Department of Pathology, Johns Hopkins University , Baltimore, Maryland 21231, United States.
Biological Sciences Division, Pacific Northwest National Laboratory , Richland, Washington 99352, United States.
J Proteome Res. 2017 Dec 1;16(12):4523-4530. doi: 10.1021/acs.jproteome.7b00362. Epub 2017 Nov 16.
Clinical proteomics requires large-scale analysis of human specimens to achieve statistical significance. We evaluated the long-term reproducibility of an iTRAQ (isobaric tags for relative and absolute quantification)-based quantitative proteomics strategy using one channel for reference across all samples in different iTRAQ sets. A total of 148 liquid chromatography tandem mass spectrometric (LC-MS/MS) analyses were completed, generating six 2D LC-MS/MS data sets for human-in-mouse breast cancer xenograft tissues representative of basal and luminal subtypes. Such large-scale studies require the implementation of robust metrics to assess the contributions of technical and biological variability in the qualitative and quantitative data. Accordingly, we derived a quantification confidence score based on the quality of each peptide-spectrum match to remove quantification outliers from each analysis. After combining confidence score filtering and statistical analysis, reproducible protein identification and quantitative results were achieved from LC-MS/MS data sets collected over a 7-month period. This study provides the first quality assessment on long-term stability and technical considerations for study design of a large-scale clinical proteomics project.
临床蛋白质组学需要对人体标本进行大规模分析，以达到统计学意义。我们评估了一种基于 iTRAQ（相对和绝对定量的同重同位素标记）的定量蛋白质组学策略的长期重现性，该策略在不同 iTRAQ 组的所有样本中使用一个通道作为参考。总共完成了 148 次液相色谱串联质谱（LC-MS/MS）分析，生成了 6 个 2D LC-MS/MS 数据集，代表了基底和腔型亚型的人源乳腺癌异种移植组织。如此大规模的研究需要实施稳健的指标来评估定性和定量数据中技术和生物学变异性的贡献。因此，我们基于每个肽谱匹配的质量得出了一个定量置信得分，以从每个分析中去除定量异常值。在结合置信得分过滤和统计分析后，从为期 7 个月收集的 LC-MS/MS 数据集中实现了可重复的蛋白质鉴定和定量结果。这项研究提供了对大规模临床蛋白质组学项目的长期稳定性和技术设计的第一个质量评估。
