Inference of differentiation time for single cell transcriptomes using cell population reference data.

Area of Science:

• Developmental Biology
• Computational Biology
• Genomics

Background:

• Single-cell RNA sequencing (scRNA-seq) is crucial for understanding cellular heterogeneity during development.
• Traditional trajectory analyses often overlook cell-cycle events, treating them as noise.
• Integrating cell population RNA sequencing (cpRNA-seq) with scRNA-seq offers a more comprehensive view.

Purpose of the Study:

• To develop an integrative analysis package (iCpSc) for cpRNA-seq and scRNA-seq data.
• To establish a computational model for biological differentiation time using reference cpRNA-seq data.
• To unbiasedly associate cell-cycle checkpoints with single-cell developmental timing.

Main Methods:

• Developed the "iCpSc" package for integrative analysis of cpRNA-seq and scRNA-seq data.
• Generated a computational model for biological differentiation time from cell population data.
• Inferred network flow between cpRNA-seq and scRNA-seq data to identify regulatory events.

Main Results:

• Unbiasedly associated cell-cycle checkpoints with the internal molecular timer of single cells.
• Predicted a role for M phase in regulating neural differentiation speed in mouse embryonic stem cells.
• Validated the role of M phase through gene knockout experiments.

Conclusions:

• The iCpSc approach effectively links temporally matched cpRNA-seq and scRNA-seq data.
• Provides an unbiased method for identifying developmental trajectories.
• Identifies timing-related regulatory events crucial for developmental processes.