SEARCH

SEARCH BY CITATION

Keywords:

  • metabolomics;
  • mass spectrometry;
  • subcellular localization;
  • non-aqueous fractionation;
  • metabolite annotation;
  • systems biology

Summary

  1. Top of page
  2. Summary
  3. Introduction
  4. Targeted and Non-Targeted Analysis in MS-Based Metabolomics
  5. Compound Annotation: Making Use of High-Resolution, Accurate Mass, Isotope Labeling and Compound Fragmentation for the Annotation of Metabolites
  6. Increasing the Resolution by Increasing the Separation
  7. Getting an Overview: ‘Metabolite Analysis at the tissue and Cellular Level’
  8. Zooming in: large scale analysis at the Subcellular Level
  9. Non-Aqueous Fractionation (NAF): A Method Allowing the Application of Metabolomics Technologies to Subcellular Compartments Determined by Continuous Distributions
  10. Conclusion
  11. Acknowledgements
  12. References

The main goal of metabolomics is the comprehensive qualitative and quantitative analysis of the time- and space-resolved distribution of all metabolites present in a given biological system. Because metabolite structures, in contrast to transcript and protein sequences, are not directly deducible from the genomic DNA sequence, the massive increase in genomic information is only indirectly of use to metabolomics, leaving compound annotation as a key problem to be solved by the available analytical techniques. Furthermore, as metabolites vary widely in both concentration and chemical behavior, there is no single analytical procedure allowing the unbiased and comprehensive structural elucidation and determination of all metabolites present in a given biological system. In this review the different approaches for targeted and non-targeted metabolomics analysis will be described with special emphasis on mass spectrometry-based techniques. Particular attention is given to approaches which can be employed for the annotation of unknown compounds. In the second part, the different experimental approaches aimed at tissue-specific or subcellular analysis of metabolites are discussed including a range of non-mass spectrometry based technologies.


Introduction

  1. Top of page
  2. Summary
  3. Introduction
  4. Targeted and Non-Targeted Analysis in MS-Based Metabolomics
  5. Compound Annotation: Making Use of High-Resolution, Accurate Mass, Isotope Labeling and Compound Fragmentation for the Annotation of Metabolites
  6. Increasing the Resolution by Increasing the Separation
  7. Getting an Overview: ‘Metabolite Analysis at the tissue and Cellular Level’
  8. Zooming in: large scale analysis at the Subcellular Level
  9. Non-Aqueous Fractionation (NAF): A Method Allowing the Application of Metabolomics Technologies to Subcellular Compartments Determined by Continuous Distributions
  10. Conclusion
  11. Acknowledgements
  12. References

Every biological organism relies for its proper function on the interaction of a multitude of cellular molecules. The interplay between these molecules is complex and needs to be coordinated with respect to both time and space. Over the years, the study of both small molecules and macromolecules has progressed from the analysis of single compounds to the study of complex mixtures, enabling the detection and identification of several hundred to several thousand different entities (Feng et al., 2008; Hsu et al., 2011). Due to technological developments the major molecular classes studied comprise transcripts, proteins, and metabolites. The most comprehensive analytical techniques have been developed for RNA molecules, and present-day technologies allow a full analysis of all RNAs present in a given biological system (Gresham et al., 2008; Forrest and Carninci, 2009). In contrast, there is yet no single technique that allows monitoring of all proteins, including their post-translational modified derivatives (Wade et al., 2004; Gstaiger and Aebersold, 2009; Yates et al., 2009; Walther and Mann, 2010). This is because proteins, in contrast to RNA, differ significantly in their chemical properties and thus also their analytical behavior. Even more difficult is the study of small molecules and metabolites, where analytical tools face three challenges: (i) metabolites differ vastly in their concentration; (ii) they comprise compounds of extremely different chemical properties (e.g. a hydrophilic organic acid versus a highly hydrophobic triacylglyceride); and, most importantly, (iii) metabolomics benefits only indirectly from completed or ongoing genome sequencing projects. The reason for this is obvious: whereas RNA and protein sequences are essentially collinear with genomic information, metabolic structures represent a completely different level of realization of genome information which does not necessarily display collinearity with the genome sequence (Saito and Matsuda, 2010).

Two main technologies, nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS), have been employed in the field of metabolomics research. Both technologies are highly complex and provide different advantages and limitations (Dettmer et al., 2007; Kim et al., 2011). Compared with the MS-based approaches, NMR requires almost no sample preparation and, in principle, allows structural determination and quantitative analysis of the most abundant signals (Kim et al., 2011). Unfortunately, the lack of sensitivity and the associated lack of brought compound annotation represents a serious drawback when using NMR for metabolomics on complex mixtures (Kim et al., 2011).

In contrast, MS-based metabolomics approaches in combination with different separation techniques (gas or liquid chromatography but also capillary electrophoresis) provide a much higher sensitivity and flexibility (Villas-Boas et al., 2005; Dunn, 2008; Allwood and Goodacre, 2010). As a consequence, MS-based approaches provide deeper and more detailed insight into the metabolic inventory of a biological sample (Kim et al., 2011), even though precise compound identification still represents a major bottleneck (Saito and Matsuda, 2010; Lei et al., 2011).

In this review article we focus on MS-based metabolomic analysis in plants, since only the high sensitivity achieved with this technology provides the means to potentially tackle the whole metabolome, defined as the complete set of small molecules present in a single cell/organism (Oliver et al., 1998). In the second part we further review several strategies used for the analysis of subcellular metabolite distributions within a single cell or different cell types.

Targeted and Non-Targeted Analysis in MS-Based Metabolomics

  1. Top of page
  2. Summary
  3. Introduction
  4. Targeted and Non-Targeted Analysis in MS-Based Metabolomics
  5. Compound Annotation: Making Use of High-Resolution, Accurate Mass, Isotope Labeling and Compound Fragmentation for the Annotation of Metabolites
  6. Increasing the Resolution by Increasing the Separation
  7. Getting an Overview: ‘Metabolite Analysis at the tissue and Cellular Level’
  8. Zooming in: large scale analysis at the Subcellular Level
  9. Non-Aqueous Fractionation (NAF): A Method Allowing the Application of Metabolomics Technologies to Subcellular Compartments Determined by Continuous Distributions
  10. Conclusion
  11. Acknowledgements
  12. References

Hyphenated MS-based metabolomics, meaning the combination of, for example, chromatographic separation techniques and MS, are available in various different flavors (Villas-Boas et al., 2005; da Silva et al., 2006; Dettmer et al., 2007; Dunn, 2008; Feng et al., 2008; Lei et al., 2011). The main parameter for the choice of a given method depends on the initial biological question and on the nature of the compounds to be measured (Figure 1). Restriction to a single or several analytical methods has to be taken, since currently neither an all-inclusive extraction method nor a technological platform able to measure all possible metabolites in a single analysis is available or in sight of being developed soon (Saito and Matsuda, 2010; Lei et al., 2011). This means that selection of the technology should be matched to the specific set of metabolites or based on the function of the target gene of interest. If more than one method is available, the most sensitive and robust analytical technology should normally be selected.

image

Figure 1.  Schematic diagram indicating the work flow from biological sample to metabolite functional assignment. Two main routes are given, which either follow a targeted or a non-targeted metabolome analysis. NMR, nuclear magnetic resonance; MS, mass spectrometry; PC, principal component.

Download figure to PowerPoint

An approach aiming to measure and profile metabolites with known chemical structures is commonly called targeted metabolomics (Albinsky et al., 2010; Dudley et al., 2010). The best established systems for such a strategy are gas chromatographic (GC) separation systems, coupled either to time-of-flight (TOF) or quadrupole mass spectrometers (Lisec et al., 2006) or liquid chromatography (LC) systems coupled to, for example, triple quadrupole (QqQ) mass spectrometers (Lu et al., 2008). In plant science the GC-TOF MS system has already been in use for several years as one of the workhorses for the analysis of primary metabolism (Schauer et al., 2005; Lisec et al., 2006; Williams et al., 2007; Lytovchenko et al., 2009; Shuman et al., 2011) but to some extent also for secondary compounds like triterpenes (Field and Osbourn, 2008) or sterols (Klahre et al., 1998). The LC-QqQ systems can, depending on the chromatography used, cover several compound classes ranging from highly polar compounds of the Calvin cycle (Arrivault et al., 2009) to semi-polar plant hormones (Pan et al., 2010) and non-polar compounds like plant lipids (Markham and Jaworski, 2007).

The major limitation of targeted metabolic profiling techniques is that they require authentic reference compounds for the identification and quantification of the measured metabolic profiles, limiting the analysis to compounds that are either commercially available (Last et al., 2007; Nakabayashi et al., 2009) or already identified and validated by orthogonal technologies like NMR, for example (Kim et al., 2011).

In many cases, the class of metabolites of interest might not be certain at the beginning of the study. For example, studies might seek to understand the metabolomic influence of a gene with a more generic function, such as a kinase or a transcription factor, or even a gene of unknown function. As there is no a priori knowledge or idea about which metabolites will be influenced by this gene, a targeted metabolome analysis runs a high risk of missing the most significant changed metabolites. Obviously in this case a consequent step of the study is to expand the analysis beyond the known targets and include signals of unknown identity. Of course the strategy of broadening the analysis beyond the targeted pathway of interest is also useful if the primary function of the gene is known. This approach is commonly called non-targeted, or unbiased, analysis (De Vos et al., 2007; Hanhineva et al., 2008; Tianniam et al., 2009; Oliver et al., 2011). Non-targeted analysis can be performed in two ways. One approach is so-called metabolic fingerprinting, where no chromatographic separation of the samples prior to the MS-based measurement is performed (Last et al., 2007). The second approach, called metabolite profiling, usually includes a chromatographic separation step prior to the mass spectrometric measurement (Last et al., 2007; Allwood and Goodacre, 2010). Both methods provide complex metabolic signatures which can be analyzed by multivariate statistical methods (Bylesjo et al., 2007; Sumner et al., 2007b; Eliasson et al., 2011). This provides information on metabolic features that are significantly different between the analyzed samples. At this stage of analysis it is not important if these features correspond to an immediately identifiable chemical compound. As long as these known unknowns can be reproducibly detected, they can serve many useful purposes in diagnostics or systems biology. Once their importance as a diagnostic marker, for example, is validated, more sophisticated analytical technologies can be applied to unravel the exact chemical structure of the compounds of interest.

A number of studies have successfully used metabolic fingerprinting or profiling strategies to discriminate or associate different gene functions, determine system-specific metabolic responses, or simply associate quality features of a sample (Messerli et al., 2007; Beckmann et al., 2008; Cuadros-Inostroza et al., 2010; Scherling et al., 2010; Lisec et al., 2011; Smith and Bluhm, 2011). Still, in the long run, the ultimate goal of every non-targeted study should be not only to make use of the whole metabolic signature for sample discrimination, or marker detection, but also to identify those discriminative compounds.

Compound Annotation: Making Use of High-Resolution, Accurate Mass, Isotope Labeling and Compound Fragmentation for the Annotation of Metabolites

  1. Top of page
  2. Summary
  3. Introduction
  4. Targeted and Non-Targeted Analysis in MS-Based Metabolomics
  5. Compound Annotation: Making Use of High-Resolution, Accurate Mass, Isotope Labeling and Compound Fragmentation for the Annotation of Metabolites
  6. Increasing the Resolution by Increasing the Separation
  7. Getting an Overview: ‘Metabolite Analysis at the tissue and Cellular Level’
  8. Zooming in: large scale analysis at the Subcellular Level
  9. Non-Aqueous Fractionation (NAF): A Method Allowing the Application of Metabolomics Technologies to Subcellular Compartments Determined by Continuous Distributions
  10. Conclusion
  11. Acknowledgements
  12. References

As discussed above, the most straightforward way to identify a compound by MS-based methods is to match a signal of a measured analyte to a pure reference compound. Unfortunately, in many cases this is not possible due to the unavailability of several chemical standards potentially present in plants (Saito and Matsuda, 2010; Lei et al., 2011). As a consequence the structural elucidation of an unknown analyte, not matching any of the commercially available reference compounds, represents one of the major bottlenecks in metabolomics studies (Neumann and Bocker, 2010). A true de novo determination of the final structure of a small molecule, including its stereochemistry, might be possible with higher-dimension NMR methods; this as a rule requires extensive purification and enrichment of the compound under study and thus cannot be achieved for every measured metabolite within complex mixtures (Kim et al., 2011).

As a consequence of this identification problem a hierarchical annotation system was developed for MS-based metabolomics studies, where different levels of compound identification are described (Sumner et al., 2007a). In Figure 2 a number of commonly used tools or strategies for MS-based compound annotation are summarized. But still it must be understood that none of these MS-based methods alone can fully solve the structure of an unknown compound. However, the combination of several of these methods can serve to predict a handful of possible structures, which can be tested with commercial or newly synthesized standards (Neumann and Bocker, 2010).

image

Figure 2.  Overview of commonly used mass spectrometry-based metabolite annotation strategies. The represented compound structure in the center of the figure, with the elemental composition C12H22NO10S3 is 4-methylsulphinylbutyl glucosinolate (glucoraphanin). The five surrounding graphical illustrations represent physicochemical properties of the measured compound, which next to the measured mass, can be exploited for compound annotation or at least for the rejection of unlikely annotation. The combinatorial use of several of the displayed analyses might therefore lead at least to a partial elucidation of the structure. RIA, relative isotopic abundance; fwhm, full width half maximum.

Download figure to PowerPoint

One of the most significant improvements in compound identification in MS-based metabolomics analysis came with advancements in mass accuracy (measured in parts per million, p.p.m.) and the resolving power or resolution (mm) of the instruments (Schaub et al., 2005; Makarov et al., 2009; Andrews et al., 2011; Pelander et al., 2011). These improvements, in combination with more sophisticated software packages, facilitate a more efficient use of the measured mass spectrometric and chromatographic features (Bocker et al., 2009; Wolf et al., 2010; Boswell et al., 2011a,b; Rasche et al., 2011; Rojas-Cherto et al., 2011).

The ever increasing resolving power and the improved mass accuracy, which can reach a resolution of more than 1 000 000 (at full width half-maximum, fwhm) and sub-p.p.m. mass accuracies in commercially available Fourier transform ion cyclotron (FT-ICR) mass spectrometers (Marshall and Hendrickson, 2008) significantly boosted metabolomics studies. These two features first of all help to separate overlapping mass signals (avoiding confounding signal interference), but also, if isotopic fine structures are to be determined, help to predict the elemental composition of measured metabolic signals with a mass up to 350 Da (Koch et al., 2007). Of course it has to be kept in mind that resolution and accurate mass alone will not be sufficient to predict the elemental composition of a whole metabolome.

Kind and Fiehn have shown that a mass spectrometer with 3 p.p.m. and 2% relative isotope abundance accuracy can predict and annotate elemental compositions more efficiently than a FT-ICR with a mass accuracy of better than 0.1 p.p.m. (Kind and Fiehn, 2006; Koch et al., 2007). Additionally, it should be kept in mind that most of the ultra-high resolution (resolution >350 000) instruments have rather low scanning rates (depending on a measured resolution in the range of seconds), and so are not compatible with the fast chromatography of the <2 μm particle-size ultra-high performance chromatography (UPLC) systems, which achieve chromatographic peak widths of 5 sec and less (Plumb et al., 2004). As a consequence these slow-scanning high-mass-accuracy instruments can only use their full resolution in combination with either direct-infusion-based measurements (Aharoni et al., 2002; Giavalisco et al., 2008), where scanning rates are not limiting, or in combination with sample fractionation strategies, where peaks of interest are collected during LC-MS measurements (Li et al., 2007).

In recent years quadrupole TOF instruments have improved not only their resolution (up to 50 000 fwhm) but also their mass accuracy and their scanning rates (Andrews et al., 2011; Pelander et al., 2011). These instruments are now, with low p.p.m. mass errors, extremely accurate (Stroh et al., 2007). Due to the use of TOF detectors, these instruments have always been fast, but now they reach extremely fast scanning rates with up to 100 mass scans per second (Andrews et al., 2011); this enables several different scan modes, including several tandem MS (MS/MS) scans (Matsuda et al., 2009, 2010). In addition, the new TOF detector provides an excellent dynamic range and relative isotopic abundance (RIA) accuracy of 3% and better (Bocker et al., 2009; Andrews et al., 2011; Pelander et al., 2011). Isotopic abundance distribution is a valuable compound-specific feature, which in combination with a high RIA accuracy can be used to predict or discriminate elemental compositions. Within the last years a number of software packages have therefore implemented RIA (Bocker et al., 2009; Pluskal et al., 2010).

Unfortunately, the RIA accuracy in mass spectrometers of the Orbitrap type is, with a median error of approximately 20%, not as good (Xu et al., 2010) as the one obtained from the TOF-type instruments. However, these slow-scanning ion trap-like systems still have a distinct advantage, since they are capable of performing high-order tandem MS (MSn) experiments. Meaning that these instruments are not only capable of producing primary fragments, but also secondary, tertiary and so on (higher-order MSMS). These generated higher-order fragmentation trees can subsequently be used for efficient elucidation of elemental composition and partial structure (Rojas-Cherto et al., 2011).

Another updated approach to the improved annotation of elemental compositions of metabolites from LC-MS studies was published recently (Boswell et al., 2011a,b). This approach, which is theoretically independent of the type of MS used and also of the LC system (even though high-mass-accuracy systems will benefit more from the approach), aims to help validate the compound annotation by calculating a predicted retention time for each measured compound in a gradient LC run. This calculation is based on the isocratic log κ to volume of organic modifier (Ø) relationship. To get started the system needs to be tuned by a number of internal reference compounds. Subsequent prediction of the potential annotated known structures can reach an accuracy of better than 2% (Boswell et al., 2011a,b). Still, the general applicability of this tool to annotate unknown compounds in a real metabolic study still awaits its validation.

An additional, MS-platform independent approach for the accurate annotation of elemental compositions is based on the use of isotopically labeled compounds. Here one can either make use of single labeled compounds and follow their intercellular metabolism (Feldberg et al., 2009) or label whole metabolomes with isotopically labeled carbon, nitrogen or sulfur (Giavalisco et al., 2008, 2009, 2011). These labeled metabolites, which show elemental composition-specific mass shifts, can be used to significantly improve annotation of elemental composition and reduce false positive rates, but also to allow a clear distinction between biological and non-biological contaminating compounds (Giavalisco et al., 2008, 2009, 2011).

Increasing the Resolution by Increasing the Separation

  1. Top of page
  2. Summary
  3. Introduction
  4. Targeted and Non-Targeted Analysis in MS-Based Metabolomics
  5. Compound Annotation: Making Use of High-Resolution, Accurate Mass, Isotope Labeling and Compound Fragmentation for the Annotation of Metabolites
  6. Increasing the Resolution by Increasing the Separation
  7. Getting an Overview: ‘Metabolite Analysis at the tissue and Cellular Level’
  8. Zooming in: large scale analysis at the Subcellular Level
  9. Non-Aqueous Fractionation (NAF): A Method Allowing the Application of Metabolomics Technologies to Subcellular Compartments Determined by Continuous Distributions
  10. Conclusion
  11. Acknowledgements
  12. References

Eukaryotic organisms are often multicellular and highly compartmentalized. Many metabolic pathways involve reactions in more than one tissue type or subcellular compartment requiring numerous transport processes across different membrane systems. Probably the most prominent example of a well-described multi-compartment pathway in plants is the photorespiration pathway, which includes chemical reaction in chloroplasts, peroxisomes, and mitochondria (Bauwe, 2010; Maurino and Peterhansel, 2010). In addition, many other metabolic pathways might involve metabolic shuttling since the required metabolic enzymes are localized as isoenzymes in more than one subcellular compartment (Kruger and von Schaewen, 2003; Sweetlove et al., 2010). Within those pathways enzymatic activities and therefore metabolic fluxes are regulated by substrate concentration, the presence of different co-factors and intermediates, as well as allosteric activation or feedback inhibition of key enzymes (Stitt et al., 1983; Gunasekaran et al., 2004; Kruger et al., 2007; Takahashi et al., 2011). Therefore, detailed information about tissue-specific or subcellular compartmentalization of metabolites is fundamental for the understanding of metabolic networks and the regulation of metabolic fluxes. A major hurdle for the reliable determination of the spatial distribution of metabolites is their fast turnover and rapid transport between the different compartments (Flugge et al., 1980; Stitt et al., 1982; Ratcliffe and Shachar-Hill, 2005; Furumoto et al., 2011). Although analysis of a wide range of primary and secondary metabolites by GC- and LC-MS is frequently done to discover metabolic phenotypes (Saito and Matsuda, 2010), little information is available about the spatial distribution of these metabolites within different cell types. Methods have been developed to study the spatial distribution of metabolites at the tissue level, like matrix-associated laser desorption ionization mass spectrometry imaging (MALDI-MSI), desorption electrospray ionization mass spectrometry imaging (DESI-MSI), laser capture microdissection (LCM), or cell sap sampling (CSS) and on a subcellular level like fluorescence resonance energy transfer (FRET)-based nanosensor technology, NMR spectroscopy, protoplast fractionation (PF), or non-aqueous fractionation (NAF).

Getting an Overview: ‘Metabolite Analysis at the tissue and Cellular Level’

  1. Top of page
  2. Summary
  3. Introduction
  4. Targeted and Non-Targeted Analysis in MS-Based Metabolomics
  5. Compound Annotation: Making Use of High-Resolution, Accurate Mass, Isotope Labeling and Compound Fragmentation for the Annotation of Metabolites
  6. Increasing the Resolution by Increasing the Separation
  7. Getting an Overview: ‘Metabolite Analysis at the tissue and Cellular Level’
  8. Zooming in: large scale analysis at the Subcellular Level
  9. Non-Aqueous Fractionation (NAF): A Method Allowing the Application of Metabolomics Technologies to Subcellular Compartments Determined by Continuous Distributions
  10. Conclusion
  11. Acknowledgements
  12. References

Increasing the spatial resolution is an important challenge in the field of metabolite analysis, especially in multicellular organisms. In recent years several different approaches have been undertaken to study metabolism/metabolites at a tissue and cellular level. Imaging technologies like MALDI-MSI or DESI-MSI have been successful applied to study the spatial distribution of a broad range of molecules directly from tissue or tissue sections (Kaspar et al., 2011; Muller et al., 2011). The MALDI ionization method produces intact ions due to the use of a pulsed laser beam in combination with an energy-absorbing matrix which co-crystallizes with the analyte (Figure 3D). The absorption of the laser energy leads to explosive desorption of the matrix and the analyte into the gas phase. Matrix-associated laser desorption ionization, like ESI, is one of the so-called soft ionization techniques (Zaikin and Halket, 2006). The use of different matrices allows the efficient ionization and subsequent analysis of molecules between a few hundred Da and 100 kDa (Chughtai and Heeren, 2010; Muller et al., 2011). In plants, MALDI-MSI was successfully applied to study the spatial distribution of molecules such as sugars, lipids, secondary metabolites like glucosinolates, flavonoids and alkanes in leaf tissue and tissue sections of different plants (Cha et al., 2008, 2009; Shroff et al., 2008; Jun et al., 2010; Vrkoslav et al., 2010). For example, the analysis of glucosinolate distribution within Arabidopsis thaliana using MALDI-MSI revealed a non-uniform distribution throughout the leaf tissue (Vrkoslav et al., 2010). The accumulation of glucosinolates close to the leaf margin and the middle vein strongly influences the feeding preference of Helicoverpa armigera larvae and might play a key role as a first barrier against chewing herbivores (Vrkoslav et al., 2010). The MALDI-MSI approach has classically been limited by its requirement for a high vacuum for the ionization of the analyzed molecules, making this technology incompatible with in vivo analysis. However, more recently Coello et al. (2010) have described the use of a near-infrared laser imaging MS approach obviating the need for high vacuum. Even though the presented approach demonstrates the potential of this technique for in vivo imaging studies, it still lacks precision and sensitivity to fully analyze cell-specific or even subcellular metabolic distributions.

image

Figure 3.  Methods for studying metabolism with varying degrees of spatial resolution. Cell fractionation (A) is the classical method for studying metabolism on a subcellular level. Two different methods for subcellular fractionation have been used. Protoplast fractionation (PF) is based on the fractionation of protoplast by centrifugation through a nylon net (1). Cellular content is released (2) and chloroplasts are separated by centrifugation through a silicon oil layer (3). Metabolism within isolated chloroplast is inhibited by perchloric acid (4). For non-aqueous fractionation (NAF) freeze-dried tissue homogenate is fractionated on a non-aqueous density gradient. Using NAF, at least three compartments, namely chloroplasts (a), the cytosol (b), and the vacuole (c), can be unambiguously delineated. To study metabolism on a single-cell level, microcapillary approaches (B) have been used. Sap from a single cell is sampled and analyzed by metabolite profiling. Fluorescence resonance energy transfer (FRET)-based metabolite nanosensors (C) are a promising new technology to study spatial and time-resolved changes in metabolite concentrations in vivo and with a high degree of resolution. Analysis of metabolite composition on a whole tissue level is realized by desorption electrospray ionization mass spectrometry imaging (DESI-MSI) and matrix-associated laser desorption ionization mass spectrometry imaging (MALDI-MSI) (D) by generating spatial-resolved images. Nuclear magnetic resonance (NMR) technology (E) facilitates the analysis of metabolites in non-invasive ways at the cellular and subcellular level. Microdissection (F) allows isolation of specific cell types from a tissue section. Within the isolated cells a broad range of molecules can be analyzed.

Download figure to PowerPoint

The development of atmospheric pressure ionization methods like the DESI represents an additional method with potential for imaging MS in a real in vivo imaging experiment (Costa and Cooks, 2008). For DESI, charged solvent droplets are produced in an electric field and accelerated by a nitrogen stream (Figure 3D). The impact of the droplet on the sample surface results first in a liquid film which leads to the extraction of metabolites. Further droplet collision leads to the generation of analyte-containing microdroplets which became conventionally ionized by ESI. Using DESI-MSI, several different metabolite classes such as sugars, amino acids, fatty acids, lipids and alkaloids could be detected in leaf samples of different plant species, indicating the sensitivity of this method (Jackson et al., 2009; Liu et al., 2011; Muller et al., 2011). The DESI-MSI and MALDI-MSI techniques are so far not applicable to single-cell analysis as the spot size of the laser beam and the nitrogen stream has to be smaller than a single cell. Recently, a reduction in the laser spot size for MALDI-MSI from 150 μm to below 20 μm for analysis at the single cell level has been achieved. However, thus far this reduced laser spot size technique has not yet been applied to plant tissues (Holle et al., 2006; Grassl et al., 2011).

Another approach involving sampling of specific tissue or cell types is LCM (Figure 3F). This approach has already been used in plant systems to analyze the metabolite composition in certain cell types (Kerk et al., 2003; Nakazono et al., 2003; Schad et al., 2005). In general, histological sections are prepared and fixed. The cells of interest have to be identified by either histological staining, reporter gene expression, or morphological properties. Therefore they are selected under a microscope, isolated by a laser beam and collected in a tube by a laser pressure pulse (Nelson et al., 2006). The collected cells can be used for different analytical methods, such as transcript, protein, or metabolite analysis (Westphal et al., 2002; Wittliff and Erlander, 2002; Kerk et al., 2003; Nakazono et al., 2003; Schad et al., 2005). However, one disadvantage of this method is the sample preparation, because histological fixation is required which might affect molecule composition within the selected sections (Nelson et al., 2006). Furthermore, and probably even more important, LCM techniques are very time intensive with respect to the preparation of enough cells for later analysis and are thus definitely not applicable in a high-throughput mode.

Beside LCM, other methods have been used to study metabolite composition at the tissue-specific level. In some special cases such as trichome cels a cellular preparation using classical fractionation techniques is possible and can be used in order to determine the metabolic composition of these special cell types (McDowell et al., 2011; Weinhold and Baldwin, 2011).

In cases where these fractionation methods do not work, microcapillary methods can be used to collected cell sap (Figure 3B), mainly from the vacuole of epidermis cells. Cell sap sampling in combination with capillary electrophoresis (CE) or GC-MS was used to detect a number of different metabolites like organic acids, amino acids, sugars, inorganic anions, and secondary metabolites (Lochmann et al., 1998, 2001; Ebert et al., 2010).

Due to technical limitations the CSS methods are generally not suitable for the analysis of subcellular compartments like the chloroplasts or mitochondria. Cell sap sampling has only been applied in a very special case for analysis of a subcellular compartment, i.e. the vacuole of a single internodal cell of the giant alga Chara australis (Oikawa et al., 2011). After the turgor of the 20-cm Chara australis cell is lost, the sap of its large vacuole can be easily harvested and either directly analyzed or further metabolically fractionated (Oikawa et al., 2011).

Zooming in: large scale analysis at the Subcellular Level

  1. Top of page
  2. Summary
  3. Introduction
  4. Targeted and Non-Targeted Analysis in MS-Based Metabolomics
  5. Compound Annotation: Making Use of High-Resolution, Accurate Mass, Isotope Labeling and Compound Fragmentation for the Annotation of Metabolites
  6. Increasing the Resolution by Increasing the Separation
  7. Getting an Overview: ‘Metabolite Analysis at the tissue and Cellular Level’
  8. Zooming in: large scale analysis at the Subcellular Level
  9. Non-Aqueous Fractionation (NAF): A Method Allowing the Application of Metabolomics Technologies to Subcellular Compartments Determined by Continuous Distributions
  10. Conclusion
  11. Acknowledgements
  12. References

As mentioned above, next to mass spectrometry, NMR spectroscopy is the second main method for metabolite profiling in plants (Figure 3E). Nuclear magnetic resonance is based on the detection of isotopes with non-zero nuclear magnetic moments like, for example, 1H, 13C, 14N, 15N, or 31P (Ratcliffe and Shachar-Hill, 2005; Kim et al., 2011). As those nuclei naturally occur – or can be incorporated into plants – NMR allows us to determine the in vivo metabolite composition in cell suspension cultures, tissues, and even whole plants (Gout et al., 1993, 2000; Scheenen et al., 2000; Libourel et al., 2006; Kim et al., 2011). For the analysis of metabolites on a subcellular level, a compartment-specific NMR signal for the metabolite of interest is required. This can be achieved because the chemical shift of many metabolites depends on the pH (Bligny and Douce, 2001). Using 13C- and 31P-NMR, the compartmentalization of carbohydrates and amino acids within Acer pseudoplatanus cells was analyzed (Aubert et al., 1998; Gout et al., 2011). This study showed how sugar starvation in A. pseudoplatanus cells leads to a very rapid (<2.5 min) response characterized by a dramatic change in phosphorylated intermediates. Irrespective of this successful example, the use of NMR in a non-invasive way still has several limitations because it is restricted to metabolites which exhibit strong NMR signals and more importantly to metabolites which display a chemical shift in dependence on the subcellular compartment. This is true for phosphorylated compounds due to the pH dependence of the chemical shift of the phosphate atom; however, it is not generally true. In addition, due to the complexity of the analyte composition of a living cell, in vivo NMR spectra often have to deal with poor signal to noise ratios and resolution, which limits spectral interpretation (Ratcliffe and Shachar-Hill, 2001; Kim et al., 2011).

A more accurate and sensitive technique to study metabolite compartmentalization is the use of FRET-based nanosensors (Figure 3C). These genetically encoded molecular nanosensors, with their prototype being developed for detecting calcium at nanomolar concentrations (Persechini et al., 1997), represent a new and promising technology to follow the spatial and temporal metabolic changes on the cellular and even subcellular level in living cells (Fehr et al., 2005; Lalonde et al., 2005). However, they do suffer from the severe limitation that each metabolite requires its own sensor protein for detection, thus making it essentially not applicable for true metabolomics.

Historically, the metabolite content in purified organelles has been studied following fractionation of leaf cell protoplasts (Figure 3A; Robinson and Walker, 1980). Protoplast fractionation in combination with enzymatic determination of metabolites has been widely used to quantify a subset of metabolites like adenylates, phosphorylated sugars and Calvin cycle intermediates in different compartments (Robinson and Walker, 1980; Wirtz et al., 1980; Lilley et al., 1982; Stitt et al., 1982; Gardeström, 1993). Besides the isolation of chloroplast and mitochondria, protoplast fractionation has also been used for the isolation of intact vacuoles (Martinoia et al., 1991; Ramos et al., 2011; Tohge et al., 2011).

Although isolation of organelles after protoplast fractionation represents an excellent method for purifying the organelles, this approach must be taken with great caution because the procedure as a rule takes several hours and includes several centrifugation steps, thus presenting a major disturbance of the physiology and thus the biochemistry of the system. In consequence changes in the composition and compartmentalization of metabolites due to the fractionation procedure are very likely, leading to a partly artificial representation of the subcellular concentrations of metabolites (Robinson and Walker, 1980).

In conclusion many of the techniques discussed above allow the reliable and partly in vivo analysis of a small subset of metabolites. However, with the goal of comprehensive metabolite coverage including cellular and subcellular distributions that faithfully reflect in vivo conditions, most of the above-described methods suffer from one shortcoming or another.

Non-Aqueous Fractionation (NAF): A Method Allowing the Application of Metabolomics Technologies to Subcellular Compartments Determined by Continuous Distributions

  1. Top of page
  2. Summary
  3. Introduction
  4. Targeted and Non-Targeted Analysis in MS-Based Metabolomics
  5. Compound Annotation: Making Use of High-Resolution, Accurate Mass, Isotope Labeling and Compound Fragmentation for the Annotation of Metabolites
  6. Increasing the Resolution by Increasing the Separation
  7. Getting an Overview: ‘Metabolite Analysis at the tissue and Cellular Level’
  8. Zooming in: large scale analysis at the Subcellular Level
  9. Non-Aqueous Fractionation (NAF): A Method Allowing the Application of Metabolomics Technologies to Subcellular Compartments Determined by Continuous Distributions
  10. Conclusion
  11. Acknowledgements
  12. References

Non-aqueous fractionation (NAF) is the most widely used method for studying metabolite pool sizes at a subcellular level in plants (Gerhardt and Heldt, 1984; Dancer et al., 1990; Riens et al., 1991; Winter et al., 1992; Farre et al., 2001; Fettke et al., 2005; Krueger et al., 2011). Instead of purifying intact organelles, the NAF method is based on the enrichment of compartments within a continuous non-aqueous density gradient (Figure 4). The main advantage with respect to analyzing metabolism is the fast quenching of metabolic processes to avoid reallocation and conversion of metabolites. As NAF results in a continuous compartment distribution computer-based evaluation of the obtained data is important for obtaining reliable results (Krueger et al., 2011; Klie et al., 2011). If the volumes for the subcellular compartments, the total tissue concentration, and the subcellular distribution of a certain metabolite are known, the exact subcellular concentration of this metabolite can be calculated (Winter et al., 1992). Information about the subcellular localization of different metabolites obtained from NAF studies represents an important step forward for the understanding of plant metabolism. For example, by using NAF technology the prevalent cytosolic localization of pyrophosphate was discovered, whereas it could be shown that the pyrophosphatase is highly active in chloroplasts (Weiner et al., 1987). Non-aqueous fractionation in combination with field flow fractionation (FFF) was applied to analyze the subcellular localization of a recently identified water-soluble heteroglucan (Fettke et al., 2005). In recent years NAF technology has been linked with true metabolomics studies allowing the subcellular localization of a large number of metabolites to be analyzed in parallel (Farre et al., 2001; Benkeblia et al., 2007; Krueger et al., 2011). The evaluation of such data sets using biostatistical and bioinformatic tools suggests the presence of previously unidentified plant compartments within the NAF gradient and shows that the resolution of this method is yet not fully explored (Krueger et al., 2011). Although NAF does not allow purification of intact organelles, and the evaluation of the obtained data is rather complex, NAF is still the method of choice and its full potential has not yet been reached. For example, metabolic flux analysis often ignores metabolite compartmentalization, leading to misinterpretation of the obtained results (Kruger et al., 2007). As NAF allows direct quenching of metabolism by snap-freezing in liquid nitrogen, the combination of NAF with metabolic flux analysis using 13C labeled CO2 is a very attractive approach for the future. However, the complexity of the obtained data from such an approach might require new methods for the computation, interpretation, and visualization of the data as evaluated by Klie and co-workers (Klie et al., 2011).

image

Figure 4.  The entire non-aqueous fractionation (NAF) procedure can be divided into experimental- and computational-driven analyses. The experimental part starts with the fast quenching of metabolism by snap-freezing of plant material in liquid nitrogen. The frozen leaf material is homogenized and freeze-dried in a pre-cooled lyophilizer. The dried powder is resuspended in a non-aqueous mixture of tetrachloroethylene (TCE)/heptane and sonicated on ice. The obtained suspension is poured through a nylon sieve and loaded on a linear density gradient. After 50-min centrifugation at 5000 g and 4°C equilibrium distribution of the fractionated particles is reached. Fractions are taken from the top of the gradient and separated into 10 1-ml aliquots. For the analysis of marker activity and metabolite profiling, aliquots of each fraction are dried and then extracted in the respective buffer or solvent by strong vortexing or shaking in a pre-cooled Retsch mill. The computational phase of the work includes the validation, classification, visualization, and interpretation of the obtained data (cf. Klie et al., 2011).

Download figure to PowerPoint

Conclusion

  1. Top of page
  2. Summary
  3. Introduction
  4. Targeted and Non-Targeted Analysis in MS-Based Metabolomics
  5. Compound Annotation: Making Use of High-Resolution, Accurate Mass, Isotope Labeling and Compound Fragmentation for the Annotation of Metabolites
  6. Increasing the Resolution by Increasing the Separation
  7. Getting an Overview: ‘Metabolite Analysis at the tissue and Cellular Level’
  8. Zooming in: large scale analysis at the Subcellular Level
  9. Non-Aqueous Fractionation (NAF): A Method Allowing the Application of Metabolomics Technologies to Subcellular Compartments Determined by Continuous Distributions
  10. Conclusion
  11. Acknowledgements
  12. References

Metabolic analyses have become an integral part of plant science within recent years. Since the developed technologies have become more and more sophisticated, and often also specific for particular questions, the gap between the width (number of compounds analyzed) and depth (quality of annotated compounds) within metabolomics studies seems to be expanding. As a consequence the key challenges in metabolomics, as in all ‘omics’ technologies, will be to establish and combine methods and strategies which allow us to increase the number of measured and annotated compounds – this means qualitatively, quantitatively and spatially resolved without losing sample throughput and quality controls. In particular, quality control and standardization of the applied methods are necessary to allow cross-laboratory comparisons.

Although in principle many technical solutions are already available for these challenges none of them is yet fully applicable to high-throughput approaches which will become an important part of plant science in the future.

References

  1. Top of page
  2. Summary
  3. Introduction
  4. Targeted and Non-Targeted Analysis in MS-Based Metabolomics
  5. Compound Annotation: Making Use of High-Resolution, Accurate Mass, Isotope Labeling and Compound Fragmentation for the Annotation of Metabolites
  6. Increasing the Resolution by Increasing the Separation
  7. Getting an Overview: ‘Metabolite Analysis at the tissue and Cellular Level’
  8. Zooming in: large scale analysis at the Subcellular Level
  9. Non-Aqueous Fractionation (NAF): A Method Allowing the Application of Metabolomics Technologies to Subcellular Compartments Determined by Continuous Distributions
  10. Conclusion
  11. Acknowledgements
  12. References
  • Aharoni, A., Ric de Vos, C.H., Verhoeven, H.A., Maliepaard, C.A., Kruppa, G., Bino, R. and Goodenowe, D.B. (2002) Nontargeted metabolome analysis by use of Fourier transform ion cyclotron mass spectrometry. Omics 6, 217234.
  • Albinsky, D., Sawada, Y., Kuwahara, A., Nagano, M., Hirai, A., Saito, K. and Hirai, M.Y. (2010) Widely targeted metabolomics and coexpression analysis as tools to identify genes involved in the side-chain elongation steps of aliphatic glucosinolate biosynthesis. Amino Acids 39, 10671075.
  • Allwood, J.W. and Goodacre, R. (2010) An introduction to liquid chromatography-mass spectrometry instrumentation applied in plant metabolomic analyses. Phytochem. Anal. 21, 3347.
  • Andrews, G.L., Simons, B.L., Young, J.B., Hawkridge, A.M. and Muddiman, D.C. (2011) Performance characteristics of a new hybrid quadrupole time-of-flight tandem mass spectrometer (TripleTOF 5600). Anal. Chem. 83, 54425446.
  • Arrivault, S., Guenther, M., Ivakov, A., Feil, R., Vosloh, D., van Dongen, J.T., Sulpice, R. and Stitt, M. (2009) Use of reverse-phase liquid chromatography, linked to tandem mass spectrometry, to profile the Calvin cycle and other metabolic intermediates in Arabidopsis rosettes at different carbon dioxide concentrations. Plant J. 59, 826839.
  • Aubert, S., Curien, G., Bligny, R., Gout, E. and Douce, R. (1998) Transport, compartmentation, and metabolism of homoserine in higher plant cells. Carbon-13- and phosphorus-31-nuclear magnetic resonance studies Carbon-13- and Phosphorus-31-Nuclear Magnetic Resonance Studies. Plant Physiol. 116, 547557.
  • Bauwe, H. (2010) Recent developments in photorespiration research. Biochem. Soc. Trans. 38, 677682.
  • Beckmann, M., Parker, D., Enot, D.P., Duval, E. and Draper, J. (2008) High-throughput, nontargeted metabolite fingerprinting using nominal mass flow injection electrospray mass spectrometry. Nat. Protoc. 3, 486504.
  • Benkeblia, N., Shinano, T. and Osaki, M. (2007) Metabolite profiling and assessment of metabolome compartmentation of soybean leaves using non-aqueous fractionation and GC-MS analysis. Metabolomics 3, 297305.
  • Bligny, R. and Douce, R. (2001) NMR and plant metabolism. Curr. Opin. Plant Biol. 4, 191196.
  • Bocker, S., Letzel, M.C., Liptak, Z. and Pervukhin, A. (2009) SIRIUS: decomposing isotope patterns for metabolite identification. Bioinformatics 25, 218224.
  • Boswell, P.G., Schellenberg, J.R., Carr, P.W., Cohen, J.D. and Hegeman, A.D. (2011a) Easy and accurate high-performance liquid chromatography retention prediction with different gradients, flow rates, and instruments by back-calculation of gradient and flow rate profiles. J. Chromatogr. A 1218, 67426749.
  • Boswell, P.G., Schellenberg, J.R., Carr, P.W., Cohen, J.D. and Hegeman, A.D. (2011b) A study on retention “projection” as a supplementary means for compound identification by liquid chromatography-mass spectrometry capable of predicting retention with different gradients, flow rates, and instruments. J. Chromatogr. A 1218, 67326741.
  • Bylesjo, M., Eriksson, D., Kusano, M., Moritz, T. and Trygg, J. (2007) Data integration in plant biology: the O2PLS method for combined modeling of transcript and metabolite data. Plant J. 52, 11811191.
  • Cha, S., Zhang, H., Ilarslan, H.I., Wurtele, E.S., Brachova, L., Nikolau, B.J. and Yeung, E.S. (2008) Direct profiling and imaging of plant metabolites in intact tissues by using colloidal graphite-assisted laser desorption ionization mass spectrometry. Plant J. 55, 348360.
  • Cha, S., Song, Z., Nikolau, B.J. and Yeung, E.S. (2009) Direct profiling and imaging of epicuticular waxes on Arabidopsis thaliana by laser desorption/ionization mass spectrometry using silver colloid as a matrix. Anal. Chem. 81, 29913000.
  • Chughtai, K. and Heeren, R.M. (2010) Mass spectrometric imaging for biomedical tissue analysis. Chem. Rev. 110, 32373277.
  • Coello, Y., Jones, A.D., Gunaratne, T.C. and Dantus, M. (2010) Atmospheric pressure femtosecond laser imaging mass spectrometry. Anal. Chem. 82, 27532758.
  • Costa, A.B. and Cooks, R.G. (2008) Simulated splashes: elucidating the mechanism of desorption electrospray ionization mass spectrometry. Chem. Phys. Lett. 464, 18.
  • Cuadros-Inostroza, A., Giavalisco, P., Hummel, J., Eckardt, A., Willmitzer, L. and Pena-Cortes, H. (2010) Discrimination of wine attributes by metabolome analysis. Anal. Chem. 82, 35733580.
  • Dancer, J., Neuhaus, H.E. and Stitt, M. (1990) Subcellular compartmentation of uridine nucleotides and nucleoside-5′ -diphosphate kinase in leaves. Plant Physiol. 92, 637641.
  • De Vos, R.C., Moco, S., Lommen, A., Keurentjes, J.J., Bino, R.J. and Hall, R.D. (2007) Untargeted large-scale plant metabolomics using liquid chromatography coupled to mass spectrometry. Nat. Protoc. 2, 778791.
  • Dettmer, K., Aronov, P.A. and Hammock, B.D. (2007) Mass spectrometry-based metabolomics. Mass Spectrom. Rev. 26, 5178.
  • Dudley, E., Yousef, M., Wang, Y. and Griffiths, W.J. (2010) Targeted metabolomics and mass spectrometry. Adv. Protein Chem. Struct. Biol. 80, 4583.
  • Dunn, W.B. (2008) Current trends and future requirements for the mass spectrometric investigation of microbial, mammalian and plant metabolomes. Phys. Biol. 5, 011001.
  • Ebert, B., Zoller, D., Erban, A., Fehrle, I., Hartmann, J., Niehl, A., Kopka, J. and Fisahn, J. (2010) Metabolic profiling of Arabidopsis thaliana epidermal cells. J. Exp. Bot. 61, 13211335.
  • Eliasson, M., Rannar, S. and Trygg, J. (2011) From data processing to multivariate validation – essential steps in extracting interpretable information from metabolomics data. Curr. Pharm. Biotechnol. 12, 9961004.
  • Farre, E.M., Tiessen, A., Roessner, U., Geigenberger, P., Trethewey, R.N. and Willmitzer, L. (2001) Analysis of the compartmentation of glycolytic intermediates, nucleotides, sugars, organic acids, amino acids, and sugar alcohols in potato tubers using a nonaqueous fractionation method. Plant Physiol. 127, 685700.
  • Fehr, M., Okumoto, S., Deuschle, K., Lager, I., Looger, L.L., Persson, J., Kozhukh, L., Lalonde, S. and Frommer, W.B. (2005) Development and use of fluorescent nanosensors for metabolite imaging in living cells. Biochem. Soc. Trans. 33, 287290.
  • Feldberg, L., Venger, I., Malitsky, S., Rogachev, I. and Aharoni, A. (2009) Dual labeling of metabolites for metabolome analysis (DLEMMA): a new approach for the identification and relative quantification of metabolites by means of dual isotope labeling and liquid chromatography-mass spectrometry. Anal. Chem. 81, 92579266.
  • Feng, X., Liu, X., Luo, Q. and Liu, B.F. (2008) Mass spectrometry in systems biology: an overview. Mass Spectrom. Rev. 27, 635660.
  • Fettke, J., Eckermann, N., Tiessen, A., Geigenberger, P. and Steup, M. (2005) Identification, subcellular localization and biochemical characterization of water-soluble heteroglycans (SHG) in leaves of Arabidopsis thaliana L.: distinct SHG reside in the cytosol and in the apoplast. Plant J. 43, 568585.
  • Field, B. and Osbourn, A.E. (2008) Metabolic diversification – independent assembly of operon-like gene clusters in different plants. Science 320, 543547.
  • Flugge, U.I., Freisl, M. and Heldt, H.W. (1980) Balance between metabolite accumulation and transport in relation to photosynthesis by isolated spinach chloroplasts. Plant Physiol. 65, 574577.
  • Forrest, A.R. and Carninci, P. (2009) Whole genome transcriptome analysis. RNA Biol. 6, 107112.
  • Furumoto, T., Yamaguchi, T., Ohshima-Ichie, Y. et al. (2011) A plastidial sodium-dependent pyruvate transporter. Nature 476, 472475.
  • Gardeström, P. (1993) Metabolite levels in the chloroplast and extrachloroplast compartments of barley leaf protoplasts during the initial phase of photosynthetic induction. Biochim. Biophys. Acta 1183, 327332.
  • Gerhardt, R. and Heldt, H.W. (1984) Measurement of subcellular metabolite levels in leaves by fractionation of freeze-stopped material in nonaqueous media. Plant Physiol. 75, 542547.
  • Giavalisco, P., Hummel, J., Lisec, J., Inostroza, A.C., Catchpole, G. and Willmitzer, L. (2008) High-resolution direct infusion-based mass spectrometry in combination with whole 13C metabolome isotope labeling allows unambiguous assignment of chemical sum formulas. Anal. Chem. 80, 94179425.
  • Giavalisco, P., Kohl, K., Hummel, J., Seiwert, B. and Willmitzer, L. (2009) 13C isotope-labeled metabolomes allowing for improved compound annotation and relative quantification in liquid chromatography-mass spectrometry-based metabolomic research. Anal. Chem. 81, 65466551.
  • Giavalisco, P., Li, Y., Matthes, A., Eckhardt, A., Hubberten, H.M., Hesse, H., Segu, S., Hummel, J., Kohl, K. and Willmitzer, L. (2011) Elemental formula annotation of polar and lipophilic metabolites using (13) C, (15) N and (34) S isotope labelling, in combination with high-resolution mass spectrometry. Plant J. 68, 364376.
  • Gout, E., Bligny, R., Pascal, N. and Douce, R. (1993) 13C nuclear magnetic resonance studies of malate and citrate synthesis and compartmentation in higher plant cells. J. Biol. Chem. 268, 39863992.
  • Gout, E., Aubert, S., Bligny, R., Rebeille, F., Nonomura, A.R., Benson, A.A. and Douce, R. (2000) Metabolism of methanol in plant cells. Carbon-13 nuclear magnetic resonance studies. Plant Physiol. 123, 287296.
  • Gout, E., Bligny, R., Douce, R., Boisson, A.M. and Rivasseau, C. (2011) Early response of plant cell to carbon deprivation: in vivo 31P-NMR spectroscopy shows a quasi-instantaneous disruption on cytosolic sugars, phosphorylated intermediates of energy metabolism, phosphate partitioning, and intracellular pHs. New Phytol. 189, 135147.
  • Grassl, J., Taylor, N.L. and Millar, A.H. (2011) Matrix-assisted laser desorption/ionisation mass spectrometry imaging and its development for plant protein imaging. Plant methods 7, 21.
  • Gresham, D., Dunham, M.J. and Botstein, D. (2008) Comparing whole genomes using DNA microarrays. Nat. Rev. Gen. 9, 291302.
  • Gstaiger, M. and Aebersold, R. (2009) Applying mass spectrometry-based proteomics to genetics, genomics and network biology. Nat. Rev. Gen. 10, 617627.
  • Gunasekaran, K., Ma, B. and Nussinov, R. (2004) Is allostery an intrinsic property of all dynamic proteins? Proteins 57, 433443.
  • Hanhineva, K., Rogachev, I., Kokko, H., Mintz-Oron, S., Venger, I., Karenlampi, S. and Aharoni, A. (2008) Non-targeted analysis of spatial metabolite composition in strawberry (Fragariaxananassa) flowers. Phytochemistry 69, 24632481.
  • Holle, A., Haase, A., Kayser, M. and Hohndorf, J. (2006) Optimizing UV laser focus profiles for improved MALDI performance. J. Mass Spectrom. 41, 705716.
  • Hsu, C.S., Hendrickson, C.L., Rodgers, R.P., McKenna, A.M. and Marshall, A.G. (2011) Petroleomics: advanced molecular probe for petroleum heavy ends. J. Mass Spectrom. 46, 337343.
  • Jackson, A.U., Tata, A., Wu, C., Perry, R.H., Haas, G., West, L. and Cooks, R.G. (2009) Direct analysis of Stevia leaves for diterpene glycosides by desorption electrospray ionization mass spectrometry. Analyst 134, 867874.
  • Jun, J.H., Song, Z., Liu, Z., Nikolau, B.J., Yeung, E.S. and Lee, Y.J. (2010) High-spatial and high-mass resolution imaging of surface metabolites of Arabidopsis thaliana by laser desorption-ionization mass spectrometry using colloidal silver. Anal. Chem. 82, 32553265.
  • Kaspar, S., Peukert, M., Svatos, A., Matros, A. and Mock, H.P. (2011) MALDI-imaging mass spectrometry – An emerging technique in plant biology. Proteomics 11, 18401850.
  • Kerk, N.M., Ceserani, T., Tausta, S.L., Sussex, I.M. and Nelson, T.M. (2003) Laser capture microdissection of cells from plant tissues. Plant Physiol. 132, 2735.
  • Kim, H.K., Choi, Y.H. and Verpoorte, R. (2011) NMR-based plant metabolomics: where do we stand, where do we go? Trends Biotechnol. 29, 267275.
  • Kind, T. and Fiehn, O. (2006) Metabolomic database annotations via query of elemental compositions: mass accuracy is insufficient even at less than 1 ppm. BMC Bioinformatics 7, 234.
  • Klahre, U., Noguchi, T., Fujioka, S., Takatsuto, S., Yokota, T., Nomura, T., Yoshida, S. and Chua, N.H. (1998) The Arabidopsis DIMINUTO/DWARF1 gene encodes a protein involved in steroid synthesis. Plant Cell 10, 16771690.
  • Klie, S., Krueger, S., Krall, L., Giavalisco, P., Flügge, U.-I., Willmitzer, L. and Steinhauser, D. (2011) Analysis of the compartmentalized metabolome – a validation of the non-aqueous fractionation technique. Front. Plant Sci. 2, 55, doi: 10.3389/fpls.2011.00055.
  • Koch, B.P., Dittmar, T., Witt, M. and Kattner, G. (2007) Fundamentals of molecular formula assignment to ultrahigh resolution mass data of natural organic matter. Anal. Chem. 79, 17581763.
  • Krueger, S., Giavalisco, P., Krall, L., Steinhauser, M.C., Bussis, D., Usadel, B., Flugge, U.I., Fernie, A.R., Willmitzer, L. and Steinhauser, D. (2011) A topological map of the compartmentalized Arabidopsis thaliana leaf metabolome. PLoS ONE 6, e17806.
  • Kruger, N.J. and von Schaewen, A. (2003) The oxidative pentose phosphate pathway: structure and organisation. Curr. Opin. Plant Biol. 6, 236246.
  • Kruger, N.J., Le Lay, P. and Ratcliffe, R.G. (2007) Vacuolar compartmentation complicates the steady-state analysis of glucose metabolism and forces reappraisal of sucrose cycling in plants. Phytochemistry 68, 21892196.
  • Lalonde, S., Ehrhardt, D.W. and Frommer, W.B. (2005) Shining light on signaling and metabolic networks by genetically encoded biosensors. Curr. Opin. Plant Biol. 8, 574581.
  • Last, R.L., Jones, A.D. and Shachar-Hill, Y. (2007) Towards the plant metabolome and beyond. Nat. Rev. Mol. Cell Biol. 8, 167174.
  • Lei, Z., Huhman, D.V. and Sumner, L.W. (2011) Mass spectrometry strategies in metabolomics. J. Biol. Chem. 286, 2543525442.
  • Li, X., Fekete, A., Englmann, M., Frommberger, M., Lv, S., Chen, G. and Schmitt-Kopplin, P. (2007) At-line coupling of UPLC to chip-electrospray-FTICR-MS. Anal. Bioanal. Chem. 389, 14391446.
  • Libourel, I.G., van Bodegom, P.M., Fricker, M.D. and Ratcliffe, R.G. (2006) Nitrite reduces cytoplasmic acidosis under anoxia. Plant Physiol. 142, 17101717.
  • Lilley, R.M., Stitt, M., Mader, G. and Heldt, H.W. (1982) Rapid fractionation of wheat leaf protoplasts using membrane filtration: the determination of metabolite levels in the chloroplasts, cytosol, and mitochondria. Plant Physiol. 70, 965970.
  • Lisec, J., Schauer, N., Kopka, J., Willmitzer, L. and Fernie, A.R. (2006) Gas chromatography mass spectrometry-based metabolite profiling in plants. Nat. Protoc. 1, 387396.
  • Lisec, J., Romisch-Margl, L., Nikoloski, Z., Piepho, H.P., Giavalisco, P., Selbig, J., Gierl, A. and Willmitzer, L. (2011) Corn hybrids display lower metabolite variability and complex metabolite inheritance patterns. Plant J. 68, 326336.
  • Liu, J., Wang, H., Cooks, R.G. and Ouyang, Z. (2011) Leaf spray: direct chemical analysis of plant material and living plants by mass spectrometry. Anal. Chem. 83, 76087613.
  • Lochmann, H., Bazzanella, A. and Bächmann, K. (1998) Analysis of solutes and metabolites in single plant cell vacuoles by capillary electrophoresis. J. Chromatogr. A 817, 337343.
  • Lochmann, H., Bazzanella, A., Kropsch, S. and Bachmann, K. (2001) Determination of tobacco alkaloids in single plant cells by capillary electrophoresis. J. Chromatogr. A 917, 311317.
  • Lu, W., Bennett, B.D. and Rabinowitz, J.D. (2008) Analytical strategies for LC-MS-based targeted metabolomics. J. Chromatogr. B Analyt. Technol. Biomed. Life Sci. 871, 236242.
  • Lytovchenko, A., Beleggia, R., Schauer, N., Isaacson, T., Leuendorf, J.E., Hellmann, H., Rose, J.K. and Fernie, A.R. (2009) Application of GC-MS for the detection of lipophilic compounds in diverse plant tissues. Plant methods 5, 4.
  • Makarov, A., Denisov, E. and Lange, O. (2009) Performance evaluation of a high-field Orbitrap mass analyzer. J. Am. Soc. Mass Spectrom. 20, 13911396.
  • Markham, J.E. and Jaworski, J.G. (2007) Rapid measurement of sphingolipids from Arabidopsis thaliana by reversed-phase high-performance liquid chromatography coupled to electrospray ionization tandem mass spectrometry. Rapid Commun. Mass Spectrom. 21, 13041314.
  • Marshall, A.G. and Hendrickson, C.L. (2008) High-resolution mass spectrometers. Annu. Rev. Anal. Chem. (Palo Alto, Calif) 1, 579599.
  • Martinoia, E., Vogt, E., Rentsch, D. and Amrhein, N. (1991) Functional reconstitution of the malate carrier of barley mesophyll vacuoles in liposomes. Biochim. Biophys. Acta 1062, 271278.
  • Matsuda, F., Yonekura-Sakakibara, K., Niida, R., Kuromori, T., Shinozaki, K. and Saito, K. (2009) MS/MS spectral tag-based annotation of non-targeted profile of plant secondary metabolites. Plant J. 57, 555577.
  • Matsuda, F., Hirai, M.Y., Sasaki, E., Akiyama, K., Yonekura-Sakakibara, K., Provart, N.J., Sakurai, T., Shimada, Y. and Saito, K. (2010) AtMetExpress development: a phytochemical atlas of Arabidopsis development. Plant Physiol. 152, 566578.
  • Maurino, V.G. and Peterhansel, C. (2010) Photorespiration: current status and approaches for metabolic engineering. Curr. Opin. Plant Biol. 13, 249256.
  • McDowell, E.T., Kapteyn, J., Schmidt, A. et al. (2011) Comparative functional genomic analysis of Solanum glandular trichome types. Plant Physiol. 155, 524539.
  • Messerli, G., Partovi Nia, V., Trevisan, M., Kolbe, A., Schauer, N., Geigenberger, P., Chen, J., Davison, A.C., Fernie, A.R. and Zeeman, S.C. (2007) Rapid classification of phenotypic mutants of Arabidopsis via metabolite fingerprinting. Plant Physiol. 143, 14841492.
  • Muller, T., Oradu, S., Ifa, D.R., Cooks, R.G. and Krautler, B. (2011) Direct plant tissue analysis and imprint imaging by desorption electrospray ionization mass spectrometry. Anal. Chem. 83, 57545761.
  • Nakabayashi, R., Kusano, M., Kobayashi, M., Tohge, T., Yonekura-Sakakibara, K., Kogure, N., Yamazaki, M., Kitajima, M., Saito, K. and Takayama, H. (2009) Metabolomics-oriented isolation and structure elucidation of 37 compounds including two anthocyanins from Arabidopsis thaliana. Phytochemistry 70, 10171029.
  • Nakazono, M., Qiu, F., Borsuk, L.A. and Schnable, P.S. (2003) Laser-capture microdissection, a tool for the global analysis of gene expression in specific plant cell types: identification of genes expressed differentially in epidermal cells or vascular tissues of maize. Plant Cell 15, 583596.
  • Nelson, T., Tausta, S.L., Gandotra, N. and Liu, T. (2006) Laser microdissection of plant tissue: what you see is what you get. Annu. Rev. Plant Biol. 57, 181201.
  • Neumann, S. and Bocker, S. (2010) Computational mass spectrometry for metabolomics: identification of metabolites and small molecules. Anal. Bioanal. Chem. 398, 27792788.
  • Oikawa, A., Matsuda, F., Kikuyama, M., Mimura, T. and Saito, K. (2011) Metabolomics of a single vacuole reveals metabolic dynamism in an alga Chara australis. Plant Physiol. 157, 551554.
  • Oliver, S.G., Winson, M.K., Kell, D.B. and Baganz, F. (1998) Systematic functional analysis of the yeast genome. Trends Biotechnol. 16, 373378.
  • Oliver, M.J., Guo, L., Alexander, D.C., Ryals, J.A., Wone, B.W. and Cushman, J.C. (2011) A sister group contrast using untargeted global metabolomic analysis delineates the biochemical regulation underlying desiccation tolerance in Sporobolus stapfianus. Plant Cell 23, 12311248.
  • Pan, X., Welti, R. and Wang, X. (2010) Quantitative analysis of major plant hormones in crude plant extracts by high-performance liquid chromatography-mass spectrometry. Nat. Protoc. 5, 986992.
  • Pelander, A., Decker, P., Baessmann, C. and Ojanpera, I. (2011) Evaluation of a high resolving power time-of-flight mass spectrometer for drug analysis in terms of resolving power and acquisition rate. J. Am. Soc. Mass Spectrom. 22, 379385.
  • Persechini, A., Lynch, J.A. and Romoser, V.A. (1997) Novel fluorescent indicator proteins for monitoring free intracellular Ca2 + . Cell Calcium 22, 209216.
  • Plumb, R., Castro-Perez, J., Granger, J., Beattie, I., Joncour, K. and Wright, A. (2004) Ultra-performance liquid chromatography coupled to quadrupole-orthogonal time-of-flight mass spectrometry. Rapid Commun. Mass Spectrom. 18, 23312337.
  • Pluskal, T., Castillo, S., Villar-Briones, A. and Oresic, M. (2010) MZmine 2: modular framework for processing, visualizing, and analyzing mass spectrometry-based molecular profile data. BMC Bioinformatics 11, 395.
  • Ramos, M.S., Abele, R., Nagy, R., Grotemeyer, M.S., Tampe, R., Rentsch, D. and Martinoia, E. (2011) Characterization of a transport activity for long-chain peptides in barley mesophyll vacuoles. J. Exp. Bot. 62, 24032410.
  • Rasche, F., Svatos, A., Maddula, R.K., Bottcher, C. and Bocker, S. (2011) Computing fragmentation trees from tandem mass spectrometry data. Anal. Chem. 83, 12431251.
  • Ratcliffe, R.G. and Shachar-Hill, Y. (2001) Probing plant metabolism with Nmr. Annu. Rev. Plant Physiol. Plant Mol. Biol. 52, 499526.
  • Ratcliffe, R.G. and Shachar-Hill, Y. (2005) Revealing metabolic phenotypes in plants: inputs from NMR analysis. Biol. Rev. Camb. Philos. Soc. 80, 2743.
  • Riens, B., Lohaus, G., Heineke, D. and Heldt, H.W. (1991) Amino-acid and sucrose content determined in the cytosolic, chloroplastic, and vacuolar compartments and in the phloem sap of spinach leaves. Plant Physiol. 97, 227233.
  • Robinson, S.P. and Walker, D.A. (1980) Distribution of metabolites between chloroplast and cytoplasm during the induction phase of photosynthesis in leaf protoplasts. Plant Physiol. 65, 902905.
  • Rojas-Cherto, M., Kasper, P.T., Willighagen, E.L., Vreeken, R.J., Hankemeier, T. and Reijmers, T.H. (2011) Elemental composition determination based on MSn. Bioinformatics 27, 23762383.
  • Saito, K. and Matsuda, F. (2010) Metabolomics for functional genomics, systems biology, and biotechnology. Annu. Rev. Plant Biol. 61, 463489.
  • Schad, M., Mungur, R., Fiehn, O. and Kehr, J. (2005) Metabolic profiling of laser microdissected vascular bundles of Arabidopsis thaliana. Plant methods 1, 2.
  • Schaub, T.M., Hendrickson, C.L., Quinn, J.P., Rodgers, R.P. and Marshall, A.G. (2005) Instrumentation and method for ultrahigh resolution field desorption ionization fourier transform ion cyclotron resonance mass spectrometry of nonpolar species. Anal. Chem. 77, 13171324.
  • Schauer, N., Steinhauser, D., Strelkov, S. et al. (2005) GC-MS libraries for the rapid identification of metabolites in complex biological samples. FEBS Lett. 579, 13321337.
  • Scheenen, T.W., van Dusschoten, D., de Jager, P.A. and Van As, H. (2000) Quantification of water transport in plants with NMR imaging. J. Exp. Bot. 51, 17511759.
  • Scherling, C., Roscher, C., Giavalisco, P., Schulze, E.D. and Weckwerth, W. (2010) Metabolomics unravel contrasting effects of biodiversity on the performance of individual plant species. PLoS ONE 5, e12569.
  • Shroff, R., Vergara, F., Muck, A., Svatos, A. and Gershenzon, J. (2008) Nonuniform distribution of glucosinolates in Arabidopsis thaliana leaves has important consequences for plant defense. Proc. Natl Acad. Sci. USA 105, 61966201.
  • Shuman, J.L., Cortes, D.F., Armenta, J.M., Pokrzywa, R.M., Mendes, P. and Shulaev, V. (2011) Plant metabolomics by GC-MS and differential analysis. Methods Mol. Biol. 678, 229246.
  • da Silva, A.L., Sperling, P., Horst, W., Franke, S., Ott, C., Becker, D., Stass, A., Lorz, H. and Heinz, E. (2006) A possible role of sphingolipids in the aluminium resistance of yeast and maize. J. Plant Physiol. 163, 2638.
  • Smith, J.E. and Bluhm, B.H. (2011) Metabolic fingerprinting in Fusarium verticillioides to determine gene function. Methods Mol. Biol. 722, 237247.
  • Stitt, M., Lilley, R.M. and Heldt, H.W. (1982) Adenine nucleotide levels in the cytosol, chloroplasts, and mitochondria of wheat leaf protoplasts. Plant Physiol. 70, 971977.
  • Stitt, M., Gerhardt, R., Kurzel, B. and Heldt, H.W. (1983) A role for fructose 2,6-bisphosphate in the regulation of sucrose synthesis in spinach leaves. Plant Physiol. 72, 11391141.
  • Stroh, J.G., Petucci, C.J., Brecker, S.J., Huang, N. and Lau, J.M. (2007) Automated sub-ppm mass accuracy on an ESI-TOF for use with drug discovery compound libraries. J. Am. Soc. Mass Spectrom. 18, 16121616.
  • Sumner, L.W., Amberg, A., Barrett, D.A. et al. (2007a) Proposed minimum reporting standards for chemical analysis. Chemical Analysis Working Group (CAWG) Metabolomics Standards Initiative (MSI). Metabolomics 3, 211221.
  • Sumner, L.W., Urbanczyk-Wochniak, E. and Broeckling, C.D. (2007b) Metabolomics data analysis, visualization, and integration. Methods Mol. Biol. 406, 409436.
  • Sweetlove, L.J., Beard, K.F., Nunes-Nesi, A., Fernie, A.R. and Ratcliffe, R.G. (2010) Not just a circle: flux modes in the plant TCA cycle. Trends Plant Sci. 15, 462470.
  • Takahashi, H., Kopriva, S., Giordano, M., Saito, K. and Hell, R. (2011) Sulfur assimilation in photosynthetic organisms: molecular functions and regulations of transporters and assimilatory enzymes. Annu. Rev. Plant Biol. 62, 157184.
  • Tianniam, S., Bamba, T. and Fukusaki, E. (2009) Non-targeted metabolite fingerprinting of oriental folk medicine Angelica acutiloba roots by ultra performance liquid chromatography time-of-flight mass spectrometry. J. Sep. Sci. 32, 22332244.
  • Tohge, T., Ramos, M.S., Nunes-Nesi, A. et al. (2011) Towards the storage metabolome: profiling the barley vacuole. Plant Physiol. 157, 14691482.
  • Villas-Boas, S.G., Mas, S., Akesson, M., Smedsgaard, J. and Nielsen, J. (2005) Mass spectrometry in metabolome analysis. Mass Spectrom. Rev. 24, 613646.
  • Vrkoslav, V., Muck, A., Cvacka, J. and Svatos, A. (2010) MALDI imaging of neutral cuticular lipids in insects and plants. J. Am. Soc. Mass Spectrom. 21, 220231.
  • Wade, J.T., Hall, D.B. and Struhl, K. (2004) The transcription factor Ifh1 is a key regulator of yeast ribosomal protein genes. Nature 432, 10541058.
  • Walther, T.C. and Mann, M. (2010) Mass spectrometry-based proteomics in cell biology. J. Cell Biol. 190, 491500.
  • Weiner, A., Stitt, M. and Heldt, H.W. (1987) Subcellular compartmentation of pyrophosphate and alkaline pyrophosphatase in leaves. Biochim. Biophys. Acta 893, 1321.
  • Weinhold, A. and Baldwin, I.T. (2011) Trichome-derived O-acyl sugars are a first meal for caterpillars that tags them for predation. Proc. Natl Acad. Sci. USA 108, 78557859.
  • Westphal, G., Burgemeister, R., Friedemann, G. et al. (2002) Noncontact laser catapulting: a basic procedure for functional genomics and proteomics. Methods Enzymol. 356, 8099.
  • Williams, B.J., Cameron, C.J., Workman, R., Broeckling, C.D., Sumner, L.W. and Smith, J.T. (2007) Amino acid profiling in plant cell cultures: an inter-laboratory comparison of CE-MS and GC-MS. Electrophoresis 28, 13711379.
  • Winter, H., Lohaus, G. and Heldt, H.W. (1992) Phloem transport of amino acids in relation to their cytosolic levels in Barley leaves. Plant Physiol. 99, 9961004.
  • Wirtz, W., Stitt, M. and Heldt, H.W. (1980) Enzymic determination of metabolites in the subcellular compartments of spinach protoplasts. Plant Physiol. 66, 187193.
  • Wittliff, J.L. and Erlander, M.G. (2002) Laser capture microdissection and its applications in genomics and proteomics. Methods Enzymol. 356, 1225.
  • Wolf, S., Schmidt, S., Muller-Hannemann, M. and Neumann, S. (2010) In silico fragmentation for computer assisted identification of metabolite mass spectra. BMC Bioinformatics 11, 148.
  • Xu, Y., Heilier, J.F., Madalinski, G., Genin, E., Ezan, E., Tabet, J.C. and Junot, C. (2010) Evaluation of accurate mass and relative isotopic abundance measurements in the LTQ-orbitrap mass spectrometer for further metabolomics database building. Anal. Chem. 82, 54905501.
  • Yates, J.R., Ruse, C.I. and Nakorchevsky, A. (2009) Proteomics by mass spectrometry: approaches, advances, and applications. Annu. Rev. Biomed. Eng. 11, 4979.
  • Zaikin, V.G. and Halket, J.M. (2006) Derivatization in mass spectrometry – 8. Soft ionization mass spectrometry of small molecules. Eur. J. Mass Spectrom. (Chichester, Eng) 12, 79115.