Oxylipins in human plasma – method development and dietary effects on levels
Jana Späth
Jana Späth
Degree Thesis in Chemistry 30 ECTS Master’s Level
Report passed: 05 March 2014
Supervisor: Malin L. Nording and Sandra Gouveia-Figueira
ABSTRACT
The emerging field of lipidomics, as a subfield of metabolomics, studies the complex lipid composition of biological samples and investigates their structures, pathways and functions in the metabolism.
Nutritional lipidomics examines the network of dietary lipids and the effect of diets on lipid molecules.
New understanding can be applied for improving human health. Oxylipins, as a part of the lipidome, are a group of oxidized metabolites that derive from various fatty acids and perform a variety of different functions in the human body. They are linked to many physiological processes such as cell proliferation, apoptosis, tissue repair, blood clotting, blood vessel permeability, inflammation, and immune cell behavior, and are associated to a multitude of autoimmune diseases.
In this master thesis, the effect of both vegan and vegetarian diets on the oxylipin profile of human plasma was examined. Oxylipin levels were analyzed in human plasma samples as a part of a study that further includes the analysis of another group of regulatory lipids, endocannabinoids, and other metabolites related to nutrition. One subject changed diet from vegan (year 1) to vegetarian (year 2).
On three different days (both year 1 and 2), plasma samples were collected at four different time points, one at a fasting state, and at 0.5h, 1h, and 2h after a well‐defined meal, representing the postprandial response. In total, 24 plasma samples were collected. Our specific aim was to find out if there are any general differences in oxylipin profiles due to vegetarian and vegan diet as well as during the postprandial response after a well‐defined meal.
A method based on ultra‐performance liquid chromatography coupled to electrospray ionization tandem mass spectrometry (UPLC‐ESI‐MS/MS) for simultaneously analyzing 37 oxylipins was developed and validated in accordance to the U.S. Food and Drug Administration guidelines. The method showed good linearity (0.9938 < R2 < 0.9996), limit of detection and quantification (0.0005 ‐ 4.21 pg on column), inter‐ and intraday accuracy (80 ‐ 120%; except for 8 compounds) and precision (<
18%), as well as recovery in PBS and plasma (71 ‐ 109%; except for 12(13)‐EpOME‐d4). The method was successfully applied to quantify oxylipins in human plasma samples. 35 of the 37 oxylipins could be detected in the samples with levels ranging over three orders of magnitude from 0.01 to 76 nM.
Significantly different levels during the postprandial response were shown by five compounds (9,10‐
DiHOME, 12,12‐DiHOME, 13‐oxo‐ODE, 13‐HODE, and 9‐HODE) in vegan and by two compounds (13‐
HODE and 9‐HODE) in vegetarian samples. Comparing vegan and vegetarian diet, one compound (TXB2) was found to have a significantly decreased baseline level in vegetarian samples, whereas six compounds (9‐HODE, 13‐oxo‐ODE, 9,10‐EpOME, 12,13‐EpOME, 20‐HETE, and 11,12‐DHET) showed significantly lower levels in vegetarian samples at different time points during the postprandial response.
In summary, the developed UPLC‐ESI‐MS/MS method may be applied for the quantification of oxylipins in different biological samples. This application can be useful to gain deeper knowledge about the various physiological processes of these important regulatory lipids.
LIST OF ABBREVIATIONS
AA Arachidonic acid
ACN Acetonitrile
AJS Agilent Jet Stream
ALA α‐linolenic acid
AV Average
BHT Butylhydroxytoluene
BMI Body mass index
COX Cyclooxygenase
CUDA 12‐[[(cyclohexylamino)carbonyl]amino]‐dodecanoic acid
CYP450 Cytochrome P450
DGLA Dihomo‐γ‐linolenic acid
DHA Docosahexaenoic acid
dMRM Dynamic multiple reaction monitoring EDTA Ethylenediaminetetraacetic acid
EETs Epoxyeicosatrienoic acids
EPA Eicosapentaenoic acid
ESI Electrospray ionization
FA Fatty acid
FDA Food and Drug administration
GC Gas chromatography
HETEs Hydroxyeicosatetraenoic acids HLB Hydrophobic‐lipophilic balance
HPLC High performance liquid chromatography
LA Linoleic acid
LC Liquid chromatography
LGC Laboratory of the Government Chemist
LOD Limit of detection
LOQ Limit of quantification
LOX Lipoxygenase
m/z Mass to charge ratio
MRM Multiple reaction monitoring
MS Mass spectrometry
MS/MS Tandem mass spectrometry
PBS Phosphate buffer saline
PG Prostaglandin
PUFA Poly unsaturated fatty acid
QC Quality Control
QQQ Triple quadrupole
R2 Coefficient of regression
S/N Signal to noise ratio
SD Standard deviation
sEH Soluble epoxide hydrolase
SPE Solid phase extraction
SPE Solid phase extraction
TX Thromboxane
UPLC‐ESI‐MS/MS Ultra‐performance liquid chromatography electrospray coupled to tandem mass spectrometry
INDEX OF CONTENTS
ABSTRACT ... I LIST OF ABBREVIATIONS ... III
INDEX OF CONTENTS ... IV
1. Introduction ... 1
1.1. Lipidomics in nutritional research ... 1
1.2. Aim ... 2
2. Background ... 3
2.1. Vegetarian and vegan diets ... 3
2.2. Oxylipins ... 4
2.3. Oxylipin analysis ... 7
2.3.1. The analytical protocol ... 7
2.3.2. Agilent 6490 Triple Quadrupole ... 10
2.4. Method development ... 12
2.5. Method validation ... 13
3. Experimental section ... 14
3.1. Chemicals and materials ... 14
3.2. Method development for Agilent 6490 Triple Quadrupole ... 14
3.3. Method Validation ... 16
3.3.1. Linearity, LOQ and LOD ... 16
3.3.2. Precision and accuracy ... 17
3.3.3. Recovery ... 17
3.3.4. Stability ... 17
3.4. Oxylipin quantification of plasma samples from the diet study ... 17
3.4.1. Standard selection ... 17
3.4.2. Standard curve preparation ... 19
3.4.3. Internal standards and internal standard curve preparation ... 20
3.4.4. Solid phase extraction (SPE) ... 20
3.4.5. Statistical analysis ... 21
3.4.6. Diet study design ... 21
4. Results and discussion ... 21
4.1. Method development ... 21
4.2. Method validation ... 28
4.3. Oxylipin profiling of plasma samples from the diet study ... 31
4.4. Statistical analysis of plasma oxylipin levels ... 36
5. Conclusion ... 41
6. Future perspectives ... 42
ACKNOWLEDGEMENTS ... 43
REFERENCES ... 44
APPENDIX ... 48
1. Introduction
1.1. Lipidomics in nutritional research
The term lipidomics is relatively new and has emerged in the past decade. In lipidomics research, as a subfield of metabolomics investigations, the complex lipid composition of biological samples is analyzed, structures are characterized, and roles in lipid metabolism are investigated [1]. In summary, it can be defined as the study of the lipidome, which is the complete lipid profile present in any cell, tissue, or biofluid [2].
The spectrum of lipids covers various structurally and functionally different metabolites playing diverse roles in nutrition and health [3]. They are the major component of the cell membrane (membrane lipids), and function as important signaling molecules (signaling lipids), and energy source (storage lipids). Whereas many lipids are endogenously produced in the body from simple precursors, some essential fatty acids (FAs) cannot be synthesized and must therefore be ingested with the diet [3].
Lipids can be divided into eight classes (fatty acyls, glycerolipids, glycerophospholipids, sphingolipids, sterol lipids, prenol lipids, saccharolipids, and polyketides) [3].
In contrast to proteins, lipids are not consisting of similar units, and their large number, complex structures, chemical diversity, and wide concentration ranges (from attomolar to micromolar), makes their analysis a challenging task. Covering the whole lipidome cannot be enabled by one single analytical technique. The most commonly analytical approach is based on mass spectrometry (MS), combined with chromatographic separation methods such as gas chromatography (GC) and liquid chromatography (LC). Advances in mass spectrometry in terms of speed, accuracy, and sensitivity, as well as in the field of bioinformatics, has provided rapid progress of lipidomics research in the past years [1], [3].
The main goal of lipidomics research is to gain better understanding of the complex lipid‐signaling pathways [3] and further apply this knowledge with the purpose of improving human health [1].
Applications span from the detection of lipid alterations connected to various diseases, the assessment of medicine or nutritional supplementation, and the investigation of biomarkers. Studied diseases cover many areas, such as neurological diseases (e.g. Alzheimer’s and Parkinson’s diseases), cardiovascular, ocular, dermatological, and pulmonary (e.g. asthma) diseases, especially those that are linked to immune or inflammatory responses [2]. Lipidomics has further increasingly been utilized in nutritional studies, the development of novel food technologies, and evaluation of food quality [3].
Former research showed that the response to diet is unique to each individual and therefore individualized analytical approaches are indispensable when investigating dietary effects [3], [4].
variations, making the characterization of dietary effects challenging [3]. A common established approach in nutritional lipidomics are cross‐over studies, where the subject undergoes both treatment and control arm. Combined with the response‐to‐challenge model that measures metabolites not only at the fasted state but also over the time course following a well‐defined meal (postprandial state), this approach allows for revealing complex dietary effects on the lipidome [1], [4].
The group of signaling lipids includes oxylipins that are formed by oxidation and linked to the regulation of various biological processes, such as both initiation and termination of inflammation [5]. Compared to analyzing structures, investigating the signaling functions of lipids is even more ambitious. The influence of lipid oxidation on biological processes is therefore still poorly understood [1]. Currently, a lot of research deals with the development of suitable methods for profiling oxylipins in order to better understand their roles, with focus on clinical implications [6]. It has for instance been shown that the oxylipin metabolome is affected by supplementation and challenge tests [7], [8], [9].
1.2. Aim
The overall aim of the master thesis work was to develop and validate an ultra‐performance liquid chromatography coupled to electrospray ionization tandem mass spectrometry (UPLC‐ESI‐MS/MS) method for the analysis of oxylipins.
Further, the master thesis work was intended to build a bridge between oxylipin research and nutritional lipidomics by further investigating the oxylipin metabolome. Growing interest in alternative diets and lifestyles like vegetarianism and veganism demands fundamental knowledge about expected effects on health and therefore comprehensive analysis of involved metabolic pathways. To that end, the effect of an altered diet (from vegan to vegetarian) on the oxylipin metabolome of human plasma was studied. According to the response‐to‐challenge model, oxylipin levels were measured during the time course after a well‐defined meal to investigate the postprandial response. The hypothesis was that dietary changes have an impact on the individual lipid profile and may also result in alterations of the oxylipin profile. The aim was to answer the following questions:
1) Is there a general difference in oxylipin profiles due to vegetarian and vegan diets?
2) If so, is it a diet‐dependent postprandial oxylipin response?
3) Is it possible to detect consistent postprandial responses in the circulating oxylipin metabolome after a well‐defined meal?
To that end, the developed and validated method for the analysis of oxylipins was applied to human plasma samples. Besides the study on dietary effects, two more studies investigating the oxylipin metabolome after exposure with different chemicals and applying the same method are ongoing. The date of these studies are used for the comparison of oxylipin baseline levels.
2. Background
2.1. Vegetarian and vegan diets
In Asia, people have lived by vegetarian principles for centuries, especially followers of the Hindu, Buddhist, and Jain religions. In the last decades the interest in vegetarian and vegan diets has immensely increased in the western world [10]. The share of vegetarians is between 1% and 10% of the population in the European Union, the USA, and Canada [11]. Results of polls report for Germany a number of about seven million people (8‐9% of the population) that consider themselves vegetarian and about 800.000 people consider themselves vegan [12].
A vegetarian diet is traditionally interpreted to imply the absence of meat. Further, it can mean the exclusion of all animal meat and all animal products. There are therefore several variations possible.
Lacto‐vegetarians include dairy products and lacto‐ovo‐vegetarians include dairy products and eggs in their diet. Vegans abstain from all foods of animal origin, including dairy products, eggs, and honey, and may also exclude any products tested on animals, products manufactured from animal products, as well as clothing from animals. Semi‐vegetarians include fish, seafood, and sometimes even poultry to their diets, although this definition can be debated. All these diets are additionally characterized by a greater consumption of fruit, vegetables, legumes, grains, and soy products [11], [13].
The estimates on health effects of vegetarian and vegan diets differ. There are several benefits associated with these diets. The American Dietetic Association claims that vegetarian and vegan diets – if appropriately planned – are healthful and have favorable effects on the prevention and treatment of certain diseases [14]. Compared to omnivores, vegetarians have a reduced risk of chronic diseases and increased life expectancy. Furthermore, they tend to have lower low‐density lipoprotein cholesterol levels, lower blood pressure, a lower body mass index, and fewer cases of type 2 diabetes and cancer [11].
The health benefits cannot distinctively be linked to the absence of meat only. There are more factors that come into play. Beneficial health effects may be due to the increased consumption of plant foods and therefore higher intake of fiber, phytochemicals, antioxidants, folic acid, vitamin C and D. At the same time vegetarians and vegans have a lower intake of saturated fat and cholesterol. This as well as other lifestyle components associated with vegetarian diet possibly offering protective metabolic advantages, a decreased blood pressure and BMI [11], [14].
Excluding all products of animal origin from the diet causes concerns related to insufficient nutrient provision. Likely deficient nutrients are iron, vitamin D and B12, as well as n‐3 fatty acids. Fish, for instance, contains the essential long‐chain n‐3 fatty acids eicosapentaenoic acid (C20:5n3, EPA) and
They are components of the cell membrane and play a role in eye and brain development. The body can convert the plant‐based n‐3 α‐linolenic acid (18:3n3, ALA) into EPA and DHA, but with a low efficiency. Excluding fish from the diet results in vegetarians and vegans having lower blood concentrations of EPA and DHA [11]. To compensate for nutrient deficiencies, foods containing nutrients of concern should be added to the diet. As for all diets it is therefore vital to assure a balance of nutrients from a diversity of foods [11], [15].
The term vegetarian describes a large group of different diets in terms of nutrient and food content, making a significant comparison challenging. Clear definitions are necessary. Current studies demonstrating beneficial health effects of vegetarian and vegan diets often do not address whether those effects are based on the absence of meat, on components of the replaced food, or on a generally different vegetarian and vegan lifestyle [15]. In order to make a precise statement, more studies on vegetarians and vegans are needed.
2.2. Oxylipins
The selection of oxylipins included in the method development is presented in Figure 1. Oxylipins are a group of oxidized metabolites deriving from various polyunsaturated fatty acids (PUFAs) and performing a variety of different functions in the human body [17]. Eicosanoids are oxylipins derived from n‐6 arachidonic acid (C20:4n6, AA). AA is a component of membrane phospholipids. It can be released by the phospholipase A2, as well as formed from diacylglycerol by diacylglycerol lipase.
Eicosanoids include the well investigated prostaglandins (PG) and thromoxanes (TX), as well as leukotrienes, hydroxyeicosatetraenoic acids (HETEs), epoxyeicosatrienoic acids (EETs), and lipoxins [6].
Other PUFAs included in the oxylipin formation are for instance the n‐6 fatty acids linoleic acid (C18:2n6, LA), γ‐ linolenic acid (C18:3n6), and dihomo‐γ‐linolenic acid (C20:3n6, DGLA), as well as the n‐3 ALA, DHA, and EPA [18]. These precursor PUFAs interact with reactive oxygen and are converted to oxylipins by three main classes of enzymes, cyclooxygenase (COX), lipoxygenase (LOX), and cytochrome P450 (CYP), as well as by non‐enzymatic auto‐oxidation (Figure 2). Oxidation via the COX pathway generates prostaglandins and thromboxanes, e.g. PGE2 and TXB2 with arachidonic acid as precursor PUFA. They are also referred to as class 2 PGs and TXs due to the two remaining double bonds. Class 1 and 3 PGs and TXs are formed from DGLA and EPA, respectively. PGE2 functions as an anti‐inflammatory, anti‐proliferative metabolite, whereas TXB2 has pro‐coagulant, proliferative properties.
The LOX pathway can be subdivided into the 5‐LOX, 12‐LOX, and 15‐LOX pathways. Leukotrienes are generated via 5‐LOX. Other LOX products include alcohols such as the hydroxyeicosatetraenoic acids (HETEs) from AA, hydroxyoctadecadienoic acids (HODEs) from LA, and hydroxypentaenoic acids
(HEPEs) from EPA. HETEs serve as bioactive lipid mediators in chemotaxis and degranulation progresses of neutrophils [5]. AA derived metabolites of the 5‐LOX pathway include pro‐inflammatory 5‐HETE, 5‐oxo‐ETE, as well as leukotriene B4 (LTB4), while LA products include 9‐HODE and trihydroxyoctadecenoic acids (TriHOMEs). 12‐HETE, 12‐oxo‐HETE, and pro‐inflammatory 9‐HETE are generated from AA via the 12‐ and 15‐LOX pathways. Several HODEs and HETEs can also be produced by non‐enzymatic oxidation [5].
Oxidation via the CYP pathway generates epoxides such as LA derived epoxyoctadecenoic acids (EpOMEs) and AA derived epoxyeicosatrienoic acids (EETs). They are known to play a role in cardiovascular functions such as vasodilatation of coronary arteries. EpOMEs and EETs are further converted to diols, dihydroxyoctadecenoic acids (DiHOMEs) and dihydroxyeicosatrienoic acids (DHETs), by soluble epoxide hydrolase (sEH) [18], [19].
Oxylipins belong amongst other substances to the group of regulatory lipids and play important roles in many physiological processes such as cell proliferation, apoptosis, tissue repair, blood clotting, blood vessel permeability, inflammation, and immune cell behavior [6]. They are employed as signals in all cell types, e.g. in platelets for inducing the blood clotting cascade and in smooth muscle cells for promoting vasodilation. Further, they are associated to a multitude of autoimmune diseases such as osteoarthritis, and inflammatory diseases such as heart disease and neural degeneration, as well as cancer and have also been used as medication in these conditions [18]. Some of the oxylipin functions have already been allocated, whereas others remain unclear. Further investigations on oxylipins may provide novel insights into the regulation of biological processes [5]. The development of an accurate, sensitive, and specific method for determining oxylipin levels will lead to a better understanding of the often opposing functions of these regulatory lipids. As oxylipins are produced from numerous PUFAs and via different enzymatic pathways, they form a diversity of different molecules with yet similar structures, chemistries, and physical properties [17], [20]. The analysis of these metabolites is therefore a challenging task and requires high class instrumentation and advanced method development.
Figure 1: Chemical structures of oxylipins derived from linoleic acid (A), eicosapentaenoic acid / docosahexaenoic acid (B), and arachidonic acid (C). Full oxylipin names are shown in Table A1 in the appendix.
Figure 2: Formation of oxylipins via different enzymatic pathways and PUFA precursors. Adapted from Zivkovic et al [18].
2.3. Oxylipin analysis
There are several approaches for analyzing bioactive lipids such as oxylipins. Immunoassays (e.g.
radioimmunoassay, enzyme‐linked immunosorbent assay) utilize antibodies to target only one or a few analytes. Gas chromatography coupled to mass spectrometry (GC‐MS) is a sensitive technique for a larger spectrum of compounds. However, as derivatization is required, additional steps in the analytical protocol are necessary and it is not suitable for thermally unstable analytes. Liquid chromatography coupled to mass spectrometry (LC‐MS) avoids problems associated with GC‐MS. It is a method with high sensitivity and currently used for simultaneously analyses of many compounds [5], [21].
2.3.1. The analytical protocol
For the LC‐MS analysis of bioactive lipids, samples undergo a process including preparation by solid phase extraction (SPE), separation by high performance liquid chromatography (HPLC), and detection by MS, followed by quantification.
Figure 3: Overview of the analytical protocol used for oxylipin analysis of human plasma samples.
Solid phase extraction
SPE is a separation technique based on selective partitioning of the analytes of interest between a solid and a liquid phase. It is used for extracting the analytes from plasma to remove compounds that might cause matrix effects during analysis later on and concentrating the analytes at the same time.
Therefore, a cartridge containing a solid adsorbent is first conditioned, loaded with the sample (in our study usually 500 µL) and then washed to remove residues while the analytes retain in the adsorbent.
The analytes are eluted from the cartridge with an appropriate solvent. This, as well as the solvent for washing and the adsorbent material in the cartridge, should be chosen with regard to the chemical and physical characteristics in relation to the analyte of interest. The Hydrophobic‐Lipophilic Balance (HLB) cartridges used in our experimental procedure contain a polymeric reversed‐phase sorbent [22].
Due to mainly deviations caused by differences between cartridge batches and irregular use of the required vacuum, the use of type I internal standard is recommended to compensate for those deviations [23], [24].
High performance liquid chromatography
HPLC is an analytical technique used to separate a sample into its single compounds giving a series of chromatographic peaks, to utilize identification and quantification of each compound. Therefore, a liquid sample, or a solid sample dissolved in an appropriate solvent, is carried through a chromatographic column (stationary phase) by a solvent (mobile phase). Chromatographic separation is achieved by different kinds of interactions of the compounds with the stationary phase and the mobile phase, such as solute/stationary‐phase interactions (liquid‐solid adsorption, liquid‐liquid‐
partitioning, ion exchange, and size‐exclusion) and solute/mobile‐phase interactions. The higher the affinity of the solute to the column material, the more it is retained in the column and the later it elutes (longer retention time). The higher the affinity of the solute to the liquid phase, the earlier it elutes (shorter retention time).
Depending on the properties of stationary and mobile phases, two forms of liquid chromatography are distinguished. For both, the elution of compounds is controlled by their polarity. In normal‐phase chromatography the stationary phase is polar and the mobile phase nonpolar. More commonly used is reversed‐phase chromatography with a nonpolar stationary phase and a polar mobile phase. The more polar a compound, the less it interacts with the stationary phase and therefore the sooner it elutes from the column. On the other hand, the least polar compound is retained the longest in the column and elutes at a later stage from the column. Longer or shorter retention times can be achieved by increasing or decreasing the polarity of the mobile phase, respectively. The most commonly used column material for reversed‐phase chromatography is an organochlorosilane for which the alkyl group is an n‐octyl (C8) or n‐octyldecyl (C18) hydrocarbon chain. Separation can be performed using either a mobile phase with constant composition (isocratic elution) or varying composition (gradient elution). For gradient elution, the composition of the mobile phase changes linearly, nonlinearly, or in steps, during the analytical run. For reversed‐phase chromatography the mobile phase composition is relatively polar at the beginning and becomes less polar during the run. It allows for optimal conditions for both early and late eluting solvents.
HPLC enables a highly resolved separation of a broad range of different substances in a short time.
Contrary to GC it is suitable to low‐volatile and thermally unstable compounds if they are soluble in a solvent.
For detecting the separated compounds several types of detectors (e.g. UV/Vis, fluorescence, electrochemical) may be used. In LC‐MS the effluent from the column is directly transferred to a mass spectrometer [23], [24], [25].
Tandem mass spectrometry
Mass spectrometry is an analytical technique that is used for generation, separation, and detection of ions in gas phase. A mass spectrometer consists of an ion source (for generating ions), a mass analyzer (for separating ions), and an ion detector (for receiving and amplifying the signal). They are operated under high vacuum conditions. The effluent from the LC column is led to the ionization chamber of the mass spectrometer where the solvent is evaporated and solutes are ionized. Each solute is thereby fragmented into characteristic ions. In the analyzer, the ions are separated by their mass to charge ratio (m/z). The qualitatively and quantitatively detection of the ions by their respective m/z and abundance then occurs in the detector [25], [26].
There are several ways for chemically or physically generating ions in the ion source. According to the type of ion source either hard ionization (e.g. electron ionization) or soft ionization (e.g. electrospray ionization) of analytes can be performed. For polar analytes and especially when coupling LC to MS, as
it accomplishes the transfer of ions from the liquid to the gas phase, electrospray ionization (ESI) is one of the most commonly used technique. None or only minor fragmentations are then generated. The molecules are being desolvated and ionized to small droplets and further to molecular ions. An ESI ion source consists of a capillary needle where the liquid sample is nebulized by a high pressure being applied and charged by a surrounding electrode at a potential of 3000‐4000 V. The mist consists then of charged molecular ions. Entering the desolvating capillary the mist is being desolvated by a drying gas (e.g. dehydrated nitrogen) or heat and the existing droplets divide into smaller droplets of either positive or negative charge. The charged ions are then focused towards the mass analyzer [26], [27].
In the mass analyzer ions are separated according to their mass to charge ratio. To operate tandem mass spectrometry (MS/MS) the mass spectrometer contains more than one analyzer. A triple quadrupole (QQQ) is typically used for electrospray ionization. Three quadrupoles (Q1, Q2, and Q3) are arranged sequentially, each quadrupole consists of two positively and two negatively charged rods with alternating AC and DC currents. Potential differences accelerate the molecular ions through the quadrupole. High vacuum conditions prevent the ions from colliding with the four rods and therefore ensure they maintain charge and can be detected. Q1 and Q3 of the triple quadrupole serve as normal mass filters and Q2 is used as a collision cell [27], [28].
For a triple quadrupole, tandem mass spectrometer multiple reaction monitoring (MRM) mode is most commonly utilized. A precursor ion with a specific m/z is selected in the first quadrupole Q1 and then transferred to the second quadrupole Q2 where it is fragmented. Then, a specific product ion is selected by Q3. The two stages of mass selection enables a specific detection of analytes and little background interferences even in a complex matrix like plasma [29].
2.3.2. Agilent 6490 Triple Quadrupole
Plasma samples were analyzed using an Agilent 6490 Triple Quadrupole LC‐MS system promising remarkable sensitivity (zeptomole), reliability, and up to six orders of dynamic range. It is equipped with Agilent’s iFunnel technology consisting of three novel components, the Agilent Jet Stream (AJS) technology, a hexabore capillary, and a dual ion funnel, enabling delivering far more ions to the mass spectrometer while efficiently removing the gas load [30]. The iFunnel technology ensures high ion generation and allows a more efficient ion transfer and therefore more ions to be captured by the mass spectrometer.
The AJS technology consists of a sprayer that surrounds the droplets that are created during nebulization with a sheath of superheated gas. Gas temperatures of up to 400 °C effectively desolvate the droplets and produce substantially smaller droplet sizes. The fast flowing gas focuses the spray near the MS inlet and minimizes the ion rich zone to about one fifth the size of a regular ESI ion source (Figure 4). This leads to much higher signal intensities.
Figure 4: Comparison of Agilent Jet Stream spray (orange) and regular ESI spray (green). Adapted from Agilent Technologies [31].
The hexabore capillary is composed of a circular array of six capillaries that span a horizontal distance of 3 mm within the ion rich zone (Figure 5). Compared to a single inlet capillary (0.6 mm) more gas is conducted to the MS. The capillary length is about half the size of a single inlet capillary enabling decreased capillary gas flow resistance and minimized ion loss caused by high mobility. Therefore, more ions are sampled from the AJS without the need of separating them from a large volume of gas.
Figure 5: Hexabore capillary consisting of six capillaries and spanning within the ion rich zone. Adapted from Agilent Technologies [31].
The dual ion funnel consists of an initial funnel operating at high pressure and a second funnel operating at low pressure (Figure 6) Voltages and radio frequencies (RF) of the first funnel focus and accelerate ions to the second funnel and further to the entrance of the mass analyzer, while removing
gas and neutral species. Combining these three elements enable the iFunnel technology to increase ion sampling, gain in sensitivity, and lower the limits of detection [31].
Figure 6: Dual ion funnel removes gas and neutral species and focuses ions to the mass analyzer.
Adapted from Agilent Technologies [31].
The Agilent 6490 Triple Quadrupole further includes the dynamic MRM mode. MRM is currently most widely utilized and therefore time segmentation is usually the common approach. Thereby the chromatographic run is dived into time segments. Mass scans are only performed for analytes that elute during each segment so that there are fewer concurrent transitions during each MS scan.
However, if the number of analytes increases so will the number of concurrent transitions. Dynamic MRM offers a reduction of those transitions without the requirement of reducing dwell times for these transitions or increasing the cycle time for each scan. Retention time windows for every analyte transition are selected and analytes are only monitored at those times, whereas no monitoring occurs where no compounds elute. In addition, modifications in the method e.g. adding more analytes can easily be made. Thus, dynamic MRM provides methods for accurately analyzing and quantifying hundreds of compounds as well as easier method development [29]. Furthermore, method development is facilitated by the MassHunter Optimizer software since it allows automated compound optimization for transitions and collision energies. The MassHunter Source Optimizer automatically optimizes iFunnel RF and seven ion source parameters (capillary voltage, nozzle voltage, drying gas temperature, drying gas flow, sheet gas temperature, sheet gas flow, and nebulizer gas flow).
2.4. Method development
There are special requirements when a method for analyzing bioactive lipids is developed. As most of them are present in plasma at very low concentrations, a method with good sensitivity and low limit of detection is indispensable. At the same time, their concentrations can deviate for more than three orders of magnitude, so calibration curves should cover this range [6]. Many of them are structurally
similar or even isomers, and some of them also have the same MRM transitions. Therefore good chromatographic separation is needed. Furthermore, the instability of some compounds as well as matrix effects must be considered. Another important aspect is to analyze a broad range of analytes simultaneously during as short as possible run time. Thus, to achieve a method suitable to these requirements, it is necessary to carefully optimize all steps of the method, especially liquid chromatography and mass spectrometry parameters [17].
2.5. Method validation
A developed method must be tested to assure the suitability for its intended use. To that end it can be validated according to guidelines given by different organizations such as the Laboratory of the Government Chemists (LGC) and the United States Food and Drug administration (FDA). The validation process should be performed before the initial use of a method, when transferring to another equipment or laboratory, and whenever single conditions change (e.g. using samples with a different matrix).
The validation can be established with the help of laboratory studies, several parameters are therefore examined [32]. A method for the analysis of 39 oxylipins in plasma has previously been developed by Yang et al [8]. For validation purposes, they determined linearity, limit of quantification (LOQ), accuracy, precision, and the recovery.
Linearity is defined as the ability of a method to obtain responses that are directly proportional to the amount of the compounds in the sample. It can be assessed by injecting and analyzing serial dilutions of standard solutions that span 80‐120% of the expected concentration range [32]. Peak areas are plotted against the amount of compounds and curve equations are calculated by linear regression. The coefficient of regression (R2) can be used to evaluate linearity. The closer it is to 1, the more linear the equation.
Limit of quantification is defined as the amount of sample giving a signal to noise ratio equal or greater than 10 [17].
The accuracy identifies how close the result of an experiment is to the expected result. For determination of accuracy, quality control (QC) samples with known concentration are measured and the results are compared to the true values.
Precision is considered to be the scatter of values when a sample is measured a couple of times. It can also be determined with the help of QC samples. By injecting them 3 times a day and replicates on 3 different days intraday precision and accuracy and interday precision and accuracy, respectively, can be determined.
When spiking the matrix with a specific amount of a compound, the recovery is defined as the amount of this compound that can be detected after extraction and analysis. As the recovery provides information about the effectiveness of the sample preparation, the process of determining the recovery should mimic the sample preparation as much as possible [17], [23].
3. Experimental section
3.1. Chemicals and materials
Native (PGF2a, PGE2, TXB2, PGD2, 5(6)‐EET, 8(9)‐EET, 11(12)‐EET, 14(15)‐EET, 5,6‐DHET, 8,9‐DHET, 11,12‐DHET, 14,15‐DHET, 9(10)‐EpOME, 12(13)‐EpOME, 9,10‐DiHOME, 12,13‐DiHOME, 5‐HETE, 8‐
HETE, 9‐HETE, 11‐HETE, 12‐HETE, 15‐HETE, 20‐HETE, 9‐HODE, 13‐HODE, 15(S)‐HETrE, 12‐HEPE, 17‐
HDoHE, 5‐oxo‐ETE, 12‐oxo‐ETE, 15‐oxo‐ETE, 13‐oxo‐ODE, LTB4, Resolvin D2, and Resolvin D1) and internal standards (12,13‐DiHOME‐d4, 12(13)‐EpOME‐d4, 9‐HODE‐d4, PGE2‐d4, and TXB2‐d4) were purchased from Cayman Chemical (Ann Arbor, MI, USA) with exception of 9,10,13‐TriHOME and 9,12,13‐TriHOME obtained from Larodan (Sweden, Malmö). Recovery standard (CUDA) was from Cayman Chemical (Ann Arbor, MI, USA).
All solvents used for analysis were of HPLC grade. Ethyl acetate was purchased from Fisher Scientific (Loughborough, UK), methanol and acetonitrile (ACN) from Fisher Scientific (Loughborough, UK) or Merck (Darmstadt, Germany), isopropanol from VWR PROLABO (Fontenay‐sous‐Bois, France). Acetic acid was purchased from Aldrich Chemical Company, Inc. (Milwaukee, WI, USA). Phosphate buffer saline was from Fluka Analytical, Sigma‐Aldrich (Buchs, Switzerland). Butylhydroxytoluene from Cayman Chemical (Ann Arbor, MI, USA) and ethylenediaminetetraacetic acid from Fluka Analytical, Sigma‐Aldrich (Buchs, Switzerland). Glycerol was from Fischer Scientific (Loughborough, UK). Oasis HLB cartridges (60 mg) were purchased from Waters (Milford, MA, USA), 5 mL cryo tubes from Sarstedt (Nümbrecht, Germany), vials from Thermo Scientific (Langerwehe, Germany).
3.2. Method development for Agilent 6490 Triple Quadrupole
An in‐house method for solid phase extraction (SPE) and ultra‐performance liquid chromatography coupled to mass spectrometry (UPLC‐ESI‐MS/MS) for analyzing 16 oxylipins in human plasma had already been applied. To achieve better sensitivity and faster data acquisition, this method was transferred to a new and more sensitive instrument. Furthermore, the number of analytes in a single analysis was increased from 16 to 37. Several steps of optimization were therefore taken.
The UPLC system consisted of an Agilent 6490 Triple Quadrupole system equipped with the iFunnel Technology source (Agilent Technologies, Santa Clara, CA, USA). Using the MassHunter Optimizer software (Agilent), transitions and collision energies for all standards and internal standards (solutions
at 0.5 µg/mL) could be determined using negative mode. The collision energy range was set from 0 to 40 V. The two most intense transitions were selected for each compound, one to use as quantifier and the other as qualifier. After comparing the results with data found in the literature, for two compounds also a manual optimization was performed.
Different gradients and different solvents found in the literature were compared and tested. A comparison is shown in Table A2 (see Appendix). Different flow rates were tested as well. The optimal conditions are summarized in Table 1. For chromatographic separation an Acquity UPLC BEH C18 column (Waters, Milford, MA, USA) with In‐Line filter (1.7 µm, 2.1 x 150 mm) and a constant flow rate of 0.3 mL/min was used. The autosampler temperature was kept at 10 °C, the column at 40 °C.
Table 1: Applied HPLC gradient. Eluent A: 0.1% acetic acid in water. Eluent B: acetonitrile / isopropanol (90:10).
Time [min] [%] B
0.0‐3.5 10‐35
3.5‐5.5 40
5.5.‐7.0 42
7.0‐9.0 50
9.0‐15.00 65
15.0‐17.0 75
17.0‐18.5 85
18.5‐19.5 95
19.5‐21.0 10
21.0‐25.0 10
The Source and iFunnel Optimization was executed for optimizing eight parameters. A mixture of all native and internal standards was injected and run with column. The electrospray ionization was conducted in negative mode with the capillary and nozzle voltage set at 4000 V and 1500 V, respectively. Drying gas temperature was set at 230 °C with a gas flow of 15 L/min. Sheet gas temperature was set at 400 °C with a gas flow of 11 L/min. The nebulizer gas flow was 35 psi, high and low pressure RF at 90 and 60 V, respectively. The dynamic MRM option was used and performed for all compounds with optimized transitions and collision energies. The determination and integration of
all peaks was manually performed using MassHunter Workstation software. Peaks were smoothed before integration and peak to peak S/N was determined using the area.
iFunnel high pressure and low pressure RF were optimized together because they were dependent on each other. Sheath gas temperature and flow, drying gas temperature and flow, as well as capillary voltage, nozzle voltage, and nebulizer pressure were optimized individually. In total around 100 runs were performed. For each run and for each compound, peak areas and S/N were determined and compared. The settings that gave highest areas were considered to be optimal. But since compounds were optimized at different parameters, compromises had to be made.
Instead of setting up certain time segments to maximize dwell times, a dynamic MRM method was used. Retention times and a delta retention time windows were therefore set for each compound.
By comparing retention times, internal standards were assigned to each native standard.
The retention times were established by injecting individually 10 µL of each compound’s stock solution (10 µg/mL).
3.3. Method Validation
The method was validated according to the U.S. Food and Drug Administration (FDA) guidelines over three consecutive days for linearity, limit of detection (LOD) and limit of quantification (LOQ), inter‐
and intraday precision and accuracy, and recovery. Stability tests are still undergoing.
3.3.1. Linearity, LOQ and LOD
To determine the linearity of the method, a standard mixture (S1) consisting of stock solutions of each native standard was prepared and then diluted with methanol several times to establish LOD and LOQ (S2‐S18). For injection, 90 µL of the standard mixture (Sn) was spiked with 10 µL internal standard mixture (IS4, 50000/25000 pg/mL) and 10 µL recovery standard solution (0.005 µg/mL CUDA (12‐
[[(cyclohexylamino)carbonyl]amino]‐dodecanoic acid)). After UPLC‐ESI‐MS/MS analysis, the ratios of standard peak area and corresponding internal standard peak area were plotted against the amount of standard on column. Calibration curve equations for each native standard were calculated by linear regression using the equation y = m(x) + b where “y” was equal to the ratio of standard area and corresponding internal standard area, “m” was equal to the slope of the curve, and “b” was equal to the y‐interception of the curve (set to 0). Dilutions and concentrations of the standard mixtures are shown in Table A3 in the appendix.
LOQ and LOD were defined as the concentration at which the S/N was equal or greater than 10 and 3, respectively.
3.3.2. Precision and accuracy
Accuracy and precision were determined using 4 quality control samples (QC) of 100 mM phosphate buffer saline (PBS) spiked with native standard mixtures at different concentrations (S2: 130000 pg/mL, S4: 33000 pg/mL , S5: 1600 pg/mL, S7: 4100 pg/mL). Three replicates of each QC sample were prepared and extracted by SPE as described below and analyzed by UPLC‐ESI‐MS/MS in the same batch together with a complete set of calibration standards on three different days. The concentration of each QC sample was determined using the calibration curve obtained in each batch.
The intraday precision was calculated by dividing the standard deviation by the average value of the 3 injections. The intraday accuracy was determined by comparing the average value of the 3 injections and the expected value, and expressed as fraction of the expected value (in %).
Interday precision and accuracy were determined as the average value of the 3 intraday precision and accuracy values, respectively.
3.3.3. Recovery
The recovery of the method was established by spiking 250 µL of 100 mM phosphate buffer saline (PBS) solutions with 10 µL of different internal standard mixtures (IS1: 1000000/500000 pg/mL, IS2: 200000/100000 pg/mL, IS4: 50000/25000 pg/mL) prior to extraction. The same procedure was repeated for internal standards in human plasma.
The ratios of the internal standard area and the recovery standard area were plotted against the on column amount, and concentrations were calculated using internal standard curve equations. The recovery for each internal standard was determined by the fraction of the measured value in relation to the expected value.
3.3.4. Stability
The stability of each analyte was determined by measuring the concentrations in the samples directly after collection and at different time points after storage at ‐20 and ‐80 °C and with two freeze‐thaw cycles. Stability of working solutions was determined by comparing fresh solutions and solutions stored at ‐80 °C for four weeks.
3.4. Oxylipin quantification of plasma samples from the diet study
Plasma samples obtained from the diet study were extracted and analyzed by UPLC‐ESI‐MS/MS according to the previously developed and validated method.
3.4.1. Standard selection
The aim was to increase the number of analytes measured simultaneously in a single analytical run. A
compared to assess importance of investigated oxylipins [33], [17], [34], [35], [36], [5]. Main emphasis was put on n‐6 AA and LA derived metabolites because they have been found to be present in plasma the most [5]. The final selection of 37 oxylipins is shown in Table 2.
Table 2: Selected oxylipins
Oxylipin Abbreviation Class Precursor
fatty acid
Pathway
Prostaglandin F2a PGF2a Triol AA COX
Prostaglandin E2 PGE2 Diol/Ketone AA COX
Thromboxane B2 TXB2 Triol AA COX
Prostaglandin D2 PGD2 Diol/Ketone AA COX
5(6)‐epoxy‐eicosatrienoic acid 5(6)‐EET Epoxide AA CYP
8(9)‐epoxy‐eicosatrienoic acid 8(9)‐EET Epoxide AA CYP
11(12)‐epoxy‐eicosatrienoic acid 11(12)‐EET Epoxide AA CYP 14(15)‐epoxy‐eicosatrienoic acid 14(15)‐EET Epoxide AA CYP
5,6‐dihydroxy‐eicosatrienoic acid 5,6‐DHET Diol AA CYP
8,9‐dihydroxy‐eicosatrienoic acid 8,9‐DHET Diol AA CYP
11,12‐dihydroxy‐eicosatrienoic acid 11,12‐DHET Diol AA CYP 14,15‐dihydroxy‐eicosatrienoic acid 14,15‐DHET Diol AA CYP
9(10)epoxy‐octadecenoic acid 9(10)‐EpOME Epoxide LA CYP
12(13)epoxy‐octadecenoic acid 12(13)‐EpOME Epoxide LA CYP
9,10‐dihydroxy‐octadecenoic acid 9,10‐DiHOME Diol LA CYP
12,13‐dihydroxy‐octadecenoic acid 12,13‐DiHOME Diol LA CYP
5‐hydroxy‐eicosatetraenoic acid 5‐HETE Alcohol AA 5‐LOX
8‐hydroxy‐eicosatetraenoic acid 8‐HETE Alcohol AA 15‐LOX
9‐hydroxy‐eicosatetraenoic acid 9‐HETE Alcohol AA 15‐LOX
11‐hydroxy‐eicosatetraenoic acid 11‐HETE Alcohol AA 15‐LOX
12‐hydroxy‐eicosatetraenoic acid 12‐HETE Alcohol AA 15‐LOX
15‐hydroxy‐eicosatetraenoic acid 15‐HETE Alcohol AA 15‐LOX
20‐hydroxy‐eicosatetraenoic acid 20‐HETE Alcohol AA CYP
9‐hydroxy‐octadecadienoic acid 9‐HODE Alcohol LA 5‐LOX
13‐hydroxy‐octadecadienoic acid 13‐HODE Alcohol LA 5‐LOX
15‐hydroxy‐eicosatrienoic acid 15(S)‐HETrE Alcohol AA 15‐LOX 12‐hydroxy‐eicosapentaenoic acid 12‐HEPE Alcohol EPA 15‐LOX 17‐hydroxy‐docosahexaenoic acid 17‐HDoHE Alcohol DHA 15‐LOX
5‐oxo‐eicosatetraenoic acid 5‐oxo‐ETE Ketone AA 5‐LOX
12‐oxo‐eicosatetraenoic acid 12‐oxo‐ETE Ketone AA 15‐LOX
15‐oxo‐eicosatetraenoic acid 15‐oxo‐ETE Ketone AA 15‐LOX
13‐oxo‐octadecadienoic acid 13‐oxo‐ODE Ketone LA 5‐LOX
9,10,13‐trihydroxy‐octadecenoic acid 9,10,13‐TriHOME Triol LA 5‐LOX 9,12,13‐trihydroxy‐octadecenoic acid 9,12,13‐TriHOME Triol LA 5‐LOX
Leukotriene B4 LTB4 Diol AA 5‐LOX
7S,16R,17S‐trihydroxy‐
4Z,8E,10Z,12E,14E,19Z‐
docosahexaenoic acid
Resolvin D2 Triol DHA 5, 15‐
LOX
7S,8R,17S‐trihydroxy‐
4Z,9E,11E,13Z,15E,19Z‐
docosahexaenoic acid
Resolvin D1 Triol DHA 5, 15‐
LOX
3.4.2. Standard curve preparation
As methyl acetate is unsuitable for analysis due to its high volatility, standards supplied in methyl acetate were first evaporated under nitrogen and reconstituted in either 50 or 500 µL ethanol. To solid standards, 1000 µL ethanol was added. Stock solutions of each standard in methanol with concentrations of 10 µg/mL were prepared by adding either 10 or 100 µL of the commercial solution to 990 or 900 µL methanol, respectively. For the strongest standard mixture (S1) 100 µL of each stock solution was added to a final volume of 3.8 mL. Exact steps of preparations are shown in Table A3 in the appendix.
To obtain the calibration curve solutions, serial dilutions in methanol were prepared (S2‐S13). The dilution steps and concentrations of the standard curve solutions are shown in Table A4 in the appendix. The calibration curves were determined as previously described in the method validation section.
Only standard mixtures generating peaks with S/N > 10 were used for the calibration curves of each standard. Thus, for many standards the lowest standard mixture (S13) was not included.
3.4.3. Internal standards and internal standard curve preparation
Stock solutions of each internal standard in methanol with concentrations of either 5 or 10 µg/mL were prepared. Internal standards supplied in methyl acetate were evaporated under nitrogen and reconstituted in either 100, 250, or 500 µL ethanol. The strongest internal standard mixture (IS1) was prepared by adding 250 µL of each stock solution to a final volume of 1250 µL It was then diluted 5 times to obtain the internal standard curves (IS2‐IS6). Dilutions and concentrations are found in Table A5 and Table A6 in the appendix. 90 µL of methanol was spiked with 10 µl of each internal standard mixture and 10 µL of the recovery standard solution (0.005 µg/mL CUDA) and the solutions were analyzed by UPLC‐ESI‐MS/MS. The calibration curves were determined as previously described in the method validation section. To determine the internal standard mixture for spiking samples and standards, the obtained internal standards areas were compared to match with the native standard areas.
For the recovery calculations, solutions of CUDA in methanol were prepared. 5 mg CUDA was dissolved in 5 mL of methanol (concentration: 1000 µg/mL) and then diluted in methanol several times to obtain the stock solution (100 µg/mL), a working solution (1 µg/mL) and potential spiking solutions (0.05 and 0.005 µg/mL). Spiking solutions were analyzed by UPLC‐ESI‐MS/MS to determine the proper concentration for spiking samples and standards.
3.4.4. Solid phase extraction (SPE)
A previously published SPE protocol was adapted for sample preparation [37]. Briefly, on the day of extraction the plasma samples were thawed at room temperature and centrifuged.
SPE Waters Oasis HLB cartridges (60 mg sorbent, 30 μm particle size) were first washed with one column volume of ethyl acetate, two column volume of methanol, and then conditioned with two column volumes of wash solution (5% methanol, 0.1% acetic acid). A quantitative volume of plasma (300 ‐ 500 µL depending on the amount supplied) was loaded onto the SPE cartridges and spiked with 10 μL of internal standard solution (SI4) and 10 µL antioxidant solution (0.2 mg/mL BHT/EDTA in methanol/water (1:1)).
Cartridges were washed with two CV wash solution, dried under high vacuum for about 2 minutes, and eluted with 2 mL methanol and 2 mL ethyl acetate into tubes containing 6 µL of a glycerol solution (30% in methanol). Glycerol operates as a trap solution for the compounds. After extraction, high vacuum was applied for 1‐3 minutes to remove remaining ethyl acetate. Afterwards, the solvents were evaporated under vacuum (SpeedVAC system, Farmingdale, NY, USA) until only glycerol remained and the tubes were stored at ‐80 °C until analysis. The residues were then reconstituted in 100 µL methanol
and tubes were vortexed. The solutions were transferred to LC vials and 10 µL of the recovery standard solution was added (0.005 µg/mL CUDA) and UPLC‐ESI‐MS/MS analysis was performed immediately.
3.4.5. Statistical analysis
Analyte levels were calculated and expressed as mean ± standard error of the mean, using the GraphPad Prism 6 (San Diego, CA, U.S.A.). Differences between time points within the vegan and vegetarian diet for each compound were assessed using two‐way ANOVA conducted with post hoc Tukey’s multiple comparison test. Student’s t‐test was used to detect significant differences between the vegan and vegetarian diets for each time point. The significant level was defined as p < 0.05.
3.4.6. Diet study design
Plasma samples were collected from a subject at two different dietary conditions. At first collection occasion, the subject was on a vegan diet excluding all foods of animal origin. Samples were collected at 4 different time points, one at a fasting state after an overnight fast, and at 0.5h, 1h, and 2h after a well‐defined meal in order to monitor the postprandial response. The meal consisted of approximately 350 g bananas and provided 15 % of daily calories. The sample collection was repeated on three different days. After sampling, the subject changed the diet from vegan to vegetarian and therefore adding dairy products and egg to the diet. The same sampling protocol was carried out after 1.5 years on the new diet, on three different days in accordance to the previous time points (at a fasting state, and at 0.5h, 1h, and 2h after the same well‐defined meal). In total 24 plasma samples were collected.
The subject was a healthy female aged 29‐30 with BMI 20.
4. Results and discussion
4.1. Method development
A UPLC‐ESI‐MS/MS method was developed and validated for the analysis of 37 oxylipins in human plasma samples. Several parameters were tested. For LC separation, a previously published gradient [17] consisting of 0.1% acetic acid in water (eluent A) and acetonitrile / methanol / acetic acid (85:15:0.1%) (eluent B) led to a poor separation of the two isomers PGE2 and 8‐iso‐PGE2 that are especially relevant for further studies. Separation of all compounds including PGE2 and 8‐iso‐PGE2 could be achieved by changing eluent B to acetonitrile / isopropanol (90:10), in accordance with Strassburg et al [5]. Optimized conditions and the applied gradient are shown above (in the Method development for Agilent 6490 Triple Quadrupole section). Adding 0.1% acetic acid to eluent B improved signals for the prostaglandins PGD2, PGE2, PGF2α, and TXB2, but worsened signals for the other compounds.
MassHunter Optimizer software gave transitions and collision energies for all native and internal
(on standard mixtures) and therefore transitions reported in the literature [5] were manually added.
MRM chromatograms for all native and internal standards are shown in Figure 7 and Figure 8. A detailed list of MRM transitions and collision energies can be found in Table 3. The most intense transitions were used for quantification purposes and are marked in bold; the second transition was used as qualifier fragment. Internal standards were assigned to each native standard according to their retention times, with the exception of 14(15)‐EET, 11(12)‐EET, 5‐oxo‐ETE, 8(9)‐EET, and 5(6)‐EET that were eluting close to 12(13)‐EpOME‐d4 but were assigned to 9‐HODE‐d4, because better linearity could be achieved since there was low recovery of 12(13)‐EPOME‐d4.
Strassburg et al. [5] and Yang et al. [38] have developed methods for the analysis of oxylipins.
Strassburg et al. analyze 100 oxylipins in a single analytical run using a similar equipment to ours, while Yang et al use a triple quadropole MS of another brand. The method by Strassburg et al comprehends oxylipins that are also included in our method, with the exception of Resolvin D2 and D1 (only included in our method). They determined different transitions for PGE2, 5,6‐DHET, 12‐HEPE, 13‐HODE, 9‐HODE, 15‐HETE, 5‐HETE, and 12(13)‐EpOME (Table 3). The other transitions match our results.
Eight parameters were optimized using the Source and iFunnel Optimization software and peak areas for each run and each compound were compared. Table 4 shows the parameters that were found to be optimal for most of the compounds. Strassburg et al. found similar optimal parameters.
Dynamic MRM with defined retention time windows instead of time segments was performed allowing the instrument to monitor transitions only during the stated time window and therefore reducing the number of concurrent transitions. An extracted ion chromatogram of all oxylipins included in the method is shown in Figure 9.
23
Figure 7: Multiple reaction monitoring (MRM) chromatograms of native standards included in the method.
24
Figure 8: Multiple reaction monitoring (MRM) chromatograms of native and internal standards included in the method.
25
Table 3: Optimized multiple reaction monitoring (MRM) transitions and collision energies for all 37 native and 5 internal standards. Most intense transitions are marked in bold.
Standard Internal standard Retention time [min]
Parent ion [M‐H]‐
Product ions
Collision energy [V]
Fragmentor voltage [V]
Product ions Strassburg [5]
Product ions Yang [38]
TXB2‐d4 ‐ 7.545 373.25 173.00
199.00
9 9
380 173.10 ‐
TXB2 TXB2‐d4 7.573 369.23 169.10
195.00
13 9
380 169.10 195.10
9,12,13‐TriHOME TXB2‐d4 8.145 329.23 211.10
229.10
21 17
380 211.20 211.10
9,10,13‐TriHOME TXB2‐d4 8.317 329.23 171.00
139.10
21 21
380 171.10 171.10
PGF2a TXB2‐d4 8.332 353.23 193.30
211.00
21 21
380 193.20 309.30
PGE2‐d4 ‐ 8.601 355.24 319.20
275.20
5 13
380 275.20 ‐
PGE2 PGE2‐d4 8.640 351.21 315.10
271.20
5 13
380 315.00 271.10
PGD2 PGE2‐d4 9.082 351.21 315.20
271.10
9 13
380 315.20 271.10
Resolvin D2 PGE2‐d4 9.456 375.21 215.10
216.10
13 13
380 ‐ ‐
Resolvin D1 PGE2‐d4 9.865 375.21 215.10
217.1
17 13
380 ‐ ‐
LTB4 PGE2‐d4 12.912 335.22 195.10
317.20
13 9
380 195.20 195.10
12,13‐DiHOME‐d4 ‐ 13.152 317.26 185.10
‐
21
‐
380 185.20 ‐
12,13‐DiHOME 12,13‐DiHOME‐d4 13.224 313.24 183.20 99.00
17 25
380 183.20 183.20
9,10‐DiHOME 12,13‐DiHOME‐d4 13.686 313.20 201.00 17 380 201.10 201.20
