Event-driven acquisition for content-enriched microscopy

Abstract

A common goal of fluorescence microscopy is to collect data on specific biological events. Yet, the event-specific content that can be collected from a sample is limited, especially for rare or stochastic processes. This is due in part to photobleaching and phototoxicity, which constrain imaging speed and duration. We developed an event-driven acquisition (EDA) framework, in which neural network-based recognition of specific biological events triggers real-time control in an instant structured illumination microscope (iSIM). Our setup adapts acquisitions on-the-fly by switching between a slow imaging rate while detecting the onset of events, and a fast imaging rate during their progression. Thus, we capture mitochondrial and bacterial divisions at imaging rates that match their dynamic timescales, while extending overall imaging durations. Because EDA allows the microscope to respond specifically to complex biologi-cal events, it acquires data enriched in relevant content.