Mass Spectrometry Strategies in Metabolomics

Zhentian Lei, David V. Huhman and Lloyd W. Sumner*

Plant Biology Division, The Samuel Roberts Noble Foundation,

Ardmore, OK 73402

Running Title: MS in Metabolomics

*Address correspondence to: Lloyd W. Sumner, The Samuel Roberts Noble Foundation, Plant Biology Division, 2510 Sam Noble Parkway, Ardmore, OK 73401. Telephone: (580) 224-6710; Fax: (580) 224-6692; Email: lwsumner@noble.org Summary

Mass spectrometry (MS) has evolved as a critical component in metabolomics which seeks to answer biological questions through large-scale qualitative and quantitative analyses of the metabolome. MS based metabolomics techniques offer an excellent combination of sensitivity and selectivity, and they have become an indispensable platform in biology and metabolomics. In this minireview, various MS technologies used in metabolomics are briefly reviewed and future needs suggested.

Introduction

Metabolomics is idealized as the large-scale, qualitative and quantitative study of all metabolites in a given biological system. Unlike transcripts and proteins, the molecular identity of metabolites cannot be deduced from genomic information. Thus, the identification and quantification of metabolites must rely on sophisticated instrumentation such as mass spectrometry (MS), nuclear magnetic resonance (NMR) spectroscopy and laser induced fluorescence (LIF) detection. Each of these technologies has its own unique advantages and disadvantages. Optimal selection of a particular technology depends on the goals

of the study and is usually a compromise among sensitivity, selectivity and speed.

NMR is highly selective, non-destructive and generally accepted as the gold standard in metabolite structural elucidation, but it suffers from relatively lower sensitivities. LIF is one of the most sensitive techniques, but it lacks the chemical selectivity that is critical in structural identification. In contrast, MS offers a good combination of sensitivity and selectivity. Modern MS provides highly specific chemical information that is directly related to the chemical structure, such as accurate mass, isotope distribution pattern for elemental formula determination, and characteristic fragment-ions for structural elucidation or identification via spectral matching to authentic compound data. Moreover, the high sensitivity of MS allows detection and measurement of picomole to femtomole levels of many primary and secondary metabolites. These unique advantages make MS an important tool in metabolomics (1,2).

Modern MS offers an array of technologies that differ in operational principles and performance. Variations include ionization technique, mass analyzer technology, resolving power and mass accuracy. The most common ionization techniques in metabolomics include electron

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http://www.jbc.org/cgi/doi/10.1074/jbc.R111.238691The latest version is at JBC Papers in Press. Published on June 1, 2011 as Manuscript R111.238691

Copyright 2011 by The American Society for Biochemistry and Molecular Biology, Inc.

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ionization (EI), electrospray ionization (ESI) and atmospheric pressure chemical ionization (APCI). Other ionization techniques such as chemical ionization (CI), matrix assisted laser desorption ionization (MALDI), and more recently desorption electrospray ionization (DESI) (3) and extractive electrospray ionization (EESI) (4) have also been used. Mass analyzers with different resolving powers have also been used in metabolomics. These include ultrahigh and high resolution MS such as Fourier transform ion cyclotron resonance (FT-ICRMS), Orbitrap MS, and multi-pass TOFMS. However, lower resolution instruments such as ion traps (both linear and three dimensional quadrupoles) and single quadrupoles are utilized by many. Each of these mass analyzers has its own advantage and limitation. Selection of a specific MS platform for metabolomics depends on the goal of the metabolomics projects, throughput and instrumental costs. In this article, we review MS strategies currently incorporated into metabolomics including direct MS analysis and MS coupled to chromatography for the analysis of the chemically complex metabolome.

Direct Mass Spectrometry Analysis

Direct MS analyses sample crude mixtures without chromatographic separations. This approach is the least informative, but does provide a high-throughput screening tool that is often the only practical choice for large sample numbers such as those encountered during clinical trials or screening of large mutant populations. For example, direct MS analyses enabled successful screening of thousands of yeast mutants to help elucidate the functions of mutated genes (5). Direct MS applicability in metabolomics is broadened by advanced instrumentation capable of high resolution, accurate mass

measurements and tandem MS (such as FT-ICRMS and Orbitrap MS).

FT-ICRMS is an important and powerful tool in direct MS analyses due to its ultrahigh resolution (>1,000,000) and mass accuracy (<1 ppm). The high mass resolution is useful in empirical formula calculations and compound identification. For example, direct FT-ICRMS analysis of a crude oil sample revealed more than 111,000 features in a singular mass spectrum, from which over 8,300 peaks could be assigned a unique elemental composition (6). Similarly, more than 1000 unambiguous chemical formulas were reportedly identified from the aerial parts of Arabidopsis using direct FT-ICRMS (7). The disadvantage of FT-ICRMS is its formidable cost that prohibits its wide-spread availability and routine use in many metabolomics laboratories.

The orbitrap is a relatively newer mass analyzer that uses an electrostatic field to trap ions (8). Orbitraps also have very high resolving power (typically 150,000), excellent mass accuracy (1–5 ppm) and have been widely used in direct MS analyses of bovine lipids (9), yeast sphingolipids (10) and plant metabolites (11). Multiple-pass TOFMS instruments with high resolving power (40,000) and mass accuracy (less than 5 ppm) have also been recently developed and further facilitate the use of high resolution MS in metabolomics. Three-dimensional and linear ion traps have been used in direct MS analysis of metabolites as well. However, their relatively lower resolution and mass accuracy limit their roles in direct analysis of complex samples.

The development of several new ionization techniques such as DESI, DART and EESI facilitates the use of direct MS in metabolomics. DESI uses a charged ESI aerosol focused on a separate surface to ionize the surface analytes, allowing not only liquid, but also solid sample analyses

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directly by MS (12). For example, DESI MS revealed heterogeneous distribution of antifungal compounds on the native surface of seaweed (13) and spatial accumulation of different lipids on rat spinal cords (14). Other techniques such as DART-MS (15) and EESI-MS (4) have been used too in direct MS analyses, including analyses of insect hormones (16), metabolic fingerprinting of human serum (17), screening of pesticides in produce (18), analyses of urine, milk and polluted water (4), and fingerprinting of olive oil without sample preparation (19). EESI has also been used to directly analyze gaseous samples (20).

Overall, direct MS analyses have been used for a diversity of analytes; however, direct MS methods are very susceptible to ion suppression or enhancement. In addition, direct MS data interpretation can be challenging as unique metabolite ions are difficult to distinguish from adduct and product ions. Another disadvantage is the inability to differentiate isomers. The majority of these disadvantages can be surmounted by coupling MS to chromatographic separations.

Chromatography Coupled to Mass Spectrometry

Coupling chromatography to MS offers an excellent solution to complex mixture analyses and has been extensively used in metabolomics. Chromatographic separation of metabolites prior to MS analyses has several advantages: (1) reduction of matrix effects and ionization suppression, (2) separation of isomers, (3) provides additional and orthogonal data (i.e., retention time/factor/index) valuable for metabolite annotation, and (4) allows for more accurate quantification of individual metabolites. Currently, three predominant chromatographic techniques have been incorporated in MS based metabolomics,

i.e., gas chromatography (GC), liquid chromatography (LC) and capillary electrophoresis (CE). Multidimensional separation techniques such as two-dimensional GC and LC (aka GCxGC and LCxLC) have further enabled the separation of even more complex biological mixtures, but are less widely employed. This section reviews the applications of chromatography coupled to MS for metabolite profiling.

Gas chromatography coupled to mass spectrometry (GC-MS).

GC-MS is ideally suited for the analyses of both volatile and nonvolatile compounds following derivatization. The high resolution and reproducible chromatographic separations offered by modern capillary GC make it an excellent tool for complex metabolic mixture analyses. In addition, the standardized MS electron ionization energy of 70 eV leads to reproducible mass spectra and highly transferable EI-MS spectral libraries that allow compound identification through mass spectral library matching. The highly reproducible retention indices can also be used for orthogonal confirmation of compound identification, such as in identification of stereoisomers that often produce similar mass spectra, but distinctly separate in the chromatographic domain. Several spectral libraries have incorporated retention indices such as NIST (21) and FiehnLib (22).

Volatiles are a specialized class of metabolites that contribute to vegetable and fruit aromas and plant defense responses. Current metabolomics technologies used to study volatiles center on GC-MS coupled to headspace, solid-phase microextraction (SPME) or other sorbent-based sampling techniques. SPME is a sensitive and robust technique. The effectiveness of various commercial SPME fibers has recently been evaluated for the analysis of fruit volatiles and led to the identification of 14 novel

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volatiles (23). GC-MS based analyses have also been used to identify volatile tomato repellents against whitefly (24) and to profile volatiles from various plant species including tomato (25) and grape (26).

GC requires volatile and thermally stable analytes such as those described above. However, relatively few compounds meet this requirement in their native state (e.g., short-chain alcohols, acid, esters and hydrocarbons). Many other compounds can be analyzed by GC only following derivatization, i.e., alkylation and silylation (27,28). An in-liner derivatization method for ultra-small sample volumes (2 μL down to 10 nL) was recently developed and used to profile the intracellular content of frog oocytes (29). While derivatization is often necessary in GC analyses, it does introduce variability and produce derivatization artifacts.

GC has often been coupled to single quadrupole MS detectors, which have the advantages of high sensitivity and good dynamic range, but suffer from slower scan rates and lower mass accuracy relative to TOFMS. However, the availability, reliability, effectiveness and affordable cost of GC-quadrupole MS have made them a popular and robust metabolomics platform. Other mass analyzers such as TOFMS and triple quadrupoles (QqQMS) have also been interfaced to GC. GC-QqQMS/MS is capable of multiple reaction monitoring (MRM) of analytes, which can overcome the challenging identification and quantification problems associated with coeluting analytes in complex matrices. It has been employed to detect multiple pesticide classes in various fruits and vegetables (30-32), profile sugar in olive fruits, leaves and stems (33), reveal responses of cell cultures to external stresses (27) and fatty acid amides in human plasma (34).

GC-TOFMS technology offers high mass resolution, high mass accuracy and fast scan

speeds. The relatively faster scan rates associated with TOFMS are extremely useful for the accurate deconvolution of overlapping high resolution or ultra fast GC peaks such as those encountered during complex metabolic mixture analyses. Recent application of GC-TOFMS in metabolomics includes large scale metabolite profiling of human serum (28) and plant samples (35-37).

A unique innovation was the development of GC×GC which offers dramatically increased separation efficiencies and peak capacities (38). In GC×GC, two capillary columns of different stationary-phase selectivity are coupled in series through a flow modulator. Effluents from the first column (usually a long nonpolar column) are captured and transferred by the modulator onto the second column. The second column is normally a short polar or semi-polar column that quickly separates the effluent within seconds before the next effluent enters the column. The sharp and narrow peaks generated in fast GC or GC×GC require the use of fast scanning analyzers such as TOFMS (ie ~>100 Hz) or ‘semi-fast’ scan quadrupoles (ie ~20 Hz) (39). Current GC×GC-TOFMS can operate with very high acquisition rates, typically up to 500 Hz and offers higher resolution and sensitivity. Recent metabolomics applications of GC×GC-MS include animals (40), plants (41-44), microorganisms (45) and other samples such as human serum and tissues (46-48). Figure 1 (reproduced from ref (41) with permission) shows a recent application of GC×GC-TOFMS to resolve plant terpenoids.

GC-MS is limited to volatile, thermally stable, and energetically stable compounds. Unfortunately, it is less amenable to large, highly polar metabolites due to their poor volatility. Chromatographic analyses of these compounds usually rely on other

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chromatographic techniques such as LC and CE.

Liquid chromatography coupled to mass spectrometry (LC-MS)

LC-MS is an important tool in metabolomics and can be tailored for targeted or non-targeted metabolomics. While both normal phase (NP) and reversed phase (RP) columns have been employed in metabolomics, RP columns such as C18 and C8 are by far the most utilized. However, NP separations provide complementary views of the metabolome, as demonstrated in metabolic profiling of urine using hydrophilic interaction liquid chromatography (HILIC)-MS and RP-HPLC-MS (49). HILIC is ideal for highly polar and ionic compounds and therefore suitable for samples that contain predominantly polar metabolites such as urine. LC-MS using conventional C18 columns with particle sizes of 3 to 5 μm has been widely used in metabolomics elucidation of plant secondary metabolism (27,50-52). Many established lipidomics programs also rely upon LC-MS for the large-scale study of cellular lipids (53,54) which has improved our understanding of lipid metabolism, signaling and neurodegenerative disorders such as Alzheimer's disease (55-57).

The development of fast and more efficient ultra high pressure liquid chromatography (UHPLC or UPLC), which utilizes higher pressures (12,000–15,000 psi as compared to ~6000 psi for HPLC) and sub 2 μm packing particles, has substantially increased chromatographic resolution and peak capacity compared to HPLC. Figure 2 shows a UPLC-TOFMS base peak chromatogram of a highly complex metabolic plant mixture. The superiority of UPLC has also been demonstrated by Nordstrom and coworkers who reported that UPLC-MS resulted in more than a 20%

increase in detectable components compared to a similar HPLC-MS based approach (58). More recently, HILIC-UPLC-MS has been introduced and used in urinary metabolic profiling (59).

Two-dimensional (2D) HPLC utilizing two columns of different selectivity offers an effective platform for separating both polar and nonpolar compounds simultaneously. The peak capacity of 2D HPLC is much higher and is the product of the two independent dimensional peak capacities given that the first and second separations are truly orthogonal. The higher peak capacities offer greater metabolome coverage. Figure 3 (reproduced from ref (60) with permission) illustrates 2D NPxRP and 2D RPxRP HPLC separations; with the NPxRP system achieving an overall peak capacity of 1095 when applied to the analysis of a lemon oil extract (60). 2D HPLC is typically superior to 1D HPLC even if the columns used in 2D LC are of similar chemistry (61). 2D HPLC-MS has been employed for the analyses of carotenoids in different orange juices (62), drug metabolism (63) and triacylglycerols in vegetable oil (64). 2D HPLC-MS has also been tailored for target analyses. For example, Aturki and coworkers used a RP C18 column in the first dimension to separate flavanone-7-O-glycosides from a complex sample, and then used a carboxymethylated β-cyclodextrin-based column in the second dimension to resolve the individual flavanone-7-O-glycoside stereoisomer (65). The disadvantages of 2D HPLC are its relatively complex setup and the loss of sensitivity due to a sample dilution effect in second dimension (66).

The ionization technique selected for LC-MS based metabolomics can also have a substantial impact on metabolite profiles. Generally, ESI is ideal for semi polar and polar compounds; whereas APCI is more suitable for neutral or less polar compounds.

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These two ionization techniques provide complementary data, both desirable in large scale non-targeted metabolomics. For example, complementary ESI APCI analysis resulted in about a 20% increase in the number of detected ions in a human blood serum extract (67). Multi-ionization mode or dual APCI/ESI ion sources are now commercially available from some instrument vendors, allowing for simultaneous acquisition of ESI and APCI data.

Many modern MS instruments are now capable of fast polarity switching during data acquisition and have been exploited in simultaneous acquisition of both positive and negative ion mode data (68,69). Use of both positive and negative ionization LC-MS offers more comprehensive metabolome coverage than using a single polarity (50,67). Figure 4 (reprinted from (50) with permission) shows the positive and negative ion mode HPLC-MS chromatograms of M. truncatula cell extracts. Several analytes were detected only in negative ion mode; whereas others were observed only in the positive ion mode. Similarly, Nordstrom and coworkers noted that over 90% of the human blood plasma ions observed in the positive ion ESI mode were not found in negative ion mode and vice versa (67).

It is expected that LC-MS will continue to play an important role in MS based metabolomics and with the continuous advancement of LC and MS technologies, both sensitivity and depth-of-coverage of LC-MS based metabolomics will continue to improve. As demonstrated in a recent human serum metabolome study, higher metabolome coverage can best be achieved by using multiple metabolomics technologies (70). Using a combination of platforms including LC-MS/MS, GC-MS, TLC-GC-FID, direct infusion MS/MS and NMR, 4229 compounds were tentatively identified from human serum with each

platform identifying a subset of unique compounds (70).

Capillary electrophoresis chromatography coupled to mass spectrometry (CE-MS).

CE separates analytes based on charge and size, and it is particularly suitable for the analysis of highly polar and ionic metabolites. Separation of neutral compounds can be achieved using micellar electrokinetic chromatography (MEKC) that employs charged surfactants such as sodium dodecyl sulfate (SDS) to form charged micelles containing the analyte. CE is a fast, relatively inexpensive and a highly efficient separation technique. Capillary zone electrophoresis (CZE) is the most utilized separation technique in CE-MS based metabolomics because many compounds can be separated readily by CZE. CE can be interfaced with various MS analyzers, however TOFMS is the most commonly used CE-MS analyzer due to its fast acquisition rates which are necessary to statistically sample the narrow CZE peaks. ESI is the ionization technique of choice for CE-MS.

CE-MS has been used in both targeted and non-targeted metabolomics. CE-TOFMS was used for global profiling of endogenous metabolites in tumor and normal tissues, and the results revealed elevated glycolysis in tumor tissues evidenced by extremely low glucose, high lactate and high glycolytic intermediates in tumor tissues (71). CE-TOFMS metabolic profiling of Illicium anisatum seed, pulp, stem and leaf tissues detected more than 1000 tentative polar metabolites in 40 min and revealed spatial distributions of numerous metabolites (72). Use of capillary electrochromatography (CEC) coupled to MS for metabolomics has been reported as well. CEC achieves separations using an electrostatic field imposed on a packed particle or a monolithic column, which

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allows for the high separation efficiency of CE with the high selectivity of the stationary phase of LC columns. For example, CEC-ESI-MS methods have been developed and used to analyze the metabolome of a human hepatocellular carcinoma cell line (73) and drug abuse through urine analysis (74).

Unfortunately, CE-MS has inherent limitations. These are mainly low sensitivity, poor reproducibility, and electrochemical reactions of metabolites. Recently, the performance of GC-MS, LC-MS, and CE-MS were compared in quantitative metabolomics and it was concluded that CE lacked the necessary robustness and was the least suitable platform for analyzing complex biological samples (75).

Progressive MS Based Metabolomics Applications and Quantifications

The above noted MS technologies form the core analytical platforms used by most in metabolomics. However, metabolomics applications and techniques continue to expand. Following are several highlighted areas including spatially resolved metabolomics, fluxomics, integrated metabolomics, personalized medicine, and computational methods for metabolite annotation.

Advanced sampling and MS technologies have made it possible to perform spatially resolved metabolomics which can be achieved through the profiling of laser dissected tissues (76), single cell sampling using microcapillaries (77), or through metabolite imaging mass spectrometry (IMS). Micro dissection and single cell sampling using microcapillaries provide information on differential metabolite accumulation in specific tissue or cell types, whereas IMS enables the spatial visualization of metabolites and their relative abundances across various tissues. Spatial metabolomics can be used to

decipher the functional roles of the metabolites based upon their localization and/or co-loalization with other metabolites/proteins/transcripts.

Flux analyses or fluxomics is another important application of metabolomics (78), which has been used for mechanistic studies (79), integrated metabolomics for gene discovery (80) and personalized medicine (81). For example, analysis of flux through the TCA cycle in a human cancer cell line confirmed glutaminolysis and reductive carboxylation as the major cancer cell pathways that provide nitrogen for amino acid and nucleotide (adenine) syntheses (78). Differential regulation of the same metabolic pathways in response to different elicitors in plant cells were identified through large scale metabolic profiling (79). Combined with transcriptomics, metabolomics was successfully used to identify two Arabidopsis Myb transcription factor genes that regulate aliphatic glucosinolate biosynthesis (80). The emerging MS based omics technologies are providing a systems approach to disease and transforming medicine from reactive to proactive (i.e., predictive, personalized, preventive and participatory or 4P) (82).

In general, a significant proportion of profiled metabolites remain unannotated, but several groups are creating large MS/MS spectral libraries as part of an advanced scheme to characterize and cross correlate both known and unknown metabolites. For example, Matsuda and colleagues have generated a MS2T library that contains nearly 1.6 million MS2T spectra to facilitate peak annotation in non-targeted plant metabolomics studies (83). Annotation of metabolites through MS or MS/MS spectra typically involves spectral matching against spectral libraries compiled with authentic standards. Identification of metabolites whose MS/MS spectra are not present in the spectral libraries remains very challenging

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and is being addressed through both empirical and computational mass spectrometry. MetFrag, a computer program for metabolite identification based upon in silico spectral fragmentation matching was recently described (84). It is expected that continued development of computational methods such MetFrag and other fragment tree related software (85) will further assist metabolite identification in metabolomics.

Quantification in metabolomics is critical in understanding biological processes, and it is generally performed in two manners, i.e., relative or absolute quantification. Relative quantification which normalizes the metabolite signal intensity to that of an internal standard or another metabolite is typically used in non-targeted large scale profiling. Absolute quantification uses external standards or internal isotopically labeled standards to determine the absolute metabolite quantity and is mostly used in targeted metabolomics. The major obstacle in metabolite quantification is that the metabolite’s signal intensity is not only dependent on its concentration, but also its chemical structure and matrix. Ion suppression and enhancement caused by matrix effects can result in inaccurate quantification of the metabolites. Stable isotope dilution is one solution for absolute quantification (i.e., stable isotope-labeled standards are added to the samples to account for sample processing variation and matrix effects encountered during MS analysis). Absolute quantification in metabolic profiling of obese and lean humans clearly linked branched-chain amino acid related metabolites to the development of obesity-associated insulin resistance (86). Quantitative MS intermediary metabolite profiling and 13C NMR based flux analyses identified a critical link between pyruvate and tricarboxylic acid (TCA) cycle medicated by pyruvate carboxylase, which plays an important role in regulating

glucose-stimulated insulin secretion (87,88). Quantitative lipidomics revealed that abnormal lipid profiles occur even at the very earliest stages of diabetes (89). Stable isotope dilution GC-MS of cholesterol in human plasma revealed that oxysterols were sensitive and specific biomarkers for Niemann-Pick C1 disease (90).

It is clear that stable isotope standards are critical for absolute quantification in metabolomics. However, the availability of commercial isotope-labeled standards is limited and the costs can be prohibitive to large scale use. Several groups have begun to chemically synthesize a diverse range of stable isotope labeled compounds for absolute quantification. These include fatty acids (91), human steroids (92) and plant hormones (93), but this approach is laborious and requires synthetic expertise not available in every metabolomics group. In addition, preparing multiple isotope-labeled standards for a large scale metabolomics is challenging, given the diverse and complex structures of the metabolites. One way to circumvent this problem is to introduce a stable isotope tag to the metabolites through chemical labeling. A dimethylation method to label amine-containing metabolites using commercially available and inexpensive 13C- or deuterium-labeled formaldehyde has been reported (94). Standards of known concentrations are labeled in the same manner and used to generate calibration curves for absolute quantification.

Conclusions and Future Challenges MS has become an indispensable, productive tool in metabolomics due to its high sensitivity and selectivity. However, there are still many metabolomics challenges including limited dynamic range, lack of comprehensive coverage and limited metabolite annotations. Currently, the best dynamic range of modern MS is

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approximately 106 and is significantly lower than the estimated concentration range of cellular metabolites as 1012 or more. In addition, the estimated number of metabolites within a given plant species can be 10,000 or more, and the current metabolome depth of coverage is roughly

<20%. Thus, there is substantial need for improvement. Advancements and solutions to the above limitations will continue to expand the scope and propel the utility of mass spectrometry in metabolomics!

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Acknowledgments: The authors would like to acknowledge financial personnel support from the Samuel Roberts Noble Foundation. Current support for metabolomics projects within the Sumner Lab has also been provided by Oklahoma Center for the Advancement of Science and Technology (#PSB10-027); National Science Foundation MCB#1024976 and MCB#0520140; LECO Corporation; and Agilent Technologies. The abbreviations used are: LIF, laser induced fluorescence; EI, electron ionization; ESI, electrospray ionization; APCI, atmospheric pressure chemical ionization; CI; chemical ionization, MALDI, matrix assisted laser desorption ionization; DESI, desorption electrospray ionization; EESI, extractive electrospray ionization; FT-ICR, Fourier transform ion cyclotron resonance; TOF, time of flight; GC, gas chromatography; LC, liquid chromatography; CE, capillary electrophoresis; NIST, National Institute of Standards and Technology; 2D, two-dimensional; UHPLC or UPLC, ultra high pressure liquid chromatography; NP, normal phase; RP, reversed phase; HILIC, hydrophilic interaction liquid chromatography; QqQ, triple quadrupoles; HS-SPME, headspace solid-phase microextraction; MS/MS, tandem MS; MRM, multiple reaction monitoring; MEKC, micellar electrokinetic chromatography; CEC, capillary electrochromatography; IMS, imaging MS; TCA tricarboxylic acid.

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FIGURE 1. Contour plot of 2D GC-TOF MS analysis of an n-hexane extract of transgenic Artemisia annua L. The upper panel (A) is the total ion chromatogram plot (first dimension: x-axis; second dimension: y-axis, time unit: second). The lower panel (B) shows the expanded view of the rectangular area in the total ion chromatogram plot. Peaks that are numbered in both panels are terpenoids. (Reproduced from ref (41) with permission).

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FIGURE 2. UPLC-QTOF-MS base-peak ion chromatogram of a combined methanol extracts from soybean and Medicago truncatula (A17). Separations were performed on a Waters Acquity using a Waters Acquity UPLC C18 column (2.1mm x 100 mm) with 1.7-µm particles with a flow of 600 µl/min and a linear gradient of 0.1% acetic acid:acetonitrile (5:95 to 30:70 over 30 min). Mass spectra were recorded on a Waters QTOFMS Premier.

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FIGURE 3. Contour plot of 2D separation of a test mix using NP x RP 2D LC (A) and a steroid mix using RP  x RP 2D LC (B). Compounds that were poorly separated in the first dimensional NP separation (x-axis) such as aromatic ethers (peaks 1-3), aromatic esters (peaks 4-6), phenones (peaks 7-10) and steroids (peaks 19-28) were clearly separated in the second dimensional RP separation (y-axis) (A). Despite its apparent lower orthogonality, RP x RP 2D LC still offers higher separations relative to 1D LC (B). When two RP columns (Cyano and C18) were used, steroids were separated, which could not be achieved using either Cyano or C18 1D LC (B). (Reprinted from ref (60) with kind permission).

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FIGURE 4. HPLC–positive-ion ESI–MS chromatogram (B) with insert 3 showing a full positive-ion MS spectra of peak 15 (afrormosin β-d-glucoside) and HPLC–negative-ion ESI–MS chromatogram of M. truncatula cell extracts (C). Some peaks such as peaks 4, 30, 31 and 32 were only detected by negative ion mode (C), but not in positive ion mode (B). In contrast, peaks 3, 13 and 28 were observed in positive ion mode only (B). (Reprinted from our previous work (50) with permission).

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Zhentian Lei, David Huhman and Lloyd W. SumnerMass Spectrometry Strategies in Metabolomics

published online June 1, 2011J. Biol. Chem. 

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