A typical lipidomics approach aims at the simultaneous analysis of a multitude of lipid species from different lipid classes with highest possible sensitivity for all target lipids. Efficient extraction of lipids from the biological matrix is a crucial step in the analytical workflow. Whereas numerous applications of classical and more recently published extraction methods have been reported for blood serum or plasma samples, very little is known about the applicability of these methods for cerebrospinal fluid (CSF). CSF though represents a highly interesting biofluid for the investigation of neurological disorders, such as Alzheimer’s disease, Parkinson’s disease, multiple sclerosis, amyotrophic lateral sclerosis, or brain cancer. Since CSF comprises substantially lower endogenous lipid concentrations compared to serum or plasma, the use of highly efficient extraction methods is of utmost importance. In addition, literature on lipid extraction methods is often inconsistent in terms of methodological parameters like temperature, mixing times, or the number of repeated extraction cycles. In this study, four liquid-liquid extraction methods (Folch, Bligh & Dyer, MTBE and BUME) and one protein precipitation method (MMC method) were evaluated using a pooled CSF sample, followed by the investigation of key process parameters (temperature and mixing times) and modifications of the most promising methods, in order to achieve a broad coverage of lipid classes as well as high recoveries and repeatabilities. A modified Folch method turned out as most suitable for the efficient extraction of a broad range of lipid classes from CSF including glycerophospholipids, glycerolipids and sphingolipids. In addition, using cooled solvents and equipment was shown to significantly improve lipid extraction efficiencies. Mixing times should be thoroughly optimized for the lipid classes of interest in order to achieve high recoveries without lipid degradation due to unnecessarily long mixing. Finally, acidification led to improved extraction efficiency for acidic glycerophospholipids.
Copyright © 2020 The Authors. Published by Elsevier B.V. All rights reserved.

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