Extended Data Fig. 9: Seurat and PAGODA single-cell RNA-seq analyses of fibroblasts identify distinct fibroblast subpopulations associated with fast- or slow-healing trajectories. | Nature

Extended Data Fig. 9: Seurat and PAGODA single-cell RNA-seq analyses of fibroblasts identify distinct fibroblast subpopulations associated with fast- or slow-healing trajectories.

From: Heterogeneity in old fibroblasts is linked to variability in reprogramming and wound healing

Extended Data Fig. 9

ag, Analysis of cells identified as fibroblasts from the single-cell RNA-seq dataset described in Fig. 4c. a, t-SNE clustering of cells identified as fibroblasts (2,678 cells in total) coloured by significant clusters identified using a k-nearest neighbour (KNN) graph-based algorithm as implemented by Seurat, or by mouse. b, log2-transformed fold change in the number of cells in each of the three subpopulations identified by Seurat between fast-healing old wounds and slow-healing old wounds. c, Seurat analysis of fibroblasts (2,678 cells in total) identified three main clusters. Heat map depicts the expression of the top 10 marker genes for each significant cell cluster identified by Seurat, which are defined as the genes that are most specific to each population. The identity of each cell subpopulation assigned to each cluster is indicated below each column. d, PAGODA of fibroblasts. PAGODA was performed using raw expression counts and all KEGG pathways, and the in vitro fibroblast ageing and the fibroblast activation signatures (see Supplementary Table 2b, f). Top, heat map of single cells from wounds of old mice and cell clusters identified by Seurat and PAGODA analyses. Bottom, heat map of the separation of cells based on their principal component scores for the significantly overdispersed gene sets. Top heat map, PAGODA clustering of cells. Maroon and blue colours indicate increased and decreased expression of the associated gene sets, respectively. e, PAGODA as described in d. Bottom, heat map of the expression of the genes that are part of the fibroblast activation signature (see Supplementary Table 2f); expression is shown as log-transformed and normalized gene expression values as calculated by Seurat and scaled row-wise. The scale for expression fold changes is indicated on the right. f, Expression of the genes that are part of the KEGG cytokine–cytokine receptor interaction gene set as in e. g, Expression of the genes that are part of the KEGG TNF signalling pathway as in e. h–l, Analysis of the combined single-cell RNA-seq datasets described in Fig. 4b, c. h, Seurat analysis of combined datasets clusters fibroblasts from both datasets together. t-SNE clustering of all live, high-quality cells from both datasets (13,833 cells in total) coloured by significant clusters identified using a KNN graph-based algorithm as implemented by Seurat, or by mouse. i, t-SNE clustering of combined fibroblasts from the datasets described in Fig. 4b (PDGFRα+Lin) and Fig. 4c. Combined fibroblasts (5,716 cells in total) are coloured by significant clusters identified using a KNN graph-based algorithm as implemented by Seurat, or by mouse. j, Seurat analysis of combined fibroblasts (5,716 cells in total) identified three main subpopulations. Heat map depicts the expression of the top 10 marker genes for each significant subpopulation identified by Seurat, which are defined as the genes that are most specific to each population. The cell subpopulation identity assigned to each cluster is indicated below each column. k, PAGODA of combined fibroblasts. PAGODA was performed using Seurat normalized counts and all KEGG pathways, the in vitro fibroblast ageing, the fibroblast activation signatures (see Supplementary Table 2b, f). Top, heat map of single fibroblasts from wounds of young and old mice or wounds from old fast- or slow-healing mice, and cell clusters identified by Seurat and PAGODA analyses. Bottom, heat map of separation of cells based on their principal component scores for a subset of the top significantly overdispersed gene sets. Top heat map, PAGODA clustering of cells. Maroon and blue colours indicate increased and decreased expression of the associated gene sets, respectively. Note that fibroblast subpopulation B did not contain cells from old/young in the combined analysis. This is probably owing to the fact that this subpopulation of fibroblast has some markers of the haematopoietic lineage, and is probably depleted in the PFGDRα+Lin FACS-sorting scheme used to isolate fibroblasts from the wounds of young and old mice. l, log2-transformed fold change in each of the three combined fibroblast subpopulations identified by PAGODA between wounds of old fast- and slow-healing mice, or between wounds from young and old mice, at day 7.

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