Parallel single-cell sequencing links transcriptional and epigenetic heterogeneity

By Christof Angermueller, Stephen J. Clark, Heather J. Lee, I. Macaulay, Mabel J Teng, T. X. Hu, Felix Krueger, Sébastien A. Smallwood, Chris P. Ponting, T. Voet and 3 more. Nature Methods, 2015

648 citations recorded by the source corpus.

We report scM&T-seq, a method for parallel single-cell genome-wide methylome and transcriptome sequencing that allows for the discovery of associations between transcriptional and epigenetic variation. Profiling of 61 mouse embryonic stem cells confirmed known links between DNA methylation and transcription. Notably, the method revealed previously unrecognized associations between heterogeneously methylated distal regulatory elements and transcription of key pluripotency genes.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/c164958d436aff9fd596d4e2881559c877d268e1

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Citations in the corpus

The Human Cell Atlas (2017, neutral): “…simultaneously profiled the transcriptome together with either the genome (Angermueller et al., 2016; Dey et al., 2015; Macaulay et al., 2015), the epigenome (Angermueller et al., 2016), or protein signatures (Albayrak et al., 2016; Darmanis et al., 2016; Frei et al., 2016; Genshaft et al., 2016).”
From Big Data to Precision Medicine (2019, positive): “It is now possible to measure concurrent transcriptomes and genetics [so-called G&T seq (28)] or epigenetic modifications (29) on a single cell.”
Computational principles and challenges in single-cell data integration (2021, methodology): “Similarly, the first parallel RNA expression, DNA methylation and/or chromatin accessibility assays have been used to probe dependencies between proximal regulatory elements and gene expression levels 3,6,37–39 .”
Multi‐Omics Factor Analysis—a framework for unsupervised integration of multi‐omics data sets (2018, methodology): “The data were obtained from Angermueller et al (2016), where details on the data generation and pre-processing can be found.”
Orchestrating single-cell analysis with Bioconductor (2019, background): “In addition, recent advances in technology and protocols allow the simultaneous measurement of genetic, epigenetic, and transcriptomic information from the same cells [46–52].”
Design and computational analysis of single-cell RNA-sequencing experiments (2016, background): “Advances will also facilitate the study of single-cell gene-expression profiles over time, space [89], or lineage [90], as well as of multiple types of -omics data within a single cell [91].”
Revealing the vectors of cellular identity with single-cell genomics (2016, background): “Early successes include parallel measurement of the transcriptome and either the genome (DR-seq 219 and G&T-seq 220 ), the methylome (scM&T-seq 221 , an adaptation of G&T-seq), or the proteome 222–224 .”
DeepCpG: accurate prediction of single-cell DNA methylation states using deep learning (2016, methodology): “Specifically, let rij be the linkage between expression levels of gene i and the mean methylation levels of an adjacent region j [10].”