-->PCOMPBIOL-D-25-01276

Cell-type resolved transcriptional networks reveal molecular hallmarks of cellular senescence

PLOS Computational Biology

Dear Dr. Garcia-Ojalvo,

Thank you for submitting your manuscript to PLOS Computational Biology. After careful consideration, we feel that it has merit but does not fully meet PLOS Computational Biology's publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process. Especially, reviewers noted the necessity of citing other foundational work in the field and further differentiating your methodological contributions.

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Academic Editor

PLOS Computational Biology

Mark Alber

Section Editor

PLOS Computational Biology

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At this stage, the following Authors/Authors require contributions: Alda Sabalic, Victoria Moiseeva, Oleg Deryagin, Andres Cisneros, Eusebio Perdiguero, Pura Munoz-Canoves, and Jordi Garcia-Ojalvo. Please ensure that the full contributions of each author are acknowledged in the "Add/Edit/Remove Authors" section of our submission form.

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Note: If the reviewer comments include a recommendation to cite specific previously published works, please review and evaluate these publications to determine whether they are relevant and should be cited. There is no requirement to cite these works unless the editor has indicated otherwise.

Reviewers' comments:

Reviewer's Responses to Questions

Reviewer #1: In their ms, Sabalic and coworkers present an integrative analysis combining bulk RNA-seq from FACS-sorted muscle cells with network-based feature selection and community detection to identify transcriptional correlates of cellular senescence. They apply eigenvector centrality-based ranking and entropy-guided clustering and identify a minimal gene set of six genes (including Lncpint, Fabp3, Cd59a) that distinguishes senescent, non-senescent, and basal states across multiple cell types. They validated their key findings in an independent single-cell RNA-seq dataset from fibrotic liver tissue.

The study addresses a biologically significant and computationally challenging problem by identifying robust molecular markers for molecular characterization of cellular senescence. The methodology is clearly described, and the code is made available via GitHub. The validation by using independent scRNA-seq data enhances confidence in the biological relevance and generalizability of the results.

The manuscript is well structured and written with a concise message.

Major Comments

1. While the results are compelling and rather convincing, the ms would benefit from a more detailed comparison with between their ECFS and community detection approach with other methodologies like WGCNA, random forest feature selection or detailed PCA and ICA analyses. This would help the readers to contextualize the novelty and utility of their pipeline. This said, a more comprehensive description of the PCA analysis would be also interesting in terms of the explained variability of the different PCs, how the other PCs may discriminate the different cell states and if there is biological meaningful information in the PC loads/weights.

2. The biological interpretation how the gene candidates relate to senescence could be more extensive. Particularly, Cd59a would benefit from deeper discussion of potential mechanisms or prior evidence in senescence biology. Furthermore, some more detailed discussion on the limitations of the study would allow to put the results in a broader biological context. While the confirmation in liver data is interesting, the potential context could be discussed further.

3. Some more clarification on the experimental set-up is needed. Were some experiments performed for this study or was it all done in relation to ref 23? Accordingly to the statement “There are no primary data in the paper.“ no experiments were performed, but then the FACS sorting description in Sec. 8.1 is confusing. If experiments were done for this study, the ethics should be also specified.

Minor comments

- Page 2: replace “-“ with “,” or adda space in “… between their elements –which in the context of this investigation”.

- Page 7: use lower case “s” in “is explained in the next Section.”.

- Page 9: Something is missing in the sentence “Similar to the mixing of basal and non-senescent cells, this might be because both of these cell states are extracted from the injured leg.”?

- Page 12: Please clarify the sentence “It is worth noting that although the cell types used for validation originated from an entirely distinct tissue from those in our analysis, we could use our results to find the senescent cells that overlapped with our dataset, indicating that our conclusions for myeloid cells might be generalizable.”

- It would be nice to have a README in the github repository giving the context of the repository and maybe also mention the corresponding python version. For me it worked at least with python 3.9.17.

- It would be interesting to see the distribution of the ECSF sores to have a better intuition on the gene selection.

- What is the reasoning for 1.5 as a threshold in splitting the dTomato population in Figure 7?

Besides these comments, the manuscript could be a very valuable contribution to PLoS Computational Biology when the points above are addressed accordingly.

Reviewer #2: The authors have used cell-type resolved bulk RNA-seq with eigenvector centrality analysis and community detection (Louvain) to compress senescence vs. non-senescence transcriptomes into a small discriminative set pf six genes that separates cell states across FAPs, myeloid cells, and satellite cells during muscle regeneration after injury. Additional analysis is used to quantify the robustness of the separation analysis. As far as I know these approaches are sound and original and the findings. While the analysis highlights some potentially novel candidate markers of senescence in the context of skeletal muscle regeneration and their transcriptional regulatory networks, I am less certain about the originality and significance of these findings given that several major studies in the field appear not to have been cited.

• Ogrodnik M, Carlos Acosta J, Adams PD, d'Adda di Fagagna F, Baker DJ, Bishop CL, Chandra T, Collado M, Gil J, Gorgoulis V, Gruber F, Hara E, Jansen-Dürr P, Jurk D, Khosla S, Kirkland JL, Krizhanovsky V, Minamino T, Niedernhofer LJ, Passos JF, Ring NAR, Redl H, Robbins PD, Rodier F, Scharffetter-Kochanek K, Sedivy JM, Sikora E, Witwer K, von Zglinicki T, Yun MH, Grillari J, Demaria M. Guidelines for minimal information on cellular senescence experimentation in vivo. Cell 2024 Aug 8;187(16):4150-4175. doi: 10.1016/j.cell.2024.05.059. PMID: 39121846; PMCID: PMC11790242.

This paper describes minimum information for cellular senescence experimentation in vivo that the authors should acknowledge and confirm that they followed.

• Suryadevara V, Hudgins AD, Rajesh A, Pappalardo A, Karpova A, Dey AK, Hertzel A, Agudelo A, Rocha A, Soygur B, Schilling B, Carver CM, Aguayo-Mazzucato C, Baker DJ, Bernlohr DA, Jurk D, Mangarova DB, Quardokus EM, Enninga EAL, Schmidt EL, Chen F, Duncan FE, Cambuli F, Kaur G, Kuchel GA, Lee G, Daldrup-Link HE, Martini H, Phatnani H, Al-Naggar IM, Rahman I, Nie J, Passos JF, Silverstein JC, Campisi J, Wang J, Iwasaki K, Barbosa K, Metis K, Nernekli K, Niedernhofer LJ, Ding L, Wang L, Adams LC, Ruiyang L, Doolittle ML, Teneche MG, Schafer MJ, Xu M, Hajipour M, Boroumand M, Basisty N, Sloan N, Slavov N, Kuksenko O, Robson P, Gomez PT, Vasilikos P, Adams PD, Carapeto P, Zhu Q, Ramasamy R, Perez-Lorenzo R, Fan R, Dong R, Montgomery RR, Shaikh S, Vickovic S, Yin S, Kang S, Suvakov S, Khosla S, Garovic VD, Menon V, Xu Y, Song Y, Suh Y, Dou Z, Neretti N. SenNet recommendations for detecting senescent cells in different tissues. Nat Rev Mol Cell Biol. 2024 Dec;25(12):1001-1023. doi: 10.1038/s41580-024-00738-8. Epub 2024 Jun 3. PMID: 38831121; PMCID: PMC11578798.

This article includes recommendations for detecting senescent cells in based on literature reported senescence markers in 14 tissues in mice and humans. It seems highly relevant but is not cited or discussed.

• Li Y, Li C, Zhou Q, Liu X, Qiao Y, Xie T, Sun H, Ong MT, Wang H. Multiomics and cellular senescence profiling of aging human skeletal muscle uncovers Maraviroc as a senotherapeutic approach for sarcopenia. Nat Commun. 2025 Jul 5;16(1):6207. doi: 10.1038/s41467-025-61403-y. PMID: 40617829; PMCID: PMC12228793.

This paper reports the first senescence atlas profiling senescence in human skeletal muscle.

• Walter LD, Orton JL, Ntekas I, Fong EHH, Maymi VI, Rudd BD, De Vlaminck I, Elisseeff JH, Cosgrove BD. Transcriptomic analysis of skeletal muscle regeneration across mouse lifespan identifies altered stem cell states. Nat Aging. 2024 Dec;4(12):1862-1881. doi: 10.1038/s43587-024-00756-3. Epub 2024 Nov 22. PMID: 39578558; PMCID: PMC11645289.

Although focused more specifically on stem cell states, this paper identifies molecular markers of age-related renerative potential.

In summary, the manuscript reports a compact, network-derived gene set that separates senescent from non-senescent states across muscle cell types in regeneration, but it ignores major recent multiomic studies in the field. This casts some doubt on the extent to which the authors have really identified novel “hallmarks” of skeletal muscle senescence.

Reviewer #3: Sabalic et al. report novel targets of cellular senescence in a skeletal muscle injury model. The paper primarily focuses on the development of computational methods to identify senescent cells within a mixed cell population, rather than presenting new biological findings. However, a general definition of senescent cells, including well-known biomarkers, should be provided. Additionally, the use of cells identified as senescent in aged humans or model organisms—rather than those resulting from artificially-induced muscle injury—may be necessary to justify the methodological approach.

Since this study is predominantly based on a chemically induced muscle injury model, both the overall scope and the title should more accurately reflect this focus instead of adopting a broadly generalized view. This consideration should also be integrated into the Introduction section, given that the model system was previously reported by the co-authors (Moiseeva et al., Nature 2023, doi: 10.1038/s41586-022-05535-x). Because the earlier paper employed a very similar mouse muscle injury model for both bulk and single-cell RNA sequencing, it is important to highlight the biological and methodological differences and the novel aspects of this current study.

Similar bioinformatic approaches using eigenvector centrality have been published for RNA-seq network community analysis by multiple groups, although none have specifically focused on senescence or muscle injury (Pashaei et al., NAR 2025, doi: 10.1093/NAR/GKAF609; Sarmah and Bhattacharyya, J Biosci 2024, doi: 10.1007/s12038-024-00437-8; Wei et al., LSA 2023, doi: 10.26508/LSA.202302253; Liu et al., iScience 2018, doi: 10.1016/J.ISCI.2018.08.002). If the authors’ methodology is primarily computational, they need to clearly articulate what makes their approach novel or more advantageous compared to these existing methods.

Given that the utility of the current approach for identifying novel and generalizable senescence markers is considered, the analysis should be extended to more published datasets, coupled with biological validation. Currently, the manuscript lacks clarity throughout; a thorough revision is recommended to emphasize the novel methodological and biological contributions.

Other Issues:

1. The first paragraph of the Introduction should adopt a broader perspective, as cellular aging is inherently heterogeneous. For example, some senescence pathways are not triggered by molecular damage, whereas DNA damage remains an important cause—either directly or indirectly—of senescence. This may be a writing issue, as heterogeneity is acknowledged later in the manuscript.

2. In relation to the above, it would be more effective to include an overview of the muscle injury model within the context of senescence research, given that this is the primary focus of the study.

3. While citing many review articles can be helpful, the authors should incorporate more primary research articles where relevant to strengthen their arguments.

4. The Experimental Data section should specify the sources of key materials, such as cardiotoxin and the mouse strain used, to ensure reproducibility. This basic information is currently lacking.

5. The statement, “We focused on three different cell types because of their crucial role in muscle tissue regeneration—satellite cells (muscle stem cells), fibroadipogenic progenitor (FAP) cells, and myeloid cells”—raises questions about their specific roles and the rationale behind selecting these cell types. Clarification is needed, particularly since the focus on these cells may be specific to this experimental model rather than applicable to cellular senescence broadly.

6. The manuscript should specify which gene database was used for RNA-seq analysis (e.g., GENCODE, REFSEQ, or others). Although the authors mention a total of 46,078 genes, the specific database and version used should be clearly indicated, as gene counts can vary between releases.

7. Providing clearer information about the genes identified in Table 1 and Figure 3 would be helpful. Notably, the color-coding legend in Figure 3 appears to use the same blue for both cell cycle and apoptosis resistance, which could be confusing. Furthermore, since transcription factors are depicted hierarchically, a label like “gene expression” is too broad; the database used for functional annotations should be cited. For example, there appears to be no match for Elf1 in mouse or human KEGG pathways, despite the authors’ statement. Additionally, the phrase “The pathways shown are related to established senescence” in the figure legend would benefit from specific citations or source references.

8. In Figure 7, the graph appears to represent a histogram, but it is unclear what scale is being used. Since tdTomato expression is intended to report p16 levels, clarification is needed. The cited Cell Metabolism paper describes tissues in terms of p16^high^ or p16^low^, reflecting their differing distributions of p16. The authors should clearly explain what they did in the current study to reassess the previously reported p16 data (Figures 7 and 8).

9. Regarding the computational implementations, did the authors use the MATLAB code provided in the cited paper on the eigenvector centrality ranking algorithm (Roffo and Melzi, 2016)? The mathematical formulations in Sections 8.2–8.3 are already presented in that paper, unless the authors introduced modifications. The current manuscript provides no information on how the method was actually implemented—whether as a computational package or as executable resources—which is a significant omission.

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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: No: Written in the comments

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Reviewer #1: No

Reviewer #2: No

Reviewer #3: No

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PCOMPBIOL-D-25-01276R1

Cell-type resolved transcriptional network analysis of in vivo cellular senescence following injury

PLOS Computational Biology

Dear Dr. Garcia-Ojalvo,

Thank you for submitting your manuscript to PLOS Computational Biology. After careful consideration, we feel that it has merit but does not fully meet PLOS Computational Biology's publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please submit your revised manuscript by Mar 28 2026 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at ploscompbiol@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pcompbiol/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

* A letter that responds to each point raised by the editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'. This file does not need to include responses to formatting updates and technical items listed in the 'Journal Requirements' section below.

* A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.

* An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, competing interests statement, or data availability statement, please make these updates within the submission form at the time of resubmission. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

We look forward to receiving your revised manuscript.

Kind regards,

Christoph Kaleta

Section Editor

PLOS Computational Biology

Mark Alber

Section Editor

PLOS Computational Biology

Journal Requirements:

If the reviewer comments include a recommendation to cite specific previously published works, please review and evaluate these publications to determine whether they are relevant and should be cited. There is no requirement to cite these works unless the editor has indicated otherwise.

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Authors:

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

Reviewer #2: The authors have satisfactorily addressed my review comments

Reviewer #3: The revised manuscript by Sabalic et al. now includes many essential lines of information that was lacking in the original submission. As a result, its readability has been improved substantially for broad audience.

Remaining Issue:

A minor point first but in Figure 3 Significant filtering -- it appears the same as the 'discriminating filtering' shown in Figure 1. Filtering (and RNA-seq in general) is using statistical criteria (in this case the KS test). For this, using one term will reduce confusion.

More importantly, however, I must question whether the Kolmogorov-Smirnov test is really appropriate to compare the means of gene 'i' expression between sample groups with small sample size. In a total of the 36 samples without counting replicates, the sample size of each group could be around 10-20 (looks like 12 from the right panel of Fig 3B), considered too small below the generalized effective size range of 50 and 1000 for KS test. Although the authors applied Bonferroni correction, it tries to control overall Type I errors but it does not mitigate this issue. Therefore, it is advisable to find an alternative that tolerates small sample sizes better than KS test.

In the same token, I wonder if a negative binomial method may simply suffice without separating the two filtering steps during bulk RNA-seq analysis as the authors used in Ref.20. Simply, the use of ranking by the degree of differential expression available in the DEG list generated previously by EdgeR, may work similarly without raising the sample size issue above. Have the authors already tried it? Since the authors' main point of this manuscript is about application of the eigenvector centrality feature selection algorithm developed by another group earlier for network community analysis, the goal of RNA-seq analysis may be relatively peripheral. However, it is important to find a confidence in selected approach.

By choosing a different test appropriate for small sample sizes in the second filtering step (or whole in RNA-seq data analysis), I believe that the manuscript requires a revision. Depending on the outcome, results and discussion (as well as methods) may also need adjustments.

**********

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 #2: Yes

Reviewer #3: Yes

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Reviewer #2: No

Reviewer #3: No

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-->While revising your submission, we strongly recommend that you use PLOS’s NAAS tool (https://ngplosjournals.pagemajik.ai/artanalysis) to test your figure files. NAAS can convert your figure files to the TIFF file type and meet basic requirements (such as print size, resolution), or provide you with a report on issues that do not meet our requirements and that NAAS cannot fix.-->-->

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PCOMPBIOL-D-25-01276R2

Cell-type resolved transcriptional network analysis of in vivo cellular senescence following injury

PLOS Computational Biology

Dear Dr. Garcia-Ojalvo,

Thank you for submitting your manuscript to PLOS Computational Biology. After careful consideration, we feel that it has merit but does not fully meet PLOS Computational Biology's publication criteria as it currently stands. Following the assessment of one reviewer that their statistical concerns may not have been fully addressed, another reviewer was invited to consider those concerns. Based on their assessment, I'd like to ask you to use their advice how to remedy those concerns.

Please submit your revised manuscript by Jun 08 2026 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at ploscompbiol@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pcompbiol/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

* A letter that responds to each point raised by the editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'. This file does not need to include responses to formatting updates and technical items listed in the 'Journal Requirements' section below.

* A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.

* An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, competing interests statement, or data availability statement, please make these updates within the submission form at the time of resubmission. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

We look forward to receiving your revised manuscript.

Kind regards,

Christoph Kaleta

Section Editor

PLOS Computational Biology

Mark Alber

Section Editor

PLOS Computational Biology

Journal Requirements:

If the reviewer comments include a recommendation to cite specific previously published works, please review and evaluate these publications to determine whether they are relevant and should be cited. There is no requirement to cite these works unless the editor has indicated otherwise.

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Authors:

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

Reviewer #3: The authors have revised the manuscript in response to the previous comments regarding the applicability of the Kolmogorov–Smirnov (KS) test. In the revised version, the authors state that the KS test was conducted using 108 samples (36 groups × 3 replicates). Based on this clarification, however, I remain concerned about the validity of the test, as the current description appears to overlook the distinction between the intra-group distribution (across replicates) and the inter-group distribution (between groups).

Because this test is used as a key gene-filtering step that feeds directly into the subsequent network construction, the statistical validity of this step is important for evaluating the reliability of the downstream results.

If the primary goal of this step is to reduce or filter the number of genes, several alternative approaches could be considered, such as applying a stringent p-value threshold, fold-change filtering, variance-based filtering, or potentially a mixed-effects model. The way the KS test is applied remains somewhat unclear from a statistical perspective, and further clarification or reconsideration of this step would be appreciated.

Reviewer #4: I appreciate the authors’ clarification of the application of the Kolmogorov-Smirnov test. The modified figures and updated pipeline description improve the original ambiguity in the manuscript. It is acknowledged that the sample sizes are larger than what was initially inferred from the previous version of the figures.

However, my primary concern is not yet fully addressed. Considering the new information about sample composition, the issue seems to extend beyond sample size to the independence assumption underlying the KS test. The 36 senescent observations are not independent samples from a single "senescent" distribution. Rather, they seem to comprise a structured combination of 3 cell types, 2 age groups, 2 time points, and 3 biological replicates. This structure also applies to the 72 non-senescent and basal observations.

This pooling approach may cause the test to identify genes that differ systematically between cell types, age groups, or time points, rather than specifically between senescent and non-senescent states. For example, if the composition of cell types or age groups differs even just slightly between the senescent and combined non-senescent/basal groups, the KS test would detect this compositional difference instead of a true senescence effect. While the Bonferroni correction controls the familywise error rate under valid test assumptions, it does not resolve any violations of those assumptions.

I’d like to emphasize that I do not consider this issue to be fatal to the manuscript. In this case, the KS test serves as an intermediate filtering step, and downstream eigenvector centrality and community-detection analyses provide further refinement. The final set of genes is biologically coherent, which supports confidence in the overall pipeline. However, the statistical methodology at this stage should be defensible on its own merits, especially in a manuscript proposing a computational workflow for wider use.

I therefore would appreciate it if the authors could undertake one of the following actions:

(1) Conduct a sensitivity analysis comparing the genes identified by the KS test to those identified by either a standard differential expression framework (such as edgeR or DESeq2) using an appropriate design matrix that accounts for cell type, age group, and time point, or at the very least a statistical test that is more robust to small sample sizes and dependent conditions. If the overlap is substantial, this would demonstrate that the downstream results are robust to the choice of filtering method and would wholly resolve my concern.

(2) Alternatively, if the authors prefer to maintain the KS test, they should provide an explicit discussion acknowledging that the test does not account for the nested experimental structure, along with an explanation for why this is acceptable in the context of a coarse pre-filtering step. This should include, at a minimum, a brief argument for why confounding by cell type, age, or time point is unlikely to influence the results.

I consider either of these requests minor in scope and do not anticipate that they would require extensive changes to the results or conclusions. My expectation is that option (1) would likely confirm the robustness of the existing findings and thereby significantly strengthen the manuscript.

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

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Reviewer #3: Yes

Reviewer #4: None

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Reviewer #3: No

Reviewer #4: No

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Dear Dr. Garcia-Ojalvo,

We are pleased to inform you that your manuscript 'Cell-type resolved transcriptional network analysis of in vivo cellular senescence following injury' has been provisionally accepted for publication in PLOS Computational Biology.

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Reviewer's Responses to Questions

Comments to the Authors:

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Reviewer #4: I would like to thank the authors for the time and effort they invested in this well-structured review. The team not only performed the requested sensitivity analysis but also went beyond that to address the confounding issue in figure S3. I follow their reasoning regarding the results of this new analysis and agree with their conclusions. My previous concerns have now been fully addressed and resolved. In my opinion, the paper has been strengthened more than sufficiently, and I see no further issues that would oppose publication.

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

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Reviewer #4: Yes

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Reviewer #4: Yes: Kristina Schlicht