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.

中文翻译：

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使用QuPath软件对大型动物脑组织的免疫组织化学染色进行自动定量。

免疫组织化学（IHC）的大规模无偏定量分析非常耗时，昂贵和/或范围有限。异类组织类型（例如脑组织）已对自动化分析的发展提出了进一步的挑战，因为不同的细胞形态导致适用性有限或需要大量的训练组织用于机器学习方法。在这里，我们介绍使用QuPath（一种免费和开源的软件）来量化分别用免疫组织化学标记IBA1和AT8染色的全脑切片，分别用于小胶质细胞和磷酸化的tau蛋白。本文中基于像素的分析方法可对大脑之间的全局蛋白质表达进行统计比较，并生成染色强度的热图，可视化整个切片上的染色信号，并允许对感兴趣区域进行更具体的研究。此方法快速，自动化，无偏见且易于复制。我们将经历了闭合性头部外伤性脑损伤的猪脑与假动物的脑进行了比较，发现小胶质细胞信号表达和磷酸化tau的全球增加。我们讨论了利用此分析所必需的IHC方法，并提供了有关在整个幻灯片图像的基于像素的分析中使用QuPath的详细说明。