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Review

Methods of the Analysis of Oxylipins in Biological Samples

by
Ivan Liakh
1,
Alicja Pakiet
2,
Tomasz Sledzinski
1 and
Adriana Mika
1,2,*
1
Department of Pharmaceutical Biochemistry, Medical University of Gdansk, Debinki 1, 80-211 Gdansk, Poland
2
Department of Environmental Analysis, Faculty of Chemistry, University of Gdansk, Wita Stwosza 63, 80-308 Gdansk, Poland
*
Author to whom correspondence should be addressed.
Molecules 2020, 25(2), 349; https://doi.org/10.3390/molecules25020349
Submission received: 13 December 2019 / Revised: 8 January 2020 / Accepted: 13 January 2020 / Published: 15 January 2020
(This article belongs to the Section Analytical Chemistry)
Browse Figure
Versions Notes

Abstract

:
Oxylipins are derivatives of polyunsaturated fatty acids and due to their important and diverse functions in the body, they have become a popular subject of studies. The main challenge for researchers is their low stability and often very low concentration in samples. Therefore, in recent years there have been developments in the extraction and analysis methods of oxylipins. New approaches in extraction methods were described in our previous review. In turn, the old analysis methods have been replaced by new approaches based on mass spectrometry (MS) coupled with liquid chromatography (LC) and gas chromatography (GC), and the best of these methods allow hundreds of oxylipins to be quantitatively identified. This review presents comparative and comprehensive information on the progress of various methods used by various authors to achieve the best results in the analysis of oxylipins in biological samples.

1. Introduction

Oxylipins are important lipid mediators that are formed from polyunsaturated fatty acids (PUFAs) such as arachidonic acid (ARA), linoleic acid (LA), α-linolenic acid (ALA), eicosapentaenoic acid (EPA), and docosahexaenoic acid (DHA) [1,2,3] in reactions catalyzed by cyclooxygenase (COX), lipoxygenase (LOX), and cytochrome P450 (CYP 450) enzymes, and non-enzymatic oxidation pathways [1,2]. Oxylipins are involved in various biological processes but, primarily, are important for the regulation of inflammation [4,5,6,7,8]. The direction of oxylipins influence on inflammation depends on their PUFA precursor, usually n-3 PUFA derived oxylipins are anti-inflammatory and pro-resolving [1], while n-6 PUFA metabolites can promote inflammation [1,3]. The ability of oxylipins to act as molecular mediator arises from their binding to peroxisome proliferator-activated receptors (PPARs) and G protein-coupled receptors (GPCRs) [9,10]. In epithelial-derived cancers altered ARA metabolism by COX and LOX leads to production of pro-inflammatory factors that promote tumor growth and facilitate formation of tumor microenvironment conductive to angiogenesis and immunosuppression [6,10]. Altered PUFA metabolism was also shown in obesity [4], where the interaction with PPAR and GPCR can modulate adipogenesis [9]. Plasma oxylipin levels have also been correlated with outcome of cardiovascular disease, metabolic syndrome, preeclampsia, due to vasoconstrictive effects of some oxylipins, or cardiac arrythmias [11].
The presence of most oxylipins in low concentrations, as well as their enormous heterogeneity and the emergence of many structurally similar oxylipins, makes their qualitative and quantitative determination difficult due to the low sensitivity of traditional methods. In the previous review [12], we described the current methods of sample preparation from various biological materials preceding the analysis of oxylipins. This paper included a description of the stages of sample collection and storage and a summary of the used pre-extraction additives and standards. These are especially important to consider in order to minimize oxidation, photodegradation or heat destruction of oxylipins during sample handling. Furthermore, the main extraction methods including protein precipitation (PPT), liquid-liquid extraction (LLE), solid-phase extraction (SPE), and the derivatization process were described. Choosing appropriate extraction method and solvents is essential in obtaining good target analyte recoveries and reproducibility needed for further quantitative analysis and depends on the oxylipin group of interest. SPE, due to the availability of various types of sorbents and solvents, is the most widely used extraction method in the analysis of oxylipins. For LLE and SPE, the extraction methods have been grouped according to the characteristics of the studied biological material (biofluids, solid tissues, cell cultures). Finally, new approaches and trends in material collection (dried blood spot), precipitation (ferromagnetic particle enhanced deproteination), and extraction (microextraction, online SPE, mixed-mode extraction with a spin column) for the analysis of oxylipins were described. Owing to the fact that oxylipins are usually present at very low concentrations in biological samples, the quality of sample collection, storage and extraction methods is paramount for achieving accurate quantification. The present review, being an extension of the previous paper, describes further stages of analysis and the quantitative determination of oxylipin levels in the prepared samples using existing analytical techniques.
Immunoassay, thin layer chromatography (TLC), HPLC with a diode array or fluorescent detector and capillary electrophoresis with a photodiode array detector were used to analyze oxylipins [13,14,15,16,17,18,19]. However, a very similar structure, limited stability and extremely low concentrations of oxylipins in the tissues impose some restrictions on these methods. Therefore, recently, gas chromatography–mass spectrometry (GC–MS) and liquid chromatography–mass spectrometry (LC–MS) have most often been used to determine oxylipin levels in biological samples [20,21,22,23,24]. A summary of the most frequently used analytical methods in the analysis of oxylipins as well as their advantages and limitations is presented in Table 1. Also, the comparison of mass spectra of PGE2 and PGD2 obtained by two most popular techniques for lipids identification: GC–MS and LC–MS is presented on Figure 1 [25].

2. Immunoassay Methods

For a long time, immunoassays (enzyme immunoassay (EIA) and radioimmunoassay (RIA)), due to their high sensitivity, were the most widely used quantitative techniques for oxylipins: RIA has been developed to quantify concentrations of IsoPs (8-iso-PGF2α) in human plasma and urine [26], 15-keto-dihydro-PGF2α in human plasma [62], Prostaglandin E2 (PGE2) and LTB4 in human prostate tissues [63], PGE2 in human plasma [64,65], PGE2, PGF2α, PGI2, 6-oxo-PGF2α, TXA2, TXB2 [66] and PGF2α, PGI2, TXA2, 13,14-dihydro-15-keto PGF2α (M-PGF2α), 6-keto PGF1α, and TXB2 [67] in human follicular fluid. ELISA, similar to EIA and RIA, requires specific antibodies, and due to the structural similarity of oxylipins, it is unlikely that antibodies that will sufficiently distinguish them can be obtained [68]. RIA and EIA are sensitive enough to measure subpicomole amounts of oxylipins, but have some limitations for tissue and plasma samples, which diminishes immunoassay sensitivity. Plasma proteins can bind to eicosanoids, and there is a significant degree of immunological cross-reactivity among commercially available eicosanoid antibodies (e.g., the PGE2 antibody can cross-react to a significant extent with PGE3 and 8-iso-PGF2a) [69,70]. To solve these problems, and to separate eicosanoids from plasma proteins, LLE or SPE is used, and the chromatographic separation of eicosanoids is necessary to avoid immunological cross-reactivity [71]. Miller et al. showed that oxylipin levels determined by ELISA correlate well with the levels determined by RIA (LTC4 and LTB4) and HPLC (LTC4) [30]. However, later Schmidt et al. showed the advantages of LC–ESI–MS/MS over previously used immunoassays in the determination of PGE2 and PGD2 in rat microdialysis samples [72]. Also, among the shortcomings of these types of analysis, can be noted a lack of specificity for complex biological fluids, and immunoassays are designed to determine only single metabolites [73]. Also, Gandhi et al. found that the LC–MS/MS method offers a cost-effective and more sensitive alternative to ELISA for the quantification of PGE2, PGD2, PGF2α, LTB4 and TXB2 in rat brain and spinal cord tissues [74]. Henkel et al. compared EIA and LC–MS/MS to determine LTs, PGE2, and TXB2 in human macrophage supernatants and found that EIA showed both inflated levels and higher analyte variability compared to LC–MS/MS [75]. Thus, due to these disadvantages of immunoassay, in recent years, LC–MS/MS has largely replaced this method for determining oxylipin levels.

3. Mass Spectrometry Coupled with Separation Techniques

3.1. Separation Techniques

3.1.1. High-Performance Liquid Chromatography

In recent decades, HPLC has been the main technique of LC-analysis for the separation of oxylipins, and has superseded TLC. However, conventional analysis using HPLC with UV detection is a difficult task, due to the fact that the conjugated dienes and keto groups absorb at a low UV range (235–280 nm), and most of the oxylipin molecules do not have suitable chromophores, thus the sensitivity/selectivity is insufficient for their analysis in complex extracts from cells or tissue [69,76]. However, UV absorption can be taken into account to further confirm the LC–MS measurement results [77]. For these reasons, a plasma that contains high levels of numerous UV-absorbing compounds is not suitable for determining eicosanoids, the concentrations of which in plasma are lower, as well as PGs, which do not absorb UV light [47]. Since these compounds do not contain aromatic or natural fluorescent systems, GC–MS and, likewise, HPLC combined with fluorescent detectors requires these compounds to be derivatized into a complex that fluoresces, which makes the analysis time-consuming and expensive [50,78]. Also, HPLC with electrochemical detection allows the determination of picogram amounts of lipoxin (LX) A4 (LXA4) and LXB4 in extracts of human polymorphonuclear granulocytes [79], and leukotriene (LT) B4 (LTB4) from human polymorphonuclear leukocytes [80]. However, this method is only suitable for the determination of electrochemically active substances, to which most oxylipins do not apply, in this case additional transformations of substances are required [81,82]. In the HPLC method, the use of various stationary phases allows the use of different modes of separation of oxylipins—reversed-phase (RP)–HPLC, normal-phase (NP)–HPLC, chiral HPLC and hydrophilic interaction chromatography (HILIC). Separation in RP–HPLC relies on the hydrophobic properties of the analytes and therefore remains the main method for the separation of the metabolites of arachidonic acid (ARA) [24]. The RP mode performed mainly on octadecyl silica (C18) or octyl silica (C8) columns provides the highest selectivity for the resolution of isobaric oxylipins [83]. Also, Chen et al., when comparing the chromatographic characteristics of an HSS T3 C18 column and a core-shell C30 column, showed the great potential of the latter in the separation of lipid molecules [84]. The use of a core-shell column instead of a standard C18 column is promising from the point of view of reducing analysis time and improving the chromatographic resolution of PGs [85]. In addition, in terms of the stability of stationary phases, core–shell or fused-core columns are comparable to the conventional columns used in UHPLC but have a lower back pressure (350 bar) [86].
Optimization of chromatographic separation can be achieved by changing the mobile phase composition, gradient conditions, stationary phases and run time. However, some compounds such as IsoP, nitro fatty acids, prostaglandins (PGs), sphingoids, and lysophosphatidic acid species in biological samples are difficult to separate in a single chromatographic run under normal conditions. Schumann et al. solved this problem using two runs: with low pH (between 3.2 and 3.5) and with high pH (between 8.5 and 10.3) [87]. Chromatographic separation also depends on the chemical structure of the compounds. Aoyagi et al. found that the position of the hydroxyl and epoxy groups in the acyl chains of oxylipins can influence the retention time of LC, while epoxy-containing oxylipins elute later than hydroxyl-containing ones. In addition, oxylipins in which the hydroxyl or epoxy group is located at the end of the acyl chain elute earlier than others [88]. Chromatographic separation is usually achieved using gradient elution, which is obtained by mixing solvents A and B. 0.1% acetic acid (HAc) or 0.1% formic acid (FAc) (rarely phosphoric acid) in water (v/v) is commonly used as solvent A; solvent B can consist of different solvents: Acetonitrile (ACN), isopropanol (IPA), methanol (MeOH), individually or mixed in different proportions (e.g., ACN/IPA–90/10, 70/30 or 50/50 v/v; ACN/MeOH–80/15 v/v). Also, solvents A and B may contain ammonium acetate and ammonium formate as additives. By the addition of additives (HAc (0.02 and 0.05%), FAc (0.02 and 0.05%), 5mM ammonium formate and 5 mM ammonium acetate) in solvent A, Berkecz et al. studied the effects of solvent on the retention times of oxylipins from human plasma [83]. Using HAc and FAc at 0.05% showed the highest retention times compared to ammonium acetate where the lowest retention was observed. Using FAc and HAc without any additive allowed higher chromatographic resolution to be obtained for isomers. However, for ALA, γ-linolenic acid, 5-hydroxyeicosatetraenoic acid (HETE), 11,12-epoxyeicosatrienoic acid (EET), 15-HETE, 5,15-dihydroxyeicosatetraenoic acid (DiHETE), 6-trans LTB4, 11-HETE, 12-HETE, 4-hydroxydo-cosahexaenoic acid (HDHA) and 14-HDHA, mobile phases with ammonium acetate demonstrate better results [84]. Shaik et al. found that water and ACN mobile phases with HAc provide optimal conditions when measuring prostanoids in rat brain. They also obtained a 2-fold increase in peak area and sensitivity with the addition of 15 μL of 1% (v/v) HAc in methanol to the eluent before sample evaporation [89]. Also, for the analysis of oxylipins, a technique such as ultra-high performance supercritical fluid chromatography (UHPSF) was successfully used. To improve the separation of oxidized phospholipids such as hydroxides, epoxides and hydroperoxides of linoleic acid and arachidonic acid, Uchikata et al. established an analytical system using supercritical fluid chromatography on a 2-ethylpyridine column. Oxidized phosphatidylcholine isomers from mice liver were separated and further detected using the MRM method with SFC/MS/MS, as RP chromatography cannot solve this problem [90]. But in another study, Berkecz et al. compared the methods UHPSFC/ESI–MS with UHPLC/ESI–MS and found that the sensitivity of UHPLC/MS for the determination of oxylipins is higher (except for prostaglandins) [83].

3.1.2. Other Separation Techniques

Chiral Chromatography

One of the problems in the analysis of oxylipins is the presence of critical separation pairs (compounds that have an identical molecular composition, similar fragmentation and close retention time), as well as a large number of isomers that can be formed during non-enzymatic oxidation. To solve this problem, several alternative solutions exist. One of them is the use of chiral chromatography and the application of special chiral columns (Immobilized Polysaccharide or Protein-Based columns) [91]. Deems et al. used an LC system with a Chiralpak AD-H derivatized amylose column (250 × 4.6 mm; Chiral Technologies, West Chester, PA) coupled with MS to isolate eight pairs of isomeric eicosanoids from a cell culture medium [92]. Oh et al. used a Chiralpak AD-RH column (150 × 2.1 mm, 5 μm; Chiral Technologies, West Chester, PA, USA) for the chiral HPLC–MS–MS lipidomic profiling of 18-R/S-HEPEs and 18R/S-resolvin (Rv) E2 stereoisomer pairs from human sera [93,94]. This approach allowed for identification of novel EPA-derived 18S resolvins, which were not apparent without chiral separation, and, upon analyzing biological samples, identification of 18S-HEPE as precursor to this series of resolvins. Later, Deng et al. used this method (LC–MS/MS with a Chiralpak AD-RH column) to study the formation of 14R-HDHA and 14S-HDHA in human macrophages incubated with 12-LOX and identification of novel 13R,14S-diHDHA—maresin 2 [95]. Mesaros et al. used a chiral LC/ECAPCI–MS method with a Chiralpak AD-H column (250×4.6 mm, 5 μm; Daicel Chemical Industries Ltd., Tokyo, Japan) for the analysis of six EET enantiomers formed in urine and tissue samples [96]. Kolmert et al., using a Chiralpak AD-RH analytical column (150 × 2.1 mm, 5 μm; Daicel Collaboration, France), achieved the separation of 13 chiral oxylipins from a human bronchus during an LC–MS/MS analysis [97]. Thus, the use of chiral chromatography is critical not only for separation of known isomer pairs or groups, but allows for the identification of novel compounds, which may poses interesting bioactivity, such as maresin 2, which displayed both anti-inflammatory and pro-resolving action [95]. The disadvantages of chiral chromatography include the need for long-term equilibration, the use of an isocratic gradient and thus a longer analysis time [35,74,98,99], and the use of barely accessible internal standards [100].
Another solution for the separation of isomer pairs is the use of a smaller particle size (UHPLC) or core-shell material columns [86], and more careful optimization of the gradient conditions and solvent composition [101]. Gouveia-Figueira et al. [102] used a Waters BEH C18 column (150 × 2.1 mm, 2.5 μm) during a UHPLC–ESI–MS/MS analysis of oxylipins in human plasma to separate critical pairs of isomers (Resolvin D1/D2 and PGE2/PGD2). In order to obtain the best results, they tried different compositions and gradients of the mobile phase [102]. Using a Zorbax Eclipse Plus C18 column with a diameter of particles less than 2 microns (150 × 2.1 mm, 1.8 μm; Agilent, Waldbronn, Germany) and an optimized gradient, Kutzner et al. separated the maresin (Mar)1 and 7 (S)-Mar1 and protectin (N)PD1 and PDX stereoisomers from human serum and the results were comparable to those achieved by using a chiral column [103]. The high resolution power of approaches using these columns is usually used for separation of known isomers, not for identification of novel compounds.
Finally, isomer pairs were analyzed using combination of both above mentioned approaches. Fuchs et al. combined UHPLC–MS/MS and chiral LC–MS to investigate the stereochemistry of trihydroxyoctadecenoic acids (TriHOMEs) in human BALF samples. They used a UHPLC BEH C18 column (150 × 2.1 mm, 1.7 µm; Waters, Milford, MA, USA) for the rapid quantification of diastereomers and a Chiralpak AD-RH column (150 × 2.1 mm, 3 µm; Daicel, Illkirch, France) for the relative abundance of all stereoisomers. As a result, for the first time, the authors were able to measure all 16 TriHOME isomers in a single chromatographic run. [35]. Also, to identify HETEs/EETs isomers (16-HETE, 17-HETE, and 18-HETE), Chen et al. used a unique heatmap-assisted strategy which included excluding the most abundant common critical pair ions, and a heatmap analysis of unique fragment ions and transitions [84]. Thus, chiral chromatography effectively separates the isomers of oxylipins; however, due to some disadvantages (see Table 1), it is more often used for the targeted analysis of specific oxylipins.

Ion Mobility Spectrometry

Ion mobility (IMS) is another separation technique which can be used to improve identification and characterization of oxylipins in biological samples, especially separation of isomers. The IMS implementation between chromatographic and MS step provides another dimension of separation, increasing certainty of oxylipin identification-addition of collision-cross-section (CCS) value. This approach has been successfully used by Kyle et al. [104] for separation of 42 isomer pairs or groups in either positive or negative mode, for example R and S isomers of HETEs; better separation was obtained for sodiated [M + Na]+ ions rather than deprotonated species [M − H]−. A variation of IMS was also used by Hinz et al. [105]. In this study ions were travelling through drift tube filled with low pressure nitrogen gas in which their drift time was affected by ion size and shape, which allowed for formation of different oxylipin conformers. However, the use of IMS requires building additional CSS libraries, use of either computationally generated standards, or literature CCS [105], which complicates implementation of this method.

Immunoaffinity Column Chromatography

Immunoaffinity chromatography (IAC) is a type of LC in which the stationary phase consists of an antibody or synthetic protein-binding reagent, and it is a highly efficient method to isolate a particular compound from biological samples for measurement by GC–MS or LC–MS [106]. IAC is highly selective and specific for the identification and quantification of prostanes, isoprostanes (IsoP) and their metabolites, which commonly occur at very low concentrations in biological fluids such as plasma and urine [107]. However, there is a limitation to IAC as it is exclusively commercialized for the analysis of 15-F2t-isoprostane and not for other metabolites of n-3 and n-6 PUFAs [2]. Gijon et al. showed the possibility of using immunoaffinity extraction enrichment (IAE) to improve the analysis of certain lipids present in trace amounts in certain test samples. The use of this double extraction protocol for the extraction of leukotrienes (LTs) gave such advantages, when compared to traditional SPEs, as minimal risk of column overload, cleaner samples, and the flow-through fluid can be used to analyze compounds not retained by the antibodies [108]. Tsikas et al. used Sepharose 4-based IAC columns (4-mL, 1-mL gel resin; Cayman Chemicals, Ann Arbor, MI, USA) for the extraction and GC–tandem mass spectrometric (MS/MS) quantification of prostaglandin (PG) E1 (PGE1) in human plasma [42]. However, the lack of ready-made antibodies, in combination with the complexity of antibody production methods, limits the widespread use of IAC [106].

Thin-Layer Chromatography

TLC, popular in the last century, can be used as an additional sample preparation technique before MS analysis. Liu et al. developed a method including SPE, TLC purification, chemical derivatization, and GC–MS detection for the quantification of F2-IsoPs from a variety of biological sources [38]. Also, TLC was applied to separate thromboxane (TX) B2 (TXB2) and 11-Dehydro-TXB2 in human plasma [109] and for the quantitation of 15-F2t-IsoP in rat plasma [39]. Moreover, Tsikas et al. compared IAC (4-mL (1-mL gel resin) 8-Isoprostane Affinity Column) with TLC for the quantification of 8-iso-PGF2α in human urine. The combination of TLC followed by quantification by GC–MS yields twice as high values for 8-iso-PGF than the combination of IAC extraction with GC–MS [107].

3.2. GC–MS

Maximum development in the study of oxylipin levels by GC–MS took place in the 1980s. For GC analysis, the molecule must be volatile and thermally stable, which is not the case with oxylipins; as a result, the analysis requires the derivatization of carboxyl and hydroxyl groups to increase their volatility [14]. Reagents such as N, O-bis(trimethylsilyl)-trifluoroacetamide (BSTFA) are used for the silylation of hydroxyl groups, with consequent detection in electron impact (EI) mode [110]. To increase the sensitivity of the analysis, GC is almost always coupled with MS detection, which allows multiple analytes to be detected in one sample, greatly reducing the cost of routine detection [47]. The GC–MS technique has wide application in the analysis of oxylipins in urine and plasma samples. Tsikas notes the widespread use of GC–MS in the quantitative measurement of PGs, TXs, LTs, IsoPs, and other ARA metabolites in human urine [55]. Later Tsikas and Zoerne reviewed various reports of research on eicosanoids in plasma, serum, and other biological fluids of healthy humans, measured by validated GC–MS, GC–MS/MS, and LC–MS/MS methods. GC–MS allows the analysis of such different compounds as LTs (LTB4), TXs (TXB2, 11-dh-TXB2), prostacyclins (6keto–PGF1α), prostaglandins (PGF2α, PGE1, PGE2, PGD2), and F2-IsoPs (15(S)-8-iso-PGF2α) [21]. The authors also note that most of the reported LC–MS/MS methods have several-fold higher lower limits of quantitation (LLOQ) values than the reported GC–MS/MS methods, which is the undoubted disadvantage of LC–MS/MS [2,21]. At the same time, Puppolo et al. summarized analytical methods for ARA and its metabolites in the brain, and showed that GC–MS is widely used for PGE2, PGD2, PGF2α, 8,9-dihydroxyeicosatrienoic acid (DiHETrE), 5,6-DiHETrE, 12-HHT, 2-HETE, 3-HETE, 5-HETE, 8,9-HETE, 11,12-HETE, 15-HETE, 8-iso-PGF2α, 9α,11β-PGF2α, 9α,11 β-PGF2α, PGE2, PGD2, TXB2, PGF1α, PGF2α, F2-IsoP, and ARA quantification in brain tissue [24].
GC–MS is widely used for the quantification of F2-IsoPs from a variety of biological sources [2,39,111,112,113]. Liu et al. validated the methodology to quantify F2-IsoPs in biological fluids and tissues using GC–MS that includes SPE, TLC purification, derivatization, and MS detection using negative ion chemical ionization (NICI) [38]. At the same time, GC–MS methods allow all possible F2-IsoPs stereoisomers to be quantified, while LC–MS methods permit the separation and identification of selected regioisomers and diastereomers of F2-IsoPs [38]. Also GC–MS was used as an alternative to enzyme-linked immunosorbent assay (ELISA) for the quantification of IsoPs in human urine and plasma [114], because results from ELISA and GC–MS cannot be compared (ELISA overestimates urinary 15-F2t-IsoP concentrations) [31,115]. Milne et al. used GC–MS to simultaneously quantify IsoPs and isofurans (IsoFs). While numerous methodologies for the quantification of F2-IsoPs have been presented in the literature, including alternate GC/MS based assays as well as LC/MS assays and enzyme immunoassays [116], no alternative methodologies to quantify IsoFs have been reported. Also GC–MS was used for the measurement of F2-IsoP in rat liver plasma and urine [32]. GC–MS analysis has been widely used to estimate other types of oxylipins in various types of biological tissues. Margalit et al. used GC–MS for the assessment of 14 biologically significant eicosanoids (ARA, PGE2, PGE2,-d4, PGD2, PGF2α, PGE1, 6-Keto PGF1α, TXB2, hepoxilin A3 (HxA3), 12-HPETE, 12-hydroxyeicosatrienoic acid (HETrE), 12-HETE, 15-HETE, LTB4, LTC4) in rat air pouches and human whole blood [117]. Tsukamoto et al. used GC–MS for the simultaneous quantification of PGE2, PGD2, PGF2α, 8-epi-PGF2α, 6-keto-PGF1α, TXB2 from a cell-cultured medium (RAW264.7 and U937 cells) [118]. Also GC–MS was used for the determination of peripheral plasma prostanoid concentrations (PGE2, PGF2α, 6-keto-PGF1α and TXB2) [119,120], PGE1, PGE0 and 15-keto-PGE0 in human plasma [121], LTB4 in human serum [122], and the analysis of 11-dehydro-TXB2 in human urine [123] and plasma [109]. Nithipatikom et al. used GC–MS/NICI to analyze EETs, DHETs and 20-HETE in coronary venous plasma during coronary artery occlusion and reperfusion in dogs [124]. Werner et al. used GC–MS after the purification of cell cultures by RP–HPLC to investigate the formation of dihydroxyeicosatetraenoic acids (DHETs) and HETEs (5,6-DHET, 8,9-DHET, 11,12-DHET, 14,15-DHET, 5-HETE, 8-HETE, 9-HETE, 11-HETE, 12-HETE, and 15-HETE) in human peritoneal macrophages [125].

3.3. LC–MS/MS

Since LC–MS/MS is the most frequently chosen technique for analyzing oxylipins (a more detailed description of the used conditions and analytical tools is presented in Table 2), in this section we would like to draw attention to the difficulties encountered in LC–MS/MS analysis, and the methods used by different authors to eliminate them.
One of the main advantages of LC–MS/MS is that derivatization is not required, which prevents impurities from being introduced and increases sensitivity in MS analysis, as well as reducing time and cost [24]. Tandem MS/MS instruments coupled with HPLC or ultra-high performance liquid chromatography (UHPLC) are capable of analyzing multiple analytes simultaneously [150]. The main two scanning modes of an MS/MS instrument are multiple reaction monitoring (MRM) and selected reaction monitoring (SRM) modes. While in scanning mode specific ranges of mass are studied in the first or second analyzer, in MRM mode product ions are produced from precursor ions after the collision-induced dissociation (CID) which increases the specificity of the analysis [24] and allows the identification of oxylipins with structural similarity [140]. Using identification only by the retention time and SRM transition of the analytes makes difficult the fact that oxylipins can be represented by several isomers. Detection in SRM mode can help in the case where LC cannot separate 8 and 12-HETE, and they coelute. Conversely, 9 and 12-HETE have similar MS–MS spectra and they must be separated chromatographically [150], as well as in the case of PGD2 and PGE2 [151] and 8-iso-PGF2 and PGF2 [86], which have identical patterns of fragmentation. Also, to maximize the specificity and sensitivity of oxylipin analysis, tandem mass spectrometry (MS/MS) with a triple quadrupole (QqQ) detector in MRM mode can be used in the case of co-eluting metabolites [140].
Presently, targeted metabolomic LC–MS approaches for ARA metabolites allow the simultaneous quantification of more than 100 oxylipins with high sensitivity (LOD 0.01–0.21 pg on the column) in a run time of about 25 min. In all of these methods, the RP LC is connected to a highly sensitive QqQ MS system operating in negative electrospray ionization (ESI) mode [150]. A combination of UHPLC chromatographic separation and MRM transitions performed on a QqQ allowed 184 eicosanoid metabolites to be separated and quantified in a 5-min running time [60].
When coupling LC with MS, the solvent selection is important as mobile phase additives and buffers can lead to ion suppression. In most of the mobile phases, weak acids are used for the analysis of oxidized PUFA species by LC–MS (e.g., 0.1% FAc or HAc), preventing the formation of carboxylate anions in the ESI source [74]. Also, HAc enhanced the ESI (-) ionization of plasma oxylipins compared to the mobile phase without any additive (ca. 90% on average for 0.02%, and 40% for 0.05% of HAc), while the addition of ammonium acetate or formate resulted in a drastic loss of sensitivity [83]. Golovko and Murphy found that using different brands of methyl formate during tissue extraction before PG LC–MS analysis led to very high chemical background noise, accounting for a 5- to 20-fold reduction in sensitivity for different PGs compared with acetone LLE [152]. Chen et al., when optimizing the ionization processes (MS signal), compared various additives (HAc, FA, ammonium formate) in composition with the same mobile phase (50/50, ACN/H2O), and it was shown that 0.1% FA showed less variation and higher average responses [84].
LC–MS analysis not only requires the highest level of purity of the reactants, but also additional purification steps of the sample (PPT, LLE, SPE) to eliminate the effect of the matrix components and reduce the matrix effect [56,60,74,153]. The matrix effect is the suppression or enhancement of the ionization of analytes by the presence of matrix components in biological samples [154]. Matrix effects occur when molecules co-eluting with the analytes alter the ionization efficiency of the electrospray interface. Matrix effects are also compound-dependent; the most polar compound was found to have the largest ion suppression, and the least polar was affected less by ion suppression. The interfering matrix components may come from the current sample, a previously injected sample or the overload of the analytical column [155]. To most effectively remove or minimize the influence of matrix effects, the following improvements can be used: modifications to the sample extraction methodology, improved chromatographic separation, and using stable isotope-labeled internal standards (IS) [154]. Deuterium-labeled standards may have disadvantages such as different retention times as compared to analytes, undesired amplification or the weakening of ionization, while 13C-labeled standards theoretically may be better for analysis but they are not commercially available [37,156]. The choice of the optimal amount of IS is also important, since the interaction of the analyte/internal standard affects the accuracy [157,158]. For the efficient removal of the ESI interfering matrix in plasma, special SPE procedures are undertaken using anion-exchange stationary phases (oxylipin carboxy acid moiety) or non-polar (water) and polar (n-hexane) washing stages before the elution of oxylipins [150]. If the IS does not compensate for all matrix effects, it is possible to use the IS with a more heavy isotope label, which will increase the accuracy and reliability of oxylipin analysis [150], and it should also be remembered that, despite the fact that most non-certified standards are good quality, it is recommended to calculate correction factors in order to adjust concentrations and compensate for differences during their use [78]. In addition, in order to reduce the effect of the phospholipid-based matrix in LC–ESI–MS, the matrix can be diluted as far as possible before loading it into the sorbent, and a slightly higher concentration of organic modifier used for washing and elution can be taken. [159]. To obtain low matrix effects and a good yield of extraction, Dupuy et al. optimized the sample preparation and the extraction process of iso-prostanoids from human plasma, which included Folch extraction, basic hydrolysis and SPE (Oasis Max, 60 mg; Waters) purification [142]. Also, using a short UHPLC column containing very small particles (50 × 2.1 mm, 1.7 µm), compared to a HPLC column (100 × 3.0 mm, 3.5 µm), for the quantification of seven urinary eicosanoid species, allowed Sterz et al. to obtain shorter run-times and sharper peaks and thus improved signal-to-noise ratios [98].
The matrix effect also depends on the type of tissue being examined, and how complex and rich the matrix is [87]. For colon tissue, the matrix effect was very important with a signal loss of about 50% for PUFA metabolites [135], in contrast to the determination of urinary levels of oxylipins (LTB4) when the matrix effect led to a 10% signal loss [160], or the matrix effect was absent for urinary eicosanoids [98] and oxylipins [161].
UHPLC increases the resolution, speed and sensitivity of the analysis of metabolites, and additionally, lowers the use of solvent, which decreases costs [24]. The application of columns 2-μm less instead of the traditional LC columns has significantly improved the chromatographic separation of eicosanoids in human plasma and reduced the analysis time from 20~60 min to 4–12 min [84]. Kortz et al. compared a standard C18 column (C18, 250 × 2.1 mm, 5 μm; Vydac, Grace Vydac) and a Kinetex core-shell column (C18, 100 mm ×  2.1 mm, 2.6 μm; Kinetex, Phenomenex) for the LC–ESI–MS/MS (5500 QTrap; AB SCIEX) analysis of standard mixtures of 15 eicosanoids. Using a Kinetex core-shell column at a flow rate of 0.5 mL/min (P = 315 bar) improved the resolution of PGD2 and PGE2 from 1.4 to 2.9 and reduced the analysis time by half [85].
Brose et al. tested reverse-phase UHPLC columns like: BEH C18 (150 × 2.1 mm,1.7 μm), BEH HILIC (100×2.1mm, 1.7 μm), ACQUITY (Waters, Milford, MA, USA), CSH C18 (150 × 2.1 mm, 1.7 μm), HSS T3 (150 × 2.1 mm, 1.8 μm) and HSS C18 (150 × 2.1 mm, 1.8 μm). They used gradients with acidified ACN/water, MeOH/IPA/water, and MeOH/water, to quantify PGs (Q-TOF, Synapt G2-S; Waters, Milford, MA) with the ESI source. All commercially available PGD2- and PGE2 like iso-PG was separated on the HSS T3 column (ACN/water acidified with 0.1% FAc gradient); it was also possible to separate brain endogenous iso-PG (except for PGE2/ent-PGE2 and 8iso-PGE2/15R-PGE2). That allowed 5 times sharper peaks to be obtained compared to the previously used LC–MS/MS method, with a fast 4 min separation [162]. Gouveia-Figueira et al. elaborated and validated a novel UHPLC–ESI–MS/MS method for the analysis of the oxylipins in human plasma. Using a Waters BEH C18 column (150 × 2.1 mm, 2.5 μm) coupled with an Agilent 6490 Triple Quadrupole system with the iFunnel Technology source (Agilent Technologies, Santa Clara, CA, USA), they achieved the separation of 37 oxylipins, (including some critical pairs of isomers) [102]. Song et al. developed a UHPLC–MRM/MS platform for the simultaneous determination of 122 eicosanoids in human whole blood (HWB). An AB SCIEX API 4000 system (AB SCIEX, Foster City, CA, USA) was used for MS detection. Using UHPLC separation with a sub-2-μm column (150 × 2.1 mm, 1.7 µm, Kinetex C18 column; Phenomenex) led to a total run time reduction to 6.5 min (including equilibration of the column) without the loss of peak resolution or detection sensitivity. The average peak width narrowed to 3 s with a UHPLC/sub-2-μm column from 9 s compared to a conventional HPLC column. Also, injecting only 5 μL of the sample obtained from 2.5 μL of HWB (which consists of 1/4 of the volume needed for the conventional HPLC protocol) showed the equivalent sensitivity [137].
Mass spectrometric optimization also plays a big role and can lead to a significant gain in the signal and thus lower LLOQ values. Various parameters such as source temperature (ST), collision energy (CE), declustering potential (DP), collision cell exit potential (CEP), collision activated dissociation (CAD), and gas pressure affect the signal intensity in the SRM mode. Simultaneously, caution should be taken with increasing the temperature of nebulizer gas as well as auxiliary gas, because the optimum temperature for some compounds may lead to the thermal degradation of others [103]. To obtain better detection limits and avoid interference from nearby isomers, at the last step of optimization, the most intense and specific fragments and transitions are usually chosen [101,103,163]. However, in the case of interference ions, it is better to select more characteristic rather than stronger fragmentation ions. For example, when Yang et al. developed a method for murine sera and BALF oxylipin profiling, for 8, 9 EET the weaker transition 319.2/123 was chosen, because the stronger transition 319.2/167 overlaps with the transition 319.2/167 for 11, 12 EET [101]. Also, to increase the sensibility of the MS method, the optimization of MS operating conditions should be carried out for each individual compound [15,69,103,131,164]. By the careful selection of transitions, it is possible to optimize sensitivity and selectivity so that the overall sensitivity will improve several times [85,101].
Sometimes, simple operations can significantly improve the performance of the analysis. Kolmert et al. determined 109 analytes from human and guinea pig lung tissue using the LC−MS/MS method. They showed that the average recovery increased by 13% by adding 30 μL of 30% glycerol (in MeOH) before eluate evaporation. In addition, even washing the inner surface of the tube with a reconstitution solvent for 10 s increased recovery by 19% [97]. Mengesha also showed that the use of nitrogen gas at room temperature in a vacuum dryer for pre-concentration leads to a decrease in the signals of eicosanoids, with an especially high loss of analytes with a large proportion of ACN, while adding other solvents to ACN reduced the loss of analytes [158]. Also, the organic composition of the mixture for sample re-dissolution can be modified by adding 10–30% water to methanol; this does not affect the solubility of hydrophobic analytes but improves the peak shape for polar analytes [97]. After the optimization of MS operating conditions (DP, CE, CEP, CAD), Kutzner et al. [103] achieved a significant signal gain. Additionally, the increase of the injection volume to 10 μL resulted in lower LLOQ values. A further increase in injection volume to 20 μL was possible only when the extract of the sample was dissolved in 1:1 MeOH/water, because the recovery of the extract in pure organic solvent led to an unacceptable peak shape [103].

3.4. Mass Spectrometry—“Shotgun” Lipidomics

There are two fundamentally different approaches for mass spectrometry-based lipidomic analyses. One approach—comprehensive lipidomic analysis by separation simplification (CLASS), is based on the separation of different lipid categories using optimal extraction and chromatographic techniques prior to mass analysis [165]. The second approach (shotgun lipidomics) is based on the ability of ESI to analyze multiple components in the sample simultaneously, which allows the profiling of many lipid classes in biological tissue in parallel, wherein the sample is introduced into the mass spectrometer without separation, and fragmented components are scanned in MS/MS modes [163]. Wang et al. used the direct infusion of human plasma after LLE and one-step derivatization for the structural identification of the composition of eicosanoid isomers by using ESI–MS/MS [166]. Also, Milic et al. described a new derivatization technique for the simultaneous detection of the oxidation products of DHA, ARA, LA, and oleic acid by ESI–MS in positive ion mode [167]. And if this approach causes difficulties (isobaric species, ion suppression) in complex matrices as plasma, such simple matrices as urine are devoid of this problem [168]. However, in most cases, when analyzing oxylipins an absolute quantitation is required, which needs prior separation [129].

4. Oxylipin Identification/Annotation

Further analysis of oxylipins, depending on the purpose of the study, can be carried out using two main approaches. The first “targeted approach” includes targeted analysis of specific types of oxylipins. Most often, for positive identification, the MRM of the analyte as well as the retention time (in order to differentiate analytes with identical MRM transitions) are compared with those of the standard. Usually for each analyte, after single injections, unique transitions are selected that are additionally compared with the literature. Scanning “transitions” on a QqQ provides a sensitive quantitative analysis, but this approach is limited by the number of available commercial standards, so most published methods analyze only a small fraction of the known oxylipins.
To obtain high-quality lipidomic information about all the main molecular species of oxylipins in the sample, the second approach, namely “untargeted” (non-targeted), is used. In this procedure, non-targeted lipidomics are performed using high-resolution MS (mainly QTOF) and all the detected ions are identified using a database based on fragmentation values. Recently, Watrous et al. developed a new approach for using non-targeted LC–MS/MS to classify putative known oxylipins. Based on the fragmentation patterns obtained from a large number of standards, they maintained a network due to which it was possible to classify more than 500 different oxylipins in human plasma [169]. It is also possible to combine both approaches like Wheelock et al., who used targeted (QqQ) metabolomics to identify 36 oxylipins and non-targeted (orbitrap) to annotate 219 metabolites [170].
Oxylipin researchers currently work with a large number of databases in manual or automatic mode to identify compounds based on their mass spectra. Among the most popular are the following databases: The LIPIDMAPS open source lipid database, which, based on mass generation by computational methods, allows the search for existing masses in the LIPID MAPS Structure Database (LMSD), the calculation of the exact mass of lipid ions, the prediction of MS/MS spectra, and also shows such structural properties as the exact mass, formula, abbreviation, etc. [171,172]. The LipidBlast open source computer-generated (in-silico) tandem mass spectral (MS/MS) lipid database, which contains about 8000 unique structures [173]. The Lipid Mass Spectrum Analysis Database (LMSAD), which is a database containing MS data, structures and annotations of biologically significant lipids [174]. The LipidHome database of theoretical lipid structures providing theoretically generated lipid molecules [175]. LipidBank, the official open source lipid database of the Japanese Conference on the Biochemistry of Lipids (JCBL) [176,177]. The Human Metabolome Database (HMDB), which is a freely accessible electronic database containing information on the metabolites of small molecules found in the human body [178]. MassBank—a storage of mass spectral data that includes several thousands of lipids and lipid metabolites [179]. The METLIN database, obtained by the individual analysis of substances using MS/MS data generated on various types of devices [180]. In oxylipin analysis also databases can be used such as MINE databases [181], LipiDAT, [182], NIST [183], CYBERLIPID [184], LipidPedia [185], and omicX databases for lipidomics analysis (https://omictools.com), and the American Oil Chemistry Society (AOCS) lipid library (https://lipidlibrary.aocs.org).

Programs and Tools

To simplify and automate the process of searching data in known databases special software is often used. These tools allow not only for automatic identification from open databases and an in-house library, but also the quantification of lipids according to LC–MS data. Many of them allow the primary MS/MS raw data received from various devices to be directly exported, and often contain statistics modules for the analysis of results. To minimize the possibility of errors, these programs are usually equipped with various filters to eliminate unwanted matches; some of them allow the use of rule-based identification. Below, are the most popular commercial and free available ones used for these purposes: Lipid View [186], Lipid Search [177], SimLipid [187], LipidXplorer [188], LipiDex [189], LIMSA [190], Lipidyzer [191], Lipid Data Analyzer [192], LipidQA [193], CEU Mass Mediator [194], LipidLama [195], LipidMatch [196], LipidMiner [197], dGOT-MS [198], ALEX [199], The Lipid Annotation Service (LAS) [200], and LOBSTAHS [201]. Thus, the data accumulated over many years make the non-targeted approach simpler, and therefore more popular among researchers. At the same time, the traditional targeted approach will remain indispensable for the quantification of oxylipin levels.

5. Conclusions

Among the various analytical tools, tandem MS/MS instruments have recently become the most popular due to their high sensitivity for analyzing the levels of oxylipins in biological samples, while UHPLC offers the highest resolution, speed and sensitivity for analyzing oxylipins. The development of instrumentation makes operation easier, speeds up analysis, and provides improved selectivity and sensitivity, and lower detection limits.

Author Contributions

A.M. conceived and designed the review. I.L. and A.P. studied the literature and wrote the manuscript. T.S. wrote and verified the manuscript. All authors accepted the final version of the review. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by the National Science Centre of Poland (grant no. NCN 2016/21/D/NZ5/00219 and 2016/22/E/NZ4/00665).

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Gabbs, M.; Leng, S.; Devassy, J.G.; Monirujjaman, M.; Aukema, H.M. Advances in Our Understanding of Oxylipins Derived from Dietary PUFAs. Adv. Nutr. Am. Int. Rev. J. 2015, 6, 513–540. [Google Scholar] [CrossRef] [Green Version]
  2. Vigor, C.; Bertrand-Michel, J.; Pinot, E.; Oger, C.; Vercauteren, J.; Le Faouder, P.; Galano, J.M.; Lee, J.C.Y.; Durand, T. Non-enzymatic lipid oxidation products in biological systems: ASSESSMENT of the metabolites from polyunsaturated fatty acids. J. Chromatogr. B Anal. Technol. Biomed. Life Sci. 2014, 964, 65–78. [Google Scholar] [CrossRef]
  3. Shearer, G.C.; Walker, R.E. An overview of the biologic effects of omega-6 oxylipins in humans. Prostaglandins Leukot. Essent. Fat. Acids 2018, 137, 26–38. [Google Scholar] [CrossRef] [PubMed]
  4. Pickens, C.A.; Sordillo, L.M.; Zhang, C.; Fenton, J.I. Obesity is positively associated with arachidonic acid-derived 5- and 11-hydroxyeicosatetraenoic acid (HETE). Metabolism 2017, 70, 177–191. [Google Scholar] [CrossRef] [PubMed]
  5. Zhou, J.; Chen, L.; Liu, Z.; Sang, L.; Li, Y.; Yuan, D. Changes in erythrocyte polyunsaturated fatty acids and plasma eicosanoids level in patients with asthma. Lipids Health Dis. 2018, 17, 206. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  6. Rigas, B.; Goldman, I.S.; Levine, L. Altered eicosanoid levels in human colon cancer. J. Lab. Clin. Med. 1993, 122, 518–523. [Google Scholar] [PubMed]
  7. Yang, J.; Eiserich, J.P.; Cross, C.E.; Morrissey, B.M.; Hammock, B.D. Metabolomic profiling of regulatory lipid mediators in sputum from adult cystic fibrosis patients. Free Radic. Biol. Med. 2012, 53, 160–171. [Google Scholar] [CrossRef] [Green Version]
  8. Kumar, N.; Gupta, G.; Anilkumar, K.; Fatima, N.; Karnati, R.; Reddy, G.V.; Giri, P.V.; Reddanna, P. 15-Lipoxygenase metabolites of α-linolenic acid, [13-(S)-HPOTrE and 13-(S)-HOTrE], mediate anti-inflammatory effects by inactivating NLRP3 inflammasome. Sci. Rep. 2016, 6, 31649. [Google Scholar] [CrossRef]
  9. Barquissau, V.; Ghandour, R.A.; Ailhaud, G.; Klingenspor, M.; Langin, D.; Amri, E.-Z.; Pisani, D.F. Control of adipogenesis by oxylipins, GPCRs and PPARs. Biochimie 2017, 136, 3–11. [Google Scholar] [CrossRef]
  10. Wang, D.; DuBois, R.N. Eicosanoids and cancer. Nat. Rev. Cancer 2010, 10, 181–193. [Google Scholar] [CrossRef]
  11. Harris, W.S.; Shearer, G.C. Omega-6 Fatty Acids and Cardiovascular Disease. Circulation 2014, 130, 1562–1564. [Google Scholar] [CrossRef] [Green Version]
  12. Liakh, I.; Pakiet, A.; Sledzinski, T.; Mika, A. Modern Methods of Sample Preparation for the Analysis of Oxylipins in Biological Samples. Molecules 2019, 24, 1639. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  13. Yuan, Z.-X.; Majchrzak-Hong, S.; Keyes, G.S.; Iadarola, M.J.; Mannes, A.J.; Ramsden, C.E. Lipidomic profiling of targeted oxylipins with ultra-performance liquid chromatography-tandem mass spectrometry. Anal. Bioanal. Chem. 2018, 410, 6009–6029. [Google Scholar] [CrossRef]
  14. O’Donnell, V.B.; Maskrey, B.; Taylor, G.W. Eicosanoids: Generation and detection in mammalian cells. Methods Mol. Biol. 2009, 462, 5–23. [Google Scholar] [PubMed]
  15. Rund, K.M.; Ostermann, A.I.; Kutzner, L.; Galano, J.-M.; Oger, C.; Vigor, C.; Wecklein, S.; Seiwert, N.; Durand, T.; Schebb, N.H. Development of an LC-ESI (-)-MS/MS method for the simultaneous quantification of 35 isoprostanes and isofurans derived from the major n3- and n6-PUFAs. Anal. Chim. Acta 2018, 1037, 63–74. [Google Scholar] [CrossRef] [PubMed]
  16. VanderNoot, V.A.; VanRollins, M. Capillary Electrophoresis of Cytochrome P-450 Epoxygenase Metabolites of Arachidonic Acid. 2. Resolution of Stereoisomers. Anal. Chem. 2002, 74, 5866–5870. [Google Scholar] [CrossRef] [PubMed]
  17. VanRollins, M.; VanderNoot, V.A. Simultaneous resolution of underivatized regioisomers and stereoisomers of arachidonate epoxides by capillary electrophoresis. Anal. Biochem. 2003, 313, 106–116. [Google Scholar] [CrossRef]
  18. Toyo’oka, T.; Ishibashi, M.; Terao, T.; Imai, K. Sensitive fluorometric detection of prostaglandins by high performance liquid chromatography after precolumn labelling with 4-(N,N-dimethylaminosulphonyl)-7-(1-piperazinyl)-2,1,3-benzoxadiazole (DBD-PZ). Biomed. Chromatogr. 1992, 6, 143–148. [Google Scholar] [CrossRef] [PubMed]
  19. Rathbun, B.R.K.; Faulkner, G.R.; Ostroski, M.H.; Christianson, T.A.; Hughes, G.; Jones, G.; Cahn, R.; Maziarz, R.; Royle, G.; Keeble, W.; et al. Fluorescent HPLC assay for 20-HETE and other P-450 metabolites of arachidonic acid. Am. J. Physiol. Heart Circ. Physiol. 2000, 509, 863–871. [Google Scholar]
  20. Hewawasam, E.; Liu, G.; Jeffery, D.W.; Muhlhausler, B.S.; Gibson, R.A. A stable method for routine analysis of oxylipins from dried blood spots using ultra-high performance liquid chromatography–tandem mass spectrometry. Prostaglandins Leukot. Essent. Fat. Acids 2018, 137, 12–18. [Google Scholar] [CrossRef]
  21. Tsikas, D.; Zoerner, A.A. Analysis of eicosanoids by LC-MS/MS and GC-MS/MS: A historical retrospect and a discussion. J. Chromatogr. B 2014, 964, 79–88. [Google Scholar] [CrossRef]
  22. Quehenberger, O.; Armando, A.M.; Brown, A.H.; Milne, S.B.; Myers, D.S.; Merrill, A.H.; Bandyopadhyay, S.; Jones, K.N.; Kelly, S.; Shaner, R.L.; et al. Lipidomics reveals a remarkable diversity of lipids in human plasma. J. Lipid Res. 2010, 51, 3299–3305. [Google Scholar] [CrossRef] [Green Version]
  23. Ostermann, A.I.; Willenberg, I.; Schebb, N.H. Comparison of sample preparation methods for the quantitative analysis of eicosanoids and other oxylipins in plasma by means of LC-MS/MS. Anal. Bioanal. Chem. 2015, 407, 1403–1414. [Google Scholar] [CrossRef]
  24. Puppolo, M.; Varma, D.; Jansen, S.A. A review of analytical methods for eicosanoids in brain tissue. J. Chromatogr. B Anal. Technol. Biomed. Life Sci. 2014, 964, 50–64. [Google Scholar] [CrossRef] [Green Version]
  25. MoNA—MassBank of North America Home Page. Available online: https://mona.fiehnlab.ucdavis.edu (accessed on 12 December 2019).
  26. Basu, S. Radioimmunosassay of 8-iso-prostaglandin F2α: An index for oxidative injury via free radical catalysed lipid peroxidation. Prostaglandins Leukot. Essent. Fat. Acids 1998, 58, 319–325. [Google Scholar] [CrossRef]
  27. Tsikas, D.; Suchy, M.-T. Assessment of Urinary F 2 -Isoprostanes in Experimental and Clinical Studies: Mass Spectrometry Versus ELISA. Hypertension 2012, 60, 199315. [Google Scholar] [CrossRef] [Green Version]
  28. Taylor, G.; Black, P.; Turner, N.; Taylor, I.; Maltby, N.; Fuller, R.; Dollery, C. Urinary leukotriene E4 after antigen challenge and in acute asthma and allergic rhinitis. Lancet 1989, 333, 584–588. [Google Scholar] [CrossRef]
  29. Chiang, N.; Bermudez, E.A.; Ridker, P.M.; Hurwitz, S.; Serhan, C.N. Aspirin triggers antiinflammatory 15-epi-lipoxin A4 and inhibits thromboxane in a randomized human trial. Proc. Natl. Acad. Sci. USA 2004, 101, 15178–15183. [Google Scholar] [CrossRef] [Green Version]
  30. Miller, D.K.; Sadowski, S.; DeSousa, D.; Maycock, A.L.; Lombardo, D.L.; Young, R.N.; Hayes, E.C. Development of enzyme-linked immunosorbent assays for measurement of leukotrienes and prostaglandins. J. Immunol. Methods 1985, 81, 169–185. [Google Scholar] [CrossRef]
  31. Klawitter, J.; Haschke, M.; Shokati, T.; Klawitter, J.; Christians, U. Quantification of 15-F2t-isoprostane in human plasma and urine: Results from enzyme-linked immunoassay and liquid chromatography/tandem mass spectrometry cannot be compared. Rapid Commun. Mass Spectrom. 2011, 25, 463–468. [Google Scholar] [CrossRef]
  32. Roberts, L.J.; Morrow, J.D. Measurement of F2-isoprostanes as an index of oxidative stress in vivo. Free Radic. Biol. Med. 2000, 28, 505–513. [Google Scholar] [CrossRef]
  33. Kempen, E.C.; Yang, P.; Felix, E.; Madden, T.; Newman, R.A. Simultaneous Quantification of Arachidonic Acid Metabolites in Cultured Tumor Cells Using High-Performance Liquid Chromatography/Electrospray Ionization Tandem Mass Spectrometry. Anal. Biochem. 2001, 297, 183–190. [Google Scholar] [CrossRef] [PubMed]
  34. Bayer, M.; Mosandl, A.; Thaçi, D. Improved enantioselective analysis of polyunsaturated hydroxy fatty acids in psoriatic skin scales using high-performance liquid chromatography. J. Chromatogr. B Anal. Technol. Biomed. Life Sci. 2005, 819, 323–328. [Google Scholar] [CrossRef]
  35. Fuchs, D.; Hamberg, M.; Sköld, C.M.; Wheelock, Å.M.; Wheelock, C.E. An LC-MS/MS workflow to characterize 16 regio- and stereoisomeric trihydroxyoctadecenoic acids. J. Lipid Res. 2018, 59, 2025–2033. [Google Scholar] [CrossRef] [Green Version]
  36. Lee, S.-H.; Blair, I.A. Targeted chiral lipidomics analysis of bioactive eicosanoid lipids in cellular systems. BMB Rep. 2009, 42, 401–410. [Google Scholar] [CrossRef]
  37. Massey, K.A.; Nicolaou, A. Lipidomics of oxidized polyunsaturated fatty acids. Free Radic. Biol. Med. 2013, 59, 45–55. [Google Scholar] [CrossRef]
  38. Liu, W.; Morrow, J.D.; Yin, H. Quantification of F2-isoprostanes as a reliable index of oxidative stress in vivo using gas chromatography–mass spectrometry (GC-MS) method. Free Radic. Biol. Med. 2009, 47, 1101–1107. [Google Scholar] [CrossRef] [Green Version]
  39. Parker, C.E.; Graham, L.B.; Nguyen, M.-N.; Gladen, B.C.; Kadiiska, M.B.; Barrett, J.C.; Tomer, K.B. An Improved GC/MS-Based Procedure for the Quantitation of the Isoprostane 15-F2t-IsoP in Rat Plasma. Mol. Biotechnol. 2001, 18, 105–118. [Google Scholar] [CrossRef]
  40. Schweer, H.; Watzer, B.; Seyberth, W.; Nu, R.M. Improved Quantiücation of 8-epi-Prostaglandin F and F -isoprostanes by Gas Quadrupole Mass Spectrometry: Partial Cyclooxygenase-dependent Formation of 8-epi-Prostaglandin F in Humans 2a. J. Mass Spectrom. 1997, 32, 1362–1370. [Google Scholar] [CrossRef]
  41. Nourooz-Zadeh, J.; Gopaul, N.K.; Barrow, S.; Mallet, A.I.; Änggård, E.E. Analysis of F2-isoprostanes as indicators of non-enzymatic lipid peroxidation in vivo by gas chromatography-mass spectrometry: Development of a solid-phase extraction procedure. J. Chromatogr. B Biomed. Sci. Appl. 1995, 667, 199–208. [Google Scholar] [CrossRef]
  42. Tsikas, D.; Suchy, M.-T.; Tödter, K.; Heeren, J.; Scheja, L. Utilizing immunoaffinity chromatography (IAC) cross-reactivity in GC–MS/MS exemplified at the measurement of prostaglandin E 1 in human plasma using prostaglandin E 2-specific IAC columns. J. Chromatogr. B 2016, 1021, 101–107. [Google Scholar] [CrossRef]
  43. Isobe, Y.; Arita, M.; Matsueda, S.; Iwamoto, R.; Fujihara, T.; Nakanishi, H.; Taguchi, R.; Masuda, K.; Sasaki, K.; Urabe, D.; et al. Identification and structure determination of novel anti-inflammatory mediator resolvin E3, 17,18-dihydroxyeicosapentaenoic acid. J. Biol. Chem. 2012, 287, 10525–10534. [Google Scholar] [CrossRef] [Green Version]
  44. Terragno, A.; Rydzik, R.; Terragno, N.A. High performance liquid chromatography and UV detection for the separation and quantitation of prostaglandins. Prostaglandins 1981, 21, 101–112. [Google Scholar] [CrossRef]
  45. Nithipatikom, K.; Pratt, P.F.; Campbell, W.B. Determination of EETs using microbore liquid chromatography with fluorescence detection. Am. J. Physiol. Circ. Physiol. 2000, 279, H857–H862. [Google Scholar] [CrossRef]
  46. Aghazadeh-Habashi, A.; Asghar, W.; Jamali, F. Simultaneous determination of selected eicosanoids by reversed-phase HPLC method using fluorescence detection and application to rat and human plasma, and rat heart and kidney samples. J. Pharm. Biomed. Anal. 2015, 110, 12–19. [Google Scholar] [CrossRef]
  47. Maskrey, B.H.; O’Donnell, V.B. Analysis of eicosanoids and related lipid mediators using mass spectrometry. Biochem. Soc. Trans. 2008, 36, 1055–1059. [Google Scholar] [CrossRef]
  48. Kim, H.G.N.; Huh, Y.N.; Park, K.S. k Simultaneous HPLC analysis of arachidonic acid metabolites in biological samples with simple solid phase extraction. Korean J. Physiol. Pharmacol. 1998, 2, 779–786. [Google Scholar]
  49. Eberhard, J.; Jepsen, S.; Albers, H.K.; Açil, Y. Quantitation of Arachidonic Acid Metabolites in Small Tissue Biopsies by Reversed-Phase High-Performance Liquid Chromatography. Anal. Biochem. 2000, 280, 258–263. [Google Scholar] [CrossRef]
  50. Yue, H.; Strauss, K.I.; Borenstein, M.R.; Barbe, M.F.; Rossi, L.J.; Jansen, S.A. Determination of bioactive eicosanoids in brain tissue by a sensitive reversed-phase liquid chromatographic method with fluorescence detection. J. Chromatogr. B 2004, 803, 267–277. [Google Scholar] [CrossRef]
  51. Manna, J.D.; Reyzer, M.L.; Latham, J.C.; Weaver, C.D.; Marnett, L.J.; Caprioli, R.M. High-Throughput Quantification of Bioactive Lipids by MALDI Mass Spectrometry: Application to Prostaglandins. Anal. Chem. 2011, 83, 6683–6688. [Google Scholar] [CrossRef] [Green Version]
  52. Solati, Z.; Ravandi, A. Lipidomics of Bioactive Lipids in Acute Coronary Syndromes. Int. J. Mol. Sci. 2019, 20, 1051. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  53. Johanson, R.A.; Buccafusca, R.; Quong, J.N.; Shaw, M.A.; Berry, G.T. Phosphatidylcholine removal from brain lipid extracts expands lipid detection and enhances phosphoinositide quantification by matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry. Anal. Biochem. 2007, 362, 155–167. [Google Scholar] [CrossRef] [PubMed]
  54. Halket, J.M.; Waterman, D.; Przyborowska, A.M.; Patel, R.K.P.; Fraser, P.D.; Bramley, P.M. Chemical derivatization and mass spectral libraries in metabolic profiling by GC/MS and LC/MS/MS. J. Exp. Bot. 2005, 56, 219–243. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  55. Tsikas, D. Application of gas chromatography-mass spectrometry and gas chromatography-tandem mass spectrometry to assess in vivo synthesis of prostaglandins, thromboxane, leukotrienes, isoprostanes and related compounds in humans. J. Chromatogr. B Biomed. Appl. 1998, 717, 201–245. [Google Scholar] [CrossRef]
  56. Martin-Venegas, R.; Jáuregui, O.; Moreno, J.J. Liquid chromatography-tandem mass spectrometry analysis of eicosanoids and related compounds in cell models. J. Chromatogr. B 2014, 964, 41–49. [Google Scholar]
  57. Murphy, R.C.; Barkley, R.M.; Zemski Berry, K.; Hankin, J.; Harrison, K.; Johnson, C.; Krank, J.; McAnoy, A.; Uhlson, C.; Zarini, S. Electrospray ionization and tandem mass spectrometry of eicosanoids. Anal. Biochem. 2005, 346, 1–42. [Google Scholar]
  58. Ståhlman, M.; Ejsing, C.S.; Tarasov, K.; Perman, J.; Borén, J.; Ekroos, K. High-throughput shotgun lipidomics by quadrupole time-of-flight mass spectrometry. J. Chromatogr. B 2009, 877, 2664–2672. [Google Scholar] [CrossRef]
  59. Stepnowski, P.; Mika, A.; Sledzinski, T. Current progress of lipid analysis in metabolic diseases by mass spectrometry methods. Curr. Med. Chem. 2017, 24, 1–45. [Google Scholar]
  60. Wang, Y.; Armando, A.M.; Quehenberger, O.; Yan, C.; Dennis, E.A. Comprehensive ultra-performance liquid chromatographic separation and mass spectrometric analysis of eicosanoid metabolites in human samples. J. Chromatogr. A 2014, 1359, 60–69. [Google Scholar] [CrossRef] [Green Version]
  61. Wong, A.; Sagar, D.R.; Ortori, C.A.; Kendall, D.A.; Chapman, V.; Barrett, D.A. Simultaneous tissue profiling of eicosanoid and endocannabinoid lipid families in a rat model of osteoarthritis. J. Lipid Res. 2014, 55, 1902–1913. [Google Scholar] [CrossRef] [Green Version]
  62. Basu, S. Radioimmunoassay of 15-keto-13, 14-dihydro-prostaglandin F2alpha: An index for inflammation via cyclooxygenase catalysed lipid peroxidation. Prostaglandins Leukot. Essent. Fat. Acids 1998, 58, 347–352. [Google Scholar] [CrossRef]
  63. Larré, S.; Tran, N.; Fan, C.; Hamadeh, H.; Champigneulles, J.; Azzouzi, R.; Cussenot, O.; Mangin, P.; Olivier, J.L. PGE2 and LTB4 tissue levels in benign and cancerous prostates. Prostaglandins Lipid Mediat. 2008, 87, 14–19. [Google Scholar] [CrossRef] [PubMed]
  64. Aslan, M.; Aslan, I.; Özcan, F.; Eryılmaz, R.; Ensari, C.O.; Bilecik, T. A pilot study investigating early postoperative changes of plasma polyunsaturated fatty acids after laparoscopic sleeve gastrectomy. Lipids Health Dis. 2014, 13, 62. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  65. Neupert, W.; Oelkers, R.; Brune, K.; Geisslinger, G. A new reliable chemiluminescence immunoassay (CLIA) for prostaglandin E2 using enhanced luminol as substrate. Prostaglandins 1996, 52, 385–401. [Google Scholar] [CrossRef]
  66. Jeremy, J.Y.; Okonofua, F.E.; Thomas, M.; Wojdyla, J.; Smith, W.; Craft, I.L.; Dandona, P. Oocyte maturity and human follicular fluid prostanoids, gonadotropins, and prolactin after administration of clomiphene and pergonal. J. Clin. Endocrinol. Metab. 1987, 65, 402–406. [Google Scholar] [CrossRef]
  67. Ylikorkala, O.; Tenhunen, A. Follicular fluid prostaglandins in endometriosis and ovarian hyperstimulation. Fertil. Steril. 1984, 41, 66–69. [Google Scholar] [CrossRef]
  68. Morgan, A.H.; Hammond, V.J.; Morgan, L.; Thomas, C.P.; Tallman, K.A.; Garcia-Diaz, Y.R.; McGuigan, C.; Serpi, M.; Porter, N.A.; Murphy, R.C.; et al. Quantitative assays for esterified oxylipins generated by immune cells. Nat. Protoc. 2010, 5, 1919–1931. [Google Scholar] [CrossRef] [Green Version]
  69. Wang, D.; DuBois, R.N. Measurement of Eicosanoids in Cancer Tissues. Methods Enzymol. 2007, 433, 27–50. [Google Scholar]
  70. Yang, P.; Felix, E.; Madden, T.; Fischer, S.M.; Newman, R.A. Quantitative high-performance liquid chromatography/electrospray ionization tandem mass spectrometric analysis of 2-and 3-series prostaglandins in cultured tumor cells. Anal. Biochem. 2002, 308, 168–177. [Google Scholar] [CrossRef]
  71. Jaffe, B.M.; Behrman, H.R.; Parker, C.W. Radioimmunoassay Measurement of Prostaglandins E, A, and F in Human Plasma. J. Clin. Investig. 1973, 52, 398–405. [Google Scholar] [CrossRef]
  72. Schmidt, R.; Coste, O.; Geisslinger, G. LC-MS/MS-analysis of prostaglandin E2 and D2 in microdialysis samples of rats. J. Chromatogr. B Anal. Technol. Biomed. Life Sci. 2005, 826, 188–197. [Google Scholar] [CrossRef] [PubMed]
  73. Astarita, G.; Kendall, A.C.; Dennis, E.A.; Nicolaou, A. Targeted lipidomic strategies for oxygenated metabolites of polyunsaturated fatty acids. Biochim. Biophys. Acta Mol. Cell Biol. Lipids 2015, 1851, 456–468. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  74. Gandhi, A.S.; Budac, D.; Khayrullina, T.; Staal, R.; Chandrasena, G. Quantitative analysis of lipids: A higher-throughput LC–MS/MS-based method and its comparison to ELISA. Futur. Sci. OA 2017, 3, FSO157. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  75. Henkel, F.D.R.; Friedl, A.; Haid, M.; Thomas, D.; Bouchery, T.; Haimerl, P.; de los Reyes Jiménez, M.; Alessandrini, F.; Schmidt-Weber, C.B.; Harris, N.L.; et al. House dust mite drives pro-inflammatory eicosanoid reprogramming and macrophage effector functions. Allergy 2019, 74, 1090–1101. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  76. Gatti, R.; Gotti, R.; Cavrini, V.; Soli, M.; Bertaccini, A.; Carparelli, F. Stability study of prostaglandin E1(PGE1) in physiological solutions by liquid chromatography (HPLC). Int. J. Pharm. 1995, 115, 113–117. [Google Scholar] [CrossRef]
  77. Hartung, N.M.; Mainka, M.; Kampschulte, N.; Ostermann, A.I.; Schebb, N.H. A strategy for validating concentrations of oxylipin standards for external calibration. Prostaglandins Lipid Mediat. 2019, 141, 22–24. [Google Scholar] [CrossRef]
  78. Moraes, L.A.; Giner, R.M.; Paul-Clark, M.J.; Perretti, M.; Perrett, D. An isocratic HPLC method for the quantitation of eicosanoids in human platelets. Biomed. Chromatogr. 2004, 18, 64–68. [Google Scholar] [CrossRef]
  79. Herrmann, T.; Steinhilber, D.; Knospe, J.; Roth, H.J. Determination of picogram amounts of lipoxin A4 and lipoxin B4 by high-performance liquid chromatography with electrochemical detection. J. Chromatogr. B Biomed. Sci. Appl. 1988, 428, 237–245. [Google Scholar] [CrossRef]
  80. Herrmann, T.; Steinhilber, D.; Roth, H.J. Determination of leukotriene B4 by high-performance liquid chromatography with electrochemical detection. J. Chromatogr. B Biomed. Sci. Appl. 1987, 416, 170–175. [Google Scholar] [CrossRef]
  81. Kotani, A.; Kusu, F. HPLC with electrochemical detection for determining the distribution of free fatty acids in skin surface lipids from the human face and scalp. Arch. Dermatol. Res. 2002, 294, 172–177. [Google Scholar] [CrossRef]
  82. Potter, P.E.; Meek, J.L.; Neff, N.H. Acetylcholine and Choline in Neuronal Tissue Measured by HPLC with Electrochemical Detection. J. Neurochem. 1983, 41, 188–194. [Google Scholar] [CrossRef] [PubMed]
  83. Berkecz, R.; Lísa, M.; Holčapek, M. Analysis of oxylipins in human plasma: Comparison of ultrahigh-performance liquid chromatography and ultrahigh-performance supercritical fluid chromatography coupled to mass spectrometry. J. Chromatogr. A 2017, 1511, 107–121. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  84. Chen, G.; Zhang, Q. Comprehensive analysis of oxylipins in human plasma using reversed-phase liquid chromatography-triple quadrupole mass spectrometry with heatmap-assisted selection of transitions. Anal. Bioanal. Chem. 2019, 411, 367–385. [Google Scholar] [CrossRef] [PubMed]
  85. Kortz, L.; Helmschrodt, C.; Ceglarek, U. Fast liquid chromatography combined with mass spectrometry for the analysis of metabolites and proteins in human body fluids. Anal. Bioanal. Chem. 2011, 399, 2635–2644. [Google Scholar] [CrossRef] [PubMed]
  86. Kortz, L.; Dorow, J.; Becker, S.; Thiery, J.; Ceglarek, U. Fast liquid chromatography–quadrupole linear ion trap-mass spectrometry analysis of polyunsaturated fatty acids and eicosanoids in human plasma. J. Chromatogr. B 2013, 927, 209–213. [Google Scholar] [CrossRef]
  87. Schoeman, J.C.; Harms, A.C.; van Weeghel, M.; Berger, R.; Vreeken, R.J.; Hankemeier, T. Development and application of a UHPLC–MS/MS metabolomics based comprehensive systemic and tissue-specific screening method for inflammatory, oxidative and nitrosative stress. Anal. Bioanal. Chem. 2018, 410, 2551–2568. [Google Scholar] [CrossRef] [Green Version]
  88. Aoyagi, R.; Ikeda, K.; Isobe, Y.; Arita, M. Comprehensive analyses of oxidized phospholipids using a measured MS/MS spectra library. J. Lipid Res. 2017, 58, 2229–2237. [Google Scholar] [CrossRef] [Green Version]
  89. Shaik, J.S.B.; Miller, T.M.; Graham, S.H.; Manole, M.D.; Poloyac, S.M. Rapid and simultaneous quantitation of prostanoids by UPLC–MS/MS in rat brain. J. Chromatogr. B 2014, 945–946, 207–216. [Google Scholar] [CrossRef] [Green Version]
  90. Uchikata, T.; Matsubara, A.; Nishiumi, S.; Yoshida, M.; Fukusaki, E.; Bamba, T. Development of oxidized phosphatidylcholine isomer profiling method using supercritical fluid chromatography/tandem mass spectrometry. J. Chromatogr. A 2012, 1250, 205–211. [Google Scholar] [CrossRef]
  91. Schneider, C.; Yu, Z.; Boeglin, W.E.; Zheng, Y.; Brash, A.R. Enantiomeric Separation of Hydroxy and Hydroperoxy Eicosanoids by Chiral Column Chromatography. Methods Enzymol. 2007, 433, 145–157. [Google Scholar]
  92. Deems, R.; Buczynski, M.W.; Bowers-Gentry, R.; Harkewicz, R.; Dennis, E.A. Detection and Quantitation of Eicosanoids via High Performance Liquid Chromatography-Electrospray Ionization-Mass Spectrometry. Methods Enzymol. 2007, 432, 59–82. [Google Scholar] [PubMed]
  93. Oh, S.F.; Pillai, P.S.; Recchiuti, A.; Yang, R.; Serhan, C.N. Pro-resolving actions and stereoselective biosynthesis of 18S E-series resolvins in human leukocytes and murine inflammation. J. Clin. Investig. 2011, 121, 569–581. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  94. Oh, S.F.; Vickery, T.W.; Serhan, C.N. Chiral lipidomics of E-series resolvins: Aspirin and the biosynthesis of novel mediators. Biochim. Biophys. Acta Mol. Cell Biol. Lipids 2011, 1811, 737–747. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  95. Deng, B.; Wang, C.W.; Arnardottir, H.H.; Li, Y.; Cheng, C.Y.C.; Dalli, J.; Serhan, C.N. Maresin biosynthesis and identification of maresin 2, a new anti-inflammatory and pro-resolving mediator from human macrophages. PLoS ONE 2014, 9, e102362. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  96. Mesaros, C.; Lee, S.H.; Blair, I.A. Analysis of epoxyeicosatrienoic acids by chiral liquid chromatography/electron capture atmospheric pressure chemical ionization mass spectrometry using [13C]-analog internal standards. Rapid Commun. Mass Spectrom. 2010, 24, 3237–3247. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  97. Kolmert, J.; Fauland, A.; Fuchs, D.; Säfholm, J.; Gómez, C.; Adner, M.; Dahlén, S.E.; Wheelock, C.E. Lipid Mediator Quantification in Isolated Human and Guinea Pig Airways: An Expanded Approach for Respiratory Research. Anal. Chem. 2018, 90, 10239–10248. [Google Scholar] [CrossRef]
  98. Sterz, K.; Scherer, G.; Ecker, J. A simple and robust UPLC-SRM/MS method to quantify urinary eicosanoids. J. Lipid Res. 2012, 53, 1026–1036. [Google Scholar] [CrossRef] [Green Version]
  99. Neilson, A.P.; Ren, J.; Hong, Y.H.; Sen, A.; Smith, W.L.; Brenner, D.E.; Djuric, Z. Effect of fish oil on levels of R-And S-enantiomers of 5-, 12-, and 15-hydroxyeicosatetraenoic acids in mouse colonic mucosa. Nutr. Cancer 2012, 64, 163–172. [Google Scholar] [CrossRef] [Green Version]
  100. Mesaros, C.; Blair, I.A. Targeted Chiral Analysis of Bioactive Arachidonic Acid Metabolites Using Liquid-Chromatography-Mass Spectrometry. Metabolites 2012, 2, 337–365. [Google Scholar] [CrossRef]
  101. Yang, J.; Schmelzer, K.; Georgi, K.; Hammock, B.D. Quantitative Profiling Method for Oxylipin Metabolome by Liquid Chromatography Electrospray Ionization Tandem Mass Spectrometry. Anal. Chem. 2009, 81, 8085–8093. [Google Scholar] [CrossRef] [Green Version]
  102. Gouveia-Figueira, S.; Späth, J.; Zivkovic, A.M.; Nording, M.L. Profiling the oxylipin and endocannabinoid metabolome by UPLC-ESI-MS/MS in human plasma to monitor postprandial inflammation. PLoS ONE 2015, 10, 1–29. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  103. Kutzner, L.; Rund, K.M.; Ostermann, A.I.; Hartung, N.M.; Galano, J.-M.; Balas, L.; Durand, T.; Balzer, M.S.; David, S.; Schebb, N.H. Development of an Optimized LC-MS Method for the Detection of Specialized Pro-Resolving Mediators in Biological Samples. Front. Pharmacol. 2019, 10, 169. [Google Scholar] [CrossRef] [PubMed]
  104. Kyle, J.E.; Aly, N.; Zheng, X.; Burnum-Johnson, K.E.; Smith, R.D.; Baker, E.S. Evaluating lipid mediator structural complexity using ion mobility spectrometry combined with mass spectrometry. Bioanalysis 2018, 10, 279–289. [Google Scholar] [CrossRef] [PubMed]
  105. Hinz, C.; Liggi, S.; Mocciaro, G.; Jung, S.; Induruwa, I.; Pereira, M.; Bryant, C.E.; Meckelmann, S.W.; O’Donnell, V.B.; Farndale, R.W.; et al. A Comprehensive UHPLC Ion Mobility Quadrupole Time-of-Flight Method for Profiling and Quantification of Eicosanoids, Other Oxylipins, and Fatty Acids. Anal. Chem. 2019, 91, 8025–8035. [Google Scholar] [CrossRef]
  106. Moser, A.C.; Hage, D.S. Immunoaffinity chromatography: An introduction to applications and recent developments. Bioanalysis 2010, 2, 769–790. [Google Scholar] [CrossRef] [Green Version]
  107. Tsikas, D.; Schwedhelm, E.; Suchy, M.T.; Niemann, J.; Gutzki, F.M.; Erpenbeck, V.J.; Hohlfeld, J.M.; Surdacki, A.; Frölich, J.C. Divergence in urinary 8-iso-PGF2α (iPF2α-III, 15-F2t-IsoP) levels from gas chromatography-tandem mass spectrometry quantification after thin-layer chromatography and immunoaffinity column chromatography reveals heterogeneity of 8-iso-PGF2α. J. Chromatogr. B Anal. Technol. Biomed. Life Sci. 2003, 794, 237–255. [Google Scholar] [CrossRef]
  108. Gijon, M.A.; Kennedy, P.D.; Soulika, A.M. Immunoaffinity Extraction and Bioactive Lipid Profiling. Genet. Eng. Biotechnol. News 2018, 38, 14–15. [Google Scholar] [CrossRef]
  109. Catella, F.; Healy, D.; Lawson, J.A.; FitzGerald, G.A. 11-Dehydrothromboxane B2: A quantitative index of thromboxane A2 formation in the human circulation. Proc. Natl. Acad. Sci. USA 1986, 83, 5861–5865. [Google Scholar] [CrossRef] [Green Version]
  110. Mueller, M.J.; Mène-Saffrané, L.; Grun, C.; Karg, K.; Farmer, E.E. Oxylipin analysis methods. Plant J. 2006, 45, 472–489. [Google Scholar] [CrossRef]
  111. Wiswedel, I.; Hirsch, D.; Nourooz-Zadeh, J.; Flechsig, A.; Lück-Lambrecht, A.; Augustin, W. Analysis of Monohydroxyeicosatetraenoic Acids and F 2-isoprostanes as Markers of Lipid Peroxidation in Rat Brain Mitochondria. Free Radic. Res. 2002, 36, 1–11. [Google Scholar] [CrossRef]
  112. Morrow, J.D.; Roberts, L.J. Mass spectrometric quantification of F2-isoprostanes in biological fluids and tissues as measure of oxidant stress. In Methods in Enzymology; Academic Press: Cambridge, MA, USA, 1999; Volume 300, pp. 3–12. [Google Scholar]
  113. Lee, C.-Y.J.; Jenner, A.; Halliwell, B. Rapid preparation of human urine and plasma samples for analysis of F2-isoprostanes by gas chromatography-mass spectrometry. Biochem. Biophys. Res. Commun. 2004, 320, 696–702. [Google Scholar] [CrossRef] [PubMed]
  114. Tsikas, D.; Zoerner, A.A.; Haufe, S.; Engeli, S.; Stichtenoth, D.O.; Jordan, J. Microdialysis of Prostaglandins, Thromboxane, and Other Eicosanoids: Recall Past Knowledge. Hypertension 2012, 59, 191908. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  115. Il’yasova, D.; Morrow, J.D.; Ivanova, A.; Wagenknecht, L.E. Epidemiological marker for oxidant status: Comparison of the ELISA and the gas chromatography/mass spectrometry assay for urine 2,3-dinor-5,6-dihydro-15-F2t-isoprostane. Ann. Epidemiol. 2004, 14, 793–797. [Google Scholar] [CrossRef] [PubMed]
  116. Milne, G.L.; Gao, B.; Terry, E.S.; Zackert, W.E.; Sanchez, S.C. Measurement of F2- isoprostanes and isofurans using gas chromatography–mass spectrometry. Free Radic. Biol. Med. 2013, 59, 36–44. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  117. Margalit, A.; Duffin, K.L.; Isakson, P.C. Rapid Quantitation of a Large Scope of Eicosanoids in Two Models of Inflammation: Development of an Electrospray and Tandem Mass Spectrometry Method and Application to Biological Studies. Anal. Biochem. 1996, 235, 73–81. [Google Scholar] [CrossRef]
  118. Tsukamoto, H.; Hishinuma, T.; Mikkaichi, T.; Nakamura, H.; Yamazaki, T.; Tomioka, Y.; Mizugaki, M. Simultaneous quantification of prostaglandins, isoprostane and thromboxane in cell-cultured medium using gas chromatography–mass spectrometry. J. Chromatogr. B 2002, 774, 205–214. [Google Scholar] [CrossRef]
  119. Schweer, H.; Kammer, J.; Seyberth, H.W. Simultaneous determination of prostanoids in plasma by gas chromatograp—negative-ion chemical-ionization mass spectrometry. J. Chromatogr. B Biomed. Sci. Appl. 1985, 338, 273–280. [Google Scholar] [CrossRef]
  120. Schweer, H.; Kammer, J.; Kühl, P.G.; Seyberth, H.W. Determination of peripheral plasma prostanoid concentration: An unreliable index of “in vivo” prostanoid activity. Eur. J. Clin. Pharmacol. 1986, 31, 303–305. [Google Scholar] [CrossRef]
  121. Hammes, W.; Büchsler, U.; Kinder, P.; Bökens, H. Simultaneous determination of prostaglandin E1, prostaglandin E0 and 15-keto-prostaglandin E0 in human plasma by gas chromatography/negative-ion chemical-ionization tandem mass spectrometry. J. Chromatogr. A 1999, 847, 187–202. [Google Scholar] [CrossRef]
  122. Hughes, H.; Mitchell, J.R.; Gaskell, S.J. Quantitation of leukotriene B4 in human serum by negative ion gas chromatography-mass spectrometry. Anal. Biochem. 1989, 179, 304–308. [Google Scholar] [CrossRef]
  123. Schweer, H.; Meese, C.O.; Fürst, O.; Kühl, P.G.; Seyberth, H.W. Tandem mass spectrometric determination of 11-dehydrothromboxane B2, an index metabolite of thromboxane B2 in plasma and urine. Anal. Biochem. 1987, 164, 156–163. [Google Scholar] [CrossRef]
  124. Nithipatikom, K.; DiCamelli, R.F.; Kohler, S.; Gumina, R.J.; Falck, J.R.; Campbell, W.B.; Gross, G.J. Determination of Cytochrome P450 Metabolites of Arachidonic Acid in Coronary Venous Plasma during Ischemia and Reperfusion in Dogs. Anal. Biochem. 2001, 292, 115–124. [Google Scholar] [CrossRef] [PubMed]
  125. Werner, K.; Schaefer, W.R.; Schweer, H.; Deppert, W.R.; Karck, U.; Zahradnik, H.P. Characterization and identification of cytochrome P450 metabolites of arachidonic acid released by human peritoneal macrophages obtained from the pouch of Douglas. Prostaglandins Leukot. Essent. Fat. Acids 2002, 67, 397–404. [Google Scholar] [CrossRef] [PubMed]
  126. Arnold, C.; Markovic, M.; Blossey, K.; Wallukat, G.; Fischer, R.; Dechend, R.; Konkel, A.; von Schacky, C.; Luft, F.C.; Muller, D.N.; et al. Arachidonic Acid-metabolizing Cytochrome P450 Enzymes Are Targets of ω-3 Fatty Acids. J. Biol. Chem. 2010, 285, 32720–32733. [Google Scholar] [CrossRef] [Green Version]
  127. Bollinger, J.G.; Thompson, W.; Lai, Y.; Oslund, R.C.; Hallstrand, T.S.; Sadilek, M.; Turecek, F.; Gelb, M.H. Improved Sensitivity Mass Spectrometric Detection of Eicosanoids by Charge Reversal Derivatization. Anal. Chem. 2010, 82, 6790–6796. [Google Scholar] [CrossRef] [Green Version]
  128. Brose, S.A.; Thuen, B.T.; Golovko, M.Y. LC/MS/MS method for analysis of E 2 series prostaglandins and isoprostanes. J. Lipid Res. 2011, 52, 850–859. [Google Scholar] [CrossRef] [Green Version]
  129. Dumlao, D.S.; Buczynski, M.W.; Norris, P.C.; Harkewicz, R.; Dennis, E.A. High-throughput lipidomic analysis of fatty acid derived eicosanoids and N-acylethanolamines. Biochim. Biophys. Acta Mol. Cell Biol. Lipids 2011, 1811, 724–736. [Google Scholar] [CrossRef] [Green Version]
  130. Balvers, M.G.J.; Verhoeckx, K.C.M.; Bijlsma, S.; Rubingh, C.M.; Meijerink, J.; Wortelboer, H.M.; Witkamp, R.F. Fish oil and inflammatory status alter the n-3 to n-6 balance of the endocannabinoid and oxylipin metabolomes in mouse plasma and tissues. Metabolomics 2012, 8, 1130–1147. [Google Scholar] [CrossRef] [Green Version]
  131. Shinde, D.D.; Kim, K.-B.; Oh, K.-S.; Abdalla, N.; Liu, K.-H.; Bae, S.K.; Shon, J.-H.; Kim, H.-S.; Kim, D.-H.; Shin, J.G. LC–MS/MS for the simultaneous analysis of arachidonic acid and 32 related metabolites in human plasma: Basal plasma concentrations and aspirin-induced changes of eicosanoids. J. Chromatogr. B 2012, 911, 113–121. [Google Scholar] [CrossRef]
  132. Strassburg, K.; Huijbrechts, A.M.L.; Kortekaas, K.A.; Lindeman, J.H.; Pedersen, T.L.; Dane, A.; Berger, R.; Brenkman, A.; Hankemeier, T.; van Duynhoven, J.; et al. Quantitative profiling of oxylipins through comprehensive LC-MS/MS analysis: Application in cardiac surgery. Anal. Bioanal. Chem. 2012, 404, 1413–1426. [Google Scholar] [CrossRef] [Green Version]
  133. Zivkovic, A.M.; Yang, J.; Georgi, K.; Hegedus, C.; Nording, M.L.; O’Sullivan, A.; German, J.B.; Hogg, R.J.; Weiss, R.H.; Bay, C.; et al. Serum oxylipin profiles in IgA nephropathy patients reflect kidney functional alterations. Metabolomics 2012, 8, 1102–1113. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  134. Balgoma, D.; Larsson, J.; Rokach, J.; Lawson, J.A.; Daham, K.; Dahlén, B.; Dahlén, S.-E.; Wheelock, C. E Quantification of Lipid Mediator Metabolites in Human Urine from Asthma Patients by Electrospray Ionization Mass Spectrometry: Controlling Matrix Effects. Anal. Chem. 2013, 85, 7866–7874. [Google Scholar] [CrossRef] [PubMed]
  135. Le Faouder, P.; Baillif, V.; Spreadbury, I.; Motta, J.-P.; Rousset, P.; Chêne, G.; Guigné, C.; Tercé, F.; Vanner, S.; Vergnolle, N.; et al. LC–MS/MS method for rapid and concomitant quantification of pro-inflammatory and pro-resolving polyunsaturated fatty acid metabolites. J. Chromatogr. B 2013, 932, 123–133. [Google Scholar] [CrossRef] [PubMed]
  136. Schuchardt, J.P.; Schmidt, S.; Kressel, G.; Dong, H.; Willenberg, I.; Hammock, B.D.; Hahn, A.; Schebb, N.H. Comparison of free serum oxylipin concentrations in hyper-vs. normolipidemic men. Prostaglandins Leukot. Essent. Fat. Acids 2013, 89, 19–29. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  137. Song, J.; Liu, X.; Wu, J.; Meehan, M.J.; Blevitt, J.M.; Dorrestein, P.C.; Milla, M.E. A highly efficient, high-throughput lipidomics platform for the quantitative detection of eicosanoids in human whole blood. Anal. Biochem. 2013, 433, 181–188. [Google Scholar] [CrossRef]
  138. Caligiuri, S.P.B.; Aukema, H.M.; Ravandi, A.; Guzman, R.; Dibrov, E.; Pierce, G.N. Flaxseed Consumption Reduces Blood Pressure in Patients with Hypertension by Altering Circulating Oxylipins via an α-Linolenic Acid–Induced Inhibition of Soluble Epoxide Hydrolase. Hypertension 2014, 64, 53–59. [Google Scholar] [CrossRef] [Green Version]
  139. Caligiuri, S.P.B.; Love, K.; Winter, T.; Gauthier, J.; Taylor, C.G.; Blydt-Hansen, T.; Zahradka, P.; Aukema, H.M. Dietary Linoleic Acid and α-Linolenic Acid Differentially Affect Renal Oxylipins and Phospholipid Fatty Acids in Diet-Induced Obese Rats. J. Nutr. 2013, 143, 1421–1431. [Google Scholar] [CrossRef] [Green Version]
  140. Petta, T.; Moraes, L.A.B.; Faccioli, L.H. Versatility of tandem mass spectrometry for focused analysis of oxylipids. J. Mass Spectrom. 2015, 50, 879–890. [Google Scholar] [CrossRef]
  141. Takuji Fujihara, T.S. Improvements in the High-Performance Liquid Chromatography and Extraction Conditions for the Analysis of Oxidized Fatty Acids Using a Mixed-Mode Spin Column. Mod. Chem. Appl. 2015, 3, 1000161. [Google Scholar] [CrossRef]
  142. Dupuy, A.; Le Faouder, P.; Vigor, C.; Oger, C.; Galano, J.-M.; Dray, C.; Lee, J.C.-Y.; Valet, P.; Gladine, C.; Durand, T.; et al. Simultaneous quantitative profiling of 20 isoprostanoids from omega-3 and omega-6 polyunsaturated fatty acids by LC–MS/MS in various biological samples. Anal. Chim. Acta 2016, 921, 46–58. [Google Scholar] [CrossRef] [Green Version]
  143. Galvão, A.F.; Petta, T.; Flamand, N.; Bollela, V.R.; Silva, C.L.; Jarduli, L.R.; Malmegrim, K.C.R.; Simões, B.P.; de Moraes, L.A.B.; Faccioli, L.H. Plasma eicosanoid profiles determined by high-performance liquid chromatography coupled with tandem mass spectrometry in stimulated peripheral blood from healthy individuals and sickle cell anemia patients in treatment. Anal. Bioanal. Chem. 2016, 408, 3613–3623. [Google Scholar] [CrossRef] [PubMed]
  144. Rauzi, F.; Kirkby, N.S.; Edin, M.L.; Whiteford, J.; Zeldin, D.C.; Mitchell, J.A.; Warner, T.D. Aspirin inhibits the production of proangiogenic 15 (S)-HETE by platelet cyclooxygenase-1. FASEB J. 2016, 30, 4256–4266. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  145. Gouveia-Figueira, S.; Nording, M.L. Validation of a tandem mass spectrometry method using combined extraction of 37 oxylipins and 14 endocannabinoid-related compounds including prostamides from biological matrices. Prostaglandins Lipid Mediat. 2015, 121, 110–121. [Google Scholar] [CrossRef] [PubMed]
  146. Gouveia-Figueira, S.; Karimpour, M.; Bosson, J.A.; Blomberg, A.; Unosson, J.; Pourazar, J.; Sandström, T.; Behndig, A.F.; Nording, M.L. Mass spectrometry profiling of oxylipins, endocannabinoids, and N-acylethanolamines in human lung lavage fluids reveals responsiveness of prostaglandin E2 and associated lipid metabolites to biodiesel exhaust exposure. Anal. Bioanal. Chem. 2017, 409, 2967–2980. [Google Scholar] [CrossRef] [Green Version]
  147. Yasumoto, A.; Tokuoka, S.M.; Kita, Y.; Shimizu, T.; Yatomi, Y. Multiplex quantitative analysis of eicosanoid mediators in human plasma and serum: Possible introduction into clinical testing. J. Chromatogr. B 2017, 1068, 98–104. [Google Scholar] [CrossRef]
  148. Pier, B.; Edmonds, J.W.; Wilson, L.; Arabshahi, A.; Moore, R.; Bates, G.W.; Prasain, J.K.; Miller, M.A. Comprehensive profiling of prostaglandins in human ovarian follicular fluid using mass spectrometry. Prostaglandins Lipid Mediat. 2018, 134, 7–15. [Google Scholar] [CrossRef]
  149. Thakare, R.; Chhonker, Y.S.; Gautam, N.; Nelson, A.; Casaburi, R.; Criner, G.; Dransfield, M.T.; Make, B.; Schmid, K.K.; Rennard, S.I.; et al. Simultaneous LC-MS/MS analysis of eicosanoids and related metabolites in human serum, sputum and BALF. Biomed. Chromatogr. 2018, 32, e4102. [Google Scholar] [CrossRef]
  150. Willenberg, I.; Ostermann, A.I.; Schebb, N.H. Targeted metabolomics of the arachidonic acid cascade: Current state and challenges of LC—MS analysis of oxylipins. Anal. Bioanal. Chem. 2015, 407, 2675–2683. [Google Scholar] [CrossRef]
  151. Masoodi, M.; Nicolaou, A. Lipidomic analysis of twenty-seven prostanoids and isoprostanes by liquid chromatography/electrospray tandem mass spectrometry. Rapid Commun. Mass Spectrom. 2006, 20, 3023–3029. [Google Scholar] [CrossRef] [Green Version]
  152. Golovko, M.Y.; Murphy, E.J. An improved LC-MS/MS procedure for brain prostanoid analysis using brain fixation with head-focused microwave irradiation and liquid-liquid extraction. J. Lipid Res. 2008, 49, 893–902. [Google Scholar] [CrossRef] [Green Version]
  153. Wang, W.; Qin, S.; Li, L.; Chen, X.; Wang, Q.; Wei, J. An Optimized High Throughput Clean-Up Method Using Mixed-Mode SPE Plate for the Analysis of Free Arachidonic Acid in Plasma by LC-MS/MS. Int. J. Anal. Chem. 2015, 2015, 374819. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  154. Viswanathan, C.T.; Bansal, S.; Booth, B.; DeStefano, A.J.; Rose, M.J.; Sailstad, J.; Shah, V.P.; Skelly, J.P.; Swann, P.G.; Weiner, R. Quantitative Bioanalytical Methods Validation and Implementation: Best Practices for Chromatographic and Ligand Binding Assays. Pharm. Res. 2007, 24, 1962–1973. [Google Scholar] [CrossRef] [PubMed]
  155. Taylor, P.J. Matrix effects: The Achilles heel of quantitative high-performance liquid chromatography–electrospray–tandem mass spectrometry. Clin. Biochem. 2005, 38, 328–334. [Google Scholar] [CrossRef] [PubMed]
  156. Hsu, F.-F. Mass spectrometry-based shotgun lipidomics—A critical review from the technical point of view. Anal. Bioanal. Chem. 2018, 410, 6387–6409. [Google Scholar] [CrossRef] [PubMed]
  157. Araujo, P.; Janagap, S.; Holen, E. Application of Doehlert uniform shell designs for selecting optimal amounts of internal standards in the analysis of prostaglandins and leukotrienes by liquid chromatography-tandem mass spectrometry. J. Chromatogr. A 2012, 1260, 102–110. [Google Scholar] [CrossRef] [PubMed]
  158. Mengesha, Z. Development of an Extraction Method for the Analysis of Pro-Inflammatory Prostaglandin-E 2 and Leukotriene-B4 in Human Plasma by Liquid Chromatography Tandem Mass Spectrometry of the Requirement for European. Master in Quality in Analytical Laboratories. Master’s Thesis, The University of Bergen, Bergen, Norway, 2013. [Google Scholar]
  159. Rahman, M.; Ahmad, S.; Gupta, A.; Hussain, A.; Kalra, H.; Raut, B. HybridSPE: A novel technique to reduce phospholipid-based matrix effect in LC-ESI-MS Bioanalysis. J. Pharm. Bioallied Sci. 2012, 4, 267–275. [Google Scholar] [CrossRef]
  160. Perestrelo, R.; Silva, C.L.; Câmara, J.S. Determination of urinary levels of leukotriene B4 using ad highly specific and sensitive methodology based on automatic MEPS combined with UHPLC-PDA analysis. Talanta 2015, 144, 382–389. [Google Scholar] [CrossRef]
  161. Newman, J.W.; Watanabe, T.; Hammock, B.D. The simultaneous quantification of cytochrome P450 dependent linoleate and arachidonate metabolites in urine by HPLC-MS/MS. J. Lipid Res. 2002, 43, 1563–1578. [Google Scholar] [CrossRef] [Green Version]
  162. Brose, S.A.; Baker, A.G.; Golovko, M.Y. A Fast One-Step Extraction and UPLC–MS/MS Analysis for E2/D2 Series Prostaglandins and Isoprostanes. Lipids 2013, 48, 411–419. [Google Scholar] [CrossRef] [Green Version]
  163. Spickett, C.M.; Pitt, A.R. Oxidative Lipidomics Coming of Age: Advances in Analysis of Oxidized Phospholipids in Physiology and Pathology. Antioxid. Redox Signal. 2015, 22, 1646–1666. [Google Scholar] [CrossRef] [Green Version]
  164. Derogis, P.B.M.C.; Freitas, F.P.; Marques, A.S.F.; Cunha, D.; Appolinário, P.P.; de Paula, F.; Lourenço, T.C.; Murgu, M.; Di Mascio, P.; Medeiros, M.H.G.; et al. The Development of a Specific and Sensitive LC-MS-Based Method for the Detection and Quantification of Hydroperoxy- and Hydroxydocosahexaenoic Acids as a Tool for Lipidomic Analysis. PLoS ONE 2013, 8, e77561. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  165. Harkewicz, R.; Dennis, E.A. Applications of Mass Spectrometry to Lipids and Membranes. Annu. Rev. Biochem. 2011, 80, 301–325. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  166. Wang, M.; Han, R.H.; Han, X. Fatty Acidomics: Global Analysis of Lipid Species Containing a Carboxyl Group with a Charge-Remote Fragmentation-Assisted Approach. Anal. Chem. 2013, 85, 9312–9320. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  167. Milic, I.; Hoffmann, R.; Fedorova, M. Simultaneous Detection of Low and High Molecular Weight Carbonylated Compounds Derived from Lipid Peroxidation by Electrospray Ionization-Tandem Mass Spectrometry. Anal. Chem. 2013, 85, 156–162. [Google Scholar] [CrossRef]
  168. Min, H.K.; Lim, S.; Chung, B.C.; Moon, M.H. Shotgun lipidomics for candidate biomarkers of urinary phospholipids in prostate cancer. Anal. Bioanal. Chem. 2011, 399, 823–830. [Google Scholar] [CrossRef]
  169. Watrous, J.D.; Niiranen, T.J.; Lagerborg, K.A.; Henglin, M.; Xu, Y.-J.; Rong, J.; Sharma, S.; Vasan, R.S.; Larson, M.G.; Armando, A.; et al. Directed Non-targeted Mass Spectrometry and Chemical Networking for Discovery of Eicosanoids and Related Oxylipins. Cell Chem. Biol. 2019, 26, 433–442. [Google Scholar] [CrossRef]
  170. Anas Kamleh, M.; McLeod, O.; Checa, A.; Baldassarre, D.; Veglia, F.; Gertow, K.; Humphries, S.E.; Rauramaa, R.; de Faire, U.; Smit, A.J.; et al. Increased levels of circulating fatty acids are associated with protective effects against future cardiovascular events in non-diabetics. J. Proteome Res. 2018, 17, 870–878. [Google Scholar] [CrossRef]
  171. Sud, M.; Fahy, E.; Cotter, D.; Brown, A.; Dennis, E.A.; Glass, C.K.; Merrill, A.H.; Murphy, R.C.; Raetz, C.R.H.; Russell, D.W.; et al. LMSD: LIPID MAPS structure database. Nucleic Acids Res. 2007, 35, D527–D532. [Google Scholar] [CrossRef] [Green Version]
  172. Fahy, E.; Subramaniam, S.; Murphy, R.C.; Nishijima, M.; Raetz, C.R.H.; Shimizu, T.; Spener, F.; van Meer, G.; Wakelam, M.J.O.; Dennis, E.A. Update of the LIPID MAPS comprehensive classification system for lipids. J. Lipid Res. 2009, 50, S9–S14. [Google Scholar] [CrossRef] [Green Version]
  173. Cajka, T.; Fiehn, O. LC-MS-Based Lipidomics and Automated Identification of Lipids Using the LipidBlast In-Silico MS/MS Library. Methods Mol. Biol. 2017, 1609, 149–170. [Google Scholar]
  174. Yu, R.; Ma, T.; Zhang, Y.; Zhang, S.; Liu, G.; Ma, X.; Yang, J.; Liu, Z.; Du, L.; Liu, Q. LMSAD: Lipid Mass Spectrum Analysis Database. In Proceedings of the 2010 International Conference on Biomedical Engineering and Computer Science, Wuhan, China, 23–25 April 2010; pp. 1–4. [Google Scholar]
  175. Foster, J.M.; Moreno, P.; Fabregat, A.; Hermjakob, H.; Steinbeck, C.; Apweiler, R.; Wakelam, M.J.O.; Vizcaíno, J.A. LipidHome: A Database of Theoretical Lipids Optimized for High Throughput Mass Spectrometry Lipidomics. PLoS ONE 2013, 8, e61951. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  176. Watanabe, K.; Yasugi, E.; Oshima, M. How to Search the Glycolipid data in “LIPIDBANK for Web” the Newly Developed Lipid Database in Japan. Trends Glycosci. Glycotechnol. 2000, 12, 175–184. [Google Scholar] [CrossRef] [Green Version]
  177. Taguchi, R.; Nishijima, M.; Shimizu, T. Basic analytical systems for lipidomics by mass spectrometry in Japan. Methods Enzymol. 2007, 432, 185–211. [Google Scholar] [PubMed]
  178. Wishart, D.S.; Feunang, Y.D.; Marcu, A.; Guo, A.C.; Liang, K.; Vázquez-Fresno, R.; Sajed, T.; Johnson, D.; Li, C.; Karu, N.; et al. HMDB 4.0: The human metabolome database for 2018. Nucleic Acids Res. 2018, 46, D608–D617. [Google Scholar] [CrossRef] [PubMed]
  179. Horai, H.; Arita, M.; Kanaya, S.; Nihei, Y.; Ikeda, T.; Suwa, K.; Ojima, Y.; Tanaka, K.; Tanaka, S.; Aoshima, K.; et al. MassBank: A public repository for sharing mass spectral data for life sciences. J. Mass Spectrom. 2010, 45, 703–714. [Google Scholar] [CrossRef]
  180. Guijas, C.; Montenegro-Burke, J.R.; Domingo-Almenara, X.; Palermo, A.; Warth, B.; Hermann, G.; Koellensperger, G.; Huan, T.; Uritboonthai, W.; Aisporna, A.E.; et al. METLIN: A Technology Platform for Identifying Knowns and Unknowns. Anal. Chem. 2018, 90, 3156–3164. [Google Scholar] [CrossRef] [Green Version]
  181. Jeffryes, J.G.; Colastani, R.L.; Elbadawi-Sidhu, M.; Kind, T.; Niehaus, T.D.; Broadbelt, L.J.; Hanson, A.D.; Fiehn, O.; Tyo, K.E.J.; Henry, C.S. MINEs: Open access databases of computationally predicted enzyme promiscuity products for untargeted metabolomics. J. Cheminform. 2015, 7, 44. [Google Scholar] [CrossRef] [Green Version]
  182. Caffrey, M.; Hogan, J. LIPIDAT: A database of lipid phase transition temperatures and enthalpy changes. DMPC data subset analysis. Chem. Phys. Lipids 1992, 61, 1–109. [Google Scholar] [CrossRef]
  183. Babushok, V.I.; Linstrom, P.J.; Reed, J.J.; Zenkevich, I.G.; Brown, R.L.; Mallard, W.G.; Stein, S.E. Development of a database of gas chromatographic retention properties of organic compounds. J. Chromatogr. A 2007, 1157, 414–421. [Google Scholar] [CrossRef]
  184. Matos, M. Cyberlipid Center: What Do You Want to Know About Lipids? Biotech Softw. Internet Rep. 2001, 2, 2–4. [Google Scholar] [CrossRef]
  185. Kuo, T.-C.; Tseng, Y.J. LipidPedia: A comprehensive lipid knowledgebase. Bioinformatics 2018, 34, 2982–2987. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  186. Ejsing, C.S.; Duchoslav, E.; Sampaio, J.; Simons, K.; Bonner, R.; Thiele, C.; Ekroos, K.; Shevchenko, A. Automated Identification and Quantification of Glycerophospholipid Molecular Species by Multiple Precursor Ion Scanning. Anal. Chem. 2006, 78, 6202–6214. [Google Scholar] [CrossRef] [PubMed]
  187. Rajanayake, K.K.; Taylor, W.R.; Isailovic, D. The comparison of glycosphingolipids isolated from an epithelial ovarian cancer cell line and a nontumorigenic epithelial ovarian cell line using MALDI-MS and MALDI-MS/MS. Carbohydr. Res. 2016, 431, 6–14. [Google Scholar] [CrossRef] [PubMed]
  188. Soares, V.; Taujale, R.; Garrett, R.; da Silva, A.J.R.; Borges, R.M. Extending compound identification for molecular network using the LipidXplorer database independent method: A proof of concept using glycoalkaloids from Solanum pseudoquina A. St.-Hil. Phytochem. Anal. 2019, 30, 132–138. [Google Scholar] [CrossRef]
  189. Hutchins, P.D.; Russell, J.D.; Coon, J.J. LipiDex: An Integrated Software Package for High-Confidence Lipid Identification. Cell Syst. 2018, 6, 621–625. [Google Scholar] [CrossRef] [Green Version]
  190. Haimi, P.; Chaithanya, K.; Kainu, V.; Hermansson, M.; Somerharju, P. Instrument-independent software tools for the analysis of MS-MS and LC-MS lipidomics data. Methods Mol. Biol. 2009, 580, 285–294. [Google Scholar]
  191. Cao, Z.; Schmitt, T.C.; Varma, V.; Sloper, D.; Beger, R.D.; Sun, J. Evaluation of the Performance of Lipidyzer Platform and Its Application in the Lipidomics Analysis in Mouse Heart and Liver. J. Proteome Res. 2019. [Google Scholar] [CrossRef]
  192. Hartler, J.; Trötzmüller, M.; Chitraju, C.; Spener, F.; Köfeler, H.C.; Thallinger, G.G. Lipid Data Analyzer: Unattended identification and quantitation of lipids in LC-MS data. Bioinformatics 2011, 27, 572–577. [Google Scholar] [CrossRef] [Green Version]
  193. Song, H.; Hsu, F.-F.; Ladenson, J.; Turk, J. Algorithm for processing raw mass spectrometric data to identify and quantitate complex lipid molecular species in mixtures by data-dependent scanning and fragment ion database searching. J. Am. Soc. Mass Spectrom. 2007, 18, 1848–1858. [Google Scholar] [CrossRef] [Green Version]
  194. Gil-de-la-Fuente, A.; Godzien, J.; Saugar, S.; Garcia-Carmona, R.; Badran, H.; Wishart, D.S.; Barbas, C.; Otero, A. CEU Mass Mediator 3.0: A Metabolite Annotation Tool. J. Proteome Res. 2019, 18, 797–802. [Google Scholar] [CrossRef]
  195. Kochen, M.A.; Chambers, M.C.; Holman, J.D.; Nesvizhskii, A.I.; Weintraub, S.T.; Belisle, J.T.; Islam, M.N.; Griss, J.; Tabb, D.L. Greazy: Open-Source Software for Automated Phospholipid Tandem Mass Spectrometry Identification. Anal. Chem. 2016, 88, 5733–5741. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  196. Koelmel, J.P.; Kroeger, N.M.; Ulmer, C.Z.; Bowden, J.A.; Patterson, R.E.; Cochran, J.A.; Beecher, C.W.W.; Garrett, T.J.; Yost, R.A. LipidMatch: An automated workflow for rule-based lipid identification using untargeted high-resolution tandem mass spectrometry data. BMC Bioinform. 2017, 18, 331. [Google Scholar] [CrossRef] [PubMed]
  197. Meng, D.; Zhang, Q.; Gao, X.; Wu, S.; Lin, G. LipidMiner: A software for automated identification and quantification of lipids from multiple liquid chromatography/mass spectrometry data files. Rapid Commun. Mass Spectrom. 2014, 28, 981–985. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  198. Shi, X.; Wang, S.; Jasbi, P.; Turner, C.; Hrovat, J.; Wei, Y.; Liu, J.; Gu, H. Database Assisted Globally Optimized Targeted Mass Spectrometry (dGOT-MS): Broad and Reliable Metabolomics Analysis with Enhanced Identification. Anal. Chem. 2019, 91, 13737–13745. [Google Scholar] [CrossRef]
  199. Husen, P.; Tarasov, K.; Katafiasz, M.; Sokol, E.; Vogt, J.; Baumgart, J.; Nitsch, R.; Ekroos, K.; Ejsing, C.S. Analysis of lipid experiments (ALEX): A software framework for analysis of high-resolution shotgun lipidomics data. PLoS ONE 2013, 8, e79736. [Google Scholar] [CrossRef] [Green Version]
  200. Fernández-López, M.; Gil-de-la-Fuente, A.; Godzien, J.; Rupérez, F.J.; Barbas, C.; Otero, A. LAS: A Lipid Annotation Service Capable of Explaining the Annotations It Generates. Comput. Struct. Biotechnol. J. 2019, 17, 1113–1122. [Google Scholar] [CrossRef]
  201. Collins, J.R.; Edwards, B.R.; Fredricks, H.F.; Van Mooy, B.A.S. LOBSTAHS: An Adduct-Based Lipidomics Strategy for Discovery and Identification of Oxidative Stress Biomarkers. Anal. Chem. 2016, 88, 7154–7162. [Google Scholar] [CrossRef]
Molecules 25 00349 g001 550
Figure 1. Representative mass spectra of prostaglandin E2 (PGE2) and prostaglandin D2 (PGD2) obtained by using hard (gas chromatography–mass spectrometry (GC–MS)) and soft ionization (liquid chromatography–mass spectrometry (LC–MS)/MS) in spectrometry mass.
Figure 1. Representative mass spectra of prostaglandin E2 (PGE2) and prostaglandin D2 (PGD2) obtained by using hard (gas chromatography–mass spectrometry (GC–MS)) and soft ionization (liquid chromatography–mass spectrometry (LC–MS)/MS) in spectrometry mass.
Molecules 25 00349 g001
Table
Table 1. Advantages and limitations of the most frequently used analytical methods in the analysis of oxylipins.
Table 1. Advantages and limitations of the most frequently used analytical methods in the analysis of oxylipins.
Analytical TechniqueApplicationAdvantagesLimitations
Immunoassay methodsEicosanoids, IsoPs, LTs, TxBs, PGsEnzyme immunoassay (EIA) and dioimmunoassay (RIA) are easy to use, sensitive and do not require expensive instrumentation [2,26]Unspecificity due to cross-reactivity [27]
RIA combined with HPLC can greatly enhance sensitivity [28]Limited to a single metabolite at a time [29]
ELISA is comparable to RIA and HPLC in sensitivity and selectivity [30]Target only a few compounds [30]
Metabolite overestimation due to cross-reactivity [31,32]
RIA requires long analysis run times and generates hazardous waste [33]
Chiral chromatographyHETEs, PGs, EETs, TriHOMEs, Maresins, Protectins, Lipoxins Allows the enantiomeric separation of oxylipins [34]Long equilibration and run time, and limited sensitivity [34]
Reduced sensitivity (with increasing peak width and elution time, the signal/noise decreases) [35]
Not a high throughput (used for targeted analysis) [36]
Very sensitive to changes in mobile-phase composition [34,37]
Capillary electrophoresis EETs, DHETsAllows the resolution of EET and DHET regioisomers [20]Limited number of analytes [16]
Long run time [17]
Thin layer chromatographyIsoPs, PGs, keto-PGs, TXsUsed for the preliminary separation of analytes [38,39,40]Loss of products at the TLC stages [41]
Requires large plasma volumes [42]
HPLC-UV or fluorescence detection PGs, EETs, HETEs, LTsProvides structural information [43]Limited number of UV-light absorbing analytes [44]
Sufficient sensitivity and selectivity [45]Sensitivity is low [46,47]
Requires two distinct separation methods to quantify the prostaglandin products and the HETE and leukotriene products [48]
Long analysis time required [49]
HPLC with fluorescence detection also requires derivatization [50]
MALDI-TOF/MSPGsAllows the direct quantification of prostaglandins at levels similar to LC–MS/MS analysis [51]Needs the co-crystallization of a matrix with the sample, which affects the quantification of the analytes [52]
Soft ionization technique allows for the analysis of non-volatile compounds [52]Needs derivatization steps [51]
Less sensitive to impurities (buffer salts) compared to other MS methods [53]
GC-EI/MSAll classesWhen coupled with MS detection, GC is both highly sensitive and selective [21,24]Needs derivatization of the analytes [54]
Multiple analytes can be detected in one sample [47]Requires the thermal stability of the analytes [55]
Higher sensitivity compared to LC–MS/MS [21]There is no derivatization strategy for some compounds [56,57]
Absolute requirement of volatility [57]
ESI-MS/MSAll classesSimple, fast, no sample carry-over [58]
(shotgun analysis)		Creates a common product ion spectrum, inability to separate the isomers [59]
(shotgun analysis)
High selectivity and good sensitivity [60]Ion suppression [15]
During switching of the ionization mode (negative/positive) a loss of sensitivity occurs [61]
Table
Table 2. Analytical methods based on LC–MS used in oxylipin analysis.
Table 2. Analytical methods based on LC–MS used in oxylipin analysis.
OxylipinBiological MaterialExtraction MethodAnalytical ColumnMobile PhasesInstrumental AnalysisAnalyzerLODReference
11-trans-LT (C4, E4,)
15-deoxy-Δ12,14-PG (D2, J2)
6-trans-LT (B4, 12-epi-LTB4)
DiHETE (5,6-, 5,15-)
DiHETrE (5,6-, 8,9-, 11,12-, 14,15-)
EpETrE (5(6)-, 8(9)-
, 11(12)-, 14(15)-)
HETE (5-, 8-, 9-, 10-, 11-, 12-, 15-, 20-)
HHTrE (12-); HpETE (5-, 12-, 15-)
LT (B4, C4, E4); LX (A4);
Oxo-ETE (5-, 12-)
PG (B2, D2, D2-EA, 6-keto-E1, E2, E2-EA, F2α, F2α-EA, J2)
PGE2 (bicyclo-, dhk-, 19-hydroxy-, 20-hydroxy-, 15-keto)
PGF2α (11β-, dhk-, 2,3- dinor-11β-, 20-hydroxy-, 15-keto-)
tetranor (PGEM, PGFM); TX (B2)		cell culture, cell mediumSPERPLC:
Grace-Vydac C18
2.1 × 250 mm		RPLC:
A: H2O/ACN/FAc (63/37/0.02, v/v/v)
B: ACN/IPA (50/50, v/v)		LC–MS/MSQQQ-LIT1–1000 pg[92]
Chiral LC:
Chiralpak AD-H
4.6 × 250 mm		Chiral LC:
A: Hex/anhydrous ethanol/H2O/FAc (96/4/0.08/0.02, v/v/v)
B: 100% anhydrous ethanol
EDP (7,8-, 10,11-, 13,14-, 16,17-, 19,20-)
EpETE (5(6)-, 8(9)-, 11(12)-, 14(15)-, 17(18)-)
EpETrE (5(6)-, 8(9)-, 11(12)-, 14(15)-)
HDoHE (20-); HEPE (20-); HETE (20-)		rat heart, kidney, brain, liver, lung, pancreas, red blood cells, plasmaSPEZorbax Eclipse Plus-C18
4.6 × 150 mm, 1.8 μm		A: ACN
B: 0.01 M ammonia acetate		HPLC–MS/MSQQQ0.05–0.1 ng[126]
6-keto-PGF1α
HETE (5-, 8-, 11-, 12-, 15-)
LTB4; PG (D2, E2, F2α); TX (B2)		mouse serum, human lung epithelial cells, rat fibroblast cell line culture mediumSPE, derivatization Ascentis Express C18
2.1 × 150 mm, 2.7 μm		System I
A: H2O/ACN/HAc (95/5/1, v/v/v)
B: 1% HAc in ACN		LC-ESI-MS/MSQQQ–[127]
System II
A: 0.1% FAc in H2O
B: 0.1% FAc in ACN
PG (D2, 15R-PGD2, E2, 11β-PGE2, 8-iso-PGE2)mice brain LLELuna C18
2.0 × 150 mm, 3 μm		A: 0.1% FAc in H2O
B: 0.1% FAc in ACN		HPLC-ESI-MS/MSQQQ~3 pg[128]
11-trans-LT (C4, D4, E4)
14,15-LT (C4, D4, E4)
15-deoxy-PG (A2, D2, J2)
15-keto-PG (E1α, E2, F2α)
5-iso-PGF2αVI; 6-keto-PGs (E1, F1α)
8-iso-PGF2αIII; dhk PGs (D2, E2, F2α)
DiHETE (5,6-, 5,15-, 8,15-)
DiHETrE (5,6-, 8,9-, 11,12-, 14,15-)
DiHoHE (10S,17S-);
DiHOME (9,10-, 12,13-)
dihomo PG (15-deoxy PGJ2, D2, E2, F2α, J2)
EpETE (14(15)-, 17(18)-)
EpETrE (5(6)-, 11(12)-, 14(15)-)
EpOME (9(10)-, 12(13)-)
HDoHE (4-, 7-, 8-, 10-, 11-, 13-, 14-, 16-, 17-, 20-)
HEPE (5-, 8-, 9-, 11-, 12-, 15-, 18-)
HETE (5-, 8-, 9-, 11-, 12-, 15-, 16-, 17-, 18-, 19-, 20-)
HHTrE (12-); HODE (9-, 13)
HOTrE (9-, 13-, 13-HOTrE-γ)
HX (A3, B3); LT (B4, C4, D4, E4)
LX (A4, A5, B4)
Oxo-EDE (15-); Oxo-ETE (5-, 15-)
Oxo-ODE (9-, 13-); PD1
PG (A2, B2, D1, D2, D3, E1, E2, E3, F1α, F2α, F3α, J2, K2)
PGE2 (11β-, 19-hydroxy-, 20-hydroxy-, bicyclo-)
PGF1α (6,15-dk-dh-, 17 6-keto-)
PGF2α (11β-, 11β-dhk-, 19-hydroxy-, 20-hydroxy-, 2,3-dinor-11β-, dh-)
Rv (D1, E1)
tetranor (12-HETE, PGEM, PGFM)
TXs (B1, B2, B3)		cell medium, rat spinal cord tissue, murine papilloma, murine tibiotarsal ankle jointSPE Synergi reverse-phase C18
2.1 × 250 mm		A: H2O/ACN/HAc (70/30/0.02, v/v/v)
B: ACN/IPA (50/50, v/v)		LC–MS/MSQQQ-LIT0.1–1 pg[129]
dhk-PG (E2, F2α, D2)
DiHDoHE (10,17-)
DiHETrE (5,6-, 8,9-, 11,12-, 14,15-)
DiHoPE (19,20-)
EpETrE (5(6)-, 11(12)-, 14(15)-)
HDoHE (17-); HEPE (5-, 12-)
HETE (5-, 11-, 12-, 15-, 20-)
HHTrE (12-); HODE (13-); LT (B4, D4)
PG (E2, F2α, 8-iso-PGF2α, D3, E3)
TriHOME (9,10,13-); TX (B2, B3)		mouse plasma, liver, ileum and adipose tissueSPE Acquity C18 BEH
2.1 × 100 mm, 1.7 μm		A: 0.1% FAc in H2O
B: 0.1% FAc in ACN		UHPLC–MS/MSQQQ–[130]
DiHETrE (5,6-, 8,9-, 11,12-, 14,15-)
EpETrE (8(9)-, 11(12)-, 14(15)-)
HETE (5-, 8-, 9-, 11-, 12-, 15-, 20-)
HpETE (5S-); LT (B4, C4, D4, E4)
LX (A4); PG (D2, E2, J2, 6-keto-PGF1α, dhk-PGF1α, F2α, 8-iso-PGF2α)
tetranor (PGEM, PGFM); TX (B2, 11-dehydro-TXB2, 2,3-dinor-TXB2)		human plasma SPELuna C8
2.0 × 150 mm, 5 μm		A: 0.5 mM ammonium formate in H2O (pH 3.3)
B: 0.5 mM ammonium formate in ACN (pH 3.3)		HPLC–MS/MSQQQ-LIT10–400 pg/mL (LLOQ)[131]
HETE (12-); LT (E4)
PGF2α (2,3-dinor-8-iso-, 8-iso-)
tetranor (PGEM); TX (2,3-dinor-TXB2, 11-dehydro-TXB2)		human urineLLEBEH C18
2.1 × 50 mm, 1.7 μm		A: 0.1% FAc in H2O
B: 0.1% FAc in ACN		UHPLC-SRM/MSQQQ0.002–0.06 ng/mL urine[98]
DiHDPA (19,20-);
DiHETE (14,15-, 17,18-)
DiHETrE (5,6-, 8,9-, 11,12-, 14,15-)
DiHOME (9,10-, 12,13-)
EpDPE (19(20)-);
EpOME (9(10)-, 12(13)-)
HEPE (5-, 12-, 15-);
HETE (5-, 8-, 11-, 12-, 15-)
HETrE (15S-); HODE (9-, 13-)
HOTrE (9-); HpODE (9-, 13-)
Oxo-ODE (9-, 13-)
PG (F2α, dh-PGF2α, F1α, E2, 11β-PGE2)
TriHOME (9,10,13-, 9,12,13-); TX (B2)		human plasmaSPEAscentis Express
2.1 × 150 mm, 2.7 μm		A: 0.1% HAc in H2O
B: ACN/IPA (90/10, v/v)		LC-ESI-MS/MSQQQ0.1–11.4 nM[132]
DiHDPE (4,5-, 7,8-, 10,11-, 13,14-, 16,17-, 19,20-)
DiHETE (5,6-, 5,15-, 8,15-, 8,9-, 11,12-, 14,15-, 17,18-)
DiHETrE (5,6-, 8,9-, 11,12-, 14,15-)
DiHODE (9,10-, 12,13-, 15,16-)
DiHOME (9,10-, 12,13-)
EpDPE (7(8)-, 10(11)-, 13(14)-, 16(17)-, 19(20)-)
EpETE (8(9)-, 11(12)-, 14(15)-, 17(18)-)
EpETrE (5(6)-, 8(9)-, 11(12)-, 14(15)-)
EpODE (9(10)-, 12(13)-, 15(16)-)
EpOME (9(10)-, 12(13)-)
HDoHE (17-); HEPE (5-, 8-, 12-, 15-)
HETE (5-, 8-, 9-, 11-, 12-, 15-, 20)
HETrE (15S-); HODE (9-, 13-)
HOTrE (9-, 13-); LT (B3, B4, B5)
LTB4 (6-trans-, 20-OH-, 20-COOH-)
LX (A4); Oxo-ETE (5-, 12-, 15-)
Oxo-ODE (9-)
PG (D1, E1, B2, D2, E2, J2, 15-deoxy-PGJ2, D3, E3, F2α)
RvE1; TriHETrE (10,12,15-)
TriHOME (9,12,13-, 9,10,13-); TX (B2)		human plasmaSPEEclipse Plus C18
2.1 × 150, 1.8 μm		A: 0.1% glacial HAc in H2O
B: 0.1% glacial HAc in ACN/MeOH (84/16, v/v)		LC–MS/MSQQQ-LIT–[133]
2,3-dinor-6- keto-PGF1α
TX (B2, 2,3-dinor-TXB2)		human urineSPE, derivatization Acquity UHPLC BEH C18
2.1 × 150 mm, 1.7 μm		A: 0.1% HAc in H2O
B: ACN/IPA (90/10, v/v)		UHPLC–MS/MSQQQ0.85–15.2 fmol[134]
2,3-dinor-6-keto-PGF1α
8,12-iso-iPF2α-VI
dhk-PG (E1, E2, F2α)
PG (D2, E1, E2, F2α);
PGF2α (2,3-dinor-8-iso-, 2,3-dinor-11β-, 8-iso-, 11β-)
tetranors (PGDM, PGEM)		SPE 0.55–15.4 fmol
14,15-LT (C4, D4, E4)
LT (B4, 6-trans-LTB4, C4, D4, E4,)		SPE A: 0.2% FAc in H2O
B: 0.2% FAc in ACN/IPA (90:10, v/v)		3.02–4.59 fmol
11β-PGs (F2α, E2); 14,15-LT (C4, E4)
15-deoxy-Δ12.14-PG (D2, J2)
15-keto-PG (E2, F2α)
20-hydroxy-PG (F2α, E2)
6-keto-PG (E1, F1α)
8-iso-15-keto-PG (E2, F2α)
8-iso-PG (A2, E2, E3, F2α, F3α)
dhk-PG (F2α, E2, tetranor-F1α, D2)
DiHDoHe (10S,17S-)
DiHETE (5,6-, 8,15-, 5,15-)DiHETrE (5,6-, 8,9-, 11,12-, 14,15-)
EpETrE (5(6)-, 8(9)-, 11(12)-, 14(15)-)
HEPE (5-, 11-, 12-)
HETE (5-, 9-, 11-, 12-, 15-, 16-, 17-, 18-, 19-, 20-)
HHTrE (12-); HpETE (5-, 12-, 15-)
iPF2α-IV; LT (B4, C4, E4, D4)
LTB4 (20-hydroxy-, 15,15-dehydro-, 18-carboxy-dinor-, 20-carboxy-, 12-oxo-); LX (5S,6R-LXA4, 5S,14R-LXB4)
Mar1; Oxo-ETE (5-, 12-, 15-)
PG (A2, B2, D1, D2, D3, E1, E2, E3, F1α, F2α, F3α, J2)
PGF2α (11β-dhk-, 2,3-dinor-11β-, 2,3-dinor-8-iso-); Rv (D1)tetranor (12-HETE, PGFM, PGEM, PGDM)
TX (B1, B2, 11-dehydro-TXB2, B3)		human plasmaonline SPE Kinetex C18
2.1 × 100 mm, 2.6 μm		A: H2O/ACN/FAc (63/37/0.02, v/v/v)
B: IPA/ACN (50/50, v/v)		online-SPE-LC–MS/MS systemQQQ-LIT–[86]
DiHETE (5,6-)
EpETrE (5(6)-, 8(9)-, 11(12)-, 14(15)-)
HDoHE (14-, 17-); HEPE (18-)
HETE (5-, 8-, 12-, 15-, 19-, 20-)
LT (B4, B5); LX (A4, B4)
Mar1 (7-); Oxo-ETE (5-); PDx
PG (E2, E3, 6-keto-PGF1α)
Rv (D1); TX (B2)		mouse colon tissue, human epithelial colorectal adenocarcinoma cells supernatant,
foam macrophages supernatant,
mouse peritoneal exudate		SPEZorBAX SB-C18
2.1 × 50 mm, 1.8 μm		A: H2O/ACN/FAc (75/25/0.1, v/v/v)
B: ACN/FAc (100:0.1, v/v)		HPLC–MS/MSQQQ0.06–15.63 ng/mL[135]
DiHDPE (10,11-, 13,14-, 16,17-, 19,20-)
DiHETE (11,12-, 14,15-, 17,18-)
DiHETrE (8,9-, 11,12-, 14,15-)
DiHODE (9,10-, 12,13-, 15,16-)
DiHOME (9,10-, 12,13-)
EpDPE (10(11)-, 13(14)-, 16(17)-, 19(20)-)
EpETE (11(12)-, 14(15)-, 17(18)-)
EpETrE (8(9)-, 11(12)-, 14(15)-)
EpODE (9(10)-, 12(13)-, 15(16)-)
EpOME (9(10)-, 12(13)-)
HEPE (5-, 12-, 15-);
HETE (5-, 8-, 9-, 11-, 12-, 15-)
HODE (9-, 13-); HOTrE (9-, 13-)		human serumSPEZorbax Eclipse Plus C18
2.1 × 150 mm, 1.7 μm		A: 0.1% HAc in H2O
B: ACN/MeOH/HAc (80/15/0.1, v/v/v)		LC-ESI-MSQQQ-LIT0.25–7 nM vial (LLOQ)[136]
11-trans-LT (C4, E4, D4)
14,15-LT (C4, E4,)
15-deoxy-PG (A2, D2, J2)
6-keto-PG (E1, F1α)
dhk PG (PGF2α, E2, D2)
DiHDPA (19,20-)
DiHETE (5,6-, 5,15-, 8,15-)
DiHETrE (5,6- 8,9-, 11,12-, 14,15-)
DiHOME (9,10-, 12,13-)
EpDPE (16(17)-, 19(20)-)
EpETE (14(15)-, 17(18)-)
EpETrE (5(6)-, 8(9)-, 11(12)-, 14(15)-)
EpOME (9(10)-, 12(13)-)
HDoHE (4-, 7-, 8-, 10-, 11-, 13-, 14-, 16-, 17-, 20-)
HEPE (5-, 8-, 11-, 12-, 15-, 18)
HETE (5-, 8S-, 8R-, 9-, 11-, 15-, 16-, 17-, 18-, 19-, 20-)
HETγE (5-, 8-, 15-); HHTrE (12-)HODE (9-, 13-); HOTγE (13-, 13-HOTγEg, 9-)
LT (B4, C4, E4, D4)
LTB4 (12-epi, 12-oxo-, 20-carboxy, 20-hydroxy-, 6-trans-, 6-trans-12-epi-)
LX (14R-LXA4, A4, A5, B4)Oxo-EDE (15-); Oxo-ETE (5-, 12-, 15-)
Oxo-ODE (9-, 13-); PD1PG (A2, B2, D1, D2, D3, E1, E2, E3, F1α, F2α, F3α, J2, K2)
PGE2 (11β-, 19-hydroxy-, 15-keto-, 20-hydroxy-, bicyclo)
PGF1α (15-keto-, 6,15-dk-dh-)
PGF2α (11β-, 11β-dhk-, 15-keto-, 19-hydroxy-, 20-hydroxy-, 5-isoPGF2αVI, 8-iso PGF2αIII)
PGF2α (Δ17 6-keto-, 2,3-dinor-11β-, dh-, dihomo-)
Rv (D1, D2)
tetranor (12-HETE, PGEM, PGDM)
TX (B1, B2, B3)		human whole bloodSPE Kinetex C18
2.1 × 150 mm, 1.7 μm		A: H2O/ACN/HAc (70/30/0.1, v/v/v)
B: IPA/ACN/HAc (50/50/0.02, v/v/v)		UHPLC–MS/MSQQQ–[137]
DiHDPA (19,20-)
DiHETrE (5,6-, 8,9-, 11,12-, 14,15-)
DiHOME (9,10-, 12,13-)
EpOME (12(13)-)
HDoHE (4-, 7-, 10-, 11-, 13-, 14-, 16-, 17-, 20-)
HEPE (5-, 12-, 18-)
HETE (5-, 8-, 11-, 12-, 15-, 16-, 18-)
HETrE (15-); HODE (9-, 13-)
HOTrE (9-, 13-); MarOxo-ODE (13-)
PG (D2, E2, 6-keto-PGF1α, F2α)
TriHOME (9,10,13-, 9,12,13-); TX (B2)		human plasma, rat kidneySPE Luna C18
2.0 × 250 mm, 5 μm		A: H2O/ACN/FAc (70/30/0.02, v/v/v)
B: IPA/ACN (50:50, v/v)		LC–MS/MSQQQ-LIT–[138,139]
11β-PG (E2, F2α, dhk-PGF2α)
15-deoxy-PG (J2, D2, A2)
6-keto-PG (E1, -2,3-dinor-F1α)
8-iso-PG (F3α, F2αIII, 8-iso-15-keto PGF2β)
dhk-PG (D2, E2, F2α);
DiHDPA (19,20-)
DiHETE (5,6-, 5,15-, 8,15-)
DiHETrE (5,6-, 8,9-, 11,12-, 14,15-)
DiHOME (9,10-, 12,13-)
EpDPE (16(17)-, 19(20)-)
EpETE (14(15)-, 17(18)-)
EpETrE (8(9)-, 11(12)-, 14(15)-)
EpOME (9(10)-, 12(13)-)
HDoHE (4-, 7-, 8-, 10-, 11-, 13-, 14-, 16-, 17-)
HEPE (5-, 8-, 9-, 11-, 12-, 15-)
HETE (5-, 8-, 9-, 11-, 12-, 15-, 18-, 19-, 20-)
HETrE (5-, 8-, 15-); HHTrE (12-)
HODE (9-,13-); HOTrE (13-, 13-HOTrEγ)
HX (A3, B3); LT (B4, E4)
LTB4 (12-epi, 12-oxo-, 20-carboxy, 20-hydroxy-, 6-trans-, 6-trans-12-epi-)
LTE4 (11-trans-, 14,15-)
LX (B4, 15R-LXA4, 6S-LXA4, 6R-LXA4, LXA5)
Mar1 (17R-); Oxo-EDE (15-)
Oxo-ETE (5-, 12-, 15-)
Oxo-ODE (9-, 13-); PD (10S-, 15-trans-)
PG (A2, B2, D1, D2, D3, E1, E2, E3, F1α, F2α, F3α, J2, K2)
PGE2 (19-hydroxy, 20-hydroxy, bicyclo)
PGF2α (2,3-dinor-11β-, 2,3-dinor-8-iso-, 20-hydroxy-, 5-iso-PGF2αVI, dh-)
Rv (D1, E1)
TX (B1, B2, B3, 11-dehydro-TXB2, 2,3-dinor-TXB2)		control human plasma, mouse and human tissue: adipose, liver, muscleSPE Acquity UHPLC BEH shield RP18
2.1 × 100 mm, 1.7 μm		A: ACN/H2O/HAc (60/40/0.02, v/v/v)
B: ACN/IPA (50/50, v/v)		UHPLC–MS/MSQQQ-LIT3–300 pg (LOQ)[60]
DiHDPE (4,5-, 7,8-, 10,11-, 13,14-, 16,17-, 19,20-)
DiHETE (5,6-, 5,15-, 8,15-, 8,9-, 11,12-, 14,15-, 17,18-)
DiHETrE (5,6-, 8,9-, 11,12-, 14,15-)
DiHODE (9,10-, 12,13-, 15,16-)
DiHOME (9,10-, 12,13-)
EKODE; EpDPE (10(11)-, 13(14)-, 16(17)-, 19(20)-)
EpETE (8(9)-, 11(12)-, 14(15)-, 11(12)-)
EpETrE (5(6)-, 8(9)-, 11(12)-, 14(15)-)
EpODE (9(10)-, 12(13)-, 15(16)-)EpOME (9(10)-, 12(13)-)
HEPE (5-, 8-, 12-, 15-)
HETE (5-, 8-, 9-, 11-, 12-, 15-, 20-)
HETrE (15S-); HODE (9-, 13-)
HOTrE (9-, 13-); LT (B3, B4, B5)
LTB4 (20-carboxy-, 20-hydroxy-, 6-trans-); LX (A4)
Oxo-ETE (5-, 15-); Oxo-ODE (9-, 13-)
PG (B2, D1, D2, D3, E1, E2, E3, 6-keto-PGF1α, F2α, J2, 15-deoxy-PGJ2)Rv (E1); THF diolTriHETrE (1,12,15-)
TriHOME (9,10,13-, 9,12,13-); TX (B2)		human plasmaSPE, LLEAgilent Zorbax Eclipse Plus C-18
2.1 × 150 mm, 1.8 μm		A: 0.1% HAc in H2O
B: ACN/MeOH/HAc (800/150/1, v/v/v)		LC–MS/MSQQQ-LIT0.1–1.30 nM[23]
DiHETE (5,6-)
DiHETrE (11,12-, 14,15-)
EpETrE (11(12)-)
HETE (5-, 12-, 15-, 20-)
LT (B4, 6-trans-LTB4, C4, D4, E4)
LX (A4); Oxo-ETE (5-)
PG (D2, E2, J2, 15-deoxy-Δ12,14-PGJ2)
PGE2 (20-hydroxy-, 15-keto-); TX (B2)		mouse hypothalmusSPE Supelco C18
3.0 × 100 mm, 2.7 μm		A: H2O/ACN/HAc (69.98/20/0.02, v/v/v)
B: ACN/IPA (70/30, v/v)		LC–MS/MSQQQ–[140]
dhk-PG (D2, E2)
EpDPE (16(17)-, 19(20)-)
EpETE (8(9)-, 11(12)-, 14(15)-, 17(18)-)
EpETrE (5(6)-, 8(9)-, 11(12)-, 14(15)-)
EpOME (9(10)-, 12(13)-)
HDoHE (4-, 7-, 8-, 10-, 11-, 13-, 14-, 16-, 17-, 20-)
HEPE (5-, 8-, 9-, 11-, 12-, 15-, 18-)
HETE (5-, 8-, 9-, 11-, 12-, 15-, 16-, 17-, 18-); HETrE (5-, 8-, 15-)
HODE (9-, 13-); HOTrE (9-, 13-, 13-HOTrEr)
LT (B4); LX (A4); Mar1 (7S-)
PD1; PG (D2, E2, 6-keto-PGF1α, F2α, 15-deoxy-PGJ2)
Rv (17R-RvD1); TX (B2)		mouse lung homogenate SPE mixed-mode spin column,
SPE		Acquity UHPLC BEH C18
2.1 × 100 mm, 1.7 μm		A: H2O/1 M ammonium acetate/5 mM phosphoric acid/FAc (990/10/1/1, v/v/v/v)
B: ACN/IPA/1 M ammonium acetate/FAc (495/495/10/1, v/v/v/v)		LC–MS/MSQQQ–[141]
AdA IsoP (ent-7(R,S)-7-F2t-dihomo-IsoP, 17(R,S) -F2t-dihomo-IsoP)
ARA IsoP (15-epi-15-F2t-IsoP, 15-F2t-IsoP, 5-F2t-IsoP, 5-epi-5-F2t-IsoP, 2,3-dinor-15-F2t-IsoP, ent-15(R,S)-2,3-dinor-5,6-dihydro-15-F2t-IsoP, d4-15-F2t-IsoP)
ALA PhytoP (ent-16-epi-16-F1t-PhytoP, ent-16-F1t-PhytoP, 9-F1t-PhytoP, 9-epi-9-F1t-PhytoP)
EPA IsoP (8-F3t-IsoP, 8-epi-8-F3t-IsoP, 5-F3t-IsoP, 5-epi-5-F3t-IsoP)
DHA NeuroP (10-F4t-NeuroP, 4(R,S)-F4t-NeuroP, 10-epi-10-F4t-NeuroP, d4-10-epi-10-F4t-NeuroP, d4-10-F4t-NeuroP, d4-4(R,S)-4-F4t-NeuroP)		human: plasma, CSF, mouse: plasma, urine, brain, liver, muscle SPE, derivatization Zorbax SB-C18 Rapid Resolution HD
2.1 × 100 mm, 1.8 μm		A: 0.1% FAc in H2O
B: 0.1% FAc in ACN		LC–MS/MSQQQ0.49–15.63 ng/mL[142]
DiHETE (5,6-)
DiHETrE (11,12-, 14,15-)
EpETrE (11(12)-)
HETE (5-, 12-, 15-, 20-)
LT (D4, E4, B4, 6-trans-LTB4, 12-epi-LTB4)
LX (A4); Oxo-ETE (5-)
PG (D2, E2, J2, 15-deoxy-Δ12,14-PGJ2, 20-hydroxy-PGE2, 15-keto-PGE2)
TX (B2)		human plasmaSPEAscentis Express C18
30 × 100 mm, 2.7 μm		A: H2O/ACN/FAc (69.98/30/0.02, v/v/v)
B: ACN/IPA 970/30, v/v)		LC–MS/MSQQQ5.0 ng/mL (LLOQ)[143]
DiHDPA (19,20-)
DiHETrE (5,6-, 8,9-, 11,12-, 14,15-, 17,18-)
EpDPE (19(20)-); EpETE (17(18)-)
EpETrE (5(6)-, 8(9)-, 11(12)-, 14(15)-)
EpOME (9(10)-, 12(13)-)
HETE (5-, 11-, 12-, 15-, 19-, 20-)
HODE (9-, 13-)PG (6-keto-PGF1α, F2α, 8-iso-PGF2α, E2, D2)
TriHOME (9,10,13-, 9,12,13-); TX (B2)		human whole blood, human platelet-rich plasmaSPELuna C18
1 × 150 mm, 5 μm		-HPLC–MS/MSQQQ–[144]
DiHDPE (19,20-)
DiHETE (5,6-, 5,15-, 8,15-)
DiHETrE (5,6-, 14,15-)
DiHETrE (5,6-, 14,15-)
DiHOME (12,13-)
EpETE (14(15)-)
EpETrE (5(6)-, 11(12)-)
EpOME (9(10)-, 12(13)-)
HDoHE (4-, 7-, 8-, 9-, 10-, 11-, 14-, 17-, 20-); HEPE (5-, 11-, 15-)
HETE (5-, 8-, 9-, 11-, 12-, 15-, tetranor-12-); HETrE (5-, 15-)
HHTrE (12-); HOTrE (9-, 13-)
LT (B4, E4, 6-trans-LTB4)
Oxo-ETE (5-, 15-); Oxo-ODE (9-, 13-)
PG (A2, B2, D2, 15-deoxy-Δ12,14-PGD2, D3, H2, E2, F2α, J2)
PGE2 (11β-, dhk-)
PGF1α (6-keto-, 2,3-dinor-6-keto-)
PGF2α (8-iso-, dhk-, 15-keto-, 5-iso-PGF2αVI); Rv (D1)
tetranor (PGDM); TX (B2)		human plasmaSPE Acquity UHPLC BEH C18
2.1 × 150 mm, 1.7 μm		A: ACN/H2O/HAc (45/55/0.02, v/v/v)
B: IPA/ACN (50/50, v/v)		UHPLC–MS/MSQ-IM-TOF–[83]
Acquity UPC2 Torus 1-Aminoanthracene
3 × 100 mm, 1.7μm		A: supercritical MeOH
B: 0.1% HAc in MeOH		UHPSFC-MS/MS
DiHETrE (5,6-, 8,9- 11,12-, 14,15-)
DiHOME (9,10-, 12,13-)
endocannabinoids
EpETrE (5(6)-, 8(9)-, 11(12)-, 14(15)-)
EpOME (9(10)-, 12(13)-); HEPE (12S-)
HETE (5-, 8-, 9-, 11-, 12-, 15-, 20-)
HETrE (15S-); HODE (9S-, 13-)
LT (B4); Oxo-ETE (5-, 12-, 15-)
Oxo-ODE (13-)
PG (D2, E2, E2-EA, F2α, F2α-EA)
Rv (D1, D2)
TriHOME (9,10,13-, 9,12,13-); TX (B2)		cow heart, cow liver, pig/elk/cow brain, human plasma, lung lavage fluid, milk, cell medium SPEWaters BEH C18
2.1 × 150 mm, 1.7 μm		A: 0.1% HAc in H2O
B: ACN/IPA (90/10, v/v)		UHPLC–MS/MSQQQ0.25–533 fg[102,145,146]
DiHETrE (5,6-, 8,9-, 11,12-, 14,15-)
HETE (5-, 8-, 9-, 11-, 12-, 15-, 16-, 18-)
HHTrE (12-); Oxo-ETE (5-, 12-)
LT (B4); PG (A2, D2, E2, F2α, J2, 6-keto-PGF1α, dhk-PGF2α)
tetranor (PGEM)
TX (B2, 11-dehydro-TXB2)		human serum, plasma, washed plateletsSPEKinetex C8
2.1 × 150 mm, 2.6 μm		A: 0.1% FAc in H2O
B: 0.1% FAc in ACN		LC–MS/MSQQQ–[147]
DiHOME (9,10-)
EpETrE (5(6)-, 8(9)-, 11(12)-)
EpDPA (16(17)-, 19(20)-)
EpETE (14(15)-); EpOME (9(10)-)
HDoHE (4-)HETE (5-, 8-, 9-, 11-, 12-, 15-)
HODE (13-); HOTrE (9S-)
LT (B4); LX (A4); Oxo-ODE (9-, 13-)		dried blood spotLEEclipse plus C8
2.1 × 100 mm, 1.8 μm		A: 0.05% FAc in ACN/H2O (50/50, v/v)
B: 0.05% FAc in ACN 		UHPLC–MS/MSQQQ1–20 pg/μL[20]
PG (A1, A2, D1, D2, E1, E2, F1α, 6-keto-PGF1α, F2α, 8-iso-PGF2α, F3α)human ovarian follicular fluidLLENano cHiPLC ChromXP C18-CL
0.5 mm × 200 μm, 3 μm 120 Å		A: 0.1% FAc in H2O
B: 0.1% FAc in ACN		nano LC–MS/MSQQQ-TOF–[148]
Synergy hydro RP-C18A: 0.1% FAc in H2O
B: 0.1% FAc in ACN		LC–MS/MSQQQ
10S,17S-diH (AdA, n3 DPA, n6 DPA)
11-dehydro-TX (B2, B3)
2,3-dinor-TX (B1, B2)
7S,17S-diH (n3 DPA)
AdA IsoP/F (ent-7(R,S)-7-F2t-dihomo-IsoP, 17(R,S)-17-F2t-dihomo-IsoP 1 and 2, 7(R,S)-ST-Δ8-11-dihomo-isoF, 17(R,S)-10-epi-SC-Δ15-11-dihomo-IsoF 1 and 2)
ALA PhytoP/F (ent-9-F1t-PhytoP, ent-9-epi-F1t-PhytoP, ent-16-epi-16-F1t-PhytoP, ent-16-F1t-PhytoP, 9-L1-PhytoP, ento-16(R,S)-13-epi-ST-Δ14-9-PhytoF 1 and 2)
ARA IsoP (5(R,S)-5-F2t-IsoP, 15-F2t-IsoP (8-iso-PGF2α), 15(R,S)-2,3-dinor-15-F2t-isoP)
DHA NeuroP/F (4(R,S)-4-F4t-NeuroP, 10-F4t-NeuroP, 10-epi-10-F4t-NeuroP, 14(R,S)-14-F4t-NeuroP, 4(R,S)-ST- Δ5-8-NeuroF)
dhk PG (E1, F2α, tetranor PGE2)
DiHDPE (4,5-, 7,8-, 10,11-, 13,14-, 16,17-, 19,20-)
DiHETE (5,15-, 8,9-, 8,15-, 11,12-, 14,15-, 17,18-)
DiHETrE (5,6-, 8,9- 11,12-, 14,15-)
DiHODE (9,10-, 12,13-, 15,16-)
DiHOME (9,10-, 12,13-)
EPA IsoP (5(R,S)-5-F3t-IsoP, 8-F3t-IsoP, 8-epi-8-F3t-IsoP)
EpDPE (10(11)-, 13(14)-, 16(17)-, 19(20)-)
EpETE (8(9)-, 11(12)-, 14(15)-, 17(18)-, 12-OH-17(18)-)
EpETrE (5(6)-, 8(9)-, 11(12)-, 14(15)-)
EpODE (9(10)-, 12(13)-, 15(16)-)
EpOME (9(10)-, 12(13)-)
HDoHE (4-, 7-, 8-, 10-, 11-, 13-, 14-, 16-, 17-, 20-, 21-, 22-)
HEPE (5-, 8-, 9-, 11-, 12-, 15-, 18-, 19-, 20-)
HETE (5-, 8-, 9-, 11-, 12-, 15-, 19-, 20-)
HETrE (5S-, 15S-); HHTrE (12S-)
HODE (9-, 13-); HOTrE (9-,13-)
LT (B3, B4, B5)
LTB4 (20-carboxy-, 20-hydroxy-, 6-trans-); LX (A4)
n6 DPA NeuroP (4(R,S)-F3t-NeuroPn6)
Oxo-ETE (5-, 15-); Oxo-ODE (9-, 13-)
PD1; PG (B2, D1, D2, D3, E1, E2, 20-hydroxy-PGE2, F1α, F2α, J2)
PGF1α (15-keto-, 6-keto-, Δ17-6-keto-)
PGF2α (11β-, dihomo-)
PGJ2 (15-deoxy-, Δ12-)
Rv (D1, E1, E2, 18R-RvE3, 18S-RvE3)
stearic acid (9,10-diH-, 9(10)-ep-)
THF diol; TriHETrE (11,12,15-)
TX (B1, B2, B3)		human plasma, human colorectal carcinoma cellsSPEZorbax Eclipse Plus C18
2.1 × 150 mm, 1.8 μm		A: 0.1% HAc in H2O
B: ACN/MeOH/HAc (800/150/1, v/v/v)		LC-(ESI-)-MS/MSQQQ-LIT0.05–1.0 nM[15]
dhk-PG (E1, E2, F2α); dh-PG (E1, F2α)
HETE (5-, 8-, 11-, 12-, 15-)
LT (B4, C4, 14,15-LTC4, D4, E4)
PG (B2, D1, D2, D3, E1, E2, E3, F1α, 6-keto-PGF1α, F2α, F3α, J2, 15-deoxy-Δ12,14-PGJ2)
PGE2 (20-hydroxy-, 15-keto-)
PGF2α (8-iso-, 11β-, 2,3-dinor-8-iso-)
tetranor (PGEM, PGFM)
TX (B2, B3, 11-dehydro-TXB3)		human serum, sputum, BALFSPEAcquity UHPLC BEH shield RP18
2.1 × 150 mm, 1.7 μm		A: 0.1% HAc in H2O
B: 0.1% HAc in ACN/MeOH (90/10, v/v)		UHPLC–MS/MSQQQ-LIT0.2–1 ng/mL depending on matrix (LLOQ)[149]
DiHOME (9,10-, 12,13-)
EDP (16,17-, 19,20-)
EpETE (14(15)-, 17(18)-)
EpETrE (5(6)-, 8(9)-, 11(12)-, 14(15)-)
EpOME (9(10)-, 12(13)-)
HDoHE (4-, 7-, 10-, 14-, 17-)
HEPE (18-)
HETE (5-, 8-, 9-, 11-, 12-, 15-, 20-)
HODE (9-, 13-); HOTrE (9-, 13-)
hydroxy-epoxy-, keto-epoxy-octadecenoic acids,
LT (B4); LX (A4, B4); Mar1
Oxo-ETE (5-); Oxo-ODE (9-, 13-)
PD (X, 1/NPD1)
PG (E2, F2α, 8-isoPGF2α)
Rv (D1, D2)TriHOME (9,12,13-, 9,10,11-, 9,10,13-)
TX (B4)		human plasmaSPEZorBAX RRHD Eclipse Plus C18
4.6 × 100 mm, 1.8 μm		A: 12 mM ammonium acetate/HAc (100/0.02, v/v)
B: 12 mM ammonium acetate in ACN/H2O/HAc (90/10/0.02, v/v/v)		UHPLC–MS/MSQQQ-LIT0.02–1 ng/mL[13]
11-trans-LT (C4, D4, E4)
11β-PG (F2α, E2); 14,15-LT (C4, D4)
15-deoxy PG (D2, J2);
15-keto-PG (E2, F1α); 6-keto-PG (E1)
dhk-PG (D2, E2, F2α)
DiHDPA (19,20-)
DiHETE (5,6-, 5,15-, 8,15-)
DiHETrE (5,6-, 8,9-, 11,12-, 14,15-)
DiHOME (9,10-, 12,13-)
dihomo-PG (E2, F2α)
EpDPE (16(17)-, 19(20)-)
EpETE (17(18)-, 14(15)-)
EpETrE (5(6)-, 8(9)-, 11(12)-, 14(15)-)
EpOME (9(10)-, 12(13)-)
HDoHE (4-, 7-, 8-, 10-, 11-, 13-, 14-, 16-, 17-, 20-)
HEPE (5-, 8-, 9-, 11-, 12-, 15-, 18-)
HETE (5-, 8-, 9-, 11-, 12-, 15-, 16-, 17-, 18-, 19-, 20-)
HETrE (5-, 8-, 15-); HHTrE (12-)
HODE (9-, 13-)
HOTrE (9-, 13-, 13-HOTrEγ)
LT (B4, C4, D4, E4)
LTB4 (12-oxo-, 20-carboxy-, 20-hydroxy-)
LX (B4, A5, 15R-LXA4, 6S-LXA4)
Mar1 (7R-); nitrooleate (9-, 10-)
Oxo-EDE (15-); Oxo-ETE (5-, 12-, 15-)
Oxo-ODE (9-, 13-); PD1
PG (A2, B2, D1, D2, D3, E1, E2, E3, F1α, F2α, 8-iso PGF2αIII, 8-iso-15-keto-PGF2b, F3α, J2, K2)
PGEM, PGFM
PGF1α (6,15-dk-dh-, 6-keto-, d17 6-keto-)
Rv (D1, E1)
tetranor (12-HETE, PGDM)
TX (B1, B2, B3, 12-dehydro-TXB2, 2,3-dinor-TXB2)		human plasmaSPEHSS T3
2.1 × 100 mm, 1.8 μm		A: 0.1% FAc in H2O
B: 0.1% FAc in ACN		LC–MS/MSQQQ0.24–156.25 pg[84]
ACN: Acetonitrile; CSF: Cerebrospinal fluid; EA: Ethyl acetate; FAc: Formic acid; HAc: Acetic acid; LOD: Limit of detection; LOQ: Limit of quantification; LLOQ: Lower limit of quantification; MeOH: Methanol; Q: Quadrupole; Q-IM-TOF: Quadrupole-ion mobility-time of flight; QQQ: Triple quadrupole; QQQ-TOF: Triple quadrupole-time of flight.

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MDPI and ACS Style

Liakh, I.; Pakiet, A.; Sledzinski, T.; Mika, A. Methods of the Analysis of Oxylipins in Biological Samples. Molecules 2020, 25, 349. https://doi.org/10.3390/molecules25020349

AMA Style

Liakh I, Pakiet A, Sledzinski T, Mika A. Methods of the Analysis of Oxylipins in Biological Samples. Molecules. 2020; 25(2):349. https://doi.org/10.3390/molecules25020349

Chicago/Turabian Style

Liakh, Ivan, Alicja Pakiet, Tomasz Sledzinski, and Adriana Mika. 2020. "Methods of the Analysis of Oxylipins in Biological Samples" Molecules 25, no. 2: 349. https://doi.org/10.3390/molecules25020349

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