Formation of correlated chromatin domains at nanoscale dynamic resolution during transcription

Abstract

Intrinsic dynamics of chromatin contribute to gene regulation. How chromatin mobility responds to genomic processes, and whether this response relies on coordinated chromatin movement is still unclear. Here, we introduce an approach called Dense Flow reConstruction and Correlation (DFCC), to quantify correlation of chromatin motion with sub-pixel sensitivity at the level of the whole nucleus. DFCC reconstructs dense global flow fields of fluorescent images acquired in real-time. We applied our approach to analyze stochastic movements of DNA and histones, based on direction and magnitude at different time lags in human cells. We observe long-range correlations extending over several μm between coherently moving regions over the entire nucleus. Spatial correlation of global chromatin dynamics was reduced by inhibiting elongation by RNA polymerase II, and abolished in quiescent cells. Furthermore, quantification of spatial smoothness over time intervals up to 30 s points to clear-cut boundaries between distinct regions, while smooth transitions in small (<1 μm) neighborhoods dominate for short time intervals. Rough transitions between regions of coherent motion indicate directed squeezing or stretching of chromatin boundaries, suggestive of changes in local concentrations of actors regulating gene expression. The DFCC approach hence allows characterizing stochastically forming domains of nuclear activity.

Hi-D: Nanoscale mapping of nuclear dynamics in single living cells

“…These parameter maps clearly demonstrate that chromatin exhibits spatially heterogeneous and partitioned dynamics forming a mosaic of domains with similar values characterizing chromatin motion. This view is in line with current insights that chromatin dynamics are spatially correlated in the micrometer-range [18,20]. To further explore and characterize this heterogeneous distribution, parameter value distributions were deconvolved into contributing subpopulations using a General Mixture model (GMM) (Figure 1c, Methods, and Supplementary Figure 4).…”

“…Motion observed within a series of conventional confocal fluorescence microscopy images was quantitatively reconstructed by a dense Optical Flow method [20] and trajectories were calculated by integration of the resulting flow fields (Figure 1a, Supplementary Note 1). The type of diffusion characterizing each pixel's chromatin motion was determined in an unbiased manner using Bayesian inference to simultaneously test a set of common models to fit each trajectory's Mean Squared Displacement (MSD) curve [21] (Figure 1b, left panel, Supplementary Figure 2).…”

“…adding serum to the medium brings cells back into the cell cycle and stimulates transcriptional activity [20,24,25]. Diffusion constants were calculated for each pixel based on the model selected by Bayesian inference.…”

“…A first study analyzing bulk chromatin motion at nanoscale resolution revealed dynamic partitioning of chromatin into a number of nuclear sub-regions which show correlated motion of chromatin in the micrometer range [20]. However, to further elucidate underlying physical mechanisms driving chromatin dynamics and giving rise to their spatial coherence, an integrated characterization of the stochastic motion of nuclear molecules in their native, and physiological environment at the local scale is needed.…”

Characterizing chromatin folding coordinate and landscape with deep learning

“…The identification of the reaction coordinate itself, however, is nontrivial and often requires kinetic measurements of the folding process. Though significant progress has been made in live-cell imaging (42)(43)(44)(45), monitoring chromatin with high spatial and temporal resolution remains out of reach.…”