A stochastic model dissects cell states in biological transition processes

Area of Science:

• Systems biology
• Computational biology
• Cellular dynamics

Background:

• Cellular transitions are crucial for biological processes like differentiation and disease.
• Investigating cell states and transitions is difficult due to limitations in single-cell assays.
• Existing methods struggle to capture the underlying single-cell dynamics from population data.

Purpose of the Study:

• To develop a stochastic model for estimating single-cell parameters and transition rates from population-averaged time-course data.
• To provide a general framework for studying cell state transitions, including epigenetic transformations.
• To gain insights into the intermediate cell states during cellular reprogramming.

Main Methods:

• Developed a latent stochastic model at the single-cell level.
• Aggregated single-cell models to create a population-level likelihood.
• Estimated cell-state-specific parameters and transition rates from genome-wide, population-averaged time-course data.

Main Results:

• Successfully applied the model to study reprogramming to pluripotency.
• Identified and profiled two intermediate cell states during reprogramming.
• Findings are supported by independent single-cell studies.

Conclusions:

• The stochastic model enables unbiased investigation of cell states and transitions using population data.
• The approach provides a powerful tool for understanding complex cellular dynamics and epigenetic transformations.
• This framework offers new insights into the mechanisms underlying cell state changes.