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Untargeted Profiling of Tracer-Derived Metabolites Using Stable Isotopic Labeling and Fast Polarity-Switching LC–ESI-HRMS
[Image: see text] An untargeted metabolomics workflow for the detection of metabolites derived from endogenous or exogenous tracer substances is presented. To this end, a recently developed stable isotope-assisted LC–HRMS-based metabolomics workflow for the global annotation of biological samples ha...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American
Chemical
Society
2014
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4255957/ https://www.ncbi.nlm.nih.gov/pubmed/25372979 http://dx.doi.org/10.1021/ac503290j |
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author | Kluger, Bernhard Bueschl, Christoph Neumann, Nora Stückler, Romana Doppler, Maria Chassy, Alexander W. Waterhouse, Andrew L. Rechthaler, Justyna Kampleitner, Niklas Thallinger, Gerhard G. Adam, Gerhard Krska, Rudolf Schuhmacher, Rainer |
author_facet | Kluger, Bernhard Bueschl, Christoph Neumann, Nora Stückler, Romana Doppler, Maria Chassy, Alexander W. Waterhouse, Andrew L. Rechthaler, Justyna Kampleitner, Niklas Thallinger, Gerhard G. Adam, Gerhard Krska, Rudolf Schuhmacher, Rainer |
author_sort | Kluger, Bernhard |
collection | PubMed |
description | [Image: see text] An untargeted metabolomics workflow for the detection of metabolites derived from endogenous or exogenous tracer substances is presented. To this end, a recently developed stable isotope-assisted LC–HRMS-based metabolomics workflow for the global annotation of biological samples has been further developed and extended. For untargeted detection of metabolites arising from labeled tracer substances, isotope pattern recognition has been adjusted to account for nonlabeled moieties conjugated to the native and labeled tracer molecules. Furthermore, the workflow has been extended by (i) an optional ion intensity ratio check, (ii) the automated combination of positive and negative ionization mode mass spectra derived from fast polarity switching, and (iii) metabolic feature annotation. These extensions enable the automated, unbiased, and global detection of tracer-derived metabolites in complex biological samples. The workflow is demonstrated with the metabolism of (13)C(9)-phenylalanine in wheat cell suspension cultures in the presence of the mycotoxin deoxynivalenol (DON). In total, 341 metabolic features (150 in positive and 191 in negative ionization mode) corresponding to 139 metabolites were detected. The benefit of fast polarity switching was evident, with 32 and 58 of these metabolites having exclusively been detected in the positive and negative modes, respectively. Moreover, for 19 of the remaining 49 phenylalanine-derived metabolites, the assignment of ion species and, thus, molecular weight was possible only by the use of complementary features of the two ion polarity modes. Statistical evaluation showed that treatment with DON increased or decreased the abundances of many detected metabolites. |
format | Online Article Text |
id | pubmed-4255957 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | American
Chemical
Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-42559572014-12-09 Untargeted Profiling of Tracer-Derived Metabolites Using Stable Isotopic Labeling and Fast Polarity-Switching LC–ESI-HRMS Kluger, Bernhard Bueschl, Christoph Neumann, Nora Stückler, Romana Doppler, Maria Chassy, Alexander W. Waterhouse, Andrew L. Rechthaler, Justyna Kampleitner, Niklas Thallinger, Gerhard G. Adam, Gerhard Krska, Rudolf Schuhmacher, Rainer Anal Chem [Image: see text] An untargeted metabolomics workflow for the detection of metabolites derived from endogenous or exogenous tracer substances is presented. To this end, a recently developed stable isotope-assisted LC–HRMS-based metabolomics workflow for the global annotation of biological samples has been further developed and extended. For untargeted detection of metabolites arising from labeled tracer substances, isotope pattern recognition has been adjusted to account for nonlabeled moieties conjugated to the native and labeled tracer molecules. Furthermore, the workflow has been extended by (i) an optional ion intensity ratio check, (ii) the automated combination of positive and negative ionization mode mass spectra derived from fast polarity switching, and (iii) metabolic feature annotation. These extensions enable the automated, unbiased, and global detection of tracer-derived metabolites in complex biological samples. The workflow is demonstrated with the metabolism of (13)C(9)-phenylalanine in wheat cell suspension cultures in the presence of the mycotoxin deoxynivalenol (DON). In total, 341 metabolic features (150 in positive and 191 in negative ionization mode) corresponding to 139 metabolites were detected. The benefit of fast polarity switching was evident, with 32 and 58 of these metabolites having exclusively been detected in the positive and negative modes, respectively. Moreover, for 19 of the remaining 49 phenylalanine-derived metabolites, the assignment of ion species and, thus, molecular weight was possible only by the use of complementary features of the two ion polarity modes. Statistical evaluation showed that treatment with DON increased or decreased the abundances of many detected metabolites. American Chemical Society 2014-11-05 2014-12-02 /pmc/articles/PMC4255957/ /pubmed/25372979 http://dx.doi.org/10.1021/ac503290j Text en Copyright © 2014 American Chemical Society This is an open access article published under a Creative Commons Attribution (CC-BY) License (http://pubs.acs.org/page/policy/authorchoice_ccby_termsofuse.html) , which permits unrestricted use, distribution and reproduction in any medium, provided the author and source are cited. |
spellingShingle | Kluger, Bernhard Bueschl, Christoph Neumann, Nora Stückler, Romana Doppler, Maria Chassy, Alexander W. Waterhouse, Andrew L. Rechthaler, Justyna Kampleitner, Niklas Thallinger, Gerhard G. Adam, Gerhard Krska, Rudolf Schuhmacher, Rainer Untargeted Profiling of Tracer-Derived Metabolites Using Stable Isotopic Labeling and Fast Polarity-Switching LC–ESI-HRMS |
title | Untargeted Profiling of Tracer-Derived Metabolites
Using Stable Isotopic Labeling and Fast Polarity-Switching LC–ESI-HRMS |
title_full | Untargeted Profiling of Tracer-Derived Metabolites
Using Stable Isotopic Labeling and Fast Polarity-Switching LC–ESI-HRMS |
title_fullStr | Untargeted Profiling of Tracer-Derived Metabolites
Using Stable Isotopic Labeling and Fast Polarity-Switching LC–ESI-HRMS |
title_full_unstemmed | Untargeted Profiling of Tracer-Derived Metabolites
Using Stable Isotopic Labeling and Fast Polarity-Switching LC–ESI-HRMS |
title_short | Untargeted Profiling of Tracer-Derived Metabolites
Using Stable Isotopic Labeling and Fast Polarity-Switching LC–ESI-HRMS |
title_sort | untargeted profiling of tracer-derived metabolites
using stable isotopic labeling and fast polarity-switching lc–esi-hrms |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4255957/ https://www.ncbi.nlm.nih.gov/pubmed/25372979 http://dx.doi.org/10.1021/ac503290j |
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