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LipidHunter Identifies Phospholipids by High-Throughput Processing of LC-MS and Shotgun Lipidomics Datasets

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    LipidHunter Identifies Phospholipids by High-Throughput Processing of LC-MS and Shotgun Lipidomics Datasets
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    † ‡ †Institute of Bioanalytical Chemistry, Faculty of Chemistry and Mineralogy and ‡Center for Biotechnology and Biomedicine, Universität Leipzig, Deutscher Platz 5, 04103 Leipzig, Germany
    *E-mail: [email protected]. Institut für Bioanalytische Chemie, Biotechnologisch-Biomedizinisches Zentrum, Deutscher Platz 5, 04103 Leipzig, Germany.
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    Analytical Chemistry

    Cite this: Anal. Chem. 2017, 89, 17, 8800–8807
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    https://doi.org/10.1021/acs.analchem.7b01126
    Published July 28, 2017
    Copyright © 2017 American Chemical Society
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    Abstract

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    Lipids are dynamic constituents of biological systems, rapidly responding to any changes in physiological conditions. Thus, there is a large interest in lipid-derived markers for diagnostic and prognostic applications, especially in translational and systems medicine research. As lipid identification remains a bottleneck of modern untargeted lipidomics, we developed LipidHunter, a new open source software for the high-throughput identification of phospholipids in data acquired by LC-MS and shotgun experiments. LipidHunter resembles a workflow of manual spectra annotation. Lipid identification is based on MS/MS data analysis in accordance with defined fragmentation rules for each phospholipid (PL) class. The software tool matches product and neutral loss signals obtained by collision-induced dissociation to a user-defined white list of fatty acid residues and PL class-specific fragments. The identified signals are tested against elemental composition and bulk identification provided via LIPID MAPS search. Furthermore, LipidHunter provides information-rich tabular and graphical reports allowing to trace back key identification steps and perform data quality control. Thereby, 202 discrete lipid species were identified in lipid extracts from rat primary cardiomyocytes treated with a peroxynitrite donor. Their relative quantification allowed the monitoring of dynamic reconfiguration of the cellular lipidome in response to mild nitroxidative stress. LipidHunter is available free for download at https://bitbucket.org/SysMedOs/lipidhunter.

    Copyright © 2017 American Chemical Society

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    Supporting Information

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    The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.analchem.7b01126.

    • Descriptions of the supplementary tables and Figures S1–S7 (PDF)

    • LipidHunter User Guide (PDF)

    • Examples of LipidHunter identification results for lipids from different PL classes with low, medium, and high abundances of precursor ions (Figures 1–20) (PDF)

    • Table S-1. Configuration file used to define the white list for fatty acid residues (XLSX)

    • Table S-2. Configuration file used to define PL class specific ions (XLSX)

    • Table S-3. Configuration file used to define weight factors Wfrag (XLSX)

    • Table S-4. LipidHunter output tables for PC, PE, PS, PI, PG, and PA lipids exemplified for 70 min_SIN sample (XLSX)

    • Table S-5. Summary of PLs identified by LipidHunter in lipid extracts from cardiomyocytes treated with SIN-1 (XLSX)

    • Table S-6. Comparison between LipidHunter and LipidBlast results for PL identification (XLSX)

    • Table S-7. Summary of relative quantification for discrete PLs identified by LipidHunter in extracts from SIN-1 treated cardiomyocytes using Progenesis QI (XLSX)

    • Table S-8. Human serum PLs identified by LipidHunter (XLSX)

    • Table S-9. PLs identified by LipidHunter in shotgun DDA data sets (XLSX)

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    45. Caroline Gaud, Bebiana C. Sousa, An Nguyen, Maria Fedorova, Zhixu Ni, Valerie B. O’Donnell, Michael J.O. Wakelam, Simon Andrews, Andrea F. Lopez-Clavijo. BioPAN: a web-based tool to explore mammalian lipidome metabolic pathways on LIPID MAPS. F1000Research 2021, 10 , 4. https://doi.org/10.12688/f1000research.28022.1
    46. Caroline Gaud, Bebiana C. Sousa, An Nguyen, Maria Fedorova, Zhixu Ni, Valerie B. O’Donnell, Michael J.O. Wakelam, Simon Andrews, Andrea F. Lopez-Clavijo. BioPAN: a web-based tool to explore mammalian lipidome metabolic pathways on LIPID MAPS. F1000Research 2021, 10 , 4. https://doi.org/10.12688/f1000research.28022.2
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    48. Patrick O. Helmer, Ansgar Korf, Heiko Hayen. Analysis of artificially oxidized cardiolipins and monolyso‐cardiolipins via liquid chromatography/high‐resolution mass spectrometry and Kendrick mass defect plots after hydrophilic interaction liquid chromatography based sample preparation. Rapid Communications in Mass Spectrometry 2020, 34 (1) https://doi.org/10.1002/rcm.8566
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    50. Zhixu Ni, Laura Goracci, Gabriele Cruciani, Maria Fedorova. Computational solutions in redox lipidomics – Current strategies and future perspectives. Free Radical Biology and Medicine 2019, 144 , 110-123. https://doi.org/10.1016/j.freeradbiomed.2019.04.027
    51. Kai P. Law, Chuanlun L. Zhang. Current progress and future trends in mass spectrometry-based archaeal lipidomics. Organic Geochemistry 2019, 134 , 45-61. https://doi.org/10.1016/j.orggeochem.2019.04.001
    52. Angela Criscuolo, Martin Zeller, Ken Cook, Georgia Angelidou, Maria Fedorova. Rational selection of reverse phase columns for high throughput LC–MS lipidomics. Chemistry and Physics of Lipids 2019, 221 , 120-127. https://doi.org/10.1016/j.chemphyslip.2019.03.006
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    Analytical Chemistry

    Cite this: Anal. Chem. 2017, 89, 17, 8800–8807
    Click to copy citationCitation copied!
    https://doi.org/10.1021/acs.analchem.7b01126
    Published July 28, 2017
    Copyright © 2017 American Chemical Society
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