Using pseudotime derivative on single-cell RNA sequencing data to identify genes undergoing cell cycle regulation

Area of Science:

• Molecular Biology
• Genomics
• Cell Biology

Background:

• The cell cycle is fundamental to cellular life and is typically studied via synchronization or selection experiments.
• These traditional methods involve chemical modifications or cell sorting, which can introduce artifacts and bypass natural cellular processes.

Purpose of the Study:

• To develop a method for studying the cell cycle using single-cell RNA sequencing (scRNA-seq).
• To circumvent the need for cell synchronization or selection experiments in cell cycle research.
• To provide a robust approach for mapping gene velocities throughout cell cycle phases.

Main Methods:

• Utilized a pseudotime method to calculate gene velocity based on predicted and real gene expression.
• Applied statistical analysis to identify genes with significant velocity shifts within pseudotime.
• Incorporated a method for merging technical replicates to enhance robustness and account for variations.
• Demonstrated observation of gene regulatory behaviors like mRNA splicing and degradation rates.

Main Results:

• Developed a robust computational approach to map gene velocities across cell cycle phases using scRNA-seq.
• Identified biologically and statistically significant genes exhibiting dynamic velocity changes.
• Showcased the ability to infer gene regulatory dynamics, including splicing and degradation.
• Successfully merged technical replicates for improved analysis of cell line experiments.

Conclusions:

• The developed method offers a powerful, non-invasive way to study cell cycle progression at single-cell resolution.
• This approach provides deeper insights into gene regulation and dynamics during the cell cycle.
• The methodology is robust and applicable to cell line experiments, with data and code publicly available.