Characterizing and inferring quantitative cell cycle phase in single-cell RNA-seq data analysis.

Area of Science:

• Genomics
• Stem Cell Biology
• Computational Biology

Background:

• Cellular heterogeneity in gene expression arises from intrinsic cellular processes and external environmental factors.
• The cell cycle is a major contributor to gene expression variability, even within uniform cell populations.
• Single-cell RNA sequencing (scRNA-seq) offers powerful tools to dissect gene expression heterogeneity.

Purpose of the Study:

• To investigate the role of cell cycle progression in driving gene expression heterogeneity in human induced pluripotent stem cells (iPSCs).
• To develop and validate a novel computational method for quantifying cell cycle progression using scRNA-seq data.
• To establish a foundation for accounting for cell cycle-related noise in iPSC gene expression studies.

Main Methods:

• Integrated fluorescence imaging with scRNA-seq to simultaneously measure cell cycle phase and gene expression profiles.
• Developed a novel computational approach to model cell cycle progression as a continuous variable, moving beyond discrete stages.
• Assessed the predictive accuracy of gene expression signatures for cell cycle phase determination.

Main Results:

• Gene expression data from as few as five genes could predict a cell's position on the continuous cell cycle with 14% accuracy.
• Increasing the number of genes did not significantly improve the accuracy of cell cycle phase prediction.
• The developed method accurately quantifies cell cycle progression on a continuum.

Conclusions:

• Cell cycle progression is a critical determinant of gene expression heterogeneity in iPSCs.
• A small set of genes is sufficient to accurately place cells within the cell cycle continuum.
• The developed method and data provide valuable resources for future studies on iPSC heterogeneity and cell cycle dynamics in other cell types.