PCOMPBIOL-D-25-00230

Quantifying hiPSC-CM structural organization at scale with deep learning-enhanced SarcGraph

PLOS Computational Biology

Dear Dr. Lejeune,

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We look forward to receiving your revised manuscript.

Kind regards,

Anna Grosberg, Ph.D.

Academic Editor

PLOS Computational Biology

Stacey Finley, Ph.D.

Section Editor

PLOS Computational Biology

Additional Editor Comments :

The reviewers' consider this work to be important to the field. However, there are important questions about the availability of all necessary components in the GitHub repository. Further, there are some methodological questions that should be addressed based on the reviewer comments.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Authors:

Please note that one of the reviews is uploaded as an attachment.

Reviewer #1: This manuscript significantly advances our ability to quantify cardiomyocyte structural organization at scale by enhancing the previously published SarcGraph pipeline. Introducing a deep-learning-based z-disc classification coupled with the ensemble graph-scoring method represents an essential methodological leap forward, particularly for challenging datasets involving immature hiPSC-CMs. Its use of graph theory and deep learning methods clearly provides strong potential for generalizability beyond classical Z-disk markers. It is highly exciting and warrants publication in PLOS Computational Biology.

The primary issue with the manuscript in its current form is the accessibility and completeness of the provided GitHub repository. The repository currently lacks crucial resources, such as the pre-trained neural network models, the labeled dataset used for training, and the clearly delineated training/testing splits. While this omission may be inadvertent or intended to avoid duplication, providing direct access to all relevant datasets and models within a single, comprehensive repository would substantially improve reproducibility and uptake by the community. Additionally, enhancing the documentation to guide new users step-by-step through replicating training and prediction would make this valuable tool even more impactful.

In addition, it would be great for the manuscript to add insights and discussions around the following:

1. Comparative Benchmarking: The original SarcGraph paper (Zhao et al., 2021) set a high standard by providing explicit numerical benchmarking against multiple state-of-the-art methods, synthetic datasets, and varied experimental conditions. It would be ideal for this new manuscript to perform similar explicit comparative benchmarking against leading tools such as SarcTrack, ZlineDetection, SOTA, SarcOptiM, and CONTRAX on identical datasets. Alternatively, the authors should expand the discussion to clarify why specific direct comparisons might not be informative or necessary, considering the different methodological goals or practical use cases between these tools.

2. Computational Scalability and Performance Metrics: Providing clear, quantitative performance metrics for computational efficiency—such as runtime benchmarks on representative large-scale datasets, including details of the computing resources used—would significantly enhance the manuscript's practical relevance, particularly given its emphasis on high-throughput analysis.

Finally, figures clarity could be improved as follows:

Figure 2D could benefit from a clearer visual representation of the z-disc correction process, perhaps by illustrating intermediate steps explicitly.

Figure 3C-4 should visually or textually differentiate between various scoring methods (pruning-based, z-disc probability-based, original SarcGraph scoring).

Figure 1A-iii is somewhat visually crowded; simplifying or enlarging the depicted contours would improve readability.

Figure 4B captions should explicitly state the specific structural differences demonstrated between the original and modified SarcGraph results.

Figures 6 and 7 could include brief annotations explaining discrepancies between predicted scores and expert scores, helping readers understand the structural features leading to these differences.

In conclusion, this manuscript represents an exciting methodological advancement with strong potential for wide-ranging applications. Addressing these highlighted issues will further enhance its clarity, rigor, and utility to the computational biology community.

Reviewer #2: This is a high quality manuscript that describes new machine-learning inspired code to analyze sarcomeres and myofibrils in microscopic images. The text is very detailed and presents strengths and limitations of the proposed algorithms. The figures are impressive. The work is likely to be of substantial value to the field.

This reviewer has only on specific comment which relates to the upload to GitHub. While the source code is (likely) all there, there is not enough information in the GitHub repo to make it easy for a new user to implement the code. Including a tutorial or some worked examples with the GitHub repo would markedly increase the impact of this work.

Reviewer #3: The review is uploaded as an attachment

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Have the authors made all data and (if applicable) computational code underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data and code underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data and code should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data or code —e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: No: example datasets, pre-trained network, labeled datasets are missing from the repo.

Reviewer #2: Yes

Reviewer #3: Yes

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Reviewer #1: Yes: Francesco Pasqualini

Reviewer #2: No

Reviewer #3: No

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Attachments

Attachment

Submitted filename: sarcomere_review.docx

Dear Dr. Lejeune,

We are pleased to inform you that your manuscript 'Quantifying hiPSC-CM structural organization at scale with deep learning-enhanced SarcGraph' has been provisionally accepted for publication in PLOS Computational Biology.

Before your manuscript can be formally accepted you will need to complete some formatting changes, which you will receive in a follow up email. A member of our team will be in touch with a set of requests.

Please note that your manuscript will not be scheduled for publication until you have made the required changes, so a swift response is appreciated.

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Thank you again for supporting Open Access publishing; we are looking forward to publishing your work in PLOS Computational Biology. 

Best regards,

Anna Grosberg, Ph.D.

Academic Editor

PLOS Computational Biology

Stacey Finley, Ph.D.

Section Editor

PLOS Computational Biology

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Reviewer's Responses to Questions

Comments to the Authors:

Please note here if the review is uploaded as an attachment.

Reviewer #1: All comments were addressed and the qualtiy of the GitHub repo and data resources has massively improved. Thanks for your work and congrats on a great piece of research.

Reviewer #2: The authors responded appropriately to prior comments.

Reviewer #3: The authors have addressed my comments. I have no further comments.

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Have the authors made all data and (if applicable) computational code underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data and code underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data and code should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data or code —e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: Yes: Francesco Pasqualini

Reviewer #2: No

Reviewer #3: No