Research
| Title： | Genome-wide analysis of the biophysical properties of chromatin and nuclear proteins in living cells with Hi-D |
|---|---|
| First author： | Valades-Cruz, Cesar Augusto; Barth, Roman; Abdellah, Marwan; Shaban, Haitham A. |
| Journal： | NATURE PROTOCOLS |
| Years： | 2025 |
| DOI： | 10.1038/s41596-024-01038-3 |
| Abstract： | To understand the dynamic nature of the genome, the localization and rearrangement of DNA and DNA-binding proteins must be analyzed across the entire nucleus of single living cells. Recently, we developed a computational light microscopy technique, called high-resolution diffusion (Hi-D) mapping, which can accurately detect, classify and map diffusion dynamics and biophysical parameters such as the diffusion constant, the anomalous exponent, drift velocity and model physical diffusion from the data at a high spatial resolution across the genome in living cells. Hi-D combines dense optical flow to detect and track local chromatin and nuclear protein motion genome-wide and Bayesian inference to characterize this local movement at nanoscale resolution. Here we present the Python implementation of Hi-D, with an option for parallelizing the calculations to run on multicore central processing units (CPUs). The functionality of Hi-D is presented to the users via user-friendly documented Python notebooks. Hi-D reduces the analysis time to less than 1 h using a multicore CPU with a single compute node. We also present different applications of Hi-D for live-imaging of DNA, histone H2B and RNA polymerase II sequences acquired with spinning disk confocal and super-resolution structured illumination microscopy. This protocol covers the implementation in Python of a computational technique, termed high-resolution diffusion mapping, to detect, classify and map chromatin and protein dynamics via dense optical flow detection.High-resolution diffusion mapping relies on dense labeling of biomolecules. Alternative methods for the analysis of abundant and densely labeled molecules include image mean square displacement analysis (iMSD) and displacement correlation spectroscopy. High-resolution diffusion uses a dense optical flow algorithm to quantify and classify the motion of nuclear macromolecules, assigning biophysical parameters such as the diffusion constant, the anomalous exponent and the drift velocity. |
