Review Reports

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

Major Comments

1. Sample Size Justification and Biological Replicates

The study relies on ADSCs from four donors, yet the manuscript does not justify why this number is adequate for the large‑scale proteomic and transcriptomic analyses performed. Moreover, the RNA‑seq section reports n = 12 per group, which requires clarification regarding biological vs. technical replication. The independence of samples must be clearly explained, and donor should be incorporated into the statistical model as a blocking factor if technical replicates are used

2. Inconsistent Differential Gene Expression Numbers

Section 3.2 reports over 16,000 differentially expressed genes, which is inconsistent with Table 1, the abstract, and the thresholds described (adjusted p < 0.05, |log2FC| > 1). This discrepancy requires correction and clarification of the filtering and statistical criteria actually used

3. Proteomics QC, Imputation, and Statistical Rigor

Three proteomic samples were excluded due to reduced identification depth, but no predefined QC thresholds are provided. The impact of these exclusions on group balance (OM vs. SM, donor, and media background) must be reported.

Additionally, MNAR imputation (shift 1.8 SD, width 0.3) was applied without sensitivity analysis. Given that downstream biological interpretations depend heavily on the imputed data, robustness checks are essential. Furthermore, the use of a nominal p < 0.05 cutoff to expand the dataset to 854 proteins—without FDR correction—is not appropriate for inferential conclusions.

4. Medium Effects and Dexamethasone Confounding

ADSCs were expanded in two different media (DMEM + FBS or NutriStem®), yet the manuscript does not explain how medium‑related variability was controlled.

More critically, OM contains 100 nM dexamethasone, a potent immunomodulator. Without proper controls (e.g., OM without dexamethasone), it is not possible to distinguish osteogenic differentiation–driven immunological changes from glucocorticoid‑mediated effects. This must be addressed analytically or discussed as a major limitation

5. Osteogenesis Validation

Osteogenic induction is only demonstrated using qualitative Alizarin Red staining. Quantitative elution‑based analysis or additional early markers (ALP activity, RUNX2, OSX, IBSP) are needed to support claims about osteogenic commitment at day 10

6. Interpretation of Transcriptome–Proteome Concordance

The reported Pearson correlation of r = 0.074 indicates extremely weak agreement between RNA and protein levels. It is therefore not appropriate to infer mechanistic concordance from small subsets of overlapping features without significance testing. The manuscript states a 60.1% concordance, but does not demonstrate whether this exceeds random expectation (e.g., via binomial or permutation test). Spearman correlation should also be assessed.

7. HLA Reporting and Fold‑Change Inconsistencies

The statement that HLA‑B expression is not induced by osteogenic induction requires supporting quantitative evidence. Relatedly, HLA‑A and HLA‑C fold‑changes appear in both log2 and linear forms across the paper, leading to confusion. Please report all fold‑changes consistently and clarify detection thresholds for HLA‑B

8. Over‑Interpretation of Immunomodulatory Findings

The manuscript draws mechanistic conclusions about macrophage polarization, osteoclast suppression, immune tolerance, and therapeutic suitability. However, no functional immunological assays (PBMC suppression, cytokine profiling, macrophage polarization, osteoclastogenesis assays) were performed. These conclusions must be substantially tempered, or functional validation added

9. Use of Outdated Genome Build (hg19)

RNA‑seq quantification was performed against hg19, which is outdated relative to hg38. A justification is needed, or datasets should be re‑processed against a current reference genome

10. Source of Clinical Trials Claim

The introduction states that 165 clinical studies involving ADSCs have been completed as of July 28, 2025. Please provide the database source and search strategy

 

 

Minor Comments

1. Donor demographics (age, sex, BMI, comorbidities) should be reported, as they may influence ADSC immunomodulatory properties
2. Handling of “NS/ND” transcript–protein pairs in Table 4 should be clarified with respect to how they affected concordance analyses
3. Selection of the 13 immune‑related proteins needs clarification: were these predefined or selected post hoc based on the data?
4. Unusual proteomics thresholds (−log10 p > 5 and > 10) must be justified or replaced with FDR‑controlled significance measures
5. Please specify how many proteomic samples remained per group after exclusions and whether donor effects were modeled in PCA or differential analysis
6. The regulatory argument against 14–21 day osteogenic induction requires citation to EMA, FDA, or GMP guidelines
7. Minor typographical and formatting inconsistencies should be addressed, especially regarding fold‑changes and log2 notation.

Author Response

Dear Reviewer,

We would like to express our sincere gratitude for your valuable comments and suggestions, which have undoubtedly enriched and enhanced the scientific value of our manuscript. We have made every effort to address all the points raised.

For our responses, please see the attachment.

Yours sincerely,

dr. Ilona Szabłowska-Gadomska

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

While the manuscript presents interesting proteomic and transcriptomic data suggesting changes in immune-related proteins and genes associated with the early osteogenic differentiation of ADSCs, several conceptual and interpretative logical gaps need to be addressed in order to properly interpret the immunomodulatory potential of ADSCs under osteogenic differentiation conditions:

First, the conclusion that osteogenic differentiation still exert immunomodulatory effects is based on the protein or gene expressions rather than functional assays. For example, MHC class I complexes are responsible for recognizing and presenting antigens to T-cells; Without functional assays, it is not possible to determine whether these changes result in immune tolerance or immune activation.

Second, some of the reported fold changes appear to be overinterpreted. For example, the function of STAT3 is context dependent. In addition, although changes in EV markers CD63 and CD81 are reported, no EV isolation experiments are presented to substantiate these claims.

Third, although proteomic - transcriptomic discordance is well documented in the literature, it remains unclear whether the discordance observed in this manuscript is due to phenotypic shifts, inter-donor variability, or transient adaptive responses.

Other suggestion:
1. Figure 5: There are two distinct populations of the differentiating group; it is recommended that the data points from all four patients be clearly labeled accordingly.
2. Figure 6 : From which population of differentiating ADSCs in Figure 5 were the data in Figure 6 derived?
3. Please strengthen the explanation of how the data presented in Figures 4, 5 and 8 are related to immunoregulation.
4.    The Discussion contains a substantial amount of content unrelated to immunomodulation. It is recommended to place greater emphasis on describing the changes in immune-related genes or proteins.

Author Response

Dear Reviewer,

We would like to express our sincere gratitude for your valuable comments and suggestions, which have undoubtedly enriched and enhanced the scientific value of our manuscript. We have made every effort to address all the points raised.

For our responses, please see the attachment.

Yours sincerely,

dr. Ilona Szabłowska-Gadomska

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

Major Comments

1. The molecular pathway investigated in this study has been previously reported in related contexts. The current work appears to provide incremental advancement rather than a fundamentally novel mechanism.The authors should better clarify how their findings differ from prior studies. Additional mechanistic validation (e.g., rescue experiments or pathway-specific inhibition) would strengthen causal interpretation.
2. Several conclusions are based primarily on expression changes. It remains unclear whether the observed phenotype is directly mediated by X or represents a secondary downstream effect. Functional rescue experiments are recommended to establish causality.
3. The manuscript does not clearly specify whether replicates represent biological or technical repeats. The statistical tests used should be explicitly justified. Information regarding multiple comparison correction is missing. The type of error bars (SD vs. SEM) should be clearly indicated.
4. The discussion section may overstate the translational significance of the findings. Claims regarding therapeutic targeting or biomarker potential should be moderated unless supported by additional validation (e.g., independent clinical cohorts).
5. Please clarify whether randomization and blinding were applied where appropriate.

Minor Comments

• Define abbreviations at first mention.
• Improve clarity and completeness of several figure legends.
• Provide catalog numbers for key reagents.
• Streamline overly long sentences in the Discussion section.

Author Response

Dear Reviewer,

We would like to express our sincere gratitude for your valuable comments and suggestions, which have undoubtedly enriched and enhanced the scientific value of our manuscript. We have made every effort to address all the points raised.

For our responses, please see the attachment.

Yours sincerely,

dr. Ilona Szabłowska-Gadomska

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

Authors have addressed all comments.

Author Response

Thank you very much for your time and insight.

Reviewer 2 Report

Comments and Suggestions for Authors

1. For the third major concern regarding the discordance between proteomic and transcriptomic upregulation/downregulation results, this reviewer requests RT-PCR and western blot validation of at least three immune-related genes/proteins, for example, STAT3, LTA4H, and SOD2.

2. Regarding suggestions 1and 2 for Figure 5 and Figure 6: Figure 5 showed two distinct populations within the control group. The reviewer recommends clearly labeling the data points corresponding to each of the four patients. In addition, please clarify whether the presence of these two populations could influence the results of the upregulated and downregulated proteins listed in Table 4 and Figure 6.

Author Response

Comment 1. For the third major concern regarding the discordance between proteomic and transcriptomic upregulation/downregulation results, this reviewer requests RT-PCR and western blot validation of at least three immune-related genes/proteins, for example, STAT3, LTA4H, and SOD2.

Response 1: Thank you very much for this valuable comment regarding the observed discordance between transcriptomic and proteomic regulation of immune‑related genes. However, we respectfully argue that additional RT-qPCR and Western blot validation would not address the issue raised and is not methodologically necessary for the following reasons.

Of course, we do not hesitate to perform additional experiments if the Reviewer deems them necessary. However, at this stage, we are considering the feasibility of conducting them and whether they would actually provide the expected interpretive value. The literature data analyzed clearly indicate that discrepancies between transcript and protein levels are a common and biologically justified phenomenon, resulting from, among other things, post-translational regulation, differences in protein stability, and variable translation rates, which were discussed in our manuscript.

The approach for validation of high-throughput methods with classical molecular methods i.e., RT-qPCR validation of NGS transcriptomic data or Western blot validation of proteomic data, is an outdated approach which may introduce inconclusive results at best, but is not suited for comparison of transcriptomic-proteomic discordance observed in LC-MS/MS vs NGS observation.

Selective RT-qPCR and Western blot validation of a few markers is generally not considered an adequate way to assess concordance between two large-scale datasets, especially since mRNA to protein discordance is common and biologically expected. Moreover, Western blot is not a universal gold standard for proteomic validation because its performance depends strongly on antibody specificity and sample context. Therefore, such targeted follow-up would have limited value for interpreting the global transcriptome-proteome relationship. This claim is not a sole opinion of the coauthors of this manuscript but a growing concern of experts in multiomics research (1, 2).

Moreover, discordance between transcript abundance and protein levels is a well-established biological phenomenon rather than a technical artifact. Large-scale multi-omics studies consistently report only moderate correlations between mRNA and protein abundance across genes. For example, Vogel and Marcotte (2012) reported that transcript levels explain only a limited fraction of protein abundance variation due to extensive post-transcriptional regulation, while Liu et al. (2016) demonstrated that translation rates and protein degradation are major determinants of protein levels independent of mRNA expression (3, 4). Therefore, discrepancies between transcriptomic and proteomic regulation are expected and biologically meaningful rather than indicative of measurement errors.

Additionally, RNA-seq is already a quantitative method for transcript measurement with high dynamic range and reproducibility (SEQC/MAQC consortium studies). Repeating transcript quantification using RT-qPCR does not provide an independent validation of the relationship between mRNA and protein regulation. As for the Western blot validation, this method would not necessarily provide a more accurate assessment of protein abundance than the quantitative proteomic approach already employed. Modern quantitative proteomics workflows (e.g., label-free or TMT-based mass spectrometry) provide multiplexed and statistically robust measurements across hundreds to thousands of proteins, whereas Western blotting is a low-throughput method with substantial variability related to antibody specificity, epitope accessibility, and semi-quantitative signal detection (5, 6).

In light of these references, supplementing our observations with selective validation of several markers using RT-qPCR or Western blot methods would likely not allow for a more complete explanation of the nature of the described discrepancy, and would not constitute a methodologically adequate means of verifying the relationship between transcriptomic and proteomic data obtained using high-throughput methods.

Importantly, the primary objective of this study is to investigate transcriptomic and proteomic responses at the systems level. In this context, the lack of one-to-one correspondence between RNA and protein regulation is consistent with the current understanding of gene expression control and has been widely reported in multi-omics studies.

For these reasons, we believe that additional RT-qPCR and Western blot experiments would not substantially strengthen the conclusions of the manuscript. Instead, in the revised manuscript we have already expanded the Discussion section to clarify the biological mechanisms that may explain the observed transcriptome–proteome discordance and have added appropriate references to the literature.

If, in Your opinion, the results obtained from the additional experiments would substantially increase the scientific value of the presented work, we are of course ready to perform them. We would like to note, however, that their implementation will require additional time. The process of cell expansion and differentiation alone will take several weeks, and moreover, we will need to order the necessary reagents for the Western blot and RT‑qPCR analyses. As a result, the expected timeframe for obtaining the full set of results would be approximately three months.

Literature references:

1. Aebersold R, Burlingame AL, Bradshaw RA. Western blots versus selected reaction monitoring assays: time to turn the tables? Mol Cell Proteomics. 2013;12(9):2381–2382. doi:10.1074/mcp.E113.031658.
2. Edfors F, Hober A, Linderbäck K, Maddalo G, Azimi A, Sivertsson Å, et al. Enhanced validation of antibodies for research applications. Nat Commun. 2018;9(1):4130. doi:10.1038/s41467-018-06642-y.
3. Vogel C, Marcotte EM. Insights into the regulation of protein abundance from proteomic and transcriptomic analyses. Nat Rev Genet. 2012;13:227–232.
4. Liu Y, Beyer A, Aebersold R. On the dependency of cellular protein levels on mRNA abundance. 2016;165(3):535–550.
5. SEQC/MAQC Consortium. A comprehensive assessment of RNA-seq accuracy, reproducibility and information content. Nat Biotechnol. 2014;32:903–914.
6. Taylor SC, Berkelman T, Yadav G, Hammond M. A defined methodology for reliable quantification of Western blot data. Mol Biotechnol. 2013;55:217–226.

 

Comment 2: Regarding suggestions 1and 2 for Figure 5 and Figure 6: Figure 5 showed two distinct populations within the control group. The reviewer recommends clearly labeling the data points corresponding to each of the four patients. In addition, please clarify whether the presence of these two populations could influence the results of the upregulated and downregulated proteins listed in Table 4 and Figure 6.

Response 2: Thank you very much for this valuable comment. To enhance the clarity of data interpretation, we have added labels to the revised PCA figure indicating the cell sample origin of each data point (see the attachment). However, the modified version of the figure presented certain resolution limitations, we propose retaining the original image in the manuscript to ensure optimal visual quality.

We fully acknowledge the differences between the two early-stage cell populations. Nevertheless, the markedly tighter clustering observed for the differentiated samples indicates that osteogenic induction represents the predominant source of variation in our dataset. As a consequence, the differentiation process appears to substantially homogenize the cellular phenotype, thereby limiting the potential influence of early-stage heterogeneity on the protein expression patterns shown in Table 4 and Figure 6. While we cannot entirely exclude some contribution of the initial differences, our data overall suggest that the observed proteomic signatures are primarily driven by the differentiation stage rather than by subdivisions within the control group. This point is explicitly addressed in the revised Discussion section (lines 359–362), where we emphasize that the dataset used for subsequent analyses and interpretations exhibited some degree of initial heterogeneity.

We would also like to highlight that the impact of early culture stages on the cellular characteristics is the subject of our subsequent study, which is currently under peer review.

 

Author Response File: Author Response.pdf

Round 3

Reviewer 2 Report

Comments and Suggestions for Authors

Western blotting is an orthogonal approach to validate selected proteins identified by proteomic analysis.