Low-field, benchtop NMR spectroscopy as a potential tool for point-of-care diagnostics of metabolic conditions: Validation, protocols and computational models

Percival, Benita C.; Grootveld, Martin; Gibson, Miles; Osman, Yasan; Molinari, Marco; Jafari, Fereshteh; Sahota, Tarsem; Martin, Mark; Casanova, Federico; Mather, Melissa L.; Edgar, Mark; Masania, Jinit; Wilson, Philippe B.

Abstract

Novel sensing technologies for liquid biopsies offer promising prospects for the early detection of metabolic conditions through omics techniques. Indeed, high-field nuclear magnetic resonance (NMR) facilities are routinely used for metabolomics investigations on a range of biofluids in order to rapidly recognise unusual metabolic patterns in patients suffering from a range of diseases. However, these techniques are restricted by the prohibitively large size and cost of such facilities, suggesting a possible role for smaller, low-field NMR instruments in biofluid analysis. Herein we describe selected biomolecule validation on a low-field benchtop NMR spectrometer (60 MHz), and present an associated protocol for the analysis of biofluids on compact NMR instruments. We successfully detect common markers of diabetic control at low-to-medium concentrations through optimised experiments, including α-glucose (≤2.8 mmol/L) and acetone (25 µmol/L), and additionally in readily accessible biofluids, particularly human urine. We present a combined protocol for the analysis of these biofluids with low-field NMR spectrometers for metabolomics applications, and offer a perspective on the future of this technique appealing to ‘point-of-care’ applications.

Citation

Percival, B. C., Grootveld, M., Gibson, M., Osman, Y., Molinari, M., Jafari, F., …Wilson, P. B. (2019). Low-field, benchtop NMR spectroscopy as a potential tool for point-of-care diagnostics of metabolic conditions: Validation, protocols and computational models. High-Throughput, 8(1), Article 2. https://doi.org/10.3390/ht8010002

• Journal Article Type: Article
• Acceptance Date: Dec 13, 2018
• Online Publication Date: Dec 27, 2018
• Publication Date: Jan 1, 2019
• Deposit Date: Jan 10, 2019
• Publicly Available Date: Jan 11, 2019
• Journal: High-Throughput
• Print ISSN: 2571-5135
• Publisher: MDPI
• Peer Reviewed: Peer Reviewed
• Volume: 8
• Issue: 1
• Article Number: 2
• DOI: https://doi.org/10.3390/ht8010002
• Public URL: https://nottingham-repository.worktribe.com/output/1463138
• Publisher URL: https://www.mdpi.com/2571-5135/8/1/2