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UHPSFC/MS and UHPLC/MS High-throughput Quantitation of Blood Lipids of Cancer Patient
Authors: Holčapek Michal | Wolrab Denise | Chocholoušková Michaela | Jirásko Robert | Peterka Ondřej
Year: 2019
Type of publication: ostatní - přednáška nebo poster
Page from-to: nestránkováno
Titles:
Language Name Abstract Keywords
eng UHPSFC/MS and UHPLC/MS High-throughput Quantitation of Blood Lipids of Cancer Patient Blood lipidome exhibits a large complexity of several hundreds reported lipid species in eight lipid categories [1]. The lipidomic composition of human body fluids can be altered for some diseases, e.g., cancer [2]. The robust and high-throughput mass spectrometry (MS) based analytical methods are needed for the potential use of these altered lipids for early detection of such diseases. We use the coupling of the lipid class separation and mass spectrometry for the lipidomic quantitation, mainly ultrahigh-performance supercritical fluid chromatography – mass spectrometry (UHPSFC/MS) [3] or ultrahigh-performance supercritical fluid chromatography – mass spectrometry (UHPSFC/MS) [4]. Multiple exogenous internal standards (IS) for each lipid class and multiple quality controls improve the robustness of the quantitation. The first step is careful optimization of the whole methodology including preanalytical phase, sample extraction, MS analysis, data processing, and statistical evaluation [5]. The full validation of both methods is performed in line with FDA and EMA guidelines for bioanalytical validations and recommendations of Lipidomics Standards Initiative [6]. The optimized and validated methods are then applied for the standard reference material NIST 1950 human plasma, and concentrations are compared with published data in the literature [7]. Finally, the application of our methodology to high-throughput quantitation of plasma or serum lipids of cancer patients and healthy volunteers provides the molar quantitation of about 300 – 400 lipid species. Multivariate data analysis methods, such as non-supervised principal component analysis (PCA) and supervised orthogonal partial least square discriminant analysis (OPLS-DA), are applied for building the statistical models to differentiate cancer patients and healthy controls with over 90% accuracy for samples with known classification and also for blinded samples. This work was supported by project No. 18-12204S sponso

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