An optogenetic smart microscopy platform reveals signaling dynamics-dependent control over collective cell migration

Area of Science:

• Cell Biology
• Biophysics
• Optical Engineering

Background:

• All-optical experiments in cell biology traditionally involve manual design and offline data processing.
• Existing methods limit the dynamic control and measurement of cellular states and responses.

Purpose of the Study:

• To develop PyCLM, a Python-based software suite for automated, closed-loop all-optical experiments.
• To enable high-throughput measurement, image segmentation, and optogenetic control of thousands of cells simultaneously.
• To facilitate dynamic experiments for probing cell and tissue properties in real-time.

Main Methods:

• Development of PyCLM, a Python software suite integrating image segmentation, biosensor measurement, and optogenetic stimulation.
• Implementation of closed-loop feedback control for single-cell experiments.
• Application to Drosophila embryo development and epithelial cell migration studies.

Main Results:

• PyCLM successfully enabled closed-loop control on single cells and delivery of developmental signaling patterns to Drosophila embryos.
• Comparison of single-cell versus tissue-scale optogenetic control revealed the role of receptor tyrosine kinase activity waves in epithelial migration direction.
• The results align with in vivo observations in zebrafish and mouse models.

Conclusions:

• PyCLM simplifies the setup of dynamic all-optical experiments for studying cell and tissue dynamics.
• The software provides a foundation for real-time control of single-cell states at the tissue scale.
• PyCLM advances the field of optogenetics and high-throughput cell biology research.