Real-time feedback control microscopy for automation of optogenetic targeting

Optogenetics have revolutionized our ability to study cellular signaling by enabling precise control of cellular functions with light. Most classical implementations rely on fixed or manually updated illumination patterns, limiting their ability to accommodate for living systems that move, change shape or rapidly adapt their signaling states. Here, we present a real-time feedback control microscopy platform (RTM) that combines automated image segmentation, feature extraction, and adaptive hardware control to dynamically adjust optogenetic stimulation based on live cell behavior. By continuously analyzing biosensor signals, the RTM platform updates illumination patterns in real-time, maintaining region-specific stimulation, inducing traveling activity waves, or selectively activating single cells within a tissue. This fully automated, Python-based framework is built on open standards for microscope control and data handling, supporting large-scale experiments and long-term time-lapse studies. It eliminates the need for human intervention to reposition light patterns or select target cells, thereby enabling reproducible, systematic and high-throughput interrogation of spatiotemporal signaling. Automated and adaptive optogenetic perturbations provide a powerful tool to study how local signaling events shape cellular behavior, from subcellular structures, through single-cell migration, to emergent tissue-level processes.