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Updated: May 29, 2026

Determination of Total Lipid and Lipid Classes in Marine Samples
Published on: December 11, 2021
Separation and classification of lipids using differential ion mobility spectrometry
Alexandre A Shvartsburg1, Giorgis Isaac, Nathalie Leveque
1Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99352, USA. alexandre.shvartsburg@pnl.gov
Differential ion mobility spectrometry (FAIMS) coupled with mass spectrometry (MS) effectively separates lipid subclasses. This technique enhances lipidomics by creating distinct trend lines for different lipid types, improving classification of unknowns.
Area of Science:
- Analytical Chemistry
- Biochemistry
- Spectrometry
Background:
- Two-dimensional separations, like ion mobility spectrometry (IMS)/mass spectrometry (MS), use trend lines for classifying compounds based on structural and chemical properties.
- Major biomolecular classes typically occupy distinct trend line domains in IMS/MS.
- Strong correlation between conventional IMS and MS separations limits finer distinctions within compound classes.
Purpose of the Study:
- To investigate the potential of differential ion mobility spectrometry (FAIMS) for chemical class separation of lipids.
- To determine if FAIMS, being less correlated with MS than conventional IMS, can improve lipid subclass and fine category distinctions.
- To assess the impact of helium-rich gas environments on separation resolution.
Main Methods:
- Utilizing differential ion mobility spectrometry (FAIMS) coupled with mass spectrometry (MS) for 2-D separation of lipids.
- Analyzing the resulting trend lines to observe chemical class separation.
- Experimenting with helium-rich gas mixtures (up to 70% He) to evaluate resolution improvements.
- Applying the method to various lipid subclasses, including phospholipids, glycerolipids, and sphingolipids.
Main Results:
- FAIMS demonstrated effective separation of lipid subclasses, forming distinct and often non-overlapping trend line domains.
- Lipid categories with different functional groups or degrees of unsaturation were frequently separated.
- Separation resolution was enhanced in helium-rich gas environments, with glycerolipid isomers distinguished at 70% He.
- FAIMS showed greater independence from MS compared to conventional IMS for lipid analysis.
Conclusions:
- FAIMS offers a powerful approach for classifying lipids based on distinct trend line domains.
- This method enables finer distinctions within lipid subclasses and categories, surpassing conventional IMS/MS.
- The improved resolution in helium-rich gases further enhances the analytical capabilities of FAIMS for lipidomics.
- FAIMS holds significant promise for applications in shotgun lipidomics and targeted analysis of bioactive lipids.
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