Simultaneous profiling of 3D genome structure and DNA methylation in single human cells

Article metrics

Abstract

Dynamic three-dimensional chromatin conformation is a critical mechanism for gene regulation during development and disease. Despite this, profiling of three-dimensional genome structure from complex tissues with cell-type specific resolution remains challenging. Recent efforts have demonstrated that cell-type specific epigenomic features can be resolved in complex tissues using single-cell assays. However, it remains unclear whether single-cell chromatin conformation capture (3C) or Hi-C profiles can effectively identify cell types and reconstruct cell-type specific chromatin conformation maps. To address these challenges, we have developed single-nucleus methyl-3C sequencing to capture chromatin organization and DNA methylation information and robustly separate heterogeneous cell types. Applying this method to >4,200 single human brain prefrontal cortex cells, we reconstruct cell-type specific chromatin conformation maps from 14 cortical cell types. These datasets reveal the genome-wide association between cell-type specific chromatin conformation and differential DNA methylation, suggesting pervasive interactions between epigenetic processes regulating gene expression.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: Outline of the single-nucleus methyl-3C sequencing (sn-m3C-seq) method.
Fig. 2: Data processing and analysis of m3C-seq sequencing reads.
Fig. 3: Bulk and single-nucleus m3C-seq of mouse embryonic stem cells.
Fig. 4: Single-nucleus m3C-seq reconstructs cell-type specific chromatin conformation maps.
Fig. 5: Single-nucleus m3C-seq in human brain PFC.
Fig. 6: Differential mC signature associated with cell-type specific chromatin interactions.

Data availability

Raw data and processed data for culture mouse cells mESC and NMuMG are available from NCBI GEO accession code GSE124391. Raw data and processed data for human PFC are available from GEO accession code GSE130711. Intermediate files for DNA methylation and chromatin contacts can be downloaded from https://github.com/dixonlab/scm3C-seq. An AnnoJ browser for DNA methylation data can be accessed at http://neomorph.salk.edu/snm3Cseq_human_FC.php. A public HiGlass genome browser session for the human PFC data can be accessed from https://dixon.salk.edu/projects/snm3Cseq/.

Code availability

The source code used is publicly available at https://github.com/dixonlab/Taurus-MH and https://github.com/dixonlab/scm3C-seq.

References

  1. 1.

    Dixon, J. R., Gorkin, D. U. & Ren, B. Chromatin domains: the unit of chromosome organization. Mol. Cell 62, 668–680 (2016).

  2. 2.

    Rowley, M. J. & Corces, V. G. The three-dimensional genome: principles and roles of long-distance interactions. Curr. Opin. Cell Biol. 40, 8–14 (2016).

  3. 3.

    Dekker, J. & Heard, E. Structural and functional diversity of Topologically Associating Domains. FEBS Lett. 589, 2877–2884 (2015).

  4. 4.

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

  5. 5.

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

  6. 6.

    Sexton, T. et al. Three-dimensional folding and functional organization principles of the Drosophila genome. Cell 148, 458–472 (2012).

  7. 7.

    Phillips-Cremins, J. E. et al. Architectural protein subclasses shape 3D organization of genomes during lineage commitment. Cell 153, 1281–1295 (2013).

  8. 8.

    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).

  9. 9.

    Bonev, B. et al. Multiscale 3D genome rewiring during mouse neural development. Cell 171, 557–572.e24 (2017).

  10. 10.

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

  11. 11.

    Schmitt, A. D. et al. A compendium of chromatin contact maps reveals spatially active regions in the human genome. Cell Rep. 17, 2042–2059 (2016).

  12. 12.

    Nagano, T. et al. Cell-cycle dynamics of chromosomal organization at single-cell resolution. Nature 547, 61–67 (2017).

  13. 13.

    Nagano, T. et al. Single-cell Hi-C for genome-wide detection of chromatin interactions that occur simultaneously in a single cell. Nat. Protoc. 10, 1986–2003 (2015).

  14. 14.

    Nagano, T. et al. Single-cell Hi-C reveals cell-to-cell variability in chromosome structure. Nature 502, 59–64 (2013).

  15. 15.

    Liu, J., Lin, D., Yardimci, G. G. & Noble, W. S. Unsupervised embedding of single-cell Hi-C data. Bioinformatics 34, i96–i104 (2018).

  16. 16.

    Luo, C. et al. Single-cell methylomes identify neuronal subtypes and regulatory elements in mammalian cortex. Science 357, 600–604 (2017).

  17. 17.

    Hui, T. et al. High-resolution single-cell DNA methylation measurements reveal epigenetically distinct hematopoietic stem cell subpopulations. Stem Cell Rep. 11, 578–592 (2018).

  18. 18.

    Lee D. S., et al. Single-cell multi-omic profiling of chromatin conformation and DNA methylation. Protoc. Exch. https://doi.org/10.21203/rs.2.11454/v1 (2019).

  19. 19.

    Sajan, S. A. & Hawkins, R. D. Methods for identifying higher-order chromatin structure. Annu. Rev. Genom. Hum. Genet. 13, 59–82 (2012).

  20. 20.

    Luo, C. et al. Robust single-cell DNA methylome profiling with snmC-seq2. Nat. Commun. 9, 3824 (2018).

  21. 21.

    Krueger, F. & Andrews, S. R. Bismark: a flexible aligner and methylation caller for Bisulfite-Seq applications. Bioinformatics 27, 1571–1572 (2011).

  22. 22.

    Pedersen, B. S., Eyring, K., De, S., Yang, I. V. & Schwartz, D. A. Fast and accurate alignment of long bisulfite-seq reads. Preprint at https://arxiv.org/abs/1401.1129v2 (2014).

  23. 23.

    Li, H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. Preprint at https://arxiv.org/abs/1303.3997 (2013).

  24. 24.

    Habibi, E. et al. Whole-genome bisulfite sequencing of two distinct interconvertible DNA methylomes of mouse embryonic stem cells. Cell Stem Cell 13, 360–369 (2013).

  25. 25.

    Lee, D.-S. et al. An epigenomic roadmap to induced pluripotency reveals DNA methylation as a reprogramming modulator. Nat. Commun. 5, 5619 (2014).

  26. 26.

    Yang, T. et al. HiCRep: assessing the reproducibility of Hi-C data using a stratum-adjusted correlation coefficient. Genome Res. 27, 1939–1949 (2017).

  27. 27.

    Gravina, S., Dong, X., Yu, B. & Vijg, J. Single-cell genome-wide bisulfite sequencing uncovers extensive heterogeneity in the mouse liver methylome. Genome Biol. 17, 150 (2016).

  28. 28.

    Yu, B. et al. Genome-wide, single-cell DNA methylomics reveals increased Non-CpG methylation during human oocyte maturation. Stem Cell Rep. 9, 397–407 (2017).

  29. 29.

    Clark, S. J. et al. scNMT-seq enables joint profiling of chromatin accessibility DNA methylation and transcription in single cells. Nat. Commun. 9, 781 (2018).

  30. 30.

    Smallwood, S. A. et al. Single-cell genome-wide bisulfite sequencing for assessing epigenetic heterogeneity. Nat. Methods 11, 817–820 (2014).

  31. 31.

    Lu, F., Liu, Y., Jiang, L., Yamaguchi, S. & Zhang, Y. Role of Tet proteins in enhancer activity and telomere elongation. Genes Dev. 28, 2103–2119 (2014).

  32. 32.

    Lee, S.-M. et al. Intragenic CpG islands play important roles in bivalent chromatin assembly of developmental genes. Proc. Natl Acad. Sci. USA 114, E1885–E1894 (2017).

  33. 33.

    Lister, R. et al. Global epigenomic reconfiguration during mammalian brain development. Science 341, 1237905 (2013).

  34. 34.

    Ramani, V. et al. Massively multiplex single-cell Hi-C. Nat. Methods 14, 263–266 (2017).

  35. 35.

    Flyamer, I. M. et al. Single-nucleus Hi-C reveals unique chromatin reorganization at oocyte-to-zygote transition. Nature 544, 110–114 (2017).

  36. 36.

    Tan, L., Xing, D., Chang, C.-H., Li, H. & Xie, X. S. Three-dimensional genome structures of single diploid human cells. Science 361, 924–928 (2018).

  37. 37.

    Nora, E. P. et al. Targeted degradation of CTCF decouples local insulation of chromosome domains from genomic compartmentalization. Cell 169, 930–944.e22 (2017).

  38. 38.

    Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).

  39. 39.

    Zhou, J. et al. Robust single-cell Hi-C clustering by convolution- and random-walk–based imputation. Proc. Natl Acad. Sci. USA 116, 14011–14018 (2019).

  40. 40.

    Miyoshi, G. et al. Prox1 regulates the subtype-specific development of caudal ganglionic eminence-derived GABAergic cortical interneurons. J. Neurosci. 35, 12869–12889 (2015).

  41. 41.

    Merkenschlager, M. & Nora, E. P. CTCF and cohesin in genome folding and transcriptional gene regulation. Annu. Rev. Genom. Hum. Genet. 17, 17–43 (2016).

  42. 42.

    Wang, H. et al. Widespread plasticity in CTCF occupancy linked to DNA methylation. Genome Res. 22, 1680–1688 (2012).

  43. 43.

    Zimmermann, B., Bilusic, I., Lorenz, C. & Schroeder, R. Genomic SELEX: a discovery tool for genomic aptamers. Methods 52, 125–132 (2010).

  44. 44.

    ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2012).

  45. 45.

    Regev, A. et al. Science forum: the human cell atlas. eLife 6, e27041 (2017).

  46. 46.

    Langmead, B. Aligning short sequencing reads with Bowtie. Curr. Protoc. Bioinforma. 32, 11–17 (2010).

  47. 47.

    Abdennur, N. & Mirny, L. Cooler: scalable storage for Hi-C data and other genomically labeled arrays. Bioinformatics https://doi.org/10.1093/bioinformatics/btz540 (2019).

  48. 48.

    Kerpedjiev, P. et al. HiGlass: web-based visual exploration and analysis of genome interaction maps. Genome Biol. 19, 125 (2018).

  49. 49.

    Schultz, M. D. et al. Human body epigenome maps reveal noncanonical DNA methylation variation. Nature 523, 212–216 (2015).

  50. 50.

    Daley, T. & Smith, A. D. Predicting the molecular complexity of sequencing libraries. Nat. Methods 10, 325–327 (2013).

  51. 51.

    Stevens, T. J. et al. 3D structures of individual mammalian genomes studied by single-cell Hi-C. Nature 544, 59–64 (2017).

  52. 52.

    Dixon, J. R. et al. Integrative detection and analysis of structural variation in cancer genomes. Nat. Genet. 50, 1388–1398 (2018).

  53. 53.

    Hie, B., Bryson, B. & Berger, B. Efficient integration of heterogeneous single-cell transcriptomes using Scanorama. Nat. Biotechnol. 37, 685–691 (2019).

  54. 54.

    Levine, J. H. et al. Data-driven phenotypic dissection of AML reveals progenitor-like cells that correlate with prognosis. Cell 162, 184–197 (2015).

  55. 55.

    Davis, C. A. et al. The encyclopedia of DNA elements (ENCODE): data portal update. Nucleic Acids Res. 46, D794–D801 (2018).

  56. 56.

    Jolma, A. et al. DNA-binding specificities of human transcription factors. Cell 152, 327–339 (2013).

  57. 57.

    Heinz, S. et al. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol. Cell 38, 576–589 (2010).

Download references

Acknowledgements

This work was supported by the NIH (grant nos. 5U19MH114831 and 5R21HG009274 to J.R.E. and DP5OD023071 to J.R.D.). J.R.E. is a Howard Hughes Medical Institute investigator. J.R.D. is also supported by the Leona M. and Harry B. Helmsley Charitable Trust (grant no. 2017-PG-MED001) and a grant from the Salk Institute Innovation Research Fund. This work was also supported by the Flow Cytometry Core Facility of the Salk Institute with funding from NIH-NCI CCSG (grant no. P30 014195). We would like to thank the ENCODE consortium and the laboratory of M. Snyder from the Department of Genetics, Stanford University for the generation of CTCF ChIP-seq data used in this manuscript (GSE127577, ENCODE accession ENCSR822CEA).

Author information

J.R.E., J.R.D. and C.L. conceived the study. J.R.E. and J.R.D. oversaw the study. J.R.D. and C.L. designed the strategy. S.C., A.R., A.B., J.R.N., C.F. and C.O. performed the experiments. D.S.L., J.Z. and C.L. analyzed the data. C.L. and J.R.D. drafted the manuscript. D.S.L., J.Z. and J.R.E. edited the manuscript.

Correspondence to Jesse R. Dixon or Joseph R. Ecker.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information: Nicole Rusk was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Integrated supplementary information

Supplementary Figure 1 Overview of 3C-seq mapping and the quality of bulk m3C-seq.

(a) Reads are aligned using Bismark calling ungapped aligner bowtie1. To rescue reads that did not align due to the presence of a ligation junction within the reads, we split unmapped reads into 3 equal segments and these are realigned. Successfully aligned reads are then manually paired, deduplicated, and then processed for mC and chromatin contact profiles. (b) Coverage statistics of bulk m3C-seq and WGBS profiles. (c) Distribution of coverage at CpG sites for bulk m3C-seq and WGBS profiles.

Supplementary Figure 2 Chromatin interaction and DNA methylome profiles generated by m3C-seq and sn-m3C-seq are strongly correlated with published datasets.

(a) Pairwise stratum adjusted correlation coefficients between combined sn-m3C, bulk-m3C, and published datasets based on the chromatin interaction. (b) Pairwise Pearson correlation coefficients between combined sn-m3C, Bulk-m3C, and published datasets based on the DNA methylome.

Supplementary Figure 3 FANS by DNA content excludes nuclei multiplets.

(a) Single-nuclei FANS following standard in situ 3C procedure using a mixture of mESC and GM12878 results in a high fraction of wells containing both mouse and human nuclei. (b) Separate crosslinking of mESC and GM12878 nuclei followed by pooling and FANS eliminated wells containing both mouse and human nuclei. (c) Crosslinking under diluted condition reduced nuclei multiplets. (d) FANS selecting for 2N genomic content. (e) FANS selecting for 2N genomic content excluded the vast majority of nuclei multiplets.

Supplementary Figure 4 Comparison of sn-m3C-seq to existing single-cell methylome methods.

The single-cell methylome methods are compared with respect to mapping rate (a), library complexity (b), CpG island enrichment (c) and coverage uniformity (d). The elements of all box-plots are defined as following—center line, median; box limits, first and third quartiles; whiskers, 1.5× interquartile range.

Supplementary Figure 5 Comparison of sn-m3C-seq with published single-cell 3C and Hi-C studies.

(a) The number of cells analyzed and the number of cis- long range interaction detected in each cell were compared across studies. (b) The number of reads sequenced and the number of cis- long range interaction detected in each cell were compared across studies.

Supplementary Figure 6

Tbx5 locus shows differential chromatin interaction and mC patterns between mESC and NPC.

Supplementary Figure 7

Tfap2d locus shows differential chromatin interaction and mC patterns between mESC and NPC.

Supplementary Figure 8 sn-m3C-seq distinguishes mouse cell types and identify cell-type specific chromatin interactions.

(a) Contact profiles from sc-m3C-seq data in a 6.4Mb stretch of chromosome 15 show cell type specific contacts in both ES and NMuMG cells. (b) Heat map of differential interaction frequencies between mESC and NMuMG cells shown in panel (a). Regions in magenta are stronger in ES cells, regions in cyan are stronger in NMuMG. (c,d) UMAP (c) and tSNE (d) dimension reduction visualization of mESC and NMuMG cells CG methylation signature, with cells from each cell type separated to low-depth (top 50%) and low-depth (bottom 50%). (e,f) PCA of mESC and NMuMG using mCG. The cells are colored by cell type and replicate (e) or the number of non-clonal reads (f). The percentage of explained variances are labeled on the axes.

Supplementary Figure 9 Normalized mCG and mCH levels of known marker genes.

The t-SNE coordinates are based on mCG (a) or mCH (b) levels of 100kb bins. Cells are colored by their gene body mCG or mCH levels of each gene normalized by global mCG or mCH levels, respectively.

Supplementary Figure 10 Correlation of CG methylation between human PFC specimen at 1kb resolution.

Each sub-panel shows the correlation for one cell-type cluster, and the numbers in the title of the sub-panels represent the number of cells from each individual in that cluster.

Supplementary Figure 11 Separation of brain cell types by tSNE dimension reduction visualization.

Dimension reduction using mCH only (a), mCH+chromatin interaction (b) and chromatin interaction only (c).

Supplementary Figure 12 Methylation and chromosome interactions surrounding CUX2 and RORB.

The contact matrices of each cluster merged from single cells after scHiCluster imputation at 25kb resolution are shown on the top. mCG, mCH and boundary probability are shown below. The green circles on the contact maps represent the differential interactions.

Supplementary Figure 13 Methylation and chromosome interactions surrounding FOXP2 and ADARB2.

The contact matrices of each cluster merged from single cells after scHiCluster imputation at 25kb resolution are shown on the top. mCG, mCH and boundary probability are shown below. The green circles on the contact maps represent the differential interactions.

Supplementary Figure 14 Differential methylation of the methyl sensitive base at position 4 in the CTCF motif is associated with differential chromatin interactions.

(a) CTCF binding motif derived from in vitro binding to unmethylated DNA oligos (SELEX), ChIP-seq, or CTCF motifs hits showing variable methylation at position 4 across brain cell types (Variable Methylation). (b) Sequence context occurrence of position 4 and 5 in CTCF binding motif across the human genome. (c) CTCF motifs showing variable methylation at position 4 are enriched in differential interacting regions (p=1.7x10-6, Fisher’s exact test).

Supplementary Figure 15 Differential domain boundaries across cortical cell types.

(A) The normalized boundary probabilities of the differential boundaries in each cluster. (B-D) The mCG or mCH level at the differential boundaries (B), CTCF motifs at the differential boundaries (C) and gene bodies whose TSS are within 2kb of the differential boundaries (D). * represents p<0.05 in (B) and p<0.0001 in (C) and (D) (rank-sum test). The elements of all box-plots are defined as following—center line, median; box limits, first and third quartiles; whiskers, 1.5× interquartile range.

Supplementary information

Supplementary Information

Supplementary Figs. 1–15.

Reporting Summary

Supplementary Table 1

Supplementary Table 2

Supplementary Table 3

Supplementary Table 4

Supplementary Table 5

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark