Genome-wide analysis of the dynamic and biophysical properties of chromatin and nuclear proteins in living cells with Hi-D
To understand the dynamic nature of the genome in real-time, 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 new computational light microscopy technique, called high-resolution diffusion mapping (Hi-D), that can accurately detect, classify, and map the types of diffusion and biophysical parameters such as the diffusion constant, anomalous exponent, drift velocity, and physical diffusion models at a high spatial resolution over the entire genome in living cells. Hi-D combines dense optical flow to detect and track local chromatin and protein motion, and Bayesian inference to characterize this local movement at nanoscale resolution. The initial implementation requires solid experience using MATLAB (MathWorks) and computational resources, for instance, access to a computer cluster, to perform the Hi-D analysis. In addition, this implementation takes [~]18-24 hours to analyze a typical imaging stack. To avoid these limitations and emphasize high-performance implementation, we present a customized version called Hi-D-Py. The new implementation is written in the open-source Python programming language and has an option for parallelizing the calculations to run on multi-core CPUs. The functionality of Hi-D-Py is exposed to the users via user-friendly documented Python notebooks. Our efficient implementation reduces the analysis time to less than one hour using a multi-core CPU with a single compute node. We also present different applications of Hi-D for live-imaging of DNA, H2B, and RNA Pol II sequences acquired with spinning disk confocal and super-resolution structured illumination microscopy.