LipidMatch: an automated workflow for rule-based lipid identification using untargeted high-resolution tandem mass spectrometry data

Abstract

BackgroundLipids are ubiquitous and serve numerous biological functions; thus lipids have been shown to have great potential as candidates for elucidating biomarkers and pathway perturbations associated with disease. Methods expanding coverage of the lipidome increase the likelihood of biomarker discovery and could lead to more comprehensive understanding of disease etiology.ResultsWe introduce LipidMatch, an R-based tool for lipid identification for liquid chromatography tandem mass spectrometry workflows. LipidMatch currently has over 250,000 lipid species spanning 56 lipid types contained in in silico fragmentation libraries. Unique fragmentation libraries, compared to other open source software, include oxidized lipids, bile acids, sphingosines, and previously uncharacterized adducts, including ammoniated cardiolipins. LipidMatch uses rule-based identification. For each lipid type, the user can select which fragments must be observed for identification. Rule-based identification allows for correct annotation of lipids based on the fragments observed, unlike typical identification based solely on spectral similarity scores, where over-reporting structural details that are not conferred by fragmentation data is common. Another unique feature of LipidMatch is ranking lipid identifications for a given feature by the sum of fragment intensities. For each lipid candidate, the intensities of experimental fragments with exact mass matches to expected in silico fragments are summed. The lipid identifications with the greatest summed intensity using this ranking algorithm were comparable to other lipid identification software annotations, MS-DIAL and Greazy. For example, for features with identifications from all 3 software, 92% of LipidMatch identifications by fatty acyl constituents were corroborated by at least one other software in positive mode and 98% in negative ion mode.ConclusionsLipidMatch allows users to annotate lipids across a wide range of high resolution tandem mass spectrometry experiments, including imaging experiments, direct infusion experiments, and experiments employing liquid chromatography. LipidMatch leverages the most extensive in silico fragmentation libraries of freely available software. When integrated into a larger lipidomics workflow, LipidMatch may increase the probability of finding lipid-based biomarkers and determining etiology of disease by covering a greater portion of the lipidome and using annotation which does not over-report biologically relevant structural details of identified lipid molecules.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-017-1744-3) contains supplementary material, which is available to authorized users.

Optimization of Folch, Bligh-Dyer, and Matyash sample-to-extraction solvent ratios for human plasma-based lipidomics studies

“…Peak detection was performed by mass detection, chromatogram smoothing, peak deconvolution, deisotoping, feature alignment, and gap-filling. Lipid annotation was performed on the top10-ddMS2 files using Thermo LipidSearch (Score A & B) and LipidMatch, the latter of which can be accessed at <http://secim.ufl.edu/secim-tools/> [7]. …”

Methods of the Analysis of Oxylipins in Biological Samples

“…To minimize the possibility of errors, these programs are usually equipped with various filters to eliminate unwanted matches; some of them allow the use of rule-based identification. Below, are the most popular commercial and free available ones used for these purposes: Lipid View [186], Lipid Search [177], SimLipid [187], LipidXplorer [188], LipiDex [189], LIMSA [190], Lipidyzer [191], Lipid Data Analyzer [192], LipidQA [193], CEU Mass Mediator [194], LipidLama [195], LipidMatch [196], LipidMiner [197], dGOT-MS [198], ALEX [199], The Lipid Annotation Service (LAS) [200], and LOBSTAHS [201]. Thus, the data accumulated over many years make the non-targeted approach simpler, and therefore more popular among researchers.…”

Lipid Annotator: Towards Accurate Annotation in Non-Targeted Liquid Chromatography High-Resolution Tandem Mass Spectrometry (LC-HRMS/MS) Lipidomics Using a Rapid and User-Friendly Software

“…Lipid annotation using in silico libraries often leads to a relatively high false positive rate [16]. False positives often occur due to high spectral impurity (numerous co-isolated lipid precursors for fragmentation) [17], limited lipid standards for simulating MS/MS and validation, and lack of available methods to quantify the false positive rate for any given software or application [18]. In addition, lipid isomers with subtle, but biologically important structural differences co-elute in many cases.…”