Accurate lipid quantification without fragmentation bias
Lipid quantification on the molecular species level is based on the abundance of specific fragments detected in MSn spectra of endogenous lipids and standards. As species-specific internal standards are commonly not feasible, lipids are quantified utilizing only one or few internal standard(s) per class. However, the analytical response of individual lipid species is determined by its lipid class and other structural elements like double bonds, hydroxy groups, and chain length. This tutorial covers developing and applying fragmentation models for sphingolipids and glycerophospholipids, aiming to overcome challenges in accurately quantifying these critical biomolecules. The determined response factors are based on experimental data and are independent of the employed instrumentation, collision energies, and internal standard(s).
N. Troppmair, K. Schuhmann, C. Coman
Dr. Kai Schuhmann is a Senior Scientist at Lipotype GmbH with an extensive expertise in analytical chemistry, mass spectrometry and lipidomics. He is passionate about tackling complex challenges and transforming them into innovative solutions. One area of interest is the accurate quantification of lipids from MS2 spectra and the application of machine learning to resolve fragmentation-related errors.
Lipotype, Dresden, Germany
Nina Troppmair is currently doing her PhD at the Institute of Analytical Chemistry, University of Vienna. The aim of her research projects is the development of lipidomics workflows to dissect the role of sphingolipids, with a pronounced focus on adipocyte differentiation. One of her ongoing projects, thus, involves determining correction models for accurate sphingolipid quantification.
University Vienna, Vienna, Austria
Dr. Cristina Coman finished her PhD in 2019 at the Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V., in Dortmund, Germany and is currently a postdoc at the Institute of Analytical Chemistry, University of Vienna. Her interests focus on developing and implementing lipid-centered multiomics strategies (from sample collection to MS lipid behavior) to increase the understanding of cardiac related metabolism and processes.
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