µCodes: A Universal Grid Platform for Microscale Mapping, Microscopy Navigation, and Multimodal Imaging

Area of Science:

• Biotechnology
• Microscopy
• Imaging Science

Background:

• Multimodal imaging is crucial for comprehensive biological sample analysis.
• Integrating data from diverse imaging techniques presents alignment and navigation challenges.
• Accurate co-registration of images from optical and electron microscopy is often required.

Purpose of the Study:

• To present µCodes, a novel microgrid system for enhanced biological sample imaging.
• To demonstrate the utility of µCodes in improving sample navigation and image co-registration.
• To facilitate precise tracking of targets across different microscopy modalities.

Main Methods:

• Design and fabrication of the µCodes microgrid system.
• Implementation of µCodes within multimodal imaging workflows.
• Testing µCodes for sample navigation, alignment, and co-registration accuracy.

Main Results:

• µCodes enables high-precision tracking and localization of cells and targets.
• Successful co-registration of images acquired from various optical and electron microscopy techniques.
• Demonstrated enhancement of imaging workflows for biological samples.

Conclusions:

• µCodes provides a robust solution for multimodal imaging integration.
• The system significantly improves the accuracy of sample navigation and image alignment.
• µCodes is a valuable tool for advancing biological sample characterization through advanced imaging.