Kentsis Alex
Division of Hematology/Oncology, Children's Hospital Boston, and Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts, USA.
Pediatr Int. 2011 Feb;53(1):1-6. doi: 10.1111/j.1442-200X.2010.03253.x.
Modern medicine has experienced a tremendous explosion in knowledge about disease pathophysiology, gained largely from understanding the molecular biology of human disease. Recent advances in mass spectrometry and proteomics now allow for simultaneous identification and quantification of thousands of unique proteins and peptides in complex biological tissues and fluids. In particular, proteomic studies of urine benefit from urine's less complex composition as compared to serum and tissues, and have been used successfully to discover novel markers of a variety of infectious, autoimmune, oncological, and surgical conditions. This perspective discusses the challenges of such studies that stem from the compositional variability and complexity of human urine, as well as instrumental sampling limitations and the effects of noise and selection bias. Strategies for the design of observational clinical trials, physical and chemical fractionation of urine specimens, mass spectrometry analysis, and functional data annotation are outlined. Rigorous translational investigations using urine proteomics are likely to discover novel and accurate markers of both rare and common diseases. This should aid the diagnosis, improve stratification of therapy, and identify novel therapeutic targets for a variety of childhood and adult diseases, all of which will be essential for the development of personalized and predictive medicine of the future.
现代医学在疾病病理生理学知识方面经历了巨大的飞跃，这主要得益于对人类疾病分子生物学的理解。质谱分析和蛋白质组学的最新进展使得在复杂的生物组织和体液中能够同时鉴定和定量数千种独特的蛋白质和肽段。特别是，与血清和组织相比，尿液的成分相对简单，尿液蛋白质组学研究从中受益，并已成功用于发现各种感染性、自身免疫性、肿瘤学和外科疾病的新型标志物。本文讨论了此类研究面临的挑战，这些挑战源于人类尿液成分的变异性和复杂性，以及仪器采样限制、噪声和选择偏倚的影响。概述了观察性临床试验设计、尿液标本的物理和化学分级分离、质谱分析以及功能数据注释的策略。使用尿液蛋白质组学进行严格的转化研究可能会发现罕见病和常见疾病的新型准确标志物。这将有助于诊断，改善治疗分层，并为各种儿童和成人疾病确定新的治疗靶点，所有这些对于未来个性化和预测性医学的发展都至关重要。
