Single-Cell Deconvolution of Fibroblast Heterogeneity in Mouse Pulmonary Fibrosis

Abstract

Fibroblast heterogeneity has long been recognized in mouse and human lungs, homeostasis, and disease states. However, there is no common consensus on fibroblast subtypes, lineages, biological properties, signaling, and plasticity, which severely hampers our understanding of the mechanisms of fibrosis. To comprehensively classify fibroblast populations in the lung using an unbiased approach, single-cell RNA sequencing was performed with mesenchymal preparations from either uninjured or bleomycin-treated mouse lungs. Single-cell transcriptome analyses classified and defined six mesenchymal cell types in normal lung and seven in fibrotic lung. Furthermore, delineation of their differentiation trajectory was achieved by a machine learning method. This collection of single-cell transcriptomes and the distinct classification of fibroblast subsets provide a new resource for understanding the fibroblast landscape and the roles of fibroblasts in fibrotic diseases.

Keywords: fibroblast; fibrosis; lung mesenchymal cells; single-cell RNA-seq.

Figure 1. Clustering of Mesenchymal Cells by Single-Cell RNA Sequencing

(A) Sketch of bleomycin-induced pulmonary fibrosis mouse model. (B) Workflow depicts rapid dissociation and sorting of MCs from lung tissue for generating scRNA transcriptome profiles. (C and D) 2D visualization of single-cell clustering of MC profiles inferred from RNA-seq data for all MCs in normal (C) and fibrotic (D) αSMA-GFP;Tbx4-Cre;Rosa26-tdTomato lung samples. Six major classes of MCs in normal lung and seven major classes of MCs in fibrotic lung were detected. Endothelial cells also were included in the analysis. The percentage of each cell population was indicated. Colored bar coded as indicated. (E and F) Heat maps of MC normalized signal show MC subtypes changes by top genes (columns) for individual MC subtype cells (rows) in normal (E) and fibrotic (F) αSMA-GFP;Tbx4-Cre;Rosa26-tdTomato lung samples. (G and H) Clustering plots depicting single-cell RNA-seq datasets for normal (G) and fibrotic (H) Tbx4-lineage⁺α SMA⁺ MCs acquired from marker-based fluorescence-activated cell sorting (FACS).

Figure 2. Transcriptional Profile of Myofibroblasts

(A and B) Expression patterns of Acta2 (D0, A; D21, B) in representation as in Figures 1C and 1D. (C) Violin plots showing known myofibroblast markers Acta2, Myh11, and Tagln gene expression across all MC clusters. (D and E) Representative markers were distinct in normal (D) and fibrotic (E) myofibroblast clusters predicted in the scRNA-seq data. (F) Top lncRNAs enriched in myofibroblasts. The size of each circle depicts the percentage of cells in the subtype in which the marker was detected, and its color depicts the average transcript count in expressing cells (nTrans). (G) Heatmap of top significant genes, including extracellular and plasma membrane genes in this subtype. Rows correspond to normal (D0) and fibrotic (D21) myofibroblast subtypes, and columns correspond to the mean of the single-cell gene expression signature arranged by expression locations. (H) Signature transcription factor Scx expression across all MC subtypes. (I) Enrichment patterns of transcription factors in the myofibroblast subtype.

Figure 3. Matrix Fibroblast Subtype Enriched in Col13a1

(A and B) Visualization of normal (A) and fibrotic (B) Col13a1 gene expression using a t-SNE plot. (C) Expression patterns of known matrix fibroblast marker genes across MC subtypes. (D and E) Core distinct expressed genes in Col13a1 subtype from both normal (D) and fibrotic (E) lungs were indicated by violin plots. (F) lncRNAs identified in the Col13a1 subtype. (G) Heatmap of significantly expressed genes with the indication of their cellular locations was compared between normal and fibrotic Col13a1 subtypes. (H) Violin plot of Tcf21 gene expression, which is highlighted in the Col13a1 subtype. (I) Heatmap showing changes in top transcription factors between normal and fibrotic Col13a1 subtypes.

Figure 4. Characterization of Col14a1 Matrix Fibroblasts

(A and B) Col14a1 expression in normal (A) and fibrotic (B) MC subtypes projected by t-SNE (C and D) Enriched signature genes within the normal (C) and fibrotic (D) Col14a1 MC subtype. (E) Subtype significantly expressed lncRNAs. (F) Significantly differentiated genes with the indication of cellular location enriched in the Col14a1 subtype. (G) Prrx1 as the master transcription factor in the Col14a1 subtype. (H) Heatmap showing the highly distinct transcription factors.

Figure 5. Molecular Census of Lipofibroblasts

(A and B) Signature gene Plin2 expression was visualized in t-SNE plots of normal (A) and fibrotic (B) MCs. (C) Violin plots of single-cell expression levels of known lipofibroblast genes across the MC subtypes. (D and E) Top unique expressed genes in the normal (D) and fibrotic (E) lipofibroblast subtype. (F) Expression patterns of lncRNAs in normal and fibrotic lipofibroblasts. (G) Averaged expression of lipofibroblast significant genes in heatmap view. Genes were labeled with the cellular location, as indicated. (H) Nfib as the most significantly lower expressed transcription factor across the MC subtypes. (I) Top transcription factors shown in the lipofibroblast subtype.

Figure 6. Newly Emerging Fibrotic MC Subtype Expressing a High Level of Pdgfrb

(A and B) Pdgfrb expression in normal (A) and fibrotic (B) MC subtypes. (C–G) Highly unique genes (C), lncRNAs (D), significantly expressed extracellular and plasma membrane expressing genes (E), top transcription factor (F), and enriched transcription factors (G) expressed in fibrotic Pdgfrb hi subtype.

Figure 7. Metagene Analysis and Differential Potential of MCs

(A and B) Metagene profile for each MC subtype in normal status (A) and fibrotic status (B). Arrows mark overexpressed and underexpressed metagene signatures. Red shows overexpression and blue shows underexpression. (C and D) Lineage bifurcation of five MC subtypes in normal (C) and fibrotic lung (D). Cells on the same or neighboring branches are expected to be more hierarchically related. Color coding indicates pseudo-time scores of the cells.