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High-throughput Lipidomic Quantitation of Biological Samples

Autoři: Holčapek Michal | Wolrab Denise | Jirásko Robert | Chocholoušková Michaela | Peterka Ondřej

Rok: 2019

Druh publikace: ostatní - přednáška nebo poster

Strana od-do: nestránkováno

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Jazyk	Název	Abstrakt	Klíčová slova
eng	High-throughput Lipidomic Quantitation of Biological Samples	A large diversity of lipids is found in eukaryotic cells, where they fulfill important physiological functions. The dysregulation of lipids is often related to serious diseases, e.g., various types of cancer. The robust, high-throughput, and validated analytical methods for the lipidomic quantitation can be applied for biomarker discovery research and also for monitoring the progress of disease therapy. Mass spectrometry (MS) and its coupling with the liquid-phase separation techniques together with exogenous internal standards is the most common approach for the lipidomic quantitation [1]. We have developed the following MS based methods for the high-throughput clinical lipidomic quantitation: 1/ shotgun ESI-MS using characteristic neutral loss (NL) and precursor ion (PI) scans on QqQ or Q-LIT mass spectrometers [2], 2/ ultrahigh-performance supercritical fluid chromatography – mass spectrometry (UHPSFC/MS) [2], 3/ ultrahigh-performance liquid chromatography – mass spectrometry (UHPLC/MS) [3], and 3/ matrix-assisted laser desorption/ionization (MALDI) coupled to high-resolution Orbitrap mass analyzer [4]. Shotgun MS, UHPSFC/MS, and UHPLC/MS techniques are applied mainly for glycerophospholipids, sphingolipids, and glycerolipids using positive-ion electrospray ionization (ESI), while MALDI is used in the negative-ion mode to obtain complementary information on sulfatides and other anionic lipid subclasses. About 300 – 500 lipid species are typically quantified in studied biological samples from over 1000 human subjects, mainly plasma or serum from healthy volunteers and cancer patients. All mentioned methods follow the basic rule of reliable lipidomic quantitation that IS should be coionized with analytes from the same lipid subclass. Finally, multivariate data analysis (MDA) methods, such as nonsupervised principal component analysis (PCA), supervised orthogonal partial least square discriminant analysis (OPLS-DA), are applied for building the statistical models to

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