Spatially resolved transcriptomics reveals pro-inflammatory fibroblast involved in lymphocyte recruitment through CXCL8 and CXCL10
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eLife assessment
The findings of this article provide valuable information on the spatial dynamics of the human oral mucosa in chronic inflammatory disease. The strength of evidence presented is solid and should yield a better understanding of common mucosal diseases in humans.
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Abstract
The interplay among different cells in a tissue is essential for maintaining homeostasis. Although disease states have been traditionally attributed to individual cell types, increasing evidence and new therapeutic options have demonstrated the primary role of multicellular functions to understand health and disease, opening new avenues to understand pathogenesis and develop new treatment strategies. We recently described the cellular composition and dynamics of the human oral mucosa; however, the spatial arrangement of cells is needed to better understand a morphologically complex tissue. Here, we link single-cell RNA sequencing, spatial transcriptomics, and high-resolution multiplex fluorescence in situ hybridisation to characterise human oral mucosa in health and oral chronic inflammatory disease. We deconvolved expression for resolution enhancement of spatial transcriptomic data and defined highly specialised epithelial and stromal compartments describing location-specific immune programs. Furthermore, we spatially mapped a rare pathogenic fibroblast population localised in a highly immunogenic region, responsible for lymphocyte recruitment through CXCL8 and CXCL10 and with a possible role in pathological angiogenesis through ALOX5AP . Collectively, our study provides a comprehensive reference for the study of oral chronic disease pathogenesis.
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Author Response
Reviewer #1 (Public Review):
Caetano and colleagues describe the changes caused by periodontal inflammation in terms of tissue structure and provide additional evidence to understand the involvement of fibroblasts in altering the immune microenvironment.
While interesting and a concise study, the authors should improve their work on two major points:
- To improve the resolution, the authors introduced a method that addresses improving the resolution by combining more information from the neighbour structure and the existing database. This raises the question of whether the lack of previous gingival tissue spatial transcriptome sequencing results weakens the reliability of this method. Does it miss the identification of some gingival tissue-specific cells? Is the failure to match two populations of fibroblasts …
Author Response
Reviewer #1 (Public Review):
Caetano and colleagues describe the changes caused by periodontal inflammation in terms of tissue structure and provide additional evidence to understand the involvement of fibroblasts in altering the immune microenvironment.
While interesting and a concise study, the authors should improve their work on two major points:
- To improve the resolution, the authors introduced a method that addresses improving the resolution by combining more information from the neighbour structure and the existing database. This raises the question of whether the lack of previous gingival tissue spatial transcriptome sequencing results weakens the reliability of this method. Does it miss the identification of some gingival tissue-specific cells? Is the failure to match two populations of fibroblasts between single-cell sequencing and spatial transcriptome sequencing of gingival tissue fibroblasts related to this?
Thank you for raising these concerns. We don’t think that the lack of previous spatial transcriptome data of oral mucosa tissue affects the reliability of this method; however, as the technology matures our limitations will be overcome particularly regarding resolution. Understanding the exact cellular and molecular mechanisms of oral mucosa cellular remodelling processes in disease in their spatial context will be key to improve our current understanding of oral mucosa physiology. In contrast to single-cell RNA sequencing methods, we are not treating or digesting the tissue with enzymes or extracting cells from their local environment, therefore the impact on gene expression is substantially inferior compared to single-cell RNA sequencing. Because of this key difference, we expect differences between single-cell RNA sequencing and spatial data, which can preclude successful data integration. We were not successful in mapping all fibroblasts using one strategy (anchor-based integration) because this integration is performed on low resolution Visium datasets which is unable to uncover fine cell subtypes, such as fibroblasts. When we performed integration using a higher spatial resolution method, we could map these cells. In our initial single-cell RNA sequencing datasets, some gingiva cells were indeed missing due to technical limitations; for example, neutrophils were not captured given their fragile nature and low RNA content. With the spatial data, we could detect these and other immune cell types that were originally undetected. In conclusion, for a robust and unbiased molecular characterisation of human oral mucosa, spatial transcriptome data is essential.
- Although the authors did the identification of the captured tissues, the results seem to require more analysis. Take Figure 5A as an example, there is a clear overlap between endothelial cells and basal cells. In addition, it is suggested that the authors indicate the specific location of the 10 clusters of cells in Figures 1D and 2C.
Thank you for your comment. Endothelial cells in Figure 5A have a predominantly subepithelial location as shown; however, these also localise in interpapillary regions which can be confounded with basal areas given the current resolution. We highlight that these analyses are not single-cell resolution. We applied a deconvolution method to increase the original spatial data resolution (55 µm), but it is still not true single-cell resolution.
In Figure 1D and 2C we are not showing clusters of cells, but spatial/anatomical cluster regions; for example, epithelial and stromal regions. These regions contain, especially stromal areas, information of multiple cell types. We can map epithelial regions as these are generally well defined (Figure 2F), but validating stromal regions becomes more difficult. To address this, we mapped individual cell types (Figures 5 and 6) and focused on locating and validating our cell type of interest (Fibroblast 5).
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eLife assessment
The findings of this article provide valuable information on the spatial dynamics of the human oral mucosa in chronic inflammatory disease. The strength of evidence presented is solid and should yield a better understanding of common mucosal diseases in humans.
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Reviewer #1 (Public Review):
Caetano and colleagues describe the changes caused by periodontal inflammation in terms of tissue structure and provide additional evidence to understand the involvement of fibroblasts in altering the immune microenvironment.
While interesting and a concise study, the authors should improve their work on two major points:
1. To improve the resolution, the authors introduced a method that addresses improving the resolution by combining more information from the neighbour structure and the existing database. This raises the question of whether the lack of previous gingival tissue spatial transcriptome sequencing results weakens the reliability of this method. Does it miss the identification of some gingival tissue-specific cells? Is the failure to match two populations of fibroblasts between single-cell …
Reviewer #1 (Public Review):
Caetano and colleagues describe the changes caused by periodontal inflammation in terms of tissue structure and provide additional evidence to understand the involvement of fibroblasts in altering the immune microenvironment.
While interesting and a concise study, the authors should improve their work on two major points:
1. To improve the resolution, the authors introduced a method that addresses improving the resolution by combining more information from the neighbour structure and the existing database. This raises the question of whether the lack of previous gingival tissue spatial transcriptome sequencing results weakens the reliability of this method. Does it miss the identification of some gingival tissue-specific cells? Is the failure to match two populations of fibroblasts between single-cell sequencing and spatial transcriptome sequencing of gingival tissue fibroblasts related to this?
2. Although the authors did the identification of the captured tissues, the results seem to require more analysis. Take Figure 5A as an example, there is a clear overlap between endothelial cells and basal cells. In addition, it is suggested that the authors indicate the specific location of the 10 clusters of cells in Figures 1D and 2C.
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Reviewer #2 (Public Review):
This is an interesting study. In this study, the authors have linked single-cell RNA sequencing, spatial transcriptomic, and multiplex fluorescence in situ hybridization to characterize human oral mucosa in health and oral chronic inflammatory disease. They defined highly specialized epithelial and stromal compartments and spatially mapped a rare pathogenic fibroblast population likely responsible for lymphocyte recruitment and angiogenesis. They highlighted that the most dramatic variation in transcriptional/cellular spatial variability corresponds to oral mucosal tissue depth. The comparison of the list of genes with altered expression in gingival inflammation with the ones highlighted from the GWAS analysis related to patients with periodontitis is very interesting and will help to generate new hypotheses …
Reviewer #2 (Public Review):
This is an interesting study. In this study, the authors have linked single-cell RNA sequencing, spatial transcriptomic, and multiplex fluorescence in situ hybridization to characterize human oral mucosa in health and oral chronic inflammatory disease. They defined highly specialized epithelial and stromal compartments and spatially mapped a rare pathogenic fibroblast population likely responsible for lymphocyte recruitment and angiogenesis. They highlighted that the most dramatic variation in transcriptional/cellular spatial variability corresponds to oral mucosal tissue depth. The comparison of the list of genes with altered expression in gingival inflammation with the ones highlighted from the GWAS analysis related to patients with periodontitis is very interesting and will help to generate new hypotheses for future studies. Together with the recent publication from Williams et al., 2021, these studies are of particular interest and a valuable resource for researchers who study oral mucosa, especially gingiva in healthy conditions and periodontal diseases.
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