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Computer vision reveals hidden variables underlying NF-κB activation in single cells.

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Cellular heterogeneity in immune responses is predictable. Computer vision and machine learning reveal that nuclear factor κB (NF-κB) localization predetermines single-cell activation probability, offering insights into immune pathway regulation.

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Area of Science:

  • Cellular and Molecular Biology
  • Immunology
  • Computational Biology

Background:

  • Cellular responses to external signals exhibit significant heterogeneity.
  • Studying the molecular basis of this heterogeneity is challenging due to experimental limitations.
  • Understanding single-cell immune pathway activation is crucial for therapeutic development.

Purpose of the Study:

  • To develop a method for predicting single-cell responses to immune stimuli.
  • To identify the molecular determinants of cellular heterogeneity in immune activation.
  • To investigate the role of nuclear factor κB (NF-κB) in predetermining cell activation.

Main Methods:

  • Development of an image-based support vector machine learning model.
  • Application of computer vision for cell analysis.
  • Mechanistic modeling to understand regulatory dynamics.

Main Results:

  • Computer vision accurately predicts which cells will respond to cytokine stimulation.
  • Pre-existing "leaky" nuclear localization of NF-κB (p65:p50) determines cell activation.
  • The ratio of NF-κB to its inhibitor dictates leakiness and activation probability.
  • Cells maintain stable activation probabilities despite dynamic molecular state transitions.

Conclusions:

  • Image-based machine learning can uncover mechanisms of single-cell heterogeneity.
  • NF-κB dynamics and its inhibitor ratio are key regulators of immune cell activation probability.
  • This approach provides a powerful tool for studying cellular responses to proinflammatory stimuli.