Abstract
Introduction and objective
Taking into consideration the challenges of lipid analytics, present study aims to design the best high-throughput workflow for detection and annotation of lipids.
Material and methods
Serum lipid profiling was performed on CSH-C18 and EVO-C18 columns using UHPLC Q-TOF-MS and generated lipid features were annotated based on m/z and fragment ion using different software.
Result and discussion
Better detection of features was observed in CSH-C18 than EVO-C18 with enhanced resolution except for Glycerolipids (triacylglycerols) and Sphingolipids (sphingomyelin).
Conclusion
The study revealed an optimized untargeted Lipidomics-workflow with comprehensive lipid profiling (CSH-C18 column) and confirmatory annotation (LipidBlast).
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Funding
This work was supported by the Defence R&D Organization (DRDO), Ministry of Defence, India (INM 324); The Council of Scientific and Industrial Research (CSIR), India; and The University Grant Commission (UGC), India.
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SD and PR conceived and designed the study. SD, KM and AS conducted the experiments and data acquisition. SD, KM and RB analyzed the data. SD, KM, RB and DM wrote the manuscript with input and critical feedback from all authors. PR evaluated the manuscript critically and all the authors approved the final manuscript.
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Supplementary Table 1: CSH-C18 (a) and EVO-C18 (b) columns specific annotated lipid categories and their sub-classes acquired on UHPLC-MS.
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Dhariwal, S., Maan, K., Baghel, R. et al. Systematic untargeted UHPLC–Q-TOF–MS based lipidomics workflow for improved detection and annotation of lipid sub-classes in serum. Metabolomics 19, 24 (2023). https://doi.org/10.1007/s11306-023-01983-2
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DOI: https://doi.org/10.1007/s11306-023-01983-2