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首页> 外文期刊>Journal of proteome research >Cost-Effective Automated Preparation of Serum Samples for Reproducible Quantitative Clinical Proteomics
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Cost-Effective Automated Preparation of Serum Samples for Reproducible Quantitative Clinical Proteomics

机译:用于可重复定量临床蛋白质组学的血清样品的经济高效的自动制备

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摘要

Reproducible sample preparation remains a significant challenge in large-scale clinical research using selected reaction monitoring-mass spectrometry (SRM-MS), which enables a highly sensitive multiplexed assay. Although automated liquid-handling platforms have tremendous potential for addressing this issue, the high cost of their consumables is a drawback that renders routine operation expensive. Here we evaluated the performance of a liquid handling platform in preparing serum samples compared with a standard experiment while reducing the outlay for consumables, such as tips, wasted reagents, and reagent stock plates. A total of 26 multiplex assays were quantified by SRM-MS using four sets of 24 pooled human serum aliquots; the four sets used a fixed number (1, 4, 8, or 24) of tips to dispense digestion reagents. This study demonstrated that the use of 4 or 8 tips is comparable to 24 tips (standard experiment), as evidenced by their coefficients of variation: 13.5% (for 4 and 8 tips) versus 12.0% (24 tips). Thus we can save 37% of the total experimental cost compared with the standard experiment, maintaining nearly equivalent reproducibility. The routine operation of cost-effective liquid-handling platforms can enable researchers to process large-scale samples with high throughput, adding credibility to their findings by minimizing human error.
机译:可再现的样品制备在使用所选反应监测质谱(SRM-MS)中仍然是大规模临床研究中的重大挑战,这使得能够高度敏感的多重测定。虽然自动化液体处理平台具有解决此问题的巨大潜力,但其耗材的高成本是缺点,呈现昂贵的日常操作。在这里,我们评估了液体处理平台在制备血清样品方面的性能与标准实验相比,在降低消耗品的支出,例如尖端,浪费的试剂和试剂股票。通过SRM-MS通过四组24组合并的人血清等分试样来定量总共26个多重测定;四组使用固定数(1,4,8或24)的尖端来分配消化试剂。本研究表明,使用4或8个提示与24个尖端(标准实验)相当,如其变异系数所证明的:13.5%(4和8个提示)与12.0%(24个提示)。因此,与标准实验相比,我们可以节省总实验成本的37%,维持几乎等同的再现性。经济高效的液体处理平台的例程操作可以使研究人员能够通过最小化人为错误来处理具有高吞吐量的大规模样本,为其发现增加可信度。

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