Single-cell 5hmC sequencing reveals chromosome-wide cell-to-cell variability and enables lineage reconstruction

Mooijman, Dylan; Dey, Siddharth S.; Boisset, Jean Charles; Crosetto, Nicola; Oudenaarden, Alexander van

doi:10.1038/nbt.3598

Cited by 154 publications

(132 citation statements)

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“…have been published recently (Han et al 2016;Mooijman et al 2016). Compared with these two methods, which enrich 5hmC-modified regions through restriction enzyme digestion or chemical labeling, liMAB-seq and scMABseq are based on BS-seq and do not depend on enrichment, allowing a more quantitative analysis of the modifications and a direct comparison with BS-seq data.…”

Section: Discussionmentioning

confidence: 99%

Simultaneous mapping of active DNA demethylation and sister chromatid exchange in single cells

Inoue

Suzuki

et al. 2017

Genes Dev.

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To understand mammalian active DNA demethylation, various methods have been developed to map the genomic distribution of the demethylation intermediates 5-formylcysotine (5fC) and 5-carboxylcytosine (5caC). However, the majority of these methods requires a large number of cells to begin with. In this study, we describe low-input methylase-assisted bisulfite sequencing (liMAB-seq ) and single-cell MAB-seq (scMAB-seq), capable of profiling 5fC and 5caC at genome scale using ∼100 cells and single cells, respectively. liMAB-seq analysis of preimplantation embryos reveals the oxidation of 5mC to 5fC/5caC and the positive correlation between chromatin accessibility and processivity of ten-eleven translocation (TET) enzymes. scMAB-seq captures the cell-to-cell heterogeneity of 5fC and 5caC and reveals the strand-biased distribution of 5fC and 5caC. scMAB-seq also allows the simultaneous high-resolution mapping of sister chromatid exchange (SCE), facilitating the study of this type of genomic rearrangement. Therefore, our study not only establishes new methods for the genomic mapping of active DNA demethylation using limited numbers of cells or single cells but also demonstrates the utilities of the methods in different biological contexts.

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Section: Discussionmentioning

confidence: 99%

Simultaneous mapping of active DNA demethylation and sister chromatid exchange in single cells

Inoue

Suzuki

et al. 2017

Genes Dev.

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“…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%

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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“…While much of this can be inferred from transcriptomic data, tracing the precursorprogeny relationships to the level of individual clones is more difficult. It is feasible to reconstruct cellular lineage from mosaic mutations of an organism (Behjati et al, 2014;Ju et al, 2017), using epigenetic marks (Mooijman et al, 2016) or leveraging VDJ recombination for tracing lymphocyte populations (Stubbington et al, 2016). It will be compelling to integrate these methods with single-cell transcriptomics, although the scalability of combined single-cell DNA, RNA, and epigenetic assays is limited .…”

Section: The Big Picturementioning

confidence: 99%

Mapping human development at single-cell resolution

et al. 2018

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Human development is regulated by spatiotemporally restricted molecular programmes and is pertinent to many areas of basic biology and human medicine, such as stem cell biology, reproductive medicine and childhood cancer. Mapping human development has presented significant technological, logistical and ethical challenges. The availability of established human developmental biorepositories and the advent of cutting-edge single-cell technologies provide new opportunities to study human development. Here, we present a working framework for the establishment of a human developmental cell atlas exploiting single-cell genomics and spatial analysis. We discuss how the development atlas will benefit the scientific and clinical communities to advance our understanding of basic biology, health and disease.

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Single-cell 5hmC sequencing reveals chromosome-wide cell-to-cell variability and enables lineage reconstruction

Cited by 154 publications

References 29 publications

Simultaneous mapping of active DNA demethylation and sister chromatid exchange in single cells

Simultaneous mapping of active DNA demethylation and sister chromatid exchange in single cells

Manifold alignment reveals correspondence between single cell transcriptome and epigenome dynamics

Mapping human development at single-cell resolution

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