Comparison of Proteomic Assessment Methods in Multiple Cohort Studies
Comparison of Proteomic Assessment Methods in Multiple Cohort Studies
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DOI：
10.1002/pmic.201900278
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发表时间：
2020-06-01
期刊：
影响因子：
3.4
通讯作者：
Bowler, Russell P.
中科院分区：
文献类型：
--
作者：
Raffield, Laura M.;Dang, Hong;Bowler, Russell P.
新的蛋白质组学平台，如基于适体的SOMAscan平台，可以有效和成本效益地量化大量蛋白质，并迅速普及。然而，与传统免疫测定法的比较仍然未被充分探索，使研究人员不确定何时进行交叉测定比较是合适的。通过SOMAscan探索免疫测定结果与相对蛋白质定量的相关性。对于在两个慢性阻塞性肺疾病（COPD）队列、COPD研究（SPIROMICS）和COPDGene中的亚群和中间结果测量中评估的63种蛋白质，使用基于无数规则的医学多重免疫测定和SOMAscan，斯皮尔曼相关系数范围为-0.13至0.97，中位相关系数约为0.5，并且在队列间结果一致。在基于人群的多种族动脉粥样硬化研究中的免疫测定以及COPDGene和SPIROMICS中的其他测定中观察到类似的范围。在一个小的心肌梗死患者队列中，基于抗体的Olink平台和SOMAscan的相对定量比较也显示了广泛的相关性范围。最后，cispQTL数据，质谱适体确认，和其他公开可用的数据进行整合，以评估与观察到的相关性的关系。蛋白质组学检测之间的相关性显示出广泛的范围，在比较和荟萃分析检测和研究之间的蛋白质组学数据时应仔细考虑。
Novel proteomics platforms, such as the aptamer-based SOMAscan platform, can quantify large numbers of proteins efficiently and cost-effectively and are rapidly growing in popularity. However, comparisons to conventional immunoassays remain underexplored, leaving investigators unsure when cross-assay comparisons are appropriate. The correlation of results from immunoassays with relative protein quantification is explored by SOMAscan. For 63 proteins assessed in two chronic obstructive pulmonary disease (COPD) cohorts, subpopulations and intermediate outcome measures in COPD Study (SPIROMICS), and COPDGene, using myriad rules based medicine multiplex immunoassays and SOMAscan, Spearman correlation coefficients range from -0.13 to 0.97, with a median correlation coefficient of approximate to 0.5 and consistent results across cohorts. A similar range is observed for immunoassays in the population-based Multi-Ethnic Study of Atherosclerosis and for other assays in COPDGene and SPIROMICS. Comparisons of relative quantification from the antibody-based Olink platform and SOMAscan in a small cohort of myocardial infarction patients also show a wide correlation range. Finally,cispQTL data, mass spectrometry aptamer confirmation, and other publicly available data are integrated to assess relationships with observed correlations. Correlation between proteomics assays shows a wide range and should be carefully considered when comparing and meta-analyzing proteomics data across assays and studies.