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  • Book
    edited by Daniel Raftery, University of Washington and Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
    Digital : Springer2014
    Overview of mass spectrometry-based metabolomics : opportunities and challenges -- Global metabolic profiling using ultra-performance liquid chromatography/quadrupole time-of-flight mass spectrometry -- LC-MS profiling to link metabolic and phenotypic diversity in plant mapping populations -- Mitochondrial metabolomics using high-resolution Fourier-transform mass spectrometry -- Sample preparation methods for LC-MS-based global aqueous metabolite profiling -- Methods of discovery-based and targeted metabolite analysis by comprehensive two-dimensional gas chromatography with time-of-flight mass spectrometry detection -- Analysis of mouse liver metabolites by GC x GC-TOF MS -- Metabolite fingerprinting by capillary electrophoresis-mass spectrometry -- Quantitative metabolomic profiling using dansylation isotope labeling and liquid chromatography mass spectrometry -- Quantitative analysis of amino and organic acids by methyl chloroformate derivatization and GC-MS/MS analysis -- Stable isotope-labeled tracers for metabolic pathway elucidation by GC-MS and FT-MS -- Multiplexed, quantitative, and targeted metabolite profiling by LC-MS/MRM -- Multidimensional mass spectrometry-based shotgun lipidomics -- Comprehensive quantitative determination of PUFA-related bioactive lipids for functional lipidomics using high-resolution mass spectrometry -- Ultra-performance liquid chromatography-mass spectrometry targeted profiling of bile acids : application to serum, liver tissue, and cultured cells of different species -- Analysis of volatile organic compounds in exhaled breath by gas chromatography-mass spectrometry combined with chemometric analysis -- Headspace SPME-GC-MS metabolomics analysis of urinary volatile organic compounds (VOCs) -- Metabolite profiling by direct analysis in real-time mass spectrometry -- Analysis of dried blood spots using DESI mass spectrometry -- DESI-MS imaging of lipids and metabolites from biological samples -- Metabolic imaging using nanostructure-initiator mass spectrometry (NIMS) -- Statistical analysis and modeling of mass spectrometry-based metabolomics data.