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Optimized Preprocessing of Ultra-Performance Liquid Chromatography/Mass Spectrometry Urinary Metabolic Profiles for Improved Information Recovery

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† Biomolecular Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, Sir Alexander Fleming Building, South Kensington, London SW7 2AZ, United Kingdom
‡ Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland 97239, United States
§ Technologie Servier, 27 Rue Eugène Vignat, Orleans 45000, France
∥ Department of Chemistry, Princeton University, Princeton, New Jersey 08544-1014, United States
E-mail: [email protected]
Cite this: Anal. Chem. 2011, 83, 15, 5864–5872
Publication Date (Web):April 28, 2011
https://doi.org/10.1021/ac201065j
Copyright © 2011 American Chemical Society
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    Abstract

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    Ultra-performance liquid chromatography coupled to mass spectrometry (UPLC/MS) has been used increasingly for measuring changes of low molecular weight metabolites in biofluids/tissues in response to biological challenges such as drug toxicity and disease processes. Typically samples show high variability in concentration, and the derived metabolic profiles have a heteroscedastic noise structure characterized by increasing variance as a function of increased signal intensity. These sources of experimental and instrumental noise substantially complicate information recovery when statistical tools are used. We apply and compare several preprocessing procedures and introduce a statistical error model to account for these bioanalytical complexities. In particular, the use of total intensity, median fold change, locally weighted scatter plot smoothing, and quantile normalizations to reduce extraneous variance induced by sample dilution were compared. We demonstrate that the UPLC/MS peak intensities of urine samples should respond linearly to variable sample dilution across the intensity range. While all four studied normalization methods performed reasonably well in reducing dilution-induced variation of urine samples in the absence of biological variation, the median fold change normalization is least compromised by the biologically relevant changes in mixture components and is thus preferable. Additionally, the application of a subsequent log-based transformation was successful in stabilizing the variance with respect to peak intensity, confirming the predominant influence of multiplicative noise in peak intensities from UPLC/MS-derived metabolic profile data sets. We demonstrate that variance-stabilizing transformation and normalization are critical preprocessing steps that can benefit greatly metabolic information recovery from such data sets when widely applied chemometric methods are used.

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    Two figures, showing box plots of log 2-fold change in peak intensities after various normalization techniques (LOESS, MFC, TI, and QN) and the impact of variance-stabilizing normalizations (LOESS, TI, and QN) on metabolic information recovery via PCA. This material is available free of charge via the Internet at http://pubs.acs.org.

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