Bookedited by Daniel Raftery, University of Washington and Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
Contents:
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.