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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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.
Source code for analysis is available at https://github.com/timplab/nanoNOMe.
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This study was supported by National Human Genome Research Institute (project no. 5R01HG009190).
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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 figures, tables and descriptions of Supplementary Data.
nanoNOMe accessibility peaks in GM12878
CTCF-binding sites in GM12878
Estimated protein-bound regions near a subset of gene TSS in GM12878
Protein binding stratified by promoter epigenetic signatures
Allele-specific DMRs and DARs in GM12878
Gene promoter regions with allele-specific DMRs and DARs in GM12878
Heterozygous SVs in GM12878
DMRs and DARs in MCF-7 and MDA-MB-231 in comparison to MCF-10A
Summary of DMRs and DARs with respect to genomic contexts and SVs
SVs in MCF-10A, MCF-7, and MDA-MB-231
Promoter epigenetic signatures of differentially expressed genes in MCF-10A, MCF-7 and MDA-MB-231
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