Deciphering the heterogeneity of differentiating hPSC-derived corneal limbal stem cells through single-cell RNA sequencing.

A comprehensive understanding of the human pluripotent stem cell (hPSC) differentiation process stands as a prerequisite for the development of hPSC-based therapeutics. In this study, single-cell RNA sequencing (scRNA-seq) was performed to decipher the heterogeneity during differentiation of three hPSC lines toward corneal limbal stem cells (LSCs). The scRNA-seq data revealed nine clusters encompassing the entire differentiation process, among which five followed the anticipated differentiation path of LSCs. The remaining four clusters were previously undescribed cell states that were annotated as either mesodermal-like or undifferentiated subpopulations, and their prevalence was hPSC line dependent. Distinct cluster-specific marker genes identified in this study were confirmed by immunofluorescence analysis and employed to purify hPSC-derived LSCs, which effectively minimized the variation in the line-dependent differentiation efficiency. In summary, scRNA-seq offered molecular insights into the heterogeneity of hPSC-LSC differentiation, allowing a data-driven strategy for consistent and robust generation of LSCs, essential for future advancement toward clinical translation.

scANANSE gene regulatory network and motif analysis of single-cell clusters.

The recent development of single-cell techniques is essential to unravel complex biological systems. By measuring the transcriptome and the accessible genome on a single-cell level, cellular heterogeneity in a biological environment can be deciphered. Transcription factors act as key regulators activating and repressing downstream target genes, and together they constitute gene regulatory networks that govern cell morphology and identity. Dissecting these gene regulatory networks is crucial for understanding molecular mechanisms and disease, especially within highly complex biological systems. The gene regulatory network analysis software ANANSE and the motif enrichment software GimmeMotifs were both developed to analyse bulk datasets. We developed scANANSE, a software pipeline for gene regulatory network analysis and motif enrichment using single-cell RNA and ATAC datasets. The scANANSE pipeline can be run from either R or Python. First, it exports data from standard single-cell objects. Next, it automatically runs multiple comparisons of cell cluster data. Finally, it imports the results back to the single-cell object, where the result can be further visualised, integrated, and interpreted. Here, we demonstrate our scANANSE pipeline on a publicly available PBMC multi-omics dataset. It identifies well-known cell type-specific hematopoietic factors. Importantly, we also demonstrated that scANANSE combined with GimmeMotifs is able to predict transcription factors with both activating and repressing roles in gene regulation.