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A Computationally Lightweight Algorithm for Deriving Reliable Metabolite Panel Measurements from 1D (1)H NMR
[Image: see text] Small Molecule Enhancement SpectroscopY (SMolESY) was employed to develop a unique and fully automated computational solution for the assignment and integration of (1)H nuclear magnetic resonance (NMR) signals from metabolites in challenging matrices containing macromolecules (here...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Chemical
Society
2021
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8041249/ https://www.ncbi.nlm.nih.gov/pubmed/33733737 http://dx.doi.org/10.1021/acs.analchem.1c00113 |
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author | Takis, Panteleimon G. Jiménez, Beatriz Al-Saffar, Nada M. S. Harvey, Nikita Chekmeneva, Elena Misra, Shivani Lewis, Matthew R. |
author_facet | Takis, Panteleimon G. Jiménez, Beatriz Al-Saffar, Nada M. S. Harvey, Nikita Chekmeneva, Elena Misra, Shivani Lewis, Matthew R. |
author_sort | Takis, Panteleimon G. |
collection | PubMed |
description | [Image: see text] Small Molecule Enhancement SpectroscopY (SMolESY) was employed to develop a unique and fully automated computational solution for the assignment and integration of (1)H nuclear magnetic resonance (NMR) signals from metabolites in challenging matrices containing macromolecules (herein blood products). Sensitive and reliable quantitation is provided by instant signal deconvolution and straightforward integration bolstered by spectral resolution enhancement and macromolecular signal suppression. The approach is highly efficient, requiring only standard one-dimensional (1)H NMR spectra and avoiding the need for sample preprocessing, complex deconvolution, and spectral baseline fitting. The performance of the algorithm, developed using >4000 NMR serum and plasma spectra, was evaluated using an additional >8800 spectra, yielding an assignment accuracy greater than 99.5% for all 22 metabolites targeted. Further validation of its quantitation capabilities illustrated a reliable performance among challenging phenotypes. The simplicity and complete automation of the approach support the application of NMR-based metabolite panel measurements in clinical and population screening applications. |
format | Online Article Text |
id | pubmed-8041249 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | American Chemical
Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-80412492021-04-13 A Computationally Lightweight Algorithm for Deriving Reliable Metabolite Panel Measurements from 1D (1)H NMR Takis, Panteleimon G. Jiménez, Beatriz Al-Saffar, Nada M. S. Harvey, Nikita Chekmeneva, Elena Misra, Shivani Lewis, Matthew R. Anal Chem [Image: see text] Small Molecule Enhancement SpectroscopY (SMolESY) was employed to develop a unique and fully automated computational solution for the assignment and integration of (1)H nuclear magnetic resonance (NMR) signals from metabolites in challenging matrices containing macromolecules (herein blood products). Sensitive and reliable quantitation is provided by instant signal deconvolution and straightforward integration bolstered by spectral resolution enhancement and macromolecular signal suppression. The approach is highly efficient, requiring only standard one-dimensional (1)H NMR spectra and avoiding the need for sample preprocessing, complex deconvolution, and spectral baseline fitting. The performance of the algorithm, developed using >4000 NMR serum and plasma spectra, was evaluated using an additional >8800 spectra, yielding an assignment accuracy greater than 99.5% for all 22 metabolites targeted. Further validation of its quantitation capabilities illustrated a reliable performance among challenging phenotypes. The simplicity and complete automation of the approach support the application of NMR-based metabolite panel measurements in clinical and population screening applications. American Chemical Society 2021-03-18 2021-03-30 /pmc/articles/PMC8041249/ /pubmed/33733737 http://dx.doi.org/10.1021/acs.analchem.1c00113 Text en © 2021 The Authors. Published by American Chemical Society Permits the broadest form of re-use including for commercial purposes, provided that author attribution and integrity are maintained (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Takis, Panteleimon G. Jiménez, Beatriz Al-Saffar, Nada M. S. Harvey, Nikita Chekmeneva, Elena Misra, Shivani Lewis, Matthew R. A Computationally Lightweight Algorithm for Deriving Reliable Metabolite Panel Measurements from 1D (1)H NMR |
title | A Computationally Lightweight Algorithm for Deriving
Reliable Metabolite Panel Measurements from 1D (1)H NMR |
title_full | A Computationally Lightweight Algorithm for Deriving
Reliable Metabolite Panel Measurements from 1D (1)H NMR |
title_fullStr | A Computationally Lightweight Algorithm for Deriving
Reliable Metabolite Panel Measurements from 1D (1)H NMR |
title_full_unstemmed | A Computationally Lightweight Algorithm for Deriving
Reliable Metabolite Panel Measurements from 1D (1)H NMR |
title_short | A Computationally Lightweight Algorithm for Deriving
Reliable Metabolite Panel Measurements from 1D (1)H NMR |
title_sort | computationally lightweight algorithm for deriving
reliable metabolite panel measurements from 1d (1)h nmr |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8041249/ https://www.ncbi.nlm.nih.gov/pubmed/33733737 http://dx.doi.org/10.1021/acs.analchem.1c00113 |
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