Unraveling cellular heterogeneity and phenotypic drug responses using chromatin profiles

Abstract

For effective treatment regimens, decisions should be based on specific genetic variability present across different human body cells by taking advantage of already accessible large-scale omics data like genomics, epigenomics, proteomics, and metabolomics databases. As of lately, cellular heterogeneity in phenotypic conditions (like cancer, neurodegenerative diseases, bone disease, metabolic disorders, and immune-related disorders) is inferred using genomic and epigenetic biomarkers for clinical diagnosis, patient stratification, prognosis and treatment monitoring. For understanding regulatory changes due to disease and external stimuli in a cell, it is important to consider the role of chromatin structures as it is the regulation of the expression of the genes. But current existing datasets about chromatin interaction are derived from only a few cell-types, thereby providing limited insights for many cell-types. Single-cell open-chromatin profiles can be used to infer the pattern of chromatin-interaction in a cell-type. To study chromatin-interaction data for more cell-types, we developed a method called as single-cell epigenome-based chromatin-interaction analysis (scEChIA) that utilizes imputation of read-counts and refined L1 regularization for predicting interactions among genomic sites using single-cell open-chromatin profiles . Unlike other methods scEChiA is not biased for only short-range interaction but it opens avenues for studying long-range chromatin interaction by using single-cell open-chromatin profile . Using scEChIA, to predict chromatin interaction using single-cell open-chromatin profile of seven human brain cell types lead to identification of almost 0.7 million cis-regulatory interactions. Further analysis helped in finding the cell-type where there could be a connection to the known expression quantitative trait locus (eQTL) and their target genes the human brain. It also lead to the identification of possible target genes of human-accelerated-elements and disease-associated mutatio