Deconvolving the spatiotemporal chromatin landscape through the cell cycle

Area of Science:

• Genomics
• Molecular Biology
• Computational Biology

Background:

• Cell cycle progression involves dynamic genomic changes, but profiling chromatin occupancy is difficult due to loss of synchrony and large data requirements.
• Existing methods for cell cycle transcription deconvolution do not extend to chromatin occupancy due to replication-associated copy number variations.

Purpose of the Study:

• To develop a computational framework, CyCLOPS, for deconvolving genome-wide chromatin occupancy during the cell cycle.
• To create the first high-resolution dynamic atlas of chromatin occupancy throughout the cell cycle.

Main Methods:

• Developed CyCLOPS, a computational framework to handle large datasets and copy number effects.
• Applied CyCLOPS to MNase-seq data from synchronized yeast populations.
• Generated genome-wide chromatin occupancy profiles at sub-minute resolution.

Main Results:

• Created a dynamic atlas of genome-wide chromatin occupancy across the cell cycle.
• Identified functional gene groups via chromatin-based clustering.
• Uncovered chromatin regulatory dynamics at both genic and non-genic loci.
• Observed largely independent fluctuations between chromatin occupancy and transcription.

Conclusions:

• CyCLOPS enables high spatiotemporal resolution profiling of the cell cycle chromatin landscape.
• The dynamic atlas provides insights into chromatin regulation and gene expression coordination.
• Chromatin occupancy and transcription dynamics are largely uncoupled during the cell cycle.