The eicosanoids are a family of lipid mediators of pain and inflammation involved in multiple pathologies. Urine has become a useful, readily accessible biofluid for monitoring the endogenous synthesis of these metabolites. The clinical interest of eicosanoids requires their targeted determination, which, in turn, warrants the development of high-throughput analytical methodology such as liquid chromatography–tandem mass spectrometry methods.
By Dr Cristina Gómez

Introduction
Eicosanoids are bioactive lipid mediators produced by the enzymatic and/or non-enzymatic oxidation of arachidonic acid (5,8,11,14-eicosatetraenoic acid), an abundant cell membrane component. Three different oxidative pathways, comprising of cyclo-oxygenase, lipoxygenase and cytochromes P450, enzymatically oxidize arachidonic acid which is converted to different eicosanoids based on the pathway involved (Fig. 1) [1–4]. These signalling mediators typically are not stored within cells but rather synthesized de novo from membrane-released arachidonic acid as required, when cells are activated by mechanical trauma or by specific cytokine, growth factor, and other stimuli. They are found in biological fluids and tissues at low concentrations. They play a fundamental role in promoting and modulating inflammation, providing both pro-inflammatory signals and terminating the inflammatory process, as well as maintaining tissue and vascular homeostasis. Disruption of the homeostasis of eicosanoids is closely related to a range of inflammatory pathophysiological conditions (fever, nephritis, cardiovascular diseases, inflammation, allergy, HIV and cancer) and act as a regulator of important processes (e.g. smooth muscle tone, platelet aggregation and vascular permeability) (1–4).
The levels of lipid mediators in activated cells may rapidly increase by several orders of magnitude compared to baseline. These signalling mediators act locally, but are rapidly removed by the circulation, meta-bolized and transported to the systemic circulation as a mixture of primary and secondary metabolites (2). The circulating metabolites are efficiently cleared by the kidney, and excreted into the urine. Many eicosanoid metabolites are frequently used as biomarkers owing to their involvement in diseases and pathophysiological conditions.

Biomarkers could be defined as compounds found in biological fluids or tissues indicating the presence of an abnormal or significantly changed condition. A biomarker can, for example, be used for disease detection, response to therapy, or to monitor exposure to toxicological compounds or conditions. The compound can either be present in higher or lower amounts than what is considered a normal range. The measurement of eicosanoid metabolites in the urine reflect activation of the different biosynthetic pathways, e.g. analysis of 8-iso-prostaglandin (PG)F_2α as a biomarker for oxidative stress, or PGE2 as a biomarker involved in inflammation. Accordingly, the analysis and quantification of these compounds in biological samples has emerged as the most practical and reliable method to assess in vivo production of primary eicosanoids.

Quantification of eicosanoids in urine
To enhance our understanding of disease progression and eicosanoid concentration, measurement of the complete profile of produced eicosanoids is essential. Given the clinical interest in eicosanoids and the complexity of their responses to biological stimuli, it is necessary to systematically monitor the changes in their concentrations in various tissues and biological fluids. During the early inflammatory phase, eicosanoids are excreted by inflammatory cells and can increase up to 100-fold in local concentration [2]. This requires sensitive, selective and reproducible methods for their quantification.

Measurements of eicosanoids in the blood are difficult owing to low circulating levels (both primary eicosanoids and their metabolites are present in blood and urine at concentrations in the picomolar to nanomolar range), rapid hepatic and renal clearance, and induction of biosynthesis during sampling. Urine is, therefore, an optimal non-invasive biofluid for monitoring eicosanoid level, readily accessible biofluid for monitoring the endogenous synthesis of these metabolites and may be interpreted by consideration of the physiological context. The primary eicosanoids present in urine may be derived from the kidneys, whereas the urinary eicosanoid metabolites most likely reflect systemic eicosanoid metabolites [1, 2]. The quantification of primary eicosanoids and their metabolites in urine is sufficient for the assessment of both renal as well as systemic production of eicosanoids. Their quantification can serve as systematic indicators of pathological processes in the vascular, respiratory, and other cellular systems [5–7]. Reliable measurement of selected eicosanoids levels could benefit the selection of precision biomarkers assisting disease prognosis, progression and pharma-codynamic assessment.

The thorough evaluation of the eicosanoid metabolome is useful for understanding the physiology and pathology behind these processes. The utility of urinary profiling to detect inflammatory responses associated with, for example, allergen provocation has been demonstrated in several studies. As an example of the application, changes in urinary metabolites can be quantified and they reflect the local reactions in the airways after an inhalation challenge [6].
LC-MS/MS method for quantification of eicosanoids
Mass spectrometry (MS) metabolite pro-filing approaches provide a useful combination of omics-scale screening, while still focusing on biological pathways that are relevant to the pathology of interest. Historically, gas chromatography (GC)-MS and GC-tandem MS (MS/MS) have played a central role both in the identification and in the quantification of eicosanoids in biological samples. Those methods often require a complex and time-consuming sample extraction and derivatization. Due to great advances in liquid chromatography (LC)-MS/MS technology, especially over the past two decades, this relatively new approach is increasingly used both in biomedical and life sciences, and in clinical chemistry. Up to now, most reported methods examine these pathways in isolation; few studies have examined in depth the urinary eicosanoid profile for large-scale profiling of most of the major pathways at the population level [5, 7].

The quantification of eicosanoids and their metabolites in biological samples confronts an analytical challenge, even though a number of methodologies/techniques have been developed. The major difficulties encountered are related to the oxidation of eicosanoids and their low quantities in biological matrices, which requires sensitive, selective and reproducible methods for their quantification. Besides, eicosanoids are structurally similar, many of them are isomeric compounds that share the same parent mass and also the same fragmentation pattern, such as PGE₂, PGD₂ and 13, 14‐dihydro‐15‐keto‐PGE₂. Therefore, these compounds have to be chromatographically resolved, i.e. it is necessary to achieve sufficient chromatographic sepa-ration to discriminate individual eicosanoids with a high degree of specificity.

Several targeted methods for qualitative and/or quantitative determination of selected panels of eicosanoids in different biofluids have been published. A recent paper reported the development of an eicosanoid profiling method which was designed with the intent to achieve the necessary reproducibility and precision for large-scale molecular phenotyping studies and to enable the direct comparison of urinary excretion levels between independent clinical studies [6]. The method for the extraction and quantification of 32 eicosanoid urinary metabolites by LC-MS/MS covers the major synthetic pathways including prostaglandins, leukotrienes and isoprostanes (Fig. 1). In the LC optimization, column temperature and gradients were adjusted to accomplish complete separation of the critical separation pairs. After LC parameters were optimized, MS parameters were optimized to attain the lowest possible detection limit. Multiple reaction monitoring transitions were selected to yield the greatest selectivity and sensitivity. To improve the quantitative accuracy, deuterated internal standards were selected to compensate for differences in the extraction and ionization efficiency due to differences in chemical structure and chromatographic elution. Matrix effects, suppression or enhancement of the ionization efficiency, were also evaluated. In addition, the method was optimized in terms of low urine volume consumption, broad metabolic pathway coverage, and long-term precision (6).

The resulting method was highly reproducible, repeatable and stable across multiple years of analysis, and is, therefore, well suited for applications in molecular phenotyping studies, drug trials, other clinical investigations, and epidemiological monitoring of responses to exposures. The method presented some advantages with respect to previously published methods, with a focus on formatting the method to be suitable for large-scale analyses and clinical trials [6]. Previous methods were limited in metabolic coverage, required laborious, solvent intensive liquid–liquid extraction, long run-times, time-consuming absorbance measurements, large extraction volume, and/or enzymatic hydrolysis. The workflow defined monitors the majority of the eicosanoid pathways (PGF_2α, PGE₂, PGI₂, PGD₂ pathways, isoprostanes, thromboxane and leukotrienes pathways), that have established clinical or physiological functions. Further-more, one of the main advantages is the minimum required volume for analysis with the complete workflow, which simplifies planning of clinical studies and improves on previous methods in terms of simplicity, metabolic coverage and precision. Future application of this method might contribute to a better understanding of the role and relevance of eicosanoid metabolites for inflammatory diseases and conditions.

Summary
LC-MS/MS methods are being used to identify, monitor and quantify eicosanoids, signalling molecules involved in inflammatory processes and is informative for mechanistic insight into numerous pathologies. The described panel covers the major eicosanoid urinary metabolites including the prostaglandins, thromboxanes, leukotrienes, and isoprostanes. The novel method was validated in human urine and was demonstrated to be a simple, robust and sensitive method for quantification of urinary eicosanoids from different pathways by LC-MS/MS applicable in clinical studies.

References
1. Dennis EA, Norris PC. Eicosanoid storm in infection and inflammation. Nat Rev Immunol 2015; 15(8): 511–523.
2. Funk CD. Prostaglandins and leukotrienes: advances in eicosanoid biology. Science 2001; 294(5548): 1871–1875.
3. Milne GL, Yin H, Hardy KD, Davies SS, Roberts LJ. Isoprostane generation and function. Chem Rev 2011; 111(10): 5973–5996.
4. Shimizu T. Lipid mediators in health and disease: enzymes and receptors as therapeutic targets for the regulation of immunity and inflammation. Annu Rev Pharmacol Toxicol 2009; 49(1): 123–150.
5. Sterz K, Scherer G, Ecker J. A simple and robust UPLC-SRM/MS method to quantify urinary eicosanoids. J Lipid Res 2012; 53(5): 1026–1036.
6. Gómez C, Gonzalez-Riano C, Barbas C, Kolmert J, Hyung Ryu M, Carlsten C, Dahlén SE, Wheelock CE. Quantitative metabolic profiling of urinary eicosanoids for clinical phenotyping. J Lipid Res 2019; 60(6): 1164–1173.
7. Sasaki A, Fukuda H, Shiida N, Tanaka N, Furugen A, Ogura J, Shuto S, Mano N, Yamaguchi H. Determination of ω-6 and ω-3 PUFA metabolites in human urine samples using UPLC/MS/MS. Anal Bioanal Chem 2015; 407(6): 1625–1639.

The author
Cristina Gómez PhD
Division of Molecular and Systems Toxicology, Department of Pharmaceutical Sciences,
University of Basel, Switzerland

E-mail: Cristina.gomezcastella@unibas.ch

Figure 1 (a)(b). Schematic of the eicosanoid metabolic cascade displaying the urinary metabolites included in the current platform.
Arachidonic acid can be metabolized via lipoxygenase, cyclooxygenase, and oxidative stress (ROS, RNS) pathways. The chromatogram and gradient elution pattern demonstrate the separation of the eicosanoid metabolites.
cPLA2, cytosolic phospholipases A2; COX, cyclooxygenase; EX, eoxin (14,15-disubstituted analogues of the 5,6-disubstituted leukotrienes); LOX, lipoxygenase; LT, leukotriene; PG, prostaglandin; RNS, reactive nitrogen species; ROS reactive oxygen species; TX, thromboxane.

The past 15 years have shown a slow but steady expansion of the field of lipidomics, which pushes analysis beyond understanding of traditional lipids (HDL-C, LDL-C and triglycerides). This article aims to introduce readers to the world of lipidomics and the remaining hurdles involved in this exciting field.
By Matthew W..K. Wong and Dr Nady Braidy

Introduction: what is lipidomics and why is it useful?   
Lipidomics is a relatively novel subfield of ‘omics’ designed to identify and quantify hundreds to thousands of individual lipids in a given biological sample. In fact, the term ‘lipidomics’ did not exist in literature data-bases before 2004 and in 2016 lipidomics still formed under 1.% of ‘omics’ publications [1]. Despite the more established omics fields of genomics and proteomics having a clean head start on lipidomics in development and use, there has nevertheless been a huge explosion of interest in lipidomics in recent times, which can provide alternative angles of attack to answer questions relating to the biochemical basis of health and disease.

Although traditional lipids such as low-density lipoprotein cholesterol (LDL-C), high density lipoprotein (HDL-C) and total triglycerides are routinely analysed from blood and their concentrations applied to inform patients of clinical outcomes, these analyses do not capture the true complexity of lipids. In 2005, LIPID Metabolites and Pathways Strategy (LIPID MAPS) established a classification system of lipids which divided them into eight major classes: fatty acyls, glycerolipids, glycerophospholipids, sphingolipids, saccharolipids, polyketides, isoprenols, and sterols [2]. Within each class, lipid species vary considerably in their degree of saturation (number of double bonds), their fatty acyl chain lengths determined by number of carbons, and the polarity of the head group with respect to the hydrophobic tails. This enables complex permutations of lipids to exist, and it has been estimated that there are over 100.000 naturally occurring lipids, though only 40.000 have been formally identified to date.

The sheer complexity of the lipidome at the molecular level appears to suggest that there are unique and specific physiological roles for these molecules. Apart from energy storage and membrane structure, lipids also participate in cellular transport, interact with ion channels and can function as signalling molecules, especially in detergent-resistant regions known as lipid rafts. These lipid raft regions are known to anchor many transmembrane proteins and may therefore be important signalling hotspots [3]. Given the relevance of lipids to many physiological processes, not surprisingly, lipidomics has been applied to identify potential biomarkers relating to health and disease including metabolic syndrome [4], cancer, Alzheimer’s disease [1] and other neurodegenerative diseases [5]. For example, altered phospholipids and sphingolipids have frequently been implicated in Alzheimer’s disease, whereas triglyceride levels have been shown to be dysregulated in behavioural variant frontotemporal dementia [5].

Mining the complexity of analytical techniques
The main analytical tool of choice when it comes to lipid analysis is mass spectrometry (MS), where lipids are rapidly heated into the gas phase into charged ions, usually through soft ionization techniques such as electrospray ionization (ESI) [6]. Matrix assisted laser desorption/ionization (MALDI) can also be used on tissues, coupled with imaging MS to give an image of distribution intensity of various ions (Fig. 1). In ESI, the charged particles are injected into the mass spectrometer and suspended in helical motion about an orbitrap where the mass to charge ratio (m/z) of the analyte can be determined by assessing the angular momentum of the particle. Further information about the lipids of interest can be determined through experiments such as collision-induced dissociation and fragmentation of ions. Gas chromatography MS (GC-MS) is applied for fatty acid profiling, whereas liquid chromatography coupled MS (LC-MS) is particularly effective at detection of moderately polar lipids, such as glycerophospholipids. Lipidomic analysis can also be performed without chromatographic separation, in a technique known as direct infusion (or ‘shotgun lipidomics’) MS which takes into account differences in intrinsic ionization efficiency of lipid species according to their class, giving a broad fingerprint of the lipidome in a short amount of time [7]. This is a powerful and accurate technique as all lipids are analysed under the same conditions at the same time. LC-MS, however, first separates the lipids according to retention time. This way, major lipid classes are segregated on a time scale and lipids with similar m/z can be differentiated, enabling resolution of isobaric (lipids of different chemical composition but same mass) and isomeric (lipids with same chemical composition, but a different structure) lipid species which may be more difficult to resolve using direct infusion MS.

Quantitation
Quantitation of lipids can be achieved by comparing peak areas of lipids against internal standards (ISTDs). This can be either relative or absolute. ISTDs are routinely applied to correct for differences that occur owing to variation in experimental conditions, extraction efficiency, matrix effects and instrument performance, enabling the results of different LC-MS injection runs to be comparable [8].
All samples receive the same concentration of ISTDs applied consistently in the same batch, and the ISTDs are analysed simultaneously with the analytes of interest under the same experimental conditions.
For quantitation, ideally, each lipid species should have its own ISTD, but it is an expensive and time-consuming endeavour to account for tens to hundreds of lipids within the same class. Most commonly, only one or two ISTDs are applied for each lipid class. This approach requires ISTDs to have similar physio-chemical properties (ionization efficiency) to other lipids of the same class. It has been experimentally deduced that the number of carbons (chain length) and degree of saturation affect ionization efficiency to a smaller degree relative to the head group [8]. Most species of a lipid class sharing the same head group are expected to ionize similarly. In semi-quantitative analysis, where relative fold changes of lipids between groups are reported, a single ISTD per class is usually sufficient for normalization. The normalization is a simple process of determining the ratio of the analyte peak area to the corresponding internal standard peak area (Fig. 2a).

However, where more accurate and targeted quantitation is sought, multiple internal standards and calibration curves (external standards) are required (Fig. 2b). Choice of ISTD will vary depending on the analytes of interest. ISTDs should not be present endogenously in the sample and should have similar physicochemical properties. Low-physiologically occurring structural analogues, including odd chained lipids, or stable isotope-labelled standards are commonly used. ISTDs are synthesized and available for commercial use through manufacturers such as Avanti Polar Lipids, which now manufactures a cocktail of lipid standards to mimic concentrations found in plasma, called Avanti SPLASH Lipidomix.

Methodological concerns
This article focuses largely on application of lipidomics for plasma profiling, with venipuncture being relatively non-invasive and having the capacity for repeat collection. Lipidomics has been applied to the analysis of hundreds to thousands of individual samples [9] and through these experiments, researchers have identified important variables to take into consideration to maximize the available lipids for analysis. These include pre-analytical variables inherent in blood collection and storage. At the very least, lipids should be stored below −20.°C (even better at −80.°C) within 2.hours of collection, and freeze-thaw cycles should be kept at a minimum to prevent degradation of lipids. More detailed guidelines for blood collection, storage and attempts towards standardization of laboratory protocols have been reviewed [1, 10].

Further, the method of extraction will also determine the amount and type of lipids that can be analysed. For blood lipid extractions, the Folch and Matyash methods [11, 12] are considered gold standards and involve a biphasic extraction where lipids are suspended in the non-polar organic phase. More recently, a single-phase extraction method was introduced which bypasses the need to extract from the organic phase, with the entire set of lipids suspended in a single-phase supernatant [13]. Our laboratory has validated this method and confirmed that it clearly extracts lipids with as good, if not better efficiency compared to the Folch and Matyash methods [14]. Polar lipids are particularly well extracted with the single-phase method. Furthermore, the method demonstrated strong consistency, with median intra-assay and inter-assay coefficient of variation of 14.1.% and 14.4.%, respectively. Thus, repeated measurements within a batch and across batches separated over time yield consistent results and represent a strong alternative to the gold standard methods mentioned above. 

Assessing the natural variation in lipids
Perhaps the greatest hurdle facing lipidomics research today is the lack of standard measurements. Although laboratory blood analysis routinely tests for concentrations of classical lipids such as LDL-C, HDL-C and TG, and standardized concentration ranges exist for diagnostic reference, no equivalent standardized tests exist for the rest of the lipidome. Without characterization of the range of baseline plasma concentrations of lipids within and between subjects it is more difficult to compare with disease states, where confounding variables can interfere with interpretation of results.

The Wenk group in the National University of Singapore has prioritized research on identi-fying intra-individual and inter-individual differences in the healthy human plasma lipidome. Their work has shown many lipids are regulated by circadian rhythms within individuals [15]; in addition, ethnicity may be another source of variation, where Chinese, Malay and Indian subjects had differences in their lipidomes [16]. Our laboratory has also shown age, sex, use of lipid lowering medications (such as statins) to be important determinants of lipid variation, in line with some previous studies [17]. Pre-analytical differences inherent in study design and sample characteristics must be considered well before lipidomic findings can be applied to the clinic. Further, NIST Standard Reference Material plasma has been used to estimate concen-tration ranges of various lipid classes [10]. The results suggest lipids in each plasma sample can vary by several orders of magnitude, and this means that no one analysis is able to capture all the lipids of interest. It is not unusual for multiple lipidomics platforms and extraction methods to be applied to overcome this setback – if time and resources permit.

Conclusion
Despite these hurdles, lipidomics continues to grow, driven by improvements in MS enabling much higher resolution detection and identification of lipids. As a greater under-standing of how lipids contribute to health and disease and how they are regulated by genetic and environmental factors develops, it is anticipated that in the near future, lipidomics will become more routinely applied towards identifying biologically important biomarkers for diagnostic and prognostic purposes.

References
1. Wong MW, Braidy N, Poljak A, Pickford R, Thambisetty M, Sachdev PS. Dysregulation of lipids in Alzheimer’s disease and their role as potential biomarkers. Alzheimers Dement 2017; 13(7): 810-827.
2. Fahy E, Subramaniam S, Brown HA, Glass CK, Merrill AH, Jr, Murphy RC, Raetz CR, Russell DW, Seyama Y, et al. A comprehensive classification system for lipids. J Lipid Res 2005; 46(5): 839-861.
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5. Kim WS, Jary E, Pickford R, He Y, Ahmed RM, Piguet O, Hodges JR, Halliday GM. Lipidomics analysis of behavioral variant frontotemporal dementia: a scope for biomarker development. Frontiers in neurology 2018; 9: 104.
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13. Alshehry ZH, Barlow CK, Weir JM, Zhou Y, McConville MJ, Meikle PJ. An efficient single phase method for the extraction of plasma lipids. Metabolites 2015; 5(2): 389-403.
14. Wong MWK, Braidy N, Pickford R, Sachdev PS, Poljak A. Comparison of single phase and biphasic extraction protocols for lipidomic studies using human plasma. Front Neurol 2019; 10(879).
15. Chua EC, Shui G, Lee IT, Lau P, Tan LC, Yeo SC, Lam BD, Bulchand S, Summers SA, et al. Extensive diversity in circadian regulation of plasma lipids and evidence for different circadian metabolic phenotypes in humans. Proc Natl Acad Sci USA 2013; 110(35): 14468–14473.
16. Saw WY, Tantoso E, Begum H, Zhou L, Zou R, He C, Chan SL, Tan LW, Wong LP, et al. Establishing multiple omics baselines for three Southeast Asian populations in the Singapore Integrative Omics Study. Nat Commun 2017; 8: 653.
17. Wong MWK, Braidy N, Pickford R, Vafaee F, Crawford J, Muenchhoff J, Schofield P, Attia J, Brodaty H, et al. Plasma lipidome variation during the second half of the human lifespan is associated with age and sex but minimally with BMI. PLoS One 2019; 14(3): e0214141.

The authors
Matthew WK Wong BMedSci; Nady Braidy* BMedSci (Hons I Phys/Pharm), MPharm, DipInnovMan, GradCertResMan, PhD
Centre for Healthy Brain Ageing, School of Psychiatry, Faculty of Medicine, University of New South Wales, Sydney, New South Wales, Australia 2052

*Corresponding author
E-mail: n.braidy@unsw.edu.au

Figure 2 (b). Quantitation by internal standards. Absolute quantification involves setting up a calibration curve where various known concentrations of internal standards (ISTDs) and their corresponding peak areas are plotted, and a linear response obtained. The unknown concentration of lipid is then determined by taking the peak area to the calibration line and interpolating to the concentration axis.