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Autonomous Metabolomics for Rapid Metabolite Identification in Global Profiling
[Image: see text] An autonomous metabolomic workflow combining mass spectrometry analysis with tandem mass spectrometry data acquisition was designed to allow for simultaneous data processing and metabolite characterization. Although previously tandem mass spectrometry data have been generated on th...
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/PMC4303330/ https://www.ncbi.nlm.nih.gov/pubmed/25496351 http://dx.doi.org/10.1021/ac5025649 |
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author | Benton, H. Paul Ivanisevic, Julijana Mahieu, Nathaniel G. Kurczy, Michael E. Johnson, Caroline H. Franco, Lauren Rinehart, Duane Valentine, Elizabeth Gowda, Harsha Ubhi, Baljit K. Tautenhahn, Ralf Gieschen, Andrew Fields, Matthew W. Patti, Gary J. Siuzdak, Gary |
author_facet | Benton, H. Paul Ivanisevic, Julijana Mahieu, Nathaniel G. Kurczy, Michael E. Johnson, Caroline H. Franco, Lauren Rinehart, Duane Valentine, Elizabeth Gowda, Harsha Ubhi, Baljit K. Tautenhahn, Ralf Gieschen, Andrew Fields, Matthew W. Patti, Gary J. Siuzdak, Gary |
author_sort | Benton, H. Paul |
collection | PubMed |
description | [Image: see text] An autonomous metabolomic workflow combining mass spectrometry analysis with tandem mass spectrometry data acquisition was designed to allow for simultaneous data processing and metabolite characterization. Although previously tandem mass spectrometry data have been generated on the fly, the experiments described herein combine this technology with the bioinformatic resources of XCMS and METLIN. As a result of this unique integration, we can analyze large profiling datasets and simultaneously obtain structural identifications. Validation of the workflow on bacterial samples allowed the profiling on the order of a thousand metabolite features with simultaneous tandem mass spectra data acquisition. The tandem mass spectrometry data acquisition enabled automatic search and matching against the METLIN tandem mass spectrometry database, shortening the current workflow from days to hours. Overall, the autonomous approach to untargeted metabolomics provides an efficient means of metabolomic profiling, and will ultimately allow the more rapid integration of comparative analyses, metabolite identification, and data analysis at a systems biology level. |
format | Online Article Text |
id | pubmed-4303330 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | American Chemical
Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-43033302015-12-12 Autonomous Metabolomics for Rapid Metabolite Identification in Global Profiling Benton, H. Paul Ivanisevic, Julijana Mahieu, Nathaniel G. Kurczy, Michael E. Johnson, Caroline H. Franco, Lauren Rinehart, Duane Valentine, Elizabeth Gowda, Harsha Ubhi, Baljit K. Tautenhahn, Ralf Gieschen, Andrew Fields, Matthew W. Patti, Gary J. Siuzdak, Gary Anal Chem [Image: see text] An autonomous metabolomic workflow combining mass spectrometry analysis with tandem mass spectrometry data acquisition was designed to allow for simultaneous data processing and metabolite characterization. Although previously tandem mass spectrometry data have been generated on the fly, the experiments described herein combine this technology with the bioinformatic resources of XCMS and METLIN. As a result of this unique integration, we can analyze large profiling datasets and simultaneously obtain structural identifications. Validation of the workflow on bacterial samples allowed the profiling on the order of a thousand metabolite features with simultaneous tandem mass spectra data acquisition. The tandem mass spectrometry data acquisition enabled automatic search and matching against the METLIN tandem mass spectrometry database, shortening the current workflow from days to hours. Overall, the autonomous approach to untargeted metabolomics provides an efficient means of metabolomic profiling, and will ultimately allow the more rapid integration of comparative analyses, metabolite identification, and data analysis at a systems biology level. American Chemical Society 2014-12-12 2015-01-20 /pmc/articles/PMC4303330/ /pubmed/25496351 http://dx.doi.org/10.1021/ac5025649 Text en Copyright © 2014 American Chemical Society This is an open access article published under an ACS AuthorChoice License (http://pubs.acs.org/page/policy/authorchoice_termsofuse.html) , which permits copying and redistribution of the article or any adaptations for non-commercial purposes. |
spellingShingle | Benton, H. Paul Ivanisevic, Julijana Mahieu, Nathaniel G. Kurczy, Michael E. Johnson, Caroline H. Franco, Lauren Rinehart, Duane Valentine, Elizabeth Gowda, Harsha Ubhi, Baljit K. Tautenhahn, Ralf Gieschen, Andrew Fields, Matthew W. Patti, Gary J. Siuzdak, Gary Autonomous Metabolomics for Rapid Metabolite Identification in Global Profiling |
title | Autonomous Metabolomics for Rapid Metabolite Identification
in Global Profiling |
title_full | Autonomous Metabolomics for Rapid Metabolite Identification
in Global Profiling |
title_fullStr | Autonomous Metabolomics for Rapid Metabolite Identification
in Global Profiling |
title_full_unstemmed | Autonomous Metabolomics for Rapid Metabolite Identification
in Global Profiling |
title_short | Autonomous Metabolomics for Rapid Metabolite Identification
in Global Profiling |
title_sort | autonomous metabolomics for rapid metabolite identification
in global profiling |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4303330/ https://www.ncbi.nlm.nih.gov/pubmed/25496351 http://dx.doi.org/10.1021/ac5025649 |
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