CLEMSite, a software for automated phenotypic screens using light microscopy and FIB-SEM

Area of Science:

• Cell Biology
• Microscopy Techniques
• Biotechnology

Background:

• Focused Ion Beam Scanning Electron Microscopy (FIB-SEM) offers powerful ultrastructural imaging capabilities.
• Semi-automated workflows are crucial for efficient high-throughput volume imaging.
• Integrating light microscopy (LM) with electron microscopy (EM) aids in precise sample navigation.

Purpose of the Study:

• To present an automated toolset for tracking cells identified via LM within FIB-SEM.
• To enable automatic acquisition of high-resolution volume datasets for adherent cells.
• To reduce operator supervision in FIB-SEM ultrastructural imaging workflows.

Main Methods:

• Utilized a grid pattern detection in both LM and EM for common reference points.
• Implemented computer vision techniques for workflow automation, including beam coincidence and image quality assessment.
• Developed automated sample tracking within the microscope's field of view.

Main Results:

• Achieved accurate targeting of regions of interest within 5 µm in EM.
• Demonstrated unattended data acquisition and iteration between multiple targets.
• Successfully executed autonomous volume acquisition in multiple locations using FIB-SEM.

Conclusions:

• The developed toolset significantly enhances the automation of FIB-SEM ultrastructural imaging.
• Autonomous multi-location volume acquisition is feasible, improving efficiency and reducing manual intervention.
• This approach facilitates advanced cellular ultrastructure analysis in biological research.