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

INTRODUCTION: 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) an...

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Detalles Bibliográficos
Autores principales: Canlet, Cécile, Deborde, Catherine, Cahoreau, Edern, Da Costa, Grégory, Gautier, Roselyne, Jacob, Daniel, Jousse, Cyril, Lacaze, Mélia, Le Mao, Inès, Martineau, Estelle, Peyriga, Lindsay, Richard, Tristan, Silvestre, Virginie, Traïkia, Mounir, Moing, Annick, Giraudeau, Patrick
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10328857/
https://www.ncbi.nlm.nih.gov/pubmed/37418094
http://dx.doi.org/10.1007/s11306-023-02028-4
Descripción
Sumario:INTRODUCTION: 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11306-023-02028-4.