AI Article Synopsis

  • Fluorescence microscopy aims to collect specific biological event data, but its effectiveness is limited by issues like photobleaching and phototoxicity.
  • A new event-driven acquisition framework uses neural networks to recognize these events in real time, adjusting the microscope's imaging speed accordingly.
  • This setup enables the capture of dynamic processes like mitochondrial and bacterial divisions more effectively by adapting to their specific timescales and enriching the data collected.

Article 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 framework, in which neural-network-based recognition of specific biological events triggers real-time control in an instant structured illumination microscope. 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 event-driven acquisition allows the microscope to respond specifically to complex biological events, it acquires data enriched in relevant content.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7613693PMC
http://dx.doi.org/10.1038/s41592-022-01589-xDOI Listing

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