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GENERAL DESCRIPTION

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.

CITATIONS

MSⁿ annotation algorithm using decision rule sets:

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.
PM:29058722

MS¹ quantitation and peak selection algorithm:

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.
PM:21169379

Extension for oxidized lipids:

3: Krettler CA, Hartler J, Thallinger GG.
Identification and Quantification of Oxidized Lipids in LC-MS Lipidomics Data.
Studies in Health Technology and Informatics. 2020. 271:39-48.
PM:32578539

Extension for sphingolipids:

4: Hartler J, Armando AM, Trötzmüller M, Dennis EA, Köfeler HC, Quehenberger O.
Automated Annotation of Sphingolipids Including Accurate Identification of Hydroxylation Sites Using MSⁿ Data.
Analytical Chemistry. 2020. 92(20):14054-14062.
PM:33003696

Extension for glycosyl inositol phospho ceramides:

5: Panzenboeck L, Troppmair N, Schlachter S, Koellensperger G, Hartler J, Rampler E.
Chasing the Major Sphingolipids on Earth: Automated Annotation of Plant Glycosyl Inositol Phospho Ceramides by Glycolipidomics.
Metabolites. 2020. 10(9):375.
PM:32961698

Extension for accurate quantification of coeluting TG quantity based on MSⁿ spectra:

6: Vigor C, Züllig T, Eichmann TO, Oger C, Zhou B, Rechberger GN, Hilsberg L, Trötzmüller M, Pellegrino RM, Alabed HBR, Hartler J, Wolinski H, Galano JM, Durand T, Spener F.
α-Linolenic acid and product octadecanoids in Styrian pumpkin seeds and oils: How processing impacts lipidomes of fatty acid, triacylglycerol and oxylipin molecular structures.
Food Chemistry. 2022. 371:131194.
PM:34600364

Project P26148

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