obstacles to detecting isoforms using full length scrna seq data

• DOI: 10.17863/cam.53540
• Publication Date: 2020-03-23
• Authors: Jennifer Westoby; Jennifer Westoby; Pavel Artemov; Anne C. Ferguson-...; Martin Hemberg
• Keywords: 0301 basic medicine; Isoform; QH301-705.5; 0206 medical engineering; 02 engineering and technology; QH426-470; Gene; 03 medical and health sciences; scRNA-seq; Genetics; RNA Isoforms; Computer Simulation; Single cell; RNA-Seq; Biology (General); Models, Genetic; Research; Isoform choice; Isoform Choice; Alternative Splicing; Dropouts; Single-Cell Analysis; Scrna-seq; Alternative splicing

Abstract Background Early single-cell RNA-seq (scRNA-seq) studies suggested that it was unusual to see more than one isoform being produced from a gene in a single cell, even when multiple isoforms were detected in matched bulk RNA-seq samples. However, these studies generally did not consider the impact of dropouts or isoform quantification errors, potentially confounding the results of these analyses. Results In this study, we take a simulation based approach in which we explicitly account for dropouts and isoform quantification errors. We use our simulations to ask to what extent it is possible to study alternative splicing using scRNA-seq. Additionally, we ask what limitations must be overcome to make splicing analysis feasible. We find that the high rate of dropouts associated with scRNA-seq is a major obstacle to studying alternative splicing. In mice and other well-established model organisms, the relatively low rate of isoform quantification errors poses a lesser obstacle to splicing analysis. We find that different models of isoform choice meaningfully change our simulation results. Conclusions To accurately study alternative splicing with single-cell RNA-seq, a better understanding of isoform choice and the errors associated with scRNA-seq is required. An increase in the capture efficiency of scRNA-seq would also be beneficial. Until some or all of the above are achieved, we do not recommend attempting to resolve isoforms in individual cells using scRNA-seq.