Population-based 3D genome structure analysis reveals driving forces in spatial genome organization

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TitlePopulation-based 3D genome structure analysis reveals driving forces in spatial genome organization
Publication TypeJournal Article
Year of Publication2016
AuthorsTjong H, Li W, Kalhor R, Dai C, Hao S, Gong K, Zhou Y, Li H, Zhou XJ, Le Gros MA, Larabell CA, Chen L, Alber F
Journal TitleProc Natl Acad Sci U S A
Journal DateMar 22
ISBN Number1091-6490 (Electronic)0027-8424 (Linking)
Accession Number26951677
Keywords3D genome organization, Animals, Biological Evolution, Cell Line, centromere clustering, Centromere/ultrastructure, Chromatin/genetics/ultrastructure, Chromosome Positioning, Chromosomes, Human/genetics/ultrastructure, Chromosomes/genetics/*ultrastructure, Diploidy, genome structure modeling, Genome, Human, Heterochromatin/ultrastructure, Hi-C data analysis, human genome, Humans, Imaging, Three-Dimensional/*methods, In Situ Hybridization, Fluorescence, Likelihood Functions, Lymphocytes/ultrastructure, Metagenomics/*methods, Primates/genetics, Single-Cell Analysis, Stochastic Processes, Tomography, X-Ray/methods

Conformation capture technologies (e.g., Hi-C) chart physical interactions between chromatin regions on a genome-wide scale. However, the structural variability of the genome between cells poses a great challenge to interpreting ensemble-averaged Hi-C data, particularly for long-range and interchromosomal interactions. Here, we present a probabilistic approach for deconvoluting Hi-C data into a model population of distinct diploid 3D genome structures, which facilitates the detection of chromatin interactions likely to co-occur in individual cells. Our approach incorporates the stochastic nature of chromosome conformations and allows a detailed analysis of alternative chromatin structure states. For example, we predict and experimentally confirm the presence of large centromere clusters with distinct chromosome compositions varying between individual cells. The stability of these clusters varies greatly with their chromosome identities. We show that these chromosome-specific clusters can play a key role in the overall chromosome positioning in the nucleus and stabilizing specific chromatin interactions. By explicitly considering genome structural variability, our population-based method provides an important tool for revealing novel insights into the key factors shaping the spatial genome organization.