Mapping Unsaturation in Human Plasma Lipids by Data-Independent Ozone-Induced Dissociation

Over 1500 different lipids have been reported in human plasma at the sum composition level. Yet the number of unique lipids present is surely higher, once isomeric contributions from double bond location(s) and fatty acyl regiochemistry are considered. In order to resolve this ambiguity, herein, we describe the incorporation of ozone-induced dissociation (OzID) into data-independent shotgun lipidomics workflows on a high-resolution hybrid quadrupole-Orbitrap platform. In this configuration, [M + Na]⁺ ions generated by electrospray ionization of a plasma lipid extract were transmitted through the quadrupole in 1 Da segments. Reaction of mass-selected lipid ions with ozone in the octopole collision cell yielded diagnostic ions for each double bond position. The increased ozone concentration in this region significantly improved ozonolysis efficiency compared with prior implementations on linear ion-trap devices. This advancement translates into increased lipidome coverage and improvements in duty cycle for data-independent MS/MS analysis using shotgun workflows. Grouping all precursor ions with a common OzID neutral loss enables straightforward classification of the lipidome by unsaturation position (with respect to the methyl terminus). Two-dimensional maps obtained from this analysis provide a powerful visualization of structurally related lipids and lipid isomer families within plasma. Global profiling of lipid unsaturation in plasma extracts reveals that most unsaturated lipids are present as isomeric mixtures. These new insights provide a unique picture of underlying metabolism that could in the future provide novel indicators of health and disease.

Software tool for internal standard based normalization of lipids, and effect of data-processing strategies on resulting values

Background

Lipidomics, the comprehensive measurement of lipids within a biological system or substrate, is an emerging field with significant potential for improving clinical diagnosis and our understanding of health and disease. While lipids diverse biological roles contribute to their clinical utility, the diversity of lipid structure and concentrations prove to make lipidomics analytically challenging. Without internal standards to match each lipid species, researchers often apply individual internal standards to a broad range of related lipids. To aid in standardizing and automating this relative quantitation process, we developed LipidMatch Normalizer (LMN) http://secim.ufl.edu/secim-tools/ which can be used in most open source lipidomics workflows.

Results

LMN uses a ranking system (1–3) to assign lipid standards to target analytes. A ranking of 1 signifies that both the lipid class and adduct of the internal standard and target analyte match, while a ranking of 3 signifies that neither the adduct or class match. If multiple internal standards are provided for a lipid class, standards with the closest retention time to the target analyte will be chosen. The user can also signify which lipid classes an internal standard represents, for example indicating that ether-linked phosphatidylcholine can be semi-quantified using phosphatidylcholine. LMN is designed to work with any lipid identification software and feature finding software, and in this study is used to quantify lipids in NIST SRM 1950 human plasma annotated using LipidMatch and MZmine.

Conclusions

LMN can be integrated into an open source workflow which completes all data processing steps including feature finding, annotation, and quantification for LC-MS/MS studies. Using LMN we determined that in certain cases the use of peak height versus peak area, certain adducts, and negative versus positive polarity data can have major effects on the final concentration obtained.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-2803-8) contains supplementary material, which is available to authorized users.

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

Background

Lipids 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.

Results

We 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.

Conclusions

LipidMatch 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 material

The online version of this article (doi:10.1186/s12859-017-1744-3) contains supplementary material, which is available to authorized users.

DIA is not a new mass spectrometry acquisition method

Contrary to what is being said by several colleagues (and even advertised), data-independent analysis/acquisition (DIA) is not a new mass spectrometry acquisition method. Here we draw a timeline of events showing that DIA has been around since the early 2000s.

Site-specific analysis of changes in the glycosylation of proteins in liver cirrhosis using data-independent workflow with soft fragmentation

Cirrhosis of the liver is associated with increased fucosylation of proteins in the plasma. We describe a data-independent (DIA) strategy for comparative analysis of the site-specific glycoforms of plasma glycoproteins. A library of 161 glycoforms of 25 N-glycopeptides was established by data-dependent LC-MS/MS analysis of a tryptic digest of 14 human protein groups retained on a multiple affinity removal column. The collision-induced dissociation conditions were adjusted to maximize the yield of selective Y-ions which were quantified by a data-independent mass spectrometry workflow using a 10-Da acquisition window. Using this workflow, we quantified 125 glycoforms of 25 glycopeptides, covering 10 of the 14 proteins, without any further glycopeptide enrichment. Comparison of the proteins in the plasma of healthy controls and cirrhotic patients shows an average 1.5-fold increase in the fucosylation of bi-antennary glycoforms and 3-fold increase in the fucosylation of tri- and tetra- antennary glycoforms. These results show that the adjusted glycopeptide DIA workflow using soft collision-induced fragmentation of glycopeptides is suitable for site-specific analysis of protein glycosylation in complex mixtures of analytes without glycopeptide enrichment.

Emerging proteomic technologies for elucidating context-dependent cellular signaling events: A big challenge of tiny proportions

Aberrant cell signaling events either drive or compensate for nearly all pathologies. A thorough description and quantification of maladaptive signaling flux in disease is a critical step in drug development, and complex proteomic approaches can provide valuable mechanistic insights. Traditional proteomics-based signaling analyses rely heavily on in vitro cellular monoculture. The characterization of these simplified systems generates a rich understanding of the basic components and complex interactions of many signaling networks, but they cannot capture the full complexity of the microenvironments in which pathologies are ultimately made manifest. Unfortunately, techniques that can directly interrogate signaling in situ often yield mass-limited starting materials that are incompatible with traditional proteomics workflows. This review provides an overview of established and emerging techniques that are applicable to context-dependent proteomics. Analytical approaches are illustrated through recent proteomics-based studies in which selective sample acquisition strategies preserve context-dependent information, and where the challenge of minimal starting material is met by optimized sensitivity and coverage. This review is organized into three major technological themes: (i) LC methods in line with MS; (ii) antibody-based approaches; (iii) MS imaging with a discussion of data integration and systems modeling. Finally, we conclude with future perspectives and implications of context-dependent proteomics.