The cell cycle is a tightly regulated process that ensures accurate genetic replication and transmission of cellular states across generations. Pluripotent stem cells (PSCs) exhibit a unique balance of robust self-renewal and responsiveness to differentiation cues, resulting in a rapid cell cycle and a heterogeneous, asynchronous differentiation process in vitro. Evidence suggests that the activation of cell-type-specific genes is confined to the G1 phase, which lengthens during differentiation, hinting at a critical link between cell-cycle regulation, pluripotency, and differentiation. However, the regulators and epigenetic mechanisms governing this relationship remain unclear. To address this question, we have developed a novel approach to study gene expression and chromatin accessibility dynamics during the cell cycle. We combined high-depth single-cell multiome sequencing, biophysical modeling, and advanced deep learning techniques. We first used a novel deep learning tool to assign cell cycle phases to individual cells based on spliced and unspliced mRNA levels. Then, we developed a biophysical model that describes mRNA metabolism, including synthesis, splicing, nuclear export, and degradation. Our approach allowed us to unveil temporal waves of transcriptional and post-transcriptional regulation, controlling mRNA synthesis, degradation, and nuclear export during the cell cycle. Additionally, we quantified chromatin accessibility dynamics and identified transcription factor activities at high temporal resolution, uncovering key TFs that coordinate cell-cycle regulation, including known pluripotency factors. Finally, we applied our approach to spatial transcriptomics to study the spatial organization of the cell cycle in tissues and tumors. Overall, we believe that our novel approach will open the possibility to shed new light on the interplay between cell cycle regulation and cell plasticity in health and disease.

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