An untargeted metabolomic workflow to improve structural characterization of metabolites.
An untargeted metabolomic workflow to improve structural characterization of metabolites.
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DOI：
10.1021/ac400751j
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发表时间：
2013-08-20
影响因子：
7.4
通讯作者：
Patti, Gary J.
中科院分区：
文献类型：
--
作者：
Nikolskiy, Igor;Mahieu, Nathaniel G.;Chen, Ying-Jr;Tautenhahn, Ralf;Patti, Gary J.
基于质谱的代谢组学依赖于MS2数据进行代谢物的结构表征。为了获得支持代谢物鉴定所需的高质量MS2数据，必须将感兴趣的离子纯粹分离以进行碎片化。在这里，我们表明代谢组学的MS2数据经常以污染离子为特征，这些离子阻碍了结构鉴定。虽然使用窄隔离窗口可以最大限度地减少MS2碎片的污染，但即使是窄窗口也不总是有足够的选择性，并且它们可以通过从MS2光谱中去除同位素模式来使数据分析复杂化。此外，窄窗可以显著降低灵敏度。在这项工作中，我们介绍了一种新的，两部分的方法来执行代谢组学鉴定，解决这些问题。首先，我们收集MS2扫描与较不严格的隔离设置，以获得更高的灵敏度，牺牲特异性。然后，通过评估MS2片段强度作为MS2分析目标的保留时间和前体质量的函数，我们获得了与纯标准一致的反卷积MS2光谱，因此可以用于代谢物鉴定。我们的方法的价值是通过从大脑、肝脏、星形胶质细胞和神经组织中提取代谢提取物来突出，并通过使用纯代谢物标准结合基于METLIN代谢物数据库中原始MS2数据的模拟来评估性能。在我们的实验室网站（http://pattilab.wustl.edu/decoms2.php）上可以找到实现我们工作流程中使用的算法的R包。
Mass spectrometry-based metabolomics relies on MS2 data for structural characterization of metabolites. To obtain the high-quality MS2 data necessary to support metabolite identifications, ions of interest must be purely isolated for fragmentation. Here we show that metabolomic MS2 data are frequently characterized by contaminating ions that prevent structural identification. Although using narrow-isolation windows can minimize contaminating MS2 fragments, even narrow windows are not always selective enough and they can complicate data analysis by removing isotopic patterns from MS2 spectra. Moreover, narrow windows can significantly reduce sensitivity. In this work we introduce a novel, two-part approach for performing metabolomic identifications that addresses these issues. First, we collect MS2 scans with less stringent isolation settings to obtain improved sensitivity at the expense of specificity. Then, by evaluating MS2 fragment intensities as a function of retention time and precursor mass targeted for MS2 analysis, we obtain deconvolved MS2 spectra that are consistent with pure standards and can therefore be used for metabolite identification. The value of our approach is highlighted with metabolic extracts from brain, liver, astrocytes, as well as nerve tissue and performance is evaluated by using pure metabolite standards in combination with simulations based on raw MS2 data from the METLIN metabolite database. An R package implementing the algorithms used in our workflow is available on our laboratory website (http://pattilab.wustl.edu/decoms2.php).
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影响因子：
14.9
作者：
Wishart DS;Knox C;Guo AC;Eisner R;Young N;Gautam B;Hau DD;Psychogios N;Dong E;Bouatra S;Mandal R;Sinelnikov I;Xia J;Jia L;Cruz JA;Lim E;Sobsey CA;Shrivastava S;Huang P;Liu P;Fang L;Peng J;Fradette R;Cheng D;Tzur D;Clements M;Lewis A;De Souza A;Zuniga A;Dawe M;Xiong Y;Clive D;Greiner R;Nazyrova A;Shaykhutdinov R;Li L;Vogel HJ;Forsythe I
通讯作者：
Forsythe I
影响因子：
64.8
作者：
通讯作者：
--
影响因子：
64.8
作者：
Sreekumar, Arun;Poisson, Laila M.;Rajendiran, Thekkelnaycke M.;Khan, Amjad P.;Cao, Qi;Yu, Jindan;Laxman, Bharathi;Mehra, Rohit;Lonigro, Robert J.;Li, Yong;Nyati, Mukesh K.;Ahsan, Aarif;Kalyana-Sundaram, Shanker;Han, Bo;Cao, Xuhong;Byun, Jaeman;Omenn, Gilbert S.;Ghosh, Debashis;Pennathur, Subramaniam;Alexander, Danny C.;Berger, Alvin;Shuster, Jeffrey R.;Wei, John T.;Varambally, Sooryanarayana;Beecher, Christopher;Chinnaiyan, Arul M.
通讯作者：
Chinnaiyan, Arul M.
影响因子：
64.8
作者：
Picotti, Paola;Clement-Ziza, Mathieu;Lam, Henry;Campbell, David S.;Schmidt, Alexander;Deutsch, Eric W.;Roest, Hannes;Sun, Zhi;Rinner, Oliver;Reiter, Lukas;Shen, Qin;Michaelson, Jacob J.;Frei, Andreas;Alberti, Simon;Kusebauch, Ulrike;Wollscheid, Bernd;Moritz, Robert L.;Beyer, Andreas;Aebersold, Ruedi
通讯作者：
Aebersold, Ruedi
影响因子：
7.4
作者：
Yanes, Oscar;Tautenhahn, Ralf;Patti, Gary J.;Siuzdak, Gary
通讯作者：
Siuzdak, Gary