Real-time feedback control microscopy for automation of optogenetic targeting

Abstract

Optogenetics has revolutionized our ability to study cellular signaling by enabling the precise control of cellular functions with light. Most classical implementations rely on fixed or manually updated illumination patterns, limiting their ability to accommodate living systems that move, change shape or rapidly adapt their signaling states. Here, we present an experimental platform for Feedback Adaptive Real-time Optogenetics (FARO) that combines automated image segmentation, tracking, feature extraction, and adaptive hardware control to dynamically adjust optogenetic stimulation based on live cell behavior. By continuously analyzing biosensor signals, FARO updates illumination patterns in real time across biological scales; from maintaining stimulation on specific subcellular regions, to selectively activating single cells in deforming tissue. This fully automated, Python-based framework is built on open standards for data management and microscope control and data handling, supporting large-scale experiments and long-term timelapse studies, compatible with different microscope hardware. Eliminating the need for human intervention to reposition light patterns or select target cells enables reproducible, systematic and high-throughput interrogation of spatiotemporal signaling. We show how automated and adaptive optogenetic perturbations are a powerful tool to study how local signaling events shape cellular behavior, from subcellular dynamics and single-cell migration up to emergent tissue-level processes.