High-Throughput Microbore UPLC-MS Metabolic Phenotyping of Urine for Large-Scale Epidemiology Studies.
A new generation of metabolic phenotyping centers are being created to meet the increasing demands of personalized healthcare, and this has resulted in a major requirement for economical, high-throughput metabonomic analysis by liquid chromatography-mass spectrometry (LC-MS). Meeting these new demands represents an emerging bioanalytical problem that must be solved if metabolic phenotyping is to be successfully applied to large clinical and epidemiological sample sets. Ultraperformance (UP)LC-MS-based metabolic phenotyping, based on 2.1 mm i.d. LC columns, enables comprehensive metabolic phenotyping but, when employed for the analysis of thousands of samples, results in high solvent usage. The use of UPLC-MS employing 1 mm i.d. columns for metabolic phenotyping rather than the conventional 2.1 mm i.d. methodology shows that the resulting optimized microbore method provided equivalent or superior performance in terms of peak capacity, sensitivity, and robustness. On average, we also observed, when using the microbore scale separation, an increase in response of 2-3 fold over that obtained with the standard 2.1 mm scale method. When applied to the analysis of human urine, the 1 mm scale method showed no decline in performance over the course of 1000 analyses, illustrating that microbore UPLC-MS represents a viable alternative to conventional 2.1 mm i.d. formats for routine large-scale metabolic profiling studies while also resulting in a 75% reduction in solvent usage. The modest increase in sensitivity provided by this methodology also offers the potential to either reduce sample consumption or increase the number of metabolite features detected with confidence due to the increased signal-to-noise ratios obtained. Implementation of this miniaturized UPLC-MS method of metabolic phenotyping results in clear analytical, economic, and environmental benefits for large-scale metabolic profiling studies with similar or improved analytical performance compared to conventional UPLC-MS.
†Division of Computational and Systems Medicine, Department of Surgery and Cancer, South Kensington Campus, London, SW7 2AZ, United Kingdom.
Nicola
N
Gray
‡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.
Matthew R
MR
Lewis
†Division of Computational and Systems Medicine, Department of Surgery and Cancer, South Kensington Campus, London, SW7 2AZ, United Kingdom.
Robert S
RS
Plumb
†Division of Computational and Systems Medicine, Department of Surgery and Cancer, South Kensington Campus, London, SW7 2AZ, United Kingdom.
Ian D
ID
Wilson
†Division of Computational and Systems Medicine, Department of Surgery and Cancer, South Kensington Campus, 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
D002853:Chromatography, Liquid / Q000379:methods*; D016021:Epidemiologic Studies; D006801:Humans; D013058:Mass Spectrometry / Q000379:methods*; D010641:Phenotype; D015203:Reproducibility of Results; D014556:Urine
Metabolic phenotyping; UPLC−MS; microbore columns; miniaturization