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Data Processing Optimization in Untargeted Metabolomics of Urine Using Voigt Lineshape Model Non-Linear Regression Analysis

Nuclear magnetic resonance (NMR) spectroscopy is well-established to address questions in large-scale untargeted metabolomics. Although several approaches in data processing and analysis are available, significant issues remain. NMR spectroscopy of urine generates information-rich but complex spectr...

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Autores principales: Haslauer, Kristina E., Schmitt-Kopplin, Philippe, Heinzmann, Silke S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8145719/
https://www.ncbi.nlm.nih.gov/pubmed/33947160
http://dx.doi.org/10.3390/metabo11050285
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author Haslauer, Kristina E.
Schmitt-Kopplin, Philippe
Heinzmann, Silke S.
author_facet Haslauer, Kristina E.
Schmitt-Kopplin, Philippe
Heinzmann, Silke S.
author_sort Haslauer, Kristina E.
collection PubMed
description Nuclear magnetic resonance (NMR) spectroscopy is well-established to address questions in large-scale untargeted metabolomics. Although several approaches in data processing and analysis are available, significant issues remain. NMR spectroscopy of urine generates information-rich but complex spectra in which signals often overlap. Furthermore, slight changes in pH and salt concentrations cause peak shifting, which introduces, in combination with baseline irregularities, un-informative noise in statistical analysis. Within this work, a straight-forward data processing tool addresses these problems by applying a non-linear curve fitting model based on Voigt function line shape and integration of the underlying peak areas. This method allows a rapid untargeted analysis of urine metabolomics datasets without relying on time-consuming 2D-spectra based deconvolution or information from spectral libraries. The approach is validated with spiking experiments and tested on a human urine (1)H dataset compared to conventionally used methods and aims to facilitate metabolomics data analysis.
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spelling pubmed-81457192021-05-26 Data Processing Optimization in Untargeted Metabolomics of Urine Using Voigt Lineshape Model Non-Linear Regression Analysis Haslauer, Kristina E. Schmitt-Kopplin, Philippe Heinzmann, Silke S. Metabolites Article Nuclear magnetic resonance (NMR) spectroscopy is well-established to address questions in large-scale untargeted metabolomics. Although several approaches in data processing and analysis are available, significant issues remain. NMR spectroscopy of urine generates information-rich but complex spectra in which signals often overlap. Furthermore, slight changes in pH and salt concentrations cause peak shifting, which introduces, in combination with baseline irregularities, un-informative noise in statistical analysis. Within this work, a straight-forward data processing tool addresses these problems by applying a non-linear curve fitting model based on Voigt function line shape and integration of the underlying peak areas. This method allows a rapid untargeted analysis of urine metabolomics datasets without relying on time-consuming 2D-spectra based deconvolution or information from spectral libraries. The approach is validated with spiking experiments and tested on a human urine (1)H dataset compared to conventionally used methods and aims to facilitate metabolomics data analysis. MDPI 2021-04-29 /pmc/articles/PMC8145719/ /pubmed/33947160 http://dx.doi.org/10.3390/metabo11050285 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Haslauer, Kristina E.
Schmitt-Kopplin, Philippe
Heinzmann, Silke S.
Data Processing Optimization in Untargeted Metabolomics of Urine Using Voigt Lineshape Model Non-Linear Regression Analysis
title Data Processing Optimization in Untargeted Metabolomics of Urine Using Voigt Lineshape Model Non-Linear Regression Analysis
title_full Data Processing Optimization in Untargeted Metabolomics of Urine Using Voigt Lineshape Model Non-Linear Regression Analysis
title_fullStr Data Processing Optimization in Untargeted Metabolomics of Urine Using Voigt Lineshape Model Non-Linear Regression Analysis
title_full_unstemmed Data Processing Optimization in Untargeted Metabolomics of Urine Using Voigt Lineshape Model Non-Linear Regression Analysis
title_short Data Processing Optimization in Untargeted Metabolomics of Urine Using Voigt Lineshape Model Non-Linear Regression Analysis
title_sort data processing optimization in untargeted metabolomics of urine using voigt lineshape model non-linear regression analysis
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8145719/
https://www.ncbi.nlm.nih.gov/pubmed/33947160
http://dx.doi.org/10.3390/metabo11050285
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