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PyCLM: programming-free, closed-loop microscopy for real-time measurement, segmentation, and optogenetic stimulation.

Harrison R Oatman1, Beena C Lad2, Jared Toettcher1,2,3

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Summary

Researchers developed PyCLM, a Python tool for real-time cell measurement and optogenetic control. This enables dynamic, closed-loop experiments to precisely control cell behavior and tissue properties.

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Area of Science:

  • Cell biology
  • Optical techniques
  • Biophysics

Background:

  • All-optical experiments in cell biology often rely on manual, pre-determined stimulus patterns.
  • Real-time cellular information is crucial for dynamic control of cell behavior.
  • Machine learning advances enable closed-loop experimental designs.

Purpose of the Study:

  • To develop a user-friendly Python tool, PyCLM, for real-time measurement and optogenetic control of cells.
  • To facilitate dynamic, closed-loop experiments without requiring programming.
  • To enable precise control of cell states at the tissue scale.

Main Methods:

  • Development of PyCLM, a Python-based software suite.
  • Integration of real-time image segmentation and tracking.
  • Application of diverse imaging, image processing, and stimulation modalities.

Main Results:

  • PyCLM enables real-time measurement and optogenetic control of thousands of cells.
  • Demonstrated applications include studying tissue movement, guiding tissue flows, and controlling fluorescence heterogeneity.
  • Successful setup of multipoint experiments combining various modalities without programming.

Conclusions:

  • PyCLM empowers the next generation of dynamic cell and tissue experiments.
  • Provides a foundational tool for precise, real-time control of cellular states within tissues.
  • Facilitates complex all-optical experiments with enhanced simplicity and efficiency.