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

Angiotensin II Alters Mitochondrial Membrane Potential and Lipid Metabolism in Rat Colonic Epithelial Cells

An over-active renin-angiotensin system (RAS) is characterized by elevated angiotensin II (Ang II). While Ang II can promote metabolic and mitochondrial dysfunction in tissues, little is known about its role in the gastrointestinal system (GI). Here, we treated rat primary colonic epithelial cells with Ang II (1-5000 nM) to better define their role in the GI. We hypothesized that Ang II would negatively affect mitochondrial bioenergetics as these organelles express Ang II receptors. Ang II increased cellular ATP production but reduced the mitochondrial membrane potential (MMP) of colonocytes. However, cells maintained mitochondrial oxidative phosphorylation and glycolysis with treatment, reflecting metabolic compensation with impaired MMP. To determine whether lipid dysregulation was evident, untargeted lipidomics were conducted. A total of 1949 lipids were detected in colonocytes spanning 55 distinct (sub)classes. Ang II (1 nM) altered the abundance of some sphingosines [So(d16:1)], ceramides [Cer-AP(t18:0/24:0)], and phosphatidylcholines [OxPC(16:0_20:5(2O)], while 100 nM Ang II altered some triglycerides and phosphatidylserines [PS(19:0_22:1). Ang II did not alter the relative expression of several enzymes in lipid metabolism; however, the expression of pyruvate dehydrogenase kinase 2 (PDK2) was increased, and PDK2 can be protective against dyslipidemia. This study is the first to investigate the role of Ang II in colonic epithelial cell metabolism.

Sub-5-min RP-UHPLC-TIMS for high-throughput untargeted lipidomics and its application to multiple matrices

Untargeted lipidomics, with its ability to take a snapshot of the lipidome landscape, is an important tool to highlight lipid changes in pathology or drug treatment models. One of the shortcomings of most untargeted lipidomics based on UHPLC-HRMS is the low throughput, which is not compatible with large-scale screening. In this contribution, we evaluate the application of a sub-5-min high-throughput four-dimensional trapped ion mobility mass spectrometry (HT-4D-TIMS) platform for the fast profiling of multiple complex biological matrices. Human AC-16 cells and mouse brain, liver, sclera, and feces were used as samples. By using a fast 4-min RP gradient, the implementation of TIMS allows us to differentiate coeluting isomeric and isobaric lipids, with correct precursor ion isolation, avoiding co-fragmentation and chimeric MS/MS spectra. Globally, the HT-4D-TIMS allowed us to annotate 1910 different lipid species, 1308 at the molecular level and 602 at the sum composition level, covering 58 lipid subclasses, together with quantitation capability covering more than three orders of magnitude. Notably, TIMS values were highly comparable with respect to longer LC gradients (CV% = 0.39%). These results highlight how HT-4D-TIMS-based untargeted lipidomics possess high coverage and accuracy, halving the analysis time with respect to conventional UHPLC methods, and can be used for fast and accurate untargeted analysis of complex matrices to rapidly evaluate changes of lipid metabolism in disease models or drug discovery campaigns.

Comparison of Workflows for Milk Lipid Analysis: Phospholipids

Milk is a rich source of lipids, with the major components being triglycerides (TAG) and phospholipids (mainly phosphatidylcholine (PC), sphingomyelin (SM), phosphatidylethanolamine (PE), phosphatidylserine (PS) and phosphatidylinositol (PI)). Liquid chromatography-mass spectrometry (LC-MS) is the predominant technique for lipid identification and quantification across all biological samples. While fatty acid (FA) composition of the major lipid classes of milk can be readily determined using tandem MS, elucidating the regio-distribution and double bond position of the FA remains difficult. Various workflows have been reported on the quantification of lipid species in biological samples in the past 20 years, but no standard or consensus methods are currently available for the quantification of milk phospholipids. This study will examine the influence of several common factors in lipid analysis workflow (including lipid extraction protocols, LC stationary phases, mobile phase buffers, gradient elution programmes, mass analyser resolution and isotope correction) on the quantification outcome of bovine milk phospholipids. The pros and cons of the current LC-MS methods as well as the critical problems to be solved will also be discussed.

A software-assisted untargeted liquid chromatography-mass spectrometry method for lipidomic profiling of human plasma samples

INTRODUCTION

In this paper, an analytical pipeline designed for untargeted lipidomic profiling in human plasma is proposed. The analytical pipeline was developed for case-control studies nested in prospective cohorts.

METHODS

The procedure consisted of isopropanol protein precipitation followed by reverse phase liquid chromatography coupled to high resolution mass spectrometry and software-assisted data processing. The compounds are putatively annotated by matching experimental mass spectrometry data with spectral library data using LipidSearch software. The lipid profile of a pool of plasma samples from 10 healthy volunteers was detected in both positive and negative polarity modes. The impact of the chosen polarity on the number and quality of the lipid identification has been evaluated.

RESULTS

More than 1000 lipids from 12 different classes were detected, 1150 in positive mode and 273 in negative mode. Nearly half of them were unambiguously identified by the software in positive mode, and about one-third in negative mode. The method repeatability was assessed on the plasma pool samples by means of variance components analysis. The intra- and inter-assay precision was measured for 10 lipids chosen among the most abundant found within the different lipid classes. The intra-assay coefficients of variation ranged from 2.56% to 4.56% while intra- and inter-day coefficients of variance never exceeded the 15% benchmark adopted. The lipidomic profiles of the 10 healthy volunteers were also investigated.

DISCUSSION

This method detects a wide range of lipids and reports their degree of identification. It is particularly fit and well-designed for large case-control epidemiologic studies.

Norm ISWSVR: A Data Integration and Normalization Approach for Large-Scale Metabolomics

Large-scale and long-period metabolomics study is more susceptible to various sources of systematic errors, resulting in nonreproducibility and poor data quality. A reliable and robust batch correction method removes unwanted systematic variations and improves the statistical power of metabolomics data, which undeniably becomes an important issue for the quality control of metabolomics. This study proposed a novel data normalization and integration method, Norm ISWSVR. It is a two-step approach via combining the best-performance internal standard correction with support vector regression normalization, comprehensively removing the systematic and random errors and matrix effects. This method was investigated in three untargeted lipidomics or metabolomics datasets, and the performance was further evaluated systematically in comparison with that of 11 other normalization methods. As a result, Norm ISWSVR decreased the data's median cross-validated relative standard deviation (cvRSD), increased the correlation between QCs, improved the classification accuracy of biomarkers, and was well-compatible with quantitative data. More importantly, Norm ISWSVR also allows a low frequency of QCs, which could significantly decrease the burden of a large-scale experiment. Correspondingly, Norm ISWSVR favorably improves the data quality of large-scale metabolomics data.

Lipidomics and Redox Lipidomics Indicate Early Stage Alcohol-Induced Liver Damage

Alcoholic fatty liver disease (AFLD) is characterized by lipid accumulation and inflammation and can progress to cirrhosis and cancer in the liver. AFLD diagnosis currently relies on histological analysis of liver biopsies. Early detection permits interventions that would prevent progression to cirrhosis or later stages of the disease. Herein, we have conducted the first comprehensive time-course study of lipids using novel state-of-the art lipidomics methods in plasma and liver in the early stages of a mouse model of AFLD, i.e., Lieber-DeCarli diet model. In ethanol-treated mice, changes in liver tissue included up-regulation of triglycerides (TGs) and oxidized TGs and down-regulation of phosphatidylcholine, lysophosphatidylcholine, and 20-22-carbon-containing lipid-mediator precursors. An increase in oxidized TGs preceded histological signs of early AFLD, i.e., steatosis, with these changes observed in both the liver and plasma. The major lipid classes dysregulated by ethanol play important roles in hepatic inflammation, steatosis, and oxidative damage. Conclusion: Alcohol consumption alters the liver lipidome before overt histological markers of early AFLD. This introduces the exciting possibility that specific lipids may serve as earlier biomarkers of AFLD than those currently being used.

Improving quantitative analysis of spark-induced breakdown spectroscopy: Multivariate calibration of metal particles using machine learning

We have recently developed a low-cost spark-induced breakdown spectroscopy (SIBS) instrument for in-situ analysis of toxic metal aerosol particles that we call TARTA (toxic-metal aerosol real time analyzer). In this work, we applied machine learning methods to improve the quantitative analysis of elemental mass concentrations measured by this instrument. Specifically, we applied least absolute shrinkage and selection operator (LASSO), partial least squares (PLS) regression, principal component regression (PCR), and support vector regression (SVR) to develop multivariate calibration models for 13 metals (e.g., Cr, Cu, Mn, Fe, Zn, Co, Al, K, Be, Hg, Cd, Pb, and Ni), some of which are included on the US EPA hazardous air pollutants (HAPS) list. The calibration performance, adjusted coefficient of determination (R2) and normalized root mean square error (RMSE), and limit of detection (LOD) of the proposed models were compared to those of univariate calibration models for each analyte. Our results suggest that machine learning models tend to have better prediction accuracy and lower LODs than conventional univariate calibration, of which the LASSO approach performs the best with R2 > 0.8 and LODs of 40-170 ng m-3 at a sampling time of 30 min and a flow rate of 15 l min -1. We then assessed the applicability of the LASSO model for quantifying elemental concentrations in mixtures of these metals, serving as independent validation datasets. Ultimately, the LASSO model developed in this work is a very promising machine learning approach for quantifying mass concentration of metals in aerosol particles using TARTA.

Characterization of Glucuronosyl-diacyl/monoacylglycerols and Discovery of Their Acylated Derivatives in Tomato Lipid Extracts by Reversed-Phase Liquid Chromatography with Electrospray Ionization and Tandem Mass Spectrometry

Glucuronic acid containing diacylglycerols (3-(O-α-d-glucuronopyranosyl)-1,2-diacyl-sn-glycerols, GlcA-DAG) are glycolipids of plant membranes especially formed under phosphate-depletion conditions. An analytical approach for the structural characterization of GlcA-DAG in red ripe tomato (Solanum lycopersicum L.) extracts, based on reversed-phase liquid chromatography (RPLC) coupled with electrospray ionization (ESI) and tandem mass spectrometry (MS/MS) using a linear ion trap, is described in this paper. At least 14 GlcA-DAG (R₁/R₂) species, including four regioisomers, containing three predominant fatty acyl chains C16:0, C18:2, and C18:3, were identified for the first time. Moreover, 29 GlcA-DAG acylated on the glucuronosyl ring (acyl-R₃ GlcA-DAG) were discovered, alongside 15 acylated lyso-forms, i.e., acylated 3-(O-α-d-glucuronosyl)monoacylglycerols, abbreviated as acyl-R₃ GlcA-MAG (R₁/0) or (0/R₂). Although many of these acylated lyso-forms were isomeric with GlcA-DAG (i.e., acyl chains with equivalent sum composition), they were successfully separated by reversed-phase liquid chromatography (RPLC) using a solid-core C18 column packed with 2.6 μm particle size. Tandem MS (and eventually MS³) data obtained from sodium adducts ([M + Na]⁺) and deprotonated molecules ([M - H]^-) were fundamental to detect diagnostic product ions related to the glucuronosyl ring and then determine the identity of all investigated glycolipids, especially to recognize the acyl chain linked to the ring. A classification of GlcA-MAG, GlcA-DAG, and acylated GlcA-DAG and GlcA-MAG was generated by an in house-built database. The discovery of acylated derivatives emphasized the already surprising heterogeneity of glucuronic acid-containing mono- and diacylglycerols in tomato plants, stimulating interesting questions on the role played by these glycolipids.

High Throughput Semiquantitative UHPSFC-MS/MS Lipid Profiling and Lipid Class Determination

High throughput and high-resolution lipid analyses are important for many biological model systems and research questions. This comprises both monitoring at the individual lipid species level and broad lipid classes. Here, we present a nontarget semiquantitative lipidomics workflow based on ultrahigh performance supercritical fluid chromatography (UHPSFC)-mass spectrometry (MS). The optimized chromatographic conditions enable the base-line separation of both nonpolar and polar classes in a single 7-minute run. Ionization efficiencies of lipid classes vary 10folds in magnitude and great care must be taken in a direct interpretation of raw data. Therefore, the inclusion of internal standards or experimentally determined Response factors (RF) are highly recommended for the conversion of raw abundances into (semi) quantitative data. We have deliberately developed an algorithm for automatic semiquantification of lipid classes by RF. The workflow was tested and validated using a bovine liver extract with satisfactory results. The RF corrected data provide a more representative relative lipid class determination, but also the interpretation of individual lipid species should be performed on RF corrected data. In addition, semiquantification can be improved by using internal or also external standards when more accurate quantitative data are of interest but this requires validation for all new sample types. The workflow established greatly extends the potential of nontarget UHPSFC–MS/MS based analysis.

In vitro generation of oxidized standards for lipidomics. Application to major membrane lipid components

Membrane lipids (sphingolipids, glycerophospholipids, cardiolipins, and cholesteryl esters) are critical in cellular functions. Alterations in the levels of oxidized counterparts of some of these lipids have been linked to the onset and development of many pathologies. Unfortunately, the scarce commercial availability of chemically defined oxidized lipids is a limitation for accurate quantitative analysis, characterization of oxidized composition, or testing their biological effects in lipidomic studies. To address this dearth of standards, several approaches rely on in-house prepared mixtures of oxidized species generated under in vitro conditions from different sources - non-oxidized commercial standards, liposomes, micelles, cells, yeasts, and human preparations - and using different oxidant systems - UVA radiation, air exposure, enzymatic or chemical oxidant systems, among others. Moreover, high-throughput analytical techniques such as liquid chromatography coupled to mass spectrometry (LC-MS) have provided evidence of their capabilities to study oxidized lipids both in in vitro models and complex biological samples. In this review, we describe the commercial resources currently available, the in vitro strategies carried out for obtaining oxidized lipids as standards for LC-MS analysis, and their applications in lipidomics studies, specifically for lipids found in cell and mitochondria membranes.

HIGH RESOLUTION MASS SPECTROMETRY IN LIPIDOMICS

The boost of research output in lipidomics during the last decade is tightly linked to improved instrumentation in mass spectrometry. Associated with this trend is the shift from low resolution-toward high-resolution lipidomics platforms. This review article summarizes the state of the art in the lipidomics field with a particular focus on the merits of high mass resolution. Following some theoretical considerations on the benefits of high mass resolution in lipidomics, it starts with a historical perspective on lipid analysis by sector instruments and moves further to today's instrumental approaches, including shotgun lipidomics, liquid chromatography-mass spectrometry, matrix-assisted laser desorption ionization-time-of-flight, and imaging lipidomics. Subsequently, several data processing and data analysis software packages are critically evaluated with all their pros and cons. Finally, this article emphasizes the importance and necessity of quality standards as the field evolves from its pioneering phase into a mature and robust omics technology and lists various initiatives for improving the applicability of lipidomics. © 2020 The Authors. Mass Spectrometry Reviews published by John Wiley & Sons Ltd. Mass Spec Rev.

A normalized signal calibration with a long-term reference improves the robustness of RPLC-MRM/MS lipidomics in plasma

Improving the reliability of quantification in lipidomic analyses is crucial for its successful application in the discovery of new biomarkers or in clinical practice. In this study, we propose a workflow to improve the accuracy and precision of lipidomic results issued by the laboratory. Lipid species from 11 classes were analyzed by a targeted RPLC-MRM/MS method. The peak areas of species were used to estimate concentrations by an internal standard calibration approach (IS-calibration) and by an alternative normalization signal calibration schema (NS-calibration). The latter uses a long-term reference plasma material as a matrix-matched external calibrator whose accuracy was compared to the NIST SRM-1950 mean consensus values reported by the Interlaboratory Lipidomics Comparison Exercise. The bias of lipid concentrations showed a good accuracy for 69 of 89 quantified lipids. The quantitation of species by the NS-calibration schema improved the within- and between-batch reproducibility in quality control samples, in comparison to the usual IS-calibration approach. Moreover, the NS-calibration workflow improved the robustness of the lipidomics measurements reducing the between-batch variability (relative standard deviation <10% for 95% of lipid species) in real conditions tested throughout the analysis of 120 plasma samples. In addition, we provide a free access web tool to obtain the concentration of lipid species by the two previously mentioned quantitative approaches, providing an easy follow-up of quality control tasks related to lipidomics.

Normalization methods for reducing interbatch effect without quality control samples in liquid chromatography-mass spectrometry-based studies

Data normalization is an essential part of a large-scale untargeted mass spectrometry metabolomics analysis. Autoscaling, Pareto scaling, range scaling, and level scaling methods for liquid chromatography-mass spectrometry data processing were compared with the most common normalization methods, including quantile normalization, probabilistic quotient normalization, and variance stabilizing normalization. These methods were tested on eight datasets from various clinical studies. The efficiency of the data normalization was assessed by the distance between clusters corresponding to batches and the distance between clusters corresponding to clinical groups in the space of principal components, as well as by the number of features with a pairwise statistically significant difference between the batches and the number of features with a pairwise statistically significant difference between clinical groups. Autoscaling demonstrated the most effective reduction in interbatch variation and can be preferable to probabilistic quotient or quantile normalization in liquid chromatography-mass spectrometry data.

Coupling Machine Learning and Lipidomics as a Tool to Investigate Metabolic Dysfunction-Associated Fatty Liver Disease. A General Overview

Hepatic biopsy is the gold standard for staging nonalcoholic fatty liver disease (NAFLD). Unfortunately, accessing the liver is invasive, requires a multidisciplinary team and is too expensive to be conducted on large segments of the population. NAFLD starts quietly and can progress until liver damage is irreversible. Given this complex situation, the search for noninvasive alternatives is clinically important. A hallmark of NAFLD progression is the dysregulation in lipid metabolism. In this context, recent advances in the area of machine learning have increased the interest in evaluating whether multi-omics data analysis performed on peripheral blood can enhance human interpretation. In the present review, we show how the use of machine learning can identify sets of lipids as predictive biomarkers of NAFLD progression. This approach could potentially help clinicians to improve the diagnosis accuracy and predict the future risk of the disease. While NAFLD has no effective treatment yet, the key to slowing the progression of the disease may lie in predictive robust biomarkers. Hence, to detect this disease as soon as possible, the use of computational science can help us to make a more accurate and reliable diagnosis. We aimed to provide a general overview for all readers interested in implementing these methods.

A Review of Efforts to Improve Lipid Stability during Sample Preparation and Standardization Efforts to Ensure Accuracy in the Reporting of Lipid Measurements

Lipidomics is a rapidly growing field, fueled by developments in analytical instrumentation and bioinformatics. To date, most researchers and industries have employed their own lipidomics workflows without a consensus on best practices. Without a community-wide consensus on best practices for the prevention of lipid degradation and transformations through sample collection and analysis, it is difficult to assess the quality of lipidomics data and hence trust results. Clinical studies often rely on samples being stored for weeks or months until they are analyzed, but inappropriate sampling techniques, storage temperatures, and analytical protocols can result in the degradation of complex lipids and the generation of oxidized or hydrolyzed metabolite artifacts. While best practices for lipid stability are sample dependent, it is generally recommended that strategies during sample preparation capable of quenching enzymatic activity and preventing oxidation should be considered. In addition, after sample preparation, lipid extracts should be stored in organic solvents with antioxidants at -20 °C or lower in an airtight container without exposure to light or oxygen. This will reduce or eliminate sublimation, and chemically and physically induced molecular transformations such as oxidation, enzymatic transformation, and photon/heat-induced degradation. This review explores the available literature on lipid stability, with a particular focus on human health and/or clinical lipidomic applications. Specifically, this includes a description of known mechanisms of lipid degradation, strategies, and considerations for lipid storage, as well as current efforts for standardization and quality insurance of protocols.

Relationships between Very Low-Density Lipoproteins-Ceramides, -Diacylglycerols, and -Triacylglycerols in Insulin-Resistant Men

This short report describes the relationships between concentrations of ceramides (CER), diacylglycerols (DAG), triacylglycerols (TAG) in very low-density lipoproteins (VLDL) particles, and hepatic lipid accumulation. VLDL particles were isolated from male subjects (n = 12, mean ± SD, age 42.1 ± 5.4 years, BMI 37.4 ± 4.1 kg/m² , ALT 45 ± 21 U/L) and apolipoprotein B100 (apoB100), VLDL-TAG, -CER, and -DAG quantified. The contents of all three lipids were highly correlated with VLDL particle number (r ≥ 0.768, p ≤ 0.003). The molar quantity of VLDL-TAG was 3× that of DAG and 137× that of CER (14,053 ± 5714, 5004 ± 2714, and 105 ± 49 mol/mol apoB100, respectively). Reduced VLDL-CER concentrations were associated with both higher insulin levels (r = -0.645, p = 0.024) and intrahepatic-TAG (r = -0.670, p = 0.017). In fatty liver, the secretion of hepatic TAG, CER, and DAG may be suppressed and contribute to intrahepatic lipotoxicity. The mechanisms by which hepatic-CER and -DAG synthesis and assembly into VLDL is coordinately controlled with TAG will be important in understanding the emerging role of elevated CER contributing to cardiometabolic disease.

Environmental lipidomics: understanding the response of organisms and ecosystems to a changing world

BACKGROUND

Understanding the interaction between organisms and the environment is important for predicting and mitigating the effects of global phenomena such as climate change, and the fate, transport, and health effects of anthropogenic pollutants. By understanding organism and ecosystem responses to environmental stressors at the molecular level, mechanisms of toxicity and adaptation can be determined. This information has important implications in human and environmental health, engineering biotechnologies, and understanding the interaction between anthropogenic induced changes and the biosphere. One class of molecules with unique promise for environmental science are lipids; lipids are highly abundant and ubiquitous across nearly all organisms, and lipid profiles often change drastically in response to external stimuli. These changes allow organisms to maintain essential biological functions, for example, membrane fluidity, as they adapt to a changing climate and chemical environment. Lipidomics can help scientists understand the historical and present biofeedback processes in climate change and the biogeochemical processes affecting nutrient cycles. Lipids can also be used to understand how ecosystems respond to historical environmental changes with lipid signatures dating back to hundreds of millions of years, which can help predict similar changes in the future. In addition, lipids are direct targets of environmental stressors, for example, lipids are easily prone to oxidative damage, which occurs during exposure to most toxins.

AIM OF REVIEW

This is the first review to summarize the current efforts to comprehensively measure lipids to better understand the interaction between organisms and their environment. This review focuses on lipidomic applications in the arenas of environmental toxicology and exposure assessment, xenobiotic exposures and health (e.g., obesity), global climate change, and nutrient cycles. Moreover, this review summarizes the use of and the potential for lipidomics in engineering biotechnologies for the remediation of persistent compounds and biofuel production.

KEY SCIENTIFIC CONCEPT

With the preservation of certain lipids across millions of years and our ever-increasing understanding of their diverse biological roles, lipidomic-based approaches provide a unique utility to increase our understanding of the contemporary and historical interactions between organisms, ecosystems, and anthropogenically-induced environmental changes.

"Lipidomics": Mass spectrometric and chemometric analyses of lipids

Lipids are ubiquitous in the human organism and play essential roles as components of cell membranes and hormones, for energy storage or as mediators of cell signaling pathways. As crucial mediators of the human metabolism, lipids are also involved in metabolic diseases, cardiovascular and renal diseases, cancer and/or hepatological and neurological disorders. With rapidly growing evidence supporting the impact of lipids on both the genesis and progression of these diseases as well as patient wellbeing, the characterization of the human lipidome has gained high interest and importance in life sciences and clinical diagnostics within the last 15 years. This is mostly due to technically advanced molecular identification and quantification methods, mainly based on mass spectrometry. Mass spectrometry has become one of the most powerful tools for the identification of lipids. New lipidic mediators or biomarkers of diseases can be analysed by state-of-the art mass spectrometry techniques supported by sophisticated bioinformatics and biostatistics. The lipidomic approach has developed dramatically in the realm of life sciences and clinical diagnostics due to the available mass spectrometric methods and in particular due to the adaptation of biostatistical methods in recent years. Therefore, the current knowledge of lipid extraction methods, mass-spectrometric approaches, biostatistical data analysis, including workflows for the interpretation of lipidomic high-throughput data, are reviewed in this manuscript.

Lipidomics from sample preparation to data analysis: a primer

Lipids are amongst the most important organic compounds in living organisms, where they serve as building blocks for cellular membranes as well as energy storage and signaling molecules. Lipidomics is the science of the large-scale determination of individual lipid species, and the underlying analytical technology that is used to identify and quantify the lipidome is generally mass spectrometry (MS). This review article provides an overview of the crucial steps in MS-based lipidomics workflows, including sample preparation, either liquid–liquid or solid-phase extraction, derivatization, chromatography, ion-mobility spectrometry, MS, and data processing by various software packages. The associated concepts are discussed from a technical perspective as well as in terms of their application. Furthermore, this article sheds light on recent advances in the technology used in this field and its current limitations. Particular emphasis is placed on data quality assurance and adequate data reporting; some of the most common pitfalls in lipidomics are discussed, along with how to circumvent them.