Parallel single-cell sequencing links transcriptional and epigenetic heterogeneity

Angermueller, Christof; Clark, Stephen J.; Lee, Heather; Macaulay, Iain C.; Teng, Mabel; Hu, Xiaoming; Krueger, Felix; Smallwood, Sébastien A.; Ponting, Chris P.; Voet, Thierry; Kelsey, Gavin; Reik, Wolf

doi:10.1038/nmeth.3728

Cited by 645 publications

(619 citation statements)

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“…Several groups including those lead by Reik (Angermueller et al 2016;Clark et al 2016;Clark et al 2017), Vijg (Gravina et al 2015Gravina et al 2016;Yu et al 2017), Adey (Mulqueen et al 2017), and Eckardt (Luo et al 2017) have also demonstrated that methylation patterns can be generated from single cells. However, the principle caveat to these methods is that only a small amount of the genome is analyzed (often around 5%) and the same genomic regions are not analyzed in each cell, making cell-to-cell comparisons of specific loci difficult.…”

Section: Single Cell/cell-type Analysesmentioning

confidence: 99%

Analysis of DNA modifications in aging research

et al. 2018

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As geroscience research extends into the role of epigenetics in aging and age-related disease, researchers are being confronted with unfamiliar molecular techniques and data analysis methods that can be difficult to integrate into their work. In this review, we focus on the analysis of DNA modifications, namely cytosine methylation and hydroxymethylation, through nextgeneration sequencing methods. While older techniques for modification analysis performed relative quantitation across regions of the genome or examined average genome levels, these analyses lack the desired specificity, rigor, and genomic coverage to firmly establish the nature of genomic methylation patterns and their response to aging. With recent methodological advances, such as whole genome bisulfite sequencing (WGBS), bisulfite oligonucleotide capture sequencing (BOCS), and bisulfite amplicon sequencing (BSAS), cytosine modifications can now be readily analyzed with base-specific, absolute quantitation at both cytosine-guanine dinucleotide (CG) and non-CG sites throughout the genome or within specific regions of interest by next-generation sequencing. Additional advances, such as oxidative bisulfite conversion to differentiate methylation from hydroxymethylation and analysis of limited input/single-cells, have great promise for continuing to expand epigenomic capabilities. This review provides a background on DNA modifications, the current state-of-theart for sequencing methods, bioinformatics tools for converting these large data sets into biological insights, and perspectives on future directions for the field.

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Section: Single Cell/cell-type Analysesmentioning

confidence: 99%

Analysis of DNA modifications in aging research

et al. 2018

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“…Although it is very difficult in general to measure multiple genomic quantities on the same single cell, one particular protocol (scM&T-seq) has been developed for measuring DNA methylation and gene expression in the same single cell [14]. It is possible that future protocols will enable other joint measurements.…”

Section: Overview Of Matchermentioning

confidence: 99%

“…Several high-throughput single cell versions of epigenetic assays have been developed, including single cell bisulfite sequencing (DNA methylation) [14], ATAC-seq (chromatin accessibility) [13], and ChIP-seq (histone modification) [12]. Each of the initial studies that pioneered these methods applied them to mouse embryonic stem cells (mESCs) grown in serum, a classic model system of stem cell biology.…”

Section: Data Description and Processingmentioning

confidence: 99%

“…In such experiments, each sequenced cell is assumed to be at one point in the process, and sequencing enough cells can reveal the progression of gene expression changes that occur during the process [8,9]. More recently, several experimental techniques for performing single cell epigenetic have been developed [10][11][12][13][14][15][16][17], and initial analyses have demonstrated that single cell epigenetic data can be used to elucidate the series of changes in a sequential process [16,18,19].…”

Section: Introductionmentioning

confidence: 99%

“…Measuring DNA [14,[22][23][24] or RNA and proteins [25,26] from the same single cell, but experimentally performing multiple assays on either chromatin or RNA from the same cell is currently impossible.…”

Section: Introductionmentioning

confidence: 99%

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Manifold alignment reveals correspondence between single cell transcriptome and epigenome dynamics

Welch

Hartemink

Prins

2017

Preprint

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Single cell genomic techniques promise to yield key insights into the dynamic interplay between gene expression and epigenetic modification. However, the experimental difficulty of performing multiple measurements on the same cell currently limits efforts to combine multiple genomic data sets into a united picture of single cell variation. We show that it is possible to construct cell trajectories, reflecting the changes that occur in a sequential biological process, from single cell ATAC-seq, bisulfite sequencing, and ChIP-seq data. In addition, we present an approach called MATCHER that computationally circumvents the experimental difficulties inherent in performing multiple genomic measurements on a single cell by inferring correspondence between single cell transcriptomic and epigenetic measurements performed on different cells of the same type. MATCHER works by first learning a separate manifold for the trajectory of each kind of genomic data, then aligning the manifolds to infer a shared trajectory in which cells measured using different techniques are directly comparable. Using scM&T-seq data, we confirm that MATCHER accurately predicts true single cell correlations between DNA methylation and gene expression without using known cell correspondence information. We also used MATCHER to infer correlations among gene expression, chromatin accessibility, and histone modifications in single mouse embryonic stem cells. These results reveal the dynamic interplay between epigenetic changes and gene expression underlying the transition from pluripotency to differentiation priming. Our work is a first step toward a united picture of heterogeneous transcriptomic and epigenetic states in single cells.All rights reserved. No reuse allowed without permission.(which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.

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Computational approaches for interpreting scRNA‐seq data

et al. 2017

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The recent developments in high‐throughput single‐cell RNA sequencing technology (scRNA‐seq) have enabled the generation of vast amounts of transcriptomic data at cellular resolution. With these advances come new modes of data analysis, building on high‐dimensional data mining techniques. Here, we consider biological questions for which scRNA‐seq data is used, both at a cell and gene level, and describe tools available for these types of analyses. This is an exciting and rapidly evolving field, where clustering, pseudotime inference, branching inference and gene‐level analyses are particularly informative areas of computational analysis.

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Parallel single-cell sequencing links transcriptional and epigenetic heterogeneity

Cited by 645 publications

References 28 publications

Analysis of DNA modifications in aging research

Analysis of DNA modifications in aging research

Manifold alignment reveals correspondence between single cell transcriptome and epigenome dynamics

Computational approaches for interpreting scRNA‐seq data

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