Deep Characterisation of the sn-Isomer Lipidome Using High-Throughput Data-Independent Acquisition and Ozone-Induced Dissociation.
Jesse A MichaelReuben S E YoungRachelle BalezLachlan J JekimovsDavid L MarshallBerwyck L J PoadTodd W MitchellStephen J BlanksbyChrister S EjsingShane R EllisPublished in: Angewandte Chemie (International ed. in English) (2024)
In recent years there has been a significant interest in the development of innovative lipidomics techniques capable of resolving lipid isomers. To date, methods applied to resolving sn-isomers have resolved only a limited number of species. We report a workflow based on ozone-induced dissociation for untargeted characterisation of hundreds of sn-resolved glycerophospholipid isomers from biological extracts in under 20 min, coupled with an automated data analysis pipeline. It provides an order of magnitude increase in the number of sn-isomer pairs identified as compared to previous reports and reveals that sn-isomer populations are tightly regulated and significantly different between cell lines. The sensitivity of this method and potential for de novo molecular discovery is further demonstrated by the identification of unexpected lipids containing ultra-long monounsaturated acyl chains at the sn-1 position.
Keyphrases
- data analysis
- high throughput
- high glucose
- diabetic rats
- fatty acid
- mass spectrometry
- electronic health record
- hydrogen peroxide
- particulate matter
- high resolution
- small molecule
- drug induced
- transcription factor
- emergency department
- oxidative stress
- machine learning
- single cell
- air pollution
- endothelial cells
- deep learning
- big data
- liquid chromatography
- stress induced
- simultaneous determination
- human health