SPME in Omics

Solid-phase microextraction in metabolomicsBarbara Bojko a,b, Nathaly Reyes-Garcs a, Vincent Bessonneau a, Krzysztof Gorynski a,b,Fatemeh Mousavi a, Erica A. Souza Silva a, Janusz Pawliszyn a,*a Department of Chemistry, University of Waterloo, 200 University Ave. W., Waterloo, ON, Canada N2L 3G1b Department of Medicinal Chemistry, Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University, Torun, Poland

A R T I C L E I N F O

Keywords:BloodCell cultureExtraction phaseIn vivoMetabolomicsPlantPlasmaSolid-phase microextractionTissueUrine

A B S T R A C T

Among all the -omics studies, metabolomics currently has the most growth potential. The complexityof the studied matrices demands from the analytical chemist the development of new protocols and themodication of existing methods to obtain a full biochemical image of the sample. In the current review,we discuss the advantages and the disadvantages of solid-phase microextraction based on up-to-datemetabolomics studies performed on plants, biouids, tissues and cell cultures. We place particular em-phasis on the development of new extraction phases in view of the analyte coverage and on the featuresof the method, which complement solvent-based extraction technologies.

2014 Elsevier B.V. All rights reserved.

Contents

1. Introduction ........................................................................................................................................................................................................................................................ 1682. Extraction phases used for metabolomics ............................................................................................................................................................................................... 1693. Plant metabolomics .......................................................................................................................................................................................................................................... 1724. Metabolomics of biouids ............................................................................................................................................................................................................................. 1755. Tissue metabolomics ....................................................................................................................................................................................................................................... 1776. Metabolic proling of biopsy tissue, cell culture and breath ............................................................................................................................................................ 1777. Conclusions ......................................................................................................................................................................................................................................................... 178

Acknowledgements .......................................................................................................................................................................................................................................... 178References ............................................................................................................................................................................................................................................................ 178

1. Introduction

For the past few years, there has been growing interest in thecomprehensive analysis of small molecules characterizing givensystems under study. This area of study is generally referred to asmetabolomics, metabolic proling or metabonomics; however,sometimes variations of the name that are related to the investi-gated matrix can be found too, e.g., urinomics. In such cases, thename usually corresponds to an integrated toolbox of various-omics approaches, such as metabolomics, proteomics,transcriptomics and genomics. In order to obtain a full biochemi-cal prole of the studied system, there is a need for a reliableanalytical platform that can provide information about moleculesof a wide range of polarity, acidity/alkalinity, different charges, and

molecular weights. In terms of analytical instrumentation, there arecurrently two main detection platforms used in the metabolomicsarea: mass spectrometry (MS) and nuclear magnetic resonance(NMR) spectroscopy. For separation purposes, the most common-ly used approaches are still liquid or gas chromatography (LC or GC).

Due to the complexity of the studied matrices, the applicationof effective samplepreparationneeds special consideration.Anumberof publicationswere dedicated to this topic [14]. On one hand, theideal sample preparation should be as simple as possible, so as tominimize the manipulation of the matrix, including its modica-tion and losses. On the other, it must secure ecient sample clean-up,metabolism quenching and coverage of awide range of analytesin order to provide a true representation of the studied system. Tofulll that last requirement, it is very important to quench the me-tabolism of substances rapidly to protect them from degradationand decomposition. There are number of established quenching pro-tocols for investigation of biological samples, but even small timedifferences in stopping enzymatic reactions in collected samplesmay

* Corresponding author. Tel.: +1 519 888 4641; Fax: +1 519 7460435.E-mail address: [email protected] (J. Pawliszyn).

http://dx.doi.org/10.1016/j.trac.2014.07.0050165-9936/ 2014 Elsevier B.V. All rights reserved.

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lead to irreproducible results, such as the appearance of productsof degradation and losses of short-lived species. Moreover, as wasdiscussed in detail in the recent review on sample preparation forLC-basedmetabolomics [4], treatmentwith some solvents can resultin modication of the matrix by degradation of certain metabo-lites, selective quenching of the enzymes, poormetabolite coverageand matrix effect.

One of the new approaches, which tries to address these issues,is solid-phase microextraction (SPME). This method can be used forextraction purposes solely when used ex vivo and in vitro, and, insuch cases, it requires that the quenching step be performed sep-arately. However, when used in vivo, it combines extraction andmetabolism quenching of analytes by selective interaction of theextraction phase with small molecules and elimination of large mol-ecules. The direct extraction from living systems is sample-draw freeand minimizes the number of preparation steps prior to analysis.As a result, captured metabolites are not modied chemically, asmay happen during ex vivo analysis, and are protected fromenzymatic activity, thus obtaining a truer image of the systemunder investigation, including unstable compounds at the time ofsampling.

We should also mention that the SPME coating equilibrates withonly the free fraction of the analytes, and that results in lower sen-sitivity compared to exhaustive methods. However, the sensitivityof the MS instruments currently used for metabolomics compen-sates for this drawback and the information obtained about the freefraction of the metabolites directly correlates with theirbioavailability and, consequently, biological activity. The major lim-itation of the approach seems to be a lower coverage of the analyteswhen compared to standard solvent-extraction protocols [57].

Despite the short track record of SPME metabolomics studies,the applicability of the method in this area was already proved withits application towards various complex matrices (i.e., biouids, softtissues and plants) (Fig. 1).

Table 1 includes a summary of the metabolomics studies per-formed with SPME and described in this review, which covers thecurrent status of SPME technology in metabolomics analysis, de-scribes its limitations and progress inmethod development, discussesthe results of the most recent studies, and indicates future direc-tions of SPME in the metabolomics eld.

2. Extraction phases used for metabolomics

Concurrent with the development of new SPME formats is thedevelopment and the improvement of SPME extraction coatings.Since SPME was originally designed for GC applications, the com-mercial availability of a variety of coating chemistries and thicknesseshas offered reasonable exibility to researchers during experimen-tal design and method development. Conversely, in metabolomicsngerprinting and proling studies, where the aim is to detect asmany compounds as possible, it is desirable to nd the least selec-tive coating possible. Apart from a few rare studies, absorbentcoatings, such as polydimethylsiloxane (PDMS), polyacryl (PA) andCarbowax (CW), were rarely employed in proling studies; theseabsorptive coatings display selectivity trends based on polarity, soanalyte discrimination could result in poor metabolomics cover-age [8,38,45].

Accordingly, from biological specimens to fruit samples, the vastmajority of GC-based studies report the adsorptive DVB/Car/PDMS as the coating of choice when the objective is to detect avariety of compounds comprising a broad range of volatility and po-larity [917,3840]. The DVB/Car/PDMS coating contains a layer ofCarboxen particles underneath a layer of DVB particles. Consider-ing that the ability of adsorbent coatings to extract a particularanalyte is strongly dependent on the average size of the pores, largeranalytes will be retained in the outer DVB layer, while the smalleranalytes migrate through this layer and are retained by the innerlayer of Carboxen [63], so DVB/Car/PDMS extends the analyte

Fig. 1. General overview of applications of solid-phase microextraction (SPME) in metabolomics.

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Table 1Typical application of solid-phase microextraction (SPME) in metabolomics studies

SPME-ber coating Analyte Sample volume Sample matrix Analytical method Ref.

Plant100 m PDMS Volatile compounds 200 mL apple juice Apple GC-MS; HS [8]50/30 m CAR/DVB/PDMS VOCs 105 5 mg of homogenized

leaf sampleLeaf of Vitis vinifera cv. PinotNoir

GC-MS; HS [9]

50/30 m CAR/DVB/PDMS Furans, furanones, and pyranones 500 mg Coffee GC-MS; HS [10]50/30 m CAR/DVB/PDMS N-alkanes, alcohols, aldehydes, acids, pyrazines, furans and

pyridines500 mg coffee800 mg hazelnuts

Hazelnuts, coffee GCxGC-MS; HS [11]

50/30 m CAR/DVB/PDMS Volatile organic metabolites: monoterpene hydrocarbons,oxygenated monoterpenes, sesquiterpene hydrocarbons,oxygenated sesquiterpenes, pyrazines, higher alcohols, ethyl estersand carbonyl compounds

10 g of chopped fresh leaves Fresh leaves from Menthapiperita L. and Mentha spicata L.

GC-qMS; HS [12]

50/30 m CAR/DVB/PDMS Terpenoid metabolites 0.5 g of hop-essential oil Hop-essential oil from Saazvariety

GC-qMS; HS [13]

50/30 m CAR/DVB/PDMS Volatile metabolites 15 g Tomato GCqMS; HS [14]50/30 m CAR/DVB/PDMS Volatiles: alcohol, terpenes, aldehydes, esters and ketones 16 g Banana GC-MS; HS [15]50/30 m CAR/DVB/PDMS Volatile and semi-volatile analytes: ketones, aldehydes, furans,

pyridines and pyrroles1 g ground coffee Coffee GC-TOFMS [16]

50/30 m CAR/DVB/PDMS Volatile aldehydes and ketones; acids and alcohols, pyrazines,semi-volatile aldehydes and ketones, and terpenes

1 g Non-alkalized natural cocoapowder and conched chocolatepowder

GC-qMS; HS [17]

75 m CAR/PDMS VOCs 1 g Green teas GC-MS; HS [18]85 m CAR/PDMS Volatile terpenes 4 g of sage dried leaves/7 mL

sampleSalvia ocinalis L. Dried leaves GC-MS; HS [19]

65 m PDMS/DVB 224 different volatile and non-volatile compounds 1 g fruit powder Fresh sweet pepper (Capsicumannuum)

GC-MS; HS [20]

50/30 m CAR/DVB/PDMS Volatile compounds: esters, alcohols, acids, aldehydes, ketones,pyrazines

2 g Cocoa beans GC-MS; HS [21]

65 m PDMS/DVB Volatile acids, esters, alcohols and alkanes 3 cocoa beans Cocoa beans GCxGC-TOFMS; HS [22]50/30 m CAR/DVB/PDMS Volatile and semi-volatile metabolites 3 mL (HS)/10 mL (DI)

homogenateApple GCxGC-TOFMS; HS; DI [23]

50/30 m CAR/DVB/PDMS Volatiles (odor-active and aroma compounds) 0.1 g Raw hazelnutsGianduja pasta

GCxGC-MS; HS [24]

50/30 m CAR/DVB/PDMS Volatile components: aromatic hydrocarbons, thiophenes,benzofurans, phenols, pyridines, alkanes, furans, alcohols andesters

4 g Roasted barley GCxGC-TOFMS; HS [25]

65 m PDMS/DVB Volatile organic metabolites 0.5 g banana puree Banana 1D-GC-qMS; dHS [26]85 m CAR/PDMS 32 and 38 volatile compounds, fresh tomato and pasta,

respectively2 g tomatoes3 g concentrate paste

Tomato, concentrate paste GC-MS ion trap; HS [27]

65 m PDMS/DVB VOCs 500 mg of frozen tissuepowder

Peach fruit GC-MS; HS [28]

50/30 m CAR/DVB/PDMS Chiral volatile analytes 6 g Fresh fruits: peach, apricot,raspberry, coconut, strawberry,melon

GC-MS; HS [29]

65 m CAR/DVB Monoterpenoids 50 g White grapes: Vitis vinifera L. GCxGC-TOFMS; HS [30]50/30 m CAR/DVB/PDMS Volatile compounds: terpenes, esters, ketones, aldehydes, acids

and alcohols50 g Apple GC-MS; GC-FID; HS [31]

65 m PDMS/DVB Volatile and semi-volatile compounds 1 cocoa bean Cocoa beans GCxGC-TOFMS [32]65 m PDMS/DVB Volatile compounds 500 mg Apple GC-MS; sHS [33]65 m PDMS/DVB Volatile compounds: alcohols, ketones, aldehydes, esters, lactones,

carboxylic acids, phenolics and terpenoids500 mg sample of frozen tissuepowder

Peach fruit GC-MS; HS [34]

50/30 m CAR/DVB/PDMS Volatiles 10 g homogenized samples Apple (Malus domestica) GC-TOFMS; HS [35]70 m CW/DVB, 75 m CAR/PDMS, 85 m PA, 65 mPDMS/DVB, 100 m PDMS

Volatile metabolites 2 g Lemon, kiwi, papaya,Chickasaw plum

GC-qMS; HS [36]

75 m CAR/PDMS Salmonella volatile metabolites 5 mL Packaged fresh sprouts GC-TOFMS; HS [37]

(continued on next page)

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Table 1 (continued)

SPME-ber coating Analyte Sample volume Sample matrix Analytical method Ref.

Biouids8 types of ber:65 m DVB/PDMS85 m PA75 m CAR/PDMS85 m CAR/PDMS100 m PDMS7 m PDMS60 m PEG50/30 m CAR/DVB/PDMS

VOCs ~25 g Human fecal GC-MS; GC-FID; HS [38]

50/30 m CAR/DVB/PDMS VOCs n/a Hand odor, oral uid, breath,blood, and urine

GC-MS; HS [39]

50/30 m CAR/DVB/PDMS Aliphatic alkanes and aldehydes 500 L Urine (smoker vs. non-smokeradults)

GCxGC-TOFMS; HS [40]

75 m CAR/PDMS Urinary volatile metabolites: aldehydes, ketones, terpenoids,volatile fatty acids, furan compounds, volatile phenols, benzenederivatives, sulfur-containing compounds, and naphthalenederivatives

4 mL Urine/breast cancer diagnosis GC-qMS; HS [41]

MM 45 m thickness, 15 mm Lysophospholipids, triacylglycerols, mediators of plateletaggregation, and linoleic-acid metabolites

300 L Plasma samples LC-MS (Orbitrap); DI [42]

MM 45 m thickness, 15 mmC18 45 m thickness, 15 mm

Metabolites including amino acids, glycerophospholipids,eicosanoids, fatty acids and glycerolipids

300 L Saliva LC-MS (Orbitrap); DI [43]

75 m CAR/PDMS Volatile organic metabolites: aldehydes, ketones, terpenoids, acids,alcohols, benzene derivatives, furans and sulfur compounds,phenols, esters and naphthalene derivatives

4 mL Urine samples from cancer/health patients

GC-qMS; dHS [41,44]

MM 45 m thickness, 15 mm Metabolites including short-lived and/or unstable In vivo Blood of mice LC-MS (Orbitrap); DI [5]85 m PA Ketones, aldehydes, alcohols, hydrocarbons, esters and fatty acids 2 mL Urine GC-MS; HS [45]

Tissue, biopsy, breath, bacteriaand cell culture65 m PDMS/DVB Volatile compounds: alkanes, methylated alkanes, alkenes,

alcohols and aldehydes3 mm biopsy samples Skin from patients with

melanomaGC-MS; HS [46]

65 m PDMS/DVB VOCs 2 mm punch biopsy Skin from patients withmelanoma lesions

GC-MS; HS [47]

PDMS membrane20 mm 15 mm 0.45 mm

VOCs In vivo Skin GC-MS, ion trap [48]

PDMS membrane20 mm 15 mm 0.45 mm

VOCs In vivo Skin/patient with leg ulcer ofpredominantly arterial etiology

GC-MS [49]

6 mm diameter membranePDMS 254 m thickness

Volatile, semi- volatile and low-volatile compounds; includingdietary biomarkers of garlic and alcohol intake

In vial/In vivo Skin GC-qMS; HS; Directcontact

[50]

CAR/PDMS VOCs ~2 g Stomach cancer tissuespecimen and normal stomachtissues from human patients

GC-MS; HS [51]

100 m PDMS VOCs 1 L of breath Lung-cancer patients GC-MS; DI; sHS [52]75 m CAR/PDMS VOCs, including aromatic hydrocarbons, alcohols, aldehydes,

ketones, esters, ethers, sulfur compounds, nitrogen-containingcompounds and halogenated compounds

18 mL Lung-cancer patients GC-MS; HS [53]

75 m CAR/PDMS VOCs 10 mL Lung-cancer patients GC-MS, ion trap; HS [54,55]CAR/PDMS VOCs 1 g/105 cells/g Skin cell culture GC-qMS; HS [56]65 m CAR/DVB Volatile metabolites 106 cells/growth area: 30 cm2 Colon-cancer cell line GC-MS; HS [57]65 m PDMS/DVB VOCs: acids, alcohols, aldehydes, halogens, aromatic

hydrocarbons, ketones, suldes, alkanes and aminesn/a Skin of cow, pig and chicken;

remaining human tissuesample

GC-MS; HS [58,59]

85 m CAR/PDMS Decomposition and oxidation products of fatty acids 10 g Pericardial fat tissue GC; HS [60]50/30 m CAR/DVB/PDMS Bacterial volatiles: ketones, alcohols, heteroaromatics, benzenes

and aldehydes10 mL Pseudomonas aeruginosa

cultureGCxGC-TOFMS; HS [61]

50/30 m CAR/DVB/PDMS Extracelular metabolites of bacteria 5 mL E. coli BL21 sample GC-MS; HS [62]MM 45 m thickness, 7 mm Metabolites In vivo Pig liver and lung LC-MS (Orbitrap); DI [6]

1D, One-dimensional; CAR, Carboxen; CW, Carbowax; dHS, Dynamic headspace; DI, Direct immersion; DVB, Divinylbenzene; FID, Flame-ionization detector; GC, Gas chromatography; HS, Headspace; MM, Mix-mode (C18 withbenzenesulfonic acid); MS, Mass spectrometry; n/a, data not found; PA, Polyacryl; PDMS, Polydimethylsiloxane; PEG, Polyethylene glycol; qMS, Quadrupole mass spectrometer; sHS, Static headspace; TOF, Time-of-ight; VOCs,Volatile organic compounds.

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molecular weight range and still enables extraction of analytes attrace levels. Conversely, if the study focuses only on the most vol-atile fraction, PDMS/Car would be an appropriate choice of coating,since the micropores of the Carboxen retain smaller analytes betterthan other coatings [18,19,41]. However, a high degree of discrim-ination towards high-molecular-weight compounds would beexpected, in addition to desorption problems and degradation ofmethod precision and accuracy for such analytes [63]. Skin, sweetpepper, cocoa and banana are some examples of matrices proledemploying a PDMS/DVB coating [2022,46,47]. Even though thiscoating is remarkably sensitive, the weak characteristics of DVB asan adsorbent towards low-molecular-weight molecules makes thor-ough study and understanding of thematrix composition imperativein order to avoid inter-analyte displacement [63].

Although all the above-mentioned studies employed headspace(HS)-SPME, the implementation of direct-immersion (DI)-SPME inmetabolomics coupled to GC can ensure less biased extraction cov-erage and improve the capture of high-molecular-weight and polarmetabolites [23]. However, DI-SPME can be cumbersome, depend-ing onmatrix complexity. The adsorption of macromolecules on thesurface of a solid extraction phase has the potential to affect analyteuptake, reproducibility, desorption eciency and representative-ness of the sample once non-volatile and thermally-labile compoundsundergo reactions in the injector, leading to their decompositionand formation of artifacts. For this reason, the availability of PDMS-modied solid coatings that improve matrix compatibility forDI-SPME-GC would probably expand the scope of DI-SPME in GC-based metabolomics [64,65].

Although home-made coatings employ certain strategies [e.g.,sol-gel, polymeric ionic liquids, and molecularly-imprinted poly-mers (MIPs)] in target studies, they are not commonly reported inmetabolomics studies due to the high inter-coating variation thatresults from the lack of proper fabrication protocols/standards.

In light of the limitations of GC-based platforms in providing com-prehensive proling that includes thermally labile and polar analytes,SPME-coating development has taken advantage of well-establishedsolid-phase extraction (SPE) sorbents for SPME-LC-MS applica-tions. The most important advantages of such particles are:

robustness, since they are manufactured under standardizedconditions;

wide range of chemistries; and, commercial availability.

Immobilization of SPE particles in an SPME-like congurationhas been successfully achieved by using polyacrylonitrile (PAN)[66,67]. Due to the biocompatibility of this polymeric material, PAN-SPE sorbent-based coatings are suitable for direct extraction fromcomplex biological matrices and in-vivo sampling [66]. It isworth emphasizing that both ber and thin-lm SPME-samplingdevices coated with this type of extraction phase have been suc-cessfully used for several applications, showing rewarding results[6770].

Although until recently most of the work accomplished usingPAN-based biocompatible SPME coatings involved targeted analy-sis, recent studies demonstrated the potential of the technique forglobal metabolomics. With the aim of identifying potential SPMEcoatings for metabolomics, Vuckovic and Pawliszyn reported a thor-ough evaluation of various SPE-based coating chemistries in termsof metabolite coverage [71]. In this study, 42 different SPE par-ticles, including those based on silica, polymer and carbon, wereimmobilized with Loctite 349 adhesive and used as SPME-extractionphases for a broad set of metabolites (Log P range 7.9 7.4). Resultsshowed that mix-mode (C18 or C8with benzenesulfonic acid group),phenyl boronic acid (PBA) (able to form covalent bonds with diolgroups) and polystyrene-divinylbenzene (PS-DVB) coatings

performed the best in terms of coverage of hydrophilic and hydro-phobic metabolites.

Although SPME generally exhibits better extraction eciency asthe polarity of the compound decreases, these three coatings havebeen shown to be able to provide balanced metabolome coverage(at physiological pH) as long as most polar analytes are present atreasonable concentration levels. In addition, since recoveries for polarmetabolites, such as sucrose and glutamic acid, were in the range0.55%, sucient ionization of low-recovery metabolites also playsan important role in LC-MS.

Attempts have been made to develop prototype biocompatiblecoatings by immobilizing different sorbent chemistries in formatssuitable for in vivo and in vitro studies [72]. According to the nd-ings cited above, the mix-mode coating that contains C18 andbenzenesulfonic acid groups has been pointed out as a suitablecoating for global metabolomics applications [71]. The perfor-mance of this coating has been compared with ultraltration andprotein precipitation in terms of the number of features detectedfrom human plasma. Results showed that this coating is able toextract hundreds of metabolites, and provides complementary in-formation by exhibiting improved coverage of hydrophobiccompounds, when compared with the other sample-preparation ap-proaches. Similar results were found when this coating was usedfor in vivo sampling of rat brain extracellular uid [73]. Com-pounds from certain groups, such as carnitines, gangliosides, fattyacids and lysophopholipids, were successfully extracted by this SPMEcoating, but were not captured by microdialysis (MD). Conversely,other compounds, such as hydrophilic dipeptides and aliphatic aminoacids, were detected by MD but not with SPME.

In addition to SPME coatings for in vivo sampling, current re-search is being carried out on the establishment of optimumextraction phases in SPME thin-lm or blade formats for high-throughput global metabolomics. By taking advantage of theoptimized coating-preparation conditions already reported byMirnaghi et al. [67], various coating chemistries were evaluated interms of metabolite coverage in bacteria cell metabolomics (manu-script under preparation). The evaluated extraction phases wereprepared using sorbents with a polymer-based structure, such asPS-DVB, hydrophilic-lipophilic balance (HLB), and PBA, and silica-based structures, such as silica-based ionic liquid [74] and silica-based reversed-phase (RP) coating. In addition, mixtures of differentsorbents were evaluated with the aim of expanding metabolite cov-erage. Interestingly, a mixture of HLB and PS-DVB particles resultedin the best metabolite coverage with ~12,000 features detected byRP chromatography corresponding to polar and non-polar com-pounds (5.5 < Log P < 10; putative identication based on theaccurate mass and retention time). Among the metabolite classesputatively identied using this particular coating are amino acids,nucleosides, carbohydrates, vitamins, lipids, fatty acids and ste-roids. Single-sorbent coatings containing HLB and PS-DVB alsoshowed rewarding results with ~10,000 and ~10,500 featuresdetected, respectively. Fig. 2 summarizes SPME coatings anddevices.

3. Plant metabolomics

Plants are not only an important source of food and energy, theyalso provide valuable products to human life, such as pigments,wood, bers, and oils, as well as being a source of medicinal andhealth-promoting compounds. Plants have a vast diversity in theirrange of metabolites and their concentrations, as there are hun-dreds of thousands of metabolites in different categories. As such,there is no single analytical technique that has the capability of ex-tracting and detecting the whole metabolome [75]. The majority ofmetabolomics studies in plants coupled to GC-MS platforms, to date,have focused on the proling of volatile organic compounds (VOCs)

172 B. Bojko et al./Trends in Analytical Chemistry 61 (2014) 168180

emitted by plants, also known as plant volatilomics. Volatilomicsincludes the qualitative and quantitative analysis of the plantvolatilome, which is dened as the complex blend of VOCs origi-nating from different biosynthetic pathways and produced by plants

as a survivor mechanism. In other words, the plant volatilome isinvolved in some critical processes, such as plant-plant interac-tions, signaling between symbiotic organisms, attraction ofpollinating insects, and defense strategies against biotic and abiotic

Fig. 2. Workow of metabolomics study based on solid-phase microextraction (SPME).


stress. The outcome of volatilomics studies therefore has broad ag-ricultural and industrial implications. In such studies, it is essentialthat the implementation of analytical methodologies be suitable fordetecting variations in the composition of the emitted volatiles

ideally, in a dynamic manner, for example, monitoring changesoccurring when the vegetable organism is stressed [76].

Regarding sample preparation, HS-based methods play a fun-damental role in this eld, in static or dynamic modes [17,7678].

Fig. 2. (continued)


HS-SPME was the rst technique capable of pre-concentratingvolatiles, and, nowadays, it has achieved limits of detection (LODs)comparable to the detection ability of molecular sensors presentin living organisms. In addition to low LODs, some advantages ofHS-SPME that make it rst choice for volatile analysis are:

the diversity of extraction phases commercially available thathelp analysts tailor-design their methods;

autosamplers that provide full automation of methods, allow-ing for high sample throughput and improving methodrepeatability; and,

ber-SPME also offers the advantage of portability, as the smalldesign of SPME bers is ideal for on-site use during in vivo anal-ysis of dynamic changes in plant organisms.

These advantages of HS-SPME coupled to GC, especially GC-MS, are well documented in the literature; such methods have beenemployed in comprehensive volatile-prole characterization of anumber of plant matrices, including cocoa, coffee, herbs, fruits andvegetables [919,21,2431,79].

Cocoa products have been investigated by HS-SPME, providingdetailed proling and quantitative ngerprinting of volatiles fromthe aroma prole [17,22,24,32]. Humston et al. developed a methodcapable of differentiating cocoa beans according to geographicalorigins and storage conditions (either dry or high moisture). In brief,results indicated how moisture damage to cocoa beans alters thevolatile chemical signature of the beans in a way that can be trackedquantitatively over time. Changes could be readily determined usingHS-SPME technology coupled to comprehensive GC coupled to time-of-ight MS (GCxGC-TOF-MS), since the chemical signature of thebeans is monitored via sampling the HS of the vapor above a givenbean sample [22,32].

One of the most commonly adulterated food commodities iscoffee. HS-SPME, coupled to 1D or 2D GC-TOF-MS, has been imple-mented to characterize coffee samples according to geographicalorigin, botanical origin, morphological characteristics and degreeof roasting, including detection of falsications [10,11,16].

Volatile compounds are of particular agronomic and biologicalinterest due to their contribution to fruit aroma and avor, and there-fore to fruit quality. A variety of fruits, such as African star-fruits,grapes, bananas, tomatoes, apples, and peaches, have been inves-tigated via HS-SPME to characterize the volatile signatures of fruitsamples from different genetic backgrounds, locations, maturitystages and physiological responses (Fig. 3). Moreover, the combi-nation of HS-SPME-GC-MS to statistical methods provided basicinformation for biochemical studies and for commercialization[8,9,1416,26,30,31,3336].

In spite of the vast literature regarding volatile proling of fruitsand vegetables, it is worth mentioning the application of HS-SPME as part of an integrated -omics platform (volatile prolingand gene expression), in order to identify the key genes underly-ing the production of aroma compounds in peaches. In addition tothe practical implications of the results, the data provide furtherunderstanding of the physiology and the metabolism of peachfruit [34].

In addition to studies focusing on only HS-SPME, a recent studyconducted by Risticevic et al. reported the thorough use of DI-SPME in apple metabolomics studies [23]. Results clearly indicatedthat the choice of SPME conditions, in particular, the polymericcoating, has a dramatic impact on the quality and the reliability ofthe metabolomics coverage obtained. Furthermore, the authorsshowed that the implementation of DI-SPME ensures less biasedextraction coverage and improves the capture of high-molecular-weight and polar metabolites (such as lactones, carboxylic acids, fattyacids) as compared to HS-SPME. Although to date, most in vivostudies applied to plant metabolomics have focused on VOC emis-

sions, this trend is likely to change, as the development of newcoatings and miniaturized SPME assembly provide unique oppor-tunities for direct tissue sampling of endogenous compounds in livingplant organisms.

Although GC-MS platforms have been successfully employedin proling volatile and semi-volatile compounds, LC-MS is moresuitable for analysis of a large group of metabolites, including al-kaloids, saponins, phenolic acids, phenylpropanoids, avonoids,glucosinolates, polyamines and derivatives. Consequently, becauseof the very rich biochemistry of plants, covering many semi-polarmetabolites, LC-MS has an important role in plant metabolomics[80]. There is no report for SPME coupled to LC-MS for plantmetabolomics studies. As discussed in more detail in Section 2 ofthis review (Extraction phases used for metabolomics), recentlyin Pawliszyn researchs group, different coating chemistries forSPME coupled to LC-MS were developed for targeted and untar-geted metabolomics. The potential of such coatings for SPME-LC-MS applications in plant metabolomics was successfullytested in apple metabolomics studies (manuscript underpreparation).

4. Metabolomics of biouids

Biological uids (e.g., blood, plasma, serum, urine, and saliva) arevery complex matrices, containing proteins, cells, salts, acids, bases,and exogenous and endogenous metabolites at a wide range of po-larity (e.g., amino acids, steroids, and fatty acids) that are presentat very low concentrations. The selection of an appropriate sample-preparation technique is therefore critical to ensure the quality ofmetabolomics data.

The potential benets of SPME for global metabolomics studies ofblood/plasma in combination with LC-MS have already been demon-strated by Vuckovic et al. [5]. The results have shown that it is possibleto extract hundreds of metaboliteswith a single coated berwith sen-sitivity andprecision comparable to, if not better than, that of traditionalmethods, such as ultraltration and protein precipitation. This studyalso demonstrated that in vivo sampling allows detection of short-livedmetabolites, such as -NAD, AMP or glutathione, not detected byany other methodology. This result clearly indicated that blood with-drawal could have a signicant impact on the representativeness of themetabolome at the time of sampling.

DI-SPME-LC-MSwas recently applied to study the human plasmametabolome in patients undergoing cardiac surgery [42]. The resultsshowed that the metabolites extracted allowed the detection ofchanges in metabolic pathways induced by the surgery and theapplied pharmacotherapy. The most signicant changes were ob-served in metabolic proles of lysophospholipids, triacylglycerols,mediators of platelet aggregation, and linoleic-acid metabolites. Inaddition to the observation of general trends in the studied group,personalized responses to therapy characterized by different changesin metabolic pattern were also noted in individual patients. As per-sonalized therapy is one of themain aims of modernmedicine, SPME,as a fast, simple sample-preparation/extraction tool, could be em-ployed for on-site (bed-site) diagnostic assay when combined withrapid analytical instrumentation.

Saliva and urine are other matrices used inmetabolomics studies,since the collection of the samples is simple, safe, non-invasive, anddoes not require assistance of any trained personnel. A recent studyperformed on saliva samples collected from volunteers demon-strated the possibility of simultaneous extraction of around 400metabolites with a wide range of polarity (e.g., amino acids, car-boxylic acids, eicosanoids, fatty acids, and glycerophospholipids; logP in the range 3.5 10) using DI-SPME in combinationwith an RPLC-MSmethod [43]. The results also demonstrated the ability of detectedpeaks to differentiate clearly male and female individuals as wellas changes in saliva metabolome induced by 7 days of diet. SPME


Fig. 3. Solid-phase microextraction (SPME) in plant metabolomics (based on examples from [20,35] with permission of Elsevier).


was also successfully applied in HS extraction mode in combina-tion with GC-MS (HS-SPME-GC-MS) to investigate the urinarymetabolomics prole of VOCs as potential cancer biomarkers [41,44].The results of these studies demonstrated the ability of the de-tected VOCs to differentiate cancer patients from the control group.Benzene derivates, terpenoids, ketones, sulfur compounds andphenols were the chemical groups with the highest contributionfor urinary VOC proles from cancer patients [41,44]. A compre-hensive study of the urinary volatile metabolome was recentlyconducted by HS-SPME coupled to GCxGC-TOF-MS [40]. The authorstentatively identied 294 compounds, including hydrocarbons,amines, esters, ketones, aldehydes, alcohols, carboxylic acids,ethers, nitriles, halides, suldes, thiols, terpenoids, and heterocy-clic compounds.

Another metabolic proling study of urine was conducted usingHS-SPME-GC-MS in combination with NMR spectroscopy to inves-tigate gender differences [45]. The authors identied 51 volatilesmetabolites using HS-SPME-GC-MS and 18 metabolites using NMRspectroscopy. They found that gender differences were related tochanges in the metabolism of lipids and amino acids, and in the tri-carboxylic acid cycle.

5. Tissue metabolomics

Preparation of tissue samples is a very time-consuming, labor-intensive procedure. In particular for global metabolomics, whenthe primary goal is extraction of the highest possible number of me-tabolites, a multi-step solvent extraction is required to recover bothhydrophobic and hydrophilic species. Comparative studies of sixsolvent-based sample-preparation protocols for LC-MS analysisshowed that the most ecient method in terms of reproducibilityand number of metabolite features obtained used methanol/water for extraction of polar compounds with subsequent use of adichloromethane/methanol mixture for recovery of non-polar com-pounds [7]. The two-step extraction was followed by re-suspensionof the dried extracts in methanol/water. The protocol offers com-prehensive analyte coverage of a few thousand molecular features.However, this coverage involves a compromise with the total timeof the experiment. The overall time of the nal sample prepara-tion approach proposed by the same group was ~810 h using a 96-well plate format [81]. For the analysis of organs, where collectionof biopsy is relatively easy (e.g., liver), the standard protocols canbe effectively used, while, in the situations of tissue shortage (e.g.,problem with sample collection, need for repeated analysis, anddanger of side effects related to tissue collection), there is a needfor other analytical approaches able to overcome the challenge ofthe time required. As explained before, SPME can be used as an ex-traction method for ex vivo/in vitro analysis, and, when applied toin vivo analysis, it allows for the omission of a sample-collectionstep. Due to the small dimension of the probe, SPME offers minimalinvasiveness and no side effects. To date, in vivo tissue metabolomicsby DI-SPME analysis reported in the literature were performed usingthe mix-mode coating (C18 and benzenesulfonic acid) [6,74]. Inboth cases, coverage of analytes was not comparable to the onereported for solvent-based methods, but the investigationsexhibited benets of the technology not available with standardmethods.

The response of the brain to external stimuli (administration ofuoxetine) was monitored with in vivo SPME and MD as an estab-lished approach [6,73]. While MD was able to accomplish analysisof only two out of four targeted neurotransmitters, due to the lowsensitivity of the method, SPME provided information for all se-lected substances, showing superior pre-concentration abilities.However, it was also shown, during global analysis of the brainmetabolome, that integration of both analytical approaches allowsfor extension to untargeted studies of lipids and other highly hy-

drophobic species, due to the better anity of SPME-extractionphases towards non-polar compounds, and the higher anity ofMD to very polar compounds that are not extracted by the SPMEber.

In vivo SPME does not yet offer real-time data, but, as recentlypresented, the method has the potential to be used for monitoringbiochemical-prole changes as well as discovery and monitoringof biomarkers during surgery [6]. At its rst stage, the samplingmethod was optimized using liver and lung grafts. The authors in-dicated compounds discriminating in vivo versus ex vivo extractiondata, and proved therewas negligible invasivenesswhen both typesof probe, non-assembled and hypodermic needle assembled SPME,were used forDI sampling. As for the blood [5] and brain [73] studies,the mix-mode coating was used. The extraction from liver tissueshowed a larger number of metabolites obtained compared to lungtissue, and, in both cases,more compoundsweredetected inpositive-ionization mode using an Orbitrap mass spectrometer coupled toan LC system. The on-site sampling (extraction) took 20 min and30 min for lung and liver, respectively, and was followed by trans-portation to the analytical laboratory for furtherprocedures.However,considering that LC-MS analysis, particularly determination oftargeted biomarkers, can be as fast as a few minutes, and thatthe bottleneck of tissue analysis is sample preparation, theproposed variant can be a valuable addendum to tissue-extractionprotocols.

In vivo SPME analysis was also used for proling skin emis-sions [4850]. It was demonstrated that utilization of the extractionphase in thin-lm format allows metabolic proling, which permitsdifferent areas of the body to be distinguished [50] as well as changesin clinical conditions of the skin to be monitored [49]. The non-invasive nature of the method and its simplicity are features thatshowcase the strong potential of the approach in the medical di-agnostic area.

6. Metabolic proling of biopsy tissue, cell culture and breath

SPME has also been used for collected biopsy, and, in such cases,HS mode is the most commonly used approach. The prole of VOCspermitted the proposal of candidate biomarkers for melanoma andindicated involvement of the compounds in altering the metabol-ic prole of the studied tissue [21,47]. Comparative analysis of VOCssecreted from healthy and cancerous stomach tissues showed a sig-nicant difference in the extracted amounts of 1-propranolol andcarbon disulde. The proles were also matched with the HS com-position of Helicobacter pylori to verify the bacteria as a pathogenof gastric cancer in the cases investigated [51].

In similar studies, lung tissues collected from patients withlung cancer were cultured in vitro in parallel to three types oflung-cancer cell lines. Their proles of VOCs were comparedwith the composition of exhaled breath analyzed in the samepatients in order to nd the correlation between the studied ma-trices [52]. The analysis of exhaled breath alone was also used forthe investigation of the metabolic prole and potential biomarkers,which can serve as a non-invasive diagnostic tool in clinicalpractice [5355].

As mentioned above, SPME can be successfully used to obtainsignature ngerprints of the HS of cell cultures. This approach wasutilized for characterization of broblasts during their growth indifferent media [56] and identication of metabolism changes incolon cancer cells under serum-free and serum-reduced growthconditions [57].

HS-SPME analysis of tissue samples was also employed to dis-criminate human remains from decomposed tissues of animals [58]and characterization of several human-tissue samples of differenttypes and originating from different parts of the body [59]. In bothcases, it was proposed to use the method for preparing a mixture


of VOCs, which could be used as a training set for the HumanRemains Detection and Victim Recovery dogs. HS-SPME-GCwas alsoapplied to characterize decomposition of fatty acid in the pericar-dial fat tissue collected from cardiac-surgery patients and piglets[60]. It was hypothesized that lipid deposits in brain microcircu-lation can originate from the alteration of these compounds inducedby electrocautery.

The emission of volatile compounds by bacteria cultures wasalso investigated by SPME. One of the studied aspects was detec-tion of contamination of packed food with Salmonella typhimurium[37]. The combination of SPME-GC-MSwith amulti-layer perceptron(MLP) neural network with a back-propagation algorithmallowed for the prediction of a number of bacteria in unknownsamples.

Another study performed with the SPME-GCxGC platform pro-vided comprehensive insight into the volatile ngerprint ofPseudomonas aeruginosa and the enhancement of the existing listof VOCs for this species for 28 new compounds [61]. The auto-mated SPME-GC-MS platform was used for the analysis of theinuence of different concentrations of cinnamaldehyde on thegrowth of E. coli at various growth phases [62]. SPME-GC-MS wasalso used for rapid determination of microbial contamination incosmetic products (Fig. 4) [82]. The results showed that somevolatiles were common for both bacterial cultures and contami-nated samples, while others were characteristic for the specicproduct, suggesting the inuence of the substrate on the bacterialmetabolism.

7. Conclusions

In this review, we presented new improvements of SPME thatallow the technology to be successfully applied to metabolomicsanalysis in various areas of study. Despite the fact that SPME is arelatively young technology in global metabolite screening, and therebeing only a limited number of literature reports on the topic com-pared to standardmethods, SPME has been shown to offer a numberof features that permit the determination of new information un-available to date with the currently applied protocols (e.g., obtainingsnapshot of the metabolome at the time of sampling, good spatialresolution, and extraction of unstable compounds). The solvent-less nature of SPME makes the method very attractive for various

on-site analyses, such as searching for new biologically-active sub-stances in vivo in the natural environment, or clinical analysis, suchas intra-surgical monitoring of biomarkers. Because themethod offersvery ecient sample clean-up and can be easily coupled to massspectrometers with no chromatographic separation required for tar-geted analysis, the overall analytical process can even be shortenedto a few minutes.

Further development of the technology in themetabolomics areastill has a long road ahead full of great challenges, since it is throughreal-life applications that further demand is established, showingresearchers where next to concentrate their efforts. To take full ad-vantage of this new technology and to introduce it in practice inmany areas of science, collaboration is necessary between analyt-ical chemists and experts in biology, medicine and other specialties.

Acknowledgements

The authors would like to acknowledge Natural Sciences and En-gineering Research Council of Canada (NSERC IRC 184412-10 050165)and Supelco (Sigma-Aldrich) for the nancial support. K. Gorynskiwould like to acknowledge the Polish Ministry of Science and HigherEducation for his post-doctoral scholarship, Mobility Plus (1082/MOB/2013/0), which supported his stay at the University ofWaterloo,Canada.

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Solid-phase microextraction in metabolomics Introduction Extraction phases used for metabolomics Plant metabolomics Metabolomics of biofluids Tissue metabolomics Metabolic profiling of biopsy tissue, cell culture and breath Conclusions Acknowledgements References

SPME in Omics

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