Human Single-cell Chromatin+DNAm:
Part 2 - Analyzing DNAm Variability Across Cell Types

Simultaneous Analysis of Higher-order Chromatin Structure and DNA Methylation Analysis in the Same Cell?

Epigenome Technologies blog brings you the second of a three-part summary of a pre-print article from researchers led by Jingtian Zhou, Jesse R. Dixon, and Joseph R. Ecker, who sought to evaluate how DNA methylation and higher-order chromatin structure contribute to cell type-specific gene expression profiles in single cells from tissues across the human body (Zhou and Wu et al.). The authors of this fascinating new study applied single-nucleus methyl-3C (sn-m3C-seq; Lee et al.) - which permits the simultaneous analysis of two distinct epigenetic regulatory layers - to cells resident in 16 human tissues to generate the first ever single-cell human body map of DNA methylation and chromatin conformation. Overall, they hoped this resource would help explain the inherent variability of these epigenetic features in human cell types and explore how they help to establish human cell identity. Part 2 of this blog series now highlights the most exciting findings from the DNA methylation analysis undertaken in this study.

parallel analysis of individual cells for RNA expression and DNA from targeted tagmentation by sequencing or " Paired-Tag " from Epigenome Technologies generates joint epigenetic and gene expression profiles at single-cell resolution and detects histone modifications and RNA transcripts in individual nuclei with comparable efficiency to single-nucleus RNA-seq/ChIP-seq assays. Paired-Tag also avoids any requirement for cell sorting. Applying Paired-Tag technology may enable researchers to take giant leaps forward in our understanding of gene regulatory mechanisms and significantly improve disease management. What additional insight could Paired-Tag provide to this sn-m3C-seq-based study?

Analysis Suggests Variability of DNA Methylation Across Cell Types

• DNA methylation analysis (dinucleotide CpG context; mCG) revealed varying average frequencies for cell types
• mCG displayed a bimodal distribution (largely unmethylated or fully methylated) with a lack of major cell types displaying a high frequency of partial methylation
• Cells with enriched partially methylated cytosines displayed said regions as large discrete domains or "partially methylated domains" (PMDs)
• PMD analysis suggested their existence along a continuum across cell types (some lineages displaying only mild mCG depletion within PMDs and others with more pronounced depletion) and supported the division of the genome into DNA methylation compartments based on the local distribution of mCG levels across cell types
• Two major compartments comprised most of the genome, with full cytosine methylation or partial cytosine methylation, while two minor clusters associated with mCG absence and bimodal mCG
• DNA methylation compartment analysis revealed a similar distribution across major cell types
• Partially and fully methylated compartment occupies 34-72% and 20-56% of the genome
• Transcription start sites (TSSs) and candidate cis-regulatory elements (cCREs) display enrichment in the fully unmethylated compartment and associate with higher gene expression levels
• Highly expressed genes possess low promoter mCG/higher gene body mCG levels compared with more lowly expressed genes
• Most variable genes reside in the fully/partially methylated compartments, suggesting they may associate with dynamic gene regulation mechanisms
• The identification of differentially methylated regions (DMRs) between major types/subtypes aimed at understanding dynamic mCG patterns at higher resolution - 1,364,566 DMRs covered 17.9% of the genome
• 39.3% of DMRs displayed differential methylation between major types and subtypes
• 48.2% display differential methylation between subtypes but not major types, suggesting the power of predicting cCREs via higher cell type resolution
• The analysis identified 95.4% of tissue-level DMRs encountered in previous bulk tissue methylome profiling while identifying 587,648 additional DMRs
• 91.5% of DMRs lie close to TSSs but not within promoter regions, suggesting their identity as cCREs
• DMRs display hypomethylation in specific cell types and hypermethylated in most other cell types
• Comparing DMRs with snATAC-seq data generated from tissue samples revealed that, for a given major cell type, 48% of ATAC-seq peaks overlapped DMRs, while 33% of DMRs overlapped ATAC-seq peaks
• 91.9% of snATAC-seq peaks overlapped DMRs, yet only 61.9% of DMRs overlapped snATAC-seq peaks
• DMR methylation levels displayed high levels of correlation between similar lineages, which remained similar for DMRs that overlapped ATAC-seq peaks and those that did not
• These data suggest that both DMR classes contain information regarding the cis-regulatory landscape
• Analysis of transcription factor motifs within cell type-specific DMRs revealed transcription factors that contributed to establishing lineage-specific gene expression patterns
• Enriched motifs in tissues remained similar for DMRs overlapping and non-overlapping ATAC-seq peaks
• 559 motifs encountered across major types occurred in DMRs overlapping and non-overlapping with ATAC-seq peaks, while 514 motifs occurred only in DMRs overlapping with ATAC-seq peaks, and 1249 motifs occurred only in DMRs non-overlapping with ATAC-seq peaks
• The study encountered transcription factor motifs regulating cell development and functions in DMRs non-overlapping with ATAC-seq peaks

Non-CpG Methylation Patterns Across Cell Types

• Analysis of DNA methylation outside the CpG dinucleotide context (mCH) suggested its pervasive nature across human cell types and enrichment along a continuum between different lineages
• Analyzing the nature of mCH patterns revealed that they may reflect chromatin state differences between lineages but are more pronounced for specific trinucleotide contexts and distinct lineages
• The analysis of mCH presence and gene bodies and links to gene expression suggested that low mCH levels across a wide variety of lineages retain information regarding regulatory elements and cell identity

Single-cell Human Body Map Reveals the Extent of DNA Methylation Variability

Epigenome Technologies reported on the creation of the first single-cell body map of DNA methylation and chromatin conformation in human cells

Understanding DNA methylation and chromatin conformation at the single-cell level offers a means to push groundbreaking research forward; can Epigenome Technologies help in this endeavor? Profiling multiple histone modifications with simultaneous RNA sequencing in single cells provides an understanding of the complementary role of another level of epigenetic regulation. Paired-Tag from Epigenome Technologies generates joint epigenetic and gene expression profiles at single-cell resolution and detects histone modifications and RNA transcripts in individual nuclei with an efficiency comparable to single-nucleus RNA-seq/ChIP-seq assays. Epigenome Technologies also offers other single-cell products and services suitable for your research needs; therefore, applying Paired-Tag technology may enable giant leaps forward in understanding gene regulation and complement the findings of this exciting study.

For more on the analysis of DNA methylation variability in the first-ever single-cell human body map of DNA methylation and chromatin conformation, see BioRxiv, March 2025.