MiOS, an integrated imaging and computational strategy to model gene folding with nucleosome resolution

Area of Science:

• Genomics
• Molecular Biology
• Biophysics

Background:

• The linear DNA sequence alone is insufficient to understand gene function and regulation.
• Gene folding in three-dimensional nuclear space is critical for biological processes.
• Current methods lack the resolution to visualize nucleosome distribution within specific genes.

Purpose of the Study:

• To develop a super-resolution imaging strategy for visualizing nucleosome distribution within genes.
• To integrate imaging data with computational modeling for quantitative gene modeling.
• To explore gene conformation, accessibility, and variability in human cells.

Main Methods:

• Immuno-OligoSTORM: Simultaneous visualization of DNA and histones in super-resolution.
• Integration with restraint-based and coarse-grained modeling.
• Incorporation of Hi-C contact frequencies and micrococcal nuclease-sequencing data.

Main Results:

• Quantitative modeling of genes at nucleosome resolution.
• Insights into chromatin accessibility for regulatory factors like RNA polymerase II.
• Exploration of intercellular variability and transcriptional-dependent gene conformation.

Conclusions:

• Modeling immuno-OligoSTORM provides unprecedented data integration for studying gene folding.
• The method reveals insights into the conformation of housekeeping and pluripotency genes.
• This approach advances the understanding of gene regulation in human pluripotent and differentiated cells.