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Reconstructing Waddington's landscape from data.

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This study introduces a computational framework to model cell development as a landscape, directly using high-dimensional single-cell data. This approach maps gene expression to developmental dynamics, aiding in understanding and controlling cell fate transitions.

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Area of Science:

  • Developmental Biology
  • Computational Biology
  • Systems Biology

Background:

  • Zygote to organism development requires diverse cell types from a single progenitor.
  • Existing developmental signaling network models are complex and difficult to fit to data.
  • Landscape models offer an alternative by visualizing cell fate decisions as flows in an abstract topography.

Purpose of the Study:

  • To develop a computational geometry framework for fitting dynamical landscapes directly to high-dimensional single-cell data.
  • To map gene expression to developmental dynamics and characterize landscape features.
  • To provide a general framework for understanding and controlling developmental dynamics.

Main Methods:

  • Developed a computational geometry framework to fit dynamical landscapes to high-dimensional single-cell data.
  • Modeled the time evolution of probability distributions in gene expression space.
  • Applied the framework to multicolor flow-cytometry and RNA-seq data, including a stem cell system for neural tube patterning.

Main Results:

  • Successfully constructed landscapes directly from high-dimensional single-cell data with minimal free parameters.
  • Characterized dynamical features like fixed points, unstable manifolds, and basins of attraction.
  • Recovered morphogen-dependent landscapes for neural progenitor types, showing signaling dependence and irreversible behavior after transient morphogen exposure.

Conclusions:

  • The developed framework combines landscape model interpretability with direct data connection.
  • It offers a powerful tool for understanding and controlling complex developmental dynamics.
  • The method provides a general approach applicable to various single-cell datasets and biological systems.