Single-Cell RNA Sequencing Analysis of the Early Postnatal Mouse Lens Epithelium

Abstract

Purpose The lens epithelium maintains the overall health of the organ. We used single-cell RNA sequencing (scRNA-seq) technology to assess transcriptional heterogeneity between cells in the postnatal day 2 (P2) epithelium and identify distinct epithelial cell subtypes. Analysis of these data was used to better understand lens growth, differentiation, and homeostasis on P2. Methods scRNA-seq on P2 mouse lenses was performed using the 10x Genomics Chromium Single Cell 3′ Kit (v3.1) and short-read Illumina sequencing. Sequence alignment and preprocessing of data were conducted using 10x Genomics Cell Ranger software. Seurat was employed for preprocessing, quality control, dimensionality reduction, and cell clustering, and Monocle was utilized for trajectory analysis to understand the developmental progression of the lens cells. CellChat and GO analyses were used to explore cell–cell communication networks and signaling interactions. Results Lens epithelial cells (LECs) were divided into seven subclusters, classified by specific gene markers. The expression of crystallin, cell-cycle, and metabolic genes was not uniform, indicating distinct functional roles of LECs. Trajectory analysis predicted a bifurcation of differentiating and cycling cells from an Igfbp5+ progenitor pool. We also identified heterogeneity in signaling molecules and pathways, suggesting that cycling and progenitor subclusters have prominent roles in coordinating crosstalk. Conclusions scRNA-seq corroborated many known markers of epithelial differentiation and proliferation while providing further insight into the pathways and genes directing these processes. Interestingly, we demonstrated that the developing epithelium can be divided into distinct subpopulations. These clusters reflect the transcriptionally diverse roles of the epithelium in proliferation, signaling, and maintenance.

Single-Cell Transcriptome Sequencing Reveals Molecular Expression Differences and Marker Genes in Testes during the Sexual Maturation of Mongolian Horses

“…Subsequently, Seurat (4.1.1, https://satijalab.org/seurat/, accessed on 25 October 2023) was employed for further cell filtering, normalization of cellular expression quantities, subclassification of cellular subpopulations, analysis of differentially expressed genes within each subpopulation, and the screening of marker genes. Additionally, R (4.1.2, https://cloud.r-project.org, accessed on 26 October 2023) was used to identify the cell types within the clusters obtained from sequencing data and to perform single-cell data mining [30][31][32].…”