An automated approach to improve the speed and accuracy of pericyte and microglia quantification in whole mouse brain sections

Whole slide scanning technology has enabled the generation of high-resolution images of 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-labelled cells (microglia and pericytes) in whole coronal brain tissue sections. Whole brain sections from both male and female NG2DsRed x CX3CR1+/GFP mice were analysed. Small regions of interest were selected and manual counts were compared to 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 cortex, hippocampus, thalamus and hypothalamus in each section. 3.71% of all detected cells were classified as pericytes, however this proportion was significantly higher in the thalamus (6.39%) than in other regions. In contrast, microglia (4.45% 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, rapid and accurate solution to whole-slide image analysis which could lead to important new discoveries in both health and disease.