Epigenomics: Technologies and Applications

Abstract

The advent of high-throughput epigenome mapping technologies has ushered in a new era of multiomics where powerful tools can now delineate and record different layers of genomic output. Integrating various components of the epigenome from these multiomics measurements allows the interrogation of cellular heterogeneity in addition to the discovery of molecular connectivity maps between the genome and its functional output. Mapping of chromatin accessibility dynamics and higher-order chromatin structure has enabled new levels of understanding of cell fate decisions, identity, and function in normal development, physiology, and disease. We provide a perspective on the progress of the epigenomics field and applications and anticipate an even greater revolution in our understanding of the human epigenome for years to come.

Keywords: chromatin; epigenomics; genomics; humans.

Figure 1

(A) Measurement of long-range contact of DNA elements highlighted by single-cell ATAC-seq (scATACT-seq). Structured cis-variability across single epigenomes highlighted by single-cell ATAC-seq (scATACT-seq). Pearson correlation coefficient representing chromosome compartment signal of interaction frequency from a population chromatin conformation capture assay (left panel) or scATAC-seq (middle panel) from chromosome 1. Data in white represents masked regions due to highly repetitive regions. (right panel, upper box) Permuted cis-correlation map for chromosome 1. (right panel, lower box) Representative region depicting long-range covariability. From Buenrostro et al., 2015. (B) Schematic of CLOuD9 as a reversible method for manipulating chromosomal loops. Addition of abscisic acid (ABA, green) brings two complementary CLOuD9 constructs (CLOuD9 S. pyogenes (CSP), CLOuD9 S. aureus (CSA), red and blue, respectively) into proximity, remodeling chromatin structure. Removal of ABA restores the endogenous chromatin conformation. From Morgan et al., 2017.

Figure 1

(A) Measurement of long-range contact of DNA elements highlighted by single-cell ATAC-seq (scATACT-seq). Structured cis-variability across single epigenomes highlighted by single-cell ATAC-seq (scATACT-seq). Pearson correlation coefficient representing chromosome compartment signal of interaction frequency from a population chromatin conformation capture assay (left panel) or scATAC-seq (middle panel) from chromosome 1. Data in white represents masked regions due to highly repetitive regions. (right panel, upper box) Permuted cis-correlation map for chromosome 1. (right panel, lower box) Representative region depicting long-range covariability. From Buenrostro et al., 2015. (B) Schematic of CLOuD9 as a reversible method for manipulating chromosomal loops. Addition of abscisic acid (ABA, green) brings two complementary CLOuD9 constructs (CLOuD9 S. pyogenes (CSP), CLOuD9 S. aureus (CSA), red and blue, respectively) into proximity, remodeling chromatin structure. Removal of ABA restores the endogenous chromatin conformation. From Morgan et al., 2017.

Figure 2. HiChIP identifies allele-specific loops in coronary smooth muscle of coronary artery disease-associated single nucleotide polymorphisms

Genome phasing information in human coronary artery smooth muscle cells (HCASMCs) was used to measure enhancer–promoter interactions at allele-specific CAD-associated SNPs, allowing the examination of functional consequences of risk variants compared to their alternative alleles for a set of CAD-associated SNP–target genes. Many risk alleles disrupted enhancer–target gene interactions (red), but a subset of pathogenic SNPs increased enhancer–target gene interactions (blue). From Mumbach et al., 2017.