Democratizing Smart Microscopy with navigate

Neuroscientific imaging demands advanced techniques capable of resolving intricate neural circuits, fine axonal structures, and precise synaptic connections within expansive, three-dimensional volumes. Light-sheet fluorescence microscopy has emerged as a transformative tool for this purpose, enabling high-speed, three-dimensional imaging with minimal photodamage. This capability is especially critical for studying large biological specimens, such as intact cleared brains, where preserving structural and molecular integrity is paramount.

navigate is an open-source Python software designed to harness the full potential of light-sheet microscopy, empowering researchers with a flexible and innovative platform for smart microscopy. Smart microscopy decouples image acquisition from analysis, allowing for real-time data processing and dynamic adjustments to microscope operations during acquisition. This transformative approach redefines how imaging systems interact with biological specimens, enabling more efficient and adaptive workflows. For example, with navigate, the microscope can identify features of interest, switch operational modes, optimize imaging parameters, and even characterize biological structures—all in real time.

By combining modular, reusable routines—termed features—into customizable acquisition workflows, navigate enables researchers to unlock new opportunities in neuroscience. This functionality opens the door to imaging and analyzing complex nervous system architectures with unprecedented precision and efficiency, empowering both biologists with no programming background and developers creating advanced technologies.

With its accessibility, flexibility, and intelligent acquisition capabilities, navigate enhances light-sheet microscopy, supporting advancements in neuroscience and related fields.

Project Author(s)

Zach Marin, Xiaoding Wang, Dax Collison, Conor McFadden, Kevin Dean

Project Links

https://github.com/TheDeanLab/navigate

This post was automatically generated by Kevin Dean