Automated subcellular localization and quantification of protein expression in tissue microarrays
Automated subcellular localization and quantification of protein expression in tissue microarrays
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DOI：
10.1038/nm791
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发表时间：
2002-11-01
期刊：
影响因子：
82.9
通讯作者：
Rimm, DL
中科院分区：
文献类型：
--
作者：
Camp, RL;Chung, GG;Rimm, DL
组织微阵列的最新发展——由数百个不同肿瘤的组织切片排列在一张玻片上——促进了大规模结果研究的快速评估。实现这种潜力取决于快速和精确量化每个组织点内蛋白质表达的能力。我们已经开发了一套算法，允许组织微阵列的快速，自动化，连续和定量分析，包括肿瘤与基质元素的分离和信号的亚细胞定位。在乳腺癌中使用雌激素受体的验证研究表明，自动分析与传统的基于病理学的评分结果相匹配或超过。结肠癌中β -连环蛋白的自动分析和亚细胞定位确定了两个新的、具有预后意义的肿瘤亚群，这是传统的基于病理学的评分方法无法检测到的。自动化分析技术的发展使组织微阵列能够用于发现型实验（更典型的是cDNA微阵列），并具有包含长期人口统计学和患者结果信息的额外优势。
The recent development of tissue microarrays-composed of hundreds of tissue sections from different tumors arrayed on a single glass slide-facilitates rapid evaluation of large-scale outcome studies. Realization of this potential depends on the ability to rapidly and precisely quantify the protein expression within each tissue spot. We have developed a set of algorithms that allow the rapid, automated, continuous and quantitative analysis of tissue microarrays, including the separation of tumor from stromal elements and the sub-cellular localization of signals. Validation studies using estrogen receptor in breast carcinoma show that automated analysis matches or exceeds the results of conventional pathologist-based scoring. Automated analysis and sub-cellular localization of beta-catenin in colon cancer identifies two novel, prognostically significant tumor subsets, not detected by traditional pathologist-based scoring. Development of automated analysis technology empowers tissue microarrays for use in discovery-type experiments (more typical of cDNA microarrays), with the added advantage of inclusion of long-term demographic and patient outcome information.