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Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry UPLC-MS
Published on: March 14, 2013
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Lipid Discovery by Combinatorial Screening and Untargeted LC-MS/MS.
Mesut Bilgin1, Petra Born1, Filomena Fezza2,3
1Max Planck Institute for Cell Biology and Genetics, Pfotenhauerstraβe 108, 01307 Dresden, Germany.
Scientific Reports
|June 18, 2016
Summary
This study introduces a new method for identifying novel lipids in biological samples using LC-MS/MS. The technique discovered 74 new endocannabinoids and related molecules, including N-acylaspartates impacting Hedgehog signaling.
Area of Science:
- Lipidomics
- Mass Spectrometry
- Biochemistry
Background:
- Identifying novel lipids in complex biological extracts is challenging.
- Existing methods struggle with low-abundance lipid precursors.
Purpose of the Study:
- To develop a systematic method for identifying picogram quantities of new lipids.
- To discover novel endocannabinoids and related molecules in rat kidney extract.
Main Methods:
- Utilized all-ions fragmentation LC-MS/MS.
- Employed Arcadiate software to recognize lipid structural modules.
- Focused on modularity of lipid structures to identify novel molecules.
Main Results:
- Identified 58 known and 74 novel endogenous endocannabinoids and related molecules.
- Discovered a new class of N-acylaspartates.
- N-acylaspartates were found to inhibit Hedgehog signaling without affecting endocannabinoid receptors.
Conclusions:
- The developed method enables systematic identification of novel lipids at picogram levels.
- The discovery of N-acylaspartates opens new avenues for understanding lipid signaling pathways.
- This approach significantly advances lipidomics research by simplifying the analysis of complex samples.
