Sphingomyelins (SMs) are an essential class of lipids widely existing in different organisms. The sphingoid base and N-acyl chain are two building blocks which can undergo different types of modifications during lipogenesis, including desaturation, hydroxylation, and methyl branching. Current lipidomic analysis methods cannot provide detailed information on these structural motifs. Herein, we developed a tandem mass spectrometric method by harnessing radical-directed dissociation (RDD) from collision-induced dissociation (CID) of the bicarbonate anion adduct of SM ([M + HCO3]−). A major RDD channel produced a high-abundance fragment carrying the intact N-acyl chain, termed as “N-acyl fragment”, allowing the assignment of the sphingoid base/N-acyl composition and relative quantitation of compositional isomers of SM at high sensitivity. RDD also produced intrachain fragments in lower abundances, which helped localization of methyl branching and hydroxylation in SM. The acetone Paternò–Büchi (PB) reaction was found to be capable of derivatizing the Δ4 carbon–carbon double bond (C═C) in sphingosine (SPH) base and producing C═C diagnostic ions upon CID, albeit at much lower efficiencies than those of the isolated C═C in alkyl chains. A liquid chromatography–mass spectrometry workflow was developed by incorporating MS2 CID of SM via [M + HCO3]− and PB-MS2 CID. The capability of profiling SM with detailed structural information was demonstrated by analyzing complex lipid extracts from porcine brain and Caenorhabditis elegans. These results provided visualization of the sphingoid base/N-acyl compositional isomers of SM lipids and revealed large structural diversity from each sample. These included identification of the sphingadiene base [d18:1(Δ4,14)], C═C location isomers in N-acyls, C-2 hydroxylation of N-acyls, and iso-methyl branched SPH base.
The Paternò–Büchi (PB) reaction is a carbon–carbon double bond (C═C)-specific derivatization reaction that can be used to pinpoint the location(s) of C═C(s) in unsaturated lipids and quantitate the location of isomers when coupled with tandem mass spectrometry (MS/MS). As the data of PB-MS/MS are increasingly generated, the establishment of a corresponding data analysis tool is highly needed. Herein, LipidOA, a machine-learning and prior-knowledge-based data analysis tool, is developed to analyze PB-MS/MS data generated by liquid chromatography–mass spectrometry workflows. LipidOA consists of four key functional modules to realize an annotation of glycerophospholipid (GPL) structures at the fatty acyl-specific C═C location level. These include (1) data preprocessing, (2) picking C═C diagnostic ions, (3) de novo annotation, and (4) result ranking. Importantly, in the result-ranking module, the reliability of structural annotation is sorted via the use of a machine learning classifier and comparison to the total fatty acid database generated from the same sample. LipidOA is trained and validated by four PB-MS/MS data sets acquired using different PB reagents on mass spectrometers of different resolutions and of different biological samples. Overall, LipidOA provides high precision (higher than 0.9) and a wide coverage for structural annotations of GPLs. These results demonstrate that LipidOA can be used as a robust and flexible tool for annotating PB-MS/MS data collected under different experimental conditions using different lipidomic workflows.
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