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(13)C NMR Metabolomics: Applications at Natural Abundance

[Image: see text] (13)C NMR has many advantages for a metabolomics study, including a large spectral dispersion, narrow singlets at natural abundance, and a direct measure of the backbone structures of metabolites. However, it has not had widespread use because of its relatively low sensitivity comp...

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Autores principales: Clendinen, Chaevien S., Lee-McMullen, Brittany, Williams, Caroline M., Stupp, Gregory S., Vandenborne, Krista, Hahn, Daniel A., Walter, Glenn A., Edison, Arthur S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Chemical Society 2014
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4165451/
https://www.ncbi.nlm.nih.gov/pubmed/25140385
http://dx.doi.org/10.1021/ac502346h
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author Clendinen, Chaevien S.
Lee-McMullen, Brittany
Williams, Caroline M.
Stupp, Gregory S.
Vandenborne, Krista
Hahn, Daniel A.
Walter, Glenn A.
Edison, Arthur S.
author_facet Clendinen, Chaevien S.
Lee-McMullen, Brittany
Williams, Caroline M.
Stupp, Gregory S.
Vandenborne, Krista
Hahn, Daniel A.
Walter, Glenn A.
Edison, Arthur S.
author_sort Clendinen, Chaevien S.
collection PubMed
description [Image: see text] (13)C NMR has many advantages for a metabolomics study, including a large spectral dispersion, narrow singlets at natural abundance, and a direct measure of the backbone structures of metabolites. However, it has not had widespread use because of its relatively low sensitivity compounded by low natural abundance. Here we demonstrate the utility of high-quality (13)C NMR spectra obtained using a custom (13)C-optimized probe on metabolomic mixtures. A workflow was developed to use statistical correlations between replicate 1D (13)C and (1)H spectra, leading to composite spin systems that can be used to search publicly available databases for compound identification. This was developed using synthetic mixtures and then applied to two biological samples, Drosophila melanogaster extracts and mouse serum. Using the synthetic mixtures we were able to obtain useful (13)C–(13)C statistical correlations from metabolites with as little as 60 nmol of material. The lower limit of (13)C NMR detection under our experimental conditions is approximately 40 nmol, slightly lower than the requirement for statistical analysis. The (13)C and (1)H data together led to 15 matches in the database compared to just 7 using (1)H alone, and the (13)C correlated peak lists had far fewer false positives than the (1)H generated lists. In addition, the (13)C 1D data provided improved metabolite identification and separation of biologically distinct groups using multivariate statistical analysis in the D. melanogaster extracts and mouse serum.
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spelling pubmed-41654512015-08-20 (13)C NMR Metabolomics: Applications at Natural Abundance Clendinen, Chaevien S. Lee-McMullen, Brittany Williams, Caroline M. Stupp, Gregory S. Vandenborne, Krista Hahn, Daniel A. Walter, Glenn A. Edison, Arthur S. Anal Chem [Image: see text] (13)C NMR has many advantages for a metabolomics study, including a large spectral dispersion, narrow singlets at natural abundance, and a direct measure of the backbone structures of metabolites. However, it has not had widespread use because of its relatively low sensitivity compounded by low natural abundance. Here we demonstrate the utility of high-quality (13)C NMR spectra obtained using a custom (13)C-optimized probe on metabolomic mixtures. A workflow was developed to use statistical correlations between replicate 1D (13)C and (1)H spectra, leading to composite spin systems that can be used to search publicly available databases for compound identification. This was developed using synthetic mixtures and then applied to two biological samples, Drosophila melanogaster extracts and mouse serum. Using the synthetic mixtures we were able to obtain useful (13)C–(13)C statistical correlations from metabolites with as little as 60 nmol of material. The lower limit of (13)C NMR detection under our experimental conditions is approximately 40 nmol, slightly lower than the requirement for statistical analysis. The (13)C and (1)H data together led to 15 matches in the database compared to just 7 using (1)H alone, and the (13)C correlated peak lists had far fewer false positives than the (1)H generated lists. In addition, the (13)C 1D data provided improved metabolite identification and separation of biologically distinct groups using multivariate statistical analysis in the D. melanogaster extracts and mouse serum. American Chemical Society 2014-08-20 2014-09-16 /pmc/articles/PMC4165451/ /pubmed/25140385 http://dx.doi.org/10.1021/ac502346h Text en Copyright © 2014 American Chemical Society Terms of Use (http://pubs.acs.org/page/policy/authorchoice_termsofuse.html)
spellingShingle Clendinen, Chaevien S.
Lee-McMullen, Brittany
Williams, Caroline M.
Stupp, Gregory S.
Vandenborne, Krista
Hahn, Daniel A.
Walter, Glenn A.
Edison, Arthur S.
(13)C NMR Metabolomics: Applications at Natural Abundance
title (13)C NMR Metabolomics: Applications at Natural Abundance
title_full (13)C NMR Metabolomics: Applications at Natural Abundance
title_fullStr (13)C NMR Metabolomics: Applications at Natural Abundance
title_full_unstemmed (13)C NMR Metabolomics: Applications at Natural Abundance
title_short (13)C NMR Metabolomics: Applications at Natural Abundance
title_sort (13)c nmr metabolomics: applications at natural abundance
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4165451/
https://www.ncbi.nlm.nih.gov/pubmed/25140385
http://dx.doi.org/10.1021/ac502346h
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