Mobile Phase Contaminants\nAffect Neutral Lipid Analysis\nin LC-MS-Based Lipidomics Studies
Lipidomics is a well-established field, enabled by modern\nliquid\nchromatography mass spectrometry (LC-MS) technology, rapidly generating\nlarge amounts of data. Lipid extracts derived from biological samples\nare complex, and most spectral features in LC-MS lipidomics data sets\nremain unidentified. In-depth analyses of commercial triacylglycerol,\ndiacylglycerol, and cholesterol ester standards revealed the expected\nammoniated and sodiated ions as well as five additional unidentified\nhigher mass peaks with relatively high intensities. The identities\nand origin of these unknown peaks were investigated by modifying the\nchromatographic mobile-phase components and LC-MS source parameters.\nTandem MS (MS/MS) of each unknown adduct peak yielded no lipid structural\ninformation, producing only an intense ion of the adducted species.\nThe unknown adducts were identified as low-mass contaminants originating\nfrom methanol and isopropanol in the mobile phase. Each contaminant\nwas determined to be an alkylated amine species using their monoisotopic\nmasses to calculate molecular formulas. Analysis of bovine liver extract\nidentified 33 neutral lipids with an additional 73 alkyl amine adducts.\nAnalysis of LC-MS-grade methanol and isopropanol from different vendors\nrevealed substantial alkylated amine contamination in one out of three\ndifferent brands that were tested. Substituting solvents for ones\nwith lower levels of alkyl amine contamination increased lipid annotations\nby 36.5% or 27.4%, depending on the vendor, and resulted in >2.5-fold\nincreases in peak area for neutral lipid species without affecting\npolar lipid analysis. These findings demonstrate the importance of\nsolvent selection and disclosure for lipidomics protocols and highlight\nsome of the major challenges when comparing data between experiments.
Publication Details
- Journal
- Figshare
- Published
- 2024-10-07
- DOI
- https://doi.org/10.1021/jasms.4c00320.s001
- Primary Topic
- Scientific Computing and Data Management
- Type
- article
- Field-Weighted Citation Impact
- 0.00