Cell cycle gene regulation dynamics revealed by RNA velocity and deep-learning

Despite the fact that the cell cycle is a fundamental process of life, a detailed quantitative understanding of gene regulation dynamics throughout the cell cycle is far from complete. Single-cell RNA-sequencing (scRNA-seq) technology gives access to these dynamics without externally perturbing the cell. Here, by generating scRNA-seq libraries in different cell systems, we observe cycling patterns in the unspliced-spliced RNA space of cell cycle-related genes. Since existing methods to analyze scRNA-seq are not efficient to measure cycling gene dynamics, we propose a deep learning approach (DeepCycle) to fit these patterns and build a high-resolution map of the entire cell cycle transcriptome. Characterizing the cell cycle in embryonic and somatic cells, we identify major waves of transcription during the G1 phase and systematically study the stages of the cell cycle. Our work will facilitate the study of the cell cycle in multiple cellular models and different biological contexts. Single-cell RNA-sequencing technology gives access to cell cycle dynamics without externally perturbing the cell. Here the authors present DeepCycle,a robust deep learning method to infer the cell cycle state in single cells from scRNA-seq data.

Automated summary

This study utilizes single-cell RNA-sequencing technology to analyze the dynamics of gene regulation during the cell cycle. The authors propose a deep learning method, DeepCycle, to infer the cell cycle state in single cells based on scRNA-seq data. By generating scRNA-seq libraries in different cell systems and observing cycling patterns in cell cycle-related genes, the authors establish a high-resolution map of the entire cell cycle transcriptome and identify major waves of transcription during the G1 phase.