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

Area of Science:

• Cell Biology
• Biophysics
• Systems Biology

Background:

• Optical techniques like biosensors and optogenetics are crucial for studying cell states and functions.
• Current experimental designs are often manual, limiting throughput and real-time feedback capabilities.

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.

Main Methods:

• Development of PyCLM, a Python software suite integrating image segmentation, biosensor measurement, and optogenetic stimulation.
• Application of PyCLM in closed-loop feedback control on single cells and patterned stimulation of Drosophila embryos.
• Comparative analysis of single-cell versus tissue-scale optogenetic control of epithelial migration.

Main Results:

• PyCLM facilitates dynamic, closed-loop experiments for probing cell and tissue properties.
• Demonstrated feedback control on single cells and patterned signaling delivery in Drosophila embryos.
• Identified distinct roles of fast and slow receptor tyrosine kinase activity waves in directing epithelial migration.

Conclusions:

• PyCLM streamlines the setup of complex, dynamic all-optical experiments.
• The software enables real-time control of cellular states at the tissue scale.
• Findings on epithelial migration waves provide insights into developmental processes observed in vivo.