LipidOA: A Machine-Learning and Prior-Knowledge-Based Tool for Structural Annotation of Glycerophospholipids

Abstract

The Paterno−Buchi (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.

Alternative fatty acid desaturation pathways revealed by deep profiling of total fatty acids in RAW 264.7 cell line

“…Then, we used 2-acpy derivatization (the PB reaction) followed by RPLC-MS/MS to achieve relative quantitation of unsaturated total FAs at the C=C location level ( 31 ). The PB-MS/MS data were analyzed by the LipidOA module ( 39 ) embedded in LipidNovelist (35) to achieve de novo identification of FA at the C=C location level. %Relative composition of a specific isomer was calculated by normalizing the relative abundances of the C=C diagnostic ions associated with that isomer to the summed abundances of the diagnostic ions from all C=C location isomers.…”

LipidOz enables automated elucidation of lipid carbon–carbon double bond positions from ozone-induced dissociation mass spectrometry data

“…Interest in the identification of lipid double bonds has increased in recent years, and there have been multiple experimental methods (some with associated software tools) presented in the literature 16 – 18 . These works are primarily distinguished by the method used to determine double bond locations, specifically, electron-induced dissociation (EID) 16 , oxygen attachment dissociation (OAD) 17 , and Paternò-Büchi(PB) reaction 18 , 19 .…”

“…Interest in the identification of lipid double bonds has increased in recent years, and there have been multiple experimental methods (some with associated software tools) presented in the literature 16 – 18 . These works are primarily distinguished by the method used to determine double bond locations, specifically, electron-induced dissociation (EID) 16 , oxygen attachment dissociation (OAD) 17 , and Paternò-Büchi(PB) reaction 18 , 19 . While these techniques all share the underlying principle of producing fragments from lipids with masses that are characteristic to double bond position, the specific analytical configuration (and thus, the structure of the data) as well as the specific fragment masses must be accounted for in software tools in order to support analyzing data from these techniques.…”

Evaluating Software Tools for Lipid Identification from Ion Mobility Spectrometry–Mass Spectrometry Lipidomics Data

“…New technological developments involving IMS-MS lipidomics continually create the need for new software tools that facilitate the transition from proof of concept to real-world application. Two areas of particular interest are increasing the depth of structural characterization through the integration of techniques for determining lipid double bond positions (e.g., Paternò-Büchi [40,41], OzID [42,43]) or ultrahigh-resolution IMS separations [44,45] and integrating MS imaging (MSI) with IMS-MS platforms for spatial lipidomics [27,46]. Although the technological details differ significantly, these two development areas face essentially the same challenge with respect to their broader use in practical applications, i.e., the lack of software support.…”