A comparative proteomics method for multiple samples based on a 18O-reference strategy and a quantitation and identification-decoupled strategy

Hongbin Wang, Yongqian Zhang, Shuqi Gui, Yong Zhang, Fuping Lu, Yulin Deng*

*此作品的通讯作者

科研成果: 期刊稿件文章同行评审

4 引用 (Scopus)

摘要

Comparisons across large numbers of samples are frequently necessary in quantitative proteomics. Many quantitative methods used in proteomics are based on stable isotope labeling, but most of these are only useful for comparing two samples. For up to eight samples, the iTRAQ labeling technique can be used. For greater numbers of samples, the label-free method has been used, but this method was criticized for low reproducibility and accuracy. An ingenious strategy has been introduced, comparing each sample against a 18O-labeled reference sample that was created by pooling equal amounts of all samples. However, it is necessary to use proportion-known protein mixtures to investigate and evaluate this new strategy. Another problem for comparative proteomics of multiple samples is the poor coincidence and reproducibility in protein identification results across samples. In present study, a method combining 18O-reference strategy and a quantitation and identification-decoupled strategy was investigated with proportion-known protein mixtures. The results obviously demonstrated that the 18O-reference strategy had greater accuracy and reliability than other previously used comparison methods based on transferring comparison or label-free strategies. By the decoupling strategy, the quantification data acquired by LC-MS and the identification data acquired by LC-MS/MS are matched and correlated to identify differential expressed proteins, according to retention time and accurate mass. This strategy made protein identification possible for all samples using a single pooled sample, and therefore gave a good reproducibility in protein identification across multiple samples, and allowed for optimizing peptide identification separately so as to identify more proteins.

源语言英语
页(从-至)166-172
页数7
期刊Talanta
171
DOI
出版状态已出版 - 15 8月 2017

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