Systematic Optimization of Long Gradient Chromatography Mass Spectrometry for Deep Analysis of Brain Proteome

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• 作者：Hong Wang ; Yanling Yang ; Yuxin Li ; Bing Bai ; Xusheng Wang ; Haiyan Tan ; Tao Liu ; Thomas G. Beach ; Junmin Peng ; Zhiping Wu
• 刊名：Journal of Proteome Research
• 出版年：2015
• 出版时间：February 6, 2015
• 年：2015
• 卷：14
• 期：2
• 页码：829-838
• 全文大小：470K
• ISSN：1535-3907

文摘

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鈥揗S/MS platform to enhance protein identification from a complex mixture. The platform employed an in-house fabricated, reverse-phase long column (100 渭m 脳 150 cm, 5 渭m C18 beads) coupled to Q Exactive MS. The column was capable of achieving a peak capacity of 鈭?00 in a 720 min gradient of 10鈥?5% acetonitrile. The optimal loading level was 鈭? 渭g of peptides, although the column allowed loading as many as 20 渭g. Gas-phase fractionation of peptide ions further increased the number of peptide identification by 鈭?0%. Moreover, the combination of basic pH LC prefractionation with the long gradient LC鈥揗S/MS platform enabled the identification of 96鈥?27 peptides and 10鈥?44 proteins at 1% protein false discovery rate in a post-mortem brain sample of Alzheimer鈥檚 disease. Because deep RNA sequencing of the same specimen suggested that 鈭?6鈥?00 genes were expressed, the current analysis covered more than 60% of the expressed proteome. Further improvement strategies of the LC/LC鈥揗S/MS platform were also discussed.