期刊
GENOME BIOLOGY
卷 20, 期 -, 页码 -出版社
BMC
DOI: 10.1186/s13059-019-1644-0
关键词
Single-cell analysis; Alternative splicing; DNA methylation; Splicing prediction; Cell differentiation; Multi-omics
资金
- Wellcome Trust [WT090851, 095645/Z/11/Z]
- UK Medical Research Council [WT098503]
- Stiftung Familie Klee
- European Molecular Biology Laboratory
- European Union's Horizon 2020 research and innovation programme [N635290]
- EMBL Interdisciplinary Postdoc (EI3POD) program under Marie Sklodowska-Curie Actions COFUND [664726]
- European Research Council advanced grant New-Chol
- Cambridge University Hospitals National Institute for Health Research Biomedical Research Center
- Medical Research Council [PSAG028]
- BBSRC [BB/K010867/1]
- EU BLUEPRINT
- EpiGeneSys
- BBSRC [BBS/E/B/000C0422, BBS/E/B/0000H334, BB/K010867/1, BBS/E/B/000C0426] Funding Source: UKRI
- EPSRC [TS/H001220/1] Funding Source: UKRI
BackgroundAlternative 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.ResultsWe 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.ConclusionsOur study yields new insights into alternative splicing at the single-cell level and reveals a previously underappreciated link between DNA methylation variation and splicing.
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