Chen Wenjin, Reiss Michael, Foran David J
Center of Biomedical Imaging and Informatics, University of Medicine and Dentistry of New Jersey, Piscataway, NJ 08854, USA.
IEEE Trans Inf Technol Biomed. 2004 Jun;8(2):89-96. doi: 10.1109/titb.2004.828891.
The tissue microarray (TMA) technique enables researchers to extract small cylinders of tissue from histological sections and arrange them in a matrix configuration on a recipient paraffin block such that hundreds can be analyzed simultaneously. TMA offers several advantages over traditional specimen preparation by maximizing limited tissue resources and providing a highly efficient means for visualizing molecular targets. By enabling researchers to reliably determine the protein expression profile for specific types of cancer, it may be possible to elucidate the mechanism by which healthy tissues are transformed into malignancies. Currently, the primary methods used to evaluate arrays involve the interactive review of TMA samples while they are viewed under a microscope, subjectively evaluated, and scored by a technician. This process is extremely slow, tedious, and prone to error. In order to facilitate large-scale, multi-institutional studies, a more automated and reliable means for analyzing TMAs is needed. We report here a web-based prototype which features automated imaging, registration, and distributed archiving of TMAs in multiuser network environments. The system utilizes a principal color decomposition approach to identify and characterize the predominant staining signatures of specimens in color space. This strategy was shown to be reliable for detecting and quantifying the immunohistochemical expression levels for TMAs.
组织微阵列（TMA）技术使研究人员能够从组织学切片中提取小的组织圆柱体，并将它们以矩阵形式排列在受体石蜡块上，从而可以同时分析数百个样本。与传统的标本制备方法相比，TMA具有多个优点，它能最大限度地利用有限的组织资源，并为可视化分子靶点提供一种高效手段。通过使研究人员能够可靠地确定特定类型癌症的蛋白质表达谱，有可能阐明健康组织转变为恶性肿瘤的机制。目前，评估阵列的主要方法包括在显微镜下观察TMA样本时进行交互式审查，由技术人员进行主观评估和评分。这个过程极其缓慢、繁琐且容易出错。为了促进大规模、多机构研究，需要一种更自动化、更可靠的TMA分析方法。我们在此报告一个基于网络的原型，其特点是在多用户网络环境中对TMA进行自动成像、配准和分布式存档。该系统利用主颜色分解方法在颜色空间中识别和表征标本的主要染色特征。结果表明，这种策略对于检测和量化TMA的免疫组化表达水平是可靠的。
