Robust and annotation-free analysis of alternative splicing across diverse cell types in mice

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/ .

Specific splice junction detection in single cells with SICILIAN

“…Typical workflows from commodity platforms such as 10x genomics (e.g., Cell Ranger [14]) have preprocessing steps that remove all unannotated junctions before reporting spliced alignments. Further, the vast majority of publications characterizing splicing in single cells require ad hoc lower bounds on junction overlap or total number of supporting reads [15]. These approaches will miss and highlight the need for a statistically driven method to discover novel regulation of splicing in single cells.…”