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Accurate mass spectral libraries and predicted liquid chromatography retention times in bioanalysis and data independent metabolomics

Defense Thèse de doctorat : Univ. Genève, 2016 - Sc. 4894 - 2016/02/12
Abstract Analytical workflows and tools were developed to identify low molecular weight compounds in complex samples by liquid chromatography mass spectrometry in metabolomics and bioanalysis. An in-silico strategy allowed the comprehensive screening for comedication interferences in quantitative bioanalysis with LC-MS in selection reaction monitoring (SRM) mode. For non-targeted metabolomics, data independent (SWATH) analysis has emerged as a powerful tool for comprehensive qualitative and quantitative data acquisition. An accurate mass spectral library was created for SWATH metabolomics analysis with emphasis on quality control. The use of composite fragment spectra over a large range of collision energies allowed to minimize experimental setting bias and to ensure high identification score. Predicted retention windows were applied if a good fragment match was not sufficient for metabolite identification. Multiplexed fragment spectra from coeluting precursor ions can be an issue in SWATH analysis. We evaluated the use of variable Q1 windows and observed an improvement in selectivity.
Keywords MetabolomicsMetabolitesHigh Resolution Mass SpectrometrySWATHMass Spectral LibraryLibraryDatabaseData Independent AnalysisSWATHSwathTUNERProteomicsLiquid ChromatographyRetention Time PredictionPredicted Retention Time WindowsBioanalysisComedicationsScreening StrategyBiofluidsUrinePlasma
URN: urn:nbn:ch:unige-809351
Note This thesis is about the building of an accurate mass spectral library and the use of predicted retention times in metabolomics based on data independent (SWATH) analysis
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Thesis (12.3 MB) - document accessible for UNIGE members only Limited access to UNIGE
Research group Spectrométrie de masse du vivant
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BRUDERER, Tobias. Accurate mass spectral libraries and predicted liquid chromatography retention times in bioanalysis and data independent metabolomics. Université de Genève. Thèse, 2016. doi: 10.13097/archive-ouverte/unige:80935 https://archive-ouverte.unige.ch/unige:80935

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Deposited on : 2016-02-24

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