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Ion Mobility-Derived Collision Cross Section As an Additional Measure for Lipid Fingerprinting and Identification
Author(s) -
Giuseppe Paglia,
Peggi M. Angel,
Jonathan P. Williams,
Keith Richardson,
Hernando J. Olivos,
J. Will Thompson,
Lochana C. Menikarachchi,
Steven Lai,
Callee M. Walsh,
Arthur Moseley,
Robert S. Plumb,
David F. Grant,
Bernhard Ø. Palsson,
James Langridge,
Scott Geromanos,
Giuseppe Astarita
Publication year - 2014
Publication title -
analytical chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.117
H-Index - 332
eISSN - 1520-6882
pISSN - 0003-2700
DOI - 10.1021/ac503715v
Subject(s) - chemistry , lipidomics , ion mobility spectrometry , mass spectrometry , isobaric process , reproducibility , chromatography , identification (biology) , analytical chemistry (journal) , biochemistry , physics , botany , biology , thermodynamics
Despite recent advances in analytical and computational chemistry, lipid identification remains a significant challenge in lipidomics. Ion-mobility spectrometry provides an accurate measure of the molecules' rotationally averaged collision cross-section (CCS) in the gas phase and is thus related to ionic shape. Here, we investigate the use of CCS as a highly specific molecular descriptor for identifying lipids in biological samples. Using traveling wave ion mobility mass spectrometry (MS), we measured the CCS values of over 200 lipids within multiple chemical classes. CCS values derived from ion mobility were not affected by instrument settings or chromatographic conditions, and they were highly reproducible on instruments located in independent laboratories (interlaboratory RSD < 3% for 98% of molecules). CCS values were used as additional molecular descriptors to identify brain lipids using a variety of traditional lipidomic approaches. The addition of CCS improved the reproducibility of analysis in a liquid chromatography-MS workflow and maximized the separation of isobaric species and the signal-to-noise ratio in direct-MS analyses (e.g., "shotgun" lipidomics and MS imaging). These results indicate that adding CCS to databases and lipidomics workflows increases the specificity and selectivity of analysis, thus improving the confidence in lipid identification compared to traditional analytical approaches. The CCS/accurate-mass database described here is made publicly available.

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