Balboni Imelda, Chan Steven M, Kattah Michael, Tenenbaum Jessica D, Butte Atul J, Utz Paul J
Department of Medicine, Division of Immunology and Rheumatology, Stanford University School of Medicine, Stanford, California 94305, USA.
Annu Rev Immunol. 2006;24:391-418. doi: 10.1146/annurev.immunol.24.021605.090709.
Several proteomics platforms have emerged in the past decade that show great promise for filling in the many gaps that remain from earlier studies of the genome and from the sequencing of the human genome itself. This review describes applications of proteomics technologies to the study of autoimmune diseases. We focus largely on biased technology platforms that are capable of analyzing a large panel of known analytes, as opposed to techniques such as two-dimensional gel electrophoresis (2DIGE) or mass spectroscopy that represent unbiased approaches (as reviewed in 1). At present, the main analytes that can be systematically studied in autoimmunity include autoantibodies, cytokines and chemokines, components of signaling pathways, and cell-surface receptors. We review the most commonly used platforms for such studies, citing important discoveries and limitations that exist. We conclude by reviewing advances in biomedical informatics that will eventually allow the human proteome to be deciphered.
在过去十年中出现了几种蛋白质组学平台,这些平台在填补早期基因组研究以及人类基因组测序本身遗留的诸多空白方面显示出巨大潜力。本综述描述了蛋白质组学技术在自身免疫性疾病研究中的应用。我们主要关注能够分析大量已知分析物的偏向性技术平台,而不是像二维凝胶电泳(2DIGE)或质谱分析等代表无偏向性方法的技术(如文献[1]中所述)。目前,在自身免疫性疾病中可以系统研究的主要分析物包括自身抗体、细胞因子和趋化因子、信号通路成分以及细胞表面受体。我们综述了此类研究中最常用的平台,列举了存在的重要发现和局限性。我们通过回顾生物医学信息学的进展来结束本文,这些进展最终将使人类蛋白质组得以破解。