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Learning the rules of collective cell migration using deep attention networks.

Julienne LaChance1, Kevin Suh2, Jens Clausen1

  • 1Department of Mechanical and Aerospace Engineering, Princeton University, Princeton, New Jersey, United States of America.

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Summary

Deep attention networks uncover collective cell migration rules from trajectory data. These networks reveal distinct cell communication patterns in epithelial, endothelial, and cancer tissues, offering new biological insights.

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Area of Science:

  • Biophysics
  • Cell Biology
  • Computational Biology

Background:

  • Multicellular organisms rely on coordinated cell movement.
  • Understanding cell-cell interaction dynamics in collective migration is challenging.
  • Existing methods struggle to intuitively capture high-dimensional interaction data.

Purpose of the Study:

  • To develop a method for extracting collective cell migration rules.
  • To analyze and compare migration behaviors in different tissue types.
  • To visualize and interpret cell-cell interaction dynamics.

Main Methods:

  • Application of deep attention networks to cell migration trajectory data.
  • Analysis of epithelial, endothelial, and metastatic breast cancer cell behaviors.
  • Generation of attention maps to visualize neighbor influence on cell decisions.

Main Results:

  • Distinct attention patterns identified for each tissue type.
  • Endothelial cells show focused attention on forward neighbors.
  • Epithelial tissues exhibit broader forward sector influence.
  • Mesenchymal cells display symmetric, uncoordinated attention patterns.
  • Attention networks adapt to biophysical context like crowding.

Conclusions:

  • Deep attention networks can decipher complex collective cell migration rules.
  • These networks provide intuitive biological insights without prior modeling assumptions.
  • The approach offers a powerful tool for studying cellular systems.