A model-based analysis reveals three-dimensional genome organization heterogeneity and functional sub-compartments in cell populations

Abstract

The computational inference of genome organization based on Hi-C sequencing has greatly aided the understanding of chromatin and nuclear organization in three dimensions (3D). However, existing computational methods used to infer sub-compartments from Hi-C data fail to address the cell population heterogeneity. Here we describe a model-based method, called CscoreTool-M, which uses probabilistic modeling to build multiple 3D genome sub-compartments from Hi-C data. The compartment scores inferred using CscoreTool-M directly represents the probability of a genomic region locating in a specific sub-compartment. Compared to published methods, CscoreTool-M is more accurate in inferring local sub-compartment containing heterochromatin marked by Histone lysine trimethylation (H3K27me3) surrounded by the actively transcribed euchromatic regions. The compartment scores calculated by CscoreTool-M also help to quantify the levels of heterogeneity in sub-compartment localization within cell populations for different genomic regions. By comparing proliferating cells and terminally differentiated non-proliferating cells, we show that the proliferating cells have higher genome organization heterogeneity, which is likely caused by cells at different cell-cycle stages. By analyzing 10 sub-compartments, we found a sub-compartment containing chromatin potentially related to the early-G1 chromatin regions proximal to the nuclear lamina in HCT116 cells, suggesting the method can deconvolve cell cycle stage-specific genome organization among asynchronously dividing cells. Finally, we show that CscoreTool-M can further identify sub-compartments that contain genes enriched in housekeeping or cell-type-specific functions.