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Genome Biol. 2019 Feb 11;20(1):30. doi: 10.1186/s13059-019-1644-0.

Combined single-cell profiling of expression and DNA methylation reveals splicing regulation and heterogeneity.

Author information

1
European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, Cambridge, UK.
2
European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany.
3
Epigenetics Programme, The Babraham Institute, Cambridge, UK.
4
Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK.
5
Applied Bioinformatics Group, Institute of Cell Biology and Neuroscience, Goethe University Frankfurt, Max-von-Laue-Str. 13, 60438, Frankfurt, Germany.
6
Senckenberg Biodiversity and Climate Research Centre (BiK-F), Frankfurt, Germany.
7
Wellcome Trust - MRC Cambridge Stem Cell Institute, Anne McLaren Laboratory, University of Cambridge, Cambridge, CB2 0SZ, UK.
8
Department of Surgery, University of Cambridge, Cambridge, CB2 0QQ, UK.
9
Centre for Trophoblast Research, University of Cambridge, Cambridge, UK.
10
European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, Cambridge, UK. oliver.stegle@embl.de.
11
European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany. oliver.stegle@embl.de.
12
Division of Computational Genomics and Systems Genetics, German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany. oliver.stegle@embl.de.
13
European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, Cambridge, UK. bonder.m.j@gmail.com.
14
European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany. bonder.m.j@gmail.com.

Abstract

BACKGROUND:

Alternative splicing is a key regulatory mechanism in eukaryotic cells and increases the effective number of functionally distinct gene products. Using bulk RNA sequencing, splicing variation has been studied across human tissues and in genetically diverse populations. This has identified disease-relevant splicing events, as well as associations between splicing and genomic features, including sequence composition and conservation. However, variability in splicing between single cells from the same tissue or cell type and its determinants remains poorly understood.

RESULTS:

We applied parallel DNA methylation and transcriptome sequencing to differentiating human induced pluripotent stem cells to characterize splicing variation (exon skipping) and its determinants. Our results show that variation in single-cell splicing can be accurately predicted based on local sequence composition and genomic features. We observe moderate but consistent contributions from local DNA methylation profiles to splicing variation across cells. A combined model that is built based on genomic features as well as DNA methylation information accurately predicts different splicing modes of individual cassette exons. These categories include the conventional inclusion and exclusion patterns, but also more subtle modes of cell-to-cell variation in splicing. Finally, we identified and characterized associations between DNA methylation and splicing changes during cell differentiation.

CONCLUSIONS:

Our study yields new insights into alternative splicing at the single-cell level and reveals a previously underappreciated link between DNA methylation variation and splicing.

KEYWORDS:

Alternative splicing; Cell differentiation; DNA methylation; Multi-omics; Single-cell analysis; Splicing prediction

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