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In this community effort, we compare measurements between 34 laboratories from 19 countries, utilizing mixtures of labelled authentic synthetic standards, to quantify by mass spectrometry four clinically used ceramide species in the NIST (National Institute of Standards and Technology) human blood plasma Standard Reference Material (SRM) 1950, as well as a set of candidate plasma reference materials (RM 8231). Participants either utilized a provided validated method and/or their method of choice. Mean concentration values, and intra- and inter-laboratory coefficients of variation (CV) were calculated using single-point and multi-point calibrations, respectively. These results are the most precise (intra-laboratory CVs ≤ 4.2%) and concordant (inter-laboratory CVs < 14%) community-derived absolute concentration values reported to date for four clinically used ceramides in the commonly analyzed SRM 1950. We demonstrate that calibration using authentic labelled standards dramatically reduces data variability. Furthermore, we show how the use of shared RM can correct systematic quantitative biases and help in harmonizing lipidomics. Collectively, the results from the present study provide a significant knowledge base for translation of lipidomic technologies to future clinical applications that might require the determination of reference intervals (RIs) in various human populations or might need to estimate reference change values (RCV), when analytical variability is a key factor for recall during multiple testing of individuals., (© 2024. The Author(s).)

The rapid increase in lipidomic studies has led to a collaborative effort within the community to establish standards and criteria for producing, documenting, and disseminating data. Creating a dynamic easy-to-use checklist that condenses key information about lipidomic experiments into common terminology will enhance the field's consistency, comparability, and repeatability. Here, we describe the structure and rationale of the established Lipidomics Minimal Reporting Checklist to increase transparency in lipidomics research., Competing Interests: Conflict of interests Kaddurah-Daouk is an inventor on key patents in the field of metabolomics and hold equity in Metabolon, a biotech company in North Carolina. In addition, she holds patents licensed to Chymia LLC and PsyProtix with royalties and ownership. Kim Ekroos is the owner of Lipidomics Consulting Ltd., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Mass spectrometry imaging (MSI) has accelerated our understanding of lipid metabolism and spatial distribution in tissues and cells. However, few MSI studies have approached lipid imaging quantitatively and those that have focused on a single lipid class. We overcome this limitation by using a multiclass internal standard (IS) mixture sprayed homogeneously over the tissue surface with concentrations that reflect those of endogenous lipids. This enabled quantitative MSI (Q-MSI) of 13 lipid classes and subclasses representing almost 200 sum-composition lipid species using both MALDI (negative ion mode) and MALDI-2 (positive ion mode) and pixel-wise normalization of each lipid species in a manner analogous to that widely used in shotgun lipidomics. The Q-MSI approach covered 3 orders of magnitude in dynamic range (lipid concentrations reported in pmol/mm 2 ) and revealed subtle changes in distribution compared to data without normalization. The robustness of the method was evaluated by repeating experiments in two laboratories using both timsTOF and Orbitrap mass spectrometers with an ∼4-fold difference in mass resolution power. There was a strong overall correlation in the Q-MSI results obtained by using the two approaches. Outliers were mostly rationalized by isobaric interferences or the higher sensitivity of one instrument for a particular lipid species. These data provide insight into how the mass resolving power can affect Q-MSI data. This approach opens up the possibility of performing large-scale Q-MSI studies across numerous lipid classes and subclasses and revealing how absolute lipid concentrations vary throughout and between biological tissues.

Lipid analysis gained significant importance due to the enormous range of lipid functions, e.g., energy storage, signaling, or structural components. Whole lipidomes can be quantitatively studied in-depth thanks to recent analytical advancements. However, the systematic comparison of thousands of distinct lipidomes remains challenging. We introduce LipidSpace, a standalone tool for analyzing lipidomes by assessing their structural and quantitative differences. A graph-based comparison of lipid structures is the basis for calculating structural space models and subsequently computing lipidome similarities. When adding study variables such as body weight or health condition, LipidSpace can determine lipid subsets across all lipidomes that describe these study variables well by utilizing machine-learning approaches. The user-friendly GUI offers four built-in tutorials and interactive visual interfaces with pdf export. Many supported data formats allow an efficient (re)analysis of data sets from different sources. An integrated interactive workflow guides the user through the quality control steps. We used this suite to reanalyze and combine already published data sets (e.g., one with about 2500 samples and 576 lipids in one run) and made additional discoveries to the published conclusions with the potential to fill gaps in the current lipid biology understanding. LipidSpace is available for Windows or Linux (https://lifs-tools.org).

Lipids are structurally diverse molecules that play a pivotal role in a plethora of biological processes. However, deciphering the biological roles of the specific lipids is challenging due to the existence of numerous isomers. This high chemical complexity of the lipidome is one of the major challenges in lipidomics research, as the traditional liquid chromatography-mass spectrometry (LC-MS) based approaches are often not powerful enough to resolve these isomeric and isobaric nuances within complex samples. Thus, lipids are uniquely suited to the benefits provided by multidimensional liquid chromatography-ion mobility-mass spectrometry (LC-IM-MS) analysis. However, many forms of lipid isomerism, including double-bond positional isomers and regioisomers, are structurally similar such that their collision cross section (CCS) differences are unresolvable via conventional IM approaches. Here we evaluate the performance of a high resolution ion mobility (HRIM) system based on structures for lossless ion manipulation (SLIM) technology interfaced to a high resolution quadrupole time-of-flight (QTOF) analyzer to address the noted lipidomic isomerism challenge. SLIM implements the traveling wave ion mobility technique along an ∼13 m ion path, providing longer path lengths to enable improved separation of isomeric features. We demonstrate the power of HRIM-MS to dissect isomeric PC standards differing only in double bond (DB) and stereospecific number (SN) positions. The partial separation of protonated DB isomers is significantly enhanced when they are analyzed as metal adducts. For sodium adducts, we achieve close to baseline separation of three different PC 18:1/18:1 isomers with different cis -double bond locations. Similarly, PC 18:1/18:1 ( cis -9) can be resolved from the corresponding PC 18:1/18:1 ( trans -9) form. The separation capacity is further enhanced when using silver ion doping, enabling the baseline separation of regioisomers that cannot be resolved when measured as sodium adducts. The sensitivity and reproducibility of the approach were assessed, and the performance for more complex mixtures was benchmarked by identifying PC isomers in total brain and liver lipid extracts.

Reliability, robustness, and interlaboratory comparability of quantitative measurements is critical for clinical lipidomics studies. Lipids' different ex vivo stability in blood bears the risk of misinterpretation of data. Clear recommendations for the process of blood sample collection are required. We studied by UHPLC-high resolution mass spectrometry, as part of the "Preanalytics interest group" of the International Lipidomics Society, the stability of 417 lipid species in EDTA whole blood after exposure to either 4°C, 21°C, or 30°C at six different time points (0.5 h-24 h) to cover common daily routine conditions in clinical settings. In total, >800 samples were analyzed. 325 and 288 robust lipid species resisted 24 h exposure of EDTA whole blood to 21°C or 30°C, respectively. Most significant instabilities were detected for FA, LPE, and LPC. Based on our data, we recommend cooling whole blood at once and permanent. Plasma should be separated within 4 h, unless the focus is solely on robust lipids. Lists are provided to check the ex vivo (in)stability of distinct lipids and potential biomarkers of interest in whole blood. To conclude, our results contribute to the international efforts towards reliable and comparable clinical lipidomics data paving the way to the proper diagnostic application of distinct lipid patterns or lipid profiles in the future., Competing Interests: Conflict of interest The authors declare that they have no conflicts of interest with the contents of this article., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

Dysregulation of sphingomyelin and ceramide metabolism have been implicated in Alzheimer's disease. Genome-wide and transcriptome-wide association studies have identified various genes and genetic variants in lipid metabolism that are associated with Alzheimer's disease. However, the molecular mechanisms of sphingomyelin and ceramide disruption remain to be determined. We focus on the sphingolipid pathway and carry out multi-omics analyses to identify central and peripheral metabolic changes in Alzheimer's patients, correlating them to imaging features. Our multi-omics approach is based on (a) 2114 human post-mortem brain transcriptomics to identify differentially expressed genes; (b) in silico metabolic flux analysis on context-specific metabolic networks identified differential reaction fluxes; (c) multimodal neuroimaging analysis on 1576 participants to associate genetic variants in sphingomyelin pathway with Alzheimer's disease pathogenesis; (d) plasma metabolomic and lipidomic analysis to identify associations of lipid species with dysregulation in Alzheimer's; and (e) metabolite genome-wide association studies to define receptors within the pathway as a potential drug target. We validate our hypothesis in amyloidogenic APP/PS1 mice and show prolonged exposure to fingolimod alleviated synaptic plasticity and cognitive impairment in mice. Our integrative multi-omics approach identifies potential targets in the sphingomyelin pathway and suggests modulators of S1P metabolism as possible candidates for Alzheimer's disease treatment., (© 2022. The Author(s).)

A key element of successful lipidomics analysis is a sufficient extraction of lipid molecules typically by two-phase systems such as chloroform-based Bligh and Dyer (B&D). However, numerous metabolomics and lipidomics studies today apply easy to use one-phase extractions. In this work, quantitative flow injection analysis high-resolution mass spectrometry was applied to benchmark the lipid recovery of popular one-phase extraction methods for human plasma samples. The following organic solvents were investigated: methanol (MeOH), ethanol (EtOH), 2-propanol (IPA), 1-butanol (BuOH), acetonitrile (ACN) and the solvent mixtures BuOH/MeOH (3:1) and MeOH/ACN (1:1). The recovery of polar lysophospholipids was sufficient for all tested solvents. However, nonpolar lipid classes such as triglycerides (TG) and cholesteryl esters (CE) revealed extraction efficiencies less than 5% due to precipitation in polar solvents EtOH, MeOH, MeOH/ACN, and ACN. Sample pellets also contained a substantial amount of phospholipids, for example, more than 75% of total phosphatidylcholine and sphingomyelin for ACN. The loss of lipids by precipitation was directly related to the polarity of solvents and lipid classes. Although, lipid recovery increased with the volume of organic solvent, recovery in polar MeOH remains incomplete also for less polar lipid classes such as ceramides. Addition of stable isotope-labeled internal standards prior to lipid extraction could compensate for insufficient lipid recovery for polar lipid classes including lysolipids and phospholipids but not for nonpolar CE and TG. In summary, application of one-phase extractions should be limited to polar lipid classes unless sufficient recovery/solubility of nonpolar lipids has been demonstrated. The presented data reveal that appropriate lipid extraction efficiency is fundamental to achieve accurate lipid quantification.

A major challenge of lipidomics is to determine and quantify the precise content of complex lipidomes to the exact lipid molecular species. Often, multiple methods are needed to achieve sufficient lipidomic coverage to make these determinations. Multiplexed targeted assays offer a practical alternative to enable quantitative lipidomics amenable to quality control standards within a scalable platform. Herein, we developed a multiplexed normal phase liquid chromatography-hydrophilic interaction chromatography multiple reaction monitoring method that quantifies lipid molecular species across over 20 lipid classes spanning wide polarities in a single 20-min run. Analytical challenges such as in-source fragmentation, isomer separations, and concentration dynamics were addressed to ensure confidence in selectivity, quantification, and reproducibility. Utilizing multiple MS/MS product ions per lipid species not only improved the confidence of lipid identification but also enabled the determination of relative abundances of positional isomers in samples. Lipid class-based calibration curves were applied to interpolate lipid concentrations and guide sample dilution. Analytical validation was performed following FDA Bioanalytical Method Validation Guidance for Industry. We report repeatable and robust quantitation of 900 lipid species measured in NIST-SRM-1950 plasma, with over 700 lipids achieving inter-assay variability below 25%. To demonstrate proof of concept for biomarker discovery, we analyzed plasma from mice treated with a glucosylceramide synthase inhibitor, benzoxazole 1. We observed expected reductions in glucosylceramide levels in treated animals but, more notably, identified novel lipid biomarker candidates from the plasma lipidome. These data highlight the utility of this qualified lipidomic platform for enabling biological discovery., Competing Interests: Conflicts of interest The authors declare that they have no competing interests on every aspect of the work., (Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)

Lipid metabolites, beyond triglycerides and cholesterol, have been shown to have vast potential for applications in clinical applications, with substantial societal and economical value. To successfully evolve from the current research-grade methods to assays suitable for routine clinical applications, a harmonization - if not standardization - of these mass spectrometry-based workflows is necessary. Input on clinical needs and technological capabilities must be obtained from all relevant stakeholders, including wet lab scientists, informaticians and data scientists, manufacturers, and medical professionals. In order to build bridges between this diverse group of professionals, the International Lipidomics Society and its Clinical Lipidomics Interest Group were created. This opinion article is intended to provide an overview of international efforts to tackle the issues of workflow harmonization, and to serve as an open invitation for others to join this growing community., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2022 THE AUTHORS. Publishing services by ELSEVIER B.V. on behalf of MSACL.)

Metabolic profiling is an omics approach that can be used to observe phenotypic changes, making it particularly attractive for biomarker discovery. Although several candidate metabolites biomarkers for disease expression have been identified in recent clinical studies, the reference values of healthy subjects have not been established. In particular, the accuracy of concentrations measured by mass spectrometry (MS) is unclear. Therefore, comprehensive metabolic profiling in large-scale cohorts by MS to create a database with reference ranges is essential for evaluating the quality of the discovered biomarkers. In this study, we tested 8700 plasma samples by commercial kit-based metabolomics and separated them into two groups of 6159 and 2541 analyses based on the different ultra-high-performance tandem mass spectrometry (UHPLC-MS/MS) systems. We evaluated the quality of the quantified values of the detected metabolites from the reference materials in the group of 2541 compared with the quantified values from other platforms, such as nuclear magnetic resonance (NMR), supercritical fluid chromatography tandem mass spectrometry (SFC-MS/MS) and UHPLC-Fourier transform mass spectrometry (FTMS). The values of the amino acids were highly correlated with the NMR results, and lipid species such as phosphatidylcholines and ceramides showed good correlation, while the values of triglycerides and cholesterol esters correlated less to the lipidomics analyses performed using SFC-MS/MS and UHPLC-FTMS. The evaluation of the quantified values by MS-based techniques is essential for metabolic profiling in a large-scale cohort.

Matrix-assisted laser desorption ionization-mass spectrometry imaging (MALDI-MSI) is an emerging label-free method for mapping the distribution of diverse molecular species in tissue sections. Despite recent progress in MALDI-MSI analyses of lipids, it is still difficult to visualize minor bioactive lipids including lysophosphatidic acid (LPA) and sphingosine-1-phosphate (S1P). Here, we have developed a novel on-tissue derivatization method using Phos-tag, a zinc complex that specifically binds to a phosphate monoester group. MALDI-MSI with Phos-tag derivatization made it possible to image LPA and S1P in the murine brain. Furthermore, we were able to visualize other low-abundance lipids containing phosphate monoester, such as phosphatidic acid and ceramide-1-phosphate. Compared with conventional MALDI-MS, this derivatization produced LPA images with high spatial accuracy discriminating LPA artificially produced during MALDI-MS analysis. In mice with deficiencies in enzymes that degrade LPA and S1P, we observed marked S1P and/or LPA accumulation in specific regions of the brain. Thus, the present study provides a simple and optimal way to reveal the spatial localization of potent bioactive lipid phosphates such as LPA and S1P in tissues.

Defects in sphingolipid metabolism have emerged as a common link across neurodegenerative disorders, and a deeper understanding of the lipid content in preclinical models and patient specimens offers opportunities for development of new therapeutic targets and biomarkers. Sphingolipid metabolic pathways include the formation of glycosphingolipid species that branch into staggeringly complex structural heterogeneity within the globoside and ganglioside sub-lipidomes. Characterization of these sub-lipidomes has typically relied on liquid chromatography-mass spectrometry-based (LC-MS) approaches, but such assays are challenging and resource intensive due to the close structural heterogeneity, the presence of isobaric and isomeric species, and broad dynamic range of endogenous glycosphingolipids. Here, we apply Structures for Lossless Ion Manipulations (SLIM)-based High Resolution Ion Mobility (HRIM)-MS to enable rapid, repeatable, quantitative assays with deep structural information sufficient to resolve endogenous brain gangliosides at the level of individual molecular species. Analyses were performed using a prototype SLIM-MS instrument equipped with a 13-m serpentine path which enabled resolution of closely related isomeric analytes such as GD1a d36:1 and GD1b d36:1 based on recorded mass-to-charge (m/z) and arrival times. To demonstrate the power of our methodology, brain extracts derived from wild-type mice hemi-brains were analyzed by HRIM-MS using flow injection analyses (FIA) without the need for additional separation by liquid chromatography. Endogenous ganglioside species were readily resolved, identified, and quantified by FIA-SLIM-MS analyses within 2 min per sample. Thus, the FIA-SLIM-MS platform enables robust quantification across a broad range of lipid species in biological specimens in a standardized assay format that is readily scalable to support studies with large sample numbers., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier B.V. All rights reserved.)

In the last 2 decades, lipidomics has become one of the fastest expanding scientific disciplines in biomedical research. With an increasing number of new research groups to the field, it is even more important to design guidelines for assuring high standards of data quality. The Lipidomics Standards Initiative is a community-based endeavor for the coordination of development of these best practice guidelines in lipidomics and is embedded within the International Lipidomics Society. It is the intention of this review to highlight the most quality-relevant aspects of the lipidomics workflow, including preanalytics, sample preparation, MS, and lipid species identification and quantitation. Furthermore, this review just does not only highlights examples of best practice but also sheds light on strengths, drawbacks, and pitfalls in the lipidomic analysis workflow. While this review is neither designed to be a step-by-step protocol by itself nor dedicated to a specific application of lipidomics, it should nevertheless provide the interested reader with links and original publications to obtain a comprehensive overview concerning the state-of-the-art practices in the field., Competing Interests: Conflict of interest The authors declare that they have no conflicts of interest with the contents of this article., (Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.)

Aims: Smoking is an important risk factor for the development of chronic obstructive pulmonary disease and cardiovascular diseases. This study aimed to further elucidate the role of ceramides, as a key lipid class dysregulated in disease states., Main Methods: In this article we developed and validated LC-MS/MS method for ceramides (Cer(d18:1/16:0), Cer(d18:1/18:0), Cer(d18:1/24:0) and Cer(d18:1/24:1(15Z)) for the absolute quantification. We deployed it together with proteomics and transcriptomic analysis to assess the effects of cigarette smoke (CS) from the reference cigarette as well as aerosols from heat-not-burn (HnB) tobacco and e-vapor products in apolipoprotein E-deficient (ApoE -/- ) mice over several time points., Key Findings: In the lungs, CS exposure substantially elevated the ratios of Cer(d18:1/24:0) and Cer(d18:1/24:1) to Cer(d18:1/18:0) in two independent ApoE -/- mouse inhalation studies. Data from previous studies, in both ApoE -/- and wild-type mice, further confirmed the reproducibility of this finding. Elevation of these ceramide ratios was also observed in plasma/serum, the liver, and-for the Cer(d18:1/24:1(15Z)) to Cer(d18:1/18:0) ratio-the abdominal aorta. Also, the levels of acid ceramidase (Asah1) and glucocerebrosidase (Gba)-lysosomal enzymes involved in the hydrolysis of glucosylceramides-were consistently elevated in the lungs after CS exposure. In contrast, exposure to HnB tobacco product and e-vapor aerosols did not induce significant changes in the ceramide profiles or associated enzymes., Significance: Our work in mice contributes to the accumulating evidence on the importance of ceramide ratios as biologically relevant markers for respiratory disorders, adding to their already demonstrated role in cardiovascular disease risk assessment in humans., (Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved.)

Contamination from the polymeric material released by vial caps used for sample introduction in liquid chromatography can significantly affect the signal of the analyte of interest. In particular, repeated injections from the same sample vial can enhance this suppressing effect. Multiple injections of the same sample are often used in metabolomics and lipidomics during routine analyses. Here we demonstrate how the presence of contaminant polymers, originating from the vial closures, significantly influences the estimation of the relative amount of endogenous lipids in human plasma. Furthermore, this can negatively impact other operations in mass spectrometric analysis, such as instrument equilibration and tuning or the common use of technical replicates to improve confidence in data interpretation. Our observations provide critical information on how to improve future analyses through the use of appropriate vial caps, solvents, chromatographic separations and equipment., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgements Work in our laboratory is supported by grants from the National University of Singapore via the Life Sciences Institute (LSI), the National Research Foundation (NRF, NRFI2015-05 and NRFSBP-P4), the NRF and A∗STAR IAF-ICP I1901E0040, NUS iHealthtech Precision Medicine and Personalised Therapeutics., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Lipidomic analyses aim for absolute quantification of lipid species profiles in biological samples. In past years, mass spectrometry (MS) methods based on high resolution accurate masses (HRAM) have increasingly been applied to identify and quantify lipid species on the MS level. This strategy requires consideration of isobaric overlaps which may also result from various adduct ions. Generally applied solvent additives favor the formation of protonated and ammoniated ions in positive ion mode, yet sodiated ions are also frequently observed. These sodiated ions interfere with protonated ions of the species of the same lipid class with two additional CH 2 and three double bonds (Δ m / z = 0.0025) and the first isotopic peak overlaps with ammoniated ions of a species with one additional CH 2 and four double bonds (Δ m / z = 0.0057). In this work, we present an algorithm based on the sodiated to protonated/ammoniated adduct ion ratios of applied internal standards to correct for these interferences. We could demonstrate that these ratios differ significantly between lipid classes but are affected by neither chain length nor number of double bonds within a lipid class. Finally, the algorithm is demonstrated for correcting human serum samples analyzed by Fourier-transform mass spectrometry (FTMS). Here, the application of sodium correction significantly reduced overestimations and misidentifications.

For many diseases, there is an unmet need for new or better biomarkers for improved disease risk assessment and monitoring, as available markers lack sufficient specificity. Lipids are drawing major interest as potential candidates for filling these gaps. This has recently been demonstrated by the identification of selective ceramides for prediction of cardiovascular mortality, enabling improved risk assessment of cardiovascular disease compared with conventional clinical markers. In this review, we discuss current lipid biomarker findings and the possible connection between cardiovascular disease, chronic obstructive pulmonary disease, and aging. Moreover, we discuss how to overcome the current roadblocks facing lipid biomarker research. We stress the need for improved quantification, standardization of methodologies, and establishment of initial reference values to allow for an efficient transfer path of research findings into the clinical landscape, and, ultimately, to put newly identified biomarkers into practical use., Competing Interests: Declaration of Competing Interest KE is the founder and CEO of Lipidomics Consulting Ltd. OL, BT, PC, JH, and NVI are employees of Philip Morris International., (Copyright © 2020 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Lipids are essential for all facets of life. They play three major roles: energy metabolism, structural, and signaling. They are dynamic molecules strongly influenced by endogenous and exogenous factors including genetics, diet, age, lifestyle, drugs, disease and inflammation. As precision medicine starts to become mainstream, there is a huge burgeoning interest in lipids and their potential to act as unique biomarkers or prognostic indicators. Lipids comprise a large component of all metabolites (around one-third), and our expanding knowledge about their dynamic behavior is fueling the hope that mapping their regulatory biochemical pathways on a systems level will revolutionize our ability to prevent, diagnose, and stratify major human diseases. Up to now, clinical lipid measurements have consisted primarily of total cholesterol or triglycerides, as a measure for cardiovascular risk and response to lipid lowering drugs. Nowadays, we are able to measure thousands of individual lipids that make up the lipidome. nuclear magnetic resonance spectrometry (NMR) metabolomics is also being increasingly used in large cohort studies where it can report on total levels of selected lipid classes, and relative levels of fatty acid saturation. To support the application of lipidomics research, LIPID MAPS was established in 2003, and since then has gone on to become the go-to resource for several lipid databases, lipid drawing tools, data deposition, and more recently lipidomics informatics tools, and a lipid biochemistry encyclopedia, LipidWeb. Alongside this, the recently established Lipidomics Standards Initiative plays a key role in standardization of lipidomics methodologies. This article is categorized under: Laboratory Methods and Technologies > Metabolomics Analytical and Computational Methods > Analytical Methods., (© 2019 Wiley Periodicals, Inc.)