Current and emerging mass-spectrometry technologies for metabolomics

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Trends in Analytical Chemistry, Vol. 27, No. 3, 2008

Current and emerging mass-spectrometry technologies for metabolomics Mohamed Bedair, Lloyd W. Sumner Mass spectrometry (MS) has secured a central role in metabolomics that is predicted to grow based upon continuous developments and improvements in MS technologies. This article reviews both current MS technologies incorporated into many metabolomics programs as well as emerging MS technologies that hold additional promise for the advancement of metabolomics. We discuss examples of metabolic fingerprinting using direct MS analysis of complex mixtures with traditional and emerging ionization sources, spatially resolved metabolite profiling via MS imaging, metabolite profiling using chromatography coupled to MS, multi-dimensional chromatography coupled to MS, and ion-mobility MS. ª 2008 Published by Elsevier Ltd. Keywords: Mass spectrometry; Metabolic fingerprinting; Metabolic profiling; Metabolite fingerprinting; Metabolite profiling; Metabolomics

Mohamed Bedair, Lloyd W. Sumner* The Samuel Roberts Noble Foundation, Inc., Ardmore, OK 73401, USA

*

Corresponding author. Tel.: +1 580 224 6710; Fax: +1 580 224 6692; E-mail: [email protected]

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1. Introduction The metabolome represents the cumulative end products of gene expression, and the goal of metabolomics includes the comprehensive evaluation of the metabolome. Although comprehensive coverage is not yet possible, significant advancements in the large-scale profiling of metabolites have been achieved and offer unique insight into the metabolic biochemistry of organisms. Large-scale quantitative and qualitative measurements of sizeable numbers of cellular metabolites provide a high-resolution biochemical phenotype of an organism that can be used to monitor and to assess the response of the biological system and the function of specific genes. Although mRNA/transcripts represent the mechanism for information transmission from the genome to the cellular machinery for protein synthesis, mRNA levels do not always correlate well with protein levels. Furthermore, once translated, a protein

may or may not be enzymatically active, as numerous post-translational modifications, protein sorting, protein-protein interactions, and controlled proteolysis all contribute to the regulation of active enzyme levels. Due to these factors, alterations in the transcriptome or the proteome may or may not always lead to consequential changes in the metabolic phenotype. Furthermore, the majority of transcript and protein annotations are currently inferred based on sequence or structural similarity, and it is estimated that less than 10% of annotated genes have experimental evidence supporting assigned function. Thus, the accuracy and the specificity of these annotations are of some uncertainty. In the absence of functional annotated database information, transcript or protein profiling often yields limited information. Microarray or proteomics experiments may also yield putative or generic protein identification, such as a kinase or hydroxylase. However, metabolomics has the ability to reveal that the accumulated enzyme is more specifically related to a specific biochemical pathway. Thus, profiling the metabolome can often provide the most conclusive and functional information of the omics technologies. Due to the chemical complexity of the metabolome, it is generally accepted that a single analytical technique will not provide comprehensive visualization of the metabolome, so multiple technologies are generally employed [1–6]. The selection of the most suitable technology is generally a compromise between speed, chemical selectivity, and instrumental sensitivity. Tools, such as nuclear magnetic resonance spectroscopy (NMR), are rapid,

0165-9936/$ - see front matter ª 2008 Published by Elsevier Ltd. doi:10.1016/j.trac.2008.01.006

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highly selective, and non-destructive, but have relatively lower sensitivities. For more details on NMR metabolomics, see the review articles by Lindon et al. and Wishart in this issue of Trends in Analytical Chemistry. Other methods such as capillary electrophoresis (CE) coupled to laser-induced fluorescence detection are highly sensitive, but have limited chemical selectivity [7]. Mass spectrometry (MS) measurement following chromatographic separation offers the best combination of sensitivity and selectivity, so it is central to most metabolomics approaches. Mass-selective detection provides highly specific chemical information including molecular mass and/or characteristic fragment-ion information that is directly related to the chemical structure. This information can be utilized for compound identification through spectral matching with data compiled in libraries for authentic compounds or used for de novo structural elucidation. Further, chemically selective MS information can be obtained from extremely small quantities of metabolites with limits of detection in the pmole and fmole level for many primary and secondary metabolites. Coupling of highresolution separations, such as gas chromatography (GC), high-performance liquid chromatography (HPLC), ultra-high-performance liquid chromatography (UHPLC) or CE to MS offers substantial enhancement in metabolome coverage not obtainable through other direct analysis methods using MS or NMR. Based upon chemical selectivity, sensitivity, relative cost, and depth-of-coverage, MS has secured a pinnacle position in metabolomics. This article reviews both current MS technologies incorporated into many metabolomics programs as well as emerging MS technologies that hold additional promise for the advancement of metabolomics.

2. Direct analysis mass spectrometry Numerous recent developments in MS-ionization techniques accompanied with the utilization of accuratemass and tandem-mass analyzers provide very attractive techniques for metabolite fingerprinting. The high-throughput capacity of direct infusion mass fingerprinting of complex mixtures is similar to that of NMR fingerprinting. Direct MS analyses of complex mixtures in conjunction with chemometric data analysis offer a viable solution when high-throughput screening is mandatory (e.g., screening in large-scale clinical trials and screening of large mutant populations). The following section reviews numerous established and emerging direct analysis techniques that offer exciting opportunities for the direct analysis of complex metabolite mixtures in solution, metabolites on biological surfaces, and metabolites that are spatially distributed.

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2.1. Direct infusion mass spectrometry (DIMS) Direct infusion MS (DIMS), or the direct analysis of complex metabolic extracts without chromatographic separation via electrospray ionization (ESI) MS, provides a sensitive, high-throughput method for metabolic fingerprinting. The speed of the analysis, usually less than 5 min, and the greater sensitivity compared to NMR make it a very high-throughput approach useful for large-scale screening of mutant populations or in large clinical trials with several hundred sample analyses possible per day. Unfortunately, DIMS analysis is susceptible to ionization suppression, which arises from competitive ionization with other components in the matrix, such as ionic compounds (salts), charged organic compounds, organic acids/bases, and hydrophobic compounds. The utilization of nano electrospray can reduce ionization suppression effects due to the increased ionization efficiency of nano-DIMS [8], and the coupling of chip-based nanospray emitters to various mass spectrometers provides a fully automated platform for high-throughput DIMS metabolite measurements [9]. For DIMS, chip-based nanospray emitters, such as the commercially available NanoMate 100, are typically operated without chromatographic separations; however, these can be coupled with crude or high-resolution chromatographic separations, as in the Agilent 1200 HPLC-Chip/MS system. Another limitation of DIMS is its inability to differentiate between isomeric compounds based upon accurate mass alone. However, direct infusion tandem MS (DIMSMS) using ion traps, Fourier transform ion cyclotron resonance (FT-ICR) mass spectrometers or other tandem mass spectrometers can trap and accumulate fragment ions that often enable the differentiation of isomeric structures. FT-ICR MS is a powerful tool for DIMS due to its ultrahigh mass resolution (1,000,000) and mass accuracy (<1 ppm), and it has been successfully applied for metabolite-fingerprinting studies [10–12]. In the ICR cell, ions are trapped in a spatially uniform static magnetic induction in which ions undergo cyclotron motion. The cyclotron frequency of ions is measured and used to calculate mass-to-charge ratios most commonly through a Fourier transform of the frequency data [13]. The cyclotron frequencies of ions in the ICR cell are influenced by the number of ions in the cell due to the overall spacecharge density. Thus, large ion populations can negatively impact the mass-measurement accuracy and limit the dynamic range of FT-ICR when a wide (broadband) m/z range is utilized. Narrow m/z ranges are therefore commonly acquired and used to increase the dynamic range and mass accuracy for metabolic profiling. An optimized strategy for wide-scan (m/z 70–500) direct infusion nESI FT-ICR MS that increases the dynamic range and maintains high mass accuracy for metabolomic

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studies has been reported [8]. The method collected multiple adjacent selected ion monitoring (SIM) windows that were stitched together using novel algorithms. Using this SIM-stitching approach, detection sensitivity was enhanced by collecting the data as a series of narrow overlapping windows with a width of 30 m/z. The increased detection sensitivity allowed the reduction of ion transmission to the ICR cell, thus reducing spacecharge effects and allowing higher mass accuracy. The Orbitrap is a relatively new mass analyzer, which operates by radially trapping ions about a central spindle electrode [14]. Mass-to-charge values (m/z) are determined based upon the frequency of harmonic ion oscillations along the axis of the electric field of orbitally trapped ions. Orbitraps are a good alternative to the more expensive and higher maintenance FT-ICR mass spectrometers, with typical resolving powers of up to 150,000 and mass accuracies of 2–5 ppm. A hybrid instrument that couples a linear quadrupole ion trap to an Orbitrap has been utilized in the identification of human liver microsomal metabolites [15,16], the determination of growth-hormone in anti-doping screening [17] and the profiling of sphingolipid in yeast [18]. Ion-trap mass analyzers have also been utilized in DIMS metabolomic fingerprinting, although at relatively lower resolving power (<2,000) and mass accuracy around 0.1 Da. Their affordable price and tandem-MS capabilities make these instruments more accessible for metabolite fingerprinting. Screening a wide range of strains of endophytic fungi in perennial ryegrass seeds for differences in metabolic profiles was performed using direct infusion coupled with a linear ion-trap mass spectrometer [19]. Multivariate statistical analysis of the full mass spectra was used to determine discriminating ions, which were putatively identified using tandem MS2 and MS3. The utilization of MSn expands the utility of DIMS beyond metabolite fingerprinting and allows greater confidence in the putative metabolite identifications, although DIMSn is still subject to matrix effects and ion suppression. A comparison between direct infusion negative-ESI iontrap MS and GC-quadrupole MS analysis for the metabolic fingerprinting of five yeast mutants has been reported [20]. Putative ion identifications were based upon tandem MS for the ion trap and spectral library searches for the GC-MS. Results showed that the two methods complement each other by identifying different discriminating metabolites. Negative ESI LCQ ion-trap MS was reported as an effective method for the characterization of plant extracts with well defined clusters in comparison to positive-ion ESI and 1H-NMR profiling [21]. 2.2. Matrix assisted laser desorption ionization mass spectrometry (MALDI-MS) MALDI-MS is a popular analytical technique for biopolymer analysis including proteins and peptides. It has 240

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high-throughput capacity and a higher tolerance for salts than ESI. In metabolomics, MALDI has largely been confined to the targeted analysis of high-molecularweight metabolites due to the substantial chemicalbackground signals generated by the matrix in the low-molecular-weight region (<1,000 m/z) (e.g., the analysis of phospholipids in mammalian tissues [22], plant carotenoids [23], and plant cell wall xyloglycans [24]). Recent advancements in laser desorption techniques include desorption-ionization MS from porous silicon chips [25] and matrices that have minimal background signals in the low-molecular-weight region [26]. These offer exciting new opportunities for the utilization of MALDI ionization in metabolite screening and fingerprinting. MALDI-TOF/TOF employing the matrix 9-aminoacridine minimized the background signals and was successfully used to identify 285 peaks corresponding to negatively charged metabolites from mouseheart extracts [27]. However, the technique is still subject to ion suppression and yields poor quantitative detection of metabolites [28]. MALDI has been used for imaging MS (IMS) of proteins and small molecules in tissues [29]. Whole organisms or selected tissue sections are analyzed through an array of spots in which MS spectra are acquired at spatial intervals that define the image resolution. The m/z intensities of the acquired spectra are then plotted in the x and y coordinates to form a 2D image of the m/z values, which represents the spatial distribution of that metabolite/ion in the tissue [30]. MALDI-TOF IMS has been applied successfully for the study of drug and metabolite distributions in rat-brain tissues [31] and whole rat body [32] (Fig. 1). Recently, atmospheric pressure (AP) MALDI imaging was used to scan peptides and carbohydrates in bananas, grapes, and strawberries [33]. Using native water in the tissues as natural matrix, a spatial resolution of 0.04 mm, 5 times smaller than the laser-spot size, was achieved. The chemical imaging of a strawberryskin sample revealed the distribution of the sucrose, glucose/fructose, and citric acid species around the embedded seeds. 2.3. Desorption electrospray ionization (DESI) Desorption ESI (DESI) is a new, ambient, soft-ionization technique that combines features from both ESI and desorption ionization (DI) methods [34–36]. In DESI, an electrospray emitter is used to generate a spray of charged micro droplets that is directed towards an ambient sample surface. Molecules on the surface are subsequently desorbed, ionized, desolvated and directed to the MS inlet. There is virtually no sample preparation required for DESI, thus allowing the direct analysis of animal and plant tissues. For example, biological fluids can be deposited on an inert surface and then analyzed. However, DESI experimental conditions typically require

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Figure 1. The detection of drug and metabolite distributions at 2 h (left panel) and 6 h (right panel) post-dose in whole rat sagittal tissue sections. (A) An optical image of a dosed whole rat tissue section across four gold-coated MALDI target plates; organs are outlined in red. (B) A MS/MS ion image of drug (olanzapine) (m/z 313 fi m/z 256). (C) A MS/MS ion image of N-desmethyl olanzapine (m/z 299 fi m/z 256). (D) A MS/MS ion image of 2-hydroxymethyl olanzapine (m/z 329 fi m/z 272). Bar, 1 cm. (Reprinted with kind permission from [32], ª 2006 American Chemical Society).

optimization for each sample type, so time must be invested initially in optimizing the experimental parameters. The properties of the surface on which the sample is deposited (e.g., its polarity, roughness and ability to neutralize charged particles) have a large effect on the ionization efficiency. Electronegative polymers, such as polytetrafluoroethylene (PTFE), yield good signal stability in the negative-ionization (NI) mode, while polymethyl methacrylate (PMMA) has better signal stability for positive-ionization (PI) mode. Paper and etched glass are suitable for both polarities, with paper being the most commonly used sample surface for DESI [35]. The nature

of the spraying solvent also influences the DESI ionization process. The ratio of methanol/water as well the acidity or basicity of the solution will have a high impact on the efficiency and sensitivity of the ionization process. Finally, the geometry of the DESI sprayer directly affects the ionization efficiency and sensitivity (Fig. 2). The distances between the sample surface, the sprayer tip, the MS inlet, the incident-spray angle, and the ion-desorption angle leading to the MS all have a direct effect on the ionization efficiency. The optimum geometric parameters vary depending on the type of sample-deposition surface and the nature of the DESI-spray solvent.

Figure 2. DESI ion source. (Reprinted from [34], with kind permission from AAAS).

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DESI ionizes both small and macromolecules, and it usually produces multiply-charged ions similar to ESI, although with a higher tendency toward metal-adduct formation. DESI appears to have a higher tolerance to sample-matrix effects than ESI and atmospheric pressure chemical ionization (APCI). However, the quantitative precision of DESI, as other ambient ionization techniques, is less than that of ESI of solutions. The use of internal standards can improve the reproducibility and is used more commonly with solution-based samples and less so with solid-surface samples (e.g., tomato surface) [36]. The application of DESI in metabolomics is relatively new, but its ambient DI properties as well as its highthroughput capacity make it an attractive tool for metabolomics. DESI has been applied along with 1H NMR for the metabolic profiling of urine samples, as dried spots on paper and other surfaces, to differentiate between healthy and diseased (lung cancer) mice [37], and to survey patients with inborn errors of metabolism [38]. The screening of drugs of abuse and their metabolites in urine samples by DESI has been recently reported and results were found to correlate well with GC-MS data [39]. One promising area for DESI is in-vivo metabolomics, which was demonstrated through the direct profiling of alkaloids from plant tissues of Conium maculatum without sample preparation [40] while still identifying all of its previously reported alkaloids using tandem MS. Although this technique can be incorporated into an IMS configuration, the spatial resolution of the DESI source (0.5–1.0 mm) is currently less than that of MALDI ion imaging (50–100 lm) [41]. 2.4. Extractive electrospray ionization (EESI) Extractive ESI (EESI) is another new ESI technique that uses two separate sprayers. One sprayer nebulizes the sample solution that intersects with a second electrospray containing charged micro-droplets of the ionizing reagent solvent, usually an acidic aqueous methanol. Analyte molecules are ionized following collision with the reagent micro-droplets and then mass analyzed. Although the exact sample-ionization mechanism is still unclear, the ionization process depends on liquid–liquid extraction between the colliding micro-droplets of the sample spray and the charged reagent-solvent spray [42]. The advantage of EESI is its ability to analyze complex biological samples, such as urine and serum, directly with minimum or no sample preparation for an extended period of time. The direct infusion of such complex biological samples to conventional ESI/APCI ion sources causes an irrecoverable loss in signal intensity due to the formation of salt adducts, sample carry-over, or cumulative build-up of non-volatile components in the ion source. The long-term spray stability of untreated biological samples in EESI is very promising for metabolomics, in which a large number of 242

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samples need to be analyzed, as EESI significantly reduces data-collection interruptions due to frequent ionsource cleaning to remove carry-over and non-volatile accumulations associated with the ESI-DIMS of crude biological samples. EESI-MS along with 1H NMR was recently used to monitor the effect of diet on the metabolites founds in rate urine [43]. Using PCA analysis, samples were successfully clustered according to their dietary regimens. The use of a restricted set of peaks in the mass spectra that corresponded to a specific metabolic pathway increased the discrimination between the study samples.

3. Chromatography coupled to mass spectrometry The above section provides numerous examples of how direct analysis MS can contribute solutions to metabolite analyses. However, the depth-of-coverage (i.e. the number of differential metabolites) and biological context of the techniques discussed above are relatively low. Further, full-scan MS cannot discriminate between isobaric molecules, such as structural isomers or enantiomers. Thus, numerous chromatographic methods have been incorporated into the metabolomics strategy to increase the depth of metabolome coverage, separate structural isomers, and provide an additional dimension (i.e. chromatographic retention factor) sufficient for characterization and differentiation of a large number of metabolites, including isomers. At the same time, chromatographic separation prior to ionization also decreases the number and the chemical diversity of compounds simultaneously entering the mass spectrometer at any specific point in time. This significantly reduces matrixionization suppression/competitive ionization, which is considered a major limitation in direct mass analysis. Thus, coupling of chromatographic methods with MS can substantially increase the metabolome depth-ofcoverage, add an additional dimension for metabolite identification, and enhance the biological context through the more rigorous identification of a greater number of metabolites. This section reviews traditional and emerging separations techniques coupled to MS for metabolite profiling. 3.1. Gas chromatography coupled to mass spectrometry (GC-MS) GC-MS is one of the most widely used analytical techniques in metabolomics [3,5,44]. It combines the high separation efficiency and resolution of capillary GC that is essential for complex metabolic profiling with the high sensitivity of mass-selective detection. It is utilized to analyze qualitatively and quantitatively a wide range of volatile and/or derivatized non-volatile metabolites with high analytical reproducibility and lower cost compared to other hyphenated techniques, such LC-MS or LC-NMR.

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GC is traditionally coupled to quadrupole MS, which provides high sensitivity and large dynamic range, but nominal mass accuracy and slow scan speeds. Recently, GC time-of-flight MS (TOF-MS) has become more popular for metabolite profiling due to its higher mass accuracy and mass resolution relative to quadrupoles. Further, TOF-MS offers high scan speeds necessary for adequate sampling of high-resolution chromatographic peak widths in the range of 0.5–1 s. The use of high scan speeds also facilitates the implementation of fast GC methods, which can reduce the analysis time and increase productivity. A predominant requirement for GC-MS analysis is analyte volatility and thermal stability. A few metabolites meet these requirements (e.g., short-chain alcohols, esters, and low-molecular-weight hydrocarbons and lipids). However, the majority of metabolites must be made volatile through chemical derivatization prior to GC-MS analysis [45]. It is noted that derivatization steps add time and complexity to sample preparation and introduce an additional source of variance to the experimental procedures. The most commonly utilized derivatizing procedure for GC-MS metabolite profiling includes a two-step derivatization scheme [46]. The first step uses alkoxyamines to convert carbonyl groups to oximes in order to stabilize the reducing sugars in the open-chain conformation and also to prevent the decarboxylation of a-ketoacids. The second step replaces the active hydrogen in polar functional groups, such as carboxylic acid, alcohols and amines, with a trimethylsilyl group using N-methyl-Ntrimethylsilyltrifluoroacetamide (MSTFA). Silylation decreases the polarity and increases the thermal stability and volatility of a broad range of metabolites. The disadvantage of silylation is its sensitivity to water, which requires that reactions must be carried out under anhydrous conditions and that derivatized samples must be stored in a dry environment to prevent degradation. t-Butyldimethylsilylation (TBS) derivatives are less sensitive to moisture than TMS derivatives and are favored for the derivatization of amino acids and organic acids [47]. However, the higher molecular mass of TBS compared to TMS leads to a higher degree of partial derivatization in compounds with multiple function groups due to steric hindrance [44]. Other derivatization reagents have been applied in metabolomics, such as alkylation and esterification. However, they were used to derivatize a narrower range of metabolites than silylation. Derivatized samples are introduced into the GC column through split or splitless injection mode. The GCcolumn stationary phase determines the mode of separation. Non-polar stationary phases separate analytes predominantly according to their boiling point, while polar stationary phases separate the analytes according to their polarity. Although GC columns provide high resolution and peak capacity, the complexity in the

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biological samples still substantially exceeds the resolving power of commonly used capillary columns. Fortunately, there is substantial promise in two-dimensional chromatography [48] for increased peak capacities and metabolome depth of coverage. Two-dimensional GC (GC · GC) utilizes two columns having different stationary-phase selectivities, which are serially connected to increase the resolving power and maximize the peak capacity of the separation. The effluent from the first column, usually a long non-polar column, is focused into narrow sections using a cryogenic modulator, and then rapidly transferred to a second short column, usually a polar column, for the second-dimensional separation. Rapid temperature elevations accelerate the focused analyte bands onto the second column. The second short column separates this band within seconds before the next band enters the column. The focusing step along with the fast seconddimension separation reduces the peak width greatly and that enhances the sensitivity. A fast scanning MS is essential to acquire sufficient data points across the sharp narrow peaks. Thus, TOF-MS with acquisition rate of up to 500 Hz has been typically coupled with GC · GC. The cumulative result of GC · GC TOF-MS is characterized by higher resolution and higher sensitivity. Multidimensional GC generates large and complex datasets including the first-dimensional retention time, the second-dimensional retention time, and TOF-MS m/z values. The resultant 3D datasets are large and complex, and make manual qualitative and quantitative processing of the data from large biological experiments extremely difficult and time consuming. Instrumentspecific software (e.g., ChromaTOF (LECO) and HyperChrom (Thermo Fisher Scientific)) are utilized for peak picking, deconvolution, integration, and visualization of the multidimensional data. Other independent commercial software packages are available for the analysis of 2D-GC data (e.g., Transform (ITT Visual Information Solutions), GC Image (Zoex) and Ion Signature (Ion Signature Technology)). At the same time, several research groups have developed their own algorithms that implement chemometric tools, such as parallel factor analysis (PARAFAC) [49] and Fisher ratio analysis [50] for data processing or optimization of experimental parameters [51] of multidimensional GC analysis. A review on the advancement in chemometric techniques for the multidimensional separation has been published recently [52]. GC · GC has been utilized in the discovery of new metabolic biomarkers for obesity in mice [53,54] (Fig. 3), and in the separation of metabolite extracts from human infant urine to locate and to quantify target analytes for disease profiling [55]. GC · GC-MS has also been used to study the metabolomics differences in yeast cells grown under fermenting or respiring conditions [56,57], and it was http://www.elsevier.com/locate/trac

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Figure 3. A comparison of a GC-TOF chromatogram analyzed independently (top panel) with a GC · GC-TOF two-dimensional chromatogram (middle panel) of an NZO female mouse spleen extract. The lower panel is a bubble-plot representation of the identified peaks in the GC · GC chromatogram after the removal of known artifacts, with the bubble radius indicating the TIC intensity. (Reprinted from [53] with kind permission from Springer Science and Business Media).

applied, along with PARAFAC analysis, to study the metabolite responses of perennial rye grass to preharvest and post-harvest trauma [58]. GC · GC-MS is expected to become a premier metabolomics tool once the challenges of automated, high-throughput data processing have been surmounted. 3.2. Liquid chromatography coupled to mass spectrometry (LC-MS) The application of LC-MS in metabolomics has been growing over the past few years [3,59]. LC is a more universal separation technique that can be tailored for the targeted analysis of specific metabolite groups or utilized in a broader non-targeted manner. It also offers the added benefit of analyte 244

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recovery by fraction collection and/or concentration, which are much more challenging when using GC separations. Recent LC-MS metabolite-profiling examples include the identification of flavonoids and isoflavonoids in Medicago truncatula [60], the revelation of novel pathways by studying the differential and elicitor-specific responses in phenylpropanoid and isoflavonoid biosynthesis in Medicago truncatula cell cultures [61], and the investigation of small polar-metabolite responses to salt stress in Arabidopsis thaliana [62]. LC-MS has also been used in the non-targeted analysis of endogenous metabolites, which strives to detect, in an unbiased manner, as many metabolites as possible in the studied biological system [63,64]. The LC separation of analytes prior to their introduction to MS increases the number of analytes detected by decreasing the ionization competition/suppression that occurs in DIMS. ESI is the most commonly used ionization technique for LC-MS, although APCI [65] is also used to a lesser extent. In ESI, analytes preferentially ionize in either PI or NI modes. Some instruments provide the ability to switch rapidly between ESI polarities within the same experiment, thus increasing the overall number of detectable analytes, but at the cost of longer duty cycles. LC-MS operates at lower analysis temperatures than GC-MS, which enables the analysis of heatlabile metabolites that are commonly degraded during GC analysis. LC-MS analysis does not require sample derivatization, and that simplifies the sample-preparation steps as well as identification of metabolites, which can be complicated by chemical modifications of unknowns prior to GC-MS. The identification of metabolites in LC-MS is achieved through accurate-mass determination, tandem-MS analysis, and/or coupling to nuclear magnetic resonance spectroscopy (LC-MS-NMR). However, a major disadvantage of LC-MS relative to GC-MS in metabolomic profiling is the lack of transferable LCMS libraries for metabolite identifications. The massspectral variability between LC-MS systems in terms of the relative ion abundances associated with adduct formation, in-source fragmentation, tandem mass spectra fragment ions, and the lack of LC retention indices that compensate for instrumental and experimental variations hinder the comparison of LC-MS data between laboratories [45]. Thus, several research groups have constructed custom, own in-house LC-MS or LC-MS-MS libraries for automated metabolite identifications in metabolite-profiling experiments. The heart of the LC separation is the chromatographic column. Most commonly used in metabolomics are reversed-phase columns, such as C18 or C8. However, polar metabolites do not exhibit sufficient retention or separation on reversed-phase columns and often co-elute close to the column-void volume, thus hindering their detection with MS.

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Enhanced retention and separation of polar metabolites can be achieved by hydrophilic interaction liquid chromatography (HILIC) [66]. In these polar columns, a stagnant water layer is established within the stationary phase and the separation is achieved by partitioning the analytes between that polar layer and the mobile phase, which is aqueous-organic based. HILIC-MS has been used for the targeted analysis of polar metabolites in plant-leaf extracts [67] and the flow-through fraction of the reversed-phase – solid-phase extraction (RP-SPE) of rate urine [68], and to study the difference in cellular metabolites of exponentially growing versus carbonstarved E. coli [69]. A recent advancement in LC has been the introduction of the commercially available ultra-high pressure liquid chromatography (UHPLC) systems, which operate at relatively higher pressures (approximately 15,000 psi compared to 6000 psi for HPLC) and use columns packed with sub-2-lm stationary phases. Reduced particle sizes greatly enhance chromatographic resolution and efficiency [70], which provide enhanced opportunities for resolving complex biological mixtures in non-targeted metabolite profiling (Fig. 4). For recent applications in metabolomics, UHPLC-TOF-MS has been

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used to profile serum [71] and urine [72] obtained from three strains of Zucker obese rat, and successfully differentiate the three strains using multivariate data analysis. In another experiment, nine potential biomarkers for intestinal fistula were identified by the UHPLC-QTOFMS analysis of the patientsÕ sera [73]. Another promising area for advancing LC-MS is miniaturization. Reductions in column dimensions are accompanied by reduction in flow rates, which increase the sensitivity in ESI-MS. Long capillary columns (40– 200 cm) packed with either 1.4-lm or 3-lm porous C18 bonded silica have been used for the proteomics and metabolomics analysis of Shewanella oneidensis [74]. The custom-built system was operated at a constant pressure of 20 kpsi and produced peak capacities of 1000–1500. Recently, a study published by the same group reported an automated dual-capillary LC system that had a peak capacity of 900 detected from cyanobacterium [75]. Capillary columns packed with nine different stationary phases were evaluated for their separation performance. Packing materials having a pore size less than 0.01 lm and a surface area larger than 400 m2/g provided better retention for small polar analytes. Separations of polar analytes were also improved using

Figure 4. UPLC-QTOF-MS base-peak ion chromatogram obtained for the combined methanol extracts from soybean and Medicago truncatula (cv Jemalong A17). Separations were achieved using a Waters Acquity UPLC 2.1 · 100 mm, BEH C18 column with 1.7-lm particles, a flow of 600 ll/min, and a linear gradient of 0.1% acetic acid:acetonitrile (5:95 to 30:70 over 30 min). Mass spectra were collected on a Waters QTOFMS Premier. (Data generated in our laboratory by David Huhman).

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stationary phases that had a polar functional group imbedded in the C18 chain for better interaction with polar metabolites. An alternative is the use of long capillary monolithic columns. A capillary monolithic column was used for a plant metabolomics study of Arabidopsis thaliana leaf methanol extracts using a 90-cm silica-based monolithic column and gradient elution, and resulted in the detection of several hundred peaks [76]. Multi-dimensional chromatography, as mentioned above, is a promising and attractive technique to increase the separation peak capacity [77,78]. The total peak capacity of the multi-dimensional separation is the product of the peak capacities of the two independent separation dimensions. The implementation of 2D-LC/ MS for metabolomics has lagged behind that of 2D-GC/ MS due to its complicated experimental set-up and necessary parameter optimization. The separation orthogonality, column dimensions, particle sizes, flow rate, and modulation frequency have been studied in several reports [79–81]. In principle, fractions from the first column are regularly transferred at constant intervals to the second column via automated valves equipped with multiple sample loops. The first column is used for the primary separation mode, usually with long analysis times and gradient elution, while a second short column with larger inner diameter is used for the orthogonal separation of the modulated fractions from the firstdimensional separation. However, fast modulation is needed to minimize the loss in the first-dimensional resolution and decrease extra band broadening, which demands a very fast second-dimensional separation. A theoretical study showed that a sampling rate of less than 1.5 times the standard deviation of the firstdimensional peaks is required to minimize the loss in the first-dimensional resolution to 20–30% [82]. A more experimentally practical approach was recently described utilizing a fast monolithic column in the second dimension and an isocratic elution that allowed a modulation frequency of 2.2–4.0 times the standard deviation of the first-dimensional peaks to achieve 50– 70% of the theoretical maximum peak capacity [83]. Another approach to decrease the analysis time in the second dimension was the use of high-temperature (>100 C) RP gradient elution chromatography [84] using thermally stable stationary phases, which resulted in a second-dimensional analysis time of 20 s, thus permitting a high modulation rate. Two-dimensional LC has been utilized mainly in targeted metabolite analysis. For example, a 2D-LC/ESITOF-MS method was recently used for the analysis of Stevia rebaudiana glycosides [85]. The aqueous extracts of the plant leaves were separated on a C18 column in the first dimension and transferred to an amine column in the second dimension using a 10-port switching valve. All nine known sweet Stevia glycosides were successfully 246

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separated and analyzed quantitatively using the 2D-LC compared to a 1D-LC/ESI-TOF-MS because of better separation of the targeted compounds from the sample matrix and the ability to identify the target compounds on the basis of two independent retention times. 3.3. Ion-mobility mass spectrometry Ion-mobility MS (IMMS) is a gas-phase separation technique that separates ions according to their differential mobility in a buffer gas under the influence of a uniform weak electric field [86]. The separation is based on the size-to-charge ratio, in contrast to the mass-to-charge ratio in traditional MS, and also on the ionÕs interaction with the buffer gas. The mass and the polarizability of the buffer gas affect the ion-neutral interactions and can be used to alter the selectivity of the separation [87]. The ability of IMMS to separate ions according to shape and cross-section enables the separation of structural isomers that share the same m/z, so it complements traditional MS analysis [88]. It was shown that IMMS-MS can be used for the enantiomeric separation of chiral compounds using a drift gas that was doped with a volatile chiral reagent [89]. The application of IMMS has not been utilized as widely in metabolomics as it is in proteomics, such as the mapping of the human plasma proteome using 2D-LCIMMS-MS [90]. Rather, it has been confined to a few reports on targeted metabolite analyses. For example, IMMS was used for the direct detection of different volatile organic metabolites in human breath [91], and separation of isomeric flavonoid diglycosides [92], and ESI-IMMS-MS was used to separate opiates and their primary metabolites on a time scale of 70 s [93], which indicates the high-throughput capability of IMMS-MS. Currently, the peak capacity of IMMS is significantly lower than GC and LC, but it is continually progressing so it promises utility as a front-end separation device. Further, IMMS is a relatively rapid separation technique (with time of the order of ms), so it is conceivable that IMMS could be readily coupled as a second-dimensional separation following GC or LC to provide the best multiple techniques. 3.4. Capillary electrophoresis mass spectrometry (CE-MS) CE-MS is a powerful separation technique for charged metabolites [7,94]. CE has superior separation efficiencies compared to LC due to the plug-flow profile generated by the electroosmotic flow (EOF) as compared to the parabolic flow in LC. Capillary zone electrophoresis (CZE) has been the major CE mode used for CE-MS analysis of metabolites, due to the simplicity of the running buffer and the lack of surfactant or other additives necessary in other modes of separation. In CZE, charged molecules are separated based upon their differential electrophoretic mobility,

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while neutral molecules migrate through the capillary using the EOF without separation. Simultaneous separation of charged and neutral metabolites can be achieved using other CE modes (e.g., micellar electrokinetic chromatography (MEKC) or capillary electrochromatography (CEC)). The separation is based on the combined effect of the analyteÕs electrophoretic mobility as well as the analyteÕs partitioning between the mobile phase and a micellar phase in MEKC, or a stationary phase in CEC. Bioanalytical applications of CE require the use of highly sensitive detectors (e.g., laser-induced fluorescence or MS) to compensate for the relatively small oncapillary injection volumes. CE is commonly coupled with ESI-MS through a sheath-flow interface, in which the coaxial sheath liquid transfers the eluting analytes from the CE capillary to the electrospray ion source in an ESI friendly solvent. As in fast GC-MS analysis described above, high-scan-speed MS instruments are required to obtain adequate samples from the sharp peak profiles resulting from highly efficient CE separations. Other approaches that increase the sensitivity of CE in metabolomics analysis include sheathless coupling with MS, which eliminates the dilution introduced by the sheathliquid flow [95,96], or on-line sample pre concentration via transient cation isotachophoresis with dynamic pH junctions [97,98]. Sheath-flow CZE-ESI-MS/MS was utilized recently for the target profiling of amino acids in urine [99] and plant-cell cultures [100] (Fig. 5). It was also used for the quantitative analysis of 23 sulfur-related cationic metabolites during cadmium stress response in yeast [101], and for the identification of 26 flavonoid compounds in the extract of the antihyperglycemic plant Genista tenera [102]. Relatively few examples have been reported for the non-targeted profiling of charged metabolites using CEMS. Cationic and anionic CE-MS analysis of Bacillus subtilis extracts detected 1692 metabolite features of which 150 were identified [103]. A similar approach was utilized for the analysis of rice leaves, successfully detecting 88 metabolites that are involved in several primary metabolic pathways (e.g., glycolysis, tricarboxylic acid cycle, pentose phosphate pathway, photorespiration, and amino acid biosythesis) [104]. The same analytical procedure was recently used to study the alteration of metabolic pathways in transgenic rice lines that over-express dihydroflavonol-4-reductase [105]. Non-aqueous CE-ESI ion-trap MSn was utilized for quantitative and qualitative profiling of isoquinoline alkaloids in single-plant tubers of four central European Corydalis species [106]. The method used a non-aqueous electrolyte system comprising 50 mM ammonium acetate, 1 M acetic acid and 10% methanol in acetonitrile, and was coupled to MS with a sheath liquid comprised of isopropanol:water (1:1) mixture.

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Figure 5. CE-MS electropherogram of Medicago truncatula root cell culture extract. The extracted ion electropherograms for each m/z are indicated on the right. (Reprinted from [100] with kind permission, ª Wiley-VCH Verlag GmbH & Co. KGaA).

4. Metabolite identification Metabolomics is based upon large-scale qualitative and quantitative measurements, and the chemical identification of metabolites is fundamental for the extraction of biological context from the data. The bases on which metabolites are identified varies among the metabolomics community. In an effort to standardize the reporting and interpretation of metadata, the Metabolomics Standard Initiative (MSI) (http://msi-workgroups. sourceforge.net/) has formed five working groups that follow the general workflow model in metabolomics: biological context; chemical analysis; data processing; ontology; and, data exchange. Most metabolite identifications reported are typically non-novel as they have been previously characterized, identified, and reported at a rigorous level in the literature. Thus, non-novel metabolites not being identified for the first time are typically identified based upon the co-characterization with authentic samples. However, it is generally believed that a single chemical shift, m/z value, or other singular chemical parameter is insufficient for validating non-novel metabolite identifications. Thus, The Chemical Analysis Working Group has recently proposed a guideline for the identification of http://www.elsevier.com/locate/trac

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non-novel metabolites [107], in which a minimum of two independent and orthogonal data relative to an authentic standard compound analyzed under identical experimental conditions are proposed as necessary for metabolite identification. Examples would include retention time/index, mass or NMR spectrum, accurate mass and tandem MS, accurate mass and isotope pattern, 1H and/or 13C NMR spectra, and 2D NMR spectra. Identifications performed without authentic standard compounds and based upon spectral similarity with public/commercial spectral libraries, or published literatures are generally believed insufficient to validate a confident and rigorous identification. Thus, such identifications should be regarded as putatively annotated compounds. The identification of novel unknown structures or non-novel compounds in the absence of authentic standards should be elucidated using traditionally acceptable physicochemical properties, such as accurate mass, elemental analysis, UV/IR spectra, and/or NMR spectra [108]. MS can be used to predict the correct elemental composition of unknown molecules. However, accurate-mass data alone are insufficient for calculating unique elemental compositions, especially for masses above 400 amu. Additional information, such as the isotope pattern, is necessary to reduce the number of possible molecular formulae for a specific mass [109–111]. TOF mass analyzers have higher accuracy for isotope-ratio determinations as compared to FTICR or Orbitraps, due to the TOFÕs higher dynamic range and better quantification [110]. In addition to isotopic patterns, other restriction rules can be implemented on molecular formula predictions (e.g., the number of elements allowed, Lewis and Senior chemical rules, hydrogen/carbon ratio, allowed ratio of nitrogen, oxygen, phosphor, sulphur versus carbon, element-ratio probabilities and the presence of trimethylsilylated compounds). Such rules enable the exclusion of molecular formulae that are either wrong or contain unlikely high or low number of elements [112]. Formulae are ranked according to their isotopic patterns and subsequently constrained by presence in a public chemical database. When specialized target libraries are used, the correct identification rate can be as high as 98%. For novel formulae, the correct elemental composition will be among the top three matches at a probability of 65%. Metabolite annotations obtained through database queries must be regarded as preliminary hypotheses and not as ultimate identifications. Such identifications should be validated through one or more additional analytical techniques, as noted above. The incorporation of such restriction rules in commercial MS data-analysis software will be a great step toward automation of metabolite identifications. Tandem MS can also be used for the elucidation of unknown metabolites. Instrumental configurations in248

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clude tandem in space (e.g., triple quadrupoles and hybrid QTOF instruments) and tandem in time (quadrupole ion-traps and FT-ICR MS). However, all tandem mass spectrometers isolate ions of interest, fragment these ions, and measure the mass-to-charge ratios of the resultant product ions. Tandem mass spectra of unknown compounds can be compared to the tandem mass spectra of authentic compounds compiled in spectral libraries for spectral matching and identification. Currently, The National Institute of Standards and Technology (NIST) library has 5191 MS/MS spectra of 1920 different ions (1628 cations and 292 anions), while METLIN metabolomic database (http:// metlin.scripps.edu/) has 395 MS/MS spectra of metabolites, 282 spectra in PI mode and 140 spectra in NI mode. Other databases containing tandem MS data are MassBank (http://www.massbank.jp), The Human Metabolome database (http://hmdb.ca/labm/jsp/mlims/ MSDb.jsp), and MoTo database (http://appliedbioinformatics.wur.nl/moto/). The tandem MS experiment can be performed in a reiterative process (i.e. MSn) to generate fragmentation trees. These fragmentation trees can also be used for matching and probability-based metabolite identifications. Resultant product-ion mass and corresponding neutral losses obtained during tandem MS can also be used for the de novo structural elucidation of unknown metabolite structures because the observed fragment ions are highly correlated with the thermodynamic properties associated with the primary, secondary, tertiary, and quaternary structure of the ions [113]. For example, the neutral mass loss of plant flavonoids and isoflavonoids can indicate the presence of glycosyl and glycosyl malonate groups, while Retro-Diels Alder fragmentation of the molecular ion can differentiate between structural isomers [60]. Tandem MS has also been used to differentiate C-glycosidic-linked flavonoid isomers [114].

5. Conclusions MS has secured a central role in metabolomics, which is predicted to grow based upon its historical use and continuous improvements in MS technologies. This review has provided a glimpse at established and emerging MS techniques incorporated into the mass spectral array of tools in metabolomics. However, other emerging techniques, such as direct analysis in real time (DART) [115], and continued improvements in miniaturized MS, multi-dimensional chromatography, and multiplexed MS approaches will offer new opportunities in metabolomics. A fundamental key to interpreting and understanding the biological context of metabolomics experiments is an increase in the number of chemically identifiable metabolites. Although there are various degrees of

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rigor associated with metabolite identifications in the literature, the Metabolomics Standard Initiative has recently proposed that a minimum of 2 independent and orthogonal data relative to an authentic standard compound analyzed under identical experimental conditions should be necessary for metabolite identification. Only through increased qualitative and quantitative depth of metabolome coverage will the ultimate and maximal power of metabolomics be realized. We believe that MS will be central to this objective.

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