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Semi-automated non-target processing in GC × GC-MS metabolomics analysis: applicability for biomedical studies.

ABSTRACT: Due to the complexity of typical metabolomics samples and the many steps required to obtain quantitative data in GC × GC-MS consisting of deconvolution, peak picking, peak merging, and integration, the unbiased non-target quantification of GC × GC-MS data still poses a major challenge in metabolomics analysis. The feasibility of using commercially available software for non-target processing of GC × GC-MS data was assessed. For this purpose a set of mouse liver samples (24 study samples and five quality control (QC) samples prepared from the study samples) were measured with GC × GC-MS and GC-MS to study the development and progression of insulin resistance, a primary characteristic of diabetes type 2. A total of 170 and 691 peaks were quantified in, respectively, the GC-MS and GC × GC-MS data for all study and QC samples. The quantitative results for the QC samples were compared to assess the quality of semi-automated GC × GC-MS processing compared to targeted GC-MS processing which involved time-consuming manual correction of all wrongly integrated metabolites and was considered as golden standard. The relative standard deviations (RSDs) obtained with GC × GC-MS were somewhat higher than with GC-MS, due to less accurate processing. Still, the biological information in the study samples was preserved and the added value of GC × GC-MS was demonstrated; many additional candidate biomarkers were found with GC × GC-MS compared to GC-MS. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11306-010-0219-6) contains supplementary material, which is available to authorized users.

SUBMITTER: Koek MM

PROVIDER: S-EPMC3040320 | biostudies-literature | 2011 Mar

REPOSITORIES: biostudies-literature

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Semi-automated non-target processing in GC × GC-MS metabolomics analysis: applicability for biomedical studies.

Koek Maud M MM van der Kloet Frans M FM Kleemann Robert R Kooistra Teake T Verheij Elwin R ER Hankemeier Thomas T

Metabolomics : Official journal of the Metabolomic Society 20100715 1

Due to the complexity of typical metabolomics samples and the many steps required to obtain quantitative data in GC × GC-MS consisting of deconvolution, peak picking, peak merging, and integration, the unbiased non-target quantification of GC × GC-MS data still poses a major challenge in metabolomics analysis. The feasibility of using commercially available software for non-target processing of GC × GC-MS data was assessed. For this purpose a set of mouse liver samples (24 study samples and five ...[more]

PMID: 21461033

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Project description:BackgroundStandardization of analytical approaches and reporting methods via community-wide collaboration can work synergistically with web-tool development to result in rapid community-driven expansion of online data repositories suitable for data mining and meta-analysis. In metabolomics, the inter-laboratory reproducibility of gas-chromatography/mass-spectrometry (GC/MS) makes it an obvious target for such development. While a number of web-tools offer access to datasets and/or tools for raw data processing and statistical analysis, none of these systems are currently set up to act as a public repository by easily accepting, processing and presenting publicly submitted GC/MS metabolomics datasets for public re-analysis.DescriptionHere, we present MetabolomeExpress, a new File Transfer Protocol (FTP) server and web-tool for the online storage, processing, visualisation and statistical re-analysis of publicly submitted GC/MS metabolomics datasets. Users may search a quality-controlled database of metabolite response statistics from publicly submitted datasets by a number of parameters (eg. metabolite, species, organ/biofluid etc.). Users may also perform meta-analysis comparisons of multiple independent experiments or re-analyse public primary datasets via user-friendly tools for t-test, principal components analysis, hierarchical cluster analysis and correlation analysis. They may interact with chromatograms, mass spectra and peak detection results via an integrated raw data viewer. Researchers who register for a free account may upload (via FTP) their own data to the server for online processing via a novel raw data processing pipeline.ConclusionsMetabolomeExpress https://www.metabolome-express.org provides a new opportunity for the general metabolomics community to transparently present online the raw and processed GC/MS data underlying their metabolomics publications. Transparent sharing of these data will allow researchers to assess data quality and draw their own insights from published metabolomics datasets.

Project description:Highlights • Malondialdehyde (MDA) is an important biomarker of oxidative stress (lipid peroxidation)• MDA and [1,3-dideutero]MDA (d2-MDA) for use as internal standard react with pentafluorobenzyl bromide (PFB-Br) in aqueous acetone to form di-PFB derivatives.• GC–MS/MS allows for highly accurate measurement of MDA in human plasma, serum and urine.• Many pre-analytical factors including hemolysis may compromise measurement of biological MDA.• Sample storage and study design of long-term clinical studies are important factors to be considered. Malondialdehyde (MDA; 1,3-propanedial, OHC-CH2-CHO) is one of the most frequently measured biomarkers of oxidative stress in plasma and serum. L-Arginine (Arg) is the substrate of nitric oxide synthases (NOS), which convert L-arginine to nitric oxide (NO) and L-citrulline. The Arg/NO pathway comprises several members, including the endogenous NOS-activity inhibitor asymmetric dimethylarginine (ADMA) and its major metabolite dimethyl amine (DMA), and nitrite and nitrate, the major NO metabolites. Reliable measurement of MDA and members of the Arg/NO pathway in plasma, serum, urine and in other biological samples, such as saliva and cerebrospinal fluid, is highly challenging both for analytical and pre-analytical reasons. In our group, we use validated gas chromatography-mass spectrometry (GC–MS) and gas chromatography-tandem mass spectrometry (GC–MS/MS) methods for the quantitative determination in clinical studies of MDA as a biomarker of oxidative stress, and various Arg/NO metabolites that describe the status of this pathway. Here, the importance of pre-analytical issues, which has emerged from the use of GC–MS and GC–MS/MS in clinico-pharmacological studies, is discussed. Paradigmatically, two studies on the long-term oral administration of L-arginine dihydrochloride to patients suffering from peripheral arterial occlusive disease (PAOD) or coronary artery disease (CAD) were considered. Pre-analytical issues that were addressed include blood sampling, plasma or serum storage, study design (notably in long-term studies), and the alternative of measuring MDA in human urine.

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Semi-automated non-target processing in GC × GC-MS metabolomics analysis: applicability for biomedical studies.

Publications

Semi-automated non-target processing in GC × GC-MS metabolomics analysis: applicability for biomedical studies.

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