An open-source microscopy framework for simultaneous control of image acquisition, reconstruction, and analysis.

Area of Science:

• Microscopy and Imaging
• Computational Biology
• Software Engineering

Background:

• Microscopy automation is crucial for high-throughput biological research.
• Existing systems often lack flexibility and integration for simultaneous acquisition, reconstruction, and analysis.
• Open-source solutions are needed to enhance accessibility and customization in microscopy workflows.

Purpose of the Study:

• To develop a computational framework for automated, concurrent microscopy image acquisition, reconstruction, and analysis.
• To enable flexible experimental design and data access through Python scripting.
• To provide a resource-constrained, adaptable solution for diverse biological applications.

Main Methods:

• Implementation of a multi-computer unit system coordinated by Python scripts.
• Utilizing concurrent instances of ImSwitch (microscope control) and napari (image analysis).
• Employing a file-watching system for synchronization across hardware and software components.

Main Results:

• Demonstrated successful automation of tiling experiments on a MoNaLISA (molecular nanoscale live imaging with sectioning ability) microscope.
• Achieved simultaneous image acquisition, reconstruction, and analysis.
• Validated the framework's adaptability to different microscope setups and biological applications.

Conclusions:

• The presented open-source framework enables efficient and automated microscopy workflows.
• The file-watching synchronization method is robust and compatible with standard laboratory infrastructure.
• This approach enhances the capabilities of super-resolution microscopy, such as RESOLFT (reversible saturable optical fluorescence transitions).