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The accurate measurement of the lipidome permits insights into physiological and pathological processes. Of the present high-throughput technologies, LC-MS/MS especially bears potential of monitoring quantitative changes in hundreds of lipids simultaneously. In order to extract valuable information from huge amount of mass spectrometry data, the aid of automated, reliable highly sensitive and specific analysis algorithms is indispensable.

We present here a novel approach for for the identification of structural information of lipids. We achieve automated and reliable annotation of lipid species and their molecular structures in high-throughput data from chromatography-coupled tandem mass spectrometry using decision rule sets embedded in Lipid Data Analyzer. Using various low- and high-resolution mass spectrometry instruments with several collision energies, we proved the method's platform independence. We propose that the software's reliability, flexibility, and ability to identify novel lipid molecular species may now render current state-of-the-art lipid libraries obsolete.

For MS1 data, reliability is provided by two major innovations: 1) a 3D algorithm that confines the peak borders in m/z and time direction and 2) the use of the theoretical isotopic distribution of an analyte as selection/exclusion criterion. The presented algorithm has been applied to data from a controlled experiment and to biological data, containing analytes distributed over an intensity range of 10^6. Our approach shows improved sensitivity and an extremely high positive predictive value compared to existing methods. Consequently, this application is a valuable improvement in the high-throughput analysis of lipids.

1: Hartler J*, Triebl A*, Ziegl A, Trötzmüller M, Rechberger GN, Zeleznik OA,
Zierler KA, Torta F, Cazenave-Gassiot A, Wenk MR, Fauland A, Wheelock CE,
Armando AM, Quehenberger O, Zhang Q, Wakelam MJO, Haemmerle G,
Spener F, Köfeler HC, Thallinger GG.
Deciphering lipid structures based on platform-independent decision rules.
Nature Methods. 2017;14(12):1171-1174.
2: Hartler J, Trötzmüller M, Chitraju C, Spener F, Köfeler HC, Thallinger GG.
Lipid Data Analyzer: unattended identification and quantitation of lipids in
LC-MS data. Bioinformatics. 2011;27(4):572-577.
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Project P26148

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