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Anal Chem. 2016 Sep 20;88(18):9004-13. doi: 10.1021/acs.analchem.6b01481. Epub 2016 Aug 26.

Development and Application of Ultra-Performance Liquid Chromatography-TOF MS for Precision Large Scale Urinary Metabolic Phenotyping.

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MRC-NIHR National Phenome Centre, Department of Surgery and Cancer, Imperial College London , IRDB Building, Du Cane Road, London W12 0NN, United Kingdom.
Division of Computational and Systems Medicine, Department of Surgery and Cancer, Imperial College London , Sir Alexander Fleming Building, Exhibition Road, South Kensington, London SW7 2AZ, United Kingdom.
Waters Corporation , Stamford Avenue, Altrincham Road, Wilmslow SK9 4AX, United Kingdom.
Department of Psychiatry, University of Oxford, Warneford Hospital , Oxford OX3 7JX, United Kingdom.
MRC-PHE Centre for Environment and Health, Department of Epidemiology and Biostatistics, Imperial College London , St Mary's Campus, Norfolk Place, London W2 1PG, United Kingdom.
Korrigan Sciences Ltd. , 38 Wakemans, Upper Basildon, Reading RG8 8JE, United Kingdom.


To better understand the molecular mechanisms underpinning physiological variation in human populations, metabolic phenotyping approaches are increasingly being applied to studies involving hundreds and thousands of biofluid samples. Hyphenated ultra-performance liquid chromatography-mass spectrometry (UPLC-MS) has become a fundamental tool for this purpose. However, the seemingly inevitable need to analyze large studies in multiple analytical batches for UPLC-MS analysis poses a challenge to data quality which has been recognized in the field. Herein, we describe in detail a fit-for-purpose UPLC-MS platform, method set, and sample analysis workflow, capable of sustained analysis on an industrial scale and allowing batch-free operation for large studies. Using complementary reversed-phase chromatography (RPC) and hydrophilic interaction liquid chromatography (HILIC) together with high resolution orthogonal acceleration time-of-flight mass spectrometry (oaTOF-MS), exceptional measurement precision is exemplified with independent epidemiological sample sets of approximately 650 and 1000 participant samples. Evaluation of molecular reference targets in repeated injections of pooled quality control (QC) samples distributed throughout each experiment demonstrates a mean retention time relative standard deviation (RSD) of <0.3% across all assays in both studies and a mean peak area RSD of <15% in the raw data. To more globally assess the quality of the profiling data, untargeted feature extraction was performed followed by data filtration according to feature intensity response to QC sample dilution. Analysis of the remaining features within the repeated QC sample measurements demonstrated median peak area RSD values of <20% for the RPC assays and <25% for the HILIC assays. These values represent the quality of the raw data, as no normalization or feature-specific intensity correction was applied. While the data in each experiment was acquired in a single continuous batch, instances of minor time-dependent intensity drift were observed, highlighting the utility of data correction techniques despite reducing the dependency on them for generating high quality data. These results demonstrate that the platform and methodology presented herein is fit-for-use in large scale metabolic phenotyping studies, challenging the assertion that such screening is inherently limited by batch effects. Details of the pipeline used to generate high quality raw data and mitigate the need for batch correction are provided.

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