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Metabolomics. 2014;10(4):754-769. doi: 10.1007/s11306-013-0611-0. Epub 2013 Dec 4.

A novel stable isotope labelling assisted workflow for improved untargeted LC-HRMS based metabolomics research.

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Department for Agrobiotechnology (IFA-Tulln), Center for Analytical Chemistry and Institute for Biotechnology in Plant Production, University of Natural Resources and Life Sciences, Vienna (BOKU), Konrad-Lorenz-Str. 20, 3430 Tulln, Austria.
Department of Applied Genetics and Cell Biology, University of Natural Resources and Life Sciences, Vienna (BOKU), Konrad-Lorenz-Str. 24, 3430 Tulln, Austria.
Institute of Agronomy, Genetics and Field Crops, Università Cattolica del Sacro Cuore, Via Emilia Parmense 84, 29122 Piacenza, Italy.
Health and Environment Department, Bioresources - Fungal Genetics and Genomics, Austrian Institute of Technology (AIT), Konrad-Lorenz-Str. 24, 3430 Tulln, Austria.
Institute for Genomics and Bioinformatics, Graz University of Technology, Petersgasse 14, 8010 Graz, Austria.
Core Facility Bioinformatics, Austrian Centre for Industrial Biotechnology, Petersgasse 14, 8010 Graz, Austria.


Many untargeted LC-ESI-HRMS based metabolomics studies are still hampered by the large proportion of non-biological sample derived signals included in the generated raw data. Here, a novel, powerful stable isotope labelling (SIL)-based metabolomics workflow is presented, which facilitates global metabolome extraction, improved metabolite annotation and metabolome wide internal standardisation (IS). The general concept is exemplified with two different cultivation variants, (1) co-cultivation of the plant pathogenic fungi Fusarium graminearum on non-labelled and highly 13C enriched culture medium and (2) experimental cultivation under native conditions and use of globally U-13C labelled biological reference samples as exemplified with maize and wheat. Subsequent to LC-HRMS analysis of mixtures of labelled and non-labelled samples, two-dimensional data filtering of SIL specific isotopic patterns is performed to better extract truly biological derived signals together with the corresponding number of carbon atoms of each metabolite ion. Finally, feature pairs are convoluted to feature groups each representing a single metabolite. Moreover, the correction of unequal matrix effects in different sample types and the improvement of relative metabolite quantification with metabolome wide IS are demonstrated for the F. graminearum experiment. Data processing employing the presented workflow revealed about 300 SIL derived feature pairs corresponding to 87-135 metabolites in F. graminearum samples and around 800 feature pairs corresponding to roughly 350 metabolites in wheat samples. SIL assisted IS, by the use of globally U-13C labelled biological samples, reduced the median CV value from 7.1 to 3.6 % for technical replicates and from 15.1 to 10.8 % for biological replicates in the respective F. graminearum samples.


13C-labelling; Fusarium; Internal standardisation; Maize; Metabolomics; Wheat

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