Applications of nuclear magnetic resonance in lipid analyses: An emerging powerful tool for lipidomics studies

Progress in Lipid Research 68 (2017) 37–56

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Review

Applications of nuclear magnetic resonance in lipid analyses: An emerging powerful tool for lipidomics studies

MARK

Jingbo Lia,⁎, Thomas Vosegaardb, Zheng Guoa,⁎ a

Department of Engineering, Faculty of Science, Aarhus University, Gustav Wieds Vej 10, 8000 Aarhus C, Denmark Danish Center for Ultrahigh-Field NMR Spectroscopy, Interdisciplinary Nanoscience Center and Department of Chemistry, Aarhus University, Gustav Wieds Vej 14, 8000 Aarhus C, Denmark b

A R T I C L E I N F O

A B S T R A C T

Keywords: Nuclear magnetic resonance (NMR) Lipidomics Lipid analysis Disease Food adulteration Metabolites

The role of lipids in cell, tissue, and organ physiology is crucial; as many diseases, including cancer, diabetes, neurodegenerative, and infectious diseases, are closely related to absorption and metabolism of lipids. Mass spectrometry (MS) based methods are the most developed powerful tools to study the synthetic pathways and metabolic networks of cellular lipids in biological systems; leading to the birth of an emerging subject lipidomics, which has been extensively reviewed. Nuclear magnetic resonance (NMR), another powerful analytical tool, which allows the visualization of single atoms and molecules, is receiving increasing attention in lipidomics analyses. However, very little work focusing on lipidomic studies using NMR has been critically reviewed. This paper presents a first comprehensive summary of application of 1H, 13C & 31P NMR in lipids and lipidomics analyses. The scientific basis, principles and characteristic diagnostic peaks assigned to specific atoms/molecular structures of lipids are presented. Applications of 2D NMR in mapping and monitoring of the components and their changes in complex lipids systems, as well as alteration of lipid profiling over disease development are also reviewed. The applications of NMR lipidomics in diseases diagnosis and food adulteration are exemplified.

1. Introduction The suffix “-om-” originated as a back-formation from “genome”. Because “genome” refers to the complete genetic makeup of an organism, people have made the inference that there exists some root, “-ome-”, of Greek origin referring to wholeness or to completion, but such root is unknown to most or all scholars. Because of the success of largescale quantitative biology projects such as genome sequencing, the suffix “-om-” has migrated to a host of other contexts. As research scientists increasingly sought to integrate biology with information science, they took up the use of omics. For biologists, -omics easily conveyed a key concept, the implications of a complex systems approach, an approach that is closely tied to study of networks, emergent properties and encapsulation concepts of theoretical computer science. “Proteomics” has been well accepted as a term for studying the proteome. Thereafter, scientists have proposed other “-omics” which are

becoming accepted as well within biology field (http://omics.org/ index.php/Omes_and_Omics). “Lipidomics” is one of the concepts. Based on the history and definition of “-omics”, lipidomics is the study of the structure and function of the complete set of lipids (the lipidome) produced in a given cell or organism as well as their interactions with other lipids, proteins and metabolites (http://www.nature.com/ subjects/lipidomics). Through the detailed quantification of a cell's lipidome, the kinetics of lipid metabolism, and the interactions of lipids with cellular proteins, lipidomics has already provided new insights into health and disease. However, the true power and promise of lipidomics is only beginning to be realized [1]. Quantitative techniques are essential for the lipidomics. Both normal and reversed phase high performance chromatography (HPLC) systems have been developed since 1970s for lipids quantification [2–5]. However, these procedures could not meet the requirements for the application of lipidomics to the study of human disease because

Abbreviations: MS, mass spectrometry; NMR, nuclear magnetic resonance; HPLC, high performance chromatography; PC, phosphatidylcholine; PE, phosphatidylethanolamine; PI, phosphatidylinositol; PG, phosphatidylglycerol; PS, phosphatidylserine; PA, phosphatidic acid; SM, sphingomyelin; LC-NMR, liquid chromatography-NMRs; SPE, solid phase extraction; TOCSY, total correlation spectroscopy; TAG, triglycerides; DHA, docosahexaenoic acid; NOE, nuclear overhauser effect; EPA, eicosapentaenoic acid; GC, gas chromatography; HSQC, 1H, 13 C heteronuclear single quantum coherence; TLC, thin-layer-chromatography; PLs, phospholipids; CAD, coronary artery disease; AD, Alzheimer's disease; PUFAs, polyunsaturated fatty acids; FTIR, Fourier transform infrared spectroscopy; EDTA, ethylenediaminetetraacetic acid; D2O, deuterium oxide; CDTA, cyclohexane diamine tetraacetic acid; CDCl3, chloroform-d; MeOH, methanol; CHCl3, chloroform; Cs, cesium; Cr(acac)3, chromium(III) acetylacetonate; SDS, sodium dodecyl sulfate; HDL, high density lipoprotein; LDL, low density lipoprotein; PEe, alkyl ether-linked phosphatidylethanolamine; DHSM, dihydrosphingomyelin ⁎ Corresponding authors. E-mail addresses: [email protected], [email protected] (J. Li), [email protected] (T. Vosegaard), [email protected], [email protected] (Z. Guo). http://dx.doi.org/10.1016/j.plipres.2017.09.003 Received 1 June 2017; Received in revised form 25 August 2017; Accepted 11 September 2017 Available online 11 September 2017 0163-7827/ © 2017 Elsevier Ltd. All rights reserved.

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methods have their own advantages compared to MS-based methods. In Table 1, we list a comparison between NMR and MS lipidomics methods. The main advantages of the NMR-based methods are: 1) no destruction of the sample; 2) high analytical reproducibility; 3) easy identification of molecular moieties; 4) high robustness of instruments; 5) possibility to obtain molecular dynamics information; and 6) direct quantitative information. With these advantages, the NMR-based methods have the potential as a powerful tool for lipidomics analysis. Thousands of individual lipid molecular species are present in cells. This complexity implies that no single technique can effectively study all the lipid species. Therefore, even though the NMR-based method is not as sensitive as MS, it can still serve as a complementary method of MS to gain additional information and finally realize the global mapping of the lipidome. Especially, the development of 2D 1H,1H NMR [13], 1H,13C NMR [10] and 31P,1H NMR [14] techniques may bring new vitality for NMR in lipidomics analysis [15]. Although it is highly possible that the NMR-based methods could be used widely in lipidomics studies, until now the relevant studies are not a lot. Consequently, no relevant review has been published. In this paper, we attempt to review the recent development of NMRbased methods for lipid analysis. Specifically, the applications of 1H NMR, 13C NMR, and 31P NMR and combinations of them are revisited. The authors would expect, to some extent, to attract more attention of the NMR-based lipidomics through this review.

they were plagued by cumulative errors from multistep chromatographic procedures [1]. Moreover, HPLC based lipidomics is not very informative because only limited lipids can be quantified from an individual run. Mass spectrometry (MS) is an extraordinary sensitive tool for the detection of various classes, subclasses, and individual molecular species of lipids in a biological sample [1]. MS-based lipidomics has been extensively studied and reviewed by many scientists [6–9]. The limitations of most MS-based lipidomic methods include the differing abilities of lipid species to form ions and hence varying signal intensity as well as ion-quenching phenomena, in which the signal from poor ionizing lipids is quenched by more easily ionized species suppressing the former signal, which requires the prior separation of lipid species for accurate quantitation or the use of specialized MS. These factors result in a loss of sensitivity for many of the nonpolar lipid metabolites, as exemplified in a study of tuberculosis bacilli [10]. MS also destroys the sample during analysis and thus the sample cannot be recovered for other complementary analyses. Compared to the MS method, NMR-based lipid analytical methods are less sensitive and typically limited by overlapping signals in either the 1H NMR or 31P NMR, and the low natural abundance of 13C for 13C NMR [11]. Additionally, NMR generally has a poor separation of signals, which produces crowded spectra when acquired as a 1D spectrum, hampering discrimination of resonances from the various compounds in complex mixtures [12]. Saturated fatty acyl residues (such as in PC 14:0/16:0 compared to PC 16:0/16:0) can be easily differentiated by MS while this is very difficult by NMR. However, the NMR-based Table 1 Comparison of NMR and MS based methods.

Detection limits

Universality of metabolite detection

NMR

MS

Low-micromolar at typical observation frequencies (600 MHz), but nanomolar using cryoprobes for 1H NMR. Larger amount of sample (around 200 mg) for 13C NMR. 10 mg for 31P NMR. Lower amounts of sample loading can be compensated by more number of scans If metabolite contains hydrogens, carbons, or phosphorus, it will be detected, assuming the concentration is sufficient or no overlapping of chemical shift spins

Picomolar with standard techniques, but can be much lower with special techniques

Sample handling

Whole sample analyzed in one measurement for 1H NMR and 13C NMR, while 31P NMR sometimes needs different solvent; deuterated solvents needed

Sample recovery Analytical reproducibility Sample pre-preparation Ease of molecular identification

Nondestructive. Can be used for other analysis thereafter Very high Simple and rapid [37] High, both from databases of authentic material and by selfconsistent analysis of 1D and 2D spectra between 1H and 13C, or 1H and 31P

Time to collect data

1 min for 1D 1H NMR. Around 60 min for 1D 13C NMR. Less than 10 min for 31P NMR High Yes, from T1, T2 relaxation time and diffusion coefficient measurements Yes, using MAS NMR Not yet comprehensive but increasing; several are available freely on the web; some commercial products also exist

Robustness of instruments Molecular dynamics information Analysis of tissue samples Availability of databases

Reliable and reproducible quantitative data of phospholipids

Yes

Resolution for the same class of lipids [37] Derivation needed

Weak. Almost the same 1H- and/or 31P NMR spectra irrespective of the acyl chain length, number and position of unsaturation No

38

Usually needs a more targeted approach. There can be problems with poor chromatographic separation; with the loss of metabolites in void volumes; with ion suppression (but this is reduced when using UPLC); lack of ionization; ability to run both + ve and − ve ion detection gives extra information Different LC packings and conditions for different classes of metabolite; usually samples have to be extracted into a suitable solvent; samples have to be aliquoted but some recent studies have avoided the need for chromatography Destructive but only small amounts needed Fair Difficult, often only the molecular ion is available; this needs extra experiments, such as routine tandem MS; GC–MS is generally better with accurate retention times and comprehensive databases of spectra 10 min for UPLC-MS run. At least 60 min for ESI-MS run Low No No Comprehensive databases for electron impact MS allow spectral comparisons; For electrospray ionization, as is usual in LC-MS, only mass values can be compared Unreliable. Different phospholipids have quite different response factors in ESI source and a direct comparison among all these classes is often hindered by several other factors. Neutral membrane lipids (PC, pPC, ePC, SM) are usually detected and quantified in positive ESI ion-mode whilst anionic membrane lipids (PE, PS, PG, PI, CL) require negative ESI ion-mode conditions. It is not a major disadvantage with modern MS devices that switch very rapidly between positive and negative polarity High Yes. For GC–MS

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H

H

H

H

H

O

HO

R

H

O

O O R3

H2 C

H2 C

C2

C1

C

O

C

H2 C

H2 C

R1

O

C

H2 C

H2 C

R2

O

O Fig. 1. Chemical structure of cholesterols and its esters, triglycerides.

2. Brief classification of lipids

Fig. 2G–H. Rhamnolipids contain rhamnose moiety(ies), carbonyl and carboxylic groups as their markers for NMR. Sphingolipids contain a sphingosine backbone. Sphingolipids can be classified based on the substitute of X in Fig. 3 into sphingomyelin (SM), cerebroside, glucosylceramide, lactosyleramide, sulfatide, and other glycosphingolipids which contain multiple sugar rings. SM can also be identified and quantified by 31P NMR because of the phosphorus atom in the molecule. Glycolipids include glycosphingolipids and glycoglycerolipids in which the hydrophilic core is mono or multiple saccharides, which also display specific resonances in 1H and 13C NMR and can be used for structural diagnosis.

Lipids can be roughly classified into 3 groups, namely nonpolar lipids, polar lipids, and metabolites based on their relative polarities of the head group regions [9]. In each class of lipids, the lipids have common structural features while they also contain specific groups. These differences can serve as markers for the identification by NMR. To clarify this, the chemical structures of the different lipids are shown below for further discussion. 2.1. Nonpolar lipids Nonpolar lipids include predominantly cholesterol and its esters, and triglycerides [9]. A large hydrophobic region and a small neutral or less polar part are presented in all members of this class of lipids (Fig. 1). According to the different structures including backbone and branch chains, different lipids display specific NMR chemical shifts. For example, a given lipid sample can be classified either as sterols or triglycerides by their specific backbone NMR signals. Within the same class, a given lipid sample can be further cataloged by either different R chain lengths (R and R1–R3 refer to the alkyl chains on the structures in Fig. 1) or by double bonds, etc. into subclasses. Details will be discussed in the following context.

2.3. Metabolites Metabolites are derived from ‘parent’ lipids or their precursors by enzymatic reactions. They are not in a large quantity but important active secondary messengers. Common metabolites include long chain acylCoAs, long chain acylcarnitines, nonesterified fatty acids, fatty acid esters, acyl anamide, ceramides, lysolipids, eicosanoids, diglycerides, and sphingosine-3-phosphate etc. [9]. NMR has been exploited in great detail for metabolomics studies [16], as NMR provides unique fingerprints of different metabolites based on their differences in the chemical structure and the chemical environment of the individual atoms. Therefore, the importance of understanding the chemical structure cannot be emphasized enough. Several online resources, The LIPID MAPS (LIPID Metabolites And Pathways Strategy; http://www.lipidmaps.org), Lipid Library (http:// lipidlibrary.co.uk), Lipid Bank (http://lipidbank.jp), LIPIDAT (http:// www.lipidat.chemistry.ohio-state.edu), and Cyberlipids (http://www. cyberlipid.org), are available for lipid classifications, structures, and even NMR spectra.

2.2. Polar lipids Polar lipids mainly include phospholipids, sphingolipids, rhamnolipids, and glycolipids etc. The obvious characteristic of phospholipids is the presence of at least one phosphate group at the sn-3 position of glycerol backbone (Fig. 2A–F). Phospholipids can be further classified into several classes based on the phosphate groups such as phosphatidylcholine (PC), phosphatidylethanolamine (PE), phosphatidylinositol (PI), phosphatidylglycerol (PG), phosphatidylserine (PS), and phosphatidic acid (PA), etc. When R1 or R2 (Fig. 2A–F) is an H atom, the corresponding lyso-phospholipids are formed. It is clear that the phosphorus atom in each different class of phospholipids has different chemical environments, which are useful in 31P NMR for differentiating phospholipids based on different phosphate groups. Rhamnolipids are a class of glycolipids produced by microorganisms and the representative structures of mono- rhamnolipids and di-rhamnolipids are shown in

3. Nuclear magnetic resonance spectroscopy Nuclear magnetization is generated by the interaction of nuclear spins with a magnetic field. Only some isotopes, determined by the number of protons and neutrons, possess a property called ‘spin’, rendering them ‘NMR active’, including e.g. 1H, 13C, and 31P. We have prepared a special representation of the periodic table highlighting the nuclear spin properties of the different isotopes (http://periodic.pastis. 39

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B

A

O

R1 O

P

O O

O

-

R1

O O

O

N+

H

P

O

O HO

R2

R2

O

NH2

H

PE

PC D

C

O O

R1 O

O H

P

O

O HO

O

R1

OH

O

O

HO

H OH

O

HO

NH2

H

P

R2

R2

O

H

PS

PG F

E

O

O R1 O

O O

P HO

OH

OH R1

OH

HO O

O

OH

P

O

OH HO

H

O

R2

H

R2

PI

PA H

G

O

HO

O

OH O

O

O

O

O

HO

OH

O

HO HO O

O HO

O

HO

O HO O

O

3-O-a-L-rhamnopyranosyl-3hydroxydecanoyl-3-hydroxydecanoic acid

3-O-(2-O-(2E-decenoyl)-a-L-rhamnopyranosyl-(1-2)-aL-rhamnopyranosyl)-3-hydroxydecanoic acid

Fig. 2. Chemical structure of phospholipids and glycolipids. R1 and R2 are either fatty acids or hydrogen atoms.

dk). What makes NMR an important tool for chemistry, structural biology, metabolomics, and lipidomics is the fact that the immediate surroundings of the nuclei effect the nuclear spin and thereby can be exploited by NMR [17]. The most important physical effects, called nuclear spin interactions, are the shielding of the magnetic field governed by the electrons surrounding each nucleus and the couplings between nearby nuclear spins. The shielding creates a separation of the resonances from the different chemical groups in a molecule, and the couplings report on connectivities between these groups. Together this provides detailed information on molecular structure, both for pure compounds and in complex mixtures can be obtained by NMR. The information obtained can be interpreted on different levels: Without doing too any analysis of the molecular structure, the fact that each type of molecule provides a unique fingerprint in the NMR spectrum can be used for metabolite detection. More detailed analyses can provide detailed insight into the structure of a molecule, however typically requiring samples of the pure compound.

NH-COR O

(CH2)12CH3

X OH

X

Class of Sphingolipids

H

Ceramide

O P

O

N+

Sphingomyelin

OFig. 3. Chemical structure of sphingolipids and their derivatives.

40

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current 500 MHz NMR needs 20 h or longer [25]. This will enable the analysis of trace amount of lipids and related metabolites in easy and fast way. Another example from our own laboratory was a study of compounds to inhibit growth of cancer cells, which were only available in small amounts. Using a 950 MHz spectrometer with a cryogenic probe, we were able to record natural-abundance 13C NMR spectra in 15 min, whereas the use of a 400 MHz spectrometer without a cryogenic probe did not show any peaks during an overnight experiment [27]. Following the above calculation, the high-field instrument should provide ca 15 times better sensitivity translating to a factor 225 (152) in experiment time, implying that it would take more than two days to obtain the same sensitivity at the 400 MHz instrument. The sensitivity of NMR also depends on the gyromagnetic ratio, γ, of the isotope investigated. The gyromagnetic ratio defines the resonance frequency, ω0, of a nucleus at a specific magnetic field strength, B0, as ω0 = γB0. The higher gyromagnetic ratio, the higher sensitivity. To exploit this effect, socalled inverse-detection NMR experiments have been developed [28]. These techniques exploit the chemical surroundings of low-sensitivity nuclei like 13C or 15N, transfer the magnetization to neighboring 1H nuclei and detect at high sensitivity at 1H, which has the highest gyromagnetic ratio. The major drawbacks of inverse detection is that such techniques are not quantitative, as they rely on couplings between 1H and the other nucleus, which may vary across a molecule. For inverse detection of 13C, this implies, for example, that quaternary carbons like carbonyl groups are not detected, as they lack directly bonded hydrogen atoms. In LC-NMR, even we choose solvents that do not directly overlap the signals of the target compound, the dynamic range from high proportion solvents make the trace amount of components undetectable. In this case, solvent suppression in NMR will be very helpful. With the development of solvent suppression technique, it facilitates the utilization of both normal phase and reversed phase LCs [25]. This would be a very nice tool for both polar and neutral lipids analysis simultaneously. The low sensitivity of NMR can also be compensated by high loading of sample. Therefore, some of the techniques regarding giving higher detectable sample to the NMR flow cell are developed. The application of semi-micro columns in LC decreases the peak width of a compound so that all of the eluted compound can be detected in the flow-cell [25]. However, the sample loading has to be decreased to reach the same separation efficiency as normal columns. It also decreases the amount of compound in the flow-cell. LC-solid phase extraction (SPE) is another tool to obtain high concentration compounds for further NMR analyses [29]. The compounds after concentration by SPE can be either directly analyzed by NMR or stored to accumulate more for further analyses [25]. Online column trapping is another method for peak concentration for LC-NMR. The individual peak after a conventional column separation is concentrated in a trap column, and the sample is separated again by semi-micro column for NMR analyses [25]. All these techniques are available as automatic operation by software controlling [25]. The aim is to increase the sample concentration in the flow-cell of NMR. Although one of the main drawbacks, overlapping or crowded signals, may be solved by LC-NMR technique, no available lipidomics study uses LC-NMR. However, applications of the tool for natural products identification from Carthamus oxyacantha[30], phospholipidomics [31], acetaminophen metabolites in urine [32], sesame oil extracts [33], and secondary metabolites from Streptomyces violaceoruber TÜ22 [34] can be found. We will not go to detail about LC-NMR but NMR in lipidomics due to the lack of relevant published studies.

In order to determine the three-dimensional structure of a molecule by NMR, we use the fact that the dipole-dipole interaction between nearby spins is proportional to the inverse internuclear distance cubed. In the liquid state, dipole-dipole interactions are typically probed through the Nuclear Overhauser Effect (NOE) [18,19]. One appealing and intrinsic property of NMR is the fact that the signal intensity of each resonance in the NMR spectrum is directly proportional to the number of spins associated with the particular resonance. This implies that NMR is quantitative. The combination that NMR is able to identify and quantify different species in liquid mixtures makes NMR very attractive for metabolomics and lipidomics studies. In order to quantify species by NMR, certain precautions must be met [20]. In general, it is straightforward to obtain information about the relative abundances of different species in a mixture, and if the absolute concentration of a compound is targeted, internal standard of known concentration can be added. The NMR signals of the selected internal standard should not interfere the targeted ones. It should be noted that the quantification is possible only for isolated peaks without signal overlapping. Alternative methods relying only on the NMR hardware have been proposed, however requiring some more elaborate setup of the experiments [21]. Finally, NMR is sensitive to molecular dynamics on all timescales from pico-seconds to seconds [22], which has been used to provide important insight into the function of proteins, e.g. by investigation of the dynamics of protein folding, domain motions, or dynamics of binding sites. Through diffusion ordered spectroscopy (DOSY) [23], it is possible separate signals according to their diffusion coefficients, which adds chromatography-like capabilities to NMR [13]. Dynamics and diffusion will not be addressed further in this review. Focusing on lipids, NMR can be used to interrogate alterations in lipid structure and dynamics in biochemically functioning cells due to its nondestructive property. However, the suppression of lipid aggregation is crucial to obtain (more or less) high resolution spectra. The majority of NMR studies of lipids are performed subsequent to extraction of the samples with organic solvents. This procedure eliminates the interferences of other organic compounds like carbohydrates and proteins. It should be noted that lipid extraction procedures may lead to the loss of lipids, especially polar ones. Efficient extraction methods should be developed for different samples. If non-fractionated mixtures are subjected to NMR, the assignment of resonances in the –CH2/− CH3 region solely on the basis of chemical shift to lipids must be viewed with caution, as other macromolecular species such as proteins and peptides show the 1H NMR signals in the same spectral regions [24]. NMR as a detection method is in principle able to be coupled with any prior separation steps. For example, thin-layer-chromatography and flash chromatography are commonly used for purification of desired compounds and then the compounds are recovered and subjected to NMR analyses. Likewise, liquid chromatography-NMRs (LC-NMR) including high performance LC-NMR and preparative LC-NMR are nice tools for separation and subsequent structural elucidation. Hyphenated techniques connect chromatographs and spectrometers online. It is able to separate mixtures and provide spectra of the various components at the same time [25]. After separation, the individual component of a mixture flows through NMR probe, which may solve the problem of signal overlaps [25]. NMR is a comparatively insensitive spectroscopic technique and the signal-to-noise ratio is proportional to the magnetic field strength to the power of 3/2 [25] to 7/4 [26]. This implies, for example, that the sensitivity obtained at a field strength of 800 MHz (18.8 T) is ca 3 times higher than at 400 MHz (9.4 T). Therefore, the improvement of magnetic field strength is the focus in improving the sensitivity of NMR and 800 MHz LC-NMR has been available [25,26]. Sensitivity improvement of probe has also been very promising. Cryogenic probe has realized up to fourfold improvement effect on the sensitivity [25]. An 800 MHz LCNMR equipped with a cryogenic probe is able to acquire 1H NMR spectrum of approximately 1 μg sample within 30 min whereas the

3.1. 1H NMR based lipidomics Changes in lipids and lipid composition because of diseases, poison induction, or disorder, etc. may introduce observable changes in the 1H NMR signals. Accordingly, Fernando et al. [35] have employed 1H NMR 41

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Table 2 Assignments of chemical shift bins of 1H NMR to metabolites.a Chemical shift bins (ppm)

Metabolites

Chemical shift bins (ppm)

Metabolites

0.681

CH3 attached to C13 (C18H3) in cholesterol1 Cholesterol (C18)2 Cholesterol (C26, C27)2

1.61–1.623

CH2 in acyl C3 (saturated chain) or COCH2CH23

2.301 2.24–2.26, 2.27, 2.28, 2.29, 2.31–2.32, 2.333 3.67, 3.683

eCH2CO– in fatty acyl chain1 Fatty acyl chain eCOCH2–3

2.071 2.08, 2.10, 2.11–2.131 2.80–2.83, 2.84, 2.853

CH]CHeCH2– allylic1 eCH2eCH]CH in fatty acyl chain1 Diallylic = CHeCH2eCH]3

5.33–5.371 5.33–5.353 5.35 m2

0.67 s2 0.83 d, 0.85 d2 0.92–0.941

eOeCH3 in methyl ester3

4.60, 4.61–4.623 2.01–2.023 1.561

CH3 attached to C21 (C20H3) in cholesterol1 Cholesterol (C21)2 Cholesterol (C19)2 CH3 attached to C10 (C19H3) in esterified cholesterol1 C3H of esterified cholesterol3 Cholesterol acetate3 Cholesterol1

1.102

Cholesterol2

4.14–4.16, 4.173

1.84–1.871 2.26, 2.271

Cholesterol (− C]CeCH2e)1 Cholesterol C4 ⁄–CH2COOH– in fatty acyl chain1 Terminal CH3 groups in fatty acyl chain1 Terminal CH3 group in fatty acyl chain3 FA (terminal CH3; TG, Phospholipids)2 CH2 in fatty acyl chain (C4 and beyond saturated)1 eCH2– in acyl chain1 FA (CH2; TG, Phospholipids)2 Cholesterol + FA (CH2; TG, Phospholipids)2 Fatty acyl chain-COCH2CH2CH2–3 eCH2– in fatty acyl chain C4 and beyond3 CH2 in acyl C3 (saturated chain) or COCH2CH21

4.14–4.16, 4.173 5.273

CH]CH in fatty acyl chain (unsaturated fatty acid)1 CH]CH in fatty acyl chain (unsaturated fatty acyl residues)3 Fatty acyl residues (eCH]CH) in triglycerides, phospholipids and cholesterol2 Triacyglyceride-C1H2 in glycerol backbone3 Triacylglyceride-C3H2 in glycerol backbone3 Triacylglyceride-C2H in glycerol backbone3

3.731 3.64, 3.65, 3.661

eCHeOH in C2 glycerol1 OeCH3 aliphatic methyl ester1

3.96, 3.971 3.95–3.973

eCH2eOeP in PE ⁄LPE and ⁄or POCH in PS1 eCH2eOePe in PS/PI-C3H2 in glycerol phospholipid backbone3

4.08–4.123 5.22–5.233 5.20 m2

eC1H2 in glycerol phospholipid backbone3 eC2H of glycerophospholipid backbone3 Plasmalogens, Sphingolipids (− CHeC)2

4.33. 4.341 4.20 wrm2 5.401

Glycerol backbone of triglycerides (TGA)1 Triglycerides glycerol2 C2-OH in lyso-PC and/or CH]CH of acyl chain in PC1

0.89 d2 0.99 s2 1.021

0.881 0.86, 0.87, 0.91, 0.923 0.88 s2 1.26, 1.27, 1.291 1.18, 1.191 1.27 s2 1.50 m, 1.80 d, 2.00 m, 2.27 t2 1.34–1.37, 1.383 1.28–1.313 1.59, 1.601

a Samples were dissolved in CDCl3. S, singlet; d, doublet; t, triplet; m, multiplet; bs, broad singlet; bm, broad multiplet; bt, broad triplet; wrm, well-resolved multiplet. FA: fatty acyl residue. 1 [35]. 2 [48]. 3 [36].

to metabolites were given as well (summarized in Table 2). Even though some of the assignments are not consistent between the two studies, most of them agree well. Different types of lipids such as cholesterol, triglyceride, phospholipids, and double bonds can be easily identified. When the biological tissues or cells are altered by any external or internal stimulation, the 1H NMR signals proved to be a simple and easy tool to elucidate the difference among samples. Induced hypoxia stress on cervical cancer derived cells leads to significant changes in membrane lipid composition and the changes were elucidated by high resolution NMR measurements [37], wherein 1H NMR was also used for identification of the main phospholipids according to the chemical shift (Table 3). In their work, the molar ratio was quantified by the corresponding intensity of the signal at δ = 0.72 (characteristic signal from the –CH3 group) for cholesterol with respect to the intensity of the signal at δ = 3.32 for PC + SM, emphasizing the quantitative nature of 1H NMR. Most recently, 1H NMR-based lipidomic analysis of high density lipoproteins was found to be particularly useful since it provides insights into molecular features and helps in the characterization of the atheroprotective function of high density lipoproteins and in the identification of novel biomarkers of a disease state and therapy monitoring [38]. The 1H peak of the hydrogens of the C-18 methyl group in cholesterol at 0.68 ppm was used to quantify total cholesterols, while other functional groups such as alkenes, methyl group on choline, alkanes, and ω-3 fatty acyl residues, etc. could be clearly identified as well [38]. 1H NMR of lipid extracts from only HeLa cell or high density lipoproteins enables the effectivenees of the method due to the not too crowded signals in the spectra. 1H NMR can also differentiate highly

Table 3 Assignments of chemical shift bins of 1H NMR to polar lipids.a Chemical shift bins (ppm)

Phospholipids

Chemical shift bins (ppm)

Phospholipids

3.22 s1 3.30 s2

PC PC

3.20 t1 0.72 s, 1.02 s1

PE

3.96 m, 4.05 bm, 4.402 3.91–3.93, 3.943 3.883

PI Angular methyl groups in cholesterol Glycerol of phospholipids CH3 in glycerol backbone of PC CH2N+(CH3)3 in PC/ LPC

3.16 m1 2

3.20 bs

PE 1

5.96 d, 4.43 q 5.71 dt, 5.45 d1 a 1 2 3

Plasmenyl PC and/or plasmenyl PE Sphingomyelin

Dissolved in CD3OD. From [37]. From [48]. From [35].

to study the lipidomics of ethanol-induced fatty liver. Significant difference in lipid metabolome between ethanol-fed and control rats were found through cluster analysis and principal component analysis of 1H NMR data of the lipid extracts. Additionally, 1H NMR data of plasma and liver also reflected several changes. Furthermore, the level of the change is quantifiable. Later on, a dose-dependent subchronic study of hepatic lipid profiling of deer mice fed ethanol was carried out using 1H and 31P NMR [36]. Very detailed assignments of the chemical shift bins 42

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simple and accurate according to the following formula (Box 3). Compared with quantification of methyl ester, quantification of ethyl ester is more complicated because the resonance of ethoxy protons is overlapped, to some extent, with that of glyceride backbone sn-1 hydrogens. Three calculation methods (Box 4) have been compared for the purpose of quantifying fatty acid ethyl ester in transesterification system [45]. The results obtained from 1H NMR were compared with that from HPLC and the equation (Box 4) was considered to be very well. They also pointed out that the incomplete conversion of triglyceride into fatty acid ethyl ester caused strong interference because signals from monoglyceride and diglyceride overlapped. An earlier study [46] used blends of fatty acid ethyl ester and soybean oil to verify the 1H NMR method, and a linear correlation was obviously observed between ethyl ester content in oil/ester mixtures of known composition and the experimentally determined obtained with 1H NMR spectroscopy.

unsaturated fatty acyl residues from other fatty acyl residues. For example, in highly unsaturated fatty acyl residues (with unsaturated γcarbon) the protons on α and β carbon atoms relative to the carbonyl group give a signal near 2.38 ppm which is absent in olive, sunflower, and soybean oils' 1H NMR spectra [39]. This property can be applied for studying the allocation and bio-function of polyunsaturated fatty acyl residues in cells or tissues. Determination of the degree of unsaturation is also accurate by using 1H NMR [39], which makes the monitoring of unsaturated fatty acyl residues oxidation more efficient and also could be used for fast diseases diagnostics resulting from lipids unsaturation degree. However, when the sample composition is very complex, it becomes difficult to obtain the information, as the 1H NMR signals from different molecules tend to overlap. To cope with the total overlap of methylene signals at ca 1.3 ppm, the NMR technique dubbed aliphatic chain length by isotropic mixing (ALCHIM) has been developed. This technique selectively excites the Hβ-atoms and use a total correlation spectroscopy (TOCSY) technique to transfer the magnetization through the aliphatic chain to the terminal methyl group. Using this approach, Sachleben et al. [40] demonstrated that there is a linear relation between inflection point in the intensity buildup at increasing TOCSY mixing time and the number of carbons in the aliphatic chain. This technique was applied to complex natural mixtures for the identification and quantification of the constituent fatty acyl residues and represents a clever way to obtain detailed information about the aliphatic chain although its NMR signals completely overlap. The method has been applied to whole adipocytes demonstrating that it will be of great use in studies of whole tissues [40]. Admittedly, 1H NMR has some drawbacks such as signal overlapping and discrimination of resonances from complex samples. Thus, according to the published studies, we propose that an isolation and purification of lipids should be performed prior to 1H NMR. For example, run 1H NMR of phospholipids and neutral lipids individually and finally combine them to get the whole lipidomics map. The composition of unsaturated fatty acyl residues in rapeseed oil and soybean oil was determined by 1H NMR and the results were comparable with those from gas chromatography [41]. Several different calculation methods have been developed and all the results were accurate as compared with the gas chromatography data. The calculation method for 1H NMR spectroscopy is shown below (Box 1). The key idea of this calculation is that each proton in the same analysis shows the same intensity and the intensity of each specific peak corresponding either to n-3 and n-6 polyunsaturated fatty acyl residues or to monounsaturated fatty acyl residues is used for the calculation (Box 1). A new and very simple methodology was developed for triglycerides, in which no equations are necessary based on the fact that the content of each fatty acyl residue can be extracted directly from the 1H NMR spectra [43]. The fatty acid composition can be determined through the relation between the intensities of the characteristic signals of each fatty acyl chain and one of those from the glycerol backbone in the 1H NMR spectra. For example, the terminal –CH3 of linolenic acid shows unique resonance at 0.98 ppm (peak i in Fig. 4a) which can be used directly for calculating the content of linolenic acid. The peak at 2.74 ppm (peak c in Fig. 4a) is resonance from ]CHeCH2eCH] which is present in both linoleic and linolenic acids and is used for calculating the content of linoleic acid. A peak at 2.02 ppm corresponding to the resonance of ]CHeCH2eCH2 existing in linolenic, linoleic, and oleic acids is employed to calculate the content of oleic acid. The resonance of α-CH2 at 2.28 ppm is then used to calculate all other fatty acyl residues content by subtracting the content of linolenic, linoleic, and oleic acids. This method does not use mathematical formulas. However, correction coefficient is required in order to normalize the results (Box 2). Transesterification of triglyceride with methanol can also be easily quantified by 1H NMR [44]. The resonance of the methoxy and the methylene protons is completely resolved by 1H NMR as shown in Fig. 4b. Therefore, the quantification of transesterification is very

3.2.

13

C NMR based lipidomics

Due to the low natural abundance of 13C, 13C NMR is not as sensitive as 1H and 31P [10]. Therefore, a higher quantity of sample is requested when distinguishing different classes of lipid by using 13C NMR. The intake of 13C labeled substrate is another way to enrich the abundance of 13C in the lipids and their metabolites, but while this procedure is extensively used to enrich proteins for structural studies [47], to our knowledge it has not been pursued for lipidomics. In fact, not many studies have applied 13C NMR in lipidomics till now. However, some applications of 13C NMR for lipid analysis of relevance to ‘lipidomics’ are reviewed here. Tugnoli et al. [48] used 13C NMR to profile the human renal tissues. Cholesterol, fatty acyl residues, PC, and triglycerides can be clearly identified from the spectrum (Fig. 5 and Table 4). Typically, there are four clusters of resonances in a 13C NMR spectrum corresponding to different types of carbon atoms (Fig. 6). The first cluster is from 14 to 35 ppm corresponding to the terminal –CH3 and –CH2– groups in cholesterol, TAG, diglyceride, monoglyceride, phospholipids, etc. These peaks can be further assigned to individual carbons if no potential overlapping of resonances such as –CH2– in cholesterol and fatty acyl residues. For example, the 13C chemical shift of C-16 methylene of glyceride acyl chain moieties is between 31.4 and 31.9 ppm, within which the C-16 of saturated (31.86 ppm), oleate (31.84 ppm), vaccinate (31.72 ppm) and linoleate (31.45 ppm) chains can be baseline resolved [49]. Another study [50] investigated the frequency regions where the methyl (ω1) and the methylene carbons (ω2, ω3) resonate, showed that the resonances of ω1, ω2, and ω3 carbons of oleate and eicosenoate chains overlapped at 14.080, 22.693, and 31.932 ppm, respectively. It should be noted that that high field NMR helps to minimize problems with signal overlap. Meanwhile, the ω1, ω2, and ω3 carbons of oleate and eicosenoate chains each resonate as two peaks, separated by 0.008, 0.024, 0.12 ppm, respectively. The reason could be both the chains are ω9 chains, unlike the ω7 vaccenate chain whose resonances were constantly shifted toward lower frequency from the ω9 oleate chain [50]. The methylene carbons C-4 to C-7 and C-12 to C-15 of the oleate chain can be assigned according to both peak integration and measurements of T1 relaxation times, which in long chain fatty acyl residues increase regularly from the glycerol backbone up to the methyl chain end [50]. It indicates that the 13C NMR technique is a versatile tool for characterization of lipids. The second cluster is from 50 to 75 ppm where the resonance of C14, C-17, and C-3 in cholesterol, −(CH3)3 in PC, glyceryl residues in triglyceride, diglyceride, and monoglyceride, and methyl or ethyl ester carbons can be observed [48,49]. Fig. 7 shows 13C NMR spectra of substrate (Fig. 7C) and its enzymatically prepared monoglyceride (Fig. 7A, B) by enzymes. The 1(3)-monoglyceride, 2-monoglyceride, 1,2-diglyceride, and TAG are clearly resolved within the resonance of 43

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Box 1 Calculation of fatty acid composition in lipids [41].

Intensity per proton U =

1 ⎡b d (h + i) ⎤ + + 3 ⎣4 6 9 ⎦

(1) i

3 U 3

n − 3 Polyunsaturated fatty acid content (%) V =

c

n − 6 Polyunsaturated fatty acid content (%) W =

2

× 100%

− U

2i

3

(2)

× 100%

3

(3)

Monounsaturated fatty acid content (%) =

1 (a 2

− b 4) U

× 100%

3

− (2V + 3W) from olefinic protons

(4)

and e U

4

× 100% − (V + W) from allylic protons

(5)

3

where the assignments of a, b, c, d, e, f, i, and h can be found in Fig. 4a. As indicated in Fig. 4a, peak a is from resonance of –CH]CHe, peak b is from resonance of CH2 glyceride, peak c is from resonance of ]CHeCH2eCH], peak d is from resonance of α-CH2 (Fig. 4), peak e is from resonance of ]CHeCH2, peak f is from resonance of β-CH2 (Fig. 4, [42]), peak i is from resonance of terminal CH3 in n-3 polyunsaturated fatty acyl residues, and peak h is resonance from terminal CH3 of other fatty acyl residues. For peak b, there are 4 protons from CH2 glyceride, therefore, b is divided by 4. For peak d, there are 6 protons from α-CH2 (each fatty acyl residue has 2), therefore, d is divided by 6. For peak i and h, there are 9 protons from terminal –CH3, therefore they are divided by 9 in Eq. (1). Please find the example from [41] for better understanding.

triglycerides was investigated by 13C NMR and very detailed assignments for glycerol and C1 were given and shown in Table 5[54]. In general, the 13C resonance of the carbon close to the carbonyl end in triglycerides and phospholipids is influenced by esterification of the glycerol moiety, with two separate signals for the C1 and C2 carbon in the acyl chain, depending on whether the chain is present in the α- or βposition. The carbonyl signals in triglycerides appear at 173.6 and 172.1 ppm of which the higher shift is associated with the sn-1(3) chains (Table 5). Different sources of oil may have the same fatty acyl residue on sn-1 position but with a different fatty acyl residue on sn-2 position, which results in the dependent manner between 13C chemical shift and sample sources. The chemical shifts of C1 decrease with the unsaturation degree in triglycerides as the fatty acyl residue double bond is positioned closer to the carbonyl carbon. This trend is supported by other studies as well [55,56]. A very detailed assignment of the peaks is given in Fig. 10[55]. Carbonyl carbons of sn-1,3 and sn-2 acyl chains were separated by a systematic shift of about 0.4 ppm forming clusters of sn-1,3 and sn-2 peaks. Furthermore, carbonyl chemical shifts of unsaturated chains showed a marked dependency on the proximity to the first double bond, shifting to lower frequencies as the first double bond moved closer to the carbonyl center. The carbonyl carbon linked with docosahexaenoic acid (DHA) showed even lower frequencies than all the other carbonyl carbons linked with other fatty acyl residues. This is mainly because DHA is a Δ4 fatty acyl residues. It seems that the dependency of carbonyl chemical shifts of unsaturated chains on the proximity to the first double bond is more dramatic than that of sn-1,3 or sn-2 positions. The chemical shift of carbons in 13C NMR spectra of triglyceride was found to be sample concentration dependent, among which the carbonyl chemical shift was the most obvious [57]. The chemical shift of carbonyl carbon decreases with the increasing sample concentration for both sn-1,3 and sn-2 positions. An equation was also given to express the correlation between the chemical shift of carbonyl and concentration. Another study also found the similar trend for the chemical shift of carbonyl carbon in triglyceride, diglyceride, and monoglyceride [49].

68–76 ppm (Fig. 7) [51,52], which can be used for a fast determination the specificities of different lipases. Sacchi et al. [53] have summarized the 13C chemical shift of glyceryl carbons in different glycerides, from which we could also get a clear distinction of different types of glycerides. In line with this work [53], it was found out that the symmetrical 1,3-diglyceride and triglyceride give 2 signals for the glycerol moiety with an intensity ratio 1:2, the asymmetrical 1(3)-monoglyceride and 1,2-diglyceride give 3 signals with intensities in a 1:1:1 ratio [49]. The C-2 signals of glycerol resonate always downfield compared to C-1 and C-3 of glycerol backbone [49]. Methyl ester and ethyl ester display signal at 51.44 ppm and 60.13 ppm, respectively, which can be also used for example in biodiesel synthesis, flavor discovery, and pharmaceutical. The third cluster is from 120 to 135 ppm corresponding to unsaturated carbons [50]. Fig. 8 shows a 13C NMR spectrum of a standard mixture of triolein, trieicosenoin, and trivaccenin. The resonances of C9 and C-10 of oleate chain at 1(3)-positions are 129.655 and 129.959 ppm, while they are 129.629 and 129.972 ppm at 2-position. The signals of C-11 and C-12 of vaccenate chain at 1(3)-positions are 129.777 and 129.877 ppm whereas at 2-position they are129.760 and 129.885 ppm. The chemical shifts of C-11 and C-12 of eicosenoate chain are129.773 and 129.881 ppm at 1(3)-positions and are129.754 and 129.889 ppm at 2-position [50]. It indicates that the chemical shift decreases for C9 or C11 (near carbonyl group) from sn-1(3) to sn-2 while increased from C10 or C12 (near terminal –CH3) from sn-1(3) to sn-2. Except for the carbons associated with double bonds in fatty acyl residues chains, C-5 and C-6 linked by double bonds in cholesterol also give resonances at 121.70 and 140.90 ppm (Fig. 5). It indicates that this cluster mainly corresponds to carbons with double bonds. The fourth cluster is from 170 to 180 ppm where the carbonyl and carboxylic carbons resonate and it represents different glyceride acyl chains (Fig. 9). The resonances of saturated acyl chains at 173.336 ppm, vaccenate at 173.322 and 172.909 ppm, and oleate at 173.303 and 173.891 ppm of 1(3)- and 2-glycerol positions were assigned [50]. The positional distribution of ω3 fatty acyl residues in marine lipid 44

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However, the chemical shift of carbonyl carbon increased with the higher sample concentration. This implies that when one wants to record or compare the difference between two lipid samples by 13C NMR the same sample loading is important for obtaining comparable results. This also brings the message that the concentration dependence may be used for identification of some unknown compounds. Due to the low sensitivity of NMR, the spectrum is obtained by accumulating several spectra (scans) to average the noise. To obtain quantitative NMR spectra, it is essential that the nuclear spins are allowed to relax back to their equilibrium between each scan. If the system has not returned to equilibrium before a scan, the intensity will be lower in this scan. The difference is dependent upon the longitudinal relaxation time (T1), which is different for each carbon in the spectrum. In order to obtain quantitative NMR, the repetition time between scans must be long enough to ensure the complete relaxation of all nuclei. Typically, this is achieved by waiting 5 times the longest T1 (at 5 times T1 approximately 99.3% of the equilibrium value is reestablished) between two scans [58]. Furthermore, when recording quantitative 13C NMR spectra, one needs to ensure that the nuclear overhauser effect (NOE) enhancement from nearby 1H atoms is eliminated. While this effect is generally appreciable, as it may provide signal enhancements of the weak 13C signals by up to a factor of 3, the NOE enhancement depends on the distance between the 1H and 13C nuclei, and hence is by no means quantitative. To avoid the NOE enhancement, so called inverse gated decoupling may be used [59]. Inverse gated decoupling is a standard technique, in which the decoupler is only switched on during acquisition to keep the decoupling time sufficiently short to avoid NOE buildup. Longitudinal relaxation times (T1) of sn-1,3 and sn-2 carbonyl peaks of eicosapentaenoic acid (EPA) and DHA were determined for a sample of natural anchovy/sardine 2009TAG fish oil (the first two digits refer to the content of EPA and the last two digits refer to the content of DHA, both in gas chromatography (GC) peak area percentages). These measurements revealed that for EPA, the sn-2 carbonyl displayed longer T1 (2.835 s) than the sn-1,3 carbonyl (2.437 s) by a factor of almost 16%. The relaxation times of sn-1,3 and sn-2 DHA carbonyls differed only by 2% (3.231 and 3.303 s, respectively) but were substantially longer than for EPA. These values suggested that the proper repetition time for simultaneous quantification of EPA and DHA carbonyls should be set to 16.5 s, corresponding to five times the longest T1 (3.303 s) [55]. Such studies emphasize the importance of measuring T1 before using NMR for quantification. Reproducibility of 13C NMR quantification has been tested [60]. The tiny difference of 1–2 mol% in the mean values for the major fatty acid composition was reproducibly observed from both NMR spectroscopy and gas chromatography of sample oils collected from different geographic parts of Greece [60]. The differences observed in this study were thought to be reasonable as attributed to 1) GC needs chemical derivation prior to analyses; 2) difficulty in NMR is encountered with the peak which appears between the saturated fatty

Fig. 4. Assignment of peaks of typical triglyceride, free fatty acid, and fatty acid methyl esters of 1H NMR. Reproduced from [42] with permission.

Box 2 Calculation of fatty acid composition in triglycerides.

Fatty acyl residues (ester) Linolenic Linoleic Oleic Saturated a b c d e

Chemical shift (ppm) (Fig. 4a) i, 0.98 c, 2.74 e, 2.02 d, 2.28

Relative intensity of the glyceride peak

Subtraction

4a 6b 3d 6e

– 2 × [linolenic]c [linolenic] + [linoleic] [linolenic] + [linoleic] + [oleic acid]

Two glycerol hydrogens [C1 (3)] to nine methylic linolenic acid hydrogens. Two α glycerol hydrogens [C1 (3)] to six possible methylene hydrogens between olefins from the linoleic and linolenic. Linolenic acid contains two times more methylene hydrogens between olefins than linoleic acid. Two α glycerol hydrogens [C1 (3)] to twelve possible α olefin hydrogens. Two α glycerol hydrogens [C1 (3)] to six α carbonyl hydrogens.

45

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Box 3 Calculation of fatty acid methyl ester in triglyceride. Fatty acid methyl ester (%) = (2A1/3A2) × 100, where A1 and A2 are the areas of the methoxy and the methylene protons. Specifically, A1 and A2 indicate the peak area at 3.7 ppm and 2.3 ppm, respectively (Fig. 4b)

Box 4 Different calculation methods of fatty acid ethyl ester in triglyceride.

(I − ITAG ) ⎤ Y (%) = ⎡ TAG + EE × 100 ⎢ ⎥ I (1) αCH2 ⎣ ⎦ where ITAG + EE denotes the signal area of the quartet in the range of 4.05–4.25 ppm corresponding to the two hydrogens of the –OeCH2eCH3 group, which is only present in the ethyl ester products and in unreacted triglycerides. ITAG denotes the area of the double doublet signals in the range of 4.25–4.4 ppm, which are attributed to the glycerol –CH2 hydrogens in the triglycerides (TAG). IαCH2 is the area of the signal corresponding to the two carbonyl α hydrogens (− CH2eCOOR). ITAG + EE is the signal area of the hydrogens in the –OeCH2eCH3 group, which is only present in the ethyl ester products and in unreacted TAGs

assignments here are the same as those mentioned in 1D 1H NMR. However, much more information could be gained here because the signals of 1H are dispersed further according to 13C resonances. Based on the resonances of 1H and 13C, individual molecules can be accurately identified. Table 8 summarizes some representative overlapped 1H resonances but with totally different 13C resonances, clearly indicating the advantage of the 2D HSQC NMR. This method proved to be effective in lipidomics studies of adaptive changes, gene function, new mycobacterial species, and virulence factors. This method was also used for phospholipid profiling of thermophilic Geobacillus sp. strain GWE1 isolated from sterilization oven [61]. The assignments of 1H and 13C on sn-1 of glycerol backbone and –CH2CH2NH2 on head group can be further confirmed by the 2D NMR. The 2D HSQC NMR is not quantitative as the coherence transfer between 1H and 13C relies on the assumption that the one-bond scalar (1JHC) coupling has a certain value, but in practice it may fluctuate. Markley and co-workers developed a pulse sequence (HSQC0) that compensates the problem and provides quantitative information [62]. Multidimensional NMR techniques give us an unique opportunity to perform lipidomics qualitatively and quantitatively, minimal sample manipulation, and relatively short experiment time, but the full potential has yet to be explored.

acyl residues and O1α [60]. The conclusion was that NMR-based quantification is as accurate as GC methods and repeatable since samples from the same area gave repeatably the same results within a 3 year period [60].

3.3. 1H, 13C heteronuclear single quantum coherence (HSQC) for lipidomics As mentioned in 1H NMR and 13C NMR sections, the main drawback of H NMR and 13C NMR is the overlapped signals which make the assignments very difficult. Therefore, multidimensional NMR experiments are commonly used for signal assignment in various fields of NMR, as they provide better signal dispersion. Specifically, 1H-13C HSQC NMR has been applied for lipid profiling. This method provides a two-dimensional NMR spectrum which correlates the 13C resonances to the directly bonded 1H atoms. In this way, the experiment can disperse signals into two dimensions, providing an information-rich 2D lipid profile map in which key unique biomarker peaks can be resolved and used to diagnose and quantitate the presence of certain lipid species [10]. As shown in Fig. 16, signals in the HSQC spectrum are well dispersed and may be classified into three main regions: 1) the most upfield region (δ(1H) = 0.5–3.0 ppm), representing the aliphatic chain of the lipids; 2) the far downfield region (δ(1H) = 5.2–8.5 ppm), representing unsaturated and aromatic substructures; and 3) the middle region (δ(1H) = 3.2–5.4 ppm), representing sugars in glycolipids. The 1

Fig. 5. The 13C NMR spectrum of the total lipid fraction of a normal kidney. Reproduced from [48] with permission.

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Table 4 Assignments of chemical shift bins of

13

C NMR to lipids.

Chemical shift bins (ppm)

Compounds

Chemical shift bins (ppm)

Compounds

11.90a 14.15a 18.76a 19.43a 21.13a 22.61a 22.74a 22.86a 23.90a 24.33a 24.92a 25.69a 27.27a 28.06a 28.28a 29.17–29.78a 31.56a 31.73a

C-18, cholesterol Fatty acid/acyl terminal –CH3 C-21, cholesterol C-19, cholesterol C-11, cholesterol C-26, cholesterol C-17, fatty acid/acyl C-27, cholesterol C-23, cholesterol C-15, cholesterol C-3, fatty acid/acyl C-11, fatty acid/acyl C-8, fatty acid/acyl C-25, cholesterol C-12, cholesterol -CH2-, fatty acid/acyl C-16, fatty acid/acyl C-2, cholesterol

31.97a 35.84a 36.25a 36.56a 37.52a 39.57a 39.83a 42.37a 50.20a 54.60a 56.23a 56.82a 62.15a 68.95a 71.80a 121.70a 127.4–130.42a,b 172.87–173.54a,b

C-7, C-8, cholesterol C-20, cholesterol C-22, cholesterol C-10, cholesterol C-1, cholesterol C-24, cholesterol C-16, cholesterol C-4, C-13, cholesterol C-9, cholesterol (−CH3)3, PC C-17, cholesterol C-14, cholesterol C-1, C-3, glyceryl residues C-2, glyceryl residues C-3, cholesterol C-6, cholesterol Fatty acid/acyl, unsaturation Fatty acid ester carbonyls

a b

[48]. [60].

3.4.

31

As explained for 13C, to obtain quantitative NMR spectra, it is important to apply a sufficiently long repetition delay between each scan. Since the phosphorus T1 relaxation times of phospholipids are in the range of about 1.5–3.3 s (Table 6 and [64]), repetition delays of 15–20 s (5 × T1) are typically required [63]. It has been suggested that since all 31 P nuclei in phospholipids possess comparable T1 relaxation times, all intensities are affected to the same extent by shortening the repetition time [63]. It is important to use the same experimental setup (repetition time, solvent system, temperature, etc) to obtain reproducible spectra, as the 31P nuclear spin interactions are highly dependent on variations in the chemical surroundings. Additionally, unlike 13C NMR and 1H NMR, solvent mixture in 31P NMR is much more critical for obtaining a well resolved spectrum. As can be seen in Fig. 12, CDCl3 alone is absolutely not suitable for quantification of phospholipids while a mixture of CDCl3, MeOD, and D2O-EDTA gives a well resolved spectrum. It should be noted that phospholipids contain polar headgroup and nonpolar fatty acyl

P NMR for lipidomics

31 P has 100% natural abundance, while 1H and 13C have natural abundancies of 99.86% and 1.11%, respectively. The relative sensitivity of 31P is 377 relative to 13C, while that of 1H is 5680. The high sensitivity and good chemical shift dispersion of phosphorus are key elements of successful 31P NMR [63]. It may be interesting to record 31P spectra without 1H decoupling, as couplings from 1H to 31P in the eCHxeOePe moiety of phospholipids are typically in the amenable range 4JHP ≈ 0–10 Hz (4JHP is a short notation for a J coupling between 1H and 31P separated by four covalent bonds). As shown in Fig. 11, 31P NMR spectrum without proton decoupling provides multiplets for each resonance, which may potentially report on the number of nearby hydrogens. However, the spectrum is more complicated and the resonances are resolved poorly (Fig. 11a) while the proton decoupled spectrum displays highly resolved resonances (Fig. 11b).

Fig. 6.

47

13

C NMR spectrum of sunflower oil and fatty acid ester.

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Fig. 8. Olefinic carbon region of the 500 MHz 13C NMR spectrum of a standard mixture of triolein, trieicosenoin, and trivaccenin triglycerides. The resonances of C-9 and C-10 of oleate chain at 1(3)-positions (129.655 and 129.959 ppm), and 2-position (129.628 and 129.973 ppm) are indicated. The signals of C-11 and C-12 of vaccenate chain at 1(3)positions (129.777 and 129.877 ppm) and 2-position (129.759 and 129.885 ppm), and of eicosenoate chain at 1(3)-positions (129.771 and 129.881 ppm) and 2-position (129.754 and 129.889 ppm), are shown. Reproduced from [50] with permission.

Fig. 7. Monoglycerides produced by A-lipase from Candida antarctica (upper) and Lipozyme® TL IM (middle), and substrate (bottom). Reproduced from [91] with permission.

residues. Thus the phospholipids form bilayers in an aqueous environment and ‘inverse’ micelles in an organic solvent that are both characterized by considerable line broadening [65]. When a solvent mixture contains nonpolar and polar solvents, stable micelles are formed. To ease the experiment setup for 31P NMR of unknown phospholipid samples, we have in Table 7 listed the repetition times, solvent systems, and sample temperatures used in a number of articles. As can be seen, not all of the studies reported the parameters used, potentially because of unawareness of their importance. Apart from high resolution (liquidstate) 31P NMR, solid-state 31P NMR is able to give structural information about the sample phase and morphology [66], which, however, is not the focus of the current review.

Fig. 9. Carbonyl carbon region of the 500 MHz 13C NMR spectrum of Moringa oleifera oil. The resonances of saturated (173.336 ppm), vaccenate (173.322–172.909 ppm), and oleate (173.303–172.891 ppm) chains esterified at 1(3)- and 2-glycerol positions, respectively, are indicated. Reproduced from [50] with permission.

detergents, which enable quantitative analysis of the phospholipids in a complex mixture, even at the relatively low magnetic field strength. The authors attributed the narrow linewidths to the efficient averaging of chemical shift anisotropy and dipolar interactions in small micelles. HDL (high density lipoprotein) and LDL (low density lipoprotein) were analyzed by 31P NMR using the detergent (sodium cholate) based solvent and well resolved spectra were obtained with clear observation of

3.4.1. Detergent-based solvents Detergents including potassium cholate, deoxycholate, SDS, and Triton X-100 were used for 31P NMR studies of phospholipids [67]. Sharp resonances were observed in all solvents with different 48

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Detergents, structurally like phospholipids, contain one polar and one nonpolar moiety and form vesicles when they are dissolved in water. Very small micelles consisting of only few molecules yield highly resolved NMR spectra. Phospholipids then enter these micelles at an appropriate lipid/detergent ratio and thus the formation of the large phospholipids vesicles in water is suppressed, resulting in high resolution. The aggregation behavior of the detergent applied also strongly influences the spectral resolution [63]. As summarized in Ref. [63], the aggregation number of cholic acid is the smallest than that of the other detergents, which leads to enhanced resolution. The most serious drawback of using detergents is that samples are exclusively used for 31 P NMR analysis because the intense detergent resonances will cover the resonances of phospholipids in 1H and 13C NMR [63]. Additionally, recovery of phospholipids for further experiments is virtually impossible after mixing with detergents [70].

Table 5 13 C chemical shift (ppm) and assignments for glycerol, C1 and C2 carbon atoms in ω3 fatty acyl residues enriched samples [54].

Glycerol

C1

C2

Atlantic salmon

Cod liver oil

Harp seal oil

Assignment

69.11 69.02 68.96 68.60 62.26 62.18 62.09 62.03 173.52 – 173.23 173.16 173.12 173.07 172.98 172.95 172.84 172.81 – 172.68 172.63 172.59 172.51 172.48 172.11 34.83 34.20 – 34.11 34.04 – 33.73 33.57 – 33.39

69.08 68.99 68.94 68.86 62.20 62.12 62.03 61.96 – – 173.15 173.06 173.00 172.88 172.83 – 172.69 172.67 172.66 – 172.50 172.45 172.36 – 171.98 – 34.14 – 33.96 – – – 33.49 – 33.31

69.11 69.03 – 68.89 62.26 62.19 62.10 – – 173.25 173.23 173.19 173.15 173.06 173.00 – 172.86 172.82 172.78 172.76 172.66 172.60 172.53 – 172.13 – 34.21 34.20 34.05 34.01 33.97 – 33.58 33.44 33.40

22:6 β 20:5 β β β 22:6 α α α Unknown α α 20:4 n-3 α 22:5 α 18:4 α 20:5 α 20:5 α β β 20:4 n-3 β 22:5 β 18:4 β 20:5 β 22:6 α 22:6 α 22:6 β Unknown β

3.4.2. CDCl3, MeOH, and EDTA or CDTA salt based solvent Beside the aqueous detergent solvent system, another solvent is the mixture of CDCl3 and MeOH. Egg phospholipids were analyzed by 31P NMR using CDCl3:MeOH (2:1) as solvent and the resultant spectrum could completely resolve triethyl phosphate, PC, Lyso-PC, SM, PE, cardiolipin, Lyso-PE, and PA [71]. Under this condition, a spectrum obtained at 40 °C showed better resolution of PE and PI compared with that obtained at 25 °C. The standard derivation of chemical shifts less than 1% was observed at both temperatures for the reproducibility study [71]. They also found that addition of acid made the overlapping more pronounced while addition of base like trimethylamine gave sharper signals but signal overlapping and sample hydrolysis restricted its use. PC and SM are weak bases, while PE, PS, and PA are weak acids, making them very sensitive to changes of pH in their chemical environment. The proportions of phospholipids also affected the chemical shifts, suggesting identical proportions of phospholipids should be present for obtaining comparable chemical shifts. In another study, CHCl3:MeOH (2:1) was used as solvent and major phospholipids of brain membranes, namely, PC, PE, PS, PI, SM, and Lyso-PE could be clearly resolved by 31P NMR [72]. This solvent system was also employed to analyze phospholipids in profiling lipid of deer mice fed ethanol [35,36,73]. From these reports, it seems that this solvent system is sufficient to identify many phospholipid species. However, in some cases, this solvent system is not a good candidate for high resolution. For example, clear differences can be observed from Fig. 13 that the CHCl3:MeOH (2:1) solvent system is of lower resolution than CHCl3:MeOH:Cs-EDTA (0.2 M in D2O, 2:1:0.25) solvent [74]. CHCl3:MeOH:Cs-EDTA (or Cs-CDTA) solvent system is the most widely used in 31P NMR analysis for phospholipids quantification [69,75–79]. The addition of chelating agent (CDTA or EDTA) to extracts serves to “mask” divalent and, to a lesser degree, trivalent metal cations via the formation of stable cation-CDTA chelates. Unmasked paramagnetic cations would broaden phospholipids 31P resonances, thus worsening phospholipids signal resolution. CDTA is to be preferred over the more commonly used EDTA because stability constants of binary CDTA complexes are higher when compared to EDTA complexes [75]. Although these reports used CHCl3:MeOH:Cs-EDTA (or Cs-CDTA), the relative proportions of these solvent vary significantly. Consequently, some of the solvent mixtures form two phases, while others form one single phase when the solvents are mixed. Although the formed two phases, one being predominantly nonpolar (CHCl3 and MeOH) and the other predominantly polar (MeOH and water), could provide good signal resolution, they may be a source of error because the volume of nonpolar phase after separation cannot be measured accurately [69]. Therefore, one-phase solvent system (CDCl3:MeOH:CsCDTA (aqueous), 5:4:1) was developed and systematically investigated. The effects of temperature, lipid concentration, CDTA concentration, and pH value on the chemical shift, signal separation, line width, and spectral resolution were discussed [69,75]. In general, the chemical shift was increased with the increasing temperature for PC, alkyl-acyl-

α 22:6 α Unknown 20:5 β Unknown 20:5 α

α: sn-1; β: sn-2. C1 represents the carbonyl carbons and C2 indicates the methylene carbons. Please refer to Fig. 1 for α, β, C1, and C2.

PC, PI, PS, PE, and SM. Dairy phospholipids were analyzed as well by using the detergent based solvent and very well resolved spectra were obtained in which PC, PI, PS, 2LPC, PE, SM, DHSM, and 2LPE can be quantified [68]. The detergent based solvent system is versatile for different sources of sample. Chemical shifts of phospholipids species are different in different detergents. Hydrogen bonding, dielectric contact, or ring current shifts in the vicinity of the phosphorus atom are the essential effects, and even the concentration of detergent or of phospholipid may also introduce chemical shift fluctuations [67]. It underlines the importance of performing all analysis under the same conditions for comparison purpose. It was also found that the 31P chemical shift was pH dependent, which was suggested to be used in determining the pKa of an ionizable group on a phospholipid [67]. Additionally, this property can be used for resolving two identical resonances by changing the pH, which was thought to be an important advantage of the detergent based solvents over organic solvent [67]. Two different pH values, 7.1 and 9.5, were used in the detergent based solvents for 31P NMR analysis and the results from 31P NMR were compared with that from 2D–TLC [68]. The content of PE obtained at pH 7.1 showed greatest difference between NMR and TLC, which was likely because of the partial overlap of the PE and SM peaks. When the pH was adjusted to 9.5, a well resolved PE peak was given. pH affects the phospholipids 31P NMR chemical shift via three different mechanisms: (1) directly, via protonation of the PL phosphate moiety; (2) indirectly, via protonation of compounds interacting with ions that potentially complex with the phospholipids phosphate moiety. In addition, (3) indirect effects of pH on the structure of micelle-like aggregations cannot be excluded [69]. 49

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Fig. 10. Carbonyl region of the broad band decoupled 13C NMR spectrum of: a, mixture of eight homo-TAG standards; b, natural anchovy/sardine fish oil 2009TAG; c, reconstituted 2009TAG obtained upon glycerolysis of 2009FFA. The assignment of sn-1,3 and sn-2 regioisomeric peaks to individual fatty acids is annotated. Reproduced from [55] with permission.

was verified by using PC [80] and this method was successfully used for quantification of phospholipids species in rapeseed originated lecithins [81]. Although the reports systematically investigated the effects of different factors on the final results [69,75], some peaks' assignments are neither sure nor carried out. This problem always confuses people when only the 1D 31P NMR is performed. Two dimensional 31P, 1H NMR spectroscopy is then a more powerful tool for tackling the problem of assignments [14,82–84]. The 2D 31P, 1H NMR provides not only improved 31P resolution but also a good sensitivity. The gain in sensitivity is achieved by correlating the headgroup phosphorus nucleus with its proton environment separated by up to three bonds. Both the glycerol backbone and the side chain of the polar headgroup protons generated different 31P-1H cross peaks because of their unequal chemical environment [14]. Thus, the identification of phospholipids in the 2D 31P, 1 H NMR spectra was based on both 31P and 1H chemical shifts along with the characteristic 31P, 1H couplings via three bonds visible in the 1 H dimension (Fig. 14) [14]. Most recently, a fully automatic method, autoP, for identification and quantification of lipids in complex lipid mixtures from 1D 31P and 2D 1H-31P NMR spectra was developed [82]. In their study, a peak-picking algorithm was developed that

PC, PC plasmalogen, PI, PI diphosphate, PS, lyso-PC, PG, alkyl-acyl-PE, and SM, but with either different slopes or different manners. The chemical shift of PC decreased moderately as the extract concentration increased. However, the dependence of chemical shift on extract concentration was less pronounced as the sample temperature decreased and the CDTA concentration increased. The PC chemical shift decreased moderately and almost linearly with the logarithm of CDTA concentration at pH 7.4, for all extract concentrations and temperatures. The pH dependence of the chemical shifts of the PC analogs and SM essentially mirrored that of PC, but PIP2 exhibited virtually no pH dependence. It is a complicated system and there are some anomalies among all general regulations. The works [69,75] also suggest that the optimal conditions for 31P NMR phospholipids spectra may change greatly for samples of different sources. More comprehensive optimization tests may be needed for different purposes and samples. Additionally, the optimization is also decided by the information needed. For instance, if only the species of phospholipids rather than precise absolute quantities are needed, higher concentrations and less acquisition time are sufficient. If a specific phospholipid species is needed, one should focus on the optimization of the best signal separation for the relevant spectral regions. The accuracy and precision of this method 50

T1 (s) NOE (%) Reference

PC LPC Sphingomyelin PE LPE PA 3.3a; 2.2b 2.9b 1.8b 3.2a; 1.8b 2.1b 3.3a 60 – – 60 – 50 [67,71] [71] [71] [67][71] [71] [67]

−: not specified. a From [67]. b From [71].

Fig. 12. 242.94 MHz 31P NMR spectra of a coarse extract of bovine brain. (b) was recorded in pure CDCl3, whereas a mixture of CDCl3, CD3OD and D2O-EDTA (125:8:1 v/v/ v) was used in (a). The differences in chemical shifts are caused by differences in solvent polarity. The spectra are referenced to external 85% H3PO4. Reproduced from [63] with permission.

51 [65] [68] [71] [72] [35,36,73] [74,110]

3 3.5 10 2.1 – 1.52 9

30 °C 25 °C and 40 °C – 25 °C – −34 °C or 38 °C

1 or 12 30 11.2 18.165

25 °C 27 °C 29 °C 5–25 °C

[78] [69,75]

[76]

[112]

EDTA: Ethylenediaminetetraacetic acid. D2O: Deuterium oxide. CDCl3: Chloroform-d. MeOH: methanol. CHCl3: Chloroform. Cs: Cesium. Cr(acac)3: Chromium(III) acetylacetonate. SDS: Sodium dodecyl sulfate. HDL: High density lipoprotein. LDL: Low density lipoprotein. a For regular 1D 31P NMR spectra, we here report the sum of the acquisition time and relaxation delay, to reflect the full time available for the spins to relax.

Phospholipids in soybean Lipids in brains from Lewis rats

Purple membrane of halobacteria

Rat brain

2.0 ml chloroform solution of lipids + 1.08 ml of mixture of methanol-0.2 M Cs-EDTA (aq) 4:1 (v/v) + 0.40 ml of 87 mM Cr(acac)3 in CDCl3 + 4% (v/v) TMS. After shaking, the lower phase contains approximately 10.5 mM Cr(acac)3, and a ratio of CHCl3-MeOH-water (51.1:34.5:14.4) The lipids extracts were prewashed with K-EDTA. CDCl3-MeOH-0.2 M K-EDTA (pH 6.0, aqueous) or Cs-EDTA (pH 6.0, aqueous) 100.0:29.9:5.2 (v/v/v) was used solvent for 31P NMR H2O containing 3% fully deuterated-SDS with addition of EDTA or in CHCl3-MeOH-H2O (10:4:1, v/v/v) with Cs-EDTA addition CDCl3-MeOH-Cs-EDTA (0.2 M, pH 8.5) 1:1:1 (v/v/v), after vigorous shaking, lower phase was analyzed CDCl3-MeOH-Cs-CDTA (aqueous) 5:4:1

[111]

[67]

– Temperature 40 °C

5% w/v detergents, 10–125 mM EDTA and 25–75% D2O in a total volume of 0.75–1 ml. 15 s sonication and warming to 60 °C to accelerate solubilization DO2 containing 200 mM sodium cholate and 5 mM EDTA Sodium cholate-EDTA- D2O 10:1:20:80, pH 7.1 100 mg phospholipids dissolved in 3 ml of CDCl3-MeOH 2:1 (v/v) containing the internal reference (triethyl phosphate) CHCl3-MeOH (2:1) CDCl3-MeOH (2:1) CHCl3-benzene (d6)-MeOH-aqueous Cs-EDTA (0.2 M, pH 6.0) (1.9:0.1:0.8:0.2)

Standard phospholipids and sarcoplasmic reticulum phospholipids Human HDL and LDL Dairy phospholipids Rat liver Human brain Rat lipid Esophagus, distal esophageal tumor, and normal stomach Rat brain and liver phospholipids

Reference

Repetition time (s)a Sample temperature

Table 6 Summary of T1 and NOE of different species of phospholipids.

Sample preparation/solvent system

P NMR analyses of phospholipids.

Fig. 11. 31P NMR spectra (121.6 MHz) of phospholipids. a. 0.84 g liver without proton decoupling; b. 0.84 g liver with proton decoupling; c. 0.70 g brain with proton decoupling. Bu3PO4 was added as standard. AAPC, β-acyl-γ-O-alkylphosphatidylcholine; Bu3PO4, tri-n-butyl phosphate. Reproduced from [111] with permission.

Sample

31

Species

Table 7 Summary of solvent systems, sample temperatures, and repetition delays used for

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Table 8 δ1H and δ13C shifts for selected signals of mycobacterial lipids. Lipid molecule

δ1H

δ13C

Lipid molecule

δ1H

δ13C

Triglyceride, C2 of glycerolb Acyl group, CH]CHb PI, mannose, I-1a,b

5.24

69

Acylated trehalose, C1b

5.05

94.3

5.32

130.1

5.05

120

5.22

100.6

5.04

131.2

Glycerol backbone, C1c

4.47, 4.22

63.78

Menaquinone, isoprene side chain Hbb Mycolactone, macrolide ringb Head group, − CH2CH2NH2c

4.07

63.08

a Roman numerals and Arabic numerals correspond to mannose residues and the positions of carbon atoms in the pyranose ring, respectively, as described by [113]. b [10]. c [61].

Fig. 14. Two-dimensional semiconstant time 31P, 1H COSY NMR spectrum of phospholipids (PL) in cheese fat (polar liquid–liquid extraction fraction p-pp) and the corresponding 1D 31P NMR spectrum (right). Backbone and headgroup protons giving rise to cross peaks shown in a PC structure are labeled. For highly abundant PLs, weak cross peaks due to four-bond couplings can sometimes be observed (not shown). PEe, alkyl ether-linked phosphatidylethanolamine; DHSM, dihydrosphingomyelin. Reproduced from [14] with permission.

methanol/chloroform (2:1) or methanol/chloroform (1:2) was used to extract lipids from serum, plasma, or blood platelets for further 1H NMR analysis [86–88]. Unusual lipids that are caused by defects in lipid metabolism accumulate in blood and tissues. It is essential to identify these unusual lipids for correct diagnosis [87]. 1H NMR was used to simultaneously identify and quantify lipids in the blood of patients with different inborn errors of lipid metabolism [87]. Four different inborn errors of lipid metabolism were identified and quantified. Twenty-five lipid-derived resonances with 9 and 14 distinct molecular species could be assigned in the 1D spectra of healthy controls and metabolic diseases, respectively. A nonvolatile internal standard, octamethylcyclotetrasiloxane, was used as chemical shift and concentration reference for quantification of lipid species. Good correlations with conventional methods for total cholesterol and triglyceride concentrations were obtained [87]. The 1H NMR method is applicable in clinical diagnosis especially for inborn errors of lipid metabolism. Lipid metabolites in a rat hepatocarcinogenesis model were investigated by 1H NMR after extraction [89]. The trimethyl protons of choline compounds (around 3.3 ppm) serves to quantify total choline, the vinyl (5.3 ppm) and bisallyl proton (2.8 ppm) resonances serves to quantify fatty acyl residues concentrations and to probe the number of double bonds in a fatty acyl moiety, and the methylene protons of the glycerol backbone (4.13–4.42 ppm) in PLs were used to quantify the total PLs. The platelet lipid profiles of patients suffering from coronary artery disease (CAD) and healthy volunteers were analyzed by 1H NMR to explore the possible link between platelet lipid and CAD [88]. It was found that cholesterol increased by 16.5 ± 5.5% along with 15.7 ± 2.5% and 4.7 ± 4.0% of total diacylglycerophospholipids and ethanolamine-containing phospholipids reduction, respectively, in samples with CAD. The unsaturation and linoleate content of fatty acyl chains were increased by 0.2 ± 0.1% and 1.9 ± 0.5%, respectively [88]. These trends can be served as indicators to predate the development CAD. However, another study criticized that 1H NMR analysis of plasma is a weak predictor of CAD [90]. Predictions for normal coronary arteries and CAD groups were only 80.3% correct for patients not treated with statins and 61.3% for treated patients, compared with random correct predictions of 50%. However, they attribute the weak prediction to the influences of many other variables including gender and drug treatment on the lipid composition [90]. It implies that the weak prediction is not from the analytical technique but the weak

Fig. 13. 31P NMR spectra of a crude lipid preparation from the earth worm, Lumbricw tmestris. Top, the crude lipid in chloroform-methanol 2:1; bottom, the crude lipid in the Cs-containing analytical reagent (CHCl3:MeOH:Cs-EDTA (0.2 M in D2O), 2:1:0.25). Reproduced from [74] with permission.

reproducibly identifies all signals above the noise level and groups them according to their 31P chemical shift. Optimization of peak-picking was performed for accurate and tolerant signals grouping. The assignments are highlighted in Fig. 15 as bands through the different 31P traces, with the width of the band indicating the tolerance. This method can automatically and unambiguously identify and quantify approximately 20 different lipids. This automatic method can definitely serve as a very powerful tool in lipidomics specifically phospholipidomics.

4. NMR-based lipidomics for disease diagnostics Early diagnostics of diseases is a central theme in health science, and hence there is an ongoing search for reliable markers for the early onset of various diseases. Promising procedures include metabolomicsbased methods employing information-rich analytical techniques such as NMR spectroscopy and MS [85]. For metabolomics applications of 1H NMR for hydrophilic metabolites, water suppression in the one-pulse experiment depends more critically on good shimming [85]. However, when lipidomics is of interest, the prior extraction procedure would eliminate the broad signal of water in 1H NMR spectra. For example, 52

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studied by 13C NMR [92,93]. Fatty acid saturation degree, cholesteryl ester, phospholipid, and triglycerides contents were quantified based on the ratio of unique carbon signals of each molecule [92,93]. Correlation was found between the decreased polyunsaturated fatty acyl chains and cholesteryl esters with increasing obstruction [92]. Phospholipidomics by using 31P NMR has been employed to demonstrate alternations in brain membrane phospholipid metabolite levels in Alzheimer's disease (AD) [94]. Folch extraction [95] was used to obtain the phospholipids samples from brain tissues, which eliminated the interference of the other phosphorus containing hydrophilic metabolites. The peaks of main phospholipid species including PC, PI, PS, PE, PA, PE-plasmalogen, sphingomyelin, alkylacylphosphatidylcholine, and diphosphatidyl glycerol were clearly observed without overlapping. The relative intensities of each species were quantified by integration of the individual resonance peak. It was found that the AD brain levels of PE and PI were reduced with increase of sphingomyelin and PE-plasmalogen. Pearce et al. [96] used 31P NMR to determine the phospholipid composition of postmortem schizophrenic brain. The solvent system composed of CDCl3eCH3OHeH2O (10:4:2) and 0.2 M Cs-EDTA at pH 6.0 was used to realize the baseline resolution of phospholipids including PC, PI, PS, PE, LPE, alkyl, acyl-PE, PEplasmalogen, sphingomyelin, and alkyl, acyl-PC. Quantification was carried out using the relative area of each peak to the summed areas of all PLs. The total content of PE headgroups for schizophrenics was significantly lower than that for controls or psychiatric controls in the frontal cortex. However, no significant difference was found for any individual PL among the three groups. These two examples show the general procedures to use 31P NMR for lipidomics studies. PLs need to be extracted from the biological matrixes by Folch method [95] or cold hexane:isopropanol (8:2) solvent system, followed by evaporation and redissolving in appropriate solvent for NMR analysis. Both relative and absolute contents may be deduced from this data. Another advantage is that 31P NMR could distinguish LPCs according to the fatty acyl chain position at either sn-1 or sn-2 position (Fig. 15), suggesting it can be used for detection of phospholipase type such as phospholipase A1, A2, and B due to the specificities of the individual phospholipase [97]. It should be noted that an acyl migration may occur in protic solvents; particularly in the presence of a detergent. It can also be used for detecting phospholipase C and D that cleave phospholipids just before the phosphate group and the polar head group, respectively. It has been found that the defect of phospholipases associates with different diseases [98] like coronary heart disease [99] and AD [100]. 31P NMRbased phospholipidomics can be therefore a good tool to diagnose relevant diseases.

Fig. 15. Contour plot of a 2D 1He31P TOCSY spectrum of a lipid mixture with assignment of the different signals. The colored bands represent the lipids identified during the peak picking, and the individual peaks are highlighted by magenta dots. The width of the colored bands illustrates that the peak positions may fluctuate in the 31P dimension because of their imperfect lineshapes or overlap with neighboring peaks. Both the chemical shifts and the intensity distribution in the 1H dimension are important observables for a reliable peak-picking algorithm. The band labeled UI (unidentified) represents signals that do not match any entries in the database. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.) Reproduced from [82] with permission.

correlation between lipid composition and CAD. It also suggests that precise lipid-biomarkers should be explored for a specific disease before lipidomics can be used as accurate diagnostic approach. 13 C NMR could bring complementary information for 1H NMR for lipids-related disease diagnosis. Especially, glycerol backbone carbons can be clearly resonated to know the composition of glyceride and partial glyceride. Meanwhile, although 13C NMR measurements are quite insensitive because they are performed as “direct” measurements, it can distinguish carboxylic-acid groups from other carbonyls. These advantages were widely applied for lipase specificity studies [51,52,91], which can be potentially used for detecting defects of lipid metabolisms caused by lipases and further track to genomics. In addition, the relation between fatty acyl residue saturation, cholesteryl ester content and luminal obstruction in human atherosclerotic lesions was

Fig. 16. 1H, 13C heteronuclear single quantum coherence (HSQC) lipid profile of M. tuberculosis H37Rv. 1H, 13C HSQC spectrum of the 2:1 (v/v) CDCl3/CD3OD extract of M. tuberculosis H37Rv. Reproduced from [10] with permission.

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fast and nondestructive method for lipidomics investigation. 1H NMR, because of its narrow chemical shift range, complex lipid sample results in overlapped signals and further makes the identification a little difficult. However, when lipid samples from normal and abnormal tissues are compared, significant differences still can be observed. Furthermore, according to the differences of the chemical shift, the changes of types of lipid can be approximately monitored. In addition, 1 H NMR is a quantitative method if the sample is simple; for example, the unsaturation degree, content of ester, and so on. 13C NMR has a large range of chemical shift, making it more powerful than 1H NMR in lipidomics. It can resolve triglycerides and their metabolites such as mono-, di-glycerides, and free fatty acids clearly. Furthermore, cholesterols can also be identified. Fatty acyl residues with different double bonds and carbon numbers (C18:3, C18:4, C20:3, C20:4, C20:5, C22:5, C22:6) can be easily resolved in the chemical shift range of around 172–174 ppm. Polar lipids, because of their unique structure, can be identified as well by 13C NMR. However, due to the low natural abundance of 13C, the resonance of 13C is not sensitive and therefore more lipids are needed to obtain better signal noise ratio and further result in good resolution. This drawback usually limits its utilization as some trace but important lipids cannot be resolved and finally ignored. 31 P NMR, the most powerful tool for phospholipids quantification, unlike 1H NMR and 13C NMR, needs special solvent system to get highly resolved resonances and quantifiable spectra because they contain both hydrophobic fatty acyl residues and hydrophilic polar head. Detergents such as sodium cholate, SDS, and Triton X-100 are proven to be potent for obtaining sharp peaks and high resolution. EDTA or CDTA salt as chelator is critical for the high resolution. The most widely used solvent system consisting of CDCl3, MeOH, and Cs-EDTA (Cs-CDTA) and temperature, lipid concentration, pH, and chelator concentration affect the chemical shift of each phospholipid species greatly. Therefore, optimization is needed when one wants to be offered a full phospholipid map. Recently developed 2D 1H, 31P NMR could identify and quantify phospholipids at the same time, leading to a great progress in NMRbased lipidomics. For a quantitative NMR, there are some general rules that must be followed. No chemical reactions must occur between solvent and analytes. The signal and noise ratio must be in a proper range and the spins must be totally relaxed after each transient. NMR as a promising method for lipidomics study is just starting and still needs to be explored extensively.

Technically, NMR-based lipidomics is suitable for disease diagnostics as exemplified above, although failures as pointed out in ref. [90] should be prevented. However, the real problem is the weak correlation between the biomarkers and the diseases rather than the analytical methods. The complementary utilization of 1H, 13C, 31P NMR-based lipidomics would be helpful for hunting the lipid related biomarkers of diseases. 5. NMR-based lipid analysis in food adulteration Food adulteration and contamination events with serious international impacts and consequences have occurred with some regularity. Different analytical methods such as near-infrared, mid-infrared, Raman; NMR spectroscopy, as well as a range of MS techniques are developed to detect food adulteration and contamination, which has been extensively reviewed [101]. As NMR-based lipidomics is the focus of the current work, detection of food adulteration related to lipids by NMR is exemplified here. Adulteration of olive oil with hazelnut oil was realized based on the different linolenic acid, squalene, and palmitic acid contents [102]. Proton resonances at 0.297, 0.921, 0.982, and 1.626 ppm and carbon resonances at 173.17, 173.15, 173.14, and 14.13 ppm were selected according to the uniqueness of linolenic acid, squalene, and palmitic acid resonances. The developed artificial neutral network could detect the presence of hazelnut oil in olive oil at percentages higher than 8%, which is better than most other analytical techniques. The application of NMR techniques in characterization and adulteration in olive oils have been widely explored and reviewed [103–105]. The basic rule is to find the difference from the adulterants that can be identified by NMR techniques. As shown in Figs. 7 and 10, 13 C NMR could distinguish partial glycerides and different polyunsaturated fatty acids (PUFAs) on different glycerol backbone positions. The distribution of ω-3 PUFA between the sn-1,3 and sn-2 positions of muscle lipids from Atlantic salmon (Salmo salar L.), mackerel (Scomber scombrus) and herring (Clupea harengus) was obtained and found to be significantly different in sn-position specificity of the fatty acyl residues for 22:6n-3, 20:5n-3 and 18:4n-3 [106]. The difference of fatty acyl residue regiodistribution was found to be enough to distinguish fish species [106]. However, 13C NMR fingerprinting shows difficulties in sorting out important signals in a complex spectrum and this could be solved by using suitable pattern recognition techniques [107]. The compositional databases have been built up comprising more than 200 samples of marine crude oils in 2011 [107]. The most obvious advantages of 13C NMR analysis in food adulteration are: 1) it is applicable to intact lipids non-destructively; 2) it provides information of lipid classes and regiospecific distributions; 3) it provides information about sn-2 position specificities of fatty acyl residues in triglycerides; 4) it does not need any enzymatic or chemical manipulation during analyses so that it is less time-consuming [106,107]. Apart from the direct adulteration of lipid product, some other food adulterations can be realized by NMR as long as there are differences among the lipid species. Authentication of beef versus horse meat has been carried out using 60 MHz bench-top 1H NMR based on the different linolenic acid and cholesterol contents [108]. Three major signals including bisallylic, olefinic and terminal CH3 peaks were found to be particularly useful in this case. Compared to the 13C NMR method, conventional DNA-based methods require separate tests and more steps to reach the aim. 60 MHz 1H NMR bench-top spectrometer was found to deliver comparable sensitivity and better specificity than Fourier Transform Infrared Spectroscopy (FTIR) for detecting olive oil adulterated with hazelnut oil [109].

Acknowledgements Financial support from the Graduate School of Science and Technology (GSST), Aarhus University and DLG (Dansk Landbrugs Grovvareselskab) Food Oil, Denmark is gratefully acknowledged. Conflicts of interest The authors declare no competing financial interest. References [1] X. Han, R.W. Gross, Global analyses of cellular lipidomes directly from crude extracts of biological samples by ESI mass spectrometry: a bridge to lipidomics, J Lipid Res 44 (2003) 1071–1079, http://dx.doi.org/10.1194/jlr.R300004-JLR200. [2] M.J. Cooper, M.W. Anders, High pressure liquid chromatography of fatty acids and lipids, J Chromatogr Sci 13 (1975) 407–411, http://dx.doi.org/10.1093/ chromsci/13.9.407. [3] R.W. Gross, B.E. Sobel, Isocratic high-performance liquid chromatography separation of phosphoglycerides and lysophosphoglycerides, J Chromatogr A 197 (1980) 79–85. [4] J. Nordbäck, E. Lundberg, W.W. Christie, Separation of lipid classes from marine particulate material by HPLC on a polyvinyl alcohol-bonded stationary phase using dual-channel evaporative light-scattering detection, Mar Chem 60 (1998) 165–175, http://dx.doi.org/10.1016/S0304-4203(97)00109-6. [5] M. Graeve, D. Janssen, Improved separation and quantification of neutral and polar lipid classes by HPLC-ELSD using a monolithic silica phase: application to exceptional marine lipids, J Chromatogr B Anal Technol Biomed Life Sci 877 (2009) 1815–1819, http://dx.doi.org/10.1016/j.jchromb.2009.05.004.

6. Conclusions Lipidomics has been considered an essential tool for investigation of many diseases and physiological processes, such as cancers and Alzheimer's disease in recent years. NMR based method can serve as a 54

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