High-throughput data analysis for detecting and identifying differences between samples in GC/MS-based metabolomic analyses

被引:353
作者
Jonsson, P
Johansson, AI
Gullberg, J
Trygg, J
A, J
Grung, B
Marklund, S
Sjoström, M
Antti, H
Moritz, T [1 ]
机构
[1] Swedish Univ Agr Sci, Dept Forest Genet & Plant Physiol, Umea Plant Sci Ctr, SE-90187 Umea, Sweden
[2] Umea Univ, Dept Chem, Chemometr Res Grp, SE-90187 Umea, Sweden
[3] Umea Univ, Univ Umea Hosp, Dept Med Biosci, SE-90185 Umea, Sweden
[4] Univ Bergen, Dept Chem, N-5007 Bergen, Norway
关键词
D O I
10.1021/ac050601e
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
In metabolomics, the objective is to identify differences in metabolite profiles between samples. A widely used tool in metabolomics investigations is gas chromatography-mass spectrometry (GC/MS). More than 400 compounds can be detected in a single analysis, if overlapping GC/ MS peaks are deconvoluted. However, the deconvolution process is time-consuming and difficult to automate, and additional processing is needed in order to compare samples. Therefore, there is a need to improve and automate the data processing strategy for data generated in GC/MS-based metabolomics; if not, the processing step will be a major bottleneck for high-throughput analyses. Here we describe a new semiautomated strategy using a hierarchical multivariate curve resolution approach that processes all samples simultaneously. The presented strategy generates (after appropriate treatment, e.g., multivariate analysis) tables of all the detected metabolites that differ in relative concentrations between samples. The processing of 70 samples took similar time to that of the GC/TOFMS analyses of the samples. The strategy has been validated using two different sets of samples: a complex mixture of standard compounds and Arabidopsis samples.
引用
收藏
页码:5635 / 5642
页数:8
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