Optimal transport analysis reveals trajectories in steady-state systems

Area of Science:

• Cellular biology
• Developmental biology
• Computational biology

Background:

• Inferring cell identity changes and developmental trajectories from single-cell measurements is crucial in biology.
• Existing methods often struggle to unify analyses for both equilibrium (e.g., hematopoiesis) and non-equilibrium (e.g., embryonic development) systems.

Purpose of the Study:

• To introduce a unified approach for inferring cellular trajectories from single-cell data.
• To demonstrate the applicability of optimal transport analysis to inferring trajectories from a single snapshot of a population in equilibrium.
• To present the StationaryOT software package for trajectory inference.

Main Methods:

• Applied optimal transport analysis, a technique typically used for time-course data, to static single-cell population snapshots.
• Developed the StationaryOT method, mathematically grounded in Waddington's epigenetic landscape hypothesis.
• Implemented StationaryOT as a user-friendly software package.

Main Results:

• Showed that optimal transport analysis can successfully infer cellular trajectories from equilibrium systems using a single data snapshot.
• Demonstrated that the optimal transport approach provides a unified framework applicable to both stationary and non-stationary biological systems.
• Validated the efficacy of the StationaryOT package on simulated data and real single-cell data from Arabidopsis thaliana root development.

Conclusions:

• Optimal transport analysis offers a powerful and unified mathematical framework for inferring cellular trajectories across diverse biological systems.
• The StationaryOT method and software provide a novel computational tool for advancing single-cell trajectory inference.
• This approach enhances our understanding of cell fate decisions and developmental processes by analyzing static cell populations.