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

Nature Methods volume 16, pages 999–1006 (2019)Cite this article

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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.

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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.

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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.

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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).

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Author notes

  1. These authors contributed equally: Dong-Sung Lee, Chongyuan Luo, Jingtian Zhou.

Authors and Affiliations

  1. Peptide Biology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA

    Dong-Sung Lee, Sahaana Chandran & Jesse R. Dixon

  2. Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA

    Chongyuan Luo, Jingtian Zhou, Angeline Rivkin, Anna Bartlett, Joseph R. Nery & Joseph R. Ecker

  3. Howard Hughes Medical Institute, The Salk Institute for Biological Studies, La Jolla, CA, USA

    Chongyuan Luo & Joseph R. Ecker

  4. Flow Cytometry Core Facility, The Salk Institute for Biological Studies, La Jolla, CA, USA

    Conor Fitzpatrick & Carolyn O’Connor

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Contributions

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.

Corresponding authors

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

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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.

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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.

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Lee, DS., Luo, C., Zhou, J. et al. Simultaneous profiling of 3D genome structure and DNA methylation in single human cells. Nat Methods 16, 999–1006 (2019). https://doi.org/10.1038/s41592-019-0547-z

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