Dataset Information

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

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.

SUBMITTER: Linker SM

PROVIDER: S-EPMC6371455 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature

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

Linker Stephanie M SM Urban Lara L Clark Stephen J SJ Chhatriwala Mariya M Amatya Shradha S McCarthy Davis J DJ Ebersberger Ingo I Vallier Ludovic L Reik Wolf W Stegle Oliver O Bonder Marc Jan MJ

Genome biology 20190211 1

<h4>Background</h4>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 sing ...[more]

PMID: 30744673

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Project description:Osteoarthritis (OA) is a common type of arthritis, which may cause pain and disability. Alterations in gene expression and DNA methylation have been proven to be associated with the development of OA. The aim of the present study was to identify potential therapeutic targets and associated processes for OA via the combined analysis of gene expression and DNA methylation datasets. The gene expression and DNA methylation profiles were obtained from the Gene Expression Omnibus, and differentially expressed genes (DEGs) and differentially methylated sites (DMSs) were identified in the present study, using R programming software. The enriched functions of DEGs and DMSs were obtained via the Database for Annotation, Visualization and Integrated Discovery. Finally, cross analysis of DEGs and DMSs was performed to identify genes that exhibited differential expression and methylation simultaneously. The protein?protein interaction (PPI) network of overlaps between DEGs and DMSs was obtained using the Human Protein Reference Database; the topological properties of PPI network overlaps were additionally obtained. Hub genes in the PPI network were further confirmed via reverse transcription?quantitative polymerase chain reaction (RT?qPCR). The results of the present study revealed that the majority of DEGs and DMSs were upregulated and hypomethylated in patients with OA, respectively. DEGs and DMSs were primarily involved in inflammatory, immune and gene expression regulation?associated processes and pathways. Cross analysis revealed 30 genes that exhibited differential expression and methylation in OA simultaneously. Topological analysis of the PPI network revealed that numerous genes, including G protein subunit ?1 (GNAI1), runt related transcription factor 2 (RUNX2) and integrin subunit ?2 (ITGB2), may be involved in the development of OA. Additionally, RT?qPCR analysis of GNAI1, RUNX2 and ITGB2 provided further confirmation. Numerous known and novel therapeutic targets were obtained via network analysis. The results of the present study may be beneficial for the diagnosis and treatment of OA.

Dataset Information

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

Publications

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

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