Gauthier Daniel J, Lazure Claude
Neuropeptides Structure and Metabolism Research Unit, Institut de Recherches Cliniques de Montréal, University of Montréal, 110 Pine Avenue West, Montréal, Québec, Canada H2W 1R7.
Expert Rev Proteomics. 2008 Aug;5(4):603-17. doi: 10.1586/14789450.5.4.603.
Organellar proteomics aims to describe the full complement of proteins of subcellular structures and organelles. When compared with whole-cell or whole-tissue proteomes, the more focused results from subcellular proteomic studies have yielded relatively simpler datasets from which biologically relevant information can be more easily extracted. In every proteomic study, the quality and purity of the biological sample to be investigated is of the utmost importance for a successful analysis. In organellar proteomics, one of the most crucial steps in sample preparation is the initial subcellular fractionation procedure by which the enriched preparation of the sought-after organelle is obtained. In nearly all available organellar proteomic studies, the method of choice relies on one or several rounds of density-based gradient centrifugation. Although this method has been recognized for decades as yielding relatively pure preparations of organelles, recent technological advances in protein separation and identification can now reveal even minute amounts of contamination, which in turn can greatly complicate data interpretation. The scope of this review focuses on recently published innovative complementary or alternative methods to perform subcellular fractionation, which can further refine the way in which sample preparation is accomplished in organellar proteomics.
细胞器蛋白质组学旨在描述亚细胞结构和细胞器的完整蛋白质组成。与全细胞或全组织蛋白质组相比，亚细胞蛋白质组学研究的目标更集中，产生的数据集相对更简单，从中可以更容易地提取生物学相关信息。在每项蛋白质组学研究中，待研究生物样品的质量和纯度对于成功分析至关重要。在细胞器蛋白质组学中，样品制备中最关键的步骤之一是初始亚细胞分级分离程序，通过该程序可以获得所需细胞器的富集制剂。在几乎所有现有的细胞器蛋白质组学研究中，首选方法依赖于一轮或几轮基于密度的梯度离心。尽管几十年来人们一直认为这种方法能够产生相对纯净的细胞器制剂，但蛋白质分离和鉴定方面的最新技术进展现在能够揭示即使是微量的污染物，这反过来又会使数据解释变得极为复杂。本综述的范围聚焦于最近发表的用于进行亚细胞分级分离的创新性补充或替代方法，这些方法可以进一步优化细胞器蛋白质组学中样品制备的方式。
