title: Large-scale probabilistic 3D organization of human chromosome territories.
abstract: There is growing evidence that chromosome territories (CT) have a probabilistic non-random arrangement within the cell nucleus of mammalian cells including radial positioning and preferred patterns of interchromosomal interactions that are cell-type specific. While it is generally assumed that the three-dimensional (3D) arrangement of genes within the CT is linked to genomic regulation, the degree of non-random organization of individual CT remains unclear. As a first step to elucidating the global 3D organization (topology) of individual CT, we performed multi-color fluorescence in situ hybridization using six probes extending across each chromosome in human WI38 lung fibroblasts. Six CT were selected ranging in size and gene density (1, 4, 12, 17, 18 and X). In-house computational geometric algorithms were applied to measure the 3D distances between every combination of probes and to elucidate data-mined structural patterns. Our findings demonstrate a high degree of non-random arrangement of individual CT that vary from chromosome to chromosome and display distinct changes during the cell cycle. Application of a classic, well-defined data mining and pattern recognition approach termed the 'k-means' generated 3D models for the best fit arrangement of each chromosome. These predicted models correlated well with the detailed distance measurements and analysis. We propose that the unique 3D topology of each CT and characteristic changes during the cell cycle provide the structural framework for the global gene expression programs of the individual chromosomes.
authors.affiliation: Department of Biological Sciences.
authors.forename: Nitasha
authors.initials: N
authors.lastname: Sehgal
authors.affiliation: Department of Biological Sciences.
authors.forename: Andrew J
authors.initials: AJ
authors.lastname: Fritz
authors.affiliation: Department of Biological Sciences.
authors.forename: Jaromira
authors.initials: J
authors.lastname: Vecerova
authors.affiliation: Department of Computer Science and Engineering, University at Buffalo, State University of New York, Buffalo, NY 14260, USA and.
authors.forename: Hu
authors.initials: H
authors.lastname: Ding
authors.affiliation: Department of Computer Science and Engineering, University at Buffalo, State University of New York, Buffalo, NY 14260, USA and.
authors.forename: Zihe
authors.initials: Z
authors.lastname: Chen
authors.affiliation: Department of Computer Science and Engineering, University at Buffalo, State University of New York, Buffalo, NY 14260, USA and.
authors.forename: Branislav
authors.initials: B
authors.lastname: Stojkovic
authors.affiliation: Department of Mathematics and Computer Sciences, Fayetteville State University, Fayetteville, NC 28301, USA.
authors.forename: Sambit
authors.initials: S
authors.lastname: Bhattacharya
authors.affiliation: Department of Computer Science and Engineering, University at Buffalo, State University of New York, Buffalo, NY 14260, USA and.
authors.forename: Jinhui
authors.initials: J
authors.lastname: Xu
authors.affiliation: Department of Biological Sciences, berezney@buffalo.edu.
authors.forename: Ronald
authors.initials: R
authors.lastname: Berezney
mesh_terms: D000465:Algorithms; D002453:Cell Cycle / Q000235:genetics; D002460:Cell Line; D002467:Cell Nucleus / Q000737:chemistry / Q000648:ultrastructure*; D002874:Chromosome Mapping / Q000379:methods*; D002877:Chromosomes, Human / Q000737:chemistry / Q000648:ultrastructure*; D057225:Data Mining; D005333:Fetus; D005347:Fibroblasts / Q000737:chemistry / Q000648:ultrastructure*; D006801:Humans; D017404:In Situ Hybridization, Fluorescence; D010363:Pattern Recognition, Automated