Inferential Structure Determination of Chromosomes from Single-Cell Hi-C Data

Chromosome conformation capture (3C) techniques have revealed many fascinating insights into the spatial organization of genomes. 3C methods typically provide information about chromosomal contacts in a large population of cells, which makes it difficult to draw conclusions about the three-dimensional organization of genomes in individual cells. Recently it became possible to study single cells with Hi-C, a genome-wide 3C variant, demonstrating a high cell-to-cell variability of genome organization. In principle, restraint-based modeling should allow us to infer the 3D structure of chromosomes from single-cell contact data, but suffers from the sparsity and low resolution of chromosomal contacts. To address these challenges, we adapt the Bayesian Inferential Structure Determination (ISD) framework, originally developed for NMR structure determination of proteins, to infer statistical ensembles of chromosome structures from single-cell data. Using ISD, we are able to compute structural error bars and estimate model parameters, thereby eliminating potential bias imposed by ad hoc parameter choices. We apply and compare different models for representing the chromatin fiber and for incorporating singe-cell contact information. Finally, we extend our approach to the analysis of diploid chromosome data.Author Summary: Spatial interactions between distant genomic regions are of fundamental importance in gene regulation and other nuclear processes. Recent chromatin crosslinking (“Hi-C”) experiments probe the spatial organization of chromosomes on a genome-wide scale to an extent that was previously unattainable. These experiments report on contacting loci and thus provide information about the three-dimensional structure of the genome. Unfortunately, the data are noisy and do not determine the structure uniquely. There is also little quantitative prior knowledge about the large-scale organization of chromosomes. Here, we address these challenges by developing a Bayesian statistical approach that combines a minimalist polymer model with chromosome size measurements and conformation capture data. Our method generates statistical ensembles of chromosome structures from extremely sparse single-cell Hi-C data. We remove potential bias by learning modeling parameters from the experimental data and apply model comparison techniques to investigate which among a set of alternative models is most supported by the Hi-C data. Our method also allows for modeling with ambiguous contact data obtained on polyploid chromosomes, which is an important step towards three-dimensional modeling of whole genomes.