Intestinal fibroblast heterogeneity: unifying RNA-seq studies and introducing consensus-driven nomenclature
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Abstract
Abstract Figure
Graphical abstractIn the colon, single-layered epithelium lines the crypts, which descend into the underlying mucosa. Colonic crypts are surrounded by a network of fibroblasts essential for various functions, including the production and remodeling of the extracellular matrix (ECM), supporting the epithelial stem cell niche, and promoting the differentiation of epithelial cells. Recent studies indicate that fibroblasts are not a homogeneous cell population. However, differences in nomenclature and a lack of exact markers hindered their classification and functional understanding. Here, we used single-cell RNA-sequencing (scRNA-seq) to identify six distinct fibroblast subpopulations in mouse colonic mucosa, each with unique molecular signatures and functional specialization. Our analysis reveals that some fibroblasts are primarily involved in ECM production and remodeling, while others exhibit high contractility. Additionally, a subset of fibroblasts produces cytokines that promote epithelial cell differentiation, whereas another group secrete cytokines essential for maintaining the epithelial stem cell niche. We also map the spatial distribution of these fibroblast subpopulations within the colonic mucosa. Differentiation trajectory analysis suggests distinct pathways for fibroblast differentiation, while cell cycle scoring reveals that fibroblasts do not proliferate under homeostatic conditions. Furthermore, we integrated our scRNA-seq data with previously published datasets to identify common fibroblast populations and propose a standardized nomenclature for intestinal fibroblasts. This unified framework aims to improve communication within the research community and enhance understanding of fibroblast roles in gut homeostasis and gastrointestinal diseases.
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Reviewer 1
1. The structures of the lamina propria of murine colon mucosa are nicely described. However, in the introduction of the manuscript the structures of fibroblasts, myofibroblasts and ECM are not described. The structures of the lamina propria of murine colon mucosa should be well described in the induction and discussed in the discussion.
We will revise the Introduction to include a more detailed description of fibroblasts, myofibroblasts, and the ECM within the lamina propria of the murine colon mucosa. We will also expand the Discussion section to address these structures in the context of our findings.
2. The UMAP plot suggests …
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Reply to the reviewers
Reviewer 1
1. The structures of the lamina propria of murine colon mucosa are nicely described. However, in the introduction of the manuscript the structures of fibroblasts, myofibroblasts and ECM are not described. The structures of the lamina propria of murine colon mucosa should be well described in the induction and discussed in the discussion.
We will revise the Introduction to include a more detailed description of fibroblasts, myofibroblasts, and the ECM within the lamina propria of the murine colon mucosa. We will also expand the Discussion section to address these structures in the context of our findings.
2. The UMAP plot suggests potential heterogeneity within Cluster 1, raising questions about whether the chosen clustering resolution (e.g., parameter settings in Seurat's "FindClusters") optimally captures subpopulations.
We appreciate this insightful observation. We agree that the UMAP plot suggests potential heterogeneity within Cluster 1 and that the current clustering resolution may not fully capture underlying subpopulations. We could revisit the clustering parameters and explore reclustering at a lower resolution. However, we note that lowering the resolution often increases the total number of clusters, which may introduce noise and complicate biological interpretation. To more precisely dissect the heterogeneity within Cluster 1 while minimizing artificial subdivisions, we propose to perform subclustering specifically within Cluster 1.
3. Some subpopulations express marker genes characteristic of pericytes and smooth muscle cells (e.g., Desmin). How did the authors ensure proper discrimination between fibroblasts and these other cell types?
We thank the reviewer for this important comment. We acknowledge the challenge in distinguishing between fibroblasts, pericytes, and smooth muscle cells (SMCs) based solely on single-cell RNA sequencing data, particularly given the overlapping expression of markers such as Desmin.
Pericytes vs. Myofibroblast/SM-Pericyte-Like Fibroblasts: Due to the highly similar transcriptional profiles of pericytes and pericyte-like fibroblasts, scRNA-seq alone does not allow for unambiguous discrimination between these populations. However, we were able to distinguish them based on morphology and spatial localization observed in high-resolution imaging. Notably, we identified a population of large (50–150 µm), elongated myofibroblast/SM-pericyte-like fibroblasts that, unlike typical pericytes, are not positioned directly on blood vessels but are distributed around the crypts. Some of these cells also appear to contact both blood vessels and the muscle layer, raising the possibility that they represent a specialized pericyte-like population. While their precise function remains uncertain, we agree that further characterization is warranted. To address this, we propose additional staining for canonical pericyte markers to help clarify their identity and spatial relationship to the vasculature.
Smooth Muscle Cells vs. Myofibroblast/SM-Pericyte-Like Fibroblasts: We are confident that the analyzed fibroblast populations do not include smooth muscle cells. The mucosa was carefully dissected and separated from the underlying smooth muscle layer prior to RNA sequencing, which was performed exclusively on the mucosal compartment. Therefore, contamination by SMCs is unlikely.
4. The manuscript also did not show the distribution and structures of ECM. It is better to show the relationships of fibroblasts and myofibroblasts with in the lamina propria of murine colon mucosa.
In the supplementary material we show distribution of main ECM proteins such as Laminin, Collagen I, Collagen IV, and Fibronectin1.
5. The integration with previously published datasets lacks clear connection to the authors' own findings. A more detailed comparison and discussion of how these integrated analyses relate to the newly generated data would improve the manuscript's coherence.
We thank the reviewer for this helpful comment. Our RNA-seq dataset shows strong consistency with previously published datasets, supporting the robustness of our fibroblast isolation and transcriptional profiling strategy. We agree that a more explicit integration and comparison will improve the manuscript. We have now revised the Discussion to better highlight the spatial localization and organization of the different fibroblast populations identified in our study, with an emphasis on the duality of their functions. In particular, we discuss how our findings extend existing datasets by providing spatial context and functional insights that were not previously resolved. These comparisons underscore the novelty and value of our integrated approach.
6. While the authors focus on colonic mucosa, the integrated public datasets include data from both colon and small intestine. Were these distinct tissue sources accounted for in the analysis? Clarification on this point is necessary to ensure the validity of comparisons.
We thank the reviewer for raising this important point. Among the integrated datasets, only one—McCarthy et al. (GEO GSE130681)—originates from the small intestine; all others, including our own, were derived from the colon. Specifically, we used the following datasets:
- GEO GSE113043 (Degirmenci et al., PMID: 29875413) – Colon (1 sample)
- GEO GSE114374 (Kinchen et al., PMID: 30270042) – Colon (3 samples)
- GEO GSE130681 (McCarthy et al., PMID: 32884148) – Small intestine (2 samples)
- GEO GSE142431 (Roulis et al., PMID: 32322056) – Colon (5 samples) We selected these datasets based on their relevance to fibroblast biology, particularly those that specifically focused on mural fibroblasts. The inclusion of the McCarthy dataset was guided by its high-quality profiling of fibroblast populations and its utility in expanding our comparative framework.
Importantly, review by McCarthy et al. (https://doi.org/10.1038/s41556-020-0567-z) reported minimal differences in fibroblast clustering between the small intestine and colon. Our integrated analysis supports this conclusion: fibroblasts from both regions consistently co-cluster, indicating a high degree of transcriptional similarity. This suggests that inclusion of the small intestine dataset did not bias or compromise the integrity of our colon-focused findings.
Nevertheless, our primary emphasis remains on the colon, particularly due to the relative scarcity of studies addressing fibroblast localization and morphology in this tissue compared to the small intestine. Additionally, at the time of analysis, the datasets we used represented the most comprehensive publicly available single-cell profiles of intestinal mural fibroblasts.
7. Many aspects of the described fibroblast subpopulations, including their single-cell expression profiles and physiological functions, appear to have been previously reported. The authors should more explicitly highlight the novel contributions of their work to advance our understanding of intestinal fibroblast biology.
We thank the reviewer for this important observation. While it is true that aspects of fibroblast heterogeneity have been previously reported, our study provides several novel contributions that advance the current understanding of intestinal fibroblast biology. We will revise the manuscript to more explicitly highlight the following key findings:
- Functional distinction between ECM production and contractility: Our integrative analysis reveals a clearer separation between fibroblast subpopulations based on their functional specializations—specifically, ECM production versus contractile properties. This distinction has not been well delineated in prior studies and is particularly relevant in the context of inflammatory bowel disease, where fibrosis remains a major complication. Our findings may help identify specific fibroblast subtypes that contribute to pathological remodeling.
- Detailed characterization of fibroblast localization and morphology: We provide new spatial insights by demonstrating the lack of overlap between GFP⁺ and CD34⁺ basket cell populations in vivo. Additionally, we highlight the presence of large, elongated myofibroblasts and pericyte/smooth muscle-like fibroblasts that span from the vasculature to the underlying muscle layer—morphologies and arrangements that have not been thoroughly described before. These observations offer a more refined anatomical and functional framework for understanding fibroblast roles within the colonic mucosa. We will revise both the Results and Discussion sections to more explicitly emphasize these novel contributions.
Reviewer 2:
Major points:
1. The order of the present manuscript should be reconstructed. The main message is in the discussion part. It is worth bringing it to the front.
We appreciate this thoughtful suggestion. We agree that the main message of the manuscript is currently more prominent in the Discussion section, and bringing it forward would improve the overall clarity and impact of the work. We will restructure the manuscript accordingly to ensure that the key findings and their significance are introduced earlier and more clearly communicated throughout the text.
2. Figure 1A, the authors employed the "vimentin+" filter to distinguish between fibroblasts and other cell types in the single-cell RNA sequencing (scRNA-seq) data. However, they did not provide a rationale for this choice in the manuscript. It would be worthwhile to consider the incorporation of an "Epcam-" or "E-cadherin-" filter as well, given the potential impact on the subsequent analysis's significance. Notably, the original UMAP plot generated before the application of the "vimentin+, Krt8-" filter, is absent from both the main figures and the supplementary data. The availability of this data is crucial for the identification of specific fibroblast populations among the sorted cells.
The rationale for using the “vimentin⁺” filter is based on its long-standing use as a canonical marker for fibroblasts and mesenchymal cells in both developmental and adult tissues, including the intestinal lamina propria. Vimentin has consistently been used to distinguish fibroblasts from epithelial and immune cell populations in scRNA-seq studies.
Regarding the exclusion of epithelial cells, we chose to apply a “Krt8⁻” filter instead of “Epcam⁻” or “E-cadherin⁻”, as Krt8 is a highly specific marker for colonocytes in the intestinal epithelium. We found this to be a reliable criterion for excluding epithelial cells in our dataset. We will revise the Methods section to clearly explain this rationale and selection.
Additionally, we agree that the original UMAP plot—prior to the application of the “vimentin⁺, Krt8⁻” filter—would provide valuable context. We will include this plot in the supplementary figures to allow better visualization of the initial clustering and to support the identification of fibroblast populations among the sorted cells.
3. Page4 line 12, the authors claim that they did not find specific markers for the cluster 1, despite the fact that cluster 1 is distinctly separated from clusters 0, 5, 4 and 3 in figure 1B. Furthermore, the cells in the cluster 1 do not cluster together based on the resolution applied in the present manuscript. The authors claim that cells in cluster 1 are in a transition state, and therefore, they did not include them in the functional analysis. However, later they claim that the cluster 1 are multipotent progenitors, which is not clear.
We appreciate the reviewer’s careful reading and valuable critique. We acknowledge the confusion regarding the identity and interpretation of Cluster 1 and would like to clarify our reasoning and planned revisions.
When identifying marker genes using Seurat’s FindMarkers() or FindAllMarkers() functions, the output highlights genes that are significantly enriched in a given cluster relative to others—but these genes are not necessarily uniquely or exclusively expressed in that cluster. This is the case with Cluster 1: although it is spatially distinct in the UMAP (Figure 1B), many of the top-ranked marker genes are also expressed in other clusters, albeit at lower levels. As a result, defining Cluster 1 based solely on unique gene expression signatures is challenging, and we initially interpreted this cluster as a “transitional population” due to its ambiguous marker profile.
However, we acknowledge the apparent inconsistency in referring to Cluster 1 as both "in transition" and "multipotent progenitors." We will clarify our interpretation and terminology in the revised manuscript. Specifically, we will refer to Cluster 1 as a __ transitory population__, and provide a more nuanced discussion of its potential roles.
As mentioned in our response to Reviewer 1 (Comment 2), we will also perform reclustering within Cluster 1 to better explore its internal heterogeneity. Additionally, we will now include Cluster 1 in the functional enrichment analysis to further assess its biological relevance and contribution to fibroblast diversity.
4. Figure 1E and F, authors only use gene ontology to define the functions of different clusters of fibroblasts which constrain the present manuscript at the hypothesis stage. To substantiate the claims, it is imperative to conduct more precise experiments. At the very least, co-staining with cluster marker genes and candidate genes identified in GO analysis is necessary. In the event that antibodies are not available, RNA scope can serve as a viable alternative. Further functional experiments will be required to prove their unique function. For instance, the identification of specific cell surface markers to isolate different clusters of fibroblasts for coculture with intestinal organoids in vitro can be facilitated by scRNA-seq data.
We appreciate the reviewer’s insightful suggestions regarding the functional validation of GO-based predictions.
While we recognize that RNAscope is a valuable alternative when antibodies are unavailable, its use requires much thinner tissue sections than those employed in our current imaging approach. Our analysis is based on thicker sections, which preserve the 3D architecture and spatial relationships of fibroblasts within the colonic mucosa—an essential aspect of our study. Transitioning to thinner sections would compromise our ability to visualize these cells in their full anatomical context.
To suppor the GO analysis with experimental validation, we will include __co-staining for cluster __marker genes along with representative candidate genes____ identified through GO analysis to better substantiate the predicted functions of different fibroblast clusters.
We acknowledge the importance of functional studies such as co-culture assays with intestinal organoids, and indeed, several such experiments have been reported by other groups. Additionally, isolating specific fibroblast populations via FACS sorting for in vitro studies presents practical challenges, including low cell survival rates, which limit the feasibility of downstream functional assays. Thus, we believe that these types of experiments are beyond the scope of the current manuscript. We hope that our integrative approach and spatial validation will serve as a valuable foundation for future functional investigations into fibroblast biology.
5. DAPI staining is absent in the majority of the images, which complicates the task of distinguishing cells from different clusters. Multiplex staining is necessary to show all specific markers: EGFP, SMA, CD34, Desmin, Pdgfra, Pil6, and Clu, regarding six clusters in one section or image.
We appreciate the reviewer’s comment and the emphasis on the importance of cellular context in multiplex imaging.
We acknowledge that DAPI staining is absent in some of the presented images, which may limit nuclear visualization and make it more challenging to distinguish cell boundaries. However, to achieve high-content multiplexing, we employed protocols allowing up to 5–6 fluorophores per section, as previously demonstrated by Chikina et al. (Cell, 2020). Due to spectral limitations and the risk of fluorophore overlap and signal bleed-through, we occasionally excluded DAPI to allocate the 405 nm channel for markers of greater relevance to our study. In these cases, Tomato or EGFP signals served as effective surrogates for cellular localization, as they label cell membranes, providing sufficient morphological context.
Regarding multiplex staining for Pi16 and Clu, we tested several commercially available antibodies, but unfortunately, none yielded specific or reproducible signals in our hands. As a result, we were unable to include these markers reliably in our multiplex panels.
6. Figure 4, the authors utilize supervised methods to execute trajectory analysis, defining cluster 1 as the initial point based on its hybrid expression state of genes. This assertion, however, lacks sufficient substantiation, as cluster 1 could also function as a transition point, not necessarily an initial point.The data presented in the current manuscript is inadequate to support the conclusion of multipotency in cluster 1.To substantiate these claims, the authors should employ additional evidence, such as SENIC analysis, to demonstrate the expression of specific transcription factors for each lineage along the trajectory. In order to substantiate the assertion that cluster 1 is a multipotent progenitor capable of differentiating into other specific populations, such as fibroblasts, further functional experiments are required. These experiments could include isolating the population in question and conducting a differentiation test in vitro or tracking the population's response to wound healing.The absence of immunofluorescence images or gene signatures for this cluster in the study is a cause of confusion for the reader.
We thank the reviewer for this thoughtful and constructive comment. We agree that Cluster 1 could plausibly represent either an initial or transitional state. In trajectory analysis, the starting point must be defined, and we selected Cluster 1 due to its hybrid gene expression profile—exhibiting low-level expression of marker genes associated with multiple other clusters—suggesting a less differentiated or “primed” state. However, we fully acknowledge that this assignment does not preclude its interpretation as a transitional population, and we will revise the manuscript text to reflect both possibilities more clearly and cautiously.
We appreciate the suggestion to perform __SENIC __analysis (https://www.nature.com/articles/nmeth.4463). This algorithm aims to identify gene regulatory networks and their associated transcription factors for each cell cluster. While interpreting such analysis can be quite challenging, it could provide interesting insights and thus we propose to apply it.
Regarding functional validation, we agree that experiments such as isolation and in vitro differentiation assays, or in vivo lineage tracing during injury models, would offer more definitive insights. However, as we noted, the lack of specific surface markers currently makes it challenging to isolate Cluster 1 by FACS for such assays.
We also acknowledge the reviewer’s concern about the __lack of immunofluorescence images or distinct gene signatures__for Cluster 1. We will revise the text to clearly communicate that this limitation.
7. Figure 5B, the data set of Kinchen et al is from human samples. Is it relevant and significant to merge murine data and the human data together?
We appreciate the reviewer’s attention to detail. To clarify, the dataset from Kinchen et al__.__ used in Figure 5B refers exclusively to their murine samples, not the human data. Only murine datasets were included in our analysis to ensure consistency and biological relevance. Therefore, merging the Kinchen murine data with other murine datasets in Figure 5B is both appropriate and justified.
We will revise the figure legend and Methods section to clearly state that only mouse data were used throughout the analysis.
Minor points:
8. The chapter entitled "Subepithelial Fibroblast Do Not Proliferate" is not necessary to be an independent chapter. It can be considered a fusion chapter, as it is combined with Chapter 2. Further experiments such as Brdu or Edu are needed to strengthen the current hypothesis.
We agree with the reviewer that this section does not require a standalone chapter and would be better integrated into Chapter 2. We will revise the manuscript accordingly.
In addition, to further support our observations regarding fibroblast proliferation, we will perform a 2-hour EdU pulse-chase experiment and include the results in the revised manuscript. We believe this will strengthen our conclusions and provide more direct evidence regarding the proliferative status of subepithelial fibroblasts.
- DAPI staining is absent in majority of the images.
indeed this is a limitation of the unmixing technique we use.
10. Put the number of each cluster next to the arrow in all the IF images.
We will do this.
11. Immunofluorescent staining of cluster markers identified in the previous studies should be included in the present study such as: CD81, FoxL1, Myh11, Pdgfrb and Gli1.
We will include those markers.
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Referee #2
Evidence, reproducibility and clarity
Summary
The summitted article entitle "Intestinal fibroblast heterogeneity: unifying RNA-seq studies and introducing consensus-driven nomenclature" by Glisovic et al., identify six distinct populations of fibroblast with unique molecular signatures, spatial localization and specific function in mouse colon using scRNA-seq. Moreover, with different bioinformatic methods, they show the potential differentiation trajectories of fibroblast in mouse colon mucosa. Finally, they propose a standardized nomenclature for colonic fibroblast by integrating the data of this manuscript and the four published scRNA-seq data of mouse and human …
Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.
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Referee #2
Evidence, reproducibility and clarity
Summary
The summitted article entitle "Intestinal fibroblast heterogeneity: unifying RNA-seq studies and introducing consensus-driven nomenclature" by Glisovic et al., identify six distinct populations of fibroblast with unique molecular signatures, spatial localization and specific function in mouse colon using scRNA-seq. Moreover, with different bioinformatic methods, they show the potential differentiation trajectories of fibroblast in mouse colon mucosa. Finally, they propose a standardized nomenclature for colonic fibroblast by integrating the data of this manuscript and the four published scRNA-seq data of mouse and human intestinal colonic fibroblast. Several similar studies cited by the authors in the present manuscript have been done and the different populations of colonic fibroblasts have been well characterized in these previous studies. Here the authors utilized another mouse model, the "aSMAcreERT2" to target the murine colonic fibroblast population which is novel compared to previous published data. Although the authors have provided multiple bioinformatic analyses and immunofluorescent staining of certain markers to support their conclusions, many points are overclaimed or not clear based on the data of the present manuscript, especially for the differentiation trajectories and unique function of different clusters of subepithelial colonic fibroblast. Functional experiment data are absent from the present manuscript.
Major comments
- The order of the present manuscript should be reconstructed. The main message is in the discussion part. It is worth bringing it to the front.
- Figure 1A, the authors employed the "vimentin+" filter to distinguish between fibroblasts and other cell types in the single-cell RNA sequencing (scRNA-seq) data. However, they did not provide a rationale for this choice in the manuscript. It would be worthwhile to consider the incorporation of an "Epcam-" or "E-cadherin-" filter as well, given the potential impact on the subsequent analysis's significance. Notably, the original UMAP plot generated before the application of the "vimentin+, Krt8-" filter, is absent from both the main figures and the supplementary data. The availability of this data is crucial for the identification of specific fibroblast populations among the sorted cells.
- Page4 line 12, the authors claim that they did not find specific markers for the cluster 1, despite the fact that cluster 1 is distinctly separated from clusters 0, 5, 4 and 3 in figure 1B. Furthermore, the cells in the cluster 1 do not cluster together based on the resolution applied in the present manuscript. The authors claim that cells in cluster 1 are in a transition state, and therefore, they did not include them in the functional analysis. However, later they claim that the cluster 1 are multipotent progenitors, which is not clear.
- Figure 1E and F, authors only use gene ontology to define the functions of different clusters of fibroblasts which constrain the present manuscript at the hypothesis stage. To substantiate the claims, it is imperative to conduct more precise experiments. At the very least, co-staining with cluster marker genes and candidate genes identified in GO analysis is necessary. In the event that antibodies are not available, RNA scope can serve as a viable alternative. Further functional experiments will be required to prove their unique function. For instance, the identification of specific cell surface markers to isolate different clusters of fibroblasts for coculture with intestinal organoids in vitro can be facilitated by scRNA-seq data.
- DAPI staining is absent in the majority of the images, which complicates the task of distinguishing cells from different clusters. Multiplex staining is necessary to show all specific markers: EGFP, SMA, CD34, Desmin, Pdgfra, Pil6, and Clu, regarding six clusters in one section or image.
- Figure 4, the authors utilize supervised methods to execute trajectory analysis, defining cluster 1 as the initial point based on its hybrid expression state of genes. This assertion, however, lacks sufficient substantiation, as cluster 1 could also function as a transition point, not necessarily an initial point. The data presented in the current manuscript is inadequate to support the conclusion of multipotency in cluster 1.To substantiate these claims, the authors should employ additional evidence, such as SENIC analysis, to demonstrate the expression of specific transcription factors for each lineage along the trajectory. In order to substantiate the assertion that cluster 1 is a multipotent progenitor capable of differentiating into other specific populations, such as fibroblasts, further functional experiments are required. These experiments could include isolating the population in question and conducting a differentiation test in vitro or tracking the population's response to wound healing. The absence of immunofluorescence images or gene signatures for this cluster in the study is a cause of confusion for the reader.
- Figure 5B, the data set of Kinchen et al is from human samples. Is it relevant and significant to merge murine data and the human data together?
Minor comments
- The chapter entitled "Subepithelial Fibroblast Do Not Proliferate" is not necessary to be an independent chapter. It can be considered a fusion chapter, as it is combined with Chapter 2. Further experiments such as Brdu or Edu are needed to strengthen the current hypothesis.
- DAPI staining is absent in majority of the images.
- Put the number of each cluster next to the arrow in all the IF images.
- Immunofluorescent staining of cluster markers identified in the previous studies should be included in the present study such as: CD81, FoxL1, Myh11, Pdgfrb and Gli1.
Significance
In this study, the researchers employed an alternative mouse model, the "aSMAcreERT2," to target the murine colonic fibroblast population. This approach represents a novel contribution to the field, offering a fresh perspective on previous findings. While the authors have presented several bioinformatic analyses and immunofluorescent staining of specific markers to support their conclusions, certain aspects of their argument require further elaboration or clarification, particularly regarding the differentiation trajectories and unique functions of the various clusters of subepithelial colonic fibroblasts. The present manuscript is constrained at the descriptive level due to an absence of functional experiment data.
Strengths: The authors utilize "aSMAcreETR2" as a research model to target murine colonic fibroblasts, a novel approach that complements previously published data. By comparing and combining four published single-cell RNA sequencing (scRNA-seq) of colonic fibroblasts, they proposed a novel classification with five distinct subpopulations: telocytes, trophocytes/extracellular matrix (ECM) fibroblast, fibroblast, myofibroblast, and smooth muscle/pericyte-like fibroblast.This new classification, together with their unique molecular signature, can be useful for people in the colon and intestine research field. However, the manuscript is not without its limitations. First, the novel classification and unique molecular signature are not substantiated by functional experimentation, which is essential for validating the fibroblast subcluster's functionality. Additionally, the characterization of cluster 1 is lacking, particularly concerning its ability to differentiate into the five distinct subcultures, which is crucial for confirming its status as a multipotent progenitor. Despite the proposal of a novel classification and detailed molecular signature of the colonic fibroblasts, no isolation strategy is proposed in the present manuscript to allow further characterization. If the authors can address these points, the manuscript can make a significant contribution to the field. This study might interest people who perform basic research in the intestine and colon.
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Referee #1
Evidence, reproducibility and clarity
This study utilizes scRNA-seq to delineate six fibroblast subpopulations in mouse colonic mucosa, revealing their molecular heterogeneity, functional specialization, and spatial distribution. The high-quality confocal microscopy images effectively illustrate the spatial distribution of cells within the colon mucosa. However, several concerns should be addressed:
- The structures of the lamina propria of murine colon mucosa are nicely described. However, in the introduction of the manuscript the structures of fibroblasts, myofibroblasts and ECM are not described. The structures of the lamina propria of murine colon mucosa should be …
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
This study utilizes scRNA-seq to delineate six fibroblast subpopulations in mouse colonic mucosa, revealing their molecular heterogeneity, functional specialization, and spatial distribution. The high-quality confocal microscopy images effectively illustrate the spatial distribution of cells within the colon mucosa. However, several concerns should be addressed:
- The structures of the lamina propria of murine colon mucosa are nicely described. However, in the introduction of the manuscript the structures of fibroblasts, myofibroblasts and ECM are not described. The structures of the lamina propria of murine colon mucosa should be well described in the induction and discussed in the discussion.
- The UMAP plot suggests potential heterogeneity within Cluster 1, raising questions about whether the chosen clustering resolution (e.g., parameter settings in Seurat's "FindClusters") optimally captures subpopulations.
- Some subpopulations express marker genes characteristic of pericytes and smooth muscle cells (e.g., Desmin). How did the authors ensure proper discrimination between fibroblasts and these other cell types?
- The manuscript also did not show the distribution and structures of ECM. It is better to show the relationships of fibroblasts and myofibroblasts with in the lamina propria of murine colon mucosa.
- The integration with previously published datasets lacks clear connection to the authors' own findings. A more detailed comparison and discussion of how these integrated analyses relate to the newly generated data would improve the manuscript's coherence.
- While the authors focus on colonic mucosa, the integrated public datasets include data from both colon and small intestine. Were these distinct tissue sources accounted for in the analysis? Clarification on this point is necessary to ensure the validity of comparisons.
- Many aspects of the described fibroblast subpopulations, including their single-cell expression profiles and physiological functions, appear to have been previously reported. The authors should more explicitly highlight the novel contributions of their work to advance our understanding of intestinal fibroblast biology
Significance
The proposed standardized nomenclature for intestinal fibroblasts represents a valuable contribution toward unifying classification in the field.
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