State Key Laboratory of Proteomics, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing Proteome Research Center, Beijing 102206, China.
State Key Laboratory of Proteomics, National Center for Protein Sciences (The PHOENIX Center, Beijing), Beijing Proteome Research Center, Beijing 102206, China; Center for Bioinformatics, East China Normal University, Shanghai 200241, China.
EBioMedicine. 2017 Apr;18:300-310. doi: 10.1016/j.ebiom.2017.03.028. Epub 2017 Mar 22.
Urine as a true non-invasive sampling source holds great potential for biomarker discovery. While approximately 2000 proteins can be detected by mass spectrometry in urine from healthy people, the amount of these proteins vary considerably. A systematic evaluation of a large number of samples is needed to determine the range of the variations. Current biomarker studies often measure limited number of urine samples in the discovery phase, which makes it difficult to determine whether proteins differentially expressed between control and disease groups represent actual difference, or are just physiological variations among the individuals, leads to failures in the validation phase with the increased sample numbers. Here, we report a streamlined workflow with capacity of measuring 8 urine proteomes per day at the coverage of >1500 proteins. With this workflow, we evaluated variations in 497 urine proteomes from 167 healthy donors, establishing reference intervals (RIs) that covered urine protein variations. We demonstrated that RIs could be used to monitor physiological changes by detecting transient outlier proteins. Furthermore, we provided a RIs-based algorithm for biomarker discovery and validation to screen for diseases such as cancer. This study provided a proof-of-principle workflow for the use of urine proteome for health monitoring and disease screening.
尿液作为一种真正的非侵入性采样来源,在生物标志物发现方面具有巨大的潜力。虽然通过质谱法可以在健康人的尿液中检测到大约 2000 种蛋白质,但这些蛋白质的数量差异很大。需要对大量样本进行系统评估,以确定其变化范围。目前的生物标志物研究通常在发现阶段测量有限数量的尿液样本,这使得很难确定在对照组和疾病组之间表达差异的蛋白质是否代表实际差异,还是只是个体之间的生理变化,导致在验证阶段增加样本数量时出现失败。在这里,我们报告了一种简化的工作流程,每天可以测量 8 个尿液蛋白质组,覆盖 >1500 种蛋白质。使用这种工作流程,我们评估了 167 名健康供体的 497 个尿液蛋白质组中的变异,建立了参考区间(RIs),涵盖了尿液蛋白质的变化。我们证明了 RIs 可以通过检测短暂的异常蛋白来监测生理变化。此外,我们还提供了一种基于 RIs 的生物标志物发现和验证算法,用于筛查癌症等疾病。这项研究为使用尿液蛋白质组进行健康监测和疾病筛查提供了一个原理验证工作流程。