Ellington Allison A, Kullo Iftikhar J, Bailey Kent R, Klee George G
Division of Cardiovascular Diseases, Mayo Clinic, Rochester, MN, USA.
Clin Chem. 2009 Jun;55(6):1092-9. doi: 10.1373/clinchem.2008.120717. Epub 2009 Apr 16.
Multiplex arrays are increasingly used for measuring protein biomarkers. Advantages of this approach include specimen conservation, limited sample handling, and decreased time and cost, but the challenges of optimizing assay format for each protein, selecting common dilution factors, and establishing robust quality control algorithms are substantial. Here, we use measurements of 15 protein biomarkers from a large study to illustrate processing, analytic, and quality control issues with multiplexed immunoassays.
We contracted with ThermoScientific for duplicate measurements of 15 proteins in 2322 participants from a community-based cohort, a plasma control, and recombinant protein controls using 2 custom planar microarrays with 6 (panel A) or 9 (panel B) capture antibodies printed in each well. We selected constituent analytes in each panel based on endogenous concentrations and assay availability. Protocols were standardized for sample processing, storage, and freeze-thaw exposures. We analyzed data for effects of deviations from processing protocols, precision, and bias.
Measurements were within reportable ranges for each of the assays; however, concentrations for 7 of the 15 proteins were not centered on the dose-response curves. An additional freeze-thaw cycle and erroneous sample dilution for a subset of samples produced significantly different results. Measurements with large differences between duplicates were seen to cluster by analyte, plate, and participant. Conventional univariate quality control algorithms rejected many plates. Plate-specific medians of cohort and plasma control data significantly covaried, an observation important for development of alternative quality control algorithms.
Multiplex measurements present difficult challenges that require further analytical and statistical developments.
多重阵列越来越多地用于测量蛋白质生物标志物。这种方法的优点包括样本保存、样本处理有限以及时间和成本降低,但为每种蛋白质优化检测形式、选择共同稀释因子以及建立稳健的质量控制算法面临诸多挑战。在此,我们利用一项大型研究中15种蛋白质生物标志物的测量结果来说明多重免疫测定中的处理、分析和质量控制问题。
我们与赛默飞世尔科技公司签约,使用2种定制平面微阵列,对来自一个社区队列的2322名参与者、一份血浆对照和重组蛋白对照中的15种蛋白质进行重复测量,每种微阵列的每个孔中打印有6种(A组)或9种(B组)捕获抗体。我们根据内源性浓度和检测可用性在每个微阵列中选择组成分析物。对样本处理、储存和冻融暴露的方案进行了标准化。我们分析了数据,以了解偏离处理方案、精密度和偏差的影响。
每种检测的测量值均在可报告范围内;然而,15种蛋白质中的7种蛋白质的浓度未集中在剂量反应曲线上。额外的冻融循环和一部分样本的错误样本稀释产生了显著不同的结果。重复测量之间差异较大的测量值按分析物、板和参与者聚类。传统的单变量质量控制算法拒绝了许多板。队列和血浆对照数据的板特异性中位数显著协变,这一观察结果对替代质量控制算法的开发很重要。
多重测量带来了艰巨的挑战,需要进一步的分析和统计发展。