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

The quantification of labeled cells in tissue sections is crucial to the advancement of biological knowledge. Traditionally, this was a tedious process, requiring hours of careful manual counting in small portions of a larger tissue section. To overcome this, many automated methods for cell analysis have been developed. Recent advances in whole slide scanning technologies have provided the means to image cells in entire tissue sections. However, common automated analysis tools do not have the capacity to deal with the large image files produced. Herein, we present a protocol for the quantification of two fluorescently labeled cell populations, namely pericytes and microglia, in whole brain tissue sections. This protocol uses custom-made scripts within the open source software QuPath to provide a framework for the careful optimization and validation of automated cell detection parameters. Images obtained from a whole-slide scanner are first loaded into a QuPath project. Manual counts are performed on small sample regions to optimize cell detection parameters prior to automated quantification of cells across entire brain regions. Even though we have quantified pericytes and microglia, any fluorescently labeled cell with clear labeling in and around the nucleus can be analyzed using these methods. This protocol provides a user-friendly and cost-effective framework for the automated analysis of whole tissue sections.

An Automated Approach to Improve the Quantification of Pericytes and Microglia in Whole Mouse Brain Sections.

Whole slide scanning technology has enabled the generation of high-resolution images from complete tissue sections. However, commonly used analysis software is often unable to handle the large data files produced. Here, we present a method using the open-source software QuPath to detect, classify and quantify fluorescently-labeled cells (microglia and pericytes) in whole coronal brain tissue sections. Whole-brain sections from both male and female NG2DsRed x CX3CR1+/GFP mice were analyzed. Small regions of interest were selected and manual counts were compared with counts generated from an automated approach, across a range of detection parameters. The optimal parameters for detecting cells and classifying them as microglia or pericytes in each brain region were determined and applied to annotations corresponding to the entire somatosensory and motor cortices, hippocampus, thalamus, and hypothalamus in each section. 3.74% of all detected cells were classified as pericytes; however, this proportion was significantly higher in the thalamus (6.20%) than in other regions. In contrast, microglia (4.51% of total cells) were more abundant in the cortex (5.54%). No differences were detected between male and female mice. In conclusion, QuPath offers a user-friendly solution to whole-slide image analysis which could lead to important new discoveries in both health and disease.