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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
Cite this: Anal. Chem. 2011, 83, 15, 5864–5872
Publication Date (Web):April 28, 2011
Copyright © 2011 American Chemical Society

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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

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    55. Kevin Robards, Danielle Ryan. Analyses. 2022, 399-451.
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    65. Kataneh Aalaei, Bekzod Khakimov, Cristian De Gobba, Lilia Ahrné. Digestion patterns of proteins in pasteurized and ultra-high temperature milk using in vitro gastric models of adult and elderly. Journal of Food Engineering 2021, 292 , 110305.
    66. Michiel Bongaerts, Ramon Bonte, Serwet Demirdas, Edwin Jacobs, Esmee Oussoren, Ans van der Ploeg, Margreet Wagenmakers, Robert Hofstra, Henk Blom, Marcel Reinders, George Ruijter. Using Out-of-Batch Reference Populations to Improve Untargeted Metabolomics for Screening Inborn Errors of Metabolism. Metabolites 2021, 11 (1) , 8.
    67. N. Penney, W. Barton, J. M. Posma, A. Darzi, G. Frost, P. D. Cotter, E. Holmes, F. Shanahan, O. O’Sullivan, I. Garcia-Perez, . Investigating the Role of Diet and Exercise in Gut Microbe-Host Cometabolism. mSystems 2020, 5 (6)
    68. Lee S. Nguyen, Edi Prifti, Farid Ichou, Monique Leban, Christian Funck-Brentano, Philippe Touraine, Joe-Elie Salem, Anne Bachelot. Effect of congenital adrenal hyperplasia treated by glucocorticoids on plasma metabolome: a machine-learning-based analysis. Scientific Reports 2020, 10 (1)
    69. Anastasia Tzimou, Stefanos Nikolaidis, Olga Begou, Aikaterina Siopi, Olga Deda, Ioannis Taitzoglou, Georgios Theodoridis, Vassilis Mougios. Effects of Aging, Long-Term and Lifelong Exercise on the Urinary Metabolic Footprint of Rats. Metabolites 2020, 10 (12) , 481.
    70. Miao Yu, Georgia Dolios, Vladimir Yong-Gonzalez, Olle Björkqvist, Elena Colicino, Jonas Halfvarson, Lauren Petrick. Untargeted metabolomics profiling and hemoglobin normalization for archived newborn dried blood spots from a refrigerated biorepository. Journal of Pharmaceutical and Biomedical Analysis 2020, 191 , 113574.
    71. Paula Cuevas-Delgado, Danuta Dudzik, Verónica Miguel, Santiago Lamas, Coral Barbas. Data-dependent normalization strategies for untargeted metabolomics—a case study. Analytical and Bioanalytical Chemistry 2020, 412 (24) , 6391-6405.
    72. Sandip Kumar Patel, Bhawana George, Vineeta Rai. Artificial Intelligence to Decode Cancer Mechanism: Beyond Patient Stratification for Precision Oncology. Frontiers in Pharmacology 2020, 11
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    74. Dorna Varshavi, Dorsa Varshavi, Nicola McCarthy, Kirill Veselkov, Hector C. Keun, Jeremy R. Everett. Metabolic characterization of colorectal cancer cells harbouring different KRAS mutations in codon 12, 13, 61 and 146 using human SW48 isogenic cell lines. Metabolomics 2020, 16 (4)
    75. Julian Pezzatti, Julien Boccard, Santiago Codesido, Yoric Gagnebin, Abhinav Joshi, Didier Picard, Víctor González-Ruiz, Serge Rudaz. Implementation of liquid chromatography–high resolution mass spectrometry methods for untargeted metabolomic analyses of biological samples: A tutorial. Analytica Chimica Acta 2020, 1105 , 28-44.
    76. Bekzod Khakimov, Nabiollah Mobaraki, Alessia Trimigno, Violetta Aru, Søren Balling Engelsen. Signature Mapping (SigMa): An efficient approach for processing complex human urine 1H NMR metabolomics data. Analytica Chimica Acta 2020, 1108 , 142-151.
    77. Jessica R. Allegretti, Zain Kassam, Benjamin H. Mullish, Austin Chiang, Madeline Carrellas, Jonathan Hurtado, Julian R. Marchesi, Julie A.K. McDonald, Alexandros Pechlivanis, Grace F. Barker, Jesús Miguéns Blanco, Isabel Garcia-Perez, Wing Fei Wong, Ylaine Gerardin, Michael Silverstein, Kevin Kennedy, Christopher Thompson. Effects of Fecal Microbiota Transplantation With Oral Capsules in Obese Patients. Clinical Gastroenterology and Hepatology 2020, 18 (4) , 855-863.e2.
    78. Julien Boccard, Serge Rudaz. Analysis of Metabolomics Data—A Chemometrics Perspective. 2020, 483-505.
    79. Julien Boccard, Víctor González-Ruiz, Santiago Codesido, Serge Rudaz. Mass spectrometry metabolomic data handling for biomarker discovery. 2020, 369-388.
    80. Tyler Cook, Yinfa Ma, Sanjeewa Gamagedara. Evaluation of statistical techniques to normalize mass spectrometry-based urinary metabolomics data. Journal of Pharmaceutical and Biomedical Analysis 2020, 177 , 112854.
    81. Malena Manzi, Gabriel Riquelme, Nicolás Zabalegui, María Eugenia Monge. Improving diagnosis of genitourinary cancers: Biomarker discovery strategies through mass spectrometry-based metabolomics. Journal of Pharmaceutical and Biomedical Analysis 2020, 178 , 112905.
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    83. Xiaohui Wei, Nan Chen, Bin Tang, Xuan Luo, Weiwei You, Caihuan Ke. Untargeted metabolomic analysis of the carotenoid-based orange coloration in Haliotis gigantea using GC-TOF-MS. Scientific Reports 2019, 9 (1)
    84. Pierre Zalloua, Hanane Kadar, Essa Hariri, Layal Abi Farraj, Francois Brial, Lyamine Hedjazi, Aurelie Le Lay, Alexandre Colleu, Justine Dubus, David Touboul, Fumihiko Matsuda, Mark Lathrop, Jeremy K. Nicholson, Marc-Emmanuel Dumas, Dominique Gauguier. Untargeted Mass Spectrometry Lipidomics identifies correlation between serum sphingomyelins and plasma cholesterol. Lipids in Health and Disease 2019, 18 (1)
    85. Jeremy R. Ash, Melaine A. Kuenemann, Daniel Rotroff, Alison Motsinger-Reif, Denis Fourches. Cheminformatics approach to exploring and modeling trait-associated metabolite profiles. Journal of Cheminformatics 2019, 11 (1)
    86. Kei Zaitsu, Saki Noda, Tomomi Ohara, Tasuku Murata, Shinji Funatsu, Koretsugu Ogata, Akira Ishii, Akira Iguchi. Optimal inter-batch normalization method for GC/MS/MS-based targeted metabolomics with special attention to centrifugal concentration. Analytical and Bioanalytical Chemistry 2019, 411 (26) , 6983-6994.
    87. Kristian Pirttilä, Pernilla Videhult Pierre, Jakob Haglöf, Mikael Engskog, Mikael Hedeland, Göran Laurell, Torbjörn Arvidsson, Curt Pettersson. An LCMS-based untargeted metabolomics protocol for cochlear perilymph: highlighting metabolic effects of hydrogen gas on the inner ear of noise exposed Guinea pigs. Metabolomics 2019, 15 (10)
    88. Federica Murgia, Ambra Iuculano, Cristina Peddes, Maria Laura Santoru, Laura Tronci, Monica Deiana, Luigi Atzori, Giovanni Monni. Metabolic fingerprinting of chorionic villous samples in normal pregnancy and chromosomal disorders. Prenatal Diagnosis 2019, 39 (10) , 848-858.
    89. Munirah Alsaleh, Thomas A. Barbera, Ross H. Andrews, Paiboon Sithithaworn, Narong Khuntikeo, Watcharin Loilome, Puangrat Yongvanit, Isobel J. Cox, Richard R.A. Syms, Elaine Holmes, Simon D. Taylor–Robinson. Mass Spectrometry: A Guide for the Clinician. Journal of Clinical and Experimental Hepatology 2019, 9 (5) , 597-606.
    90. Kui Deng, Fan Zhang, Qilong Tan, Yue Huang, Wei Song, Zhiwei Rong, Zheng-Jiang Zhu, Kang Li, Zhenzi Li. WaveICA: A novel algorithm to remove batch effects for large-scale untargeted metabolomics data based on wavelet analysis. Analytica Chimica Acta 2019, 1061 , 60-69.
    91. Jessica R. Allegretti, Zain Kassam, Madeline Carrellas, Benjamin H. Mullish, Julian R. Marchesi, Alexandros Pechlivanis, Mark Smith, Ylaine Gerardin, Sonia Timberlake, Daniel S. Pratt, Joshua R. Korzenik. Fecal Microbiota Transplantation in Patients With Primary Sclerosing Cholangitis: A Pilot Clinical Trial. American Journal of Gastroenterology 2019, 114 (7) , 1071-1079.
    92. Tanya Monaghan, Benjamin H Mullish, Jordan Patterson, Gane KS Wong, Julian R Marchesi, Huiping Xu, Tahseen Jilani, Dina Kao. Effective fecal microbiota transplantation for recurrent Clostridioides difficile infection in humans is associated with increased signalling in the bile acid-farnesoid X receptor-fibroblast growth factor pathway. Gut Microbes 2019, 10 (2) , 142-148.
    93. Lucía Olmo‐García, Karin Wendt, Nikolas Kessler, Aadil Bajoub, Alberto Fernández‐Gutiérrez, Carsten Baessmann, Alegría Carrasco‐Pancorbo. Exploring the Capability of LC‐MS and GC‐MS Multi‐Class Methods to Discriminate Virgin Olive Oils from Different Geographical Indications and to Identify Potential Origin Markers. European Journal of Lipid Science and Technology 2019, 121 (3)
    94. Simon Moosmang, Maria Pitscheider, Sonja Sturm, Christoph Seger, Herbert Tilg, Maria Halabalaki, Hermann Stuppner. Metabolomic analysis—Addressing NMR and LC-MS related problems in human feces sample preparation. Clinica Chimica Acta 2019, 489 , 169-176.
    95. Marynka M. Ulaszewska, Christoph H. Weinert, Alessia Trimigno, Reto Portmann, Cristina Andres Lacueva, René Badertscher, Lorraine Brennan, Carl Brunius, Achim Bub, Francesco Capozzi, Marta Cialiè Rosso, Chiara E. Cordero, Hannelore Daniel, Stéphanie Durand, Bjoern Egert, Paola G. Ferrario, Edith J.M. Feskens, Pietro Franceschi, Mar Garcia‐Aloy, Franck Giacomoni, Pieter Giesbertz, Raúl González‐Domínguez, Kati Hanhineva, Lieselot Y. Hemeryck, Joachim Kopka, Sabine E. Kulling, Rafael Llorach, Claudine Manach, Fulvio Mattivi, Carole Migné, Linda H. Münger, Beate Ott, Gianfranco Picone, Grégory Pimentel, Estelle Pujos‐Guillot, Samantha Riccadonna, Manuela J. Rist, Caroline Rombouts, Josep Rubert, Thomas Skurk, Pedapati S. C. Sri Harsha, Lieven Van Meulebroek, Lynn Vanhaecke, Rosa Vázquez‐Fresno, David Wishart, Guy Vergères. Nutrimetabolomics: An Integrative Action for Metabolomic Analyses in Human Nutritional Studies. Molecular Nutrition & Food Research 2019, 63 (1)
    96. Joshua Heinemann. Machine Learning in Untargeted Metabolomics Experiments. 2019, 287-299.
    97. David P. Marciano, Michael P. Snyder. Personalized Metabolomics. 2019, 447-456.
    98. Dieter Galea, Ivan Laponogov, Kirill Veselkov. Data-Driven Visualizations in Metabolic Phenotyping. 2019, 309-328.
    99. Rui Climaco Pinto, Ibrahim Karaman, Julia C. Fussell, Evangelos Evangelou, Frank J. Kelly, Paul Elliott, Ioanna Tzoulaki. Applications of Metabolic Phenotyping in Epidemiology. 2019, 491-534.
    100. Marina Wright Muelas, Fernando Ortega, Rainer Breitling, Claus Bendtsen, Hans V. Westerhoff. Rational cell culture optimization enhances experimental reproducibility in cancer cells. Scientific Reports 2018, 8 (1)
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