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.

doi:10.3390/ht8010002

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Low-Field, Benchtop NMR Spectroscopy as a Potential Tool for Point-of-Care Diagnostics of Metabolic Conditions: Validation, Protocols and Computational Models

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Leicester School of Pharmacy, De Montfort University, The Gateway, Leicester LE1 9BH, UK

Department of Chemical Sciences, University of Huddersfield, Queensgate, Huddersfield HD1 3DH, UK

Greater Manchester NHS Trust, Stepping Hill Hospital, Poplar Grove, Hazel Grove, Stockport SK2 7JE, UK

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Magritek GmbH, Philipsstraße 8, 52068 Aachen, Germany

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Department of Electrical and Electronic Engineering, University of Nottingham, University Park, Nottingham NG7 2RD, UK

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Department of Chemistry, University of Loughborough, Epinal Way, Loughborough LE11 3TU, UK

Author to whom correspondence should be addressed.

High-Throughput 2019, 8(1), 2; https://doi.org/10.3390/ht8010002

Received: 4 October 2018 / Revised: 13 December 2018 / Accepted: 13 December 2018 / Published: 27 December 2018

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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. View Full-Text

Keywords: metabolomics; benchtop 60 MHz NMR analysis; biomarkers; biomolecules; validation; protocol; diabetes

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MDPI and ACS Style

Percival, B.C.; Grootveld, M.; Gibson, M.; Osman, Y.; Molinari, M.; Jafari, F.; Sahota, T.; Martin, M.; Casanova, F.; Mather, M.L.; Edgar, M.; Masania, J.; Wilson, P.B. Low-Field, Benchtop NMR Spectroscopy as a Potential Tool for Point-of-Care Diagnostics of Metabolic Conditions: Validation, Protocols and Computational Models. High-Throughput 2019, 8, 2. https://doi.org/10.3390/ht8010002

AMA Style

Percival BC, Grootveld M, Gibson M, Osman Y, Molinari M, Jafari F, Sahota T, Martin M, Casanova F, Mather ML, Edgar M, Masania J, Wilson PB. Low-Field, Benchtop NMR Spectroscopy as a Potential Tool for Point-of-Care Diagnostics of Metabolic Conditions: Validation, Protocols and Computational Models. High-Throughput. 2019; 8(1):2. https://doi.org/10.3390/ht8010002

Chicago/Turabian Style

Percival, Benita C., Martin Grootveld, Miles Gibson, Yasan Osman, Marco Molinari, Fereshteh Jafari, Tarsem Sahota, Mark Martin, Federico Casanova, Melissa L. Mather, Mark Edgar, Jinit Masania, and Philippe B. Wilson. 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, no. 1: 2. https://doi.org/10.3390/ht8010002

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Low-Field, Benchtop NMR Spectroscopy as a Potential Tool for Point-of-Care Diagnostics of Metabolic Conditions: Validation, Protocols and Computational Models

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