| Literature DB >> 29708737 |
Evelyn Rampler1,2,3, Angela Criscuolo4,5, Martin Zeller4, Yasin El Abiead1, Harald Schoeny1, Gerrit Hermann1,6, Elena Sokol7, Ken Cook7, David A Peake8, Bernard Delanghe4, Gunda Koellensperger1,2,3.
Abstract
Lipid identification and quantification are essential objectives in comprehensive lipidomics studies challenged by the high number of lipids, their chemical diversity, and their dynamic range. In this work, we developed a tailored method for profiling and quantification combining (1) isotope dilution, (2) enhanced isomer separation by C30 fused-core reversed-phase material, and (3) parallel Orbitrap and ion trap detection by the Orbitrap Fusion Lumos Tribid mass spectrometer. The combination of parallelizable ion analysis without time loss together with different fragmentation techniques (HCD/CID) and an inclusion list led to higher quality in lipid identifications exemplified in human plasma and yeast samples. Moreover, we used lipidome isotope-labeling of yeast (LILY)-a fast and efficient in vivo labeling strategy in Pichia pastoris-to produce (nonradioactive) isotopically labeled eukaryotic lipid standards in yeast. We integrated the 13C lipids in the LC-MS workflow to enable relative and absolute compound-specific quantification in yeast and human plasma samples by isotope dilution. Label-free and compound-specific quantification was validated by comparison against a recent international interlaboratory study on human plasma SRM 1950. In this way, we were able to prove that LILY enabled quantification leads to accurate results, even in complex matrices. Excellent analytical figures of merit with enhanced trueness, precision and linearity over 4-5 orders of magnitude were observed applying compound-specific quantification with 13C-labeled lipids. We strongly believe that lipidomics studies will benefit from incorporating isotope dilution and LC-MSn strategies.Entities:
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Year: 2018 PMID: 29708737 DOI: 10.1021/acs.analchem.7b05382
Source DB: PubMed Journal: Anal Chem ISSN: 0003-2700 Impact factor: 6.986