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NMR metabolite quantification of a synthetic urine sample: an inter-laboratory comparison of processing workflows

hal.structure.identifierToxAlim [ToxAlim]
hal.structure.identifierMetaboHUB-MetaToul
hal.structure.identifierMetatoul AXIOM [E20 ]
dc.contributor.authorCANLET, Cécile
hal.structure.identifierBiologie du fruit et pathologie [BFP]
hal.structure.identifierPlateforme Bordeaux Metabolome
dc.contributor.authorDEBORDE, Catherine
hal.structure.identifierToulouse Biotechnology Institute [TBI]
hal.structure.identifierMetaboHUB-MetaToul
hal.structure.identifierMetaToul FluxoMet [TBI-MetaToul]
dc.contributor.authorCAHOREAU, Edern
hal.structure.identifierUnité de Recherche Œnologie [Villenave d'Ornon] [OENO]
hal.structure.identifierMetaboHUB-Bordeaux
hal.structure.identifierPlateforme Bordeaux Metabolome
dc.contributor.authorDA COSTA, Grégory
hal.structure.identifierToxAlim [ToxAlim]
hal.structure.identifierMetaboHUB-MetaToul
hal.structure.identifierMetatoul AXIOM [E20 ]
dc.contributor.authorGAUTIER, Roselyne
hal.structure.identifierBiologie du fruit et pathologie [BFP]
hal.structure.identifierPlateforme Bordeaux Metabolome
dc.contributor.authorJACOB, Daniel
hal.structure.identifierInstitut de Chimie de Clermont-Ferrand [ICCF]
hal.structure.identifierMetaboHUB-Clermont
hal.structure.identifierPlateforme Exploration du Métabolisme [PFEM]
dc.contributor.authorJOUSSE, Cyril
hal.structure.identifierToxAlim [ToxAlim]
hal.structure.identifierMetaboHUB-Bordeaux
hal.structure.identifierMetatoul AXIOM [E20 ]
dc.contributor.authorLACAZE, Mélia
hal.structure.identifierUnité de Recherche Œnologie [Villenave d'Ornon] [OENO]
hal.structure.identifierMetaboHUB-Bordeaux
hal.structure.identifierPlateforme Bordeaux Metabolome
dc.contributor.authorLE MAO, Inès
hal.structure.identifierCAPACITÉS SAS
hal.structure.identifierMagnetic resonance, Isotopomics, Metabolomics, Monitoring [MIMM]
dc.contributor.authorMARTINEAU, Estelle
hal.structure.identifierToulouse Biotechnology Institute [TBI]
hal.structure.identifierMetaboHUB-MetaToul
hal.structure.identifierMetaToul FluxoMet [TBI-MetaToul]
dc.contributor.authorPEYRIGA, Lindsay
hal.structure.identifierUnité de Recherche Œnologie [Villenave d'Ornon] [OENO]
hal.structure.identifierMetaboHUB-Bordeaux
hal.structure.identifierPlateforme Bordeaux Metabolome
dc.contributor.authorRICHARD, Tristan
hal.structure.identifierMagnetic resonance, Isotopomics, Metabolomics, Monitoring [MIMM]
dc.contributor.authorSILVESTRE, Virginie
hal.structure.identifierInstitut de Chimie de Clermont-Ferrand [ICCF]
hal.structure.identifierMetaboHUB-Clermont
hal.structure.identifierPlateforme Exploration du Métabolisme [PFEM]
dc.contributor.authorTRAÏKIA, Mounir
hal.structure.identifierBiologie du fruit et pathologie [BFP]
hal.structure.identifierPlateforme Bordeaux Metabolome
dc.contributor.authorMOING, Annick
hal.structure.identifierMagnetic resonance, Isotopomics, Metabolomics, Monitoring [MIMM]
dc.contributor.authorGIRAUDEAU, Patrick
dc.date.issued2023-07-07
dc.identifier.issn1573-3882
dc.description.abstractEnIntroduction Absolute quantification of individual metabolites in complex biological samples is crucial in targeted metabolomic profiling. Objectives An inter-laboratory test was performed to evaluate the impact of the NMR software, peak-area determination method (integration vs. deconvolution) and operator on quantification trueness and precision. Methods A synthetic urine containing 32 compounds was prepared. One site prepared the urine and calibration samples, and performed NMR acquisition. NMR spectra were acquired with two pulse sequences including water suppression used in routine analyses. The pre-processed spectra were sent to the other sites where each operator quantified the metabolites using internal referencing or external calibration, and his/her favourite in-house, open-access or commercial NMR tool. Results For 1D NMR measurements with solvent presaturation during the recovery delay (zgpr), 20 metabolites were successfully quantified by all processing strategies. Some metabolites could not be quantified by some methods. For internal referencing with TSP, only one half of the metabolites were quantified with a trueness below 5%. With peak integration and external calibration, about 90% of the metabolites were quantified with a trueness below 5%. The NMRProcFlow integration module allowed the quantification of several additional metabolites. The number of quantified metabolites and quantification trueness improved for some metabolites with deconvolution tools. Trueness and precision were not significantly different between zgpr- and NOESYpr-based spectra for about 70% of the variables. Conclusion External calibration performed better than TSP internal referencing. Inter-laboratory tests are useful when choosing to better rationalize the choice of quantification tools for NMR-based metabolomic profiling and confirm the value of spectra deconvolution tools.
dc.description.sponsorshipDéveloppement d'une infrastructure française distribuée pour la métabolomique dédiée à l'innovation
dc.language.isoen
dc.publisherSpringer Verlag
dc.rights.urihttp://creativecommons.org/licenses/by/
dc.subject.enDeconvolution
dc.subject.enQuantitative NMR
dc.subject.enSynthetic urine
dc.subject.enMetabolomic profiling
dc.subject.enPeak integration
dc.title.enNMR metabolite quantification of a synthetic urine sample: an inter-laboratory comparison of processing workflows
dc.typeArticle de revue
dc.identifier.doi10.1007/s11306-023-02028-4
dc.subject.halChimie/Chimie analytique
dc.subject.halSciences du Vivant [q-bio]
dc.description.sponsorshipEuropesummit
bordeaux.journalMetabolomics
bordeaux.page65
bordeaux.volume19
bordeaux.issue7
bordeaux.peerReviewedoui
hal.identifierhal-04155487
hal.version1
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-04155487v1
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