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Top-Down Lipidomic Screens by Multivariate Analysis of High-Resolution Survey Mass Spectra

MPG-Autoren
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Schwudke,  Dominik
Max Planck Institute of Molecular Cell Biology and Genetics, Max Planck Society;

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Hannich,  J. Thomas
Max Planck Institute of Molecular Cell Biology and Genetics, Max Planck Society;

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Surendranath,  Vineeth
Max Planck Institute of Molecular Cell Biology and Genetics, Max Planck Society;

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Grimard,  Vinciane
Max Planck Institute of Molecular Cell Biology and Genetics, Max Planck Society;

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Kurzchalia,  Teymuras
Max Planck Institute of Molecular Cell Biology and Genetics, Max Planck Society;

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Shevchenko,  Andrej
Max Planck Institute of Molecular Cell Biology and Genetics, Max Planck Society;

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Zitation

Schwudke, D., Hannich, J. T., Surendranath, V., Grimard, V., Moehring, T., Burton, L., et al. (2007). Top-Down Lipidomic Screens by Multivariate Analysis of High-Resolution Survey Mass Spectra. Analytical Chemistry (Washington), 79(11), 4083-4093.


Zitierlink: http://hdl.handle.net/21.11116/0000-0001-0FC5-1
Zusammenfassung
Direct profiling of total lipid extracts on a hybrid LTQ Orbitrap mass spectrometer by high-resolution survey spectra clusters species of 11 major lipid classes into 7 groups, which are distinguished by their sum compositions and could be identified by accurately determined masses. Rapid acquisition of survey spectra was employed as a “top-down” screening tool that, together with the computational method of principal component analysis, revealed pronounced perturbations in the abundance of lipid precursors within the entire series of experiments. Altered lipid precursors were subsequently identified either by accurately determined masses or by in-depth MS/MS characterization that was performed on the same instrument. Hence, the sensitivity, throughput and robustness of lipidomics screens were improved without compromising the accuracy and specificity of molecular species identification. The top-down lipidomics strategy lends itself for high-throughput screens complementing ongoing functional genomics efforts.