Department of Diagnostic Medicine/Pathobiology, College of Veterinary Medicine, Kansas State University, Manhattan, Kansas.
Department of Comparative Medicine, University of Washington, Seattle, Washington.
J Histochem Cytochem. 2021 Feb;69(2):137-155. doi: 10.1369/0022155420959146. Epub 2020 Sep 16.
Advances in reagents, methodologies, analytic platforms, and tools have resulted in a dramatic transformation of the research pathology laboratory. These advances have increased our ability to efficiently generate substantial volumes of data on the expression and accumulation of mRNA, proteins, carbohydrates, signaling pathways, cells, and structures in healthy and diseased tissues that are objective, quantitative, reproducible, and suitable for statistical analysis. The goal of this review is to identify and present how to acquire the critical information required to measure changes in tissues. Included is a brief overview of two morphometric techniques, image analysis and stereology, and the use of artificial intelligence to classify cells and identify hidden patterns and relationships in digital images. In addition, we explore the importance of preanalytical factors in generating high-quality data. This review focuses on techniques we have used to measure proteoglycans, glycosaminoglycans, and immune cells in tissues using immunohistochemistry and in situ hybridization to demonstrate the various morphometric techniques. When performed correctly, quantitative digital pathology is a powerful tool that provides unbiased quantitative data that are difficult to obtain with other methods.
试剂、方法学、分析平台和工具的进步使研究病理学实验室发生了巨大的转变。这些进步提高了我们在健康和患病组织中高效生成大量关于 mRNA、蛋白质、碳水化合物、信号通路、细胞和结构表达和积累的客观、定量、可重复且适合统计分析的数据的能力。本文的目的是确定并介绍如何获取测量组织变化所需的关键信息。其中包括对两种形态计量技术,图像分析和体视学,以及使用人工智能对数字图像中的细胞进行分类并识别隐藏模式和关系的简要概述。此外,我们还探讨了分析前因素在生成高质量数据方面的重要性。本文重点介绍了我们使用免疫组织化学和原位杂交技术测量组织中蛋白聚糖、糖胺聚糖和免疫细胞的各种形态计量技术。如果正确执行,定量数字病理学是一种强大的工具,它提供了其他方法难以获得的客观定量数据。