Uncovering underlying physical principles and driving forces of cell differentiation and reprogramming from...

Area of Science:

• * Quantitative systems biology
• * Single-cell genomics
• * Developmental biology

Background:

• * Single-cell sequencing provides vast transcriptome data but struggles to reveal underlying cellular drivers.
• * Understanding nonequilibrium driving forces of cell function remains a challenge.
• * Existing methods for cell fate decision analysis may overlook crucial dynamic factors like flux.

Purpose of the Study:

• * To develop a method for learning cell state vector fields from single-cell RNA velocity.
• * To quantify global nonequilibrium driving forces, landscape, and flux in single cells.
• * To elucidate the physical principles governing cell differentiation and reprogramming.

Main Methods:

• * Learning cell state vector fields from discrete single-cell RNA velocity data.
• * Quantifying Waddington landscape and flux to analyze cell state transitions.
• * Inferring cell-cell interactions and gene regulatory networks from single-cell omics data.

Main Results:

• * Optimal differentiation and reprogramming paths deviate from simple landscape gradients.
• * Stem/progenitor cells require significant energy dissipation for pluripotency maintenance.
• * Identified transition states as nucleation sites and pioneer genes as nucleation seeds for cell fate decisions.
• * Developed loop flux concept to quantify cycle contributions to cell state transitions.

Conclusions:

• * The landscape and flux theory provides a validated framework for understanding cell dynamics.
• * Methodology offers insights into optimizing biological functions and predicting perturbation effects.
• * Physical principles underlying cellular processes can be explored through high-throughput single-cell experiments.