Event-driven acquisition for content-enriched microscopy.

Area of Science:

• Microscopy
• Cell Biology
• Biophysics

Background:

• Fluorescence microscopy aims to capture specific biological events, but data collection is limited by photobleaching and phototoxicity.
• Rare or stochastic biological processes pose challenges for traditional imaging due to speed and duration constraints.

Purpose of the Study:

• To develop an event-driven acquisition framework for fluorescence microscopy.
• To overcome limitations of photobleaching and phototoxicity for capturing dynamic biological events.

Main Methods:

• Implemented a neural-network-based recognition system to detect specific biological events in real-time.
• Integrated this system with an instant structured illumination microscope for adaptive acquisition control.
• Developed a dynamic imaging strategy switching between slow and fast rates based on event detection.

Main Results:

• Successfully captured mitochondrial and bacterial divisions at imaging rates matching their dynamic timescales.
• Extended overall imaging durations by optimizing acquisition based on event occurrence.
• Acquired data enriched in relevant content by responding specifically to complex biological events.

Conclusions:

• Event-driven acquisition significantly enhances the ability to collect detailed data on dynamic and rare biological processes.
• This framework offers a powerful approach to overcome imaging limitations in studying stochastic cellular events.
• The developed system provides a more efficient and informative method for fluorescence microscopy applications.