Automated high-throughput mouse transsynaptic viral tracing using iDISCO+ tissue clearing, light-sheet microscopy,...

Area of Science:

• Neuroscience
• Bioinformatics
• Microscopy

Background:

• Transsynaptic viral tracing is crucial for mapping neural circuits but is labor-intensive.
• Current methods involve manual cell counting and anatomical assignment, limiting scalability.
• Automated solutions are needed to accelerate the analysis of large-scale neural connectivity data.

Purpose of the Study:

• To introduce BrainPipe, a novel software for automated anatomical alignment and object counting in light-sheet microscopy volumes.
• To demonstrate BrainPipe's capability to be generalized for new counting tasks using custom atlases and neural network training.
• To facilitate the comprehensive mapping of cerebellar connectivity in the murine brain.

Main Methods:

• Development of BrainPipe software for automated analysis of microscopy data.
• Integration of BrainPipe with viral tracing and iDISCO+ tissue clearing techniques.
• Utilizing neural networks for object detection and a new atlas for generalized counting tasks.

Main Results:

• BrainPipe enables scalable, automated anatomical alignment and object counting in light-sheet microscopy.
• The software can be adapted to new counting tasks by training neural networks.
• Successful mapping of cerebellar connectivity in the murine brain was achieved.

Conclusions:

• BrainPipe significantly reduces the time and manual effort required for analyzing neural connectivity.
• The software provides a scalable and generalizable solution for high-throughput analysis of brain circuitry.
• This protocol enhances the ability to map complex neural connections in the brain.