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大规模磷酸化蛋白质组学常见定量策略的基准测试

Benchmarking common quantification strategies for large-scale phosphoproteomics.

作者信息

Hogrebe Alexander, von Stechow Louise, Bekker-Jensen Dorte B, Weinert Brian T, Kelstrup Christian D, Olsen Jesper V

机构信息

Novo Nordisk Foundation Center for Protein Research, Proteomics Program, Faculty of Health and Medical Sciences, University of Copenhagen, Blegdamsvej 3b, 2200, Copenhagen, Denmark.

出版信息

Nat Commun. 2018 Mar 13;9(1):1045. doi: 10.1038/s41467-018-03309-6.

Abstract

Comprehensive mass spectrometry (MS)-based proteomics is now feasible, but reproducible quantification remains challenging, especially for post-translational modifications such as phosphorylation. Here, we compare the most popular quantification techniques for global phosphoproteomics: label-free quantification (LFQ), stable isotope labeling by amino acids in cell culture (SILAC) and MS- and MS-measured tandem mass tags (TMT). In a mixed species comparison with fixed phosphopeptide ratios, we find LFQ and SILAC to be the most accurate techniques. MS-based TMT yields the highest precision but lowest accuracy due to ratio compression, which MS-based TMT can partly rescue. However, MS-based TMT outperforms MS-based TMT when analyzing phosphoproteome changes in the DNA damage response, since its higher precision and larger identification numbers allow detection of a greater number of significantly regulated phosphopeptides. Finally, we utilize the TMT multiplexing capabilities to develop an algorithm for determining phosphorylation site stoichiometry, showing that such applications benefit from the high accuracy of MS-based TMT.

摘要

基于质谱(MS)的综合蛋白质组学现已可行,但可重复定量仍具有挑战性,尤其是对于翻译后修饰(如磷酸化)而言。在此,我们比较了用于整体磷酸化蛋白质组学的最常用定量技术:无标记定量(LFQ)、细胞培养中氨基酸稳定同位素标记(SILAC)以及基于质谱和串联质谱测量的串联质量标签(TMT)。在具有固定磷酸肽比率的混合物种比较中,我们发现LFQ和SILAC是最准确的技术。基于质谱的TMT由于比率压缩而产生最高精度但最低准确性,基于质谱的TMT可部分挽救这一情况。然而,在分析DNA损伤反应中的磷酸化蛋白质组变化时,基于质谱的TMT优于基于质谱的TMT,因为其更高的精度和更大的鉴定数量能够检测到更多显著调控的磷酸肽。最后,我们利用TMT多重分析能力开发了一种用于确定磷酸化位点化学计量的算法,表明此类应用受益于基于质谱的TMT的高精度。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3ae4/5849679/6f35c6650218/41467_2018_3309_Fig1_HTML.jpg

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