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Published on: July 31, 2021
Quantitative Nuclear Magnetic Resonance for Small Biological Molecules in Complex Mixtures: Practical Guidelines and
Eva Drevet Mulard1,2, Véronique Gilard3, Stéphane Balayssac3
1Institute of Chemistry and Biochemistry (ICBMS), UMR 5246, CNRS, University Lyon, F-69622 Villeurbanne, France.
Abstract:
Nuclear magnetic resonance (NMR) spectroscopy is a powerful analytical approach that enables both the structural determination and precise quantification of small molecules, such as metabolites. However, achieving precise quantification with NMR involves more than simply comparing integrals derived from NMR peaks to a concentration reference; quantitative NMR (qNMR) is a distinct and specialized application within the field. To obtain absolute quantitative results, spectra must be acquired under strict experimental conditions. Unfortunately, these acquisition parameters can be challenging to implement experimentally and often require trade-offs that compromise high throughput or practicality. In such situations, alternative strategies based on relative quantification and advanced software tools offer valuable solutions. This review aims to provide non-specialists with the key concepts and methodologies required for accurate NMR-based quantification in biomedical research, focusing on practical guidelines and experimental considerations. Unlike prior reviews, it prioritizes accessibility and practical implementation for researchers outside the field, emphasizing key experimental workflows and applications in biological and clinical studies. It clarifies the distinctions between absolute and relative concentration determinations and emphasizes the critical importance of sample preparation, pulse sequence selection, and rigorous control of experimental parameters. Recent technological advancements, such as high-field spectrometers and cryoprobes, have significantly enhanced the sensitivity and accuracy of NMR, enabling the reliable detection of low-concentration metabolites. Quantitative NMR thus offers critical potential in elucidating metabolic processes, supporting drug development, and aiding disease diagnosis.
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