Ultra-performance liquid chromatography–mass spectrometry as a sensitive and powerful technology in lipidomic applications

Chemico-Biological Interactions 220 (2014) 181–192

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Ultra-performance liquid chromatography–mass spectrometry as a sensitive and powerful technology in lipidomic applications Ying-Yong Zhao a,b,⇑,1, Shao-Ping Wu c,1, Shuman Liu b, Yongmin Zhang c, Rui-Chao Lin d,⇑ a Key Laboratory of Resource Biology and Biotechnology in Western China, Ministry of Education, The College of Life Sciences, Northwest University, No. 229 Taibai North Road, Xi’an, Shaanxi 710069, PR China b Division of Nephrology and Hypertension, School of Medicine, University of California, Irvine, MedSci 1, C352, UCI Campus, Irvine, CA 92868, USA c Sorbonne Universités, UPMC Univ. Paris 06, CNRS UMR 8232, IPCM, 4 place Jussieu, 75005 Paris, France d School of Chinese Materia Medica, Beijing University of Chinese Medicine, No. 11 North Third Ring Road, Beijing 100029, PR China

a r t i c l e

i n f o

Article history: Received 15 April 2014 Received in revised form 31 May 2014 Accepted 30 June 2014 Available online 9 July 2014 Keywords: Lipidomics Lipid profiling Ultra-performance liquid chromatography Mass spectrometry Lipid biomarker

a b s t r a c t Lipidomics, the comprehensive illumination of lipid-based information in biology systems, involves in identifying lipids and profiling lipids and lipid-derived mediators. The development of lipidomics enables the characterization of lipid species and detailed lipid profiling in body fluid, tissue or cell, and allows for a wider understanding of the biological roles of lipid networks. Lipidomic research has been greatly facilitated by recent advances in ultra-performance liquid chromatography–mass spectrometry (UPLC–MS) and involved in lipid extraction, lipid identification and data analysis supporting applications from qualitative and quantitative assessment of multiple lipid species. UPLC technique, different mass spectrometry technique, lipid extraction and data analysis in lipidomics are reviewed. Afterwards, examples are provided on the use of UPLC–MS for finding lipid biomarkers in disease, drug, food, nutrition and plant fields. We also discuss the UPLC–MS-based lipidomics for the future perspectives and their potential problems. Ó 2014 Elsevier Ireland Ltd. All rights reserved.

Contents 1. 2.

3. 4. 5. 6.

7. 8.

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . UPLC–MS techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1. UPLC technique . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2. Mass spectrometry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Lipids and lipidomics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Lipid extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Data analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . UPLC–MS applications in lipidomics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1. UPLC–MS-based lipidomics in disease biomarkers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2. Lipid biomarkers in drug research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3. Nutrition and food of lipidomics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.4. Lipidomics of plant . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Concluding remarks and future perspectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conflict of Interest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

⇑ Corresponding authors. Address: Key Laboratory of Resource Biology and Biotechnology in Western China, Ministry of Education, The College of Life Sciences, Northwest University, No. 229 Taibai North Road, Xi’an, Shaanxi 710069, PR China. Tel.: +86 29 88304569; fax: +86 29 88304368 (Y.Y. Zhao). Tel.: +86 10 84738652; fax: +86 10 84738653 (R.C. Lin). E-mail addresses: [email protected], [email protected] (Y.-Y. Zhao), [email protected] (R.-C. Lin). 1 Ying-Yong Zhao and Shao-Ping Wu are co-first authors. http://dx.doi.org/10.1016/j.cbi.2014.06.029 0009-2797/Ó 2014 Elsevier Ireland Ltd. All rights reserved.

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Transparency Document . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 189 Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 189 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 189

1. Introduction The identification and detection of disease biomarkers play an important role in drug and medicine development. The biological samples including urine, plasma, serum and tissue extracts have been analyzed using several techniques such as proton nuclear magnetic resonance (1H NMR) spectroscopy, gas chromatography–mass spectrometry (GC–MS), liquid chromatography–mass spectrometry (LC–MS) and capillary electrophoresis–mass spectrometry (CE–MS) [1,2]. Such biomarker identification can be a time-consuming process requiring sample reanalysis. The analysis of global profiling of small molecules from complex biological samples provides a significant analytical challenge for system biology. Recently, the introduction and development of two novel analytical platforms including ultra performance liquid chromatography (UPLC) and mass spectrometry, especially mass spectrometryElevated Energy (MSE, where E represents collision energy), has increased the volume of metabolic information obtained from any single sample compared with current other analytical systems. 2. UPLC–MS techniques 2.1. UPLC technique UPLC operates with sub-2 lm chromatographic particles and this liquid chromatographic system can operate at pressures in the 6000–15,000 psi range, providing an increased chromatographic resolution compared with conventional HPLC with larger particles. UPLC provides a wider range of linear velocities while maintains good chromatographic resolution and therefore can provide more rapid analysis time. The high chromatographic resolution, which results in an increased signal/noise and narrow peak width compared with conventional HPLC, is benefit for metabolic profiling to allow the determination of an enormous number of metabolites at physiological level. The previous literature reported that UPLC offered significant advantages over conventional reversed phase HPLC amounting to a more than twofold peak capacity, an almost ten-fold increase in speed and a 3–5-fold increased sensitivity compared with a conventional 3.5 lm stationary phase [3]. Another study reported that UPLC for separation of human serum metabolites resulted in 20% more detected components compared with HPLC [4]. UPLC–MS has become a mainstay of proteomics and metabolomics for protein identification and metabolite identification, respectively [5]. A greater number of UPLC applications have been described in recent reviews and research articles [2,6–12]. 2.2. Mass spectrometry Technological advances in MS play an important role in increasing lipidomics. Traditionally, lipid is analyzed using GC–MS, The two soft-ionization techniques including electrospray ionization MS and matrix-assisted laser desorption/ionization MS have been introduced until the late 1980s and revolutionized MS [13,14]. The development of matrix-assisted laser desorption/ionization and electrospray ionization has significantly extended the range of lipids that can be analyzed by MS [15,16]. LC–MS has greatly increased the number of lipid classes that can be analyzed in a single run. Generally speaking, Identification biomarker needs two experimental procedures including low collision energy MS

conditions first to get molecular ions and then higher collision energy MS2 conditions to get the fragment ions. Fragment ions can be acquired by various different ways and the simplest method is the selection of a precursor ion and then fragmentation in the collision cell monitored using a scanning mode (tandem mass spectrometer). MS/MS instrument includes two mass analysers separated by a collision cell either in space or time (ion-traps). Beam-based MS/MS instruments include triple quadrupoles MS, quadrupole-time-of-flight MS and time-of-flight–time-of-flight MS. The crude lipid extracts can produce complex mass spectra with many different ionized lipids and many isobaric species in untargeted lipidomics. The fragment ions of lipids allow the use of precursor ion and neutral loss scans on triple quadrupole MS to observe molecules with common structural information such as lipid classes and subclasses in a single spectrum with significantly reduced chemical noise. Triple quadrupole MS was demonstrated as a powerful tool for analysis of complex lipid extracts by the use of specific precursor ion and neutral loss scans [17,18]. Iontrap MS can conduct each of the steps of MS2. The advantage of ion-traps MS can perform not only MS2 but also MS3 and up to MS10. However, the disadvantage of ion-traps MS is the loss of the bottom third of the spectrum. This is a characteristic of MSn when performed on ion-traps MS. The second major disadvantage of ion-traps is their poor mass accuracy. These approaches need know prior information of the indentified ions from full-scan MS. Alternatively, data-dependent analysis can switch from full-scan MS mode to MS2 mode when an eluting peak rises above a predefined threshold by mass spectrometer [19]. However, data-dependent analysis results in a loss of fragment ion information in the MS mode when fragment ion information of MS2 are being acquired and poor duty cycles, thus make it less than ideal for fast analysis and narrow peaks. Therefore, these approaches are perhaps less efficient that would be desired for the rapid analysis of complex biological samples. However, the ever elevating requirement for increased throughput, higher sensitivity and higher chromatographic resolution remain a key goal for research. To avoid triggering of data-dependent acquisition experiments, full-scan MS and MS2 (without specific precursor ion selection) data are collected from a single injection. MSE technique was produced and introduced to metabolomics in 2005 [20]. This type of experiment uses an orthogonal hybrid quadrupole time-of-flight mass spectrometer that leverages the fast scanning capability and nonresolving Q1 in a unique manner. Briefly, two interleaved scan functions Q1 and Q2 are used for data acquisition such that the first scan function Q1 acquires a wide mass range (m/z 50–1200) using low collision energy between Q0 and the pusher region. Function Q1 collects information on the intact ions from the sample. The second scan function Q2 now has a high collision energy that fragments all of the ions transmitted through function Q1. This function acquires data over the same mass range (m/z 50–1200); however, the collision energy is ramped from low energy to high energy (15 to 50 eV). This scan function allows for the collection of fragment information from the ions in the second scan function Q1, which is equal to a non-selective tandem mass spectrometric scan. In this way, two chromatograms are generated, one with information on the intact molecules from the first function Q1, and the other with the fragmented ion information from the second function Q2. A variety of data-processing algorithms can be used to extract metabolite information from these data [21,22]. In other words, MSE can provide parallel alternating scans for

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Fig. 1. Low-collision energy and high-collision energy exact mass spectra of PC (16:0/18:2). The low-energy spectrum only contains the precursor ion at m/z 758.5697 whereas in the high-energy spectrum various fragments appear as the loss of the various fatty acid chains at 496.3410 and 520.3407 and their respective water losses. Also, the major fragment ion at m/z 184.0733 can be seen corresponding to the polar head group.

acquisition at either low collision energies to obtain precursor ion information or high collision energies to obtain full-scan accurate mass fragment, precursor ion and neutral loss information (Fig. 1). MSE involves in a simultaneous acquisition through alternating between high and low collision energies during a single chromatographic run and Fig. 2 shows reconstructed ion chromatograms of UPLC–MSE data with low and high energy acquisitions from human plasma [23]. This ability provides the structural information required for the identification of unknown biomarkers in the context of untargeted analyses. Recently, UPLC–MSE techniques have proved to be powerful tools for the identification of trace components of complex mixtures and for confirming their presence in proteomics, metabolomics and lipidomics [2,4,24–29].

3. Lipids and lipidomics Lipidomics, a branch of metabolomics, was first put forward by Han and Gross in 2003 [30]. Lipidomics has been defined as ‘‘the full characterisation of lipid molecular species and of their biological roles with respect to expression of proteins involved in lipid metabolism and function, including gene regulation’’ [31]. The metabolome is a diverse, complex array of compounds that display varying physiochemical characteristics. Various proteins and metabolites play a fundamental role in the disease process. Proteomics focuses on characterizing and quantifying various proteins involved in gene expression. On the other hand, lipidomics is at the opposite end of the ‘omics’ spectrum. The main difference between lipids and carbohydrates, proteins and nucleic acids is their solubility in organic solvents. Historically, lipids are defined either by these physical properties-specifically their solubility in non-polar solvents or by the presence of long hydrocarbon chains; however, not all lipids satisfy both definitions. Recently,

investigators have attempted to refine this definition. A new nomenclature system has been proposed for lipids based on lipid biosynthesis, namely ‘‘hydrophobic or amphipathic small molecules that may originate entirely or in part by carbanion-based condensations of thioesters and/or by carbocation-based condensation of isoprene units’’. As a result, lipids were regrouped under the eight categories that cover eukaryotic and prokaryotic sources (Table 1) [32]. Each category contains distinct classes, subclasses, subgroups, and subsets of lipid molecules. Unlike other biomolecules, complex lipids such as sphingolipids and glycerophospholipids include a wide range of building blocks that can give rise to a bewildering array of combinations. Permutations that may arise only from common eukaryotic lipid motifs give rise to more than 180,000 theoretical phospholipid structures that could be present in a given cell or tissue extract [33]. Note that this number does not include complexity that may arise from consideration of isomeric lipids that differ only in double-bond position, backbone substitution or stereochemistry [34]. Although once considered merely a structural component of cells, it is now realized that Lipids play an important role in biological system including composing membrane bilayer, storing energy, producing signal transduction, providing functional implementations of membrane proteins and their interactions, etc. [35]. Lipidomics involves in mapping of the entire spectrum of cellular lipids in biological systems, including metabolic pathways and lipid–lipid interactions [36]. Therefore, Lipidomics complements genomics, proteomics and metabolomics to provide a more complete understanding of system biology, which enables us to understand at a fundamental level the various molecules implicated in health and disease [37]. Traditional methodologies include biochemical assays, chromatography and various imaging techniques. However, such methods were limited in their sensitivity and accuracy. Recently, an increasing number of publications have described lipidomic studies using various techniques including GC–MS, LC–MS and 1H NMR [38,39].

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H

H O

H

H

O

Cholesteryl ester (22 6)

H

H O

Cholesteryl ester (20 4)

H

H

O H

H O

Low energy

H

H

O

Cholesteryl ester (20 5)

H

H H

H

O

Cholesteryl ester (18 1)

O H

H H

H

O

Cholesteryl ester (18 2)

H

O

H H

H

O O

Cholesteryl ester (18 3) H

High energy

H

m/z 369.3515

HC

H

H

Fig. 2. Reconstructed ion chromatograms of UPLC–MSE data with low and high energy acquisitions from human plasma. The high energy reconstructed ion chromatogram of the key fragment ion from the cholesteryl (m/z 369.3515) is used to identify the possible presence of cholesteryl esters, and subsequently at the indicated in the low energy UPLC–MS trace as the key fragment ion from the cholesteryl (m/z 369.3515). Following alignment with the low energy trace it is possible to extract the unfragmented ion information, which corresponds to the cholesteryl esters. The structures shown are examples of possible structures.

Table 1 Lipid categories and typical classes. Categories

Typical classes

Fatty acyls Glycerophospholipids

Fatty acids and conjugates, fatty esters, fatty alcohols, fatty amides, eicosanoids Phosphatidylethanolamines, phosphatidylcholines, phosphatidylserines phosphatidylglycerols, phosphatidic acids, phosphatidylinositols, cardiolipinds Monogalactosyldiacylglycerol, diacylglycerides, digalactosyldiacylglycerol, triacylglycerides Ceramides, sphingosines, sphingosine-1-phosphates, ganglioside mannoside 3, sphingomyelins Polyprenols, quinines, hydroquinines, isoprenoids Cholesterols, cholesteryl esters, cholesteryl sulfates Acylaminosugars, acylaminosugar glycans, acyltrehaloses, acyltrehaloses glycans Macrolide polyketides, aromatic polyketides, non-ribosomal peptide/polyketide hybrids

Glycerolipids Sphingolipids Prenol lipids Sterol lipids Saccharolipids Polyketides

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Workflow for setting up the system

Workflow for analyzing results

Prepare solvents

Calibrate UPLC

Import data

Prepare instrument

Peak detection Data preprocessing

Create a MassLynx project

Create an LC method

Denoising and deconvolution

Creating a project

Instrument methods

Pattern recognition Statistical analysis

Create an MS method

MS and MSE method

Prepare QC samples

Create a sample list

PCA, LASSO

Lipid database

Lipid identification

Sample lists

Lipids of interest

Acquire data

Pathway analysis

Fig. 3. A typical example work flow of lipidomics using UPLC–QTOF/MS and a MSE data collection technique as research tools for discovering lipids in complex mixtures.

Among the analytical techniques, LC–MS is recognized as one of the best analytical techniques in selectivity, sensitivity and reproducibility [40]. Furthermore, among the various LC–MS platforms, UPLC–MS/MS and UPLC–MSE are considered as the most powerful tools for high-throughput lipidomic analysis, especially for largescale untargeted lipid profiling due to its enhanced reproducibility of retention time. Fig. 3 shows a sample workflow of UPLC–QTOF/ MS and a novel MSE data collection technique in lipidomics.

4. Lipid extraction Because lipids embedded in complex biological matrixes, but not appear in their free form, extraction procedures are necessary in analysis of lipids to remove other compounds such as sugars, proteins or other small molecules from the biological samples that would interfere with the chromatographic separation. The general procedures are lipids separation from the matrix; removal of any non-lipid components, such as saccharides, proteins or other small molecules; and fractionation and isolation of lipids from the extract. Generally, there are mainly two extraction methods to extract lipids from samples including liquid–liquid extraction and solid–phase extraction. The most popular methods of liquid–liquid extraction for lipids were proposed by Folch and co-workers [41] using CCl3: CH3OH (2:1, v/v) as an extraction method, and by Bligh and Dyer [42] using incorporated water as

extraction component. Different proportions of CCl3, CH3OH and H2O depend on the moisture content of the complex biological samples. The addition of salt in the extraction solvent has its origin in the fact that the formation of emulsions was one of the major disadvantages of liquid–liquid extraction method. Liquid–liquid extraction allows an instant partition of the lipids without an additional step. Other commonly method was reported using hexane–isopropanol (3:2, v/v) extraction method [43], which is a less toxic extractant. Most methods still carry out the above-mentioned lipid extraction procedure. Recently, Löfgren and co-workers developed a mixture of CH3CH2CH2CH2OH and CH3OH extraction method for total lipid extraction from human plasma [44]. The results showed this method was more suitable for total lipid extraction than the Folch’s method. In addition, the CH3CH2CH2CH2OH and CH3OH extraction method was rapid and high throughput for lipidomics. A methyl tert-butyl ether liquid–liquid extraction method to extract lipids and different classes of metabolites was developed by Chen and co-workers in 2013 [45]. Methyl tert-butyl ether extraction method achieved a complete analysis of lipids and other metabolites after a single extraction. Besides the extraction of blood, tissue or cells, a full fecal lipidome liquid– liquid extraction method was reported by Gregory and co-workers in 2013 [46]. The method utilizes two separate, complementary extraction chemistries, CH2Cl2 and a methyl tert-butyl ether/ hexafluoroisopropanol mixture, alone or with high pressure cycling. Extracts were assessed by LC–MS method. 304 endogenous

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lipid species were identified in feces, which covered six categories from LIPID MAPS as well as various related classes and subclasses. In addition, fecal lipidome liquid–liquid extraction method was developed to provide fecal lipidomics for both animal models and clinical applications [47]. Solid-phase extraction is a rapidly extraction method and can minimize degradation and set up automatic pre-analytical facilities for a simultaneous preparation of numerous samples. This method did not require for partition the solvent/water mixture and reduced the consumption of solvents and time [48]. Solid-phase extraction has been proposed for lipid extraction using aminopropyl cartridges as reported for neutral and acidic phospholipids [49], phosphatidylcholines, non-esterified fatty acids, cholesterol esters and triacylglycerols from plasma [50]. Liquid–liquid extraction and solid-phase extraction have also been carried out with phospholipids from human serum by Ferreiro-Vera and co-workers [51]. They compared the extraction efficiency of liquid–liquid extraction using different nonpolar-polar solvents with that of solid-phase extraction using three elution solvent. The results showed that the highest sensitivity and selectivity was acquired by solid-phase extraction with CH3OH as the optimum elution solvent for lipid extraction. Solid–phase extraction method is preferred in lipidomics analysis, with CH3OH, hexane, and CHCl3 as elution solvents. Compared with liquid–liquid extraction method, solid–phase extraction method reduces the consumption of time and solvents. However, when a large volume of samples were prepared, the recovery will be dramatically decreased due to the low peak capacity of solid–phase extraction method. Recently, many new extraction methods have been used for lipidomic analysis including ultrasound-assisted extraction, dispersive liquid–liquid microextraction, pressurized fluid extraction and solid–phase microextraction [52–55]. Generally speaking, extraction of organic solvents is usually the first step for the lipid analysis, so results from different groups may be variable from different methods of lipid isolation. Liquid– liquid extraction method may be preferred to extract more comprehensive lipids and it may be more suitable for nontargeted

lipidomics. When the researchers focus on all the simple and complex lipids from a tissue in a near quantitative manner, they normally use the liquid–liquid extraction method. For targeted lipidomics, solid–phase extraction method may perform better specificity and extraction efficiency.

5. Data analysis Fig. 3 represents a typical UPLC–MS-based lipidomic workflow and shows that the initial step in processing raw data is lipid identification. Several lipid databases and software packages to achieve this goal have been developed including LMSD (LIPIDMAPS), LipidView, MZmine 2 and LipidBank. Software of metabolomics may also be employed for lipid identification and quantification [56,57]. The second step of data processing is to normalize the data via a set of internal standards. Once these calculations have been carried out with raw data, the identified lipids can then be quantified by comparison to appropriate internal standards [58]. The third step is performing statistical analysis of the complex data sets. Principal component analysis (PCA) is an unsupervised multivariate data analysis method and it gives the comprehensive view of the clustering trend for the multidimensional data in lipidomics. PCA can help to visualize correlated variations in more than two dimensions. This test describes data in the form of a linear combination of scores containing information on the samples and loadings containing information on the variables. The advantage of PCA is that the outcomes are straightforward and intuitively understandable because of the graphical representation [59]. A score plot provides an indication of the clustering of observations as function of the treatment. The distance between clusters is a measure of the differential efficiency of the treatments. The greater the similarity between samples, the smaller the separation appears between labels in the score plots. A loading plot can be helpful to locate the concerted lipid variations. The load plots are also used to illuminate the compositional trends. Two lipid species that point in the same direction show a positive correlation, whereas lipid

Fig. 4. Typical base peak chromatograms from extracted heart tissue lipids for transgenic (A) and wild type (B) mice using UPLC–QTOF/MS with a Waters ACQUITY UPLC HSS T3 column (2.1 cm  100 mm, 1.8 lm), eluted with 40–100% linear gradient of acetonitrile/water (40/60, v/v) with 10 mM ammonium acetate and acetonitrile/isopropanol (10:90, v/v) with 10 mM ammonium acetate over 10 min at a flow rate of 0.4 mL/min.

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species pointing in opposite directions reveal a correlation.

negative

6. UPLC–MS applications in lipidomics Lipids are involved in various biochemical and signaling pathways, cell structure and function of health and diseases. UPLC–MS-based lipidomics are suitable for lipid analysis from a variety of samples and appropriate extraction, sample clean-up and derivatization procedures are followed [60–67]. Fig. 4 displays typical UPLC–QTOF/MS chromatograms from extracted heart tissue lipids of transgenic and wild type mice in positive ion mode. Lipidomics has been applied to disease biomarker discovery, drug development, drug safety assessment, nutritional supplementation assessment and plant research. The following section gives an overview of such studies focusing on recent reports discussing lipid biomarkers, disease biomarker discovery, drug discovery, nutritional supplementation assessment as well as some examples relevant to plant research. Table 2 displays UPLC-MS-based lipidomics applications for discovering biomarkers in the abovementioned fields. 6.1. UPLC–MS-based lipidomics in disease biomarkers UPLC-based lipidomics was applied for differential phenotyping from pet dogs developing spontaneous malignant mammary tumors and health controls. A biological signature related to cancer was successfully revealed from this lipidome analysis [68]. Type 2 diabetes is a multi-factorial disease with a complex pathogenic mechanism and a combination of metabolomic and lipidomic approach was employed to analyze serum samples from Type 2 diabetes. The results demonstrated significant perturbations in amino acid metabolism, TCA cycle and glycerol-phospholipid metabolism had an important effect on the overall glucose homeostasis in Type 2 diabetes [69]. Another study indicated that serum lipids were associated with progression to type 2 diabetes in the METSIM study [70]. The analyses of serum lipids from type 1 diabetes were performed using UPLC–MS-based lipidomics platform [71]. Type 1 diabetes is characterized by a distinct cord blood lipidomic profile that includes reduced major choline-containing phospholipids (sphingomyelins and phosphatidylcholines). Reduction in choline-containing phospholipids in cord blood therefore is specifically related to progression to type 1 diabetes but not with development of b-cell autoimmunity. A lipidomics platform using UPLC–MS was applied for the analysis of serum samples from schizophrenia in twin pairs discordant for schizophrenia as well as unaffected twin pairs [72]. Lysophosphatidylcholines are preferred carriers of polyunsaturated fatty acids across the blood–brain barrier. Decreased lysophosphatidylcholines indicated that patients might be more susceptible to infections. Their association with cognitive speed supports the opinion that altered neurotransmission in schizophrenia may be in part mediated by reactive lipids. UPLC–MS was employed to analyze serum samples collected from Lamin A/ C gene carriers, dilated cardiomyopathy patients without Lamin A/C gene mutation and controls. The analysis helped us to obtain novel insights into how the affected lipids might relate to cardiac shape and volume changes [73]. In addition, lipidomic profiling of hepatocellular carcinoma in human and animal studies was investigated by UPLC approach and lysophosphatidylcholine (24,0.0) was identified as a discriminatory biomarkers [74,75]. Non-alcoholic fatty-liver disease, as with the metabolic syndrome, is approaching epidemic proportions in many countries. Patients with non-alcoholic fatty-liver disease had increased triacylglycerols with low carbon number and double-bond content while

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lysophosphatidylcholines and ether phospholipids were diminished in those with non-alcoholic fatty-liver disease [76]. Lipidomic approach was also applied to characterization of alcohol induced metabolic changes in mouse liver [77,78]. 6.2. Lipid biomarkers in drug research UPLC–MS lipidomics was undertaken on changes of the glycerolipids upon b-amyloid peptide-induced neurotoxicity and the neuroprotective effect of (-)-epigallocatechin gallate in PC12 cells. The glycerolipids were significantly elevated in Ab-treated cells, but were restored near to normal levels after (-)-epigallocatechin gallate treatment. The increased phosphatidylcholines were associated with the reduced phospholipase A2 activity and the enhanced activity of lysophospholipid acyltransferases. In addition, an increased arachidonic acid was observed as another important response of PC12 cells to the b-amyloid peptide aggregates, indicating an active inflammatory process occurring in b-amyloid peptide induced neurotoxicity. (-)-epigallocatechin gallate treatment can reverse the deregulated metabolism of phosphatidylcholines, which might be one of the biochemical mechanisms contributing to its neuroprotective effect [79]. A lipidomics approach using UPLC–MS investigated the anti-depressive effect of the traditional Chinese medicine Allium macrostemon in a rat model of depression [80]. Several increased LPC (18:1 ? :2), LPC (O-16:2), LPC (20:1), and LPC (O-18:3) were observed in rat plasma, while some PC (32:1), PC (37:4), PC (36:4 ? :5), PC (38:4 ? :6), PC (O-36:4), PC (40:6), and PC (O-38:5) and TG (60:12), TG (58:12), and TG (62:13 ? :14) were decreased in depressed rats. These alteration showed that depression were related to inflammatory conditions and an incomplete b-oxidation of fatty acids [81,82]. Most of these abnormal lipids were returned to their normal levels after treatment with A. macrostemon. UPLC–MS lipidomic analysis was undertaken on plasma from rosuvastatin treated patients [83]. The results demonstrated that the overall lipids in plasma decreased by drug response. 6.3. Nutrition and food of lipidomics The evidence of the multiple beneficial health effects of fish, fish oil, dietary fiber and whole grain consumption are well reported. Lipidomics of the effect of fish oil supplementation was performed using UPLC–MS, where 568 lipids were detected and 260 identified. The intervention groups were well separated after three weeks. Several lipid classes including phosphatidylethanolamine, phosphatidylcholine, lysophosphatidylcholine, phosphatidylserine, sphingomyelin, triglycerides and phosphatidylglycerol contributed strongly to this separation. Significantly decreased 23 lipids were observed in the fish oil group compared with the high oleic sunflower oil group, whereas 51 were significantly increased including selected phospholipids and triglycerides of long-chain polyunsaturated fatty acids [84]. The effects and possible mechanisms of fatty fish or lean fish against coronary heart disease were studied by UPLC–MS and GC lipidomic approach [85]. Lysophosphatidylcholines, ceramides and diacylglycerols were significantly decreased in the fatty fish group, whereas cholesterol esters and specific long-chain triacylglycerols were significantly increased in the lean fish group. Diacylglycerol is the mediator of lipid-induced insulin resistance via activation of novel protein kinase C, which inhibits insulin action and is also associated with inflammatory response [86]. Ceramides are suggested to attenuate insulin signaling through multiple pathways [86–88]. Lysophosphatidylcholine is the main bioactive lipids of oxidized low-density lipoprotein and may be related to many of the inflammatory effects of oxidized low-density lipoprotein [89], which may be associated with anti-inflammatory effects of n-3 fatty acids [90]. The eight-week

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consumption of fatty fish decreased lipids, which are potential mediators of lipid-induced insulin resistance and inflammation, and may be associated with the therapeutic effects of fatty fish on the progression of insulin resistance or atherosclerotic vascular diseases. An ultra-performance convergence chromatography coupled with Q/TOF–MS was utilized to analyze triacylglycerols and diacylglycerols in cow milk fat [91]. Forty-nine triacylglycerols and seven diacylglycerols were identified in cow milk fat. The lipid class composition of the ordinary and dark muscles of chub mackerel were compared by thin-layer chromatography and UPLC–MS [92]. Diacylglycerol (18:0/18:1 and 16:0/16:0) and triacylglycerol (22:0/22:1/22:3 or 22:0/22:0/22:4) were observed in neutral lipids. Neutral and acidic glycosphingolipids were observed in the glycolipids. Phosphatidylinositol (18:0/20:5 or 18:1/20:4) and phosphatidylserine (20:5) were present in the phospholipids in thin-layer chromatography. UPLC–MS analysis found a difference in the neutral lipid fraction between the ordinary and dark muscles but the glycolipid and phospholipid patterns were similar in both muscles [92]. Lipidomic analysis was performed on the consumption of high-fiber rye bread or white-wheat bread modifies the plasma lipid profiles in postmenopausal women using UPLC–MS [93]. There were no changes in plasma lipidomic profiles during the rye bread or white-wheat bread intervention periods. The results suggest that eight-week consumption of high-fiber rye

bread increases metabolites that might mediate positive effects of rye bread on satiety and weight maintenance. Other evidence indicated that lysophosphatidylcholines was increased in the oat and wheat bread and potato group, while in the rye bread and pasta group docosahexaenoic acid (22:6n-3) increased and isoleucine decreased [94]. The lipid profiles had the correlation with the alterations in the adipose tissue differentiation pathway when using the elastic net regression model of the lipidomic profiles on selected pathways. 6.4. Lipidomics of plant The snow alga Chlamydomonas nivalis (C. nivalis) is a typical microalgal species that can adapt and resist to natural habitats in the polar region and similar extreme environments. The snow alga C. nivalis was subjected to nitrate or phosphate deprivation to study its stress responses in lipid profiles analyzed by UPLC–MS [95]. Three clusters were distinguished as the control, nitrate-deprived and phosphate-deprived groups. The lipidomic approach identified monogalactosyldiacylglycerols, digalactosyldiacylglycerols, phosphatidylethanolamine, phosphatidylglycerols, sulfoquinovosyldiacylglycerols and phosphatidylinositiol as differentiating lipid biomarkers. The changes of these lipid biomarkers provided new insights into the lipid metabolism of the snow alga in response to

Table 2 UPLC–MS-based lipidomic applications for discovering biomarkers. Application

Specimen types

Lipid classes or metabolites

References

Disease biomarkers Canine mammary cancer Type 2 diabetes Type 1 diabetes Schizophrenia patients Dilated cardiomyopathy

Serum Serum Serum Patient serum Patient serum

PC, PE, LysoPC and LysoPE Glycerophospholipid SM, PC, PE, TG and LysoPC LysoPC and TG PC (38:5e), PS (38:2), TG (49:2)/TG (49:1), TG (49:3), TG (50:10), TG (50:2)/TG (50:1), TG (54:3)/TG (54:2) LysoPC (24,0.0)

[68] [69,70] [71] [72] [73]

Odd- and short-chain TG, ether lipids, LysoPC, SM, PC, PE, PUFA-containing PC, PE and TG

[76]

PC12 cells

PC, Glycerolipids and arachidonic acid

[79]

Rat plasma

LysoPC (18:1 ? :2), LysoPC (O-16:2), LysoPC (20:1), LysoPC (O-18:3), PC (32:1), PC (37:4), PC (36:4 ? :5), PC (38:4 ? :6), PC (O-36:4), PC (40:6), PC (O-38:5), TG (60:12), TG (58:12) and TG (62:13 ? :14) PS, PG, PE, PC, TG, PI, SM, LysoPC and lysophosphatidic acid

[80]

Healthy subjects plasma Patient plasma

PE, PC, LysoPC, PS, SM, TG and PG

[84]

LysoPC, DAG, ceramides, cholesterol esters and specific long-chain TAG

[85]

Cow milk fat Ordinary and dark muscles Plasma of postmenopausal women

7 DAG and 49 TAG DAG (18:0/18:1 and 16:0/16:0), TAG (22:0/22:1/22:3 or 22:0/22:0/22:4), PI (18:0/20:5 or 18:1/20:4), PS (20:5), neutral GSL, acidic GSL and glycolipids SM (d18:1/25:1) and SM (d18:1/25:3)

[91] [92]

Tissue

MGDG, DGDG, PE, PG, PI and SQDG

[95]

Tissue

TG, PC, PG, SQDG, MGDG, LPG, LysoPC, LMGDG and lysoSQDG

[96]

Tissue

PG, TAG, SQDG, MGDG, DGDG, lysoMGDG, lysoDGDG, free fatty acid, harderoporphyrin and cholesterol TAG, PC, PG, LPG, LysoPC and lysoMGDG DGDG and DGTS PG, DGDG, MGDG and SQDG

[97]

Hepatocellular carcinoma Non-alcoholic fatty-liver disease Drug research Neuroprotective effect of epigallocatechin gallate Anti-depressive effect of Allium macrostemon Rosuvastatin Nutrition and food Fish oil supplementation Fatty fish or lean fish against coronary heart disease Cow milk Chub mackerel High-fiber rye bread or white-wheat bread Plant lipidomics Snow alga C. nivalis under nitrate or phosphate deprivation conditions Snow alga C. nivalis treated with different NaCl concentrations Nitzschia closterium Stephanodiscus sp. under cold exposure Dunaliella tertiolecta Stephanodiscus sp.

Human and animal blood Serum

Human plasma

Tissue Tissue Tissue

[74,75]

[83]

[93,94]

[98] [99] [100]

DGDG: digalactosyldiacylglycerols; DGDG: digalactosyldiacylglycerols; DGTS: diacylglyceryltrimethylhomoserines; LPG: lysophosphatidylglycerols; lysoMGDG: lysomonogalactosyldiacylglycerols; LysoPC: lysophosphatidylcholines; MGDG: monogalactosyldiacylglycerols; PC: phosphatidylcholines; PE: phosphatidylethanolamines; PG: phosphatidylglycerols; PI: phosphatidylinositols; PS: phosphatidylserines; SM: sphingomyelins; SQDG: sulfoquinovosyldiacylglycerols; TAG: triacylglycerols; TG: triacylglycerides.

Y.-Y. Zhao et al. / Chemico-Biological Interactions 220 (2014) 181–192

nitrate or phosphate deprivation stress condition. UPLC–MS approach was developed for lipidomic profiling from C. nivalis treated with different NaCl concentrations. Seven types and 35 kinds of polar lipid molecules were selected and identified as biomarkers [96]. Su and co-workers developed an UPLC–MS method to investigate the lipid changes in different growth phases of Nitzschia closterium. Thirty-one lipids were selected and identified as putative biomarkers. Further analysis on the putative biomarkers demonstrated that nitrate starvation played an important role in the transition from exponential phase to stationary phase in Nitzschia closterium [97]. UPLC–MS-based approach was developed for investigating the lipid changes during cold exposure in Stephanodiscus sp. [98]. Thirty-eight lipid molecules were selected and identified as putative biomarkers, including triacylglycerol, phosphatidylcholine, phosphatidylglycerol, lyso-phosphatidylglycerol, lysophosphatidylcholine, lyso-sulfoquinovosyldiacylglycerol, etc. These metabolites have been shown previously to function in energy storage, membrane stability and photosynthesis efficiency. A total of 29 lipids and 7 carotenoids were detected in a Dunaliella tertiolecta under light intensity and nitrogen starvation condition [99]. Alterations of digalactosyldiacylglycerol and diacylglyceryltrimethylhomoserine species were observed under stress conditions. The total carotenoid content was decreased under stress conditions, while ã-carotene was increased under nitrate-deficient cultivation. The highest productivity of carotenoid was attained under high light and nitrate sufficiency condition, which result from the highest level of biomass under high light and nitrate sufficiency. In addition, a comprehensive characterization of the photosynthetic glycerolipids of the diatom Stephanodiscus sp. was carried out by UPLC–MS [100]. Four classes of photosynthetic glycerolipid including 9 digalactosyldiacylglycerols, 16 monogalactosyldiacylglycerols, 8 phosphatidylglycerols and 23 sulfoquinovosyldiacylglycerols were identified in Stephanodiscus sp. Arabidopsis thaliana has a wide geographical range throughout the Northern Hemisphere with significant natural variation in freezing tolerance. Hummel and co-workers developed an UPLC– based method for the comprehensive profiling of more than 260 polar and non-polar lipids from Arabidopsis thaliana leaf [101]. Accumulated evidence indicated that the relative abundance of several lipid species was related to the freezing tolerance of the accessions, allowing the identification of possible marker lipids for plant freezing tolerance [102].

7. Concluding remarks and future perspectives Recently, the rapid development of UPLC–MS led to significant advances within the field of lipidomics. A sub-2 lm packing particles combined with an UPLC system enabled significantly increases in LC performance over conventional HPLC system, mainly with enhanced peak resolution, increased sensitivity and speed [4,5,103]. The improved resolution led to a reduction in the number of co-eluting components and hence a decrease in ion suppression resulting in an increase in the number of compounds detected by MS. UPLC showed better reproducibility and signal/noise ratios over HPLC and this technology was more suitable for untargeted lipidomics. Several publications have reported sensitivity of UPLC–MS and this approach which exhibits chromatographic peak area associated with a high mass accuracy allowed to lipid study in complex biological samples [51,59]. UPLC–MS had the flexibility of separating lipids from any sample type into individual lipid classes or separated lipids within the same class based on the used chromatographic phase, though choice of solvent also played a critical role. Although UPLC has high separation and quantification power, UPLC–MS may be less suitable for some class of compounds.

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Suitability of UPLC may be increased by combining hydrophilic interaction chromatography separation methods for untargeted lipid profiling to get comprehensive information on lipidome. However, other techniques may have to be employed for obtaining a comprehensive investigation of the lipidome. GC–MS may be complementary to UPLC–MS. Thus, the technological breakthroughs of UPLC have provided researchers with the capacity to measure hundreds or even thousands of various lipids in as little as a few minutes per sample, paving the way for complex diseases, drug, food and nutrition. One of the great benefits of the lipidomic application is the fact that groups of lipid biomarkers are eager for high sensitivity and specificity than other biomarkers such as gene, protein or metabolite, promoting the direct comparison of animal models with human diseases, which enhances the potential of the technique to rapidly transfer laboratory research into clinical application. The current UPLC–MS approach can allow high-throughput, sensitivity and resolution of lipid analysis and can identify lipid structures. However, continued incremental developments of new analytical techniques along with data-handling routines are still necessary for data preprocessing, data mining, statistical analysis, biomarker identification and interpretation of biochemical pathways and achieve more breakthroughs in lipid research. Better and more data on very low abundant lipids and better predicting data models will reveal more abnormalities in lipid metabolism associated with diseases, drug, food and nutrition. 8. Conflict of Interest The authors declare that there are no conflicts of interest. Transparency Document The Transparency document associated with this article can be found in the online version.

Acknowledgements This study was supported by the Program for New Century Excellent Talents in University, China (No. NCET-13-0954) and Changjiang Scholars and Innovative Research Team in University, China (No. IRT1174), National Natural Science Foundation of China, China (Nos. J1210063, 81202909, 81274025, 81001622), As a Major New Drug to Create a Major National Science and Technology Special, China (No. 2014ZX09304-307-02), China Postdoctoral Science Foundation, China (No. 2012M521831), National Innovation Training Plan Program (201310697004), Key Program for the International S&T Cooperation Projects of Shaanxi Province, China (No. 2013KW31-01), Natural Science Foundation of Shaanxi Provincial Education Department, China (No. 2013JK0811) and Administration of Traditional Chinese Medicine of Shaanxi, China (No. 13-ZY006). References [1] J.C. Lindon, E. Holmes, J.K. Nicholson, Metabonomics in pharmaceutical R&D, FEBS J. 274 (2007) 1140–1151. [2] J. van der Greef, D.C. Leegwater, Urine profile analysis by field desorption mass spectrometry, a technique for detecting metabolites of xenobiotics. Application to 3,5-dinitro-2-hydroxytoluene, Biomed. Mass Spectrom. 10 (1983) 1–4. [3] I.D. Wilson, J.K. Nicholson, J. Castro-Perez, J.H. Granger, K.A. Johnson, B.W. Smith, R.S. Plumb, High resolution ‘‘ultra performance’’ liquid chromatography coupled to oa-TOF mass spectrometry as a tool for differential metabolic pathway profiling in functional genomic studies, J. Proteome Res. 4 (2005) 591–598. [4] A. Nordström, G. O’Maille, C. Qin, G. Siuzdak, Nonlinear data alignment for UPLC–MS and HPLC–MS based metabolomics: quantitative analysis of endogenous and exogenous metabolites in human serum, Anal. Chem. 78 (2006) 3289–3295.

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