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

  1. Gonzalo Benegas
  2. Jonathan Fischer
  3. Yun S Song  Is a corresponding author
  1. University of California, Berkeley, United States
  2. University of Florida, United States

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.

Data availability

All data analyzed in this study are publicly available and URL links are provided in the Materials and Methods section of our manuscript.Our source code as well as all results represented in figures and tables are publicly available on our lab's GitHub repositories:https://github.com/songlab-cal/scquint andhttps://github.com/songlab-cal/scquint-analysis

The following previously published data sets were used

Article and author information

Author details

  1. Gonzalo Benegas

    Graduate Group in Computational Biology, University of California, Berkeley, Berkeley, United States
    Competing interests
    The authors declare that no competing interests exist.
  2. Jonathan Fischer

    Department of Biostatistics, University of Florida, Gainesville, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Yun S Song

    Computer Science Division, University of California, Berkeley, Berkeley, United States
    For correspondence
    yss@berkeley.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-0734-9868

Funding

National Institutes of Health (R35-GM134922)

  • Gonzalo Benegas
  • Yun S Song

Chan Zuckerberg Initiative (CZF2019-002449)

  • Gonzalo Benegas
  • Yun S Song

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Reviewing Editor

  1. Eduardo Eyras, Australian National University, Australia

Publication history

  1. Preprint posted: April 27, 2021 (view preprint)
  2. Received: September 1, 2021
  3. Accepted: February 27, 2022
  4. Accepted Manuscript published: March 1, 2022 (version 1)
  5. Version of Record published: April 1, 2022 (version 2)

Copyright

© 2022, Benegas et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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  1. Gonzalo Benegas
  2. Jonathan Fischer
  3. Yun S Song
(2022)
Robust and annotation-free analysis of alternative splicing across diverse cell types in mice
eLife 11:e73520.
https://doi.org/10.7554/eLife.73520
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