Quantitative Description of C. elegans mRNA Landscapes From High Coverage Single-Cell Transcriptomes

Abstract

Single-cell RNA sequencing technology dramatically changed the way we investigate transcriptomes. However, the amount and complexity of data generated by such methods poses new challenges for biologists who are trying to extract detailed insights into the genetic programs that drive cellular functions and differentiation. To provide a more intuitive understanding of cell specific gene expression programs, we developed a novel approach for exploiting scRNA-seq data that detects individual gene expression levels in each cell, by avoiding dimensional reduction methods. This was achieved by focusing our analysis on individual cells with a high sequencing coverage (above 15000 Unique Molecular Identifiers (UMIs)). Such High Coverage Cells (HCC), were found in all five C. elegans scRNA-seq datasets we investigated and constitute direct quantitative experimental observations of the mRNA content of individual cells. Clustering the complete gene expression matrix for these cells, we identified gene sets specific for most C. elegans tissues. Among each set we found genes that are dominating cell specific transcriptomes as well as genes that are restricted to particular cell types but are a thousand fold less expressed. For each cell type or subtype we characterized, we identified a set of genes with expression restricted to those cells that were not previously associated with the corresponding tissue. Our results demonstrate that by focusing on HCCs, we can provide high-resolution quantitative descriptions of cellular expression landscapes that are immediately exploitable for researchers to generate new biological hypotheses. Overall, we demonstrate that HCCs represent a powerful and largely unexplored source of biological insights and suggest that future scRNA-seq experiments could benefit from focusing on HCC enrichment to capture and exploit the full complexity of cellular transcriptomes.