Deconvolving the spatiotemporal chromatin landscape through the cell cycle

Abstract

Profiling genomic processes during the cell cycle is challenging because synchronized populations gradually lose synchrony as individual cells progress through the cycle at different rates and divide asymmetrically. Researchers have addressed this challenge by modeling the loss of synchrony and applying sophisticated branching process deconvolution methods to mitigate the effects of imperfect synchrony. Such methods have been used to deconvolve cell cycle transcription, but despite the central role of the chromatin landscape in orchestrating eukaryotic transcription and replication, comparable approaches to deconvolving cell cycle chromatin occupancy have not been developed, because the data are orders of magnitude larger and because DNA replication introduces non-uniform copy number effects across the genome during S phase. We present CyCLOPS, a computational framework that overcomes these technical challenges, enabling deconvolution of the genome-wide chromatin landscape throughout the cell cycle at high spatiotemporal resolution. We apply CyCLOPS to MNase-seq data collected from synchronized yeast populations at 10-minute intervals to produce the first dynamic atlas of genome-wide chromatin occupancy through the cell cycle, profiled at sub-minute resolution. We identify functional groups of cell cycle genes through chromatin-based clustering and uncover chromatin regulatory dynamics, including at non-genic loci. Our atlas reveals that chromatin occupancy and transcription fluctuate largely independently.