Integrative Single-Cell RNA-Seq and ATAC-Seq Analysis of Mouse Corneal Epithelial Cells

Single-Cell Multiomics Analysis of Early Wound Response Programs in the Mouse Corneal Epithelium

Purpose

Wound healing is crucial for restoring homeostasis in living organisms. Although wound response mechanisms have been studied extensively, the gene regulatory programs involved remain to be elucidated. Here, we used single-cell RNA sequencing (RNA-seq) and ATAC sequencing (ATAC-seq) analysis to profile the regulatory landscape of mouse corneal epithelium in early wound response.

Methods

We used our previously published single-cell data sets of homeostatic adult mouse corneal epithelium as the unwounded group. The wounded group data sets were obtained by sequencing the epithelium after an annular epithelial wound. Following the integration of the relevant data sets, the Seurat and ArchR packages were employed for single-cell RNA-seq and single-cell ATAC-seq data processing and downstream analysis, respectively. The Monocle 2 was used for pseudo-time analysis, CellChat for intercellular communication analysis, and pySCENIC for analyzing transcription factors. The expression of key genes was validated via immunofluorescence staining and quantitative real-time PCR.

Results

Our data show that the number of cell type-specific genes decreases and the number of common transcriptional responses increases in early wound response. Concurrently, we find that the chromatin accessibility landscape undergoes significant changes across all epithelial cell types and that the wound-induced open regions are similarly distributed across the genome. Motif enrichment analysis shows that Fosl1/AP-1 binding site is highly enriched among the opened regions. However, by assessing the correlation between changes in chromatin accessibility and gene expression, we observe that only a small subset of wound-induced genes shows a high correlation with the accessibility of nearby chromatin.

Conclusions

Our study provides a detailed single-cell landscape for transcriptomic and epigenetic changes in mouse corneal epithelium during early wound response, which improved our understanding of the mechanisms of wound healing.

SCInter: A comprehensive single-cell transcriptome integration database for human and mouse

Single-cell RNA sequencing (scRNA-seq), which profiles gene expression at the cellular level, has effectively explored cell heterogeneity and reconstructed developmental trajectories. With the increasing research on diseases and biological processes, scRNA-seq datasets are accumulating rapidly, highlighting the urgent need for collecting and processing these data to support comprehensive and effective annotation and analysis. Here, we have developed a comprehensive Single-Cell transcriptome integration database for human and mouse (SCInter, https://bio.liclab.net/SCInter/index.php), which aims to provide a manually curated database that supports the provision of gene expression profiles across various cell types at the sample level. The current version of SCInter includes 115 integrated datasets and 1016 samples, covering nearly 150 tissues/cell lines. It contains 8016,646 cell markers in 457 identified cell types. SCInter enabled comprehensive analysis of cataloged single-cell data encompassing quality control (QC), clustering, cell markers, multi-method cell type automatic annotation, predicting cell differentiation trajectories and so on. At the same time, SCInter provided a user-friendly interface to query, browse, analyze and visualize each integrated dataset and single cell sample, along with comprehensive QC reports and processing results. It will facilitate the identification of cell type in different cell subpopulations and explore developmental trajectories, enhancing the study of cell heterogeneity in the fields of immunology and oncology.