Coon Joshua J, Zürbig Petra, Dakna Mohammed, Dominiczak Anna F, Decramer Stéphane, Fliser Danilo, Frommberger Moritz, Golovko Igor, Good David M, Herget-Rosenthal Stefan, Jankowski Joachim, Julian Bruce A, Kellmann Markus, Kolch Walter, Massy Ziad, Novak Jan, Rossing Kasper, Schanstra Joost P, Schiffer Eric, Theodorescu Dan, Vanholder Raymond, Weissinger Eva M, Mischak Harald, Schmitt-Kopplin Philippe
Department of Chemistry, University of Wisconsin-Madison, Madison, WI, USA.
Proteomics Clin Appl. 2008 Jul 10;2(7-8):964. doi: 10.1002/prca.200800024.
Owing to its availability, ease of collection, and correlation with pathophysiology of diseases, urine is an attractive source for clinical proteomics. However, many proteomic studies have had only limited clinical impact, due to factors such as modest numbers of subjects, absence of disease controls, small numbers of defined biomarkers, and diversity of analytical platforms. Therefore, it is difficult to merge biomarkers from different studies into a broadly applicable human urinary proteome database. Ideally, the methodology for defining the biomarkers should combine a reasonable analysis time with high resolution, thereby enabling the profiling of adequate samples and recognition of sufficient features to yield robust diagnostic panels. Capillary electrophoresis coupled to mass spectrometry (CE-MS), which was used to analyze urine samples from healthy subjects and patients with various diseases, is a suitable approach for this task. The database of these datasets compiled from the urinary peptides enabled the diagnosis, classification, and monitoring of a wide range of diseases. CE-MS exhibits excellent performance for biomarker discovery and allows subsequent biomarker sequencing independent of the separation platform. This approach may elucidate the pathogenesis of many diseases, and better define especially renal and urological disorders at the molecular level.
由于尿液易于获取、便于收集且与疾病的病理生理学相关,因此它是临床蛋白质组学中一个有吸引力的来源。然而,由于受试者数量有限、缺乏疾病对照、定义的生物标志物数量少以及分析平台的多样性等因素,许多蛋白质组学研究的临床影响有限。因此,很难将不同研究中的生物标志物整合到一个广泛适用的人类尿液蛋白质组数据库中。理想情况下,定义生物标志物的方法应将合理的分析时间与高分辨率相结合,从而能够对足够的样本进行分析,并识别出足够的特征以产生可靠的诊断指标。毛细管电泳-质谱联用(CE-MS)用于分析健康受试者和患有各种疾病患者的尿液样本,是完成这项任务的合适方法。从尿肽编译的这些数据集的数据库能够对多种疾病进行诊断、分类和监测。CE-MS在生物标志物发现方面表现出色,并允许独立于分离平台进行后续的生物标志物测序。这种方法可能阐明许多疾病的发病机制,并在分子水平上更好地定义特别是肾脏和泌尿系统疾病。