Wang Hong, Yang Yanling, Li Yuxin, Bai Bing, Wang Xusheng, Tan Haiyan, Liu Tao, Beach Thomas G, Peng Junmin, Wu Zhiping
Department of Structural Biology, ‡St. Jude Proteomics Facility, and §̂Department of Developmental Neurobiology, St. Jude Children's Research Hospital , 262 Danny Thomas Place, Memphis, Tennessee 38105, United States.
J Proteome Res. 2015 Feb 6;14(2):829-38. doi: 10.1021/pr500882h. Epub 2014 Dec 12.
The development of high-resolution liquid chromatography (LC) is essential for improving the sensitivity and throughput of mass spectrometry (MS)-based proteomics. Here we present systematic optimization of a long gradient LC-MS/MS platform to enhance protein identification from a complex mixture. The platform employed an in-house fabricated, reverse-phase long column (100 μm × 150 cm, 5 μm C18 beads) coupled to Q Exactive MS. The column was capable of achieving a peak capacity of ∼700 in a 720 min gradient of 10-45% acetonitrile. The optimal loading level was ∼6 μg of peptides, although the column allowed loading as many as 20 μg. Gas-phase fractionation of peptide ions further increased the number of peptide identification by ∼10%. Moreover, the combination of basic pH LC prefractionation with the long gradient LC-MS/MS platform enabled the identification of 96,127 peptides and 10,544 proteins at 1% protein false discovery rate in a post-mortem brain sample of Alzheimer's disease. Because deep RNA sequencing of the same specimen suggested that ∼16,000 genes were expressed, the current analysis covered more than 60% of the expressed proteome. Further improvement strategies of the LC/LC-MS/MS platform were also discussed.
高分辨率液相色谱(LC)的发展对于提高基于质谱(MS)的蛋白质组学的灵敏度和通量至关重要。在此,我们展示了对长梯度LC-MS/MS平台的系统优化,以增强从复杂混合物中鉴定蛋白质的能力。该平台采用了自制的反相长柱(100μm×150cm,5μm C18珠)与Q Exactive质谱仪联用。在10-45%乙腈的720分钟梯度中,该柱能够实现约700的峰容量。最佳上样量约为6μg肽段,尽管该柱允许上样多达20μg。肽离子的气相分级分离进一步使肽段鉴定数量增加了约10%。此外,碱性pH值液相预分级分离与长梯度LC-MS/MS平台相结合,在阿尔茨海默病的尸检脑样本中,以1%的蛋白质错误发现率鉴定出了96,127个肽段和10,544种蛋白质。由于对同一标本的深度RNA测序表明约有16,000个基因表达,当前分析覆盖了超过60%的表达蛋白质组。还讨论了LC/LC-MS/MS平台的进一步改进策略。