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A strategy for identifying differences in large series of metabolomic samples analyzed by GC/MS
Umeå University, Faculty of Science and Technology, Department of Chemistry.
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2004 (English)In: Analytical Chemistry, ISSN 0003-2700, E-ISSN 1520-6882, Vol. 76, no 6, 1738-1745 p.Article in journal (Refereed) Published
Abstract [en]

In metabolomics, the purpose is to identify and quantify all the metabolites in a biological system. Combined gas chromatography and mass spectrometry (GC/MS) is one of the most commonly used techniques in metabolomics together with 1H NMR, and it has been shown that more than 300 compounds can be distinguished with GC/MS after deconvolution of overlapping peaks. To avoid having to deconvolute all analyzed samples prior to multivariate analysis of the data, we have developed a strategy for rapid comparison of nonprocessed MS data files. The method includes baseline correction, alignment, time window determinations, alternating regression, PLS-DA, and identification of retention time windows in the chromatograms that explain the differences between the samples. Use of alternating regression also gives interpretable loadings, which retain the information provided by m/z values that vary between the samples in each retention time window. The method has been applied to plant extracts derived from leaves of different developmental stages and plants subjected to small changes in day length. The data show that the new method can detect differences between the samples and that it gives results comparable to those obtained when deconvolution is applied prior to the multivariate analysis. We suggest that this method can be used for rapid comparison of large sets of GC/MS data, thereby applying time-consuming deconvolution only to parts of the chromatograms that contribute to explain the differences between the samples.

Place, publisher, year, edition, pages
Columbus, OH: American Chemical Society , 2004. Vol. 76, no 6, 1738-1745 p.
National Category
Chemical Sciences
URN: urn:nbn:se:umu:diva-4888DOI: 10.1021/ac0352427OAI: diva2:144160
Available from: 2005-12-22 Created: 2005-12-22 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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Jonsson, PärNordström, AndersSjöström, Michael
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