Abstract
Although isoform diversity is acknowledged as a fundamental and pervasive aspect of gene expression in higher eukaryotes, it is often omitted from single-cell studies due to quantification challenges inherent to commonly used short-read sequencing technologies. To address this issue, we have developed a suite of computational tools to investigate isoform variation by focusing on splice junction usage patterns, which can often be well characterized in spite of technical difficulties. Our method, which we name scQuint (single-cell quantification of introns), can perform accurate quantification, dimensionality reduction, and differential splicing analysis using short-read, full-length single-cell RNA-seq data. Notably, scQuint does not require transcriptome annotations and is robust to technical artifacts. In applications across diverse mouse tissues from Tabula Muris and the primary motor cortex from the BRAIN Initiative Cell Census Network, we find evidence of strong cell-type-specific isoform variation, complementary to total gene expression, and also identify a large volume of previously unannotated splice junctions. As a community resource, we provide ways to interactively visualize and explore these results, accessible at https://github.com/songlab-cal/scquint-analysis/.
Competing Interest Statement
The authors have declared no competing interest.