Automated Quantification of Multiple Cell Types in Fluorescently Labeled Whole Mouse Brain Sections Using QuPath

Area of Science:

• Neuroscience
• Cell Biology
• Computational Biology

Background:

• Manual cell counting in tissue sections is time-consuming and limited in scope.
• Whole slide imaging generates large datasets that challenge traditional analysis methods.
• Automated cell quantification is needed to analyze entire tissue sections efficiently.

Purpose of the Study:

• To develop and validate a user-friendly, cost-effective protocol for automated quantification of fluorescently labeled cells in whole brain tissue sections.
• To adapt open-source software (QuPath) for handling large whole slide images and optimizing cell detection parameters.
• To enable accurate analysis of specific cell populations, like pericytes and microglia, across entire tissue sections.

Main Methods:

• Utilized custom scripts within the open-source software QuPath for automated cell analysis.
• Integrated whole slide images into a QuPath project for comprehensive analysis.
• Employed manual counts on small regions to optimize automated cell detection parameters before whole-section analysis.

Main Results:

• Successfully quantified fluorescently labeled pericytes and microglia in whole brain tissue sections.
• Demonstrated a framework for optimizing and validating automated cell detection parameters.
• Showcased the protocol's adaptability for analyzing various fluorescently labeled cell types with clear nuclear labeling.

Conclusions:

• The developed QuPath-based protocol offers an efficient and cost-effective solution for automated cell quantification in whole tissue sections.
• This method overcomes the limitations of manual counting and standard automated tools when dealing with large imaging datasets.
• The protocol provides a robust framework for advancing biological knowledge through large-scale cell analysis in neuroscience and beyond.