Sierra: discovery of differential transcript usage from polyA-captured single-cell RNA-seq data.

Ralph Patrick,Alicia Oshlack,Vaibhao Janbandhu,David T Humphreys,Joshua W.K Ho,Kitty K Lo,Richard P Harvey

doi:10.1186/s13059-020-02071-7

Abstract

High-throughput single-cell RNA-seq (scRNA-seq) is a powerful tool for studying gene expression in single cells. Most current scRNA-seq bioinformatics tools focus on analysing overall expression levels, largely ignoring alternative mRNA isoform expression. We present a computational pipeline, Sierra, that readily detects differential transcript usage from data generated by commonly used polyA-captured scRNA-seq technology. We validate Sierra by comparing cardiac scRNA-seq cell types to bulk RNA-seq of matched populations, finding significant overlap in differential transcripts. Sierra detects differential transcript usage across human peripheral blood mononuclear cells and the Tabula Muris, and 3 ′UTR shortening in cardiac fibroblasts. Sierra is available at https://github.com/VCCRI/Sierra.

Highlights

Regulation of cellular transcriptional activity includes changes in the total expression level of genes as well as alternative usage of gene architecture
The Sierra pipeline starts with a BAM file, such as that produced by the 10x Genomics CellRanger software, and the reference GTF file used for mapping (Fig. 1)
The peak coordinates are utilised to construct a new reference file of genomic regions, enabling a unique molecular identifier (UMI) matrix of peak coordinates to be built for a supplied list of cell barcodes

Summary

Introduction

Regulation of cellular transcriptional activity includes changes in the total expression level of genes as well as alternative usage of gene architecture. RNA sequencing (RNA-seq) studies have revealed high levels of alternative transcript usage between tissues, with 95% of multi-exon genes estimated to undergo AS among human tissues [1]. APA is widespread among the mammalian genome and is estimated to occur in most genes [2]. While the extensive use of AS and APA among tissues is documented, DTU between the diverse sub-tissue cell types revealed in recent years by Patrick et al Genome Biology (2020) 21:167 single-cell RNA-seq (scRNA-seq) is relatively unexplored, nor is the regulatory logic well understood

Methods

Results

Conclusion