Gray N.,South Kensington Campus |
Lewis M.R.,National Phenome Center |
Plumb R.S.,South Kensington Campus |
Wilson I.D.,South Kensington Campus |
And 2 more authors.
Journal of Proteome Research | Year: 2015
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. © 2015 American Chemical Society. Source
Chekmeneva E.,Imperial College London |
Correia G.,Imperial College London |
Denes J.,Imperial College London |
Gomez-Romero M.,Imperial College London |
And 15 more authors.
Analytical Methods | Year: 2015
An automated chip-based electrospray platform was used to develop a high-throughput nanoelectrospray high resolution mass spectrometry (nESI-HRMS) method for multiplexed parallel untargeted and targeted quantitative metabolic analysis of urine samples. The method was demonstrated to be suitable for metabolic analysis of large sample numbers and can be applied to large-scale epidemiological and stratified medicine studies. The method requires a small amount of sample (5 μL of injectable volume containing 250 nL of original sample), and the analysis time for each sample is three minutes per sample to acquire data in both negative and positive ion modes. Identification of metabolites was based on the high resolution accurate mass and tandem mass spectrometry using authentic standards. The method was validated for 8 targeted metabolites and was shown to be precise and accurate. The mean accuracy of individual measurements being 106% and the intra- and inter-day precision (expressed as relative standard deviations) were 9% and 14%, respectively. Selected metabolites were quantified by standard addition calibration using the stable isotope labelled internal standards in a pooled urine sample, to account for any matrix effect. The multiple point standard addition calibration curves yielded correlation coefficients greater than 0.99, and the linear dynamic range was more than three orders of magnitude. As a proof-of-concept the developed method was applied for targeted quantitative analysis of a set of 101 urine samples obtained from female participants with different pregnancy outcomes. In addition to the specifically targeted metabolites, several other metabolites were quantified relative to the internal standards. Based on the calculated concentrations, some metabolites showed significant differences according to different pregnancy outcomes. The acquired high resolution full-scan data were used for further untargeted fingerprinting and improved the differentiation of urine samples based on pregnancy outcome. © 2015 The Royal Society of Chemistry. Source