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Molecular characterization of the lipidome by mass spectrometry


Ejsing,  Christer S.
Max Planck Institute of Molecular Cell Biology and Genetics, Max Planck Society;

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Ejsing, C. S. (2007). Molecular characterization of the lipidome by mass spectrometry. PhD Thesis, Technische Universität Dresden - Dresden.

Cite as: https://hdl.handle.net/21.11116/0000-0001-0EEC-7
Cells, whether bacterial, fungal or mammalian, are all equipped with metabolic pathways capable of producing an assortment of structurally and functionally distinct lipid species. Despite the structural diversity of lipids being recognized and correlated to specific cellular phenomena and disease states, the molecular mechanisms that underpin this structural diversity remain poorly understood. In part, this is due to the lack of adequate analytical techniques capable of measuring the structural details of lipid species in a direct, comprehensive and quantitative manner. The aim of my thesis study was to establish methodology for automated and quantitative analysis of molecular lipid species based on mass spectrometry. From this work a novel high-throughput methodology for lipidome analysis emerged. The main assets of the methodology were the structure-specific mass analysis by powerful hybrid mass spectrometers with high mass resolution, automated and sensitive infusion of total lipid extracts by a nanoelectrospray robot, and automated spectral deconvolution by dedicated Lipid Profiler software. The comprehensive characterization and quantification of molecular lipid species was achieved by spiking total lipid extracts with unique lipid standards, utilizing selective ionization conditions for sample infusion, and performing structure-specific mass analysis by hybrid quadrupole time-of-flight and ion trap mass spectrometry. The analytical routine allowed the comprehensive characterization and quantification of molecular glycerophospholipid species, molecular diacylglycerol species, molecular sphingolipid species including ceramides, glycosphingolipids and inositolcontaining sphingolipids, and sterol lipids including cholesterol. The performance of the methodology was validated by comparing its dynamic quantification range to that of established methodology based on triple quandrupole mass spectrometry. Furthermore, its efficacy for lipidomics projects was demonstrated by the successful quantitative deciphering of the lipid composition of T cell receptor signaling domains, mammalian tissues including heart, brain and red blood cells, and the yeast Saccharomyces cerevisiae.