Analysis of Reverse Phase Protein Array Data: From Experimental Design towards Targeted Biomarker Discovery.

Microarrays Pub Date : 2015-11-03 DOI:10.3390/microarrays4040520

Astrid Wachter, Stephan Bernhardt, Tim Beissbarth, Ulrike Korf

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引用次数: 18

Abstract

Mastering the systematic analysis of tumor tissues on a large scale has long been a technical challenge for proteomics. In 2001, reverse phase protein arrays (RPPA) were added to the repertoire of existing immunoassays, which, for the first time, allowed a profiling of minute amounts of tumor lysates even after microdissection. A characteristic feature of RPPA is its outstanding sample capacity permitting the analysis of thousands of samples in parallel as a routine task. Until today, the RPPA approach has matured to a robust and highly sensitive high-throughput platform, which is ideally suited for biomarker discovery. Concomitant with technical advancements, new bioinformatic tools were developed for data normalization and data analysis as outlined in detail in this review. Furthermore, biomarker signatures obtained by different RPPA screens were compared with another or with that obtained by other proteomic formats, if possible. Options for overcoming the downside of RPPA, which is the need to steadily validate new antibody batches, will be discussed. Finally, a debate on using RPPA to advance personalized medicine will conclude this article.

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反相蛋白质阵列数据分析:从实验设计到靶向生物标志物发现。

长期以来，掌握肿瘤组织的大规模系统分析一直是蛋白质组学的技术挑战。2001年，反相蛋白阵列(RPPA)被添加到现有的免疫测定库中，这是第一次允许在显微解剖后对微量肿瘤裂解物进行分析。RPPA的一个特点是其出色的样品容量，允许并行分析数千个样品作为常规任务。直到今天，RPPA方法已经成熟为一个强大的、高灵敏度的高通量平台，非常适合生物标志物的发现。随着技术的进步，新的生物信息学工具被开发出来用于数据规范化和数据分析，本文详细概述了这一点。此外，如果可能的话，将不同RPPA筛选获得的生物标志物特征与另一种或与其他蛋白质组学格式获得的生物标志物特征进行比较。将讨论克服RPPA缺点的选择，即需要稳定地验证新批次的抗体。最后，关于使用RPPA推进个性化医疗的辩论将结束本文。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Microarrays

自引率

0.00%

发文量

审稿时长

11 weeks

期刊介绍： High-Throughput (formerly Microarrays, ISSN 2076-3905) is a multidisciplinary peer-reviewed scientific journal that provides an advanced forum for the publication of studies reporting high-dimensional approaches and developments in Life Sciences, Chemistry and related fields. Our aim is to encourage scientists to publish their experimental and theoretical results based on high-throughput techniques as well as computational and statistical tools for data analysis and interpretation. The full experimental or methodological details must be provided so that the results can be reproduced. There is no restriction on the length of the papers. High-Throughput invites submissions covering several topics, including, but not limited to: Microarrays, DNA Sequencing, RNA Sequencing, Protein Identification and Quantification, Cell-based Approaches, Omics Technologies, Imaging, Bioinformatics, Computational Biology/Chemistry, Statistics, Integrative Omics, Drug Discovery and Development, Microfluidics, Lab-on-a-chip, Data Mining, Databases, Multiplex Assays.