FAIMS Shotgun Lipidomics for Enhanced HILIC-like Separation and Automated Annotation of Gangliosides

19 March 2024, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


The analysis of the glycosphingolipid subclass of gangliosides is extremely challenging, given their structural complexity, lack of reference standards, databases, and software solutions. Here, we introduce a fast 6 min High Field Asymmetric Ion Mobility Spectrometry (FAIMS) shotgun-based lipidomics workflow for improved ganglioside detection. By ramping compensation voltages, ideal ranges for different ganglioside classes were obtained. FAIMS revealed both class- and charge-state separation behavior based on the glycan head group moiety. The number of sialic acids attached to the glycan moiety correlated positively with their preferred charge state, i.e., trisialylated gangliosides (GT1-3) were mainly present as [M-3H]3- ions, whereas [M-4H]4- and [M 5H]5- ions were observed for GQ1 and GP1. [M-5H]5- ions were reported for the first time, primarily due to signal-to-noise enhancement and charge state filtering enabled by FAIMS. Overall, 11 ganglioside classes were covered i.e., GM1, GM2, GM3, GD1, GD2, GD3, GT1, GT2, GT3, GQ1, GP1. For data evaluation, we introduce a shotgun/FAIMS extension of the freely available, open-source Lipid Data Analyzer (LDA), which utilized combined orthogonal fragmentation spectra from CID, HCD, and 213 nm UVPD ion activation methods. Finally, 112 unique molecular gangliosides species were identified from pooled standards and porcine brain extracts. While conventional shotgun lipidomics favored the observation of singly charged ganglioside species, the incorporation of FAIMS yielded a higher number of annotated lipid species due to a gain in detection of multiply charged ion species. Therefore, this FAIMS-driven approach offers a promising strategy for complex ganglioside and glycosphingolipid characterization in shotgun lipidomics.


mass spectrometry
shotgun lipidomics
HILIC-like separation
Automated Annotation
Lipid Data Analyzer (LDA)

Supplementary materials

Supplementary Material
The Supporting Material contains an extended experimental section and supporting tables and figures


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