Candice Z. Ulmer

Harmonizing lipidomics: NIST interlaboratory comparison exercise for lipidomics using SRM 1950–Metabolites in Frozen Human Plasma

As the lipidomics field continues to advance, self-evaluation within the community is critical. Here, we performed an interlaboratory comparison exercise for lipidomics using Standard Reference Material (SRM) 1950–Metabolites in Frozen Human Plasma, a commercially available reference material. The interlaboratory study comprised 31 diverse laboratories, with each laboratory using a different lipidomics workflow. A total of 1,527 unique lipids were measured across all laboratories and consensus location estimates and associated uncertainties were determined for 339 of these lipids measured at the sum composition level by five or more participating laboratories. These evaluated lipids detected in SRM 1950 serve as community-wide benchmarks for intra- and interlaboratory quality control and method validation. These analyses were performed using nonstandardized laboratory-independent workflows. The consensus locations were also compared with a previous examination of SRM 1950 by the LIPID MAPS consortium. While the central theme of the interlaboratory study was to provide values to help harmonize lipids, lipid mediators, and precursor measurements across the community, it was also initiated to stimulate a discussion regarding areas in need of improvement.

Expanding Lipidome Coverage Using LC-MS/MS Data-Dependent Acquisition with Automated Exclusion List Generation

AbstractUntargeted omics analyses aim to comprehensively characterize biomolecules within a biological system. Changes in the presence or quantity of these biomolecules can indicate important biological perturbations, such as those caused by disease. With current technological advancements, the entire genome can now be sequenced; however, in the burgeoning fields of lipidomics, only a subset of lipids can be identified. The recent emergence of high resolution tandem mass spectrometry (HR-MS/MS), in combination with ultra-high performance liquid chromatography, has resulted in an increased coverage of the lipidome. Nevertheless, identifications from MS/MS are generally limited by the number of precursors that can be selected for fragmentation during chromatographic elution. Therefore, we developed the software IE-Omics to automate iterative exclusion (IE), where selected precursors using data-dependent topN analyses are excluded in sequential injections. In each sequential injection, unique precursors are fragmented until HR-MS/MS spectra of all ions above a user-defined intensity threshold are acquired. IE-Omics was applied to lipidomic analyses in Red Cross plasma and substantia nigra tissue. Coverage of the lipidome was drastically improved using IE. When applying IE-Omics to Red Cross plasma and substantia nigra lipid extracts in positive ion mode, 69% and 40% more molecular identifications were obtained, respectively. In addition, applying IE-Omics to a lipidomics workflow increased the coverage of trace species, including odd-chained and short-chained diacylglycerides and oxidized lipid species. By increasing the coverage of the lipidome, applying IE to a lipidomics workflow increases the probability of finding biomarkers and provides additional information for determining etiology of disease. Graphical Abstractᅟ

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

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.

Common cases of improper lipid annotation using high-resolution tandem mass spectrometry data and corresponding limitations in biological interpretation

a University of Florida, Department of Chemistry, 214 Leigh Hall, Gainesville, FL 32611, United States b National Institute of Standards and Technology, Chemical Science Division, Hollings Marine Laboratory, 331 Fort Johnson Road, Charleston, SC 29412, United States c University of Florida, Department of Pathology, Immunology, and Laboratory Medicine, College of Medicine, 1600 SW Archer Rd, Gainesville, FL 32603, United States

LipidPioneer : A Comprehensive User-Generated Exact Mass Template for Lipidomics

AbstractLipidomics, the comprehensive measurement of lipid species in a biological system, has promising potential in biomarker discovery and disease etiology elucidation. Advances in chromatographic separation, mass spectrometric techniques, and novel substrate applications continue to expand the number of lipid species observed. The total number and type of lipid species detected in a given sample are generally indicative of the sample matrix examined (e.g., serum, plasma, cells, bacteria, tissue, etc.). Current exact mass lipid libraries are static and represent the most commonly analyzed matrices. It is common practice for users to manually curate their own lists of lipid species and adduct masses; however, this process is time-consuming. LipidPioneer, an interactive template, can be used to generate exact masses and molecular formulas of lipid species that may be encountered in the mass spectrometric analysis of lipid profiles. Over 60 lipid classes are present in the LipidPioneer template and include several unique lipid species, such as ether-linked lipids and lipid oxidation products. In the template, users can add any fatty acyl constituents without limitation in the number of carbons or degrees of unsaturation. LipidPioneer accepts naming using the lipid class level (sum composition) and the LIPID MAPS notation for fatty acyl structure level. In addition to lipid identification, user-generated lipid m/z values can be used to develop inclusion lists for targeted fragmentation experiments. Resulting lipid names and m/z values can be imported into software such as MZmine or Compound Discoverer to automate exact mass searching and isotopic pattern matching across experimental data. Graphical Abstractᅟ

Examining heat treatment for stabilization of the lipidome

AIM To confidently determine lipid-based biomarkers, it is important to minimize variation introduced during preanalytical steps. We evaluated reducing variation associated with lipid measurements in invertebrate sentinel species using a state-of-the-art heat treatment technique. MATERIALS AND METHODS Earthworms (Eisenia fetida), house crickets (Acheta domestica) and ghost shrimp (Palaemonetes paludosus) were euthanized either by flash freezing or heat treatment. For both experiments, samples were either immediately extracted after removal from -80°C storage or incubated on ice for one hour prior to sample weighing and extraction. Lipidomics was performed on resulting extracts using liquid chromatography high resolution tandem mass spectrometry. LipidMatch and LipidSearch were used for lipid identification. RESULTS Lipid enzymatic products (e.g., phosphatidylmethanols, diglycerides, lysoglycerophospholipids and ether-linked/oxidized lysoglycerophospholipids), were in higher concentrations in flash-frozen samples, when compared with heat-treated samples. Results suggest that heat treatment reduces phospholipase A and phospholipase D activity. CONCLUSION Heat treatment reduced enzymatic products and increased precursors of these enzymatic products. We believe heat treatment warrants a closer interrogation for improving the robustness of lipid biomarker research, especially in tissue samples, where enzyme stabilizers are difficult to apply, and for use in field studies, where the stabilization of the collected sample is critical.

NIST lipidomics workflow questionnaire: an assessment of community-wide methodologies and perspectives

IntroductionEfforts to harmonize lipidomic methodologies have been limited within the community. Here, we aimed to capitalize on the recent National Institute of Standards and Technology lipidomics interlaboratory comparison exercise by implementing a questionnaire that assessed current methodologies, quantitation strategies, standard operating procedures (SOPs), and quality control activities employed by the lipidomics community.ObjectivesLipidomics is a rapidly developing field with diverse applications. At present, there are no community-vetted methods to assess measurement comparability or data quality. Thus, a major impetus of this questionnaire was to profile current efforts, highlight areas of need, and establish future objectives in an effort to harmonize lipidomics workflows.MethodsThe 54-question survey inquired about laboratory demographics, lipidomic methodologies and SOPs, analytical platforms, quantitation, reference materials, quality control procedures, and opinions regarding challenges existing within the community.ResultsA total of 125 laboratories participated in the questionnaire. A broad overview of results highlighted a wide methodological diversity within current lipidomic workflows. The impact of this diversity on lipid measurement and quantitation is currently unknown and needs to be explored further. While some laboratories do incorporate SOPs and quality control activities, these concepts have not been fully embraced by the community. The top five perceived challenges within the lipidomics community were a lack of standardization amongst methods/protocols, lack of lipid standards, software/data handling and quantification, and over-reporting/false positives.ConclusionThe questionnaire provided an overview of current lipidomics methodologies and further promoted the need for community-accepted guidelines and protocols. The questionnaire also served as a platform to help determine and prioritize metrological issues to be investigated.

A Robust Lipidomics Workflow for Mammalian Cells, Plasma, and Tissue Using Liquid-Chromatography High-Resolution Tandem Mass Spectrometry

Lipids have been analyzed in applications including drug discovery, disease etiology elucidation, and natural products. The chemical and structural diversity of lipids requires a tailored lipidomics workflow for each sample type. Therefore, every protocol in the lipidomics workflow, especially those involving sample preparation, should be optimized to avoid the introduction of bias. The coupling of ultra-high-performance liquid chromatography (UHPLC) with high-resolution mass spectrometry (HRMS) allows for the separation and identification of lipids based on class and fatty acid acyl chain. This work provides a comprehensive untargeted lipidomics workflow that was optimized for various sample types (mammalian cells, plasma, and tissue) to balance extensive lipid coverage and specificity with high sample throughput. For identification purposes, both data-dependent and data-independent tandem mass spectrometric approaches were incorporated, providing more extensive lipid coverage. Popular open-source feature detection, data processing, and identification software are also outlined.

Corrigendum to “Common cases of improper lipid annotation using high-resolution tandem mass spectrometry data and corresponding limitations in biological interpretation” [Biochim. Biophys. Acta 1862(8) (2017) 766–770]

a University of Florida, Department of Chemistry, 214 Leigh Hall, Gainesville, FL 32611, United States b National Institute of Standards and Technology, Chemical Science Division, Hollings Marine Laboratory, 331 Fort Johnson Road, Charleston, SC 29412, United States c University of Florida, Department of Pathology, Immunology, and Laboratory Medicine, College of Medicine, 1600 SW Archer Rd, Gainesville, FL 32603, United States