Centre for High-Throughput Biology, Department of Biochemistry and Molecular Biology, University of British Columbia, Vancouver, BC, Canada.
J Proteome Res. 2010 Apr 5;9(4):1902-12. doi: 10.1021/pr901063t.
The proteome of any cell or even any subcellular fraction remains too complex for complete analysis by one dimension of liquid chromatography-tandem mass spectrometry (LC-MS/MS). Hence, to achieve greater depth of coverage for a proteome of interest, most groups routinely subfractionate the sample prior to LC-MS/MS so that the material entering LC-MS/MS is less complex than the original sample. Protein and/or peptide fractionation methods that biochemists have used for decades, such as strong cation exchange chromatography (SCX), isoelectric focusing (IEF) and SDS-PAGE, are the most common prefractionation methods used currently. There has, as yet, been no comprehensive, controlled evaluation of the relative merits of the various methods, although some binary comparisons have been made. Here, we compare the most popular methods for fractionating samples at both the protein and peptide level, replicating all analyses to provide estimates of the variability in the analyses and controlling precisely for instrument time dedicated to each analysis, as well as directly measuring the recovery of protein or peptide from each fractionation procedure. For maximal proteome coverage, SDS-PAGE is very clearly the most effective method tested, with more than 90% of the entire data set found. When considering the amount of material recovered after each fractionation procedure, solution-based IEF and SCX performed similarly, with approximately 80% of the input being recovered.
任何细胞甚至任何亚细胞部分的蛋白质组都太复杂,无法通过一维液相色谱-串联质谱(LC-MS/MS)进行完全分析。因此,为了更深入地覆盖感兴趣的蛋白质组,大多数研究小组通常在 LC-MS/MS 之前对样品进行亚组分,以便进入 LC-MS/MS 的物质比原始样品更简单。几十年来,生物化学家使用的蛋白质和/或肽分级方法,如强阳离子交换色谱(SCX)、等电聚焦(IEF)和 SDS-PAGE,是目前最常用的预分级方法。尽管已经进行了一些二元比较,但迄今为止,还没有对各种方法的相对优点进行全面、对照评估。在这里,我们比较了在蛋白质和肽水平上分离样品的最流行方法,对所有分析进行了复制,以提供对分析中变异性的估计,并精确控制每个分析所花费的仪器时间,以及直接测量从每个分级程序中回收的蛋白质或肽的量。为了获得最大的蛋白质组覆盖范围,SDS-PAGE 显然是经过测试的最有效的方法,超过 90%的整个数据集都是通过这种方法找到的。当考虑到每种分级程序后回收的材料量时,基于溶液的 IEF 和 SCX 的性能相似,约有 80%的输入被回收。