University of PardubiceFaculty of Chemical Technology

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Lipidomic Analysis in Cancer Biomarker Research

Authors: Holčapek Michal | Wolrab Denise | Jirásko Robert | Cífková Eva | Peterka Ondřej | Chocholoušková Michaela | Brabcová Ivana | Vrána David | Melichar Bohuslav | Hrstka Roman

Year: 2018

Type of publication: ostatní - přednáška nebo poster

Page from-to: nestránkováno

Titles:

Language	Name	Abstract	Keywords
eng	Lipidomic Analysis in Cancer Biomarker Research	The state-of-art in the lipidomic analysis will be sumarized to provide the overview of chromatographic separation modes and mass spectrometry (MS) based methods for the qualitative analysis of lipids, and the quantitative MS approaches for high-throughput clinical workflows [1]. Major challenges in terms of widely accepted best practices for lipidomic analysis, nomenclature, and standards for data reporting will be discussed as well in line with the goals of recently announced the Lipidomic Standards Initiative [2]. The dysregulation of lipids in eukaryotic cells is related to serious diseases, such as cancer. In our laboratory, the following three MS based methods are mainly used for the high-throughput clinical lipidomic quantitation: A/ shotgun ESI-MS using Q-LIT mass spectrometer, B/ ultrahigh-performance supercritical fluid chromatography – mass spectrometry (UHPSFC/MS) [3], and C/ matrix-assisted laser desorption/ionization (MALDI) - high-resolution Orbitrap [4]. Shotgun MS and UHPSFC/MS techniques are applied for generic screening of glycerophospholipids, sphingolipids, and glycerolipids in positive-ion electrospray ionization (ESI) mode, while negative-ion MALDI provides complementary information on anionic lipids, e.g., sulfatides. All methods are fully validated in line with FDA and EMA recommendations for high-throughput clinical quantitation, such as tumor and surrounding normal tissues [5, 6], body fluids from cancer patients and healthy volunteers. All mentioned methods follow the basic rule of reliable lipidomic quantitation that IS should be coionized with analytes from the same lipid subclass. Our software LipidQuant is used for the semi-automated data processing, and then concentrations of lipids are statistically evaluated using multivariate data analysis (MDA) methods. This methodology has been applied to cancer research and the differentiation of healthy and cancer groups based on the lipidomic analysis of over thousand people. This work was su	Lipidomic; Cancer; Biomarker;