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Quantitative mass spectrometry based lipidomic approaches for biomarker discovery studies
Authors: Holčapek Michal | Lísa Miroslav | Cífková Eva | Jirásko Robert
Year: 2015
Type of publication: ostatní - přednáška nebo poster
Page from-to: nestránkováno
Titles:
Language Name Abstract Keywords
eng Quantitative mass spectrometry based lipidomic approaches for biomarker discovery studies Lipidomics deals with the complex lipidomic characterization of dynamic biological systems. The development of new quantitative methods is important for a better coverage of lipids applicable for high-throughput analysis in the clinical analysis and lipid biomarker discovery. Shotgun ESI-MS/MS quantitatiion is based on the direct infusion of total lipid extracts containing internal standards per each quantified lipid class. The ion suppression effects could reduce the sensitivity for determination of trace lipids. LC/MS is useful for the chromatographic separation of isobaric lipids and their quantitation. MALDI-MS is typically used without the chromatographic separation, but it is less convenient for the quantitation due to the lower signal reproducibility. Three basic mass spectrometric platforms (shotgun MS, separation - MS and MALDI-MS) are used for the development of quantitative lipidomic assays. The shotgun approach is based on well-known characteristic neutral loss and precursor ion scans characteristic for individual lipid classes together with internal standards used for each lipid class. Two separation - MS approaches (HILIC-UHPLC and UHPSFC) are compared for the lipid class separation followed by their ESI-MS detection and quantitation using internal standards per each lipid class. HILIC is applicable only for several polar lipid classes with the analysis time of 20 min, while UHSFC enables the quantitation of up to 30 lipid classes within only 6 min. The great potential of UHPSFC/ESI-MS for high-throughput lipidomic quantitation will be demonstrated on clinical analyses of tumor vs. normal tissues, patients body fluids and cancer cell lines. The large data sets of several hundreds lipid species are statistically evaluated by multivariate data analysis. Lipids with the highest impact on the differentiation of tumor and normal tissues are identified and compared with previous works.

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