Joint profiling of chromatin accessibility, DNA methylation and transcription in single cells

Parallel single-cell sequencing protocols represent powerful methods for investigating regulatory relationships, including epigenome-transcriptome interactions. Here, we report the first single-cell method for parallel chromatin accessibility, DNA methylation and transcriptome profiling. scNMT-seq (single-cell nucleosome, methylation and transcription sequencing) uses a GpC methyltransferase to label open chromatin followed by bisulfite and RNA sequencing. We validate scNMT-seq by applying it to mouse embryonic stem cells, finding links between all three molecular layers and revealing strong and widespread associations between chromatin accessibility and DNA methylation.

[1]  A. Hoffmann,et al.  A Unifying Model for the Selective Regulation of Inducible Transcription by CpG Islands and Nucleosome Remodeling , 2009, Cell.

[2]  Gangning Liang,et al.  Genome-wide mapping of nucleosome positioning and DNA methylation within individual DNA molecules , 2012, Genome research.

[3]  C. Ponting,et al.  G&T-seq: parallel sequencing of single-cell genomes and transcriptomes , 2015, Nature Methods.

[4]  Timothy E. Reddy,et al.  Dynamic DNA methylation across diverse human cell lines and tissues , 2013, Genome research.

[5]  W. Reik,et al.  Genome-wide base-resolution mapping of DNA methylation in single cells using single-cell bisulfite sequencing (scBS-seq) , 2017, Nature Protocols.

[6]  E. Pierson,et al.  ZIFA: Dimensionality reduction for zero-inflated single-cell gene expression analysis , 2015, Genome Biology.

[7]  W. Reik,et al.  FGF Signaling Inhibition in ESCs Drives Rapid Genome-wide Demethylation to the Epigenetic Ground State of Pluripotency , 2013, Clinical Epigenetics.

[8]  Lee E. Edsall,et al.  Human DNA methylomes at base resolution show widespread epigenomic differences , 2009, Nature.

[9]  James B. Brown,et al.  Modeling gene expression using chromatin features in various cellular contexts , 2012, Genome Biology.

[10]  R. Gentleman,et al.  Independent filtering increases detection power for high-throughput experiments , 2010, Proceedings of the National Academy of Sciences.

[11]  Zhigang Xue,et al.  Simultaneous profiling of transcriptome and DNA methylome from a single cell , 2016, Genome Biology.

[12]  Felix Krueger,et al.  Bismark: a flexible aligner and methylation caller for Bisulfite-Seq applications , 2011, Bioinform..

[13]  Howard Y. Chang,et al.  Single-cell chromatin accessibility reveals principles of regulatory variation , 2015, Nature.

[14]  C. Ponting,et al.  Parallel single-cell sequencing links transcriptional and epigenetic heterogeneity , 2015, Nature Methods.

[15]  Lu Wen,et al.  Single-cell triple omics sequencing reveals genetic, epigenetic, and transcriptomic heterogeneity in hepatocellular carcinomas , 2016, Cell Research.

[16]  Fabian J Theis,et al.  Diffusion pseudotime robustly reconstructs lineage branching , 2016, Nature Methods.

[17]  Iain C Macaulay,et al.  Separation and parallel sequencing of the genomes and transcriptomes of single cells using G&T–seq , 2016, Nature Protocols.

[18]  N. Brockdorff,et al.  Global hypomethylation of the genome in XX embryonic stem cells , 2005, Nature Genetics.

[19]  Åsa K. Björklund,et al.  Smart-seq2 for sensitive full-length transcriptome profiling in single cells , 2013, Nature Methods.

[20]  Wei Shi,et al.  featureCounts: an efficient general purpose program for assigning sequence reads to genomic features , 2013, Bioinform..

[21]  Aleksandra A. Kolodziejczyk,et al.  Single Cell RNA-Sequencing of Pluripotent States Unlocks Modular Transcriptional Variation , 2015, Cell stem cell.

[22]  Steven L Salzberg,et al.  HISAT: a fast spliced aligner with low memory requirements , 2015, Nature Methods.

[23]  S. Andrews,et al.  SNPsplit: Allele-specific splitting of alignments between genomes with known SNP genotypes , 2016, F1000Research.

[24]  O. Stegle,et al.  Single-Cell Genome-Wide Bisulfite Sequencing for Assessing Epigenetic Heterogeneity , 2014, Nature Methods.

[25]  W. Reik,et al.  Single-cell epigenomics: powerful new methods for understanding gene regulation and cell identity , 2016, Genome Biology.

[26]  F. Tang,et al.  Single-cell multi-omics sequencing of mouse early embryos and embryonic stem cells , 2017, Cell Research.

[27]  Joseph K. Pickrell,et al.  DNaseI sensitivity QTLs are a major determinant of human expression variation , 2011, Nature.

[28]  Nathan C. Sheffield,et al.  The accessible chromatin landscape of the human genome , 2012, Nature.

[29]  Åsa K. Björklund,et al.  Full-length RNA-seq from single cells using Smart-seq2 , 2014, Nature Protocols.

[30]  Davis J. McCarthy,et al.  A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor , 2016, F1000Research.

[31]  W. Reik,et al.  DNA methylation homeostasis in human and mouse development. , 2017, Current opinion in genetics & development.

[32]  Sebastian Pott.  Simultaneous measurement of chromatin accessibility, DNA methylation, and nucleosome phasing in single cells , 2017, bioRxiv.

[33]  Jeannie T. Lee,et al.  Tsix, a gene antisense to Xist at the X-inactivation centre , 1999, Nature Genetics.

[34]  Data production leads,et al.  An integrated encyclopedia of DNA elements in the human genome , 2012 .

[35]  Guido Sanguinetti,et al.  Higher order methylation features for clustering and prediction in epigenomic studies , 2016, Bioinform..

[36]  Po-Hao Chen,et al.  Protogenin Defines a Transition Stage during Embryonic Neurogenesis and Prevents Precocious Neuronal Differentiation , 2010, The Journal of Neuroscience.

[37]  O. Stegle,et al.  DeepCpG: accurate prediction of single-cell DNA methylation states using deep learning , 2016, Genome Biology.

[38]  J. Marioni,et al.  Pooling across cells to normalize single-cell RNA sequencing data with many zero counts , 2016, Genome Biology.