A Semi-Automated Workflow for Brain Slice Histology Alignment, Registration, and Cell Quantification (SHARCQ)

Area of Science:

• Neuroscience
• Computational Biology
• Bioinformatics

Background:

• Advanced cell-labeling methods accelerate neuroscience research, particularly in brain mapping.
• Quantifying and analyzing cell-specific data across brain regions remains a challenge.
• Existing tools require refinement for comprehensive post-imaging analysis.

Purpose of the Study:

• Introduce SHARCQ (Slice Histology Alignment, Registration, and Cell Quantification), a novel post-imaging analysis tool.
• Simplify the process of mapping histological images to mouse brain atlases for cell quantification.
• Provide an accessible interface for detailed analysis of cellular populations within brain regions.

Main Methods:

• Modified the SHARP-Track toolkit for enhanced histological image analysis.
• Developed a MATLAB-based software with a step-by-step GUI.
• Integrated compatibility with multiple mouse brain atlases (Allen Brain Atlas, Franklin-Paxinos Atlas).

Main Results:

• SHARCQ enables accurate cell-to-brain registration and quantification across various histology techniques.
• The tool generates quantitative data, including brain-wide region population counts.
• A 3D model of registered cells within the atlas space is produced.

Conclusions:

• SHARCQ offers a user-friendly solution for post-imaging analysis in neuroscience.
• The open-source software facilitates precise cell quantification and brain mapping.
• This tool advances the understanding of cellular dynamics in distinct brain regions.