Department of Human Genetics, Center for Neurodegenerative Disease, Emory University, Atlanta, GA, USA.
Proteomics Clin Appl. 2007 Nov;1(11):1342-50. doi: 10.1002/prca.200700378. Epub 2007 Oct 11.
Investigation of the human specimens is an essential element for understanding the pathogenesis of neurodegenerative disorders, such as Alzheimer's disease, Parkinson's disease, and multiple sclerosis. The studies hold promise for identifying biomarkers for diagnosis and prognosis, elucidating disease mechanisms, and accelerating the development of new strategies for therapeutic intervention. Here, we review proteomics studies of human brain samples in light of recent advances of mass spectrometry, focusing on the general strategies for experimental design and analysis (e.g., sample pooling and replication, selection of proteomics platforms, and false discovery rate in data processing), because quantitative analysis of clinical samples is confounded by a number of variables, including genetic differences, antemortem and postmortem factors, and experimental errors. Diverse proteomics platforms are also discussed with respect to sensitivity, throughput, and accuracy. Regarding the enormous complexity of the human brain and the limitation of current proteomics technologies, it may be more practical to analyze a subset of proteome in a functional context, in order to facilitate the identification of important disease-related proteins in the substantial noise reflecting biological and technical variances.
对人类标本的研究是理解神经退行性疾病(如阿尔茨海默病、帕金森病和多发性硬化症)发病机制的重要因素。这些研究有望确定用于诊断和预后的生物标志物,阐明疾病机制,并加速开发新的治疗干预策略。在此,我们根据质谱技术的最新进展,综述了人类脑组织样本的蛋白质组学研究,重点讨论了实验设计和分析的一般策略(例如,样本混合和重复、蛋白质组学平台的选择以及数据处理中的错误发现率),因为临床样本的定量分析受到许多变量的影响,包括遗传差异、生前和死后因素以及实验误差。还讨论了不同的蛋白质组学平台的灵敏度、通量和准确性。鉴于人脑的巨大复杂性和当前蛋白质组学技术的局限性,在功能背景下分析蛋白质组的一个子集可能更实际,以便在反映生物学和技术差异的大量噪声中识别重要的疾病相关蛋白。