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High-throughput data analysis for detecting and identifying differences between samples in GC/MS-based metabolomic analyses
Umeå University, Faculty of Science and Technology, Department of Chemistry.
Umeå University, Faculty of Science and Technology, Department of Chemistry.
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2005 (English)In: Analytical Chemistry, ISSN 0003-2700, E-ISSN 1520-6882, Vol. 77, no 17, 5635-5642 p.Article in journal (Refereed) Published
Abstract [en]

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.


Place, publisher, year, edition, pages
2005. Vol. 77, no 17, 5635-5642 p.
National Category
Biological Sciences
URN: urn:nbn:se:umu:diva-13582DOI: 10.1021/ac050601ePubMedID: 16131076OAI: diva2:153253
Available from: 2007-09-14 Created: 2007-09-14 Last updated: 2013-03-19
In thesis
1. Multivariate processing and modelling of hyphenated metabolite data
Open this publication in new window or tab >>Multivariate processing and modelling of hyphenated metabolite data
2005 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

One trend in the ‘omics’ sciences is the generation of increasing amounts of data, describing complex biological samples. To cope with this and facilitate progress towards reliable diagnostic tools, it is crucial to develop methods for extracting representative and predictive information. In global metabolite analysis (metabolomics and metabonomics) NMR, GC/MS and LC/MS are the main platforms for data generation. Multivariate projection methods (e.g. PCA, PLS and O-PLS) have been recognized as efficient tools for data analysis within subjects such as biology and chemistry due to their ability to provide interpretable models based on many, correlated variables. In global metabolite analysis, these methods have been successfully applied in areas such as toxicology, disease diagnosis and plant functional genomics.

This thesis describes the development of processing methods for the unbiased extraction of representative and predictive information from metabolic GC/MS and LC/MS data characterizing biofluids, e.g. plant extracts, urine and blood plasma. In order to allow the multivariate projections to detect and highlight differences between samples, one requirement of the processing methods is that they must extract a common set of descriptors from all samples and still retain the metabolically relevant information in the data. In Papers I and II this was done by applying a hierarchical multivariate compression approach to both GC/MS and LC/MS data. In the study described in Paper III a hierarchical multivariate curve resolution strategy (H-MCR) was developed for simultaneously resolving multiple GC/MS samples into pure profiles. In Paper IV the H-MCR method was applied to a drug toxicity study in rats, where the method’s potential for biomarker detection and identification was exemplified. Finally, the H-MCR method was extended, as described in Paper V, allowing independent samples to be processed and predicted using a model based on an existing set of representative samples. The fact that these processing methods proved to be valid for predicting the properties of new independent samples indicates that it is now possible for global metabolite analysis to be extended beyond isolated studies. In addition, the results facilitate high through-put analysis, because predicting the nature of samples is rapid compared to the actual processing. In summary this research highlights the possibilities for using global metabolite analysis in diagnosis.

Place, publisher, year, edition, pages
Umeå: Kemi, 2005. 66 p.
Chemometrics, Curve Resolution, GC/MS, LC/MS, Metabolomics, Metabonomics, Multivariate Analysis and Multivariate Curve Resolution.
National Category
Organic Chemistry
urn:nbn:se:umu:diva-663 (URN)91-7305-922-7 (ISBN)
Public defence
2006-01-27, KB3B1, KBC, Umeå Univeristet, Umeå, 10:00 (English)
Available from: 2005-12-22 Created: 2005-12-22 Last updated: 2009-12-03Bibliographically approved

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