Development of a Rapid Microbore Metabolic Profiling Ultraperformance Liquid Chromatography-Mass Spectrometry Approach for High-Throughput Phenotyping Studies.
A rapid gradient microbore ultraperformance liquid chromatography-mass spectrometry (UPLC-MS) method has been developed to provide a high-throughput analytical platform for the metabolic phenotyping of urine from large sample cohorts. The rapid microbore metabolic profiling (RAMMP) approach was based on scaling a conventional reversed-phase UPLC-MS method for urinary profiling from 2.1 mm × 100 mm columns to 1 mm × 50 mm columns, increasing the linear velocity of the solvent, and decreasing the gradient time to provide an analysis time of 2.5 min/sample. Comparison showed that conventional UPLC-MS and rapid gradient approaches provided peak capacities of 150 and 50, respectively, with the conventional method detecting approximately 19 000 features compared to the ∼6 000 found using the rapid gradient method. Similar levels of repeatability were seen for both methods. Despite the reduced peak capacity and the reduction in ions detected, the RAMMP method was able to achieve similar levels of group discrimination as conventional UPLC-MS when applied to rat urine samples obtained from investigative studies on the effects of acute 2-bromophenol and chronic acetaminophen administration. When compared to a direct infusion MS method of similar analysis time the RAMMP method provided superior selectivity. The RAMMP approach provides a robust and sensitive method that is well suited to high-throughput metabonomic analysis of complex mixtures such as urine combined with a 5-fold reduction in analysis time compared with the conventional UPLC-MS method.
Division of Computational and Systems Medicine, Department of Surgery and Cancer, Imperial College London , Exhibition Road, South Kensington, London SW7 2AZ, United Kingdom.
Nicola
N
Gray
Division of Computational and Systems Medicine, Department of Surgery and Cancer, Imperial College London , Exhibition Road, South Kensington, London SW7 2AZ, United Kingdom.
Kyrillos
K
Adesina-Georgiadis
Division of Computational and Systems Medicine, Department of Surgery and Cancer, Imperial College London , Exhibition Road, South Kensington, London SW7 2AZ, United Kingdom.
Elena
E
Chekmeneva
Division of Computational and Systems Medicine, Department of Surgery and Cancer, Imperial College London , Exhibition Road, South Kensington, London SW7 2AZ, United Kingdom.
Robert S
RS
Plumb
Division of Computational and Systems Medicine, Department of Surgery and Cancer, Imperial College London , Exhibition Road, South Kensington, London SW7 2AZ, United Kingdom.
Ian D
ID
Wilson
Division of Computational and Systems Medicine, Department of Surgery and Cancer, Imperial College London , Exhibition Road, South Kensington, London SW7 2AZ, United Kingdom.|MRC-NIHR National Phenome Centre, Division of Computational and Systems Medicine, Department of Surgery and Cancer, IRDB Building, Imperial College London, Hammersmith Hospital , London, W12 0NN, United Kingdom.
Jeremy K
JK
Nicholson
D000082:Acetaminophen / Q000378:metabolism / Q000652:urine*; D000818:Animals; D002851:Chromatography, High Pressure Liquid; D057166:High-Throughput Screening Assays; D013058:Mass Spectrometry; D055442:Metabolome; D055432:Metabolomics; D010636:Phenols / Q000378:metabolism / Q000652:urine*; D010641:Phenotype; D051381:Rats
D010636:Phenols; D000082:Acetaminophen; C032038:2-bromophenol