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Single-cell epigenomics: techniques and emerging applications

Key Points

  • Single-cell epigenomics combines sensitive epigenetic profiling, single-cell isolation and barcoding and high-throughput sequencing to define epigenetic landscapes across cohorts of single cells.

  • Single-cell bisulfite sequencing characterizes the methylation landscapes of rare and/or heterogeneous cell populations.

  • Single-cell ATAC-seq (assay for transposase-accessible chromatin with high-throughput sequencing) and single-cell Hi-C allow characterization of the variability in local and global physical properties of single chromosomes.

  • Partial epigenomic coverage per single cell can be compensated by increasing sample size, by computational imputation of missing values or by using reference-population epigenomics to assess epigenetic distributions in predefined groups of loci.

  • Integration of single-cell epigenomics with single-cell RNA sequencing (RNA-seq) can be approached in silico by parallel modelling of mixed cell populations at the transcriptional and epigenetic level. Experimental approaches for simultaneous transcriptional and epigenomic profiling at the single-cell level are still under development.

Abstract

Epigenomics is the study of the physical modifications, associations and conformations of genomic DNA sequences, with the aim of linking these with epigenetic memory, cellular identity and tissue-specific functions. While current techniques in the field are characterizing the average epigenomic features across large cell ensembles, the increasing interest in the epigenetics within complex and heterogeneous tissues is driving the development of single-cell epigenomics. We review emerging single-cell methods for capturing DNA methylation, chromatin accessibility, histone modifications, chromosome conformation and replication dynamics. Together, these techniques are rapidly becoming a powerful tool in studies of cellular plasticity and diversity, as seen in stem cells and cancer.

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Figure 1: Single-cell DNA methylation analysis.
Figure 2: Single-cell Hi-C.
Figure 3: Schematic of single-cell epigenomics applications.

References

  1. 1

    Bird, A. Perceptions of epigenetics. Nature 447, 396–398 (2007).

    CAS  PubMed  Google Scholar 

  2. 2

    Bernstein, B. E. et al. An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2012).

    Google Scholar 

  3. 3

    Roadmap Epigenomics Consortium et al. Integrative analysis of 111 reference human epigenomes. Nature 518, 317–330 (2015).

  4. 4

    Thurman, R. E. et al. The accessible chromatin landscape of the human genome. Nature 489, 75–82 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. 5

    Yue, F. et al. A comparative encyclopedia of DNA elements in the mouse genome. Nature 515, 355–364 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  6. 6

    Ho, J. W. K. et al. Comparative analysis of metazoan chromatin organization. Nature 512, 449–452 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. 7

    Araya, C. L. et al. Regulatory analysis of the C. elegans genome with spatiotemporal resolution. Nature 512, 400–405 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. 8

    modENCODE Consortium et al. Identification of functional elements and regulatory circuits by Drosophila modENCODE. Science 330, 1787–1797 (2010).

  9. 9

    Gerstein, M. B. et al. Integrative analysis of the Caenorhabditis elegans genome by the modENCODE project. Science 330, 1775–1787 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  10. 10

    Kharchenko, P. V. et al. Comprehensive analysis of the chromatin landscape in Drosophila melanogaster. Nature 471, 480–485 (2011).

    CAS  PubMed  Google Scholar 

  11. 11

    Farh, K. K.-H. et al. Genetic and epigenetic fine mapping of causal autoimmune disease variants. Nature 518, 337–343 (2015).

    CAS  PubMed  Google Scholar 

  12. 12

    Dick, K. J. et al. DNA methylation and body-mass index: a genome-wide analysis. Lancet 383, 1990–1998 (2014).

    CAS  PubMed  Google Scholar 

  13. 13

    Gjoneska, E. et al. Conserved epigenomic signals in mice and humans reveal immune basis of Alzheimer's disease. Nature 518, 365–369 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. 14

    Seumois, G. et al. Epigenomic analysis of primary human T cells reveals enhancers associated with TH2 memory cell differentiation and asthma susceptibility. Nat. Immunol. 15, 777–788 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. 15

    Chang, H. H., Hemberg, M., Barahona, M., Ingber, D. E. & Huang, S. Transcriptome-wide noise controls lineage choice in mammalian progenitor cells. Nature 453, 544–547 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. 16

    Landan, G. et al. Epigenetic polymorphism and the stochastic formation of differentially methylated regions in normal and cancerous tissues. Nat. Genet. 44, 1207–1214 (2012). This paper defines epigenetic polymorphism as a measure for DNA methylation heterogeneity and shows how epigenetic polymorphism emerges in vitro.

    CAS  PubMed  Google Scholar 

  17. 17

    Shalek, A. K. et al. Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells. Nature 498, 236–240 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. 18

    Ernst, J. et al. Mapping and analysis of chromatin state dynamics in nine human cell types. Nature 473, 43–49 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. 19

    Heintzman, N. D. et al. Histone modifications at human enhancers reflect global cell-type-specific gene expression. Nature 459, 108–112 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. 20

    Struhl, K. & Segal, E. Determinants of nucleosome positioning. Nat. Struct. Mol. Biol. 20, 267–273 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. 21

    Jones, P. A. Functions of DNA methylation: islands, start sites, gene bodies and beyond. Nat. Rev. Genet. 13, 484–492 (2012).

    CAS  PubMed  Google Scholar 

  22. 22

    Smith, Z. D. & Meissner, A. DNA methylation: roles in mammalian development. Nat. Rev. Genet. 14, 204–220 (2013).

    CAS  PubMed  Google Scholar 

  23. 23

    Ziller, M. J. et al. Charting a dynamic DNA methylation landscape of the human genome. Nature 500, 477–481 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. 24

    Schübeler, D. Function and information content of DNA methylation. Nature 517, 321–326 (2015).

    PubMed  Google Scholar 

  25. 25

    Buenrostro, J. D., Giresi, P. G., Zaba, L. C., Chang, H. Y. & Greenleaf, W. J. Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nat. Methods 10, 1213–1218 (2013). This article introduces ATAC-seq as an efficient method of mapping accessible sites.

    CAS  PubMed  PubMed Central  Google Scholar 

  26. 26

    Neph, S. et al. An expansive human regulatory lexicon encoded in transcription factor footprints. Nature 489, 83–90 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. 27

    Rhee, H. S. & Pugh, B. F. Comprehensive genome-wide protein–DNA interactions detected at single-nucleotide resolution. Cell 147, 1408–1419 (2011).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  28. 28

    Dixon, J. R. et al. Topological domains in mammalian genomes identified by analysis of chromatin interactions. Nature 485, 376–380 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. 29

    Nora, E. P. et al. Spatial partitioning of the regulatory landscape of the X-inactivation centre. Nature 485, 381–385 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. 30

    Sexton, T. et al. Three-dimensional folding and functional organization principles of the Drosophila genome. Cell 148, 458–472 (2012). References 28–30 introduce the notion of TADs derived from Hi-C maps.

    CAS  PubMed  Google Scholar 

  31. 31

    Hiratani, I. et al. Global reorganization of replication domains during embryonic stem cell differentiation. PLoS Biol. 6, e245 (2008).

    PubMed  PubMed Central  Google Scholar 

  32. 32

    Guelen, L. et al. Domain organization of human chromosomes revealed by mapping of nuclear lamina interactions. Nature 453, 948–951 (2008).

    CAS  PubMed  Google Scholar 

  33. 33

    Kind, J. et al. Single-cell dynamics of genome-nuclear lamina interactions. Cell 153, 178–192 (2013). This study tracks the propagation of lamina-associated domains using live-cell imaging.

    CAS  PubMed  Google Scholar 

  34. 34

    Cokus, S. J. et al. Shotgun bisulphite sequencing of the Arabidopsis genome reveals DNA methylation patterning. Nature 452, 215–219 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. 35

    Lister, R. et al. Human DNA methylomes at base resolution show widespread epigenomic differences. Nature 462, 315–322 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. 36

    Plongthongkum, N., Diep, D. H. & Zhang, K. Advances in the profiling of DNA modifications: cytosine methylation and beyond. Nat. Rev. Genet. 15, 647–661 (2014).

    CAS  PubMed  Google Scholar 

  37. 37

    Landau, D. A. et al. Locally disordered methylation forms the basis of intratumor methylome variation in chronic lymphocytic leukemia. Cancer Cell 26, 813–825 (2014). This paper exploits the single-molecule nature of bisulfite sequencing to study methylation noise in leukaemia cases.

    CAS  PubMed  PubMed Central  Google Scholar 

  38. 38

    Siegmund, K. D., Marjoram, P., Woo, Y.-J., Tavaré, S. & Shibata, D. Inferring clonal expansion and cancer stem cell dynamics from DNA methylation patterns in colorectal cancers. Proc. Natl Acad. Sci. USA 106, 4828–4833 (2009). This pioneering study uses DNA methylation to infer rare clonal populations in tumours.

    CAS  PubMed  Google Scholar 

  39. 39

    Shipony, Z. et al. Dynamic and static maintenance of epigenetic memory in pluripotent and somatic cells. Nature 513, 115–119 (2014). This paper measures the fidelity of transmission of DNA methylation from mother to daughter cells.

    CAS  PubMed  Google Scholar 

  40. 40

    Kivioja, T. et al. Counting absolute numbers of molecules using unique molecular identifiers. Nat. Methods 9, 72–74 (2012). This article coins the term UMI and develops its application in different genomic settings.

    CAS  Google Scholar 

  41. 41

    Guo, H. et al. Single-cell methylome landscapes of mouse embryonic stem cells and early embryos analyzed using reduced representation bisulfite sequencing. Genome Res. 23, 2126–2135 (2013). This is the first single-cell DNA methylation paper to use an RRBS approach.

    CAS  PubMed  PubMed Central  Google Scholar 

  42. 42

    Smallwood, S. A. et al. Single-cell genome-wide bisulfite sequencing for assessing epigenetic heterogeneity. Nat. Methods 11, 817–820 (2014). The first study to introduce single-cell DNA methylation analysis using PBAT.

    CAS  PubMed  PubMed Central  Google Scholar 

  43. 43

    Farlik, M. et al. Single-cell DNA methylome sequencing and bioinformatic inference of epigenomic cell-state dynamics. Cell Rep. 10, 1386–1397 (2015). This paper develops the idea of applying lower-depth single-cell methylation profiling for characterizing complex responses to drugs or developing cell populations.

    CAS  PubMed  PubMed Central  Google Scholar 

  44. 44

    Fang, F. et al. Genomic landscape of human allele-specific DNA methylation. Proc. Natl Acad. Sci. USA 109, 7332–7337 (2012).

    CAS  PubMed  Google Scholar 

  45. 45

    Lorthongpanich, C. et al. Single-cell DNA-methylation analysis reveals epigenetic chimerism in preimplantation embryos. Science 341, 1110–1112 (2013). This article introduces SCRAM and uses it to characterize the methylation status of imprinting control regions following perturbation of the methylation machinery.

    CAS  PubMed  Google Scholar 

  46. 46

    Cheow, L. F., Quake, S. R., Burkholder, W. F. & Messerschmidt, D. M. Multiplexed locus-specific analysis of DNA methylation in single cells. Nat. Protoc. 10, 619–631 (2015).

    CAS  PubMed  Google Scholar 

  47. 47

    Clarke, J. et al. Continuous base identification for single-molecule nanopore DNA sequencing. Nat. Nanotechnol. 4, 265–270 (2009).

    CAS  PubMed  Google Scholar 

  48. 48

    Flusberg, B. A. et al. Direct detection of DNA methylation during single-molecule, real-time sequencing. Nat. Methods 7, 461–465 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  49. 49

    Laszlo, A. H. et al. Detection and mapping of 5-methylcytosine and 5-hydroxymethylcytosine with nanopore MspA. Proc. Natl Acad. Sci. USA 110, 18904–18909 (2013).

    CAS  PubMed  Google Scholar 

  50. 50

    Pastor, W. A., Aravind, L. & Rao, A. TETonic shift: biological roles of TET proteins in DNA demethylation and transcription. Nat. Rev. Mol. Cell Biol. 14, 341–356 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  51. 51

    Wang, L. et al. Programming and inheritance of parental DNA methylomes in mammals. Cell 157, 979–991 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. 52

    Kriaucionis, S. & Heintz, N. The nuclear DNA base 5-hydroxymethylcytosine is present in Purkinje neurons and the brain. Science 324, 929–930 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  53. 53

    Globisch, D. et al. Tissue distribution of 5-hydroxymethylcytosine and search for active demethylation intermediates. PLoS ONE 5, e15367 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  54. 54

    Kinney, S. M. et al. Tissue-specific distribution and dynamic changes of 5-hydroxymethylcytosine in mammalian genomes. J. Biol. Chem. 286, 24685–24693 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  55. 55

    Hesselberth, J. R. et al. Global mapping of protein–DNA interactions in vivo by digital genomic footprinting. Nat. Methods 6, 283–289 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  56. 56

    Mavrich, T. N. et al. Nucleosome organization in the Drosophila genome. Nature 453, 358–362 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  57. 57

    Schones, D. E. et al. Dynamic regulation of nucleosome positioning in the human genome. Cell 132, 887–898 (2008).

    CAS  PubMed  Google Scholar 

  58. 58

    Yuan, G.-C. et al. Genome-scale identification of nucleosome positions in S. cerevisiae. Science 309, 626–630 (2005).

    CAS  PubMed  Google Scholar 

  59. 59

    Adli, M., Zhu, J. & Bernstein, B. E. Genome-wide chromatin maps derived from limited numbers of hematopoietic progenitors. Nat. Methods 7, 615–618 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  60. 60

    Lara-Astiaso, D. et al. Chromatin state dynamics during blood formation. Science 345, 943–949 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  61. 61

    Schmidl, C., Rendeiro, A. F., Sheffield, N. C. & Bock, C. ChIPmentation: fast, robust, low-input ChIP-seq for histones and transcription factors. Nat. Methods http://dx.doi.org/10.1038/nmeth.3542 (2015).

  62. 62

    Brind'Amour, J. et al. An ultra-low-input native ChIP-seq protocol for genome-wide profiling of rare cell populations. Nat. Commun. 6, 6033 (2015).

    CAS  PubMed  Google Scholar 

  63. 63

    Cao, Z., Chen, C., He, B., Tan, K. & Lu, C. A microfluidic device for epigenomic profiling using 100 cells. Nat. Methods http://dx.doi.org/10.1038/nmeth.3488 (2015).

  64. 64

    Cusanovich, D. A. et al. Multiplex single-cell profiling of chromatin accessibility by combinatorial cellular indexing. Science 348, 910–914 (2015). A single-cell ATAC-seq paper that develops the use of combinatorial tagging.

    CAS  PubMed  PubMed Central  Google Scholar 

  65. 65

    Buenrostro, J. D. et al. Single-cell chromatin accessibility reveals principles of regulatory variation. Nature 523, 486–490 (2015). This article describes single-cell ATAC-seq using a microfluidics-based protocol.

    CAS  PubMed  PubMed Central  Google Scholar 

  66. 66

    van Steensel, B. & Henikoff, S. Identification of in vivo DNA targets of chromatin proteins using tethered dam methyltransferase. Nat. Biotechnol. 18, 424–428 (2000).

    CAS  PubMed  Google Scholar 

  67. 67

    Sander, J. D. & Joung, J. K. CRISPR–Cas systems for editing, regulating and targeting genomes. Nat. Biotechnol. 32, 347–355 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  68. 68

    Kelly, T. K. et al. Genome-wide mapping of nucleosome positioning and DNA methylation within individual DNA molecules. Genome Res. 22, 2497–2506 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  69. 69

    Gebhardt, J. C. M. et al. Single-molecule imaging of transcription factor binding to DNA in live mammalian cells. Nat. Methods 10, 421–426 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  70. 70

    McNally, J. G., Müller, W. G., Walker, D., Wolford, R. & Hager, G. L. The glucocorticoid receptor: rapid exchange with regulatory sites in living cells. Science 287, 1262–1265 (2000).

    CAS  PubMed  Google Scholar 

  71. 71

    Stasevich, T. J. et al. Regulation of RNA polymerase II activation by histone acetylation in single living cells. Nature 516, 272–275 (2014).

    CAS  PubMed  Google Scholar 

  72. 72

    Sung, M.-H., Guertin, M. J., Baek, S. & Hager, G. L. DNase footprint signatures are dictated by factor dynamics and DNA sequence. Mol. Cell 56, 275–285 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  73. 73

    Lanctôt, C., Cheutin, T., Cremer, M., Cavalli, G. & Cremer, T. Dynamic genome architecture in the nuclear space: regulation of gene expression in three dimensions. Nat. Rev. Genet. 8, 104–115 (2007).

    PubMed  Google Scholar 

  74. 74

    Parada, L. A., Roix, J. J. & Misteli, T. An uncertainty principle in chromosome positioning. Trends Cell Biol. 13, 393–396 (2003).

    CAS  PubMed  Google Scholar 

  75. 75

    Bolzer, A. et al. Three-dimensional maps of all chromosomes in human male fibroblast nuclei and prometaphase rosettes. PLoS Biol. 3, e157 (2005).

    PubMed  PubMed Central  Google Scholar 

  76. 76

    Cremer, T., Lichter, P., Borden, J., Ward, D. C. & Manuelidis, L. Detection of chromosome aberrations in metaphase and interphase tumor cells by in situ hybridization using chromosome-specific library probes. Hum. Genet. 80, 235–246 (1988).

    CAS  PubMed  Google Scholar 

  77. 77

    Schermelleh, L. et al. Subdiffraction multicolor imaging of the nuclear periphery with 3D structured illumination microscopy. Science 320, 1332–1336 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  78. 78

    Marshall, W. F. et al. Interphase chromosomes undergo constrained diffusional motion in living cells. Curr. Biol. 7, 930–939 (1997).

    CAS  PubMed  Google Scholar 

  79. 79

    Chen, X. et al. Chromatin in situ proximity (ChrISP): single-cell analysis of chromatin proximities at a high resolution. BioTechniques 56, 117–124 (2014).

    PubMed  Google Scholar 

  80. 80

    Dekker, J., Rippe, K., Dekker, M. & Kleckner, N. Capturing chromosome conformation. Science 295, 1306–1311 (2002).

    CAS  PubMed  Google Scholar 

  81. 81

    Lieberman-Aiden, E. & van Berkum, N. Comprehensive mapping of long range interactions reveals folding principles of the human genome. Science 326, 289–293 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  82. 82

    Sanyal, A., Lajoie, B. R., Jain, G. & Dekker, J. The long-range interaction landscape of gene promoters. Nature 489, 109–113 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  83. 83

    Simonis, M. et al. Nuclear organization of active and inactive chromatin domains uncovered by chromosome conformation capture-on-chip (4C). Nat. Genet. 38, 1348–1354 (2006).

    CAS  PubMed  Google Scholar 

  84. 84

    Tanay, A. & Cavalli, G. Chromosomal domains: epigenetic contexts and functional implications of genomic compartmentalization. Curr. Opin. Genet. Dev. 23, 197–203 (2013).

    CAS  PubMed  Google Scholar 

  85. 85

    Williamson, I. et al. Spatial genome organization: contrasting views from chromosome conformation capture and fluorescence in situ hybridization. Genes Dev. 28, 2778–2791 (2014).

    PubMed  PubMed Central  Google Scholar 

  86. 86

    Nagano, T. et al. Single-cell Hi-C reveals cell-to-cell variability in chromosome structure. Nature 502, 59–64 (2013). This paper introduces single-cell Hi-C and its use to study the variability of Hi-C-derived chromosomal architectures.

    CAS  PubMed  Google Scholar 

  87. 87

    Dixon, J. R. et al. Chromatin architecture reorganization during stem cell differentiation. Nature 518, 331–336 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  88. 88

    Rao, S. S. P. et al. A 3D map of the human genome at kilobase resolution reveals principles of chromatin looping. Cell 159, 1665–1680 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  89. 89

    Splinter, E. et al. The inactive X chromosome adopts a unique three-dimensional conformation that is dependent on Xist RNA. Genes Dev. 25, 1371–1383 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  90. 90

    Pope, B. D. et al. Topologically associating domains are stable units of replication-timing regulation. Nature 515, 402–405 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  91. 91

    Rhind, N. & Gilbert, D. M. DNA replication timing. Cold Spring Harb. Perspect. Biol. 5, a010132 (2013).

    PubMed  PubMed Central  Google Scholar 

  92. 92

    Arand, J. et al. In vivo control of CpG and non-CpG DNA methylation by DNA methyltransferases. PLoS Genet. 8, e1002750 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  93. 93

    Naumova, N. et al. Organization of the mitotic chromosome. Science 342, 948–953 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  94. 94

    Huang, L., Ma, F., Chapman, A., Lu, S. & Xie, X. S. Single-cell whole-genome amplification and sequencing: methodology and applications. Annu. Rev. Genomics Hum. Genet. 16, 79–102 (2015).

    CAS  PubMed  Google Scholar 

  95. 95

    Van der Aa, N. et al. Genome-wide copy number profiling of single cells in S-phase reveals DNA-replication domains. Nucleic Acids Res. 41, e66 (2013). This article uses single-cell genomics to study replication dynamics.

    CAS  PubMed  PubMed Central  Google Scholar 

  96. 96

    Jaitin, D. A. et al. Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types. Science 343, 776–779 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  97. 97

    Stevens, M. et al. Estimating absolute methylation levels at single-CpG resolution from methylation enrichment and restriction enzyme sequencing methods. Genome Res. 23, 1541–1553 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  98. 98

    Ernst, J. & Kellis, M. Large-scale imputation of epigenomic datasets for systematic annotation of diverse human tissues. Nat. Biotechnol. 33, 364–376 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  99. 99

    Guo, H. et al. The DNA methylation landscape of human early embryos. Nature 511, 606–610 (2014).

    CAS  PubMed  Google Scholar 

  100. 100

    Brocks, D. et al. Intratumor DNA methylation heterogeneity reflects clonal evolution in aggressive prostate cancer. Cell Rep. 8, 798–806 (2014).

    CAS  PubMed  Google Scholar 

  101. 101

    Trapnell, C. et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat. Biotechnol. 32, 381–386 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  102. 102

    Heinz, S. et al. Effect of natural genetic variation on enhancer selection and function. Nature 503, 487–492 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  103. 103

    Xie, W. et al. Base-resolution analyses of sequence and parent-of-origin dependent DNA methylation in the mouse genome. Cell 148, 816–831 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  104. 104

    Schoenfelder, S. et al. Preferential associations between co-regulated genes reveal a transcriptional interactome in erythroid cells. Nat. Genet. 42, 53–61 (2010).

    CAS  PubMed  Google Scholar 

  105. 105

    Statham, A. L. et al. Bisulfite sequencing of chromatin immunoprecipitated DNA (BisChIP-seq) directly informs methylation status of histone-modified DNA. Genome Res. 22, 1120–1127 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  106. 106

    Brinkman, A. B. et al. Sequential ChIP-bisulfite sequencing enables direct genome-scale investigation of chromatin and DNA methylation cross-talk. Genome Res. 22, 1128–1138 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  107. 107

    Li, G. et al. Extensive promoter-centered chromatin interactions provide a topological basis for transcription regulation. Cell 148, 84–98 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  108. 108

    Deng, Q., Ramsköld, D., Reinius, B. & Sandberg, R. Single-cell RNA-seq reveals dynamic, random monoallelic gene expression in mammalian cells. Science 343, 193–196 (2014).

    CAS  PubMed  Google Scholar 

  109. 109

    Hashimshony, T., Wagner, F., Sher, N. & Yanai, I. CEL-seq: single-cell RNA-seq by multiplexed linear amplification. Cell Rep. 2, 666–673 (2012).

    CAS  PubMed  Google Scholar 

  110. 110

    Islam, S. et al. Quantitative single-cell RNA-seq with unique molecular identifiers. Nat. Methods 11, 163–166 (2014).

    CAS  PubMed  Google Scholar 

  111. 111

    Treutlein, B. et al. Reconstructing lineage hierarchies of the distal lung epithelium using single-cell RNA-seq. Nature 509, 371–375 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  112. 112

    Macosko, E. Z. et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161, 1202–1214 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  113. 113

    Klein, A. M. et al. Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell 161, 1187–1201 (2015). References 112 and 113 demonstrate the use of droplet technology for single-cell RNA-seq. The technique is also likely to be applicable in many single-cell epigenomics applications.

    CAS  PubMed  PubMed Central  Google Scholar 

  114. 114

    Wen, L. & Tang, F. Charting a map through the cellular reprogramming landscape. Cell Stem Cell 16, 215–216 (2015).

    CAS  PubMed  Google Scholar 

  115. 115

    Dey, S. S., Kester, L., Spanjaard, B., Bienko, M. & van Oudenaarden, A. Integrated genome and transcriptome sequencing of the same cell. Nat. Biotechnol. 33, 285–289 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  116. 116

    Macaulay, I. C. et al. G&T-seq: parallel sequencing of single-cell genomes and transcriptomes. Nat. Methods 12, 519–522 (2015).

    CAS  PubMed  Google Scholar 

  117. 117

    Patel, A. P. et al. Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science 344, 1396–1401 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  118. 118

    Fan, H. C., Fu, G. K. & Fodor, S. P. A. Combinatorial labeling of single cells for gene expression cytometry. Science 347, 1258367 (2015).

    PubMed  Google Scholar 

  119. 119

    Shalek, A. K. et al. Single-cell RNA-seq reveals dynamic paracrine control of cellular variation. Nature 510, 363–369 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  120. 120

    Kumar, R. M. et al. Deconstructing transcriptional heterogeneity in pluripotent stem cells. Nature 516, 56–61 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  121. 121

    Miura, F., Enomoto, Y., Dairiki, R. & Ito, T. Amplification-free whole-genome bisulfite sequencing by post-bisulfite adaptor tagging. Nucleic Acids Res. 40, e136 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

Research in the A.T. group is supported by the European Research Council, the Israel Science Foundation and the Flight Attendant Medical Research Institute. Work by O.S. was done as part of the requirements for Ph.D. theses at Tel Aviv University.

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Correspondence to Amos Tanay.

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Glossary

DNase-hypersensitivity mapping

An assay used to study active regulatory elements in the genome based on their association with regions of low nucleosome density. Owing to reduced protection from nucleosomes, such regions are more sensitive to DNase I-mediated cleavage. Tags of DNA derived from the cleaved chromatin are used to map these regions across the genome.

ATAC-seq

(Assay for transposase-accessible chromatin with high-throughput sequencing). Similarly to DNase- hypersensitivity mapping, this method is used to identify active regulatory sites characterized by lower density of nucleosomes. It uses the Tn5 transposase, which owing to steric hindrance can insert sequencing adaptor sequences only into regions free of nucleosomes.

Chromatin conformation capture

(3C). An assay for studying chromosomal three-dimensional structure by proximity ligation. The assay relies on crosslinking chromatin with a fixing agent (usually formaldehyde), digestion of the DNA with a six-base or four-base cutter restriction enzyme and, finally, ligation of the fixed chromatin. In the resulting chimeric DNA template, regions that were close spatially are now closed linearly.

Hi-C

A chromatin conformation capture (3C)-based method for genome-wide analysis of chromosome conformation. Hi-C involves deep sequencing of chimeric 3C DNA templates and subsequent statistical analysis of the distribution of ligation junctions over two-dimensional contact matrices. Ultra-deep sequencing, or variants of Hi-C that involve enrichment for specific regions of interest, can be used to enhance the assay's resolution.

DNA-replication domains

In mammals, these are large (1 Mb) genomic regions that consistently replicate at specific stages during S phase. Early replicating domains are strongly correlated with transcriptional activity, whereas late-replicating domains are enriched at the nuclear periphery.

Lamina-associated domains

Regions of chromatin that are in physical proximity to the nuclear envelope. These regions are usually enriched for repressed genes and heterochromatin.

Bisulfite sequencing

An assay to study 5-methylcytosine DNA methylation. Native DNA is exposed to bisulfite. Unmethylated cytosines undergo deamination and are converted to uracils, which are read as thymines, whereas methylated cytosines remain unconverted. Sequencing libraries are generated from the converted template and they allow the study of methylation at single-base resolution.

Unique molecular identifiers

(UMIs). Random or pre-designed short nucleotide sequences that are incorporated into template DNA before PCR amplification. These tags can be used to control for amplification biases.

Reduced-representation bisulfite sequencing

(RRBS). An enrichment-based bisulfite sequencing method. Digestion of the template DNA with a methylation-insensitive restriction enzyme (MspI) is followed by library construction, selection of a narrow range of DNA fragment sizes, bisulfite conversion and sequencing. This results in over tenfold enrichment for CpG-rich genomic sequences, thereby reducing the sequencing requirement in DNA methylation studies.

Post-bisulfite adaptor tagging

(PBAT). A technique for generating bisulfite sequencing libraries. DNA is first treated with bisulfite and then tagged with sequencing adaptors using random priming of the template. This method is simpler and potentially more efficient than other schemes for constructing bisulfite sequencing libraries.

Epi-haplotypes

The epigenomic markup of the two copies of each chromosome in a diploid cell. The two epi-haplotypes within a single cell are controlled by the same trans-factors and are therefore expected to be correlated. Nevertheless, regulated and stochastic allele-specific regulation can diversify epi-haplotypes substantially.

Batch effects

Components of technical experimental variation that are associated with the experimental batch but not with other known factors. The degree of batch bias depends on the assay type, along with other technical factors that are difficult to eliminate, such as reagent lots, equipment use and laboratory personnel.

Unsupervised clustering

Grouping of elements (such as single cells or genomic loci) based only on their intrinsic data features and without using external knowledge or assumptions. Several common algorithms for unsupervised clustering differ in their metrics for evaluation of high-quality solutions or in whether they impose a hierarchical structure on the clustered elements.

Footprinting

A range of techniques used to map DNA bound by proteins.

Nucleosome occupancy and methylome sequencing

(NOMe-seq). A footprinting technique in which nuclei are treated with a GpC DNA methyltransferase. Subsequent bisulfite treatment and DNA sequencing can simultaneously reveal DNA regions bound by nucleosomes or other proteins (based on protection from GpC methylation) as well as endogenous DNA methylation at CpG sites.

DNA fluorescence in situ hybridization

(DNA-FISH). A technique to localize specific loci or small groups of loci in nuclei. Fluorescent probes bind specifically to DNA and their visualization allows quantification of the intra-nuclear distances between chromosomal elements in fixed nuclei.

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Schwartzman, O., Tanay, A. Single-cell epigenomics: techniques and emerging applications. Nat Rev Genet 16, 716–726 (2015). https://doi.org/10.1038/nrg3980

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