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High-Precision Automated Workflow for Urinary Untargeted Metabolomic Epidemiology

[Image: see text] Urine is a noninvasive biofluid that is rich in polar metabolites and well suited for metabolomic epidemiology. However, because of individual variability in health and hydration status, the physiological concentration of urine can differ >15-fold, which can pose major challenge...

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Autores principales: Meister, Isabel, Zhang, Pei, Sinha, Anirban, Sköld, C. Magnus, Wheelock, Åsa M., Izumi, Takashi, Chaleckis, Romanas, Wheelock, Craig E.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Chemical Society 2021
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8041248/
https://www.ncbi.nlm.nih.gov/pubmed/33739820
http://dx.doi.org/10.1021/acs.analchem.1c00203
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author Meister, Isabel
Zhang, Pei
Sinha, Anirban
Sköld, C. Magnus
Wheelock, Åsa M.
Izumi, Takashi
Chaleckis, Romanas
Wheelock, Craig E.
author_facet Meister, Isabel
Zhang, Pei
Sinha, Anirban
Sköld, C. Magnus
Wheelock, Åsa M.
Izumi, Takashi
Chaleckis, Romanas
Wheelock, Craig E.
author_sort Meister, Isabel
collection PubMed
description [Image: see text] Urine is a noninvasive biofluid that is rich in polar metabolites and well suited for metabolomic epidemiology. However, because of individual variability in health and hydration status, the physiological concentration of urine can differ >15-fold, which can pose major challenges in untargeted liquid chromatography–mass spectrometry (LC–MS) metabolomics. Although numerous urine normalization methods have been implemented (e.g., creatinine, specific gravity—SG), most are manual and, therefore, not practical for population-based studies. To address this issue, we developed a method to measure SG in 96-well-plates using a refractive index detector (RID), which exhibited accuracy within 85–115% and <3.4% precision. Bland–Altman statistics showed a mean deviation of −0.0001 SG units (limits of agreement: −0.0014 to 0.0011) relative to a hand-held refractometer. Using this RID-based SG normalization, we developed an automated LC–MS workflow for untargeted urinary metabolomics in a 96-well-plate format. The workflow uses positive and negative ionization HILIC chromatography and acquires mass spectra in data-independent acquisition (DIA) mode at three collision energies. Five technical internal standards (tISs) were used to monitor data quality in each method, all of which demonstrated raw coefficients of variation (CVs) < 10% in the quality controls (QCs) and < 20% in the samples for a small cohort (n = 87 urine samples, n = 22 QCs). Application in a large cohort (n = 842 urine samples, n = 248 QCs) demonstrated CV(QC) < 5% and CV(samples) < 16% for 4/5 tISs after signal drift correction by cubic spline regression. The workflow identified >540 urinary metabolites including endogenous and exogenous compounds. This platform is suitable for performing urinary untargeted metabolomic epidemiology and will be useful for applications in population-based molecular phenotyping.
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spelling pubmed-80412482021-04-13 High-Precision Automated Workflow for Urinary Untargeted Metabolomic Epidemiology Meister, Isabel Zhang, Pei Sinha, Anirban Sköld, C. Magnus Wheelock, Åsa M. Izumi, Takashi Chaleckis, Romanas Wheelock, Craig E. Anal Chem [Image: see text] Urine is a noninvasive biofluid that is rich in polar metabolites and well suited for metabolomic epidemiology. However, because of individual variability in health and hydration status, the physiological concentration of urine can differ >15-fold, which can pose major challenges in untargeted liquid chromatography–mass spectrometry (LC–MS) metabolomics. Although numerous urine normalization methods have been implemented (e.g., creatinine, specific gravity—SG), most are manual and, therefore, not practical for population-based studies. To address this issue, we developed a method to measure SG in 96-well-plates using a refractive index detector (RID), which exhibited accuracy within 85–115% and <3.4% precision. Bland–Altman statistics showed a mean deviation of −0.0001 SG units (limits of agreement: −0.0014 to 0.0011) relative to a hand-held refractometer. Using this RID-based SG normalization, we developed an automated LC–MS workflow for untargeted urinary metabolomics in a 96-well-plate format. The workflow uses positive and negative ionization HILIC chromatography and acquires mass spectra in data-independent acquisition (DIA) mode at three collision energies. Five technical internal standards (tISs) were used to monitor data quality in each method, all of which demonstrated raw coefficients of variation (CVs) < 10% in the quality controls (QCs) and < 20% in the samples for a small cohort (n = 87 urine samples, n = 22 QCs). Application in a large cohort (n = 842 urine samples, n = 248 QCs) demonstrated CV(QC) < 5% and CV(samples) < 16% for 4/5 tISs after signal drift correction by cubic spline regression. The workflow identified >540 urinary metabolites including endogenous and exogenous compounds. This platform is suitable for performing urinary untargeted metabolomic epidemiology and will be useful for applications in population-based molecular phenotyping. American Chemical Society 2021-03-19 2021-03-30 /pmc/articles/PMC8041248/ /pubmed/33739820 http://dx.doi.org/10.1021/acs.analchem.1c00203 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 Meister, Isabel
Zhang, Pei
Sinha, Anirban
Sköld, C. Magnus
Wheelock, Åsa M.
Izumi, Takashi
Chaleckis, Romanas
Wheelock, Craig E.
High-Precision Automated Workflow for Urinary Untargeted Metabolomic Epidemiology
title High-Precision Automated Workflow for Urinary Untargeted Metabolomic Epidemiology
title_full High-Precision Automated Workflow for Urinary Untargeted Metabolomic Epidemiology
title_fullStr High-Precision Automated Workflow for Urinary Untargeted Metabolomic Epidemiology
title_full_unstemmed High-Precision Automated Workflow for Urinary Untargeted Metabolomic Epidemiology
title_short High-Precision Automated Workflow for Urinary Untargeted Metabolomic Epidemiology
title_sort high-precision automated workflow for urinary untargeted metabolomic epidemiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8041248/
https://www.ncbi.nlm.nih.gov/pubmed/33739820
http://dx.doi.org/10.1021/acs.analchem.1c00203
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