Characterizing chromatin folding coordinate and landscape with deep learning - Xie, Wen Jun; Qi, Yifeng; Zhang, Bin (PLOS Computational Biology, 2020)
Author summary Chromatin folding, the dynamical process during which chromatin establishes its three-dimensional organization for proper function, is of critical importance. However, it is difficult to visualize and characterize due to challenges associated with live-cell imaging at high temporal and spatial resolution. Here, using a combination of deep learning and statistical mechanical theory, we demonstrate that great insight can be gained into the folding process by analyzing snapshots of chromatin structures taken across a population of cells. Though these static structures are not connected in time, prior research on chemical reactions suggests that fluctuation within the conformational ensemble provides valuable information for uncovering the reaction mechanism. Our analysis reconciles the seemingly contradictory results from different experimental techniques and supports the presence of multiple factors in organizing the chromatin. As single-cell experimental data are becoming routine, the approaches presented here could help with their interpretation to provide more insight into chromatin folding.