Single-Cell Transcriptomics Reveals Cellular Heterogeneity and Complex Cell-Cell Communication Networks in the Mouse Cornea

Abstract

Purpose: To generate a single-cell RNA-sequencing (scRNA-seq) map and construct cell-cell communication networks of mouse corneas.

Methods: C57BL/6 mouse corneas were dissociated to single cells and subjected to scRNA-seq. Cell populations were clustered and annotated for bioinformatic analysis using the R package "Seurat." Differential expression patterns were validated and spatially mapped with whole-mount immunofluorescence staining. Global intercellular signaling networks were constructed using CellChat.

Results: Unbiased clustering of scRNA-seq transcriptomes of 14,732 cells from 40 corneas revealed 17 cell clusters of six major cell types: nine epithelial cell, three keratocyte, two corneal endothelial cell, and one each of immune cell, vascular endothelial cell, and fibroblast clusters. The nine epithelial cell subtypes included quiescent limbal stem cells, transit-amplifying cells, and differentiated cells from corneas and two minor conjunctival epithelial clusters. CellChat analysis provided an atlas of the complex intercellular signaling communications among all cell types.

Conclusions: We constructed a complete single-cell transcriptomic map and the complex signaling cross-talk among all cell types of the cornea, which can be used as a foundation atlas for further research on the cornea. This study also deepens the understanding of the cellular heterogeneity and heterotypic cell-cell interaction within corneas.

Figure 1.

ScRNA-seq analysis of adult mouse cornea. (A) Flowchart of the scRNA-seq experiment. (B) Transcriptomic profiles and unbiased clustering of two mouse cornea libraries shown by UMAPs for the 17 distinct clusters identified in this study. (C) Integrated UMAPs of the above two libraries because of identification of similar clusters. (D) Comparative heatmap of selected differentially expressed genes demonstrating the different transcriptomic profiles of the 17 cell clusters. (E) Feature plots demonstrating representative gene signatures of major cell clusters.

Figure 2.

Trajectory analysis of corneal cell subtypes. (A) Trajectory inference analysis of scRNA-seq transcriptomic profiles of 17 clusters. (B) Cell cycle scoring analysis of 17 cell clusters in the mouse cornea. (C) Distribution of root cells (left panel) and end points (right panel). (D) A representive pseudotime inference utilizing slingshot analysis with the starting point set as cluster 14 for corneal epithelial clusters.

Figure 3.

ScRNA-Seq analysis of the corneolimbal epithelium. (A) Violin plots showing common and preferential expression of representative marker genes in epithelial clusters. (B–D) Immunofluorescence staining of the whole cornea to demonstrate differential expression of genes including (B) Ocln/Areg (C) MKi67/Krt14, (D) Knstrn/PBK, (E) Gpha2/PBK, (F) Krt12/PBK, and (G) Krt15. The direction of the yellow arrowhead indicates basal layer to superficial layer.

Figure 4.

ScRNA-Seq analysis of keratocytes and the corneal endothelium. (A) Violin plots showing common and preferential expression of representative marker genes in keratocyte clusters. (B) Immunofluorescence staining of the whole cornea to demonstrate differential expression of genes, including Col14A1 and Egfl6, indicating different distributions within the stroma. (C) Violin plots showing common and preferential expression of representative marker genes in endothelial clusters. (D) Immunofluorescence staining of the whole cornea revealing the heterogenous expression of CD109 in endothelial cells.

Figure 5.

ScRNA-Seq analysis of vascular endothelial cells, immune cells, and fibroblasts. Violin plots of representative differentially expressed genes for three minor cell types in the cornea: (A) Pecam1 (CD31) for vascular endothelial cells; (C) Adrge1 (F4/80) for immune cells; and (E) Clec3b for fibroblasts. (B) Whole-cornea immunofluorescence staining for CD31. White and yellow arrowheads indicate the corneal epithelium and corneal endothelial cells, respectively. (D) Whole-cornea immunofluorescence staining for F4/80. (F) Whole-cornea immunofluorescence staining for Clec3b. White and yellow arrowheads indicate fibroblasts and corneal endothelial cells, respectively.

Figure 6.

CellChat-inferred cell–cell communication networks reveal the functional heterogeneity of corneal cell populations. Global communications are presented by circle plots showing the number (A) and weight/strength (B) of significant ligand–receptor pairs in 17 cell clusters. The edge width is proportional to the indicated number/weight of individual ligand–receptor pair signaling. The loop in the individual plot indicates the autocrine pathway of the cell cluster. (C) The dot plot shows the comparison of outgoing signaling patterns in top 39 signaling pathways of secreting cells in the 17 cell clusters. The dot size is proportional to the contribution score obtained from pattern recognition analysis. A higher contribution score indicates that the signaling pathway is more enriched in the corresponding cell cluster. Three representative signaling pathways, EGF (D–F), ncWNT (G–I), and TGF-β (J–L) pathways, were further analyzed. The inferred networks of communication between all cell types are displayed using hierarchical plots (D, G, J). The relative contribution of each ligand–receptor pair to the global signaling networks (E, H, K), and violin plots showing the expression distribution of signaling genes in each signaling network (F, I, L) are also shown.