Di Pierro, M. | bioRxiv Science

bioRxiv2018

Walking along chromosomes with super-resolution imaging, contact maps, and integrative modeling

Chromosome structure is thought to be crucial for proper functioning of the nucleus. Here, we present a method for visualizing chromosomal DNA at super-resolution and then integrating Hi-C data to produce three-dimensional models of chromosome organization. We begin by applying Oligopaint probes and the single-molecule localization microscopy methods of OligoSTORM and OligoDNA-PAINT to image 8 megabases of human chromosome 19, discovering that chromosomal regions contributing to compartments can form distinct structures. Intriguingly, our data also suggest that homologous maternal and paternal regions may be differentially organized. Finally, we integrate imaging data with Hi-C and restraint-based modeling using a method called integrative modeling of genomic regions (IMGR) to increase the genomic resolution of our traces to 10 kb.\n\nOne Sentence SummarySuper-resolution genome tracing, contact maps, and integrative modeling enable 10 kb resolution glimpses of chromosome folding.

genetics↗

bioRxiv2017

De Novo Prediction of Human Chromosome Structures: Epigenetic Marking Patterns Encode Genome Architecture

Inside the cell nucleus, genomes fold into organized structures that are characteristic of cell type. Here, we show that this chromatin architecture can be predicted de novo using epigenetic data derived from ChIP-Seq. We exploit the idea that chromosomes encode a one-dimensional sequence of chromatin structural types. Interactions between these chromatin types determine the three-dimensional (3D) structural ensemble of chromosomes through a process similar to phase separation. First, a recurrent neural network is used to infer the relation between the epigenetic marks present at a locus, as assayed by ChIP-Seq, and the genomic compartment in which those loci reside, as measured by DNA-DNA proximity ligation (Hi-C). Next, types inferred from this neural network are used as an input to an energy landscape model for chromatin organization (MiChroM) in order to generate an ensemble of 3D chromosome conformations. After training the model, dubbed MEGABASE (Maximum Entropy Genomic Annotation from Biomarkers Associated to Structural Ensembles), on odd numbered chromosomes, we predict the chromatin type sequences and the subsequent 3D conformational ensembles for the even chromosomes. We validate these structural ensembles by using ChIP-Seq tracks alone to predict Hi-C maps as well as distances measured using 3D FISH experiments. Both sets of experiments support the hypothesis of phase separation being the driving process behind compartmentalization. These findings strongly suggest that epigenetic marking patterns encode sufficient information to determine the global architecture of chromosomes and that de novo structure prediction for whole genomes may be increasingly possible.

biophysics↗