RT Journal Article
SR Electronic
T1 Cell cycle gene regulation dynamics revealed by RNA velocity and deep-learning
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2021.03.17.435887
DO 10.1101/2021.03.17.435887
A1 Andrea Riba
A1 Attila Oravecz
A1 Matej Durik
A1 Sara Jiménez
A1 Violaine Alunni
A1 Marie Cerciat
A1 Matthieu Jung
A1 Céline Keime
A1 William M. Keyes
A1 Nacho Molina
YR 2021
UL http://biorxiv.org/content/early/2021/03/19/2021.03.17.435887.abstract
AB The cell cycle is a fundamental process of life, however, a quantitative understanding of gene regulation dynamics in the context of the cell cycle is still far from complete. Single-cell RNA-sequencing (scRNA-seq) technology gives access to its dynamics without externally perturbing the cell. Here, we build a high-resolution map of the cell cycle transcriptome based on scRNA-seq and deep-learning. By generating scRNA-seq libraries with high depth, in mouse embryonic stem cells and human fibroblasts, we are able to observe cycling patterns in the unspliced-spliced RNA space for single genes. Since existing methods in scRNA-seq are not efficient to measure cycling gene dynamics, we propose a deep learning approach to fit these cycling patterns sorting single cells across the cell cycle. We characterize the cell cycle in asynchronous pluripotent and differentiated cells identifying major waves of transcription during the G1 phase and systematically study the G1-G0 transition where the cells exit the cycle. Our work presents to the scientific community a broader understanding of RNA velocity and cell cycle maps, that we applied to pluripotency and differentiation. Our approach will facilitate the study of the cell cycle in multiple cellular models and different biological contexts, such as cancer and development.Competing Interest StatementThe authors have declared no competing interest.