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Hybrid SWATH/MS and HR-SRM/MS acquisition for phospholipidomics using QUAL/QUANT data processing

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Abstract

A hybrid SWATH/MS and HR-SRM/MS acquisition approach using multiple unit mass windows and 100 u precursor selection windows has been developed to interface with a chromatographic lipid class separation. The method allows for the simultaneous monitoring of sum compositions in MS1 and up to 48 lipids in MS2 per lipid class. A total of 240 lipid sum compositions from five phospholipid classes could be monitored in MS2 (HR-SRM/MS) while there was no limitation in the number of analytes in MS1 (HR-SIM/MS). On average, 92 lipid sum compositions and 75 lipid species could be quantified in human plasma samples. The robustness and precision of the workflow has been assessed using technical triplicates of the subject samples. Lipid identification was improved using a combined qualitative and quantitative data processing based on prediction instead of library search. Lipid class specific extracted ion currents of precursors and the corresponding molecular species fragments were extracted based on the information obtained from lipid building blocks and a combinatorial strategy. The SWATH/MS approach with the post-acquisition processing is not limited to the analyzed phospholipid classes and can be applied to other analytes and samples of interest.

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Acknowledgments

The authors are grateful to Yves J.C. LeBlanc (Sciex) for valuable discussion on the setup of the SWATH experiments. GH would like to thank SystemsX and the Swiss National Sciences Foundation for the financial support: Projects 51RTP0_151032 and 206021_170779.

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Correspondence to Gérard Hopfgartner.

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Anonymized plasma samples were provided by the Centre de Transfusion Sanguine, University Hospital Geneva, Geneva, Switzerland. The Human Research Act (HRA) does not apply for the anonymized plasma samples analyzed in the present work (Art. 2 para. 2 let. b and c).

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Raetz, M., Duchoslav, E., Bonner, R. et al. Hybrid SWATH/MS and HR-SRM/MS acquisition for phospholipidomics using QUAL/QUANT data processing. Anal Bioanal Chem 411, 5681–5690 (2019). https://doi.org/10.1007/s00216-019-01946-4

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  • DOI: https://doi.org/10.1007/s00216-019-01946-4

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