Large scale unbiased quantification of immunohistochemistry (IHC) is time consuming, expensive, and/or limited in scope. Heterogeneous tissue types such as brain tissue have presented a further challenge to the development of automated analysis, as differing cellular morphologies result in either limited applicability or require large amounts of training tissue for machine-learning methods. Here we present the use of QuPath, a free and open source software, to quantify whole-brain sections stained with the immunohistochemical markers IBA1 and AT8, for microglia and phosphorylated tau respectively. The pixel-based method of analysis herein allows for statistical comparison of global protein expression between brains and generates heat-maps of stain intensity, visualizing stain signal across whole sections and permitting more specific investigation of regions of interest. This method is fast, automated, unbiased, and easily replicable. We compared swine brains that had undergone a closed head traumatic brain injury with brains of sham animals, and found a global increase in both microglial signal expression and phosphorylated tau. We discuss the IHC methods necessary to utilize this analysis and provide detailed instruction on the use of QuPath in the pixel-based analysis of whole-slide images.
Keywords: QuPath; automated analysis; immunohistochemistry; phosphorylated tau; traumatic brain injury.
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