Genome-wide tracing to decipher nuclear organization

Chromatin structure and dynamics: one nucleosome at a time

Eukaryotic genomes store information on many levels, including their linear DNA sequence, the posttranslational modifications of its constituents (epigenetic modifications), and its three-dimensional folding. Understanding how this information is stored and read requires multidisciplinary collaborations from many branches of science beyond biology, including physics, chemistry, and computer science. Concurrent recent developments in all these areas have enabled researchers to image the genome with unprecedented spatial and temporal resolution. In this review, we focus on what single-molecule imaging and tracking of individual proteins in live cells have taught us about chromatin structure and dynamics. Starting with the basics of single-molecule tracking (SMT), we describe some advantages over in situ imaging techniques and its current limitations. Next, we focus on single-nucleosome studies and what they have added to our current understanding of the relationship between chromatin dynamics and transcription. In celebration of Robert Feulgen's ground-breaking discovery that allowed us to start seeing the genome, we discuss current models of chromatin structure and future challenges ahead.

pyHiM: a new open-source, multi-platform software package for spatial genomics based on multiplexed DNA-FISH imaging

Genome-wide ensemble sequencing methods improved our understanding of chromatin organization in eukaryotes but lack the ability to capture single-cell heterogeneity and spatial organization. To overcome these limitations, new imaging-based methods have emerged, giving rise to the field of spatial genomics. Here, we present pyHiM, a user-friendly python toolbox specifically designed for the analysis of multiplexed DNA-FISH data and the reconstruction of chromatin traces in individual cells. pyHiM employs a modular architecture, allowing independent execution of analysis steps and customization according to sample specificity and computing resources. pyHiM aims to facilitate the democratization and standardization of spatial genomics analysis.