Andrea Riba

The cell cycle is a highly regulated process that ensures the accurate replication and transmission of genetic information from one generation of cells to the next. It is a fundamental biological process and plays a crucial role in development, growth, and maintenance of all living organisms, and its dysregulation can lead to a number of pathologies such as autoimmune diseases, neurodegenerative diseases, and cancer. In this work we present a novel approach to study the gene expression and chromatin accessibility dynamics during the cell cycle in mouse embryonic stem cells. To achieve this, we combined high-depth single-cell multiome sequencing, biophysical modeling, and advanced deep learning techniques. First, we used DeepCycle, a deep learning tool that assigns a cell cycle phase to every cell based on its spliced and unspliced mRNA levels. We then developed a biophysical model that describes the dynamics of gene-specific mRNA transcription, nuclear export, and degradation rates during the cell cycle. Our model obtains genome-wide estimates of mRNA transcription, nuclear retention, and degradation half-lives of genes exhibiting oscillatory dynamics in mESCs. The key feature of single-cell multiome sequencing is that it simultaneously provides readouts for gene expression as well as chromatin accessibility in the same cells. By applying our approach to these data we showcase first-of-its-kind exploration of chromatin accessibility during the cell cycle at high temporal resolution. We believe that our work will be pivotal in formulating a coherent quantitative description of mRNA metabolism. Highlights Integration of single-cell and single-nucleus multiome profiling with deep learning and biophysical modeling uncovers cell cycle regulation of RNA metabolism Genome-wide estimation of mRNA transcription, nuclear export, and degradation rates during the cell cycle in mESCs Identification of a prominent wave of transcription during early G1 in unperturbed asynchronously proliferating mESCs Characterization of temporal waves of post-transcriptional mRNA regulation controlling both mRNA degradation and nuclear export during the cell cycle Chromatin accessibility dynamics within TF footprints reveal key cell cycle regulators

Proliferating cells experience a global reduction of transcription during mitosis, yet their cell identity is maintained and regulatory information is propagated from mother to daughter cells. Mitotic bookmarking by transcription factors has been proposed as a potential mechanism to ensure the reactivation of transcription at the proper set of genes exiting mitosis. Recently, mitotic transcription and waves of transcription reactivation have been observed in synchronized populations of human hepatoma cells. However, the study did not consider that mitotic-arrested cell populations progressively desynchronize leading to measurements of gene expression on a mixture of cells at different internal cell-cycle times. Moreover, it is not well understood yet what is the precise role of mitotic bookmarking on mitotic transcription as well as on the transcription reactivation waves. Ultimately, the core gene regulatory network driving the precise transcription reactivation dynamics remains to be identified. To address these questions, we developed a mathematical model to correct for the progressive desynchronization of cells and estimate gene expression dynamics with respect to a cell-cycle pseudotime. Furthermore, we used a multiple linear regression model to infer transcription factor activity dynamics. Our analysis allows us to characterize waves of transcription factor activities exiting mitosis and predict a core gene regulatory network responsible of the transcription reactivation dynamics. Moreover, we identified more than 60 transcription factors that are highly active during mitosis and represent new candidates of mitotic bookmarking factors which could be relevant therapeutic targets to control cell proliferation.