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Simultaneous profiling of chromatin accessibility and methylation on human cell lines with nanopore sequencing

Nature Methods volume 17, pages 1191–1199 (2020)Cite this article

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Abstract

Probing epigenetic features on DNA has tremendous potential to advance our understanding of the phased epigenome. In this study, we use nanopore sequencing to evaluate CpG methylation and chromatin accessibility simultaneously on long strands of DNA by applying GpC methyltransferase to exogenously label open chromatin. We performed nanopore sequencing of nucleosome occupancy and methylome (nanoNOMe) on four human cell lines (GM12878, MCF-10A, MCF-7 and MDA-MB-231). The single-molecule resolution allows footprinting of protein and nucleosome binding, and determination of the combinatorial promoter epigenetic signature on individual molecules. Long-read sequencing makes it possible to robustly assign reads to haplotypes, allowing us to generate a fully phased human epigenome, consisting of chromosome-level allele-specific profiles of CpG methylation and chromatin accessibility. We further apply this to a breast cancer model to evaluate differential methylation and accessibility between cancerous and noncancerous cells.

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Fig. 1: Overview and assessment of nanoNOMe.
Fig. 2: DNA accessibility assessment on TSSs and CTCF-binding sites using individual reads.
Fig. 3: Single-read epigenetic analysis on gene promoters.
Fig. 4: Allele-specific methylation and accessibility in GM12878.
Fig. 5: Comparative epigenomic analysis of breast cancer model.

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Data availability

NanoNOMe data for GM12878, MCF-10A, MCF-7 and MDA-MB-231 are available at National Center for Biotechnology Information Bioproject ID PRJNA510783 (http://www.ncbi.nlm.nih.gov/bioproject/510783). Processed single-read data in select regions are deposited in Zenodo (https://zenodo.org/record/3969567) and processed methylation frequency files are available in GEO accession GSE155791.

Code availability

Source code for analysis is available at https://github.com/timplab/nanoNOMe.

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Acknowledgements

This study was supported by National Human Genome Research Institute (project no. 5R01HG009190).

Author information

Authors and Affiliations

  1. Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA

    Isac Lee, Roham Razaghi, Timothy Gilpatrick, Norah Sadowski & Winston Timp

  2. Ontario Institute for Cancer Research, Toronto, Ontario, Canada

    Michael Molnar & Jared T. Simpson

  3. Department of Molecular Biology and Genetics, Johns Hopkins University, Baltimore, MD, USA

    Ariel Gershman & Winston Timp

  4. Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA

    Fritz J. Sedlazeck

  5. Department of Biostatistics, Johns Hopkins School of Public Health, Baltimore, MD, USA

    Kasper D. Hansen

  6. Department of Computer Science, University of Toronto, Toronto, Ontario, Canada

    Jared T. Simpson

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Contributions

I.L. and W.T. conceived the study. I.L., T.G. and N.S. acquired data. I.L., R.R., T.G., M.M., A.G., F.J.S., K.D.H., J.T.S. and W.T. analyzed and interpreted data. I.L. and W.T. wrote the paper.

Corresponding author

Correspondence to Winston Timp.

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Competing interests

W.T. has two patents (8,748,091 and 8,394,584) licensed to Oxford Nanopore Technologies. I.L., T.G., N.S., F.S., J.T.S. and W.T. have received travel funds to speak at symposia organized by Oxford Nanopore Technologies. J.T.S. received research funding from Oxford Nanopore Technologies.

Additional information

Peer review information Lei Tang was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team. Nature Methods thanks Jeff Vierstra and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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

Extended data

Extended Data Fig. 1 Assessment of CpG and GpC dual methylation calling.

The ability of nanopore sequencing to distinguish cytosine methylation at CpG and GpC contexts is shown by a, examining current level shifts depending on the placement of the methylation on a 6-mer (n = 256 unique 6mers for each group). Data are presented as median values, interquartile range (IQR), and 1.5X IQR. The performance of the methylation caller was validated by b, measuring methylation frequencies for calling methylation in samples treated by methyltransferases.

Extended Data Fig. 2 Bulk NanoNOMe profiles at CTCF binding sites.

Metaplots of a, methylation and b, accessibility as a function of distance to CTCF binding motifs in nanoNOMe, WGBS, and MNAse-seq agree very closely.

Extended Data Fig. 3 Pairwise Comparison of Methylation and Accessibility at Gene Promoters.

Pairwise scatter plot of average CpG methylation to GpC accessibility for 400 bp regions centered at each gene TSS, colored by its gene expression quartile.

Extended Data Fig. 4 GpC accessibility kernel estimation on single reads.

GpC methylation calls were smoothed using a Gaussian kernel estimator. a, Distributions of length of accessible and inaccessible runs and b, metaplot of accessibility near CTCF binding sites before and after the smoothing, along with (c) example of read-level plot of accessibility from a 2 kb region around a CTCF binding site.

Extended Data Fig. 5 Single-read epigenetic assessment on CTCF binding sites.

a, Heatmaps of lengths of runs of accessible chromatin calls on individual reads with respect to distance from CTCF binding sites, separated based on presence of ChIP-seq peaks. b, Density distributions of inaccessible runs at the CTCF binding sites, showing that sites without CTCF binding have long inaccessible runs suggesting nucleosome binding while those with CTCF binding have short inaccessible runs (sub-nucleosomal footprints) suggesting CTCF binding. c, Inaccessible runs were classified as either sub-nucleosomal or nucleosome binding depending on their lengths based on mixed Gaussian models.

Extended Data Fig. 6 CTCF binding classification.

Single-read (a) methylation and (b) accessibility plots on a CTCF binding motif, clustered by the presence of sub-nucleosomal footprint at the binding motif, predicted as events of CTCF protein binding.

Extended Data Fig. 7 Comparison of protein binding prediction with ChIP-seq.

a, The fractions of CTCF-bound reads determined by sub-nucleosomal footprints were compared with ChIP-seq coverage enrichments per CTCF binding motif, showing that the ChIP-seq signal tends to increase with CTCF binding fraction, and b, the distributions of the fractions were stratified by binding motifs with ChIP-seq peaks to those without peaks, showing that sites with ChIP-seq peaks have higher fractions of CTCF binding. Data are presented as median values, interquartile range (IQR), and 1.5X IQR, as well as density distributions.

Extended Data Fig. 8 Haplotype phasing results on GM12878 nanoNOMe data.

a, The number of reads that could be phased into maternal or paternal read based on the presence of heterozygous SNV in the read, showing that 65% of reads could be phased. b, The fractions of the chromosomes that could be phased (the fraction that had > 10x coverage on each allele after phasing) shows on average, 86 % of the genome could be phased.

Extended Data Fig. 9 X-chromosome inactivation promoter comparisons.

Methylation and accessibility in 500 bp and 100 bp windows, respectively, centered at TSS compared between maternal and paternal alleles (N = number of genes in the group), a, by plotting and comparing the distributions using boxplots and one-sided Wilcoxon rank-sum test (Data are presented as median values, interquartile range (IQR), and 1.5X IQR, CpG XCI Pat > Mat p-value = 0, GpC XCI Mat > Pat p-value = 1.9e-229), and b, by density plots of the difference in methylation frequencies between the two alleles.

Extended Data Fig. 10 Differentially methylated and differentially accessible regions between alleles in GM12878.

Methylation was compared between the two alleles across the genome to find regions of significant difference and were tested using one-sided Fisher’s exact test, and accessibility peaks were compared by 1) finding peaks of accessibility on each allele separately, 2) selecting peaks that occur exclusively in one allele, 3) and comparing the accessibility frequency between the two alleles in these candidate regions. The detected DMRs and DARs are a, shown as volcano plots, with dashed lines representing thresholds for considering the region as DMR/DAR. b, Examining existing (GEO Accession GSM1155957) ATAC-seq data, we compared allele specific accessible in ATAC-seq peaks that overlapped with a heterozygous SNP. In the 321 DARs detectable via ATAC-seq, we saw high correlation with nanoNOMe (r = 0.76).

Supplementary information

Supplementary Information

Supplementary figures, tables and descriptions of Supplementary Data.

Reporting Summary

Supplementary Data 1

nanoNOMe accessibility peaks in GM12878

Supplementary Data 2

CTCF-binding sites in GM12878

Supplementary Data 3

Estimated protein-bound regions near a subset of gene TSS in GM12878

Supplementary Data 4

Protein binding stratified by promoter epigenetic signatures

Supplementary Data 5

Allele-specific DMRs and DARs in GM12878

Supplementary Data 6

Gene promoter regions with allele-specific DMRs and DARs in GM12878

Supplementary Data 7

Heterozygous SVs in GM12878

Supplementary Data 8

DMRs and DARs in MCF-7 and MDA-MB-231 in comparison to MCF-10A

Supplementary Data 9

Summary of DMRs and DARs with respect to genomic contexts and SVs

Supplementary Data 10

SVs in MCF-10A, MCF-7, and MDA-MB-231

Supplementary Data 11

Promoter epigenetic signatures of differentially expressed genes in MCF-10A, MCF-7 and MDA-MB-231

Supplementary Data 12

Protein-binding regions near differentially expressed genes in MCF-10A, MCF-7 and MDA-MB-231

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Lee, I., Razaghi, R., Gilpatrick, T. et al. Simultaneous profiling of chromatin accessibility and methylation on human cell lines with nanopore sequencing. Nat Methods 17, 1191–1199 (2020). https://doi.org/10.1038/s41592-020-01000-7

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