.

Greenspan, R.J., & Ferveur, J.F. (2000). Courtship in Drosophila. Annual Review of Genetics, 34, 205–232.

Hall, J.C. (1994). The mating of a fly. Science, 264(5163), 1702–1714.

Kabra, M., Robie, A.A., Rivera-Alba, M., Branson, S., & Branson, K. (2013). JAABA: interactive machine learning for automatic annotation of animal behavior. Nature Methods, 10(1), 64–67.

Kitamoto, T. (2001). Conditional modification of behavior in Drosophila by targeted expression of a temperature-sensitive shibire allele in defined neurons. Journal of Neurobiology, 47(2), 81–92.

Krstic, D., Boll, W., & Noll, M. (2013). Influence of the White locus on the courtship behavior of Drosophila males. PLoS ONE, 8(9), e77904.

Levin, L.R., Han, P.L., Hwang, P.M., Feinstein, P.G., Davis, R.L., & Reed, R.R. (1992). The Drosophila learning and memory gene rutabaga encodes a Ca2+/calmodulin-responsive adenylyl cyclase. Cell, 68(3), 479–489.

Pavlou, H.J., & Goodwin, S.F. (2013). Courtship behavior in Drosophila melanogaster: towards a ‘courtship connectome’. Current Opinion in Neurobiology, 23(1), 76–83.

Yapici, N., Kim, Y.J., Ribeiro, C., & Dickson, B.J. (2008). A receptor that mediates the post-mating switch in Drosophila reproductive behaviour. Nature, 451(7176), 33–37.

eLife Assessment

This study provides an important advance in understanding how disordered proteins interact with cell membranes by identifying the sequence rules that enable aromatic residues to penetrate deeply into the membrane interior. The integration of complementary computational approaches, including molecular simulations, large-scale sequence analysis, and the development of an online prediction server, makes the work potentially impactful for the membrane protein and intrinsically disordered protein communities. The evidence supporting the main conclusions is generally convincing.

Reviewer #1 (Public review):

Summary:

This work investigates the membrane insertion of aromatic-centered sequences in IDPs. Using a combination of all-atom MD simulations, the PPM method, and development of the sequence-based predictor AroMIP, the authors aim to establish a quantitative membrane insertion role for aromatic-centered motifs. The study demonstrates that flanking aliphatic and basic residues promote membrane insertion, whereas acidic and polar residues suppress insertion, and further reveals a difference between F/W-centered motifs and Y-centered motifs. The resulting AroMIP model achieves high predictive accuracy on human IDPs and is implemented as a publicly accessible web server.

Strengths:

This work addresses an important biological problem, as aromatic-driven membrane insertion remains poorly characterized despite mediating diverse functions like membrane remodeling and signaling. A key strength is the combination of complementary approaches, e.g., MD simulations provide mechanistic insight into insertion pathways, while PPM enables exhaustive sequence space exploration. The large-scale analysis clearly establishes L and R as promoters and E, N, and G as suppressors. The work also provides valuable mechanistic insight into how aromatic, aliphatic, and basic residues cooperate to stabilize membrane insertion states. Another important strength is the development of AroMIP as a practical prediction tool with a user-friendly online server that appears computationally efficient and broadly accessible to the community. The work is also well connected to prior experimental and computational literature, and the authors carefully position their findings within existing knowledge of membrane-associated IDPs.

Comments on revised version:

I think the authors have addressed all my concerns. I do not have further comments or requests for additional revisions. Thank you for all the hard work!

Author response:

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

eLife Assessment

This study provides a valuable advance in understanding how disordered proteins interact with cell membranes by identifying the sequence rules that enable aromatic residues to penetrate deeply into the membrane interior. The integration of complementary computational approaches, including molecular simulations, large-scale sequence analysis, and the development of an online prediction server, makes the work potentially impactful for the membrane protein and intrinsically disordered protein communities. The evidence supporting the main conclusions is generally convincing, although its transferability across diverse membrane compositions and its validity as a prediction tool for real protein-membrane systems remain to be further established.

We thank the editors for recognizing our study as a valuable advance. This work lays a solid foundation for future developments to account for diverse membrane compositions and further refinements after additional experimental tests.

Public review:

Reviewer #1 (Public review):

A primary limitation is the heavy reliance on computational modeling. Training for AroMIP is generated using PPM rather than direct experimental measurements, and so the model may primarily reproduce PPM behavior rather than true membrane insertion thermodynamics. Moreover, all simulations use a single lipid composition (POPC:POPS:₂ 70:25:5), but biological membranes vary substantially in cholesterol, cardiolipin, and acidic lipid content. Whether AroMIP's predictions transfer to diverse lipid environments remains untested. The 5% ₂ concentration used in the simulations is higher than that of a normal mammalian cell and may therefore overemphasize electrostatic contributions. Applicability beyond short 9-residue motifs is unclear, as longer-range interactions or secondary structure in full-length IDRs could modulate insertion in ways the current model does not capture. This could be considered for future development.

The reviewer’s point on our reliance on PPM for training, a single lipid composition, and potential effects beyond a 9-residue motif is well taken. Regarding PPM, we chose it as the optimal compromise for high-throughput data. However, we complemented the high-throughput PPM data with experimental data on an initial set of 10 peptides. Moreover, we validate AroMIP on an additional 12 IDRs (intrinsically disordered regions; Table S2). On membrane composition, we now acknowledge the limitation of our work based on a single composition and point to future developments of AroMIP involving membrane-specific parameterization (p. 19, 3rd paragraph).

On potential effects beyond a 9-residue motif, we now add justification and note neglected factors for future developments (paragraph running from p. 19-20), as suggested by the reviewer.

Reviewer #2 (Public review):

(1) Aromatic residues have been shown to partition preferentially to the headgroup region of the lipid bilayer. Most of the papers on this problem were published in the mid 1990s to early 2000s. Some of the most important papers in this regard are the following: von Heijne, Annu. Rev.

Biophys. Biomol. Struct. 1994, 23, 167-192; Doyle et al. Science 1998, 280, 69-77; Landolt-Marticorena, et al. J. Mol. Biol. 1993, 229, 602-608; Killian & von Heijne, TIBS 2000, 25, 429434; Marx & Fleming J. Am. Chem. Soc. 2021, 143, 764-772. Strangely enough, none of these articles is cited.

We have now citations to the Landolt-Marticorena paper and the von Heijne reviews [refs 25-27]. The Doyle paper is not particularly relevant. As for the Fleming paper, we cited a 2016 JACS paper (original ref 27; now ref 30) that specifically dealt with aromatic residues.

(2) This is the most important point and the most serious weakness. The authors find that the PPM method is able to reproduce the results from MD simulations, and the AroMIP model is able to perform well in comparison with PPM and MD, after training AroMIP on a large set of IDR sequences (intrinsically disordered protein regions) of the human proteome. The defining feature of the AroMIP calculation is the recognition of the importance of flanking residues in the membrane-insertion propensity of a sequence containing a central aromatic residue. All this sounds good. However, this is all theoretical. There is no connection to experiment or to any method that draws from experiment. The entire approach relies on the assumption that the MD simulations produce the correct results. There is no proof of the correctness of anything. As one of the greatest physicists of our times, Richard Feynman, wrote, "The test of all knowledge is experiment. Experiment is the sole judge of scientific "truth"."

We emphasize that we have presented substantial experimental support for AroMIP. It correctly predicts the membrane insertion status of the initial set of 10 peptides, which were characterized experimentally. In addition, we validated AroMIP on an additional set of 12 IDRs (Table S2), most of which were characterized by experimental techniques including solution and solid-state NMR, fluorescence, H/D exchange, and cryo-EM. Lastly, we now show good correlation between our insertion scores and binding free energies calculated from the scale determined experimentally by White and co-workers (new Figure S10; p. 15, second paragraph).

(3) The drawings in Figures 2 and 3 are incorrect and misleading. The size of the Tryptophan side chain is about 5.5 Å, whereas one-half of the bilayer ("a monolayer") thickness is about 15 Å. But in the figures, the lipid length and the Trp side chain seem about the same size. This is incorrect even in a qualitative sense.

We have now revised these figures.

Reviewer #3 (Public review):

(1) Membrane composition and lipid shape characteristics: The authors chose to use a model membrane bilayer of a distinct lipid composition, POPC: POPS: PI4,5P2 (70:25:5 molar ratio), for their all-atom simulations of the various model peptides. While this may be pertinent for some of these peptides, it is not for many, such as sequence 2 derived from Drp1, which preferentially binds target conical lipids such as cardiolipin (CL) and phosphatidic acid (PA). The rationale behind using PI4,5P2, which can induce positive membrane curvature when sequestered, versus CL and PA, which both induce negative membrane curvature, is not explained.

We now acknowledge the limitation of our work based on a single composition and point to future developments of AroMIP involving membrane-specific parameterization (p. 19, 3rd paragraph). In this Discussion paragraph, we also speculate that conical lipids, by promoting membrane defects, may facilitate membrane insertion.

(2) Parallel vs. perpendicular peptide orientation of sequence 2 in peripheral Drp1-lipid interactions: On page 11, the authors state that their simulation results of sequence 2 derived from Drp1 "contrasts with a transmembrane orientation proposed by Mahajan et al." However, upon review, a transmembrane orientation for this region has never been proposed anywhere. Drp1 is a peripheral membrane protein that reversibly binds CL- and PA-containing membranes via its intrinsically disordered variable domain containing an aromatic-centered WRG motif. Indeed, the model presented in Figure 9 of Mahajan et al. displays a peripheral and parallel orientation of the transiently helical WRG-containing motif rather than a transmembrane (i.e., across the bilayer) orientation. While the authors can distinguish between a parallel vs. perpendicular orientation of this sequence relative to the plane of the membrane bilayer surface from their simulations, suggesting that previous studies indicated a transmembrane orientation for Drp1 is disingenuous and misleading. The term "transmembrane" should be removed or replaced, as it presents a wrong image.

We have now deleted the sentence mentioning “transmembrane orientation”.

(2) Mutational analysis of W vs. F in membrane insertion of W-centered insertion motifs and vice versa: The PPM-based workflow suggests that F-centered sequences have the highest membrane insertion properties as opposed to W-centered ones. A W552F mutation in the WRGML sequence of Drp1 was, however, found to impair function. How do the authors rationalize this? A crossmutational analysis of W vs. F in W-centered motifs and F-centered motifs is warranted. 

AroMIP predicts a membrane insertion propensity of 0.782 for the WRGML sequence and a moderately higher propensity, 0.837, with a W552F mutation. This increase contradicts the experimental observation of a 3.6-fold increase in membrane binding affinity by Mahajan et al. We now speculate that the specific lipid, cardiolipin, as the reason for the discrepancy (p. 19, 3rd paragraph). This discrepancy provides a concrete example for the need to account for membrane composition in future developments.

Recommendations for the authors:

Reviewing Editor Comments:

(1) The membrane composition used in this study is highly specific. The manuscript would benefit from a clearer justification of the lipid composition and discussing the transferability of the approaches to other relevant membrane systems.

We now acknowledge the limitation of our work to a single composition (p. 19, 3rd paragraph). This composition was chosen because it is widely used in both computational and experimental studies (e.g., PMID: 21144818; 21344950; 29845130; 29995324; 34813727; 37406927). However, as we now point out, future developments should account for the effects of lipid composition.

(2) This work heavily relies on computational modeling. Thus, it remains unclear to what extent the model captures the thermodynamics of membrane insertion, rather than reproducing the behavior of the PPM framework. Further comparisons with available experimental results will make this work more impactful.

We note that the manuscript already has substantial experimental support. It correctly predicts the membrane insertion status of the initial set of 10 peptides, which were characterized experimentally. In addition, we validated AroMIP on an additional set of 12 IDRs (Table S2), most of which were characterized by experimental techniques including solution and solid-state NMR, fluorescence, H/D exchange, and cryo-EM. Lastly, we now show good correlation between our insertion scores and binding free energies calculated from the scale determined experimentally by White and co-workers (new Figure S10; p. 15, second paragraph).

(3) Overall, the manuscript is lengthy. Shortening will improve the readability, clarity, and accessibility to a broad audience.

We have shortened some text, as explained below.

Reviewer #1 (Recommendations for the authors):

(1) The manuscript uses a single membrane composition… The high PIP₂ content (5%) in the simulations may overemphasize electrostatic contributions from basic residues. Please discuss how different membrane compositions (e.g., lower ₂, presence of cholesterol, cardiolipin in mitochondrial membranes) might alter the q parameters and whether AroMIP predictions would change qualitatively or quantitatively.

We now discuss how different membrane compositions may alter q parameters (p. 19, 3rd paragraph). We believe that these alterations will change our predictions quantitatively but not qualitatively, given that our validation is against experimental results acquired on a variety of membrane compositions.

(2) The limitations of the current framework should be discussed more explicitly. For example, the applicability of AroMIP beyond isolated 9-residue motifs remains unclear. In full-length IDPs, membrane insertion may be modulated by longer-range sequence interactions, transient secondary structure formation, multivalent interactions, or post-translational modifications.

We now discuss the limitation of the 9-residue motif, and note these neglected factors for future developments (paragraph running from p. 19-20).

(3) The AroMIP web server is user-friendly… The utility of the server could be enhanced by enabling visualization of full-length disordered proteins. For example, if users input a >300 residue IDP, the server could output residue-wise or sliding-window membrane insertion propensity profiles.

We have revised the web server. We now display insertion propensity profiles as a plot and have added a link for users to download.

(4) The manuscript is quite long and in several sections overly descriptive, e.g., in the first MD Results section and portions of the "Additional test cases" section.

We have shortened the first subsection in Results and placed the expanded presentation in Supporting Information. However, we have kept the “Additional test cases” subsection, because Reviewer 2 appears to have overlooked the experimental validation of our method in these additional test cases.

(5) The authors used the Berendsen barostat… known not to reproduce correct volume fluctuations and is generally considered less rigorous for equilibrium simulations, although this does not affect the main conclusions of the work. For future studies, the authors may consider using more modern barostats.

Thank you for the suggestion! We will definitely be using the more modern barostats in future studies.

Reviewer #2 (Recommendations for the authors):

(1) The idea of the article seems very interesting. The problem of membrane association mediated by aromatic residues is definitely worth studying. Aromatic residues, especially Tryptophan (W), but also, albeit to a lesser extent, Phenylalanine (F), and Tyrosine (Y) are well known to partition preferentially to the headgroup region of the lipid bilayer. Some of the most important papers in this regard are the following: von Heijne, Annu. Rev. Biophys. Biomol. Struct. 1994, 23, 167-192; Doyle et al. Science 1998, 280, 69-77; Landolt-Marticorena, et al. J. Mol. Biol. 1993, 229, 602608; Killian & von Heijne, TIBS 2000, 25, 429-434; Marx & Fleming J. Am. Chem. Soc. 2021, 143, 764-772. Strangely enough, however, none of these articles is cited. Have the authors read them?

We now cite the relevant references as explained above.

(2) The authors propose to decipher the sequence code for insertion of sequences containing aromatic residues in the membrane employing three types of calculation methods with decreasing order of detail and complexity, but increasing order of efficiency. First, all-atom MD simulations; second, the PPM method (protein positioning in membranes) from Lomize et al (2006), Protein Sci 15, 1318; and third, AroMIP, a mathematical model developed by the authors. Incidentally, I don't see anywhere in the text what AroMIP stands for. Is it Aromatic Membrane Insertion Prediction, or something like that? Please define. In any case, the proposed endeavor is commendable.

We now spell out the acronym at its first occurrence in the Abstract and Introduction (Aromatic Membrane Insertion Predictor).

(3) This is the most important point and the most serious weakness. The authors find that the PPM method is able to reproduce the results from MD simulations, and the AroMIP model is able to perform well in comparison with PPM and MD, after training AroMIP on a large set of IDR sequences (intrinsically disordered protein regions) of the human proteome. The defining feature of the AroMIP calculation is the recognition of the importance of flanking residues in the membrane-insertion propensity of a sequence containing a central aromatic residue. All this sounds good. However, this is all theoretical. There is no connection to experiment or to any method that draws from experiment. The entire approach relies on the assumption that the MD simulations produce the correct results. There is no proof of the correctness of anything. As one of the greatest physicists of our times, Richard Feynman, wrote, "The test of all knowledge is experiment. Experiment is the sole judge of scientific "truth"." Thus, there must be a comparison with experiment, as elaborated in the next point.

We emphasize that we have presented substantial experimental support for AroMIP. It correctly predicts the membrane insertion status of the initial set of 10 peptides, which were characterized experimentally. In addition, we validated AroMIP on an additional set of 12 IDRs (Table S2), most of which were characterized by experimental techniques including solution and solid-state NMR, fluorescence, H/D exchange, and cryo-EM.

(4) I understand that the authors are computational or theoretical physical chemists and would not expect them to perform the experiments. However, there are plenty of data in the literature that can be used to corroborate the calculations. First and foremost, though, the authors must calculate the binding constants for their set of peptides and then compare them with experiment, or use a different set of peptides for which the experimental results are available, and test how PPM and AroMIP perform on those peptides. There are two possible approaches to calculate the binding constants. First, calculate the Gibbs energy of binding from simulation or calculation, and, from that, calculate the binding constant via the Boltzmann factor. Second, and better, is to calculate the probability of binding in the simulations or calculations from the fraction of time that the peptide spends bound to the membrane or in water. According to the ergodic principle, the ratio of the two times is the binding constant. I understand that most amphipathic peptide sequences whose binding constants to membranes have been determined by experiment are long. But some are not. For example, Mastoparan X is a 14-residue antimicrobial peptide, and its dissociation constant from POPC vesicles is known to be about 300 μM.

We would like to make it clear that the aim of our study is to predict membrane insertion propensities of aromatic-centred motifs, not the membrane binding affinity of peptides. These two properties are related but require different ways of validating predictions. For membrane insertion, validation requires that not only the motifs are bound to membranes but also the aromatic side chains are placed in the acyl chain region. Our validation of the 12 IDRs listed in Table S2 targeted these requirements.

That said, we note that free energy of binding and probability of binding calculations, mentioned by the reviewer, have been reported previously, including the free-energy cost of Ala substitutions of aromatic residues located at various depths reported by Waheed et al. (ref 35) and residue-specific insertion depths reported by Wang et al. (ref 13). Both of these studies highlighted the propensities of aromatic side chains in inserting into the acyl chain region.

(5) Furthermore, the entire Wimley-White interfacial hydrophobicity scale was determined using pentapeptides, measuring the equilibrium binding constant to POPC membranes experimentally and then using those data to build a residue-based Gibbs energy of binding (Wimley & White, Experimentally determined hydrophobicity scale for proteins at membrane interfaces. Nature Struct. Biol. 1996, 3, 842-848; White & Wimley Membrane protein folding and stability: Physical principles. Annu. Rev. Biophys. Biomol. Struct. 1999, 28, 319-365.) This allows for the calculation of the binding affinity for any sequence. The calculations have been compared to experiment and shown to have a high accuracy. A note of caution: Be careful, though, because Wimley and White used a mole fraction concentration scale, which makes the Gibbs energy of binding more favorable than the value calculated using the more common molar concentration scale by -2.4 kcal/mol. Calculating the binding constant for the peptides used by the authors from the WW interfacial scale and comparing the results with the authors' results would be a good place to start.

This is an excellent suggestion! We now compare our insertion scores with the binding free energies calculated from the WW interfacial scale (new Figure S10; p. 15, 2nd paragraph). Interestingly, we found moderately higher correlations with binding free energies calculated from the octanol scale, which we suggest is more in line with our insertion scores since octanol mimics the hydrophobic region as suggested by White and Wimley in their 1999 Annu Rev paper.

(6) When we speak of insertion in a bilayer, we normally mean insertion in the nonpolar core, not in the interfacial region, which is what the authors mean in this paper. This is extremely misleading and should be changed, namely in the title, but also throughout the paper.

Actually, by insertion we precisely mean into the nonpolar core, NOT the interfacial region. Throughout the Introduction, when we used the word “insert”, we added “into the acyl chain region” (p. 3, line 6 from bottom; p. 5, lines 6-7 from top and line 7 from bottom; p. 6, line 4 from top). To avoid any confusion, we now also explicitly add “into the membrane hydrophobic core” when the word “insertion” first occurs in the Abstract and in the opening paragraph of Introduction (replacing the previous “deep insertion”).

(7) What is q? It appears in equation (1) on page 14, and is referred to several times afterwards, but it is never defined.

We now elaborate, in the text above and below equation (1), on the meaning of the q parameters: they represent the contributions of flanking residues to the insertion score of a central aromatic residue.

(8) The drawings in Figures 2 and 3 are incorrect and misleading. The size of the Tryptophan side chain is about 5.5 Å, whereas one-half of the bilayer ("a monolayer") thickness is about 15 Å. But in the figures, the lipid length and the Trp side chain seem about the same size. This is wrong even in a qualitative sense.

We have revised these figures.

(9) In Figure 1, the location of the bilayer midplane should be indicated, for example, with a line. Currently, there is a red line on the figures, but that is not the bilayer midplane. A reader may easily - and is likely to - misinterpret the figure.

As explained in the Figure 1B, C caption, the red line is drawn at Z = -3.1 Å, which is the mean position of glycerol C2 carbon atoms (setting Z = 0 for the phosphate plane) and thus the start of the acyl chain region.

Reviewer #3 (Recommendations for the authors):

(1) Please refrain from using the term "transmembrane" when describing Drp1-membrane insertion, as Drp1 is a soluble, peripheral membrane-binding protein that reversibly associates with membranes.

We have removed the sentence mentioning “transmembrane orientation”.

eLife Assessment

This is an important study on the role of Slap in restricting Src activity and proliferation of colonic cells in vivo. The authors present solid evidence that an EPHB2-SRC signaling axis stimulates the proliferation of colon precursors and is controlled by the Src-binding protein SLAP, whose loss promotes tumorigenesis.

Reviewer #1 (Public review):

Naim et al., use genetically engineered mouse models and tissue culture cell lines to investigate the role of the SLAP adaptor protein in colonic epithelium and colon tumour formation. The SLAP adaptor protein is known to be a negative regulator of tyrosine kinase signaling in hematopoietic cells but its role outside the immune system is less well defined. Here the authors use genetically engineered SLAP deficient mice, tissue specific SLAP KO, and colonic organoids to demonstrate that SLAP is expressed in cells of the colonic epithelium where it acts as a cell autonomous regulator of proliferation and differentiation. In addition, they provide biochemical evidence that loss of SLAP expression in cultured colonic organoids results in increased Src family kinase activity and global tyrosine phosphorylation, consistent with its known role as a suppressor of tyrosine kinase activity in immune cells. Consistently, treatment with a SRC kinase inhibitor inhibited growth of SLAP deficient organoids. These data provide solid evidence of a cell autonomous role of SLAP in the colonic epithelium.

Using a chemically induced model of colitis-associated cancer the authors demonstrate that inactivation of SLAP shows a trend toward increased tumor formation as well as significantly increased Src family kinase activity within tumors. Tumor spheres from SLAP deficient animals showed enhanced growth that was suppressed by treatment with a Src family kinase inhibitor. Of note, the latter effect was specific to SLAP deficient tumor spheres. These observations are convincing and support the authors conclusion that SLAP has a tumor suppressor role in CRC through inhibition of SFK signaling.

Mechanistically, elevated expression of the EPHB2 receptor tyrosine kinase was detected in immunoblots and by IHC of SLAP KO colonic crypts. In addition, in SLAP deficient crypts, levels of phosphorylated EPHB2 are increased and associated with activated SRC family kinases. Using an EPHB2 inhibitor, the role of EPHB2 in the growth of SLAP deficient colonic organoids, and downstream SRC phosphorylation was demonstrated. The authors also show that low expression of SLAP in human CRC cell line organoids sensitizes to the growth inhibitory effects EPH inhibition which can be reversed by SLAP over expression but not expression of a SH2/SH3 mutant form of SLAP.

Overall, this work provides evidence of SLAP adaptor function in restricting EPH tyrosine kinase signaling the colonic epithelium and suggests that loss of SLAP expression promotes tumorigenesis in this context.

Reviewer #2 (Public review):

Summary:

Protein tyrosine kinases are submitted to diverse regulatory mechanisms controling their activity in normal situation. The authors previously identified SLAP (Src-like adaptor protein), a negative regulator of receptor tyrosine kinase (RTK) signaling, as a key suppressor of the cytoplasmic tyrosine kinase SRC in the normal colon and demonstrated that SLAP is downregulated in a majority of colorectal cancers (CRCs).

In this study, the authors further explored slap functions in mouse models using constitutive and inducible epithelial-specific Slap deletion (villin-CreERT2 model). They found that loss of slap augments colonic epithelial cell proliferation and that induction of tumorigenesis by the AOM/DSS protocol mimicking CRC leads to more aggressive tumors in the absence of slap. This effect is apparently cell-autonomous as growth of normal and tumoral colonic organoids is SLAP-dependent in in vitro settings. Finally, the authors define that, in colon, SLAP represses EphB2, an RTK lying upstream of SRC, and show that inhibitors of EphB2 can partially limit tumorigenic development in vitro.

Strengths:

The manuscript is clearly and concisely written, making it easy to follow. Data obtained in the mouse models are very convincing.

Weaknesses:

Direct evidence that EphB2 is activated/phosphorylated in the absence of SLAP is lacking as conclusions are only based on results obtained with inhibitors. Some other issues have to be addressed before acceptance, in particular the relevance of the findings in CRC patients.

Comments on revised version.

The authors have satisfactorily addressed my concerns.

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

Naim et al. use genetically engineered mouse models and tissue culture cell lines to investigate the role of the SLAP adaptor protein in colonic epithelium and colon tumour formation. The SLAP adaptor protein is known to be a negative regulator of tyrosine kinase signaling in hematopoietic cells, but its role outside the immune system is less well defined. Here, the authors use genetically engineered SLAP-deficient mice, tissue-specific SLAP KO, and colonic organoids to demonstrate that SLAP is expressed in cells of the colonic epithelium, where it acts as a cell-autonomous regulator of proliferation and differentiation. In addition, they provide biochemical evidence that loss of SLAP expression in cultured colonic organoids results in increased Src family kinase activity and global tyrosine phosphorylation, consistent with its known role as a suppressor of tyrosine kinase activity in immune cells. Consistently, treatment with an SRC kinase inhibitor inhibited the growth of SLAP-deficient organoids. These data provide solid evidence of a cell-autonomous role of SLAP in the colonic epithelium.

This work would be improved by further description and interpretation of the SLAP expression pattern shown in the constitutive and tissue-specific KO to further support the conclusions made. In Supplementary Figure 1, magnification of the colon epithelium areas with SLAP expression shown by b-gal and anti-SLAP staining, highlighting regions of interest, would better support the conclusions regarding SLAP expression in specific regions of the colon epithelium. In Supplementary Figure 1B, the authors should indicate that the SLAP staining referred to is epithelial and in resident immune cells, as is mentioned in the text. Also, magnification of the boxed area of LRG5 staining in Figure 1 would improve this figure.

We thank the reviewer for their positive and constructive evaluation of our work.

We have revised Fig 1 and S1 to better highlight SLAP expression patterns. Specifically, we have included higher-magnification images of the colonic epithelial regions, with clearly indicated regions of interest (new Figure S1). We have also clarified in the legend that SLAP staining is observed in both epithelial and resident immune cells, as described in the text. Additionally, we have provided a magnified view of the boxed area showing LGR5 staining in Figure 1 to improve clarity.

Using a chemically induced model of colitis-associated cancer, the authors demonstrate that inactivation of SLAP shows a trend toward increased tumor formation (though this did not reach significance) as well as increased Src family kinase activity within tumors. Tumor spheres from SLAP-deficient animals showed enhanced growth that was suppressed by treatment with a Src family kinase inhibitor. Of note, the latter effect was specific to SLAP-deficient tumor spheres. These observations are convincing and support the authors' conclusion that SLAP has a tumor suppressor role in CRC through inhibition of SFK signaling.

Mechanistically, elevated expression of the RTK, EphB2, was detected in immunoblots of SLAP KO colonic crypts, while overexpression of SLAP in CRC cell lines downregulated EphB2 protein levels. Using an EPHB2 inhibitor, the role of EPHB2 in the growth of SLAP-deficient colonic organoids was demonstrated. While these data generally support the authors' conclusion that SLAP limits colonic organoid growth by downregulating RTKS such as EphB2 and downstream Src family kinase activity, they do not show which cell types/regions in the colonic epithelium have increased EPHB2 protein and how this relates to SLAP and phospho-SRC expression, as shown in Figure 1 and Figure S1 immunocytochemistry. The expression of EphB2 and its role in colonic tumorsphere growth were not investigated.

Overall, this work provides evidence of SLAP adaptor function in restricting tyrosine kinase signaling in the colonic epithelium, and suggests that loss of SLAP expression could promote tumorigenesis in this context.

We thank the reviewer for their positive assessment of our tumour studies and for recognizing the evidence supporting a tumor suppressor role for SLAP through inhibition of SFK signaling.

To address the reviewer’s mechanistic concerns, we performed additional experiments that are now included in the revised manuscript. We confirmed that loss of Slap is associated with increased EPHB2 expression in colonic crypts by IHC (new Figure 4B) and directly tested the role of EPHB2 in the Slap-deficient phenotype: EPH inhibition reduced both pTyr levels and SRC activation in Slap-deficient organoids (new Figure S2), demonstrating that SFK hyperactivation depends on upstream EPHB2 signaling. Consistent with this mechanism, we also observed increased EPHB2 tyrosine phosphorylation and active SRC (pSRC) association in isolated colonic epithelial cells following Slap deletion (new Figure 4A).

To extend these findings to the tumour context, we examined the effect of EPH inhibition in human CRC tumoroids (new Figure 5). Pharmacological EPHB2 inhibition reduced tumoroid growth in CRC cells expressing low levels of SLAP, whereas this effect was largely lost upon SLAP overexpression. An SH2- or SH3-inactivating point SLAP mutant failed to suppress tumoroid growth and restored sensitivity to EPHB2 inhibition, further supporting EPHB2 as a critical target of SLAP-mediated tumour suppression. Together, these new data identify EPHB2 as a critical upstream activator of SRC that is negatively regulated by SLAP and strengthen our conclusion that deregulated EPHB2-SRC signaling drives the hyperproliferative phenotype associated with SLAP loss.

Reviewer #2 (Public review):

Summary:

Protein tyrosine kinases are subject to diverse regulatory mechanisms controlling their activity in normal situations. The authors previously identified SLAP (Src-like adaptor protein), a negative regulator of receptor tyrosine kinase (RTK) signaling, as a key suppressor of the cytoplasmic tyrosine kinase SRC in the normal colon and demonstrated that SLAP is downregulated in a majority of colorectal cancers (CRCs).

In this study, the authors further explored SLAP functions in mouse models using constitutive and inducible epithelial-specific Slap deletion (villin-CreERT2 model). They found that loss of SLAP augments colonic epithelial cell proliferation and that induction of tumorigenesis by the AOM/DSS protocol mimicking CRC leads to more aggressive tumors in the absence of SLAP. This effect is apparently cell-autonomous as growth of normal and tumoral colonic organoids is SLAP-dependent in in vitro settings. Finally, the authors define that, in colon, SLAP represses EphB2, an RTK lying upstream of SRC, and show that inhibitors of EphB2 can partially limit tumorigenic development in vitro.

Strengths:

The manuscript is clearly and concisely written, making it easy to follow. The data obtained in the mouse models are very convincing.

Weaknesses:

Direct evidence that EphB2 is activated/phosphorylated in the absence of SLAP is lacking, as conclusions are only based on results obtained with inhibitors. Some other issues have to be addressed before acceptance, in particular, the relevance of the findings in CRC patients.

We thank the reviewer for their positive and constructive evaluation of our work.

We agree that direct evidence linking SLAP loss to activation of the EPHB2-SRC pathway would strengthen the study. To address this point, we performed additional experiments that are now included in the revised manuscript. In addition to demonstrating increased EPHB2 expression upon Slap deletion, we found that loss of Slap enhances the EPHB2 tyrosine phosphorylation (an index of EPHB2 activity) and association between EPHB2 and pSRC in isolated colonic epithelial cells, supporting increased signaling through this pathway (new Figure 4A). Furthermore, pharmacological inhibition of EPHB2 reduced both SRC activation and the hyperproliferative phenotype observed in Slap-deficient organoids (new Figure S2). Together, these findings provide functional and biochemical evidence that deregulated EPHB2 signaling contributes to SRC activation in the absence of SLAP. We also examined the effect of EPHB2 inhibition in human CRC tumoroids (new Figure 5).

Pharmacological EPHB2 inhibition reduced tumoroid growth in CRC cells expressing low levels of SLAP, whereas this effect was largely lost upon SLAP overexpression. Together, these new data identify EPHB2 as a critical upstream activator of SRC that is negatively regulated by SLAP and strengthen our conclusion that deregulated EPHB2-SRC signaling drives the hyperproliferative phenotype associated with SLAP loss.

To address the relevance of our findings in CRC patients, we also extended our analyses to human datasets (new Figure 5D). We observed a significant inverse correlation between SLAP expression and a colorectal cancer stem cell-like activity score in TCGA tumours. In addition, co-expression of SLAP and SLAP2 with EPHB2 was associated with improved disease-free survival in microsatellite-stable (MSS) CRC patients, whereas no such association was observed in microsatellite instability (MSI) tumours. These findings support the clinical relevance of the SLAP-EPHB2 signaling axis and are consistent with a role for SLAP in restraining EPHB2-dependent CSC signaling in CRC.

Recommendations for the authors:

Reviewing Editor Comments:

Both reviewers have reported that the study of the EPHB2-SLAP-Src axis is not very developed, as most results are derived from using inhibitors. A key question is whether EPHB2 is activated by SLAP depletion and whether it is critical to SRC activation. Addressing these questions would greatly improve the paper.

We thank the Reviewing Editor for this important suggestion. We would be happy for the editors to assess the revised version without involving the reviewers again. In the revised manuscript, we have substantially strengthened the mechanistic link between SLAP loss, EPHB2 activation, and SRC signaling. We show that Slap deletion increases both EPHB2 expression and tyrosine phosphorylation, enhances EPHB2-pSRC association in colonic epithelial cells. Importantly, pharmacological inhibition of EPHB2 suppresses SRC activation and rescues the hyperproliferative phenotype of Slap-deficient organoids. We further demonstrate that EPHB2 inhibition selectively impairs growth of CRC tumoroids with low SLAP expression, whereas this effect is largely abolished upon SLAP overexpression. An SH2- or SH3-inactivating point SLAP mutant failed to suppress tumoroid growth and restored sensitivity to EPHB2 inhibition, further supporting EPHB2 as a critical target of SLAP-mediated tumour suppression. Together, these new biochemical and functional data establish EPHB2 as a critical upstream activator of SRC that is negatively regulated by SLAP and significantly reinforce the central conclusions of the study.

Reviewer #1 (Recommendations for the authors):

(1) Evidence of SLAP expression in the colon is an important basis for these studies and could be moved to the main Figure 1 rather than being in the supplementary material.

We thank the reviewer for this suggestion. While we agree that documenting SLAP expression in the colon is important, we have retained these data in Figure S1 to maintain a concise main figure set, consistent with the recommended format for Short Reports.

(2) Define AOM/DSS and briefly describe the model at first mention. In addition, the model in 3A includes TAM treatment at 45 days, but this is not mentioned in the text. Why is this done?

We have better defined the AOM/DSS protocol at first mention in the revised manuscript and specified the rationale for tamoxifen administration at day 45, which is required to maintain efficient SLAP deletion throughout the duration of the experiment (90 days).

(3) Evidence that the SRC inhibitor decreased phospho-tyrosine levels in addition to inhibiting the growth of organoids should be included.

We included data showing the inhibitory effect of the used SRC inhibitor on global phospho-tyrosine levels in organoids in the revised manuscript.

(4) Further experiments investigating the involvement of EphB2 in colonic tumor formation are of interest and would increase the significance of this work.

We included data showing that SLAP modulation affects the response of tumoroids derived from cell lines to EphB2 inhibition, providing complementary mechanistic insights.

Reviewer #2 (Recommendations for the authors):

(1) The authors should confront their findings with data obtained in normal and pathological tissues: are SLAP, SRC, and EphB2 co-expressed, at the single cell level, in normal colon and CRC? In which cell populations? Is loss of SLAP associated with poor prognosis in CRC patients?

We thank the reviewer for this important suggestion. We agree that assessing the relevance of the SLAP-EPHB2-SRC axis in human CRC is important. However, transcriptomic datasets have inherent limitations in this context, as SLAP primarily regulates signaling at the post-transcriptional level and SRC activity cannot be reliably inferred from mRNA expression.

To address the clinical relevance of our findings, we performed additional analyses of CRC patient datasets. We found a significant inverse correlation between SLAP expression and a colorectal cancer stem cell-like activity score in TCGA tumours. Furthermore, co-expression of SLAP and SLAP2 with EPHB2 was associated with improved disease-free survival in microsatellite-stable (MSS) CRC patients. These findings are consistent with a role for SLAP in restraining EPHB2-dependent signaling in CRC. Finally, while our data identify EPHB2 as a critical upstream regulator of SRC signaling controlled by SLAP, we do not exclude the possibility that additional receptor tyrosine kinases contribute to the effects of SLAP loss during colorectal tumorigenesis.

(2) In Figure 4A, total EphB2 levels are increased in the absence of SLAP in colonic crypts. However, the level of EphB2 phosphorylation is not shown. This is an important point to address. Which ligand(s) may activate EphB2?

We agree that assessing EPHB2 activation is important. To address this point, we have included new data in the revised manuscript showing that Slap deletion increases EPHB2 tyrosine phosphorylation in isolated colonic epithelial cells, providing direct evidence that EPHB2 signaling is enhanced in the absence of SLAP. In addition, we show that loss of Slap increases EPHB2-pSRC association and that pharmacological inhibition of EPHB2 reduces SRC activation and suppresses the hyperproliferative phenotype of Slap-deficient organoids. Together, these findings establish EPHB2 as a critical upstream regulator of SRC signaling following SLAP loss.

Regarding EPHB2 activation, previous studies have shown that EPHB2 is primarily activated by ephrin-B ligands expressed within the intestinal crypt compartment (Batlle et al., 2002). EPHB2 signaling may also be reinforced through cooperation with other Eph receptors, particularly EPHB3, which is highly expressed in intestinal stem and progenitor cells (Holmberg et al., 2006; Genander et al., 2009). In addition, SRC has been reported to phosphorylate EPH receptors, raising the possibility of bidirectional signaling that could further amplify EPHB2-SRC pathway activity (Leroy et al., 2009; Hochgräfe et al., 2010).

(3) In the absence of SLAP, inhibitors of EphB2 should also decrease SRC activity as EphB2 lies upstream of SRC (Figure S3C). Does this occur in organoids?

We now show that EphB2 inhibition reduces SRC activity in SLAP-deficient organoids.

(4) What is the status of EphB2 and SRC (total, phosphorylated) in SW620 and HT29 CRC cells in the absence of SLAP?

SW620 and HT29 cells are SLAP-low CRC models. Given that SLAP expression is already minimal in these cells, further depletion is unlikely to provide meaningful additional insight.

(5) Expression of SLAP is associated with a decrease in the stem cell compartment in CRC cell lines (Figure S2). Is there a stem cell signature associated with low SLAP levels in CRC?

We analyzed TCGA colorectal cancer datasets using a published colorectal cancer stem cell (CSC) signature. We found a low but significant inverse correlation between SLAP expression and the CSC-like activity score, supporting our experimental observations that SLAP restrains stem cell properties in CRC cells and organoids.

(6) Does overexpression of the mutant form of SLAP (SLAPmut) limit SLAP effects in SW620 and HT29 CRC cells in Figure S2?

We have now performed the requested experiments and found that, unlike wild-type SLAP, SLAPmut failed to inhibit tumoroid growth in CRC cells. These results are consistent with our previous findings showing that SLAPmut lacks tumour suppressor activity in CRC cells (Naudin et al., Nat Commun, 2014) and further support the requirement of SLAP signaling functions for the regulation of CRC stem-like properties.

(7) Total SRC is missing in Figure 2B

Total SRC levels are now included in the revised figure.

específica

It's offical, after the CLI which unofficial implement by API .

Also very important: intensity of targets !

La logique nous indique qu'il faut comprendre ici que l'inquiétude est générée par tout ce qui « montr[e] l'illusion » « d'entraver cette liberté », mais la formulation actuelle peut aussi laisser entendre que l'inquiétude est générée par ce qui pourrait montrer l'illusion de cette liberté. Il serait préférable de reformuler la phrase afin de clarifier.

La troncation ici indique-t-elle celle du mot ou de la phrase ? S'il s'agit de la phrase, il n'est pas nécessaire de l'indiquer puisque la citation rend implicite la troncation; si c'est celle du mot, il serait peut-être préférable de l'indiquer en encadrant seulement la dernière lettre de crochets : « il se retin[t] ».

Puisqu'il s'agit du même ouvrage qui est cité dans la phrase précédente, est-il vraiment nécessaire de répéter la référence ? L'intervalle de pages suffirait.

Bien qu'il ne s'agisse pas formellement d'une erreur à corriger, la succession des verbes « résulter » et « expliquer » pour décrire la même citation me semble un peu curieuse et accroche un peu à la lecture. Je suggère plutôt : « il en résulte que “l'amour tendre [...] ”, comme l'explique Myriam Dufour-Maître ».

search@google?"documents looks like rag"

annotated

annotated

how do we know?

of what?

貌似这里的“类存在”是指人类能够意识自己作为人这一物种的存在方式，“把类当作自己的对象”貌似是指人能够把自己和世界作为认识（理论上）和改造（实践上）的对象。“把自身当作现有的、有生命的类来对待”貌似是指人知道自己不仅是一个孤立的个体，而是“人类整体”的一部分。“普遍的存在物”则貌似意味着人可以把整个自然界和整个人类生活作为自己的活动领域。“自由的存在物”貌似指的是人（不知道在什么时代可以）能够按照自己的理性和目的主动创造自己的生活。

劳动的异化

外部世界转化为劳动产品，而劳动产品是劳动的对象化，是和工人相异且敌对的。

劳动形成的产品=劳动的现实化（这里的现实化是否是哲学语境下的？黑格尔的现实化貌似是指人的精神通过劳动进入现实世界，劳动的现实化就是人通过劳动将思维上的，观念上的东东西变为现实的），又因为劳动的产品成为了独立于劳动者，不同于劳动者，使得劳动者反倒受到劳动产品所有者控制的存在，因此它成为劳动的对象（黑格尔哲学语境下的“对象”，对象化 = 人的思想、能力通过劳动变成外部世界中的东西。），也就是劳动产品=劳动的对象化=劳动的现实化。“工人的非现实化”：劳动本来是应该让人获得自我实现的，但是在资本主义下，劳动不再成为自我实现，而成为一种被迫活动。这里描述的是劳动产品的异化

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Open.social.layer-for-public.web-annotations@lindylearn.io

quest-IPFS.in.JavaScript

Filebase

IPFS RPC API

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Reply to the reviewers

Reviewer #1 (Evidence, reproducibility and clarity (Required)):

Summary: The manuscript describes a modular, doxycycline-inducible lentiviral vector platform that enables conditional overexpression, RNAi-mediated knockdown, and CRISPR-based perturbations, combined with fluorescent and luminescent reporters for multiplexed tracking of cell populations. Using this system, the authors perform pooled, competitive in vitro and in vivo assays, focusing on hormone receptor dependencies (ER, PR, AR) in MCF7 breast cancer cells. The key biological conclusion is that AR depletion has minimal effects in vitro but significantly impairs growth in vivo, and that combined hormone receptor knockdown leads to synergistic growth suppression in a xenograft model.

Major comments: 1. Strength of evidence supporting the key conclusions: The technical demonstration of the vector platform is generally convincing, particularly the modular design and the feasibility of multiplexed fluorescent tracking. However, the biological conclusions are only partially supported by the data. The claim that hormone receptor interdependence, and in particular AR dependence, is revealed specifically in vivo rests on a single cell line (MCF7), a limited number of animals, and a small set of shRNAs-some of which appear to have weak or no functional impact in vitro. As such, the conclusions should be clearly qualified as preliminary and context-specific, rather than presented as generalizable insights into hormone receptor biology. In particular, the strong concluding statements (final paragraph of the manuscript) should be toned down to reflect: the limited number of models tested, the absence of mechanistic insight, and the reliance on RNAi-based perturbations without rescue experiments.

We thank the reviewer for this important point. We agree that reliance on a single cell line, limited animal numbers, and possible variability in shRNA performance warranted more cautious framing of the biological conclusions. We have added an explicit caveat paragraph before the closing summary stating that these findings should be regarded as proof of concept, generated in a single breast cancer cell line (MCF7) using a limited number of animals and a small panel of shRNAs whose individual potency was not exhaustively benchmarked. The AR-dependency finding, in particular, is based on RNA interference alone, without orthogonal rescue experiments or mechanistic follow-up, and should be interpreted as preliminary and context-specific rather than as a generalizable feature of AR biology. We have also softened the closing sentence of the manuscript from “accelerates the preclinical development...” to “may help inform future preclinical strategies for hormone-sensitive breast cancer and beyond, pending validation in additional models.”

1. Claims that require qualification or revision: Several claims appear overstated relative to the data provided: a. The assertion that the system enables robust temporal control of perturbations is not supported by quantitative data on leakiness, induction kinetics, or stability of editing over time.

Thank you for raising these points. To address the concerns, we repeated the experiments, including time-course analyses and DNA sequencing with the inducible CAS9 and qRNA, and now present the data in Figure 2. These results show the induction kinetics of the system and address the leakiness of the double-inducible CRISPR/CAS9 system. Because DNA editing is a permanent change to the DNA, we assume that the stability of the edit over time will also be permanent (see Figure 2 and Supplemental Table 2).

The claim that pooled multiplexed perturbation "reveals" discrepancies between in vitro and in vivo AR function is based on a narrow experimental scope and should be reframed as an illustrative example rather than a definitive finding. Thank you for pointing this out. We have revised and softened the claims by removing “reveals” and replacing it with “suggests” throughout the manuscript.

Statements implying reduced off-target effects due to temporal regulation are speculative and should be removed unless supported by data. Thank you for pointing this out. We have removed those parts from the manuscript and revised the relevant sentences to avoid speculation about off-target effects, which are largely determined by the design of gRNAs and shRNAs.

Additional experiments essential to support the paper (limited and realistic): Only minimal additional experiments are required to support the manuscript as it stands: a. Quantification of inducibility and leakiness of the dual Tet CRISPR system (e.g., untreated vs. dox-treated control in Fig. 2C, and time-course of editing efficiency).

Thank you for the suggestion. We have now performed this experiment and included the data (see the new Figure 2G).

Quantification of FUCCI reporter outputs (cell-cycle phase distributions) rather than representative images alone. Thank you for the suggestion. We repeated the experiment and quantified the FUCCI reporter output (see Figure 3).

Reproducibility and methodological clarity: Several aspects of the methodology require clarification to ensure reproducibility: a. Lentiviral titers and recombination rates are not reported. Given the complexity and size of the constructs, this information is essential. We have added more detailed descriptions of the experimental procedures to the Methods section and have repeated the Cas9 transduction experiments, including their corresponding results and viral titers (see Figure 2 and Supplementary Figure 6).

It is unclear how background editing is prevented in the dual Tet CRISPR system, since both Cas9 and gRNA are present in the same cells and may exhibit basal expression. We use serum from South America, where standard antibiotic treatment is less common than in the United States, reducing the likelihood that doxycycline is present and minimizing basal expression. The rationale for using double-inducible systems is to minimize basal expression of both components and thereby reduce the chance that either one reaches levels sufficient for editing. We have also made the underlying design logic explicit in the manuscript: because unwanted editing requires both Cas9 and gRNA to be simultaneously present above a functional threshold, and each is independently repressed through a distinct tetracycline-responsive mechanism (Tet-On for Cas9, Tet-Off for gRNA), the probability of coincident leaky expression of both components is substantially lower than for either component alone. This is now linked to the empirically measured background editing rate (~4% over 11 days without doxycycline, by ONT sequencing; Fig. 2G).

The manuscript does not adequately address integration-based leakiness of doxycycline-inducible systems in lentiviral backbones, especially compared to transposon-based approaches. Thanks for this point. While we have not directly compared our system with transposon-based approaches, we have added a brief section on this in the Discussion to address this point explicitly. Because our system relies on polyclonal, antibiotic- or FACS-selected populations rather than single-cell-validated clones, some degree of integration site–dependent leakiness in the noninduced state cannot be fully excluded and may contribute to the low background editing (~4%) we observe over extended culture. We contrast this with transposon-based delivery systems (piggyBac, Sleeping Beauty), which have distinct genomic integration profiles relative to lentivirus, and note that doxycycline-inducible piggyBac constructs have, in some contexts, achieved tighter regulation with undetectable basal leakiness in vivo. This may reflect the absence of viral LTR-associated regulatory elements and the lentiviral bias toward active chromatin. We discuss chromatin insulators, safe-harbor-targeted integration, and transposon-based delivery as potential strategies to further reduce leaky background expression in future iterations of the platform.

The description of how MCF7-luciferase cells are used to generate lentiviral vectors is confusing and must be clarified. We have updated this in the revised manuscript and hope it is clearer now.

Replication and statistical analysis: The statistical treatment of pooled competition data is insufficiently detailed. It is unclear how many animals, glands, or technical replicates contribute to each comparison. The manuscript does not clearly report the fraction of cells recovered for single, double, and triple knockdowns. Multiple comparisons and normalization strategies are not consistently explained. We apologize if these sections were difficult to follow and have substantially expanded them in the manuscript. Because a recurring concern with any pooled, barcoded competition assay is that the fluorescent tag, lentiviral integration site, or clonal origin of a population could itself influence engraftment or proliferation independently of the intended genetic perturbation, we now normalize each doxycycline-treated population to its own untreated (−Dox) baseline and then express it relative to the equivalently normalized NT population within the same tumor (Fig. 5C, F). We also report the numbers of animals and glands contributing to each comparison directly in the Fig. 5 legend, and we justify this normalization approach in more detail in a new paragraph in the Discussion.

Minor comments: a. Comparison to existing Gateway-compatible systems (e.g. the John Doench system) would help contextualize the technical advance.

Thank you. We have now added a paragraph to the Discussion that compares our platform with existing Gateway-based and CRISPR/Cas9 lentiviral resources, including the Broad Institute's Genetic Perturbation Platform co-developed by Doench and colleagues (Brunello, Dolcetto, Calabrese). This paragraph clarifies that our system is complementary to these genome-scale discovery tools rather than competing with them: it is designed for hypothesis-driven, combinatorial, multiplexed perturbation studies with tight temporal control and validated ex vivo and in vivo applications, rather than for large-scale single-modality screening.

Page 5, line 6: "The ..." should be "It ...". Thank you for the comment and we have corrected this. c. P2A sequences are not self-cleaving; the manuscript should correctly state that the ribosome skips peptide bond formation between the last two amino acids.

Thank you for the comment and we have corrected this.

Fig. 2C lacks an untreated control. Thank you for this. Instead of using an untreated control, we used two different guides targeting USP14 and USP7 to validate the inducible gRNA with constitutively expressed Cas9. For the knockdown controls, each gene served as the control for the other in the corresponding experiments, demonstrating that knockdown could be achieved with the specific inducible gRNA.

Fig. 2E (AkaLuc panel): the label "Reagent" is incorrect and overly broad; a clearer and more consistent nomenclature is needed. Thank you for bringing this to our attention; we have corrected it in the revised manuscript. The panel has also been moved and now appears as Fig. 3B, with the axis relabeled “Ctrl”/“+Substrate” in place of “Reagent.” The purpose of fluorescence-based tracking of cell populations is not clearly explained; the rationale should be explicitly stated. We have now added an explicit rationale at the start of this Results subsection: multiplexed fluorescence barcoding is used to pool several genetically distinct cell populations and track them side by side within the same well, culture, or animal. This allows different genotypes to be compared under identical experimental conditions rather than across separate parallel experiments. This internally controlled design reduces confounding variability arising from well-to-well, batch-to-batch, or animal-to-animal differences and increases the statistical power obtained per experiment. It is particularly valuable in vivo, where it substantially reduces the number of animals required, in keeping with the 3Rs principles.

Claims that hormone supplementation rescues AR depletion are not supported, particularly given the lack of a clear AR-dependent phenotype in prior assays (e.g. Fig. S3A). Thank you for bringing this up. We have rewritten this section and clarified that it behaves more like the non-targeting control shRNA (NT).

The large difference in proliferation between NT cells {plus minus} doxycycline in Fig. 4B is concerning and may reflect a normalization or analysis error; this should be addressed. Thank you for pointing this out. We have now included additional raw data for clarity and addressed the issue accordingly, so it should not be misinterpreted in the future. Figures would benefit from clearer legends specifying n, statistical tests, and normalization procedures. Thank you for pointing this out; we have addressed it in the revised manuscript. Reviewer #1 (Significance (Required)):

Nature and significance of the advance: This work represents a technical advance, rather than a conceptual or biological one. The modular lentiviral platform for inducible perturbations and multiplexed fluorescent tracking is potentially useful, particularly for pooled in vivo competition assays where reducing animal use is desirable.

Context within existing literature: Inducible lentiviral shRNA and CRISPR systems, as well as fluorescent barcoding strategies, are well established. The main contribution here is the integration of these elements into a single, modular framework. However, the manuscript would benefit from clearer comparison to existing systems (including Gateway-based and inducible CRISPR platforms) and discussion of known limitations of lentiviral Tet systems.

Audience: The work will primarily interest I) cancer biologists performing functional genetic screens, ii) researchers developing or applying genetic perturbation tools, and iii) laboratories interested in pooled in vivo assays. The biological findings regarding hormone receptor interdependence are likely of more limited interest unless further validated.

Reviewer expertise: My expertise includes functional cancer genomics, lentiviral and CRISPR-based perturbation systems, in vitro and in vivo genetic screening approaches, as well as bioinformatics. I have sufficient expertise to evaluate the technical platform and biological conclusions presented.

Reviewer #2 (Evidence, reproducibility and clarity (Required)):

Summary This study presents a novel, modular lentiviral platform integrating inducible overexpression, shRNA knockdown, and CRISPR/Cas9 editing within a single system. Its core innovation lies in the versatile design, utilizing Gateway cloning to enable rapid exchange of 14 fluorescent proteins, 3 luminescent reporters, and selection markers, facilitating high-content phenotyping. A dual doxycycline-inducible architecture ensures precise temporal control, minimizing off-target effects. Methodologically, it introduces a robust fluorescence barcoding strategy, allowing multiplexed tracking of up to nine distinct genetic perturbations in pooled assays. When applied to breast cancer intraductal xenografts, this revealed critical in vivo receptor interdependencies-such as the essential roles of AR, ER and PR, in tumor growth. By enabling combinatorial genetic analysis within single animals, this technology significantly reduces experimental variability and animal usage by up to eightfold, advancing both the efficiency of functional genomics and adherence to ethical research standards.

Major comments 1. Despite using an inducible system, shRNA and CRISPR components may still have off-target effects. In the F4 study, was whole-genome sequencing or transcriptomic analysis performed to assess unintended perturbations of non-target genes?

Because we did not aim to conduct detailed mechanistic studies or present shRNA as a new technology, and because the hairpins used are not novel and have already been described in published studies, we did not attempt to identify or test potential off-target genes in this work.

Barcode stability: Are the fluorescent barcodes stably expressed during long-term in vivo culture? Is there a risk of silencing or loss that could affect the reliability of long-term tracking?

The in vivo studies we conducted lasted more than 30 days. The cells were first validated by qPCR and Western blot, generating transgenic cells that were then expanded for all replicates and validations, including the in vivo experiments. The barcodes are driven by the EF1α promoter, which is not expected to be susceptible to methylation and, in theory, should support expression in long-term in vivo studies even beyond the 30-day duration used here.

Cell-cell interference: In mixed transplantation experiments, could different genotypes influence each other through paracrine signaling or competition for resources, leading to observed growth phenotypes that are not entirely cell-autonomous?

Thank you for raising this important point. We observed similar ex vivo effects on growth reduction in single experiments (except for AR in vitro) as we did in vivo with the mixed population. In all in vivo studies, whether mixed or single, the cancer cells injected into a mouse are never cell-autonomous. That is also why one performs in vivo studies using genetic perturbations: to demonstrate that cellular dependencies on genes are not in vitro artifacts, to show that certain genes are important in an in vivo setting, and to reveal how a gene affects the microenvironment in vivo, not just ex vivo. Because tumors are inherently heterogeneous, one could also argue that a mixture of different genotypes allows us to study tumor evolution with greater insight into different gene losses.

Applicability to non-coding genes or weak-effect genes: This platform relies on observable phenotypic changes for screening. Is it sensitive enough for genes with weak effects or functional redundancy in regulatory networks?

Thank you for the interesting question. In theory, the approach should be sensitive enough to use with genes that do not cause a growth defect. The functional readouts should then be further tailored to this purpose, using reporter assays, biomarker assays, or alternative sequencing-based readouts to study different gene-specific consequences. The barcodes can still be used to separate cells by flow cytometry and then to analyze them with the assay of choice.

Are there differences in the induction efficiency of different shRNA or CRISPR components? Could this lead to biases in certain barcode signals, thereby affecting the accuracy of competitive growth analysis?

Thank you for the question. We did not detect any induction efficiency effects that would bias the barcoding, as we used hairpins and gRNA guides that have been validated or previously used in the literature. However, this does not mean such bias could not occur in some cases.

Because we also use a “no doxycycline” control, one can not only study the intercell grafting efficacy of any transgenic cell but also estimate the theoretical growth based on NT and non-dox samples. This can normalize any calculations if such bias affects the dynamics of the hairpin or gRNA. Inclusion of the no-dox control accounts for this directly: each doxycycline-treated population is normalized to its own untreated (−Dox) baseline before being expressed relative to the similarly normalized NT population within the same tumor (Fig. 5C, F). We have now implemented and reported this normalization in the manuscript, with the rationale explained in a new Discussion paragraph, rather than treating it only as a theoretical mitigation.

Are there variations in the packaging efficiency of different shRNA or CRISPR components into lentiviral vectors? Does this affect viral titer? How is copy number consistency achieved in cells after lentiviral transduction? Has the knockdown efficiency been compared between cells transduced with 3 shRNAs and those with single shRNA? Thank you for the questions. For the shRNA and gRNA transductions, the plasmids are approximately the same size, and we have not observed any differences in transduction efficiency between these vectors. Purity and other factors during vector isolation usually have a more pronounced effect on transduction efficiency.

The vectors that are difficult to transduce are those encoding CAS9 itself; in particular, inducible CAS9 is more challenging to transduce, and achieving a good viral titer is important for efficient transduction. We repeated an experiment and transduced the cells with different viral titers and found that the number of cells surviving antibiotic selection correlated with titer, but after expansion they expressed similar levels of CAS9.

Since we do not use monoclonal cells and instead work with polyclonal transgenic lines, we assume that random integration should occur in a similar way as in the NT control, and therefore we did not karyotype the cells or analyze copy numbers. The knockdown efficiency was similar when comparing single and triple knockdown. We have now incorporated this explanation directly into the Methods section of the manuscript, stating explicitly that all shRNA and gRNA expression plasmids are of comparable size with no consistent differences in packaging or transduction efficiency, that titer is more strongly influenced by plasmid purity and preparation than by insert identity, and that knockdown efficiency assessed by Western blot was comparable between single, double, and triple knockdown lines (Fig. 4E–G), indicating that combinatorial transduction with multiple shRNAs did not measurably compromise silencing efficiency per target.

Minor comments 1. The labeling "NT" in Data F3 and Supplementary Data SF3 is unclear. Do they refer to the same condition? Is DOX added in the "NT" condition in SF3? Thank you for pointing this out. NT refers to non-targeting shRNA cells, and they are treated the same as the other cells in all panels. We have updated the figure legend to clarify this.

Cost-effectiveness ratio: Although animal use is reduced, is the cost of constructing the multiplexed barcoded viral library significantly higher than traditional methods? Is its overall economic feasibility suitable for large-scale screening? The cost of creating any shRNA or gRNA is only negligibly higher when generating different guides or hairpins and producing them with multiple barcodes, especially compared with in vivo study and animal costs. That is why we based our calculations not on construct costs but on animal costs and, more importantly, on the reduction in the number of animals needed.

For large-scale screening, the system can distinguish only among nine barcodes, gene targets, and all their combinations, so it is not currently designed for settings in which more than nine genes are targeted.

Reviewer #2 (Significance (Required)):

The most important significance of the study is integrating multiple technologies into one system enabling high-content phenotyping, which may facilitate the discovery of new biological pathways.

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 #2

Evidence, reproducibility and clarity

Summary

This study presents a novel, modular lentiviral platform integrating inducible overexpression, shRNA knockdown, and CRISPR/Cas9 editing within a single system. Its core innovation lies in the versatile design, utilizing Gateway cloning to enable rapid exchange of 14 fluorescent proteins, 3 luminescent reporters, and selection markers, facilitating high-content phenotyping. A dual doxycycline-inducible architecture ensures precise temporal control, minimizing off-target effects. Methodologically, it introduces a robust fluorescence barcoding strategy, allowing multiplexed tracking of up to nine distinct genetic perturbations in pooled assays. When applied to breast cancer intraductal xenografts, this revealed critical in vivo receptor interdependencies-such as the essential roles of AR, ER and PR, in tumor growth. By enabling combinatorial genetic analysis within single animals, this technology significantly reduces experimental variability and animal usage by up to eightfold, advancing both the efficiency of functional genomics and adherence to ethical research standards.

Major comments

1. Despite using an inducible system, shRNA and CRISPR components may still have off-target effects. In the F4 study, was whole-genome sequencing or transcriptomic analysis performed to assess unintended perturbations of non-target genes?
2. Barcode stability: Are the fluorescent barcodes stably expressed during long-term in vivo culture? Is there a risk of silencing or loss that could affect the reliability of long-term tracking?
3. Cell-cell interference: In mixed transplantation experiments, could different genotypes influence each other through paracrine signaling or competition for resources, leading to observed growth phenotypes that are not entirely cell-autonomous?
4. Applicability to non-coding genes or weak-effect genes: This platform relies on observable phenotypic changes for screening. Is it sensitive enough for genes with weak effects or functional redundancy in regulatory networks?
5. Are there differences in the induction efficiency of different shRNA or CRISPR components? Could this lead to biases in certain barcode signals, thereby affecting the accuracy of competitive growth analysis?
6. Are there variations in the packaging efficiency of different shRNA or CRISPR components into lentiviral vectors? Does this affect viral titer? How is copy number consistency achieved in cells after lentiviral transduction? Has the knockdown efficiency been compared between cells transduced with 3 shRNAs and those with single shRNA?

Minor comments

1. The labeling "NT" in Data F3 and Supplementary Data SF3 is unclear. Do they refer to the same condition? Is DOX added in the "NT" condition in SF3?
2. Cost-effectiveness ratio: Although animal use is reduced, is the cost of constructing the multiplexed barcoded viral library significantly higher than traditional methods? Is its overall economic feasibility suitable for large-scale screening?

Significance

The most important significance of the study is integrating multiple technologies into one system enabling high-content phenotyping, which may facilitate the discovery of new biological pathways.

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:

The manuscript describes a modular, doxycycline-inducible lentiviral vector platform that enables conditional overexpression, RNAi-mediated knockdown, and CRISPR-based perturbations, combined with fluorescent and luminescent reporters for multiplexed tracking of cell populations. Using this system, the authors perform pooled, competitive in vitro and in vivo assays, focusing on hormone receptor dependencies (ER, PR, AR) in MCF7 breast cancer cells. The key biological conclusion is that AR depletion has minimal effects in vitro but significantly impairs growth in vivo, and that combined hormone receptor knockdown leads to synergistic growth suppression in a xenograft model.

Major comments:

1. Strength of evidence supporting the key conclusions: The technical demonstration of the vector platform is generally convincing, particularly the modular design and the feasibility of multiplexed fluorescent tracking. However, the biological conclusions are only partially supported by the data. The claim that hormone receptor interdependence, and in particular AR dependence, is revealed specifically in vivo rests on a single cell line (MCF7), a limited number of animals, and a small set of shRNAs-some of which appear to have weak or no functional impact in vitro. As such, the conclusions should be clearly qualified as preliminary and context-specific, rather than presented as generalizable insights into hormone receptor biology. In particular, the strong concluding statements (final paragraph of the manuscript) should be toned down to reflect: the limited number of models tested, the absence of mechanistic insight, and the reliance on RNAi-based perturbations without rescue experiments.
2. Claims that require qualification or revision: Several claims appear overstated relative to the data provided:

a. The assertion that the system enables robust temporal control of perturbations is not supported by quantitative data on leakiness, induction kinetics, or stability of editing over time.

b. The claim that pooled multiplexed perturbation "reveals" discrepancies between in vitro and in vivo AR function is based on a narrow experimental scope and should be reframed as an illustrative example rather than a definitive finding.

c. Statements implying reduced off-target effects due to temporal regulation are speculative and should be removed unless supported by data. 3. Additional experiments essential to support the paper (limited and realistic): Only minimal additional experiments are required to support the manuscript as it stands:

a. Quantification of inducibility and leakiness of the dual Tet CRISPR system (e.g., untreated vs. dox-treated control in Fig. 2C, and time-course of editing efficiency).

b. Quantification of FUCCI reporter outputs (cell-cycle phase distributions) rather than representative images alone. 4. Reproducibility and methodological clarity: Several aspects of the methodology require clarification to ensure reproducibility:

a. Lentiviral titers and recombination rates are not reported. Given the complexity and size of the constructs, this information is essential.

b. It is unclear how background editing is prevented in the dual Tet CRISPR system, since both Cas9 and gRNA are present in the same cells and may exhibit basal expression.

c. The manuscript does not adequately address integration-based leakiness of doxycycline-inducible systems in lentiviral backbones, especially compared to transposon-based approaches.

d. The description of how MCF7-luciferase cells are used to generate lentiviral vectors is confusing and must be clarified. 5. Replication and statistical analysis: The statistical treatment of pooled competition data is insufficiently detailed. It is unclear how many animals, glands, or technical replicates contribute to each comparison. The manuscript does not clearly report the fraction of cells recovered for single, double, and triple knockdowns. Multiple comparisons and normalization strategies are not consistently explained.

Minor comments:

a. Comparison to existing Gateway-compatible systems (e.g. the John Doench system) would help contextualize the technical advance.

b. Page 5, line 6: "The ..." should be "It ...".

c. P2A sequences are not self-cleaving; the manuscript should correctly state that the ribosome skips peptide bond formation between the last two amino acids.

d. Fig. 2C lacks an untreated control.

e. Fig. 2E (AkaLuc panel): the label "Reagent" is incorrect and overly broad; a clearer and more consistent nomenclature is needed.

f. The purpose of fluorescence-based tracking of cell populations is not clearly explained; the rationale should be explicitly stated.

g. Claims that hormone supplementation rescues AR depletion are not supported, particularly given the lack of a clear AR-dependent phenotype in prior assays (e.g. Fig. S3A).

h. The large difference in proliferation between NT cells {plus minus} doxycycline in Fig. 4B is concerning and may reflect a normalization or analysis error; this should be addressed.

i. Figures would benefit from clearer legends specifying n, statistical tests, and normalization procedures.

Significance

Nature and significance of the advance: This work represents a technical advance, rather than a conceptual or biological one. The modular lentiviral platform for inducible perturbations and multiplexed fluorescent tracking is potentially useful, particularly for pooled in vivo competition assays where reducing animal use is desirable.

Context within existing literature: Inducible lentiviral shRNA and CRISPR systems, as well as fluorescent barcoding strategies, are well established. The main contribution here is the integration of these elements into a single, modular framework. However, the manuscript would benefit from clearer comparison to existing systems (including Gateway-based and inducible CRISPR platforms) and discussion of known limitations of lentiviral Tet systems.

Audience: The work will primarily interest I) cancer biologists performing functional genetic screens, ii) researchers developing or applying genetic perturbation tools, and iii) laboratories interested in pooled in vivo assays. The biological findings regarding hormone receptor interdependence are likely of more limited interest unless further validated.

Reviewer expertise: My expertise includes functional cancer genomics, lentiviral and CRISPR-based perturbation systems, in vitro and in vivo genetic screening approaches, as well as bioinformatics. I have sufficient expertise to evaluate the technical platform and biological conclusions presented.

mhtfkht,hyv,juyg.jyv

10^14 m^2 = 10^8 km^2

💻/thinkpad/🧊/me/📓/2026/8/0/docs/4/Wiki/Pad/about/h

http://webui.ipfs.io.ipns.localhost:8080/#/files/💻/thinkpad/🧊/me/📓/2026/8/0/docs/4/Wiki/Pad/about/h

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第一锤破冰，冰裂成“两”半；第二锤破冰，碎到了“极”限。

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中国的第一座核电站，老祖宗的规矩，当然要用“秦”字来命名开局。第一 = 秦！

卖“三万”的“牛”肉面，成本才几分钱

往“最大”的地方去 ➡️ 航天 地球装不下我们了，我们要征服最宏大的太空。 2. 往“最小”的地方去 ➡️ 原子能 物质不能再分了，我们要征服最微观的原子。 3. 往“肉体”的深处去 ➡️ 生物工程 我们要破解上帝造人的密码，征服生命本身。 4. 往“虚拟”的意识去 ➡️ 信息技术 现实世界不够玩了，我们要创造一个虚拟的网络世界。

德芙爱抬杠

eLife Assessment

This useful study describes a physical mechanism for the emergence of spiral patterns in the outer epithelial layer of the mammalian cornea independent of pre-patterning or guidance cues, using an agent-based model of self-propelled particles with alignment. The well constructed model show that spiral patterns can emerge from the interaction between the limbus position, cell division, extrusion, and collective cell migration. While the conclusions are solid, some significant questions related to the importance of topological defect remain, and the comparison between the model and data are so far mostly qualitative.

Reviewer #1 (Public review):

Summary:

The manuscript by Kostanjevec et al. investigates the mechanism behind spiral pattern formation in the cornea. The authors demonstrate that the spiral motion pattern on the mammalian corneal surface emerges from the interaction between the limbus position, cell division, extrusion, and collective cell migration. Using LacZ mosaic murine corneas, they reveal a tightening spiral flow pattern and show that their cell-based, in silico model accurately reproduces these patterns without global guidance cues. Additionally, they present a continuum model that extends the XYZ hypothesis to describe cell flux on the cornea, offering a quantitative explanation for tissue-scale processes on curved surfaces.

Strengths:

The manuscript is well-written, with a systematic approach that clearly explains experimental setups, model construction, assumptions, parameter selection, and predictions. The discussion also provides insightful perspectives on the broader implications of the results for both physics and biology.

Weaknesses:

The authors emphasize polar alignment as a key feature of the spiral pattern based on simulation results. However, they do not provide experimental evidence for this polar alignment.

Reviewer #2 (Public review):

In K. Kostanjevec et al., the authors study a possible mechanism for the formation of spiral patterns in the cornea. First the authors analyze an inferred velocity field, which is deduced from images of fixed corneas, and then determine the position-dependent spiral angle of this velocity fields. Next, the authors analysed two possible markers of cell polarity: the direction of the centrosome-nuclei and the axis of mitosis. Then the authors introduce a stochastic agent-based model of self-propelled particles with over-damped dynamics and with aligning interactions to the orientation of the nearest neighbors and to the particle's velocity. The authors claim to be able to reproduce the equal-time autocorrelation function and the velocity Fourier spectrum. Then the authors introduce the geometry of the cornea by constraining the dynamics on a spherical cap and show that their model can reproduce a typical trajectory in experiments. Finally, the authors produce a phase diagram of the states at a fixed time point as a function of the spherical cap radius and the strength of the coupling aligning constant. Finally, the authors propose an interpretation of the cell fluxes based on the equation of mass conservation.

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

Summary

The manuscript by Kostanjevec et al. investigates the mechanism behind spiral pattern formation in the cornea. The authors demonstrate that the spiral motion pattern on the mammalian corneal surface emerges from the interaction between the limbus position, cell division, extrusion, and collective cell migration. Using LacZ mosaic murine corneas, they reveal a tightening spiral flow pattern and show that their cell-based, in silico model accurately reproduces these patterns without global guidance cues. Additionally, they present a continuum model that extends the XYZ hypothesis to describe cell flux on the cornea, offering a quantitative explanation for tissue-scale processes on curved surfaces.

Strengths

The manuscript is well-written, with a systematic approach that clearly explains experimental setups, model construction, assumptions, parameter selection, and predictions. The discussion also provides insightful perspectives on the broader implications of the results for both physics and biology.

We thank the reviewer for their positive assessment of the manuscript. We are pleased that the reviewer found the work well written and systematic, and that the experimental design, model construction, assumptions, parameter selection, predictions and broader discussion were clearly presented. We have aimed to preserve these strengths in the revised manuscript while substantially expanding the discussion and analysis in response to the reviewer’s concerns.

Weaknesses

The central premise of the manuscript, that the spiral patterning of epithelial corneal cells occurs without guidance cues, is not fully supported. The authors overlook the potential role of axons in guiding epithelial cells, despite clear evidence of spiral axon patterns in their own Fig. 1b. Previous literature indicates that axon patterning precedes epithelial cell patterning, suggesting that epithelial migration might be influenced by pre-existing neural structures (e.g., Leiper et al. 2002, IOVS 2013). The authors need to address this point, possibly by exploring whether axonal patterns serve as a template for epithelial cell migration, or by providing experimental evidence to rule out axon-based guidance.

The reviewer raises an important point that we now address in the revised manuscript. We respectfully disagree with the assertion that the premise of our work is faulty. At no point do we claim that global guidance cues are absent or ignore the fact that the corneal nerves swirl; rather, our results show that such global or contact-mediated cues are not required to explain the observed swirling patterns of epithelial cell radial migration in the adult cornea.

Although our work shows that a swirling prepattern of nerves is not required to obtain radial patterns of epithelial cell migration, we now address why nerve swirling occurs and how it affects interpretation of our data. Previous literature indicates that nerve swirling is visible from approximately 3 weeks, before epithelial striping patterns become apparent in transgenic LacZ and GFP reporter mosaics at about 5 weeks. However, both experimental observations and our modelling indicate that spiral epithelial cell migration proceeds for many days before reporter stripe patterns become visible. Thus, epithelial migration may already be underway before epithelial striping is detectable. If axons are following epithelial migration, they would therefore be expected to become radially aligned before the reporter epithelial stripes are evident.

We also considered the alternative possibility that epithelial cells follow an axonal prepattern. However, several experimental observations argue against the hypothesis that epithelial migration is primarily guided by axonal projections. In situations of genetic mutation or corneal injury, epithelial cell migration can progress independently of, or ahead of, corneal axon extension. In chimeric Pax6+/− LacZ⁺ ↔ Pax6^+/+ LacZ^- mice, where the normally disrupted radial migration of Pax6+/− epithelial cells is restored, the underlying nerves may continue to exhibit abnormal projection patterns. These findings suggest that axonal organization is at least partly dependent on epithelial behaviour rather than the reverse.

Importantly, we do not exclude the possibility that additional cues, including axonal contact, neurotrophins or other environmental signals, may modulate or refine epithelial migration. We have therefore expanded the revised manuscript to discuss these possibilities and the relevant literature more fully.

While the model is well-constructed, it currently falls short of its stated goal of elucidating the mechanisms of spiral formation. Key questions remain unanswered:

Is the curvature of the cornea necessary for spiral formation, or would a simpler disk geometry suffice?

What role do boundary conditions play?

How well do the model's predictions quantitatively match experimental data?

The current comparisons in Fig. 4c-f lack quantitative agreement, and this discrepancy should be discussed with possible explanations.

We thank the reviewer for identifying these points, which we have now addressed in the revised manuscript.

First, we have examined the role of geometry more systematically. The spiral pattern also appears in a disk geometry, and the same qualitative migration pattern is observed across a broader range of simulated geometries, including different curvatures, cap angles, a prolate ellipsoid, an oblate ellipsoid and a disk. The precise shape of the spiral depends on geometric features such as curvature and cap-angle opening, but spiral formation is robust across convex cornea-like geometries. These results are now discussed in the new section “Robustness of the spiral migration pattern and requirement for limbal stem cells” and shown in the revised Fig. 10.

Second, we have clarified the role of boundary conditions, particularly the role of limbal epithelial stem cell proliferation. Without limbal stem cells, and with all other parameters unchanged, the cornea fails to produce the radial striping pattern. At the alignment strength where robust spiral formation normally appears, the simulated tissue flow is disordered and resembles our in vitro calibration simulations. At higher alignment strengths, spiral formation is still not recovered; instead, defects become anchored to the boundary. These findings show that ordered influx from the limbus is important for promoting the spiral state. They are now discussed in the same new section and shown in revised Fig. 9.

Third, we have revised the quantitative comparison between model and experiment. We agree that the original comparison was limited. We have therefore reanalysed both experimental and simulation data, focusing on the time-averaged hydrodynamic velocity field rather than short-range fluctuations amplified by divisions in the numerical model. We also replaced the Fourier-space velocity correlation functions with spatial velocity correlation functions, which are more directly interpretable. The revised analysis shows that the experiments have mesoscale spatial and temporal correlations, of the order of 5-6 cell sizes in space and about one hour in time, and that these are well captured by the simulations for both plastic and explant substrates. We have also added representative experimental and simulation snapshots in revised Fig. 4g-j.

For the full cornea, we acknowledge that direct quantitative comparison remains limited by the available experimental data. We can compare with inferred migration direction fields and resurfacing timescales, but we do not yet have direct live measurements of the full corneal velocity field.

The authors emphasize polar alignment as a key feature of the spiral pattern based on simulation results. However, they do not provide experimental evidence for this polar alignment. The manuscript includes discussions of polar and nematic symmetries that, without supporting data, feel somewhat distracting. If direct experimental evidence for polar alignment is not available, the authors could instead quantify nematic alignment as the spiral forms. This would also allow them to explore potential crosstalk between nematic cell orientation and the polar alignment of self-propulsion, especially considering recent studies showing alternative mechanisms for vortex formation in similar systems.

We thank the reviewer for pointing out that the discussion of polar and nematic alignment was confusing. We have substantially revised this part of the manuscript.

We agree that we do not have direct experimental evidence for polar alignment. However, several observations support the interpretation that the system is dominated by substrate-based polar motility with weak polar alignment. We have now quantified nematic alignment of cell orientations and find no evidence of significant elongation or local nematic order in corneal epithelial cells, as shown in the new Fig. 3d.

Our computational model begins from uncorrelated, substrate-based polar active cell migration. We tested whether polar alignment is necessary and found that the in vitro data are inconsistent with the complete absence of alignment: the flocking order parameter is too high, and the spatial and temporal correlations are larger than expected from persistent driving alone.

We also discuss that cell-cell stress patterns in the epithelium may remain nematic, but any such effect must be sufficiently weak not to dominate the observed axon motion. We have further revised the discussion to include recent related work showing spiral formation in substrate-based cell migration models with polar dynamics, as well as recent work indicating that nematic-like phenomenology can arise from minimal ingredients such as uncorrelated polar activity and cell deformability. This revision is intended to make the interpretation clearer and to avoid overemphasising unsupported claims.

Reviewer #2 (Public review):

In K. Kostanjevec et al, the authors study a possible mechanism for the formation of spiral patterns in the cornea. First the authors analyze an inferred velocity field, which is deduced from images of fixed corneas, and then determine the position-dependent spiral angle of this velocity fields. Next, the authors analysed two possible markers of cell polarity: the direction of the centrosome-nuclei and the axis of mitosis. Then the authors introduce a stochastic agent-based model of self-propelled particles with over-damped dynamics and with aligning interactions to the orientation of the nearest neighbors and to the particle's velocity. The authors claim to be able to reproduce the equal-time autocorrelation function and the velocity Fourier spectrum. Then the authors introduce the geometry of the cornea by constraining the dynamics on a spherical cap and show that their model can reproduce a typical trajectory in experiments. Finally, the authors produce a phase diagram of the states at a fixed time point as a function of the spherical cap radius and the strength of the coupling aligning constant. Finally, the authors propose an interpretation of the cell fluxes based on the equation of mass conservation.

We thank the reviewer for their careful assessment of the manuscript and for recognising the work as a solid theoretical study. We have revised the manuscript substantially in response to the reviewer’s major concerns, particularly regarding the terminology of topological defects and stagnation points, the comparison with experiments, and the role of corneal geometry.

Regarding the terminology of topological defects, we have clarified the distinction between a velocity-field stagnation point and the topological classification of the velocity direction field. Stagnation points can be assigned a topological index when one considers the direction of the vector field away from the core, while ignoring the magnitude. We agree that the physical origin of interactions in a velocity field differs from that in a director field, and we have revised the text to avoid confusion. The revised manuscript now includes Box 1, which summarises the relevant topological concepts and caveats.

Regarding the comparison with experiments, we have expanded and clarified the validation of the inferred velocity field. The LacZ reporter system was designed for lineage tracing and therefore reports coarse-grained cell motion and growth patterns. Direct live imaging of the full cornea remains technically difficult because of the macroscopic size, curved surface and long resurfacing time. However, live fluorescent reporter systems are consistent with our in vivo model and support the interpretation that inferred velocity fields recapitulate epithelial migration in vivo. We have also clarified the interpolation procedure using the XY model: approximately 30% of the corneal surface is covered by directly estimated velocity directions from stripe edges, rising to more than 50% near the central spiral. These measured directions provide sufficient boundary conditions for the annealing procedure to converge to a slowly varying field consistent with the observed stripe geometry.

We have also revised the comparison between simulation and experiment. For in vitro data, we now compare spatial velocity correlation functions of the time-averaged hydrodynamic velocity field, rather than relying on Fourier-space correlations. The revised comparison shows good agreement between experiments and simulations for both plastic and explant substrates. For the full cornea, we acknowledge that the available quantitative data are limited to inferred migration direction fields and resurfacing timescales.

Regarding the role of geometry, we have now simulated a wider range of substrate geometries, including spherical caps with different curvatures and cap angles, prolate and oblate ellipsoids, and a flat disk. The spiral migration pattern is robust across these convex geometries, although the detailed spiral shape depends on geometric features. We have also clarified that the boundary condition of inward limbal influx is crucial: without limbal stem cells, the radial striping pattern does not form, and defects may instead anchor to the boundary. These results are now shown in revised Figs. 9 and 10 and discussed in the new section “Robustness of the spiral migration pattern and requirement for limbal stem cells.”

Overall, these additions clarify that the proposed mechanism relies on the combination of polar motility, weak alignment, limbal influx, cell division and extrusion, and cornea-like confinement, while also acknowledging the current experimental limitations.

Recommendations for the authors:

Reviewing Editor:

The authors could strongly improve the manuscript by following the recommendations given below.

Reviewer #1 (Recommendations for the authors):

There are, however, substantial shortcomings that authors need to address to make their claims supported by enough evidence:

Major concerns:

(1) Neglect of Potential Axon Guidance

As it stands the premise of the paper is unfortunately faulty. The authors overlook the potential role of axons in guiding epithelial cells, despite clear evidence of spiral axon patterns in their own Fig. 1b. Previous literature indicates that axon patterning precedes epithelial cell patterning, suggesting that epithelial migration might be influenced by preexisting neural structures (e.g., Leiper et al. 2002, IOVS 2013). The authors need to address this point, possibly by exploring whether axonal patterns serve as a template for epithelial cell migration, or by providing experimental evidence to rule out axon-based guidance.

See for example:

from: https://iovs.arvojournals.org/article.aspx?articleid=2126612:

It is therefore not clear why the authors clearly show the axon vortex in Figure 1, then talk about prepatterning - and never mention the axon vortex again.

The reviewer raises an important point that we now address in the revised manuscript. We respectfully disagree with the assertion that the premise of our work is faulty. At no point do we claim that global guidance cues are absent or ignore the fact that the corneal nerves swirl; however, our results show that such global or contact-mediated cues are not required to explain the observed swirling patterns of epithelial cell radial migration in the adult cornea. Although our work shows that a swirling ‘prepattern’ of nerves is not required to obtain radial patterns of epithelial cell migration, we address below why it occurs and how it affects our data.

An intuitive interpretation of corneal anatomy would be that sensory axons follow the path of least resistance between migrating epithelial cells. We believe this is the most likely explanation in the normal wild-type cornea; however, the reviewer correctly notes that previous literature indicates that axon patterning precedes epithelial cell patterning. Nerve swirling is observed from approximately 3 weeks, earlier than the epithelial striping patterns that become apparent in transgenic LacZ and GFP reporter mosaics at about 5 weeks (e.g. Collinson et al., 2002 [PMID: 12203735]; Iannaccone et al., 2012 [PMID: 22347498]; McKenna and Lwigale 2011 [PMID: 20811061]). We now address this in the revised manuscript and below.

The early appearance of radial axonal projections can be readily explained even if axons are following the epithelial cells. Experimental observations (Collinson et al., 2002 [PMID: 12203735]) and our modelling (Fig. 6a,b) both indicate that spiral epithelial cell migration proceeds for many days before stripe patterns become visible in reporter mosaics. Thus, epithelial migration is already underway before the striping pattern becomes detectable. If axons are following the epithelial migration, they would be expected to become radially aligned before the reporter epithelial stripes were evident. The apparent precedence of radial axonal projections over epithelial striping is therefore fully consistent with the biological scenario in which axons follow the migrating epithelial cells. Corneal epithelial basal cells have been shown to wrap around individual and grouped subbasal axons, acting as surrogate glia (Stepp et al., 2016, Investigative Ophthalmology & Visual Science 57, 1292), which represents a plausible mechanism to allow migrating epithelial cells to shepherd axons in their direction of movement.

We considered that epithelial cells may follow an axonal prepattern, but several experimental observations argue against the hypothesis that epithelial migration is guided by axonal projections. In situations of genetic mutation or corneal injury, epithelial cell migration can progress independently of, or ahead of, the extension of corneal axons (Leiper et al., 2009 [PMID: 19029029]; Song et al., 2004 [PMID: 14744881]). Furthermore, in chimeric Pax6^+/− LacZ⁺ ↔ Pax6^+/+ LacZ^- mice, where the normally disrupted radial migration of Pax6^+/− epithelial cells is restored, the underlying nerves may continue to exhibit abnormal projection patterns (Leiper et al., 2009 [PMID: 19029029]). These results suggest that axonal organization is at least partially dependent on epithelial behaviour rather than the reverse.

Importantly, none of this evidence excludes the possibility that additional cues (including axonal contact or other environmental signals) may modulate or refine epithelial migration. For example, Walczysko et al. 2016 [PMID: 27563231] showed that isolated epithelial cells cultured on de-epithelialised and de-nervated corneal stroma still migrate with a small but significant radial bias, indicating that epithelial cells can respond to physical features of their environment. Our model likewise includes a component of alignment with environmental structure. Since the central radial striping in many of our simulations is somewhat less ordered than in vivo, it is plausible that additional biological guidance cues or axonal neurotrophins help refine the pattern.

We now discuss these issues and the relevant experimental evidence more fully in the revised manuscript.

(2) Model Validation and Complexity

While the model is well-constructed, it currently falls short of its stated goal of elucidating the mechanisms of spiral formation. Key questions remain unanswered:

Is the curvature of the cornea necessary for spiral formation, or would a simpler disk geometry suffice?

To address the Reviewers’ concerns about the role of geometry, we first note that the spiral pattern also appears in a disk geometry. In fact, the geometric parameters of the cornea vary across mammals, including humans, yet the spiral pattern is preserved (Dua, et al., 1993 [PMID: 8325424]; Zander and Weddell, 1951 [PMID: 14814019]). Similar variations arise in disease states, e.g. in keratoconus the cornea becomes elongated.

On a spherical cap, and all shapes with the same topology, the boundary winding number fixes the interior index, so ongoing limbal influx maintains a total index of 1. To explore the role of geometry more systematically, we simulated a broader range of geometries, including different curvatures, cap angles, a prolate and an oblate ellipsoid, and finally a disk, and compared the resulting patterns with published data across mammals and with disease states.

For all of these shapes, we find the same qualitative migration pattern – a central spiral, although its precise shape depends on geometric features such as curvature and cap-angle opening. These results are discussed in a new section “Robustness of the spiral migration pattern and requirement for limbal stem cells” and shown in Fig. 10 of the revised manuscript.

What role do boundary conditions play?

In the revised manuscript, we address the role of boundary conditions, in particular the presence of limbal epithelial stem cell proliferation. Without limbal stem cells, and with all other parameters kept unchanged, the cornea fails to make the radial striping pattern. We observe two distinct changes: First, at J=0.1, the amount of alignment at which robust spiral formation appears normally, the simulated tissue flow is instead disordered, in fact very similar to our in vitro calibration simulations (revised Figure 4). This shows that the boundary cue of an ordered influx from the limbus promotes flocking when it otherwise would not (yet) appear. Second, when we increase the alignment to J=0.15 or J=0.2, we still do not observe spiral formation. Instead of the expected central vortex shape, we have anchoring of defects to the boundary, facilitated by the effective compressibility of the tissue because of the density feedback in the division/extrusion rates.

These findings are discussed in the new section “Robustness of the spiral migration pattern and requirement for limbal stem cells” and in Fig. 9 in the revised manuscript.

How well do the model's predictions quantitatively match experimental data?

The current comparisons in Fig. 4c-f lack quantitative agreement, and this discrepancy should be discussed with possible explanations.

Regarding the in vitro cell data and matching simulations: We are aware that we have limited data to work with, and our match is intended as a rough estimate of physical parameters.

For the revision, we have reanalysed both experimental and simulation data carefully. We realised that certain details of our numerical model amplify short-range fluctuations, namely the way cell divisions induce stress dipoles, and the fact that we did not include cell-cell friction forces. This is not merely a guess but emerged from related theoretical work by some of us (Keta and Henkes [PMID: 40556485]; Kammeraat et al, arXiv:2508.01046 (2025)).

We therefore compared the time-averaged velocity field excluding divisions to the experiment, the same quantity that we already introduced as hydrodynamic velocity for the corneal surface. We also carried out further simulations that included cell-cell friction forces for comparison. They led to very similar results, albeit with a transition to flocking at somewhat lower alignment strength J.

Instead of the Fourier-space velocity correlation functions that are hard to interpret, we switched to spatial velocity correlation functions. Our experiments have ‘swirly’ velocity fields with mesoscale spatial and temporal correlations, of the order of 5-6 cell sizes in space and one hour in time. As can be seen in the revised panels 4c,d for the spatial correlations of the hydrodynamic velocity, the match between experiment and simulations is in fact good for both plastic and explant substrates. There are also systematic changes in length and time scales between the two experiments that emerge without fine-tuning from simulation. We furthermore have included snapshots of both experimental in vitro conditions and matching simulations as new panels 4g-j, showing that the mesoscale correlations appear in the hydrodynamic velocity.

Ultimately the parameter values and length and time scales that we infer for our systems (see Table 1) are quantitatively consistent with three other estimates of in vitro epithelia (Henkes et al. 2020 [PMID: 32179745]; Saraswathibhatla et al, Extreme Mechanics Letters 48, 101438 (2021), Kammeraat et al, arXiv:2508.01046 (2025)).

For cell flows on the full cornea, regrettably we lack further quantitative data to compare to beyond the migration direction fields inferred in Figure 2, and the time scales of corneal resurfacing (Figure 6).

(3) Importance of polar alignment.

Polar alignment is put forward as one main feature for the observed patterns based on the simulation results. However, no experimental confirmation for such polar alignment is presented. The authors instead present various scattered discussions about polar versus nematic symmetry, which at times reads rather unnecessary and distracting from their main message.

If they are not providing direct experimental evidence on the polar alignment, at least they could quantify nematic alignment of cell orientation as the spiral forms. One possibility is that nematic alignment of cell orientations has a crosstalk with the polar alignment associated with the self-propulsion of the cells. This is important because, as acknowledged by authors, several recent works in the context of in vitro epithelial under disk confinement have revealed alternative mechanisms for spiral vortex formation.

We thank the Reviewer for pointing out the confusion, and we have therefore completely revised our discussion of alignment. While the evidence remains indirect, the following observations lead us to conclude that our system is dominated by substrate-based polar motility and weak polar alignment:

We have investigated nematic alignment of cell orientations. As can been seen in new Figure 3d, corneal epithelial cells do not show evidence of significant elongation in any direction, i.e. we find no local nematic order.

Our computational model starts from uncorrelated, substrate-based polar active cell migration. We have carefully investigated if polar alignment is a necessary ingredient and found that the in vitro data are inconsistent with the absence of alignment: the flocking order parameter is too high, and the spatial and temporal correlations are larger than those expected from persistent driving only.

Still, cell-cell stress patterns in the epithelium may remain nematic. While one cannot exclude anything, the effect must be sufficiently weak to not affect axon motion. Their naturally long, thin shapes would strongly react to nematic stresses, but we do not see ±1/2 defects in their growth patterns, only polar ±1 defects.

We were recently made aware the work of Lång et al. (2024) [PMID: 38630812], in which the authors also observe spiral formation in cell migration on a substrate, with +1 topological defects. They explain their observations using a polar model, and our results are consistent with their observations and model.

In the active matter community, the ‘active nematic cell sheet’ paradigm is currently undergoing a revision. Notably, nematic-like phenomenology, in particular ±1/2 defects, can also arise from the minimal ingredients of uncorrelated polar activity and cell deformability (Chiang et al. 2024 [PMID: 39302997]).

Minor comments:

- It would help the reader to see some representative images of the cells, explants, and the simulation (maybe with vectors overlaid), as it could be hard to imagine what exactly is happening and the other relevant properties to compare.

Please see new panels Fig. 4g-j, and new supplementary videos S3 and S4.

- Add a colorbar that represents direction to Fig. 1a.

We assume the Reviewer meant Fig. 2a. We have added a circular orientation colour chart to the image.

- When do additional +1,-1 defects appear? The authors just say it is unlikely, but it is not clear when they are observed. Disease state?

The reviewer is correct about pathology. We now cite (in conclusion) Collinson et al. 2004 [PMID: 15037575], which shows disruption and discontinuity in Pax6-mutant corneas with chronic corneal degeneration. We also have a publication in prep that shows extra +1 and -1 discontinuities occur during wound healing. We don’t want to include these data in this manuscript, but cite Sagga et al. (in prep).

Reviewer #2 (Recommendations for the authors):

Overall, the manuscript presents a solid theoretical work. However, I have a few major concerns on this manuscript. (1) The authors use the concept of topological defect to refer to stagnation points of the velocity field (2) The comparison to experiments remains qualitative and it is unclear whether the proposed mechanism is at work, (3) The role of geometry remains unclear.

Major points:

(1) On page 3 the authors claim that topological defects exist in velocity fields, however this statement is incorrect and can confuse readers. Unlike the director field of an ordered phase, their velocity field is a vector in R^2 with a norm that is not fixed, and therefore the velocity field has no topological defects. In fluid dynamics, these special points in the velocity field are called stagnation points, fluid sinks or sources. This distinction is important because the physical nature of the interaction forces between two "topological defects" in a velocity field is fundamentally different to the interaction forces between two topological defects in a director field. For these reasons, it can confuse readers to mix the two concepts (stagnation points vs topological defects). Note that if the authors address this concern, many parts of the main manuscript should be rewritten.

Stagnation points are topological defects since the topological classification ignores the magnitude and looks only at the direction of the vector field away from the core. There are some caveats related to the role of boundary conditions, since in the case of a fluid, if boundary conditions are not fixed, one can eliminate the defect by setting the flow field to zero everywhere. Mathematically, a pair of point sources or sinks in an incompressible potential flow interacts through the same Green’s function that gives the elastic interaction between two-point disclinations in a 2D nematic director field, so their long-range pair potentials are formally identical (~ln(r) in 2D). The physical mechanism behind these forces is, as correctly pointed out by the reviewer, quite different. In addition, our coarse-grained velocity field is effectively compressible, which has consequences for the hydrodynamic equations we (can) write, see below.

In the revised version, we clarify these points by adding Box 1, which summarizes the idea of topological defects.

(2.1) How did the authors check that the velocity field that is inferred from the stripe edges matched the coarse-grained cell velocity field in a life sample? How did the authors validated the interpolation of the inferred velocity field using an XY model? What is the scale of the inferred velocity field? Can the authors clarify also this point?

The murine cornea LacZ reporter system was specifically designed to allow for lineage tracing, i.e. following coarse-grained cell motion and growth patterns. The combination of macroscopic size (3.6 mm diameter), two-week resurfacing time and the curved surface however stymied our early attempts to directly measure the cell velocity field on the cornea using confocal time-lapse microscopy. However live sample fluorescent reporter systems are fully consistent with our in vivo model and shown conclusively that inferred velocity fields are recapitulated in vivo (Park et al., 2019 [PMID: 31843909]).

For the XY model inference: We first note that approximately 30% of the corneal surface is covered by directly estimated velocity directions from the stripe edges (red arrows, Fig. 11f). This rises to more than 50% near the central spiral (red arrows, Fig. 11h) due to the way the stripes narrow near the centre due to cell extrusion. Therefore, the inferred areas are only slightly more than half of the cornea, and in regions where we expect the flow field to be largely uniform with no defects. The red arrows provide sufficient boundary conditions that a simple annealing simulation (or equivalently an energy minimisation) of the XY model rapidly converges to a slowly varying field consistent with those boundary conditions.

Furthermore, in simulation we observe that stripe edges and the macroscopic velocity field correlate strongly with each other once the spiral has fully formed (Fig. 6b-c).

The scale of the inferred velocity field is the distance between arrows in our digital version of the cornea, approximately 40 concentric rings over a 70° cone angle for a R = 1800 μm micron cornea, resulting in a spacing of 55 μm between velocity arrows. This is about at the scale of the in vitro velocity correlations. We are not able to obtain velocity magnitudes using this procedure.

Furthermore, the spiral angle profile reported in Fig. 7b-c appear to be different to that found in experiments Fig. 2b. Can the authors clarify if their theoretical framework reproduces the spiral angle profiles?

First, we note that empirically, simulations with the largest two radii (R = 1000 μm, R = 1500 μm), approaching the full experimental size, match the observed angle profile best. They both consist of a radially inward profile α(0) = 0° at the edges, only increasing, corresponding to a tightening spiral, below about θ = 20°. Note that very near the corneal centre, few cells contribute to data, and additionally the central defect position fluctuates somewhat. Therefore, the angle profile not reaching α(0) = 90° in the simulations is due to fluctuations and lack of statistics. As these simulations were run with our best fit experimentally matched parameters, this convergence is meaningful and cannot be scaled out.

Second, our partial model (eq. 4, reproduced below) links the spiral profile angle α(θ) with the macroscopic velocity magnitude v(θ) and the net cell loss rate A(θ), and it depends explicitly on the corneal radius R.

That is one equation for three radial fields. If we make the reasonable assumption that simulations at fixed 𝐽 that differ only in R have the same constitutive law A(ρ), and would follow the same continuum velocity equation that ultimately sets v(ρ), the radius R still appears explicitly in the equation. This is consistent with the different radial profiles for different R that we observe in Fig. 7c. Furthermore, in Fig. 7b we observe that above the flocking threshold, different J lead to very similar profiles. This indicates that the system enters a fully polar phase.

The same is true in experiment: We expect that cell mechanics and planar cell polarisation coordination are local effects that will set J, A(ρ), and v(ρ). Thus, we do predict that the radius R will explicitly affect the spiral profile consistent with the equation above, but we would need more direct measurements of J, A(ρ), and v(ρ) to go any further.

Properly answering this question would first require simulations of a non-dimensionalised model with different radii, boundary influx, alignment strengths and constitutive laws for the division / extrusion dynamics. Then one would want to construct a matching equation for the polarisation and / or velocity field, going beyond the Malthusian flock approximations of constant magnitude v(θ) and net cell loss rate A(θ) = 0. This is well beyond the scope of this publication, and there is certainly no experimental data to compare to.

(3.1) It appears that the formation of the spiral velocity pattern results from a combination of a radial flow of cells due to cell division at the outer boundary and apoptosis at the geometrical center and azimuthal flow of cells due to flocking in confined geometries. Is this correct? Can the authors explain how does the 3d geometry of the spherical cap modify each of these flow fields?

The Reviewer is broadly correct; however, the true picture is more subtle. Almost all of the divisions and extrusions occur in TA cells, neither at the limbus or at the corneal centre (please see the A(θ) profiles in Figure 8). Flow is then a spiral flock that is partially radial and azimuthal, and with a variable velocity magnitude. It ultimately all has to follow the flux equation 4, in steady state.

For the modification of the flow field due to 3d geometry: Broadly, they simply change the amount of corneal surface available at different angles from the limbus when switching to isomorphic surfaces like, e.g. the disk. Thus, we still observe spirals, but of somewhat modified shapes. Please see the reply to Reviewer 1 above, and new Figure 10 for simulations of different geometries.

A full theory of radially symmetric corneal shapes would again need additional velocity and constitutive equations and is beyond the scope of this publication.

(3.2) In their model, it appears that the geometry is introduced by constraining the dynamics of agents, and it has not direct influence on the alignment of agents. Can the authors explain how the geometry of the spherical cap influences the emergent spiral states? Can the authors show whether their results are robust to changes in the substrate geometry? For example, by changing the substrate geometry from a spherical cap to another convex shape. Can the authors identify differences between the spiral patterns on a spherical cap vs that on a flat disk?

Please see the response to Reviewer 1, above, and new Figure 10. Briefly, the results are robust to changes in the corneal geometry as long as they are other convex shapes. There are differences in details of the spiral shape.

Minor comments:

- On page 3, the authors claim that the Euler characteristic of a spherical cap or a disk is 1. Unfortunately, this statement is incorrect. The Euler characteristic of a spherical cap or a disk is determined by the winding of the vector field around the open boundary. In their case the Euler characteristic is 1 because the velocity field is oriented towards the top of the spherical cap, which give a winding of +1. The authors explain this correctly on page 13.

The Euler characteristic is a topological invariant of the surface and does not depend on the tangent vector field. A disc or spherical cap has Euler characteristic 1. What depends on the boundary winding is the index formula for a vector field on a surface with boundary: the winding of the field along the boundary determines the corresponding boundary contribution, and hence the sum of interior indices. Thus, while the reviewer is correct about the role of boundary winding in computing the field’s index, this does not alter the Euler characteristic of the surface itself.

We note that the orientation of the velocity field does indeed matter in our simulations: In new Figure 10, we show that in the absence of the boundary condition of inward flux at the limbus, we do not observe a spiral robustly, and we do see anchoring of defects to the boundary, with winding numbers that are now different.

- On page 5, the authors claim the clockwise and counterclockwise -oriented spirals are equally likely, however no quantification is provided to support this claim. Can the authors clarify this point?

The approximately equal likelihood is as described in Collinson et al. (2002) [PMID: 12203735].

- On page 8 the authors state "Dipolar active force cannot cause a single cell to migrate, and the flow is an emergent collective phenomenon", here I was confused, because a bacteria swimming in a Newtonian fluid can self-propel by exerting a dipolar active force on the fluid. See for instance work by E. Lauga or I. Aronson. Can the authors clarify this point?

The Reviewer is correct about how bacteria can swim using dipolar active forces. However, and unfortunately, that language was straight ported to very different conditions, that of cells migrating on a frictional substrate with no induced flow, with the equation of motion . Unless there is a net force arising from the stress profile, the cell cannot move, and with microscopic models where the active stress is a single value per cell, that statement is always true. Of course, more detailed cell models with active stresses exist, but their motion is still due to the net force arising from them. We have clarified the statement in the revised manuscript.

- On page 16, the expression for the velocity seems to miss a parenthesis.

Fixed. Thanks!

- On page 16, the authors claim that the flux profiles in the simulations and in the XYZ model are in good agreement. However, there are clear differences for theta> 50 deg. Can the authors discuss the possible explanation for these differences? Note that one may expect a between agreement between two theoretical approaches.

These disagreements are due to imperfections in the observed spiral patterns in simulations. They are not perfectly radially symmetric, and the defect is not always in the dead centre of the cornea. Thus, the theoretical predictions are not quite accurate. Numerically, what happens for θ > 50° is that the radial bins sometimes include part of the limbal zone with strong proliferation, and the corneal edge itself. Both are not described by the flux prediction. We decided not to crop out this region and rather explain where it comes from in the text.

eLife Assessment

This important study contributes to our understanding of how epithelial cells establish polarity by identifying a hierarchy in which Par3 acts upstream of centrosome positioning and apical membrane initiation. The evidence supporting the main conclusions is convincing, and the authors addressed the unresolved questions about microtubule organization and the need for clearer integration of quantitative and conceptual points raised in review. The work will be of interest to cell and developmental biologists.

Reviewer #1 (Public review):

Summary:

Wang, Po-Kai et al., utilized the de novo polarization of MDCK cells cultured in Matrigel to assess the interdependence between polarity protein localization, centrosome positioning and apical membrane formation. They show that the inhibition of Plk4 with Centrinone does not prevent apical membrane formation, but does result in its delay, a phenotype the authors attribute to the loss of centrosomes due to the inhibition of centriole duplication. However, the targeted mutagenesis of specific centrosome proteins implicated in the positioning of centrosomes in other cell types (CEP164, ODF2, PCNT and CEP120), as well as the use of dominant negative constructs to inhibit centrosomal microtubule nucleation did not affect centrosome positioning in 3D cultured MDCK cells. A screen of proteins previously implicated in MDCK polarization revealed that the polarity protein Par-3 was upstream of centrosome positioning, similar to other cell types.

Strengths:

The investigation into the temporal requirement and interdependence of previously proposed regulators of cell polarization and lumen formation is valuable. The authors have provided a detailed analysis of many of these components at defined stages of polarity establishment and well demonstrate that centrosomes are not necessary for apical polarity formation, but are involved in the efficient establishment of the apical membrane.

Weaknesses:

Key questions remain regarding the structure of the intracellular cytoskeleton following depletion of centrosomes, centrosome proteins, or abrogation of centrosome microtubule nucleation. The authors strengthen their model that centrosomes are positioned independently of microtubule nucleation using dominant negative Cdk5RAP2 and NEDD-1 constructs, however, the structure of the intracellular microtubule network remains unresolved and will be an important avenue for future investigation.

Reviewer #3 (Public review):

Here the Wang et al resubmit their manuscript describing the events in the establishment of polarity in MDCK cells cultured in vitro. As with the original version, the description is throughout and is important to the field to report as it establishes a hierarchy of events in polarization, placing Par3 upstream of centrosome positioning and apical membrane component trafficking. Unfortunately, in the revised version, the authors addressed almost none of my points. They did a cursory job of responding in the rebuttal letter but made little attempt to actually address what was being asked or to incorporate any of my suggestions into the manuscript. The particularly egregious examples are cited below:

Comments on revisions:

(1) My original main experimental concern was not addressed: I had originally asked what role microtubules play in the process of polarization (either centrosomal or non-centrosomal). An obvious model is that Gp135, Rab11, etc. are delivered to the AMIS on centrosomal microtubules. Centrosomes might also be pulled to the AMIS via cortically derived microtubules as is the case in the C. elegans intestine where the centrosome moves apically on apical microtubules via dynein directed transport to the cortically anchored minus ends. The authors do not explore the role of microtubules in the revision, citing that it was not possible to observe the microtubules directly or to perform nocodazole experiments during polarization. Instead, the authors use a relatively new genetic tool to disrupt centrosomal microtubules. They appear to succeed in displacing centrosomal g-tubulin using this tool, but without being able to observe microtubules, a remaining caveat of this experiment is that it is still unclear whether the authors have removed centrosomal microtubules. Compounding this issue is that this tool has never been used in MDCK cells. The authors conclude "we found that cells lacking centrosomal microtubules were still able to polarize and position the centrioles apically.", but they have not shown this, instead the data suggest this conclusion and the authors should acknowledge the caveat that they have no idea whether centrosomal microtubules are abolished. Similarly, the authors also state: "Additionally, although PCNT knockout cells show reduced microtubule nucleation ability, they still recruit a small amount of γ-tubulin". Where are the data that show that microtubule nucleation is reduced in these PCNT knock out cells?

(2) Many of my comments were addressed in the rebuttal, but not in the text.

The non-centrosomal GP135 in Figure 2 is not acknowledged or explained.

That the polarity index does not actually measure polarity, but nuclear-centrosome distance is not acknowledged or explained in the paper.

I still don't believe that the quantification in Figure 3D matches the images I am being shown in Figure 3A. In the centrinone treatment condition, there is certainly an enrichment of GP135 at the AMIS that is not detected in the quantification. The method described in the rebuttal might miss this enrichment if it is offset from line drawn between the centroid of the two nuclei.

Cell height changes in the centrosome depleted cysts are still referenced in the text ("the cell heights of the centrosome-depleted cysts are less uniform"), but no specific data or image is called out. Currently, Figure 3G is referenced, but that is a graph of GP135 intensity.

In my original review, I called on the authors to comment on the striking similarity of the mechanisms they documented in MDCK cells to what has been shown in in vivo systems. The authors did not do this, instead restating in the rebuttal some features of what they found. But the mechanisms shown here are remarkably similar to the polarization of primordia that generate tubular organs in vivo. Perhaps most striking is the similarity to the C> elegans intestine where Par3 localizes to the cortex at the site of an apical MTOC that pulls the centrosome to the apical surface via dynein (Feldman and Priess, 2012). Instead of discussing this similarity, the authors state: "Par3 is likely to regulate centrosome positioning through some intermediate molecules or mechanisms, but its specific mechanism is still unclear and requires further investigation." Given the acetylated tubulin signal emanating from the Par3 positive patch in Figure 5E and F, I suspect similar mechanisms to the C. elegans intestine are at play here. Such a parallel should be noted in the Discussion.

I had originally commented that "I find the results in Figure 6G puzzling. Why is ECM signaling required for Gp135 recruitment to the centrosome. Could the authors discuss what this means?" The authors responded that "The data in Figure 6G do not indicate that ECM signaling is required for the recruitment of Gp135 to the centrosome". In Figure 6G, the localization of GP135 to the centrosome appears significantly delayed compared to its localization to the centrosome in images where cells were cultured in Matrigel. Indeed, the authors argue that the centrosomal localization precedes and contributes to its localization to the AMIS. In the absence of ECM, GP135 localizes to the membrane before it localizes to the centrosome and its localization to the centrosome appears significantly reduced. Thus, my original and current interpretation is that ECM signaling is somehow required for the centrosomal targeting of GP135. One could make a competition argument, i.e. that the cortex in the absence of ECM is somehow a more desirable place to localize than the centrosome, but this experiment also argues that the centrosome does not need to be a source of this material in order for it to end up on the cortex.

(3) There needs to be precision in the language used in many places:

I don't understand this line in the abstract: "When cultured in Matrigel, de novo polarization of a single epithelial cell is often coupled with mitosis." If a cell has divided, it is no longer a single cell.

The authors state in the Introduction "Because of its strong ability to nucleate microtubules, the centrosome functions as the primary microtubule organizing center", but then state ""In polarized epithelial cells, the centrosome is localized at the apical region during interphase, which contributes to the construction of an asymmetric microtubule network conducive to polarized vesicle trafficking". In the latter statement, I assume the authors are describing the well-characterized apical microtubule network in epithelial cells that is non-centrosomal. Thus, the latter sentence is at odds with the former.

The authors continually refer to Par3 as a tight junction protein. "Par3, which controls tight junction assembly to partition the apical surface from the basolateral surface". To my knowledge, PARD3 is an apical protein with similar localization to C. elegans PAR-3 and Drosophila Bazooka. PARD3B is a junctional protein. I assume that the antibody that the authors are using is to PARD3 and not PARD3B? Can the authors please clarify this in the text.

Author response:

The following is the authors’ response to the previous reviews

Public Reviews:

Reviewer #1 (Public review):

Summary:

Wang, Po-Kai et al., utilized the de novo polarization of MDCK cells cultured in Matrigel to assess the interdependence between polarity protein localization, centrosome positioning and apical membrane formation. They show that the inhibition of Plk4 with Centrinone does not prevent apical membrane formation, but does result in its delay, a phenotype the authors attribute to the loss of centrosomes due to the inhibition of centriole duplication. However, the targeted mutagenesis of specific centrosome proteins implicated in the positioning of centrosomes in other cell types (CEP164, ODF2, PCNT and CEP120), as well as the use of dominant negative constructs to inhibit centrosomal microtubule nucleation did not affect centrosome positioning in 3D cultured MDCK cells. A screen of proteins previously implicated in MDCK polarization revealed that the polarity protein Par-3 was upstream of centrosome positioning, similar to other cell types.

Strengths:

The investigation into the temporal requirement and interdependence of previously proposed regulators of cell polarization and lumen formation is valuable. The authors have provided a detailed analysis of many of these components at defined stages of polarity establishment, and well demonstrate that centrosomes are not necessary for apical polarity formation, but are involved in the efficient establishment of the apical membrane.

Weaknesses:

Key questions remain regarding the structure of the intracellular cytoskeleton following depletion of centrosomes, centrosome proteins, or abrogation of centrosome microtubule nucleation. The authors strengthen their model that centrosomes are positioned independently of microtubule nucleation using dominant negative Cdk5RAP2 and NEDD-1 constructs, however, the structure of the intracellular microtubule network remains unresolved and will be an important avenue for future investigation.

We thank the reviewer for raising this important point. We agree that understanding the organization of the intracellular microtubule network following centrosome depletion, disruption of centrosomal proteins, or inhibition of centrosomal microtubule nucleation will be important for further mechanistic insight. However, a detailed analysis of cytoskeletal architecture under 3D culture conditions would require super-resolution or other advanced microscopy techniques and therefore falls beyond the scope of the current study. Nevertheless, several previous studies conducted under conventional 2D culture conditions provide relevant mechanistic context for interpreting our findings.

(1) Centrosome depletion by centrinone treatment

Previous studies have shown that upon centrosome loss induced by the Plk4 inhibitor centrinone, alternative microtubule-organizing centers (MTOCs), particularly the Golgi apparatus, can compensate by nucleating non-centrosomal microtubules (Chen et al., 2022; Gavilan et al., 2018; Martin, Veloso, Wu, Katrukha, & Akhmanova, 2018; Wu et al., 2016). Consistent with these findings, our microtubule regrowth assays in 2D MDCK cells (see Author response image 1) demonstrated that centriole depletion markedly altered microtubule organization. Control cells displayed the typical radial microtubule array emanating from a centralized centrosome, whereas centrinone-treated cells exhibited a dispersed microtubule network with enhanced microtubule growth from Golgi-associated sites.

Author response image 1.

Staining of control or centrinone (CN) treated MDCK cells for α-tubulin (magenta), Golgi GM130 green) and DNA (DAPI, blue) 1 min after nocodazole washout. Z-maximum projections of confocal images. The boxed cells in the overview images are magnified, and the microtubule regrowth regions are further enlarged. Scale bars: 50 μm (overview images), 10 μm (magnified cells), and 5 μm (enlarged microtubule regrowth regions).

(2) Disruption of centriole or centrosomal proteins

Previous studies have reported that depletion of the subdistal appendage protein ODF2 reduces centrosome–microtubule interactions and destabilizes centrosomal microtubules (Hung, Hehnly, & Doxsey, 2016; Ibi et al., 2011; Tateishi et al., 2013). In addition, in cells lacking the PCM protein pericentrin (PCNT), AKAP450 has been shown to partially compensate for centrosomal microtubule nucleation activity, although at a somewhat reduced level compared with wild-type cells (Figure 4—figure supplement 3A, B) (Gavilan et al., 2018).

(3) Inhibition of centrosomal microtubule nucleation

For dominant-negative Cdk5RAP2 and NEDD1 constructs, a previous study demonstrated that these constructs displace γ-tubulin from centrosomes and impair centrosomal microtubule nucleation (Vinopal et al., 2023). Consistent with this report, our MDCK cells expressing dominant-negative Cdk5RAP2 or NEDD1 also exhibited reduced γ-tubulin localization at centrioles (Figure 4—figure supplement 3C, D, and G).

Together, these findings support the interpretation that centrosomal microtubules are not strictly required for polarized vesicle trafficking, centrosome migration, or epithelial polarization, but instead enhance the efficiency and robustness of these processes. We agree with the reviewer that future studies using advanced imaging approaches under 3D culture conditions will be important to resolve the spatial organization and dynamics of the intracellular cytoskeleton during epithelial polarization.

Reviewer #3 (Public review):

Here the Wang et al resubmit their manuscript describing the events in the establishment of polarity in MDCK cells cultured in vitro. As with the original version, the description is throughout and is important to the field to report as it establishes a hierarchy of events in polarization, placing Par3 upstream of centrosome positioning and apical membrane component trafficking. Unfortunately, in the revised version, the authors addressed almost none of my points. They did a cursory job of responding in the rebuttal letter but made little attempt to actually address what was being asked or to incorporate any of my suggestions into the manuscript. The particularly egregious examples are cited below:

Comments on revisions:

(1) My original main experimental concern was not addressed: I had originally asked what role microtubules play in the process of polarization (either centrosomal or non-centrosomal). An obvious model is that Gp135, Rab11, etc. are delivered to the AMIS on centrosomal microtubules. Centrosomes might also be pulled to the AMIS via cortically derived microtubules as is the case in the C. elegans intestine where the centrosome moves apically on apical microtubules via dynein directed transport to the cortically anchored minus ends. The authors do not explore the role of microtubules in the revision, citing that it was not possible to observe the microtubules directly or to perform nocodazole experiments during polarization.

Instead, the authors use a relatively new genetic tool to disrupt centrosomal microtubules. They appear to succeed in displacing centrosomal g-tubulin using this tool, but without being able to observe microtubules, a remaining caveat of this experiment is that it is still unclear whether the authors have removed centrosomal microtubules. Compounding this issue is that this tool has never been used in MDCK cells. The authors conclude "we found that cells lacking centrosomal microtubules were still able to polarize and position the centrioles apically.", but they have not shown this, instead the data suggest this conclusion and the authors should acknowledge the caveat that they have no idea whether centrosomal microtubules are abolished.

We appreciate the reviewer’s important comments regarding the role of microtubules during epithelial polarization. We agree that determining how centrosomal and/or non-centrosomal microtubules contribute to apical trafficking and centrosome positioning represents an important mechanistic question.

We previously attempted to directly visualize microtubules during live imaging using SPY-tubulin labeling (see Author response image 2). However, under 3D Matrigel culture conditions, MDCK cells rapidly become rounded and densely packed, substantially reducing image contrast and making the majority of intracellular microtubule networks difficult to resolve, except for spindle microtubules and the cytokinetic bridge. In addition, nocodazole treatment caused mitotic arrest under our experimental conditions, thereby preventing de novo polarization from proceeding and precluding interpretation of polarity establishment.

Author response image 2.

Time-lapse maximum-intensity z-projections of MDCK cells expressing EGFP-PACT (yellow; centrosome marker) and H2B-mCherry (magenta; nuclei) embedded in Matrigel. Microtubules were labeled with SiR-tubulin (cyan) before live-cell imaging. Images show a representative dividing cell. Time stamps indicate hours and minutes relative to anaphase onset (0:00). Scale bar, 10 μm.

To partially address the role of centrosomal microtubules, we used dominant-negative Cdk5RAP2 and NEDD1 constructs that have previously been shown to displace γ-tubulin from centrosomes and impair centrosomal microtubule nucleation (Vinopal et al., 2023). Consistent with this study, we observed substantial loss of γ-tubulin from centrioles in MDCK cells expressing these constructs (Figure 4— figure supplement 3C, D, and G). However, as the reviewer correctly points out, we were unable to directly visualize centrosomal microtubules under our 3D imaging conditions. Therefore, we cannot definitively conclude that centrosomal microtubules were completely abolished. We have revised the manuscript to clarify this limitation and to more cautiously state that our data suggest centrosomal microtubules may not be strictly required, for apical polarization and centrosome positioning under these conditions.

Similarly, the authors also state: "Additionally, although PCNT knockout cells show reduced microtubule nucleation ability, they still recruit a small amount of γ-tubulin". Where are the data that show that microtubule nucleation is reduced in these PCNT knock out cells?

We thank the reviewer for this comment and apologize for not sufficiently presenting these data in the previous revision. To directly assess microtubule nucleation activity in PCNT-KO cells, we performed microtubule regrowth assays in MDCK cells following nocodazole washout (Figure 4— figure supplement 3A, B). Compared with wild-type cells, PCNT-KO cells showed reduced centrosomal microtubule regrowth, indicating impaired microtubule nucleation capacity.

Importantly, microtubule nucleation was not completely abolished in PCNT-KO cells, consistent with previous reports showing that AKAP450 can partially compensate for the loss of pericentrin and maintain residual centrosomal microtubule nucleation activity (Gavilan et al., 2018).

(2) Many of my comments were addressed in the rebuttal, but not in the text.

We sincerely thank the reviewer for the valuable suggestions. We have carefully considered all comments and incorporated many of the recommended revisions into the revised manuscript. However, we found that including every additional analysis, experiment, and discussion in the main manuscript would substantially reduce its coherence and readability. Therefore, while not all new analyses and experimental results are included in the revised manuscript, we have addressed every comment comprehensively in this response letter. Where necessary, we performed additional experiments and analyses to obtain the requested data, and the corresponding results and explanations are provided in our responses. We hope the reviewer will understand our effort to thoroughly address all comments while preserving the clarity and overall flow of the manuscript.

The non-centrosomal GP135 in Figure 2 is not acknowledged or explained.

We apologize for not sufficiently addressing the non-centrosomal Gp135 signal in Figure 2. We have now revised the manuscript to explicitly describe and discuss this point in the text (Page 5, Paragraph 4).

That the polarity index does not actually measure polarity, but nuclear-centrosome distance is not acknowledged or explained in the paper.

We have revised the manuscript to explicitly state that the “polarity index” represents the distance between the nucleus and the centrosome, which we use as a quantitative indicator of the degree of cell polarity (Page 5, Paragraph 1).

I still don't believe that the quantification in Figure 3D matches the images I am being shown in Figure 3A. In the centrinone treatment condition, there is certainly an enrichment of GP135 at the AMIS that is not detected in the quantification. The method described in the rebuttal might miss this enrichment if it is offset from line drawn between the centroid of the two nuclei.

We thank the reviewer for this comment. To better address this concern, we have now included a 3D view of the corresponding image data (see Author response image 3 and Author response image 4). This analysis clarifies that, in the centrinone-treated condition, the Gp135 signal is not localized at the geometric center of the cell doublet, but is instead offset from the axis used in our line-scan quantification. As a result, the enrichment visible in the projection image was not fully captured by the original quantification method. SiR-DNA Gp135

Author response image 3.

Author response image 4.

3D reconstructions of p53-KO control and centrinone-treated MDCK cell doublets expressing EGFP-Gp135. Images are shown after 90° rotations about the x-axis (or y-axis) to visualize the spatial distribution of Gp135. Fluorescence intensity profiles of EGFP-Gp135 were measured along the line connecting the two nuclei. White arrows indicate the central fluorescence intensity value used to quantify Gp135 accumulation at the apical membrane initiation site (AMIS) (a.u., arbitrary units). Time stamps indicate hours and minutes.

Cell height changes in the centrosome depleted cysts are still referenced in the text ("the cell heights of the centrosome-depleted cysts are less uniform"), but no specific data or image is called out. Currently, Figure 3G is referenced, but that is a graph of GP135 intensity

We have revised the manuscript to indicate representative images, ensuring that the text and figures are consistent (Page 7, Paragraph 3).

In my original review, I called on the authors to comment on the striking similarity of the mechanisms they documented in MDCK cells to what has been shown in in vivo systems. The authors did not do this, instead restating in the rebuttal some features of what they found. But, the mechanisms shown here are remarkably similar to the polarization of primordia that generate tubular organs in vivo. Perhaps most striking is the similarity to the C. elegans intestine where Par3 localizes to the cortex at the site of an apical MTOC that pulls the centrosome to the apical surface via dynein (Feldman and Priess, 2012). Instead of discussing this similarity, the authors state: "Par3 is likely to regulate centrosome positioning through some intermediate molecules or mechanisms, but its specific mechanism is still unclear and requires further investigation." Given the acetylated tubulin signal emanating from the Par3 positive patch in Figure 5E and F, I suspect similar mechanisms to the C. elegans intestine are at play here. Such a parallel should be noted in the Discussion.

We thank the reviewer for this insightful suggestion. In the revised manuscript, we have expanded the Discussion section to compare our findings with epithelial polarization mechanisms described in in vivo systems, including the C. elegans intestine and other tubular epithelial tissues (Page 13, Paragraph 2).

We agree that the hierarchical relationship we observe between Par3 localization, centrosome positioning, and apical membrane formation bears important conceptual similarities to mechanisms reported in the C. elegans intestine (Feldman & Priess, 2012). We also considered the possibility that Par3 may regulate centrosome positioning through dynein-dependent mechanisms.

To examine this possibility, we performed immunofluorescence staining for the dynein cofactor dynactin subunit p150^Glued. However, we did not observe enrichment of p150^Glued at the center of cell doublets during the cytokinetic pre-abscission stage (see Author response image 5), suggesting that dynein is not strongly concentrated together with Par3 near the AMIS under our conditions.

In addition, pharmacological inhibition of dynein resulted in cytokinesis failure and the formation of binucleated cells, preventing reliable assessment of centrosome migration and polarity establishment.

We would also like to clarify that the acetylated tubulin signal observed in Figure 5E and F does not emanate from the Par3-positive patch. Rather, this signal corresponds to the cytokinetic bridge, adjacent to which Par3 accumulates during cytokinesis. Consistent with this interpretation, γ-tubulin was not detected at the Par3-positive region (Figure 1A).

Author response image 5.

Single MDCK cells after 12 h of culture in Matrigel. Immunostaining signals of the indicated markers are shown: centrosome marker PACT-mKO1, dynactin subunit p150Glued, Gp135, and DAPI. Single confocal sections through the middle of a cyst are shown. The order of polarization is arranged from single cell (1-cell), metaphase (Meta), telophase (Telo), cytokinetic pre-abscission (Pre-Abs), post-cytokinesis (Post-CK), to lumen open (LO). Scale bar: 5 μm.

I had originally commented that "I find the results in Figure 6G puzzling. Why is ECM signaling required for Gp135 recruitment to the centrosome. Could the authors discuss what this means?" The authors responded that "The data in Figure 6G do not indicate that ECM signaling is required for the recruitment of Gp135 to the centrosome". In Figure 6G, the localization of GP135 to the centrosome appears significantly delayed compared to its localization to the centrosome in images where cells were cultured in Matrigel.

Indeed, the authors argue that the centrosomal localization precedes and contributes to its localization to the AMIS. In the absence of ECM, GP135 localizes to the membrane before it localizes to the centrosome and its localization to the centrosome appears significantly reduced. Thus, my original and current interpretation is that ECM signaling is somehow required for the centrosomal targeting of GP135. One could make a competition argument, i.e. that the cortex in the absence of ECM is somehow a more desirable place to localize than the centrosome, but this experiment also argues that the centrosome does not need to be a source of this material in order for it to end up on the cortex.

We agree that the absence of ECM substantially alters the trafficking behavior of Gp135.

Our interpretation is that ECM primarily promotes the endocytosis and internal trafficking of Gp135, thereby enabling its redistribution to membrane domains lacking ECM contact and facilitating AMIS formation (Buckley & St Johnston, 2022; O'Brien et al., 2001; Yu et al., 2005).

Under ECM-free conditions, a larger fraction of Gp135 remains associated with the plasma membrane, resulting in reduced internalized Gp135 available for centrosome-associated trafficking. During anaphase to telophase (Figure 6G, 0:05–0:20), Gp135 predominantly redistributes along the plasma membrane toward the cleavage furrow. Only after cytokinesis initiation (Figure 6G, 0:30) do we observe a small amount of internalized Gp135 associated with centrosomes near the center of the cell doublet.

Importantly, we agree with the reviewer that these findings suggest centrosomal trafficking is not absolutely required for Gp135 to localize to the plasma membrane. Rather, our data support a model in which centrosome-associated trafficking contributes specifically to the efficient and spatially restricted delivery of Gp135 to the AMIS during epithelial polarization.

We have revised the manuscript to clarify this interpretation and to avoid overstating the role of centrosome-associated Gp135 trafficking.

(3) There needs to be precision in the language used in many places:

I don't understand this line in the abstract: "When cultured in Matrigel, de novo polarization of a single epithelial cell is often coupled with mitosis." If a cell has divided, it is no longer a single cell.

We have revised the sentence to: “When cultured in Matrigel, de novo polarization of a single epithelial cell is often coupled with cytokinesis (Page 1, Paragraph 1).” This indicates that polarization happens as the cell divides. We thank the reviewer for this helpful suggestion, which has improved the readability of the sentence.

The authors state in the Introduction "Because of its strong ability to nucleate microtubules, the centrosome functions as the primary microtubule organizing center", but then state ""In polarized epithelial cells, the centrosome is localized at the apical region during interphase, which contributes to the construction of an asymmetric microtubule network conducive to polarized vesicle trafficking". In the latter statement, I assume the authors are describing the well-characterized apical microtubule network in epithelial cells that is non-centrosomal. Thus, the latter sentence is at odds with the former.

We did not intend to refer to the apical non-centrosomal microtubule network present in mature, fully polarized epithelial cells. Rather, we were referring to the off-center centrosome functions as an off-center MTOC, creating an asymmetric microtubule network during the early stages of epithelial polarization, as mentioned in previous review papers (Meiring, Shneyer, & Akhmanova, 2020).

The apical non-centrosomal microtubule network is a feature of mature, fully polarized epithelial cells. In fact, its formation is also driven by the release of microtubule minus-ends from the off-centre centrosome, which are then transferred to the apical membrane (Goldspink et al., 2017; Moss et al., 2007; Sanchez & Feldman, 2017).

The authors continually refer to Par3 as a tight junction protein. "Par3, which controls tight junction assembly to partition the apical surface from the basolateral surface". To my knowledge, PARD3 is an apical protein with similar localization to C. elegans PAR-3 and Drosophila Bazooka. PARD3B is a junctional protein. I assume that the antibody that the authors are using is to PARD3 and not PARD3B? Can the authors please clarify this in the text?

The antibody used for PARD3 staining was the Merck Millipore rabbit polyclonal antibody (Cat. No. 07-330), generated against a GST-tagged recombinant fragment corresponding to 288 amino acids from the internal region of mouse PAR-3.

Canine PARD3 and PARD3B are encoded by distinct genes located on chromosomes 2 and 37, respectively. In our study, we used two independent shRNA constructs specifically targeting canine PARD3, both of which reduced the immunoblot signal detected by this antibody, supporting the conclusion that the antibody primarily recognizes PARD3 rather than PARD3B.

However, in immunofluorescence staining, the signal was mainly localized at tight junctions, and we did not observe significant signal at the apical membrane. We will further clarify in the revised manuscript that this antibody targets PARD3 rather than PARD3B (Page 10, Paragraph 1).

Reference

Buckley, C. E., & St Johnston, D. (2022). Apical-basal polarity and the control of epithelial form and function. Nat Rev Mol Cell Biol, 23(8), 559–577. doi:10.1038/s41580-022-00465-y

Chen, F., Wu, J., Iwanski, M. K., Jurriens, D., Sandron, A., Pasolli, M., . . . Akhmanova, A. (2022). Self-assembly of pericentriolar material in interphase cells lacking centrioles. Elife, 11. doi:10.7554/eLife.77892

Feldman, J. L., & Priess, J. R. (2012). A role for the centrosome and PAR-3 in the hand-off of MTOC function during epithelial polarization. Curr Biol, 22(7), 575–582. doi:10.1016/j.cub.2012.02.044

Gavilan, M. P., Gandolfo, P., Balestra, F. R., Arias, F., Bornens, M., & Rios, R. M. (2018). The dual role of the centrosome in organizing the microtubule network in interphase. EMBO Rep, 19(11). doi:10.15252/embr.201845942

Goldspink, D. A., Rookyard, C., Tyrrell, B. J., Gadsby, J., Perkins, J., Lund, E. K., . . . Mogensen, M. M. (2017). Ninein is essential for apico-basal microtubule formation and CLIP-170 facilitates its redeployment to noncentrosomal microtubule organizing centres. Open Biol, 7(2). doi:10.1098/rsob.160274

Hung, H. F., Hehnly, H., & Doxsey, S. (2016). The Mother Centriole Appendage Protein Cenexin Modulates Lumen Formation through Spindle Orientation. Curr Biol, 26(6), 793–801. doi:10.1016/j.cub.2016.01.025

Ibi, M., Zou, P., Inoko, A., Shiromizu, T., Matsuyama, M., Hayashi, Y., . . . Inagaki, M. (2011). Trichoplein controls microtubule anchoring at the centrosome by binding to Odf2 and ninein. J Cell Sci, 124(Pt 6), 857–864. doi:10.1242/jcs.075705

Martin, M., Veloso, A., Wu, J., Katrukha, E. A., & Akhmanova, A. (2018). Control of endothelial cell polarity and sprouting angiogenesis by non-centrosomal microtubules. Elife, 7. doi:10.7554/eLife.33864

Meiring, J. C. M., Shneyer, B. I., & Akhmanova, A. (2020). Generation and regulation of microtubule network asymmetry to drive cell polarity. Curr Opin Cell Biol, 62, 86–95. doi:10.1016/j.ceb.2019.10.004

Moss, D. K., Bellett, G., Carter, J. M., Liovic, M., Keynton, J., Prescott, A. R., . . . Mogensen, M. M. (2007). Ninein is released from the centrosome and moves bi-directionally along microtubules. J Cell Sci, 120(Pt 17), 3064–3074. doi:10.1242/jcs.010322

O'Brien, L. E., Jou, T. S., Pollack, A. L., Zhang, Q., Hansen, S. H., Yurchenco, P., & Mostov, K. E. (2001). Rac1 orientates epithelial apical polarity through effects on basolateral laminin assembly. Nat Cell Biol, 3(9), 831–838. doi:10.1038/ncb0901-831

Sanchez, A. D., & Feldman, J. L. (2017). Microtubule-organizing centers: from the centrosome to noncentrosomal sites. Curr Opin Cell Biol, 44, 93–101. doi:10.1016/j.ceb.2016.09.003

Tateishi, K., Yamazaki, Y., Nishida, T., Watanabe, S., Kunimoto, K., Ishikawa, H., & Tsukita, S. (2013). Two appendages homologous between basal bodies and centrioles are formed using distinct Odf2 domains. J Cell Biol, 203(3), 417–425. doi:10.1083/jcb.201303071

Vinopal, S., Dupraz, S., Alfadil, E., Pietralla, T., Bendre, S., Stiess, M., . . . Bradke, F. (2023). Centrosomal microtubule nucleation regulates radial migration of projection neurons independently of polarization in the developing brain. Neuron, 111(8), 1241–1263 e1216. doi:10.1016/j.neuron.2023.01.020

Wu, J., de Heus, C., Liu, Q., Bouchet, B. P., Noordstra, I., Jiang, K., . . . Akhmanova, A. (2016). Molecular Pathway of Microtubule Organization at the Golgi Apparatus. Dev Cell, 39(1), 44–60. doi:10.1016/j.devcel.2016.08.009

Yu, W., Datta, A., Leroy, P., O'Brien, L. E., Mak, G., Jou, T. S., . . . Zegers, M. M. (2005). Beta1-integrin orients epithelial polarity via Rac1 and laminin. Mol Biol Cell, 16(2), 433–445. doi:10.1091/mbc.e04-05-0435

sacral nerves (5)

spinal cord

cervical nerves (8)

thoracic nerves (12)

lumbar nerves (5)

spine

axon

dendrites

cell body

nucleus

myelin sheath

synapse

cranium

cerebrum

medulla oblongata

midbrain

spinal cord

pons

cerebrospinal fluid (csf)

cerebellum

corpus callosum

meninges

autonomic nervous system

brainstem

sensory nerves

motor impulses

sensory impusles

autonomic nervous system

dendrites

myotomes

cerebrum

peripheral nervous system

referred pain

cranium

cortex

meninges

dermatomes

cell body

axon

central nervous system

motor nerves

neuron

cerebellum

corpus callosum

spinal cord

sympathetic nervous system

homeostasis

glial cells

myelin sheath

eLife Assessment

The authors show that innate defensive behavior in mice is shaped by threat intensity, reward value, and social hierarchy, highlighting how value and social context influence instinctive decisions. The authors provide a valuable characterization of escape behavior which approximates naturalistic conditions. Despite minor methodological limitations, the work provides a solid foundation for future investigation of how reward and social context interact to influence behavior.

Reviewer #1 (Public review):

[Editors' note: this version has been assessed by the Reviewing Editor without further input from the original reviewers. The authors have addressed the comments in their discussion of the limitations that were raised in the previous round of review.]

This study by Li and colleagues examines how defensive responses to visual threats during foraging are modulated by both reward level and social hierarchy. Using a semi-naturalistic paradigm, the authors test how the availability of water or sucrose, with sucrose being more rewarding than water, shapes escape behavior in mice exposed to looming stimuli of different intensities, which are used to probe perceived threat level and defensive responses. In parallel, the study compares dominant and subordinate animals to assess how social rank biases the trade-off between reward seeking and threat avoidance. By combining behavioral analyses with computational modeling, the work addresses how reward level and social context jointly influence escape decisions in an ethological setting.

Across the different experimental conditions, perceived threat level is the main determinant of behavior. The authors show that looming stimuli associated with higher threat (contrast) consistently elicit faster and more robust escape responses than lower threat stimuli. This effect is particularly evident during early exposures, when animals are highly vigilant and have not yet habituated to the looming stimulus (learned that it is not dangerous). Later they described that as animals gain experience and habituate, behavior becomes more flexible, and reward level begins to exert a graded modulation of the escape response. Importantly, the authors show that under high threat conditions increasing reward value leads to more frequent and faster escape rather than greater reward pursuit, specifically in dominant mice. This finding is particularly relevant, as it suggests that highly valued rewards can heighten vigilance and thereby enhance responsiveness to threat, highlighting that reward does not simply compete with defensive behavior but can also reshape it depending on the perceived level of danger, in contrast to low threat conditions, where threat can be more easily outweighed by reward. However, it is worth noting that the authors use an extremely low contrast for the low threat condition (20%), which may to some extent be insufficient to reliably trigger escape responses. Thus, an important conceptual contribution of the study is the introduction of vigilance as a useful framework to interpret these effects. Vigilance is treated as a behavioral state reflecting heightened attention to potential danger. In line with what is known from natural foraging, mice initially maintain high vigilance when confronted with an innate threat. This perspective helps clarify a finding that might otherwise appear counterintuitive. One might expect higher rewards to motivate animals to tolerate risk, explore more, and habituate faster in any scenario. Instead, the data suggest that highly rewarding outcomes can elevate vigilance, making animals more responsive to threat and leading to faster or more frequent escape under high threat conditions. In this sense, reward does not simply compete with threat but can also amplify sensitivity to it, depending on the internal state of the animal.

The social results are particularly interesting in this context as well. Dominant mice consistently prioritize avoidance over reward, showing stronger escape responses and slower habituation than subordinates. This behavior is well captured by the vigilance framework proposed by the authors: dominant animals appear to maintain higher vigilance, which biases decisions toward threat avoidance. The authors further suggest that stable social relationships sustain high vigilance and slow habituation, framing this as an evolutionarily conserved strategy that may enhance survival. This interpretation provides a valuable perspective on how social structure shapes defensive behavior beyond immediate physical interactions. At the same time, there are important limitations to this interpretation. All experiments were conducted in male mice, and it is possible that the relationship between social hierarchy, vigilance, and defensive behavior would differ substantially in females. In addition, the idea that stable social relationships sustain elevated vigilance should be interpreted carefully, as it does not fully align with broader views of social stability as protective against anxiety and stress and generally beneficial for mental health and resilience. These points do not undermine the findings but suggest that the social effects described here should be interpreted with caution and within the specific context of the task and sex studied.

Another important limitation is that the neural mechanisms underlying these effects remain highly speculative. Although the manuscript includes an extensive discussion of candidate circuits, particularly involving the superior colliculus and downstream structures, these interpretations go far beyond the data presented in the study and are not directly supported by experimental evidence within the paper itself. The discussion gives substantial weight to potential circuit mechanisms based primarily on previous literature rather than on findings from the current study. Given the complexity and distributed nature of the circuits likely involved in integrating vigilance, reward, social context, and defensive behavior, the present work is better viewed as providing a strong behavioral framework rather than direct mechanistic insight into the underlying neural substrates. In this context, some references discussing how animals learn to suppress defensive responses to repeated looming threats and the neural mechanisms supporting this process could further strengthen the discussion (Salay et al 2021; Fratzl et al. 2021; Conway et al. 2025; Mederos et al. 2025).

Methodologically, the behavioral paradigm is well suited for studying escape decisions in socially housed animals, and the machine learning based classification of defensive responses is a strength. The computational model provides a useful formalization of how threat level, reward level, and vigilance interact and may be valuable for other laboratories studying escape, approach avoidance, or conflict situations, particularly as a way to classify behavioral outcomes after pose estimation. More generally, the work will be of interest to the neuroethology community for its detailed characterization of escape behavior under naturalistic conditions. At the same time, some statements in the discussion slightly overstate the novelty of the methodological approach. For example, the claim that the study differs from earlier work by using machine learning rather than manual annotation overlooks that several previous studies have already implemented automated or semi-automated strategies to classify looming evoked defensive behaviors beyond manual scoring alone.

Given the ethological nature of the study and the high inter individual variability reported by the authors, clarity and precision in the methods are especially important for reproducibility. While the revised manuscript addresses many earlier concerns, some aspects remain slightly difficult to follow. For example, the main text states that animals were not water deprived to minimize differences in internal state across conditions, whereas parts of the methods describe experiments in which animals were water deprived. This distinction is not always clearly explained across the different experimental sections, despite internal state being central to the interpretation of the behavioral findings. A clearer separation and description of these conditions would further strengthen confidence in the work. In addition, it was somewhat surprising that the low contrast (20%) looming condition was still sufficient to trigger robust escape responses, and additional clarification or discussion regarding stimulus saliency at this contrast level could help readers better contextualize these findings.

Overall, this study provides a rich analysis of how reward level and social hierarchy modulate defensive behavior through changes in vigilance. It offers a useful conceptual advance for thinking about escape behavior in semi-naturalistic settings and lays a solid foundation for future work aimed at linking these behavioral states to underlying neural circuits.

Author response:

The following is the authors’ response to the previous reviews

Public Reviews:

Reviewer #1 (Public review):

This study by Li and colleagues examines how defensive responses to visual threats during foraging are modulated by both reward level and social hierarchy. Using a semi-naturalistic paradigm, the authors test how the availability of water or sucrose, with sucrose being more rewarding than water, shapes escape behavior in mice exposed to looming stimuli of different intensities, which are used to probe perceived threat level and defensive responses. In parallel, the study compares dominant and subordinate animals to assess how social rank biases the trade-off between reward seeking and threat avoidance. By combining behavioral analyses with computational modeling, the work addresses how reward level and social context jointly influence escape decisions in an ethological setting.

Across the different experimental conditions, perceived threat level is the main determinant of behavior. The authors show that looming stimuli associated with higher threat (contrast) consistently elicit faster and more robust escape responses than lower threat stimuli. This effect is particularly evident during early exposures, when animals are highly vigilant and have not yet habituated to the looming stimulus (learned that it is not dangerous). Later they described that as animals gain experience and habituate, behavior becomes more flexible, and reward level begins to exert a graded modulation of the escape response. Importantly, the authors show that under high threat conditions increasing reward value leads to more frequent and faster escape rather than greater reward pursuit, specifically in dominant mice. This finding is particularly relevant, as it suggests that highly valued rewards can heighten vigilance and thereby enhance responsiveness to threat, highlighting that reward does not simply compete with defensive behavior but can also reshape it depending on the perceived level of danger, in contrast to low threat conditions, where threat can be more easily outweighed by reward. However, it is worth noting that the authors use an extremely low contrast for the low threat condition (20%), which may to some extent be insufficient to reliably trigger escape responses. Thus, an important conceptual contribution of the study is the introduction of vigilance as a useful framework to interpret these effects. Vigilance is treated as a behavioral state reflecting heightened attention to potential danger. In line with what is known from natural foraging, mice initially maintain high vigilance when confronted with an innate threat. This perspective helps clarify a finding that might otherwise appear counterintuitive. One might expect higher rewards to motivate animals to tolerate risk, explore more, and habituate faster in any scenario. Instead, the data suggest that highly rewarding outcomes can elevate vigilance, making animals more responsive to threat and leading to faster or more frequent escape under high threat conditions. In this sense, reward does not simply compete with threat but can also amplify sensitivity to it, depending on the internal state of the animal.

We agree that the low-contrast condition (20%) represents a relatively weak threat signal by design. This level was intentionally chosen to fall within a regime that biases behavior toward freezing and escape after assessment rather than direct escape, allowing us to examine graded decision-making across threat intensities. The higher escape probability observed under low-contrast conditions relative to previous studies (Evans et al., 2018; Fratzl et al., 2021) is likely attributable to the linear arena used here, which generally promoted escape behavior over freezing.

The social results are particularly interesting in this context as well. Dominant mice consistently prioritize avoidance over reward, showing stronger escape responses and slower habituation than subordinates. This behavior is well captured by the vigilance framework proposed by the authors: dominant animals appear to maintain higher vigilance, which biases decisions toward threat avoidance. The authors further suggest that stable social relationships sustain high vigilance and slow habituation, framing this as an evolutionarily conserved strategy that may enhance survival. This interpretation provides a valuable perspective on how social structure shapes defensive behavior beyond immediate physical interactions. At the same time, there are important limitations to this interpretation. All experiments were conducted in male mice, and it is possible that the relationship between social hierarchy, vigilance, and defensive behavior would differ substantially in females. In addition, the idea that stable social relationships sustain elevated vigilance should be interpreted carefully, as it does not fully align with broader views of social stability as protective against anxiety and stress and generally beneficial for mental health and resilience. These points do not undermine the findings but suggest that the social effects described here should be interpreted with caution and within the specific context of the task and sex studied.

We thank the reviewer for this important comment and agree that findings obtained in male mice may not necessarily generalize to female mice. We have acknowledged the limitation in the Discussion and note that future studies will be required to determine the extent to which the present findings extend to female mice.

We would also like to clarify our interpretation regarding vigilance and social buffering. Vigilance should not be conflated with stress or anxiety, and the slower habituation observed in group-housed mice reflects sustained sensitivity to repeated threat exposure rather than elevated anxiety. Thus, our data do not contradict the concept of social buffering; rather, they are consistent with it, as pair-housed mice exhibited reduced defensive responses compared with individually housed animals (Lenz et al., 2022). Furthermore, in an independent study (Li, Gao, and Li, 2026; eLife 15:RP109571), we directly compared responses to looming stimuli when mice were tested alone versus in the presence of a social partner and found clear evidence of social buffering. These findings suggest that social interactions can attenuate defensive responses while prolonging vigilance during repeated threat exposure. We have revised the Discussion accordingly.

Another important limitation is that the neural mechanisms underlying these effects remain highly speculative. Although the manuscript includes an extensive discussion of candidate circuits, particularly involving the superior colliculus and downstream structures, these interpretations go far beyond the data presented in the study and are not directly supported by experimental evidence within the paper itself. The discussion gives substantial weight to potential circuit mechanisms based primarily on previous literature rather than on findings from the current study. Given the complexity and distributed nature of the circuits likely involved in integrating vigilance, reward, social context, and defensive behavior, the present work is better viewed as providing a strong behavioral framework rather than direct mechanistic insight into the underlying neural substrates. In this context, some references discussing how animals learn to suppress defensive responses to repeated looming threats and the neural mechanisms supporting this process could further strengthen the discussion (Salay et al 2021; Fratzl et al. 2021; Conway et al. 2025; Mederos et al. 2025).

We agree that the proposed neural mechanisms remain speculative and that the circuits involved in integrating internal state, reward, and social context are likely far more complex. We have revised the manuscript to acknowledge this limitation and have included a discussion of learning-dependent suppression of defensive responses to repeated looming threats and the underlying circuit mechanisms.

Methodologically, the behavioral paradigm is well suited for studying escape decisions in socially housed animals, and the machine learning based classification of defensive responses is a strength. The computational model provides a useful formalization of how threat level, reward level, and vigilance interact and may be valuable for other laboratories studying escape, approach avoidance, or conflict situations, particularly as a way to classify behavioral outcomes after pose estimation. More generally, the work will be of interest to the neuroethology community for its detailed characterization of escape behavior under naturalistic conditions. At the same time, some statements in the discussion slightly overstate the novelty of the methodological approach. For example, the claim that the study differs from earlier work by using machine learning rather than manual annotation overlooks that several previous studies have already implemented automated or semi-automated strategies to classify looming evoked defensive behaviors beyond manual scoring alone.

We have revised the manuscript to moderate our claims regarding the novelty of the machine-learning–based classification approach.

Given the ethological nature of the study and the high inter individual variability reported by the authors, clarity and precision in the methods are especially important for reproducibility. While the revised manuscript addresses many earlier concerns, some aspects remain slightly difficult to follow. For example, the main text states that animals were not water deprived to minimize differences in internal state across conditions, whereas parts of the methods describe experiments in which animals were water deprived. This distinction is not always clearly explained across the different experimental sections, despite internal state being central to the interpretation of the behavioral findings. A clearer separation and description of these conditions would further strengthen confidence in the work. In addition, it was somewhat surprising that the low contrast (20%) looming condition was still sufficient to trigger robust escape responses, and additional clarification or discussion regarding stimulus saliency at this contrast level could help readers better contextualize these findings.

To improve clarity, we have revised the Methods section to clearly distinguish between experimental conditions that involved water deprivation and those that did not.

Overall, this study provides a rich analysis of how reward level and social hierarchy modulate defensive behavior through changes in vigilance. It offers a useful conceptual advance for thinking about escape behavior in semi-naturalistic settings and lays a solid foundation for future work aimed at linking these behavioral states to underlying neural circuits.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

- Clarify which experiments involved water deprivation and which did not in methods section.

We have revised the Methods section to clearly specify which experiments involved water deprivation and which were conducted without water deprivation.

- Add discussion on why the 20% low contrast stimulus is still sufficient to trigger escape responses.

We have expanded the Discussion regarding the 20% contrast stimulus to clarify why it was sufficient to elicit escape responses.

- Tone down statements regarding the novelty of the machine learning based behavioral classification.

We have revised the manuscript to moderate claims regarding the novelty of the machine-learning–based classification approach.

- Include references related to learning-dependent suppression of looming responses and circuit mechanisms

We have incorporated the suggested references and expanded the Discussion of learning-dependent suppression of looming responses and the underlying circuit mechanisms.

- Moderate the discussion of candidate neural circuits, particularly the SC related interpretations, as these are not directly tested in the study.

We have revised the discussion of candidate circuit mechanisms.

- Clarify that the interpretation linking stable social relationships to elevated vigilance may be specific to this ethological context.

We have revised the Discussion to clarify this interpretation.

eLife Assessment

This important study addresses a discrepancy between population-level growth laws and single-cell correlations. It shows, for flagellar and synthetic genes in E. coli, that while gene expression of certain genes reduces population-average growth, expression levels positively correlate with growth at the single-cell level. The measurements are convincing, and the proposed mechanism - inheritance of growth factors such as ribosomes during asymmetric division - is consistent with the data.

Reviewer #1 (Public review):

Summary:

Garcia-Alcala, Kratz and Cluzel investigate to what extent our understanding of bacterial physiology in bulk experiments can be applied to single-cell observations. They find that intrinsic noise may be powerful enough to even inverse the trends found in the bulk. The authors hypothesize that asymmetric distribution of ribosomes to daughter cells during the cell division plays the dominant role in the intrinsic noise and is able to generate the observed phenomenon. They do not show it directly, but the data and its agreement with the model suffice to support this claim.

Strengths:

The experimental part is convincing: the positive correlation between the elongation rate and promoter activity of unnecessary protein is clear, as well as the negative correlation between the mean values while changing the promoter strength. This was demonstrated in both rich and poor media. The causality between the growth rate and the promoter activity was shown using the negative lag time of the cross-correlation function. A simple, reasonable model accounts well for the data. This paper demonstrates an interesting phenomenon and provides a plausible theory for it, advancing our understanding of bacterial physiology on the single-cell level.

Weaknesses:

(1) Mean-reversion timescales were assumed to be longer than the simulation time and much longer than the cell cycle time. It is not clear whether the results robust in case mean-reversion timescales become of the order of cell cycle or smaller. If not, is there an argument for such practically infinite reversion timescales?

(2) It is not easy to understand the simulation part unless one reads Ref. [14]. Is k(t) assumed to follow Eq. (1) from ref. [14]? Is this crucial that the ribosome noise appears only at the division? The ribosome noise strength \sigma_R=0.06 - is it lower or higher than the naively expected binomial division?<br /> Also, more intuitive explanation of the Simpson paradox would help the reader.

(3) It would be useful for the reader to see the raw data and not only the filtered one to appreciate the measurement noise level.

(4) Negative lag time of the cross-correlation function is visible, but consider adding statistical test for it.

(5) Can you make similar cross-correlation plots using the model? Can you infer using it whether the data agrees better with the assumption that ribosomes noise appear only at division or continuous fluctuations during the cell cycle?

Comments on revised version:

The authors addressed the five comments listed above.

Reviewer #2 (Public review):

Summary:

The manuscript by Garcia-Alcala et al. reports an interesting paradox: the cost of gene expression slows the population-average growth rate, whereas at the single-cell level, expression levels from these genes positively correlate with the growth rate. The effect is observed in the expression of flagellar genes and a gene under a synthetic promoter in E. coli. The findings are explained by the inheritance of growth factors, including ribosomes, during asymmetric division.

Strengths:

(1) The manuscript adds strength to an emerging body of literature showing that the population-level bacterial growth laws do not match correlations based on single-cell data. The evidence presented here is more striking than in previous works (such as Pavlou et al., Nat. Commun. 2025), as the trends in population-level data and single-cell data are reversed.

(2) A relatively simple model correctly explains the trends in the data.

Weaknesses:

(1) The differing behavior of the MG1655 and MC4100 strains remains a lingering question concerning the generality of the conclusions. It appears unlikely that ribosomes or other growth factors partition significantly differently in the MC4100 strain than in the MG1655 strain. Furthermore, based on Fig. S15, it is still unclear to what extent MC4100 exhibits growth-rate fluctuations, as stated in the text, rather than primarily size fluctuations, as shown in the figure. It is also unclear why such very slow fluctuations would lead to qualitatively different behavior, given that the proposed mechanism appears to be rather fundamental. It would be helpful for the authors to discuss these two points.

(2) It is unclear what fraction of the total proteome mVenus represents in different measurements. Adding this information would strengthen the conclusions.

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

Summary:

Garcia-Alcala, Kratz and Cluzel investigate to what extent our understanding of bacterial physiology in bulk experiments can be applied to single-cell observations. They find that intrinsic noise may be powerful enough to even inverse the trends found in the bulk. The authors hypothesize that the asymmetric distribution of ribosomes to daughter cells during cell division plays the dominant role in the intrinsic noise and is able to generate the observed phenomenon. They do not show it directly, but the data and its agreement with the model are sufficient to support this claim.

Strengths:

The experimental part is convincing: the positive correlation between the elongation rate and promoter activity of unnecessary protein is clear, as well as the negative correlation between the mean values while changing the promoter strength. This was demonstrated in both rich and poor media. The causality between the growth rate and the promoter activity was shown using the negative lag time of the cross-correlation function. A simple, reasonable model accounts well for the data. This paper demonstrates an interesting phenomenon and provides a plausible theory for it, advancing our understanding of bacterial physiology on the single-cell level.

Weaknesses:

(1) Mean-reversion timescales were assumed to be longer than the simulation time and much longer than the cell cycle time. It is not clear whether the results are robust in case mean reversion timescales become of the order of the cell-cycle or smaller. If not, is there an argument for such practically infinite reversion timescales?

Due to an error in the simulation code, the incorrect mean-reversion timescales were reported in the manuscript and have now been corrected. Instead of 1000 h and 100 h for 𝜏_R and 𝜏_U, respectively, they are 6.25 h and 0.0625 h, which are both much shorter than the total simulation time (75 h). The error had no effect on simulation results and was purely a timescale conversion mistake. We appreciate the referee’s careful review of the manuscript which allowed us to catch this error.

Given the correct values, 𝜏_U is well within the cell-cycle time, as is expected since we assume the source of noise in unnecessary protein expression is from stochastic gene expression. In contrast, 𝜏_U is still significantly longer than the cell-cycle time and needs to be in order to generate the experimentally observed behavior (i.e., the increase in growth rate with an increase in unnecessary protein expression at the single-cell level). This is consistent with our biological hypothesis that some cells inherit ribosomal surpluses from their mothers which enable bursts in protein production. 𝜏_U less than the cell-cycle time would correspond to a case where ribosomal composition quickly decays to the population average, meaning that daughter cells would never have time to capitalize on the benefit of receiving a ribosome surplus. Furthermore, 𝜏_U being longer than the cell-cycle is biologically justifiable as proteins such as ribosomes are passed from mother to daughter at division, thus allowing for memory to persist over longer timescales than a single generation.

(2) It is not easy to understand the simulation part unless one reads Ref [14]. k(t) is assumed Equation (1) from Reference [14]? Is it crucial that the ribosome noise appears only at the division? The ribosome noise strength σ_R =0.06 - is it lower or higher than the naively expected binomial division? Also, a more intuitive explanation of the Simpson paradox would help the reader.

𝜅(𝑡) is indeed Eq. (1) from Ref. [14]. To make the computational results clearer, the methods section has been updated to include the full set of equations used to perform the simulations, and the code used to produce the computational figures is now on GitHub. The relative standard deviation expected by modeling ribosome distribution at division by a binomial distribution with equal probability of being inherited by either daughter cell is in the range of 1-3% (0.01-0.03), assuming N~10³-10⁴ ribosomes. Thus, 0.06 is reasonable as it is in the same order of magnitude as what is predicted by binomial division. Furthermore, if clustering is present as already demonstrated in [39, 40, 43], we would expect the relative standard deviation to increase as clustering reduces the effective number of proteins which are distributed between daughters.

(3) It would be useful for the reader to see the raw data and not only the filtered one to appreciate the measurement noise level.

We have included the direct calculations of cell size and promoter activity in the time-lapse plots in Fig. 1. In addition, we included Fig. S2, which shows typical time traces of Class-2 activity and elongation rate, displaying both the direct measurements and the corresponding smoothed traces for the flagellar reporter strains.

(4) Negative lag time of the cross-correlation function is visible, but consider adding a statistical test for it.

We have included a statistical analysis of the lags of maximum correlation of elongation rate and activity for the flagellar reporter strains in Fig. S10. For each strain, we now show an overlay of the cross-correlation functions for all lineages and the distribution of the lag corresponding to the maximum correlation. In addition, we have added a section in Materials and Methods describing in detail how the cross-correlation and the strain-averaged lag were computed.

(5) Can you make similar cross-correlation plots using the model? Can you infer by using it, whether the data agrees better with the assumption that ribosomal noise appears only at division or continuous fluctuations during the cell cycle?

The model in its current form is unable to capture the observed cross-correlation (the correlation is sharply peaked at zero). This is because the model coarse-grains transcription and translation into one process of protein production and thus lacks any delay or memory mechanisms which would make a significant positive or negative correlation.

Both continuous fluctuations and noise from division could in principle contribute to our observation of Simpson’s paradox. We are unable to use the model in its present form to dissect the contributions from both mechanisms. However, our model simulations clearly show that noise at division by itself is sufficient to explain the observed effect with parameter values that are biologically plausible. As Chao et al., [40] has previously shown experimentally that a significant source of ribosomal noise comes from unequal distribution at division, which is the hypothesis we retained for the model.

Reviewer #2 (Public review):

Summary:

The manuscript by Garcia-Alcala et al. reports an interesting paradox: the cost of gene expression slows the population-average growth rate, whereas at the single-cell level, expression levels from these genes positively correlate with the growth rate. The effect is observed in the expression of flagellar genes and a gene under a synthetic promoter in E. coli. The findings are explained by the inheritance of growth factors, including ribosomes, during asymmetric division.

Strengths:

(1) The manuscript adds strength to an emerging body of literature showing that the population-level bacterial growth laws do not match correlations based on single-cell data. The evidence presented here is more striking than in previous works (such as Pavlou et al., Nat. Commun. 2025), as the trends in population-level data and single-cell data are reversed.

(2) A relatively simple model correctly explains the trends in the data.

Weaknesses:

(1) It is not clear whether flagellar proteins are expressed proportionally to the reporter signal. Furthermore, it is questionable if E. coli bacteria in the mother machine channels are flagellated. If they are, they could potentially swim out of the channels, which is not the case when they do not carry the MotA E98K mutation. The authors should provide some evidence that E. coli expresses the actual filament proteins in the channels.

We agree that it is important to demonstrate that our reporter reflects the production of functional flagellar structures under our experimental conditions. To this end, we first tested the swimming capabilities of our strains before introducing the MotA E98K mutation, using a standard soft‑agar motility assay. For strains in which only the Class‑1 promoter was modified (Pro2, Pro4, Pro5), after 12 h of inoculation, the diameters of the swarming rings followed the expected order based on promoter strength: Pro2 showed the smallest ring, followed by Pro4, WT, and Pro5. In contrast, control strains carrying Pro4 together with either MotA E98K (non‑rotating motors) or ΔfliC (no flagellin filament) did not form rings, consistent with their inability to swim. We also included an MG1655 strain carrying an IS5 insertion upstream of the Class‑1 promoter, which is known to enhance flagellar expression [56]; this strain showed a larger ring, as expected. A qualitative summary of ring diameters for all strains is provided in Author response table 1. We have included the figure and section “Experimental validation of functional flagella expression in reporter strains” on Supplementary Material.

Author response table 1.

We also tested whether cells assemble functional flagella on the mother‑machine. We compared MG1655 WT and MG1655 carrying the MotA E98K mutation under identical microfluidic growth conditions. Many WT cells left the channels during the experiment (∼35% of lineages over ~20 h after the onset of exponential growth inside the device), consistent with active swimming, whereas the non‑motile MotA E98K strain did not leave the channels. Because the only difference between these two strains is the MotA E98K mutation, which disables motor rotation but not flagellar assembly, this result indicates that (i) cells do express functional filaments and motors in the mother‑machine environment, and (ii) the strain used for our main experiments is immobilized by MotA E98K.

Both experiments are included in the Supplementary Information section “Experimental validation of functional flagella expression in reporter strains” and Fig. S3.

(2) It is unclear what fraction of the total proteome mVenus represents in different measurements. Some quantification is needed (for example, using the Coomassie staining). Using f_U as high as 14.4% in simulations is questionable.

We agree that we do not currently know the exact fraction of the proteome occupied by mVenus in our experiments, as we only quantified fluorescence and did not perform Coomassie staining or proteomics. The primary goal of our simulations is not to reproduce exact experimental conditions, but to illustrate how unequal ribosome partitioning affects daughter cells across a range of protein synthesis burdens. Consequently, our use of values up to F_U = 14.4% in the simulations was intended as an exploratory upper range rather than as a direct estimate of the experimental condition.

To put our experimental burden in context, we compare our growth-rate reduction to a well– characterized high-burden case in the literature. In the study by T. Hwa’s group [6], overexpression of β–galactosidase such that it constituted approximately 27% of the total proteome led to a 67% reduction in growth rate for E. coli growing in a medium similar to ours (differing only in the carbon source: glucose in their case, glycerol in ours). In our system, overexpression of mVenus from a plasmid result in a substantially smaller growth–rate reduction of 9% relative to the non–expressing control, and this is observed on the poorer carbon source (glycerol), under which burden effects are typically less pronounced [6].

Although we cannot convert our fluorescence measurements into an exact proteome fraction, the much smaller growth defect compared to the 27% β‑galactosidase case strongly suggests that mVenus does not approach such an extreme fraction of the proteome under our conditions. Under the simplifying assumption that the qualitative relationship between unnecessary‑protein fraction and growth‑rate reduction is similar in the two systems, our data are therefore consistent with a modest fraction of unnecessary protein and make it unlikely that mVenus reaches very high fractions such as 27%. This gives us confidence that exploring F_U values up to 14.4% in the simulations represents a conservative upper range relative to our experimental burden, rather than an underestimate.

(3) The data from the MC4100 strain does not directly match the trends of MG1655. The justification for filtering out the low-frequency components of MC4100 is not particularly convincing. It appears unlikely that ribosomes or other growth factors partition significantly differently in the MC4100 strain than in the MG1655 strain. Further discussion and a plot similar to Figure 1 (Left) for this strain are warranted.

We thank the reviewer for this helpful suggestion. We agree that the filtering analysis from the initial manuscript was not clear enough to be used as robust supplementary information, and we therefore removed it. Instead, we carried out with the ‘unfiltered’ MC4100 strain, the same single-cell analyses as with MG1655, including the binning analysis of instantaneous elongation rate versus Class-2 promoter activity, and a cross-correlation analysis between such measurements.

Both analyses did not reveal a significant correlation between growth and flagellar promoter activity in MC4100. We now present these results in the revised manuscript (Fig. S15), where we explicitly show the lack of association in a plot directly comparable to Fig. 1.

While ribosomes partition mechanism is likely to be the same between these two strains, MC4100 is known to exhibit very long oscillations of growth rate over 10 generations, which are absent in MG1655.

We now present MC4100 as an explicit counterexample to highlight that the positive correlation between short timescale growth fluctuations and flagellar expression observed in MG1655 is not universal across all E. coli strains, especially in strains like MC4100 whose growth rate fluctuations are dominated by long timescales, much longer than the division time. In MC4100 these slow modes are largely decoupled from flagellar gene regulation. We have also revised the text to clarify this point.

(4) The model needs to be described in more detail. A closed set of equations that have been simulated must be presented, along with all values of the model parameters and their sources. The authors should consider depositing their code on GitHub or another publicly accessible repository.

The methods section has now been updated to include the full set of equations used to perform the simulations along with all parameter values and their sources. Additionally, the code used to produce the computational figures is now on GitHub. For a full derivation and biological justification of each model component, we still refer readers to ref [14] where this model was first published.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

(1) The first paragraph of Results still belongs to the Introduction Section, I feel.

Thank you for the recommendation, we agreed with the change.

(2) Figure 1, left, is after some filter (Savitzky-Golay)? It might be useful to see the raw points.

We have included the direct calculations of cell size and promoter activity in the time-lapse plots in Fig. 1. In addition, we included Fig. S2, which shows typical time traces of Class-2 activity and elongation rate, displaying both the raw (direct) measurements and the corresponding smoothed traces for the flagellar reporter strains.

Reviewer #2 (Recommendations for the authors):

(1) Is there a statistically significant positive slope in single-cell data of the elongation rate as a function of Class-2 activity? There should be an analysis of statistical significance for the slopes in Figure 1 (Right) and Figure 4A, including both population-average and single-cell data.

Thank you for your recommendation. We have now added statistical analyses of the correlations between activity and elongation rate at both the single-cell and population levels. The results for the flagellar strains are presented in Fig. S5, S6, and S7. Also, the corresponding analysis for constitutive Venus expression is shown in Fig. S16.

(2) How does FlhC-YFP data compare with the CFP signal in the first set of measurements? What would Figure 1, Left look like for this signal?

At the single‑cell level, the relationship between Class‑1 promoter activity and elongation rate within each strain is still positive, but clearly weaker than for Class‑2, as shown in Fig. S8. When we bin the data, we can observe that the binned averages don’t display a clear positive trend, even though the Pearson correlation for each flagellar reporter strain is still positive. We think this is because Class‑1 controls a much smaller part of the proteome (it only encodes the two subunits of FlhDC) while Class‑2 promoters drive many structural and export proteins. So, changes in Class‑2 activity more directly reflect shifts in global translational capacity and are more tightly linked to growth, whereas Class‑1 activity adds only a small translational load.

(3) The information from the ER-Activity cross-correlation functions is interesting but has not been interpreted or compared with the model. Which signal precedes the other? What can explain the observed lag time on the order of Tdiv? Why do some cross-correlations show negative values in Fig. S9 while others are positive (as they should)? Can the model explain the experimentally observed cross-correlation function?

In our cross‑correlation analysis, a negative lag at the maximum means that fluctuations in elongation rate (ER) precede fluctuations in promoter activity (A) by that lag. We now describe this explicitly in Materials and Methods and quantify lag distributions for all reporter strains in Fig. S10.

In Fig. 13 (previously Fig. 9), we mainly observe positive correlations between ER and PA with negative or near zero peak lags, i.e., ER tends to lead PA by a lag by about a division time. The reason governing the negative lags is not immediately clear, but it is consistent with a resource driven mechanism: cells that inherit more growth factors (e.g. ribosomes) at division, use them to prioritize housekeeping processes and thus increase growth rate first, and only subsequently use these extra resources to increase flagellar promoter activity.

As for Class–1 activity, the correlation with growth is much weaker as it was already observed in Kim et al. Averaging the cross–correlations across all lineages yields a modest peak (mean correlation 0.10, SD 0.12; see Author response image 1) at a small positive lag of 0.5 h (while average division time is T_div ~ 1.7h). This indicates a weak but real positive correlation between Class 1 activity and ER at short lags. However, the lags of the individual maxima are widely distributed (SD ≈ 9 h; mean −1.8 h, median −0.28 h, mode ≈ 0), with only ~53% of the cells showing a negative time lag. Thus, delays are roughly symmetrically spread around zero with only a slight negative bias.

Author response image 1.

Left: cross−correlation between elongation rate and Class−1 activity for each lineage (N = 96, colored lines), and their average as a function of lag (black line). Right: distribution of the lags at which each lineage’s cross−correlation attains its maximum. The dashed line indicates zero lag, and the full line marks the lag of the peak of the mean cross correlation.

An in-depth analysis about the sign and magnitude of the lag would require measurements from a broader set of promoters. The lag is likely to depend on what genes the promoter controls (e.g. stress response, housekeeping, or large structural modules), on its strength and regulation, and on growth conditions.

The model in its current form is unable to capture the observed cross-correlation (the correlation is sharply peaked at zero). This is because the model coarse-grains transcription and translation into one single process of protein production and thus lacks any delay or memory mechanism that could generate a phase shift.

Nonetheless, this memory-free formulation shows that stochastic redistribution of growth factors at division is by itself sufficient to generate the Simpson’s paradox behavior. Capturing the experimentally observed lag time would require including explicit transcription/translation delays and additional regulatory dynamics between housekeeping and flagellar genes whose information we do not have.

(4) Figure 3, Left - it is not clear what this plot shows. Red and blue are scattered over the whole plot. How have daughter 1 and daughter 2 been assigned? Perhaps choosing one of the daughters with a higher growth rate and then plotting the data could reveal some trends.

We agree that the original left panel of Fig. 3 was difficult to follow. In the original version, the daughter labels were assigned as “Daughter–1” for the cell at the closed end of the channel and “Daughter–2” for the cell closest to the open side. After discussing this with Dr Camilla Ulla Rang (whom we now acknowledge in the manuscript), we relabeled the daughters as “new–pole daughter” and “old–pole daughter,” following the convention used in studies of aging and ribosome distribution in E. coli.

We now show the class–2 promoter activity comparison between new pole vs old pole, and in a separate panel, we plot the mean ratios of elongation rate, and class–1/class–2 promoter activities between the new–pole and old–pole daughters. These ratios show that the new–pole daughter tends to have both greater growth rate and flagellar gene activity than the old–pole daughter. Importantly, this new plot is in line with Rang and colleagues who demonstrated that new–pole daughters have greater ribosome density and faster growth rates than their old–pole sisters. Together, these results further support our hypothesis that excesses of ribosomes inherited at division underlies the observed growth boosts.

(5) Flagellar activity -> activity of flagellar gene synthesis (presumably no flagellar activity in these cells).

We corrected the terms used to reference the flagellar gene activity.

(6) Page 7: "The strain MC4100, known to exhibit slow, long period oscillations in growth ..." - some reference is- needed here.

We placed the reference some lines after, as such paper also includes the information of MG1655 short-term oscillations. Now the reference is [44]: Tanouchi, Y., et al., A noisy linear map underlies oscillations in cell size and gene expression in bacteria. Nature, 2015

(7) Page 7: Figure S11B - is Figure S11C perhaps meant?

The correct panel indeed was Fig. S11C, and we have now corrected and updated the figure label and text accordingly.

(8) Page 11: Savitsky-Golay filter.

We have corrected it, thank you.

看到流（水） ➡️ 找带水的朝代 ➡️ 清朝（明清时期）。 看到改（纠正） ➡️ 找带正的皇帝 ➡️ 雍正。

秦汉唐宋让人“极迷”（羁縻）。秦皇汉武唐宗宋祖 剩下的元明清怎么办？只能吃“土”了（土司制度）。

drop this one please, lets use the space to write the downsides of the competitors please, or what is missing with the competitors. i mean we write what is missing with plentyone, we also need to do that with the others...

okay, but please write a coulpe sentences that this does not cover the entire thign of warenwirtshaft or whatever, it cant compete with plentyone because it only does channels...we need to write that as well

again, lets add a few sentence what xentral is not good at, some objective downsides, use the wf articel to udnerstand the downsides or drawbacks rather

drop this, write something else, write the downsides here, use a coulple of downsides from the other article, the wf articles. this means nothing here what you wrote, and there is not enought space to describe it properly

unclear. ein packet ueber 100 marktplatyzuftraege kostet 25 euro pro monat? so if you have more than 100 orders, every package costs 25, or no matter how many more its 25? or what does it mean. also its not very clear, why do you say marktplatzauftrage and packages and two seaprate things, i mean they differ, but why can we compare them at the same time?

this sentence is unclear, i dont understand what you are saying. maybe this is too condences. if necessary, lets drop some arguments intsead of condensing too much.

one thing actually to mention, i think it was jtl that does not have a multilingual usere interface, so if you have an international team you have problems....this matter for interantioanl expansion...

these can capture non-linear interactions between features that logistic regression would miss. For example, maybe the relationship between damping and resonance shift only matters when Q factor is below a certain threshold. Trees find that automatically. The reason we can't do this at 15–20 is that these models have more free parameters and will fit noise in ways that are harder to detect and interpret.

stupid majority voting