Combined single-cell profiling of expression and DNA methylation reveals splicing regulation and heterogeneity
Stephanie M. Linker,Lara Urban,Stephen J. Clark,Mariya Chhatriwala,Shradha Amatya,Davis J. McCarthy,Ingo Ebersberger,Ludovic Vallier,Ludovic Vallier,Wolf Reik,Wolf Reik,Wolf Reik,Oliver Stegle,Oliver Stegle,Marc Jan Bonder +14 more
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TLDR
This study applies parallel DNA methylation and transcriptome sequencing to differentiating human induced pluripotent stem cells to characterize splicing variation (exon skipping) and its determinants and reveals a previously underappreciated link betweenDNA methylation variation and splicing.Abstract:
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. 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. 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.read more
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Single-cell RNA-sequencing of differentiating iPS cells reveals dynamic genetic effects on gene expression
Anna S E Cuomo,Daniel D Seaton,Davis J. McCarthy,Davis J. McCarthy,Iker Martinez,Marc Jan Bonder,Marc Jan Bonder,Jose Garcia-Bernardo,Shradha Amatya,Pedro Madrigal,Abigail Isaacson,Florian Buettner,Andrew J Knights,Kedar Nath Natarajan,Kedar Nath Natarajan,Ludovic Vallier,Ludovic Vallier,John C. Marioni,John C. Marioni,John C. Marioni,Mariya Chhatriwala,Oliver Stegle,Oliver Stegle +22 more
TL;DR: Induced pluripotent stem cells from 125 donors are exploited to track gene expression changes and expression quantitative trait loci at single cell resolution during in vitro endoderm differentiation to identify molecular markers that are predictive of differentiation efficiency of individual lines.
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Epigenetic heterogeneity in cancer
TL;DR: The current trend of epigenetic therapy is to use epigenetic drugs to reverse and/or delay future resistance to cancer therapies to reverse drug resistance in heterogeneous cancer.
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The epigenetic basis of cellular heterogeneity.
Benjamin Carter,Keji Zhao +1 more
TL;DR: Advances in single-cell epigenomic profiling methods are enabling high-resolution mapping of chromatin states in individual cells, providing evidence that variations in different aspects of Chromatin organization collectively define gene expression heterogeneity among otherwise highly similar cells.
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Tools for the analysis of high-dimensional single-cell RNA sequencing data.
TL;DR: This Review provides the non-expert reader with an overview of the different steps involved in the analysis of single-cell RNA sequencing data and provides insight into the strengths and pitfalls of available analysis tools.
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Direct full-length RNA sequencing reveals unexpected transcriptome complexity during Caenorhabditis elegans development.
TL;DR: A direct RNA sequencing method with ultralong reads using Oxford Nanopore Technologies is applied to study the transcriptome complexity in Caenorhabditis elegans and devised a method to classify the long reads as the same as existing transcripts or as a novel transcript using sequence mapping tracks rather than existing intron/exon structures.
References
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STAR: ultrafast universal RNA-seq aligner
Alexander Dobin,Carrie A. Davis,Felix Schlesinger,Jorg Drenkow,Chris Zaleski,Sonali Jha,Philippe Batut,Mark Chaisson,Thomas R. Gingeras +8 more
TL;DR: The Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure outperforms other aligners by a factor of >50 in mapping speed.
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