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Q-RAI data-independent acquisition for lipidomic quantitative profiling
Jing Kai Chang1,2, Guoshou Teo3, Yael Pewzner-Jung4
1Precision Medicine Translational Research Programme and Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
Scientific Reports
|November 7, 2023
Summary
This study introduces a new untargeted lipidomics workflow using Quadrupole Resolved All-Ions (Q-RAI) data independent acquisition (DIA) for reproducible lipid identification and quantification. The method shows promise for exploratory biological studies and biomarker discovery.
Area of Science:
- Biochemistry
- Analytical Chemistry
- Mass Spectrometry
Background:
- Untargeted lipidomics is crucial for generating hypotheses and discovering disease biomarkers.
- Current LC-MS untargeted methods often use data dependent acquisition (DDA) for identification and MS-only for quantification.
- There is a need for improved workflows for simultaneous lipid identification and quantification.
Purpose of the Study:
- To present a novel untargeted lipidomics workflow utilizing Q-RAI DIA acquisition.
- To enable reproducible identification and quantification of lipids in complex biological samples.
- To evaluate the workflow's performance against established methods.
Main Methods:
- Implementation of Quadrupole Resolved All-Ions (Q-RAI) acquisition in data independent acquisition (DIA) mode on an Agilent 6546 Q-TOF mass spectrometer.
- Utilizing MetaboKit software for DDA-based spectral library construction and peak area extraction (MS1 and MS2).
- Testing the workflow on human plasma lipid extracts and serum from Ceramide Synthase 2 (CerS2) null mice.
Main Results:
- The Q-RAI DIA workflow achieved comparable MS1 and MS2 quantification to multiple reaction monitoring (MRM) targeted analysis.
- Identified 88 significantly different lipid species in CerS2 null mice serum compared to wild type.
- Demonstrated reproducible relative quantification of lipids in exploratory studies.
Conclusions:
- The Q-RAI DIA workflow offers a reliable approach for simultaneous lipid identification and quantification.
- This method is suitable for hypothesis generation and biomarker discovery in untargeted lipidomics.
- The workflow enhances reproducibility and accuracy in lipidomic analyses.
