Working Paper Article Version 1 This version is not peer-reviewed

Enhanced Metabolome Coverage and Evaluation of Matrix Effects by the Use of Experimental-Condition-Matched 13C-Labeled Biological Samples in Isotope-Assisted LC-HRMS Metabolomics

Version 1 : Received: 5 October 2020 / Approved: 6 October 2020 / Online: 6 October 2020 (13:37:38 CEST)

A peer-reviewed article of this Preprint also exists.

Ćeranić, A.; Bueschl, C.; Doppler, M.; Parich, A.; Xu, K.; Lemmens, M.; Buerstmayr, H.; Schuhmacher, R. Enhanced Metabolome Coverage and Evaluation of Matrix Effects by the Use of Experimental-Condition-Matched 13C-Labeled Biological Samples in Isotope-Assisted LC-HRMS Metabolomics. Metabolites 2020, 10, 434. Ćeranić, A.; Bueschl, C.; Doppler, M.; Parich, A.; Xu, K.; Lemmens, M.; Buerstmayr, H.; Schuhmacher, R. Enhanced Metabolome Coverage and Evaluation of Matrix Effects by the Use of Experimental-Condition-Matched 13C-Labeled Biological Samples in Isotope-Assisted LC-HRMS Metabolomics. Metabolites 2020, 10, 434.

Journal reference: Metabolites 2020, 10, 434
DOI: 10.3390/metabo10110434

Abstract

Stable isotope-assisted approaches can improve untargeted LC-HRMS metabolomics studies. Here, we demonstrate at the example of chemically stressed wheat that metabolome-wide internal standardization by globally 13C-labeled metabolite extract (GLMe-IS) of experimental-condition-matched biological samples can help to improve coverage of treatment-relevant metabolites and aid in the post-acquisition assessment of putative matrix effects in samples obtained upon different treatments. For this, native extracts of toxin- and mock-treated (control) wheat ears were standardized by the addition of uniformly-13C-labeled wheat ear extracts that were cultivated under similar experimental conditions (toxin-treatment and control) and measured with LC-HRMS. The results show that 996 wheat-derived metabolites were detected with the non-condition-matched 13C-labeled metabolite extract, while another 68 were only covered by the experimental-condition-matched GLMe-IS. Additional testing is performed with the assumption that GLMe-IS enables compensation for matrix effects. Although on average no severe matrix differences between both experimental conditions were found, individual metabolites may be affected as is demonstrated by wrong decisions with respect to the classification of significantly altered metabolites. When GLMe-IS was applied to compensate for matrix effects, 272 metabolites showed significantly altered levels between treated and control samples, 42 of which would not have been classified as such without GLMe-IS.

Subject Areas

untargeted metabolomics; internal standard; deoxynivalenol; abiotic stress of wheat; matrix effects

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