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

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.