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

Gonzalo Benegas,Yun S Song,Jonathan Fischer

doi:10.7554/elife.73520

Gonzalo Benegas, Yun S Song + Show 1 more

Open Access

https://doi.org/10.7554/elife.73520

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Abstract

Although alternative splicing is 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. Here, we undertake the analysis of alternative splicing across numerous diverse murine cell types from two large-scale single-cell datasets-the Tabula Muris and BRAIN Initiative Cell Census Network-while accounting for understudied technical artifacts and unannotated events. We find strong and general cell-type-specific alternative splicing, complementary to total gene expression but of similar discriminatory value, and identify a large volume of novel splicing events. We specifically highlight splicing variation across different cell types in primary motor cortex neurons, bone marrow B cells, and various epithelial cells, and we show that the implicated transcripts include many genes which do not display total expression differences. To elucidate the regulation of alternative splicing, we build a custom predictive model based on splicing factor activity, recovering several known interactions while generating new hypotheses, including potential regulatory roles for novel alternative splicing events in critical genes like Khdrbs3 and Rbfox1. We make our results available using public interactive browsers to spur further exploration by the community.

Highlights

The past decade’s advances in single-cell genomics have enabled the data-driven characterization of a wide variety of distinct cell populations
As we demonstrate in subsequent sections, our modifications in the quantification, statistical modeling, and optimization procedures lead to improved robustness, scalability, and calibration when working with data from single cells (Figure 2–figure supplement 2, see Methods)
We instead found that Smart-seq2 data (Picelli et al, 2014) frequently contain sizable fractions of genes with coverage that decays with increasing distance from the 3’ ends of transcripts

Summary

Introduction

The past decade’s advances in single-cell genomics have enabled the data-driven characterization of a wide variety of distinct cell populations. Long-read singlecell technologies, which greatly simplify isoform quantification, are improving (Byrne et al, 2017; Gupta et al, 2018; Volden and Vollmers, 2020; Lebrigand et al, 2020; Joglekar et al, 2021), but remain more costly and lower-throughput than their short-read counterparts. For these reasons and others, short-read datasets predominate and we must work with short reads to make use of the rich compendium of available data. Researchers have developed several computational methods to investigate splicing variation despite the sizable technical challenges inherent to this regime. A selection of these challenges and methods are summarized in the Appendix

Methods

Results

Conclusion