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Metabolome analysis: the potential of in vivo labeling with stable isotopes for metabolite profiling
Opinion
TRENDS in Biotechnology
Vol.23 No.1 January 2005
Metabolome analysis: the potential of in vivo labeling with stable isotopes for metabolite profiling Claudia Birkemeyer, Alexander Luedemann, Cornelia Wagner, Alexander Erban and Joachim Kopka Department Willmitzer, Max Planck Institute of Molecular Plant Physiology, Am Mu¨hlenberg 1, D-14467 Golm, Germany
Metabolome analysis technologies are still in early development because, unlike genome, transcriptome and proteome analyses, metabolome analysis has to deal with a highly diverse range of biomolecules. Combinations of different analytical platforms are therefore required for comprehensive metabolomic studies. Each of these platforms covers only part of the metabolome. To establish multiparallel technologies, thorough standardization of each measured metabolite is required. Standardization is best achieved by addition of a specific stable isotope-labeled compound, a mass isotopomer, for each metabolite. This suggestion, at first glance, seems unrealistic because of cost and time constraints. A possible solution to this problem is discussed in this article. Saturation in vivo labeling with stable isotopes enables the biosynthesis of differentially mass-labeled metabolite mixtures, which can be exploited for highly standardized metabolite profiling by mass isotopomer ratios. Introduction The field of analytical biochemistry has recently received a novel extension: metabolomics, a concept that has been defined as the science of the comprehensive monitoring of the metabolic complement in biological systems [1–6]. Metabolomics provides information about biological systems which cannot be obtained by the classical ‘–omics’ approaches: genomics, transcriptomics and proteomics. Metabolomics could be viewed, therefore, as the missing fourth Rosetta stone [7], which fills the metabolic gap within the previously developed systems-wide approaches towards the global analysis of biological processes. Metabolomic approaches are under dynamic development and several synonyms have been suggested, such as metabonomics [8], metabolite profiling [9] or fingerprinting [4,6]. A multitude of analytical platforms has been introduced [10], including spectroscopy fingerprints at infrared (IR), near infrared (NIR) or ultraviolet (UV) wavelength ranges, gas chromatography–mass spectrometry (GC-MS) [2,3,9], liquid chromatography–electrospray ionization–mass spectrometry (LC-ESI-MS) [11–13], capillary electrophoresis coupled to mass spectrometry (CE-MS) [14] or liquid chromatography with nuclear Corresponding author: Kopka, J. ([email protected]).
magnetic resonance spectroscopy (LC-NMR) [8,15], just to mention a few. There is no single analytical platform currently conceivable that would enable the multiparallel analysis of the complete metabolome [10,16], which comprises the full range of chemically diverse biomolecules Glossary Dynamic range: The range of concentrations, between detection limit and maximum amount of a substance to be quantified by one analytical technology. Hyphenated technologies: Hyphenation stands for the combination of at least two principles of chemical separation in a single instrument, such as gas chromatography–mass spectrometry (GC-MS), capillary electrophoresis– electrospray mass spectrometry (CE-ESI-MS) or high-performance liquid chromatography–nuclear magnetic resonance spectroscopy (HPLC-NMR). Exploiting two chemical properties for compound separation is prerequisite for in-parallel analysis of multiple compounds from a complex mixture. Mass isotopomer: Chemical substances are composed of naturally occurring (or technically enriched) mixtures of atomic isotopes, which have, as a rule, the same chemical properties but exhibit different mass. A modern mass spectrometer can resolve the mass differences of a single isotope substitution within a molecule. Each mass variant of a chemical substance is called a mass isotopomer. Mass isotopomer distribution: The mass isotopomer distribution of molecules can be precisely calculated and is dependent: (i) on the number of atoms present in a molecule and (ii) on the natural or technically enriched isotope abundances of each element [46]. High isotope enrichment and low atom numbers result in highly abundant, fully labeled mass isotopomers. Low enrichment and high atom numbers favor partial labeling and cause a broad distribution. For example, a four-carbon molecule might carry a 12C- or 13C-label at either of the positions. The chances for a fully 13C-labeled four-carbon mass isotopomer at ambient 1.1% enrichment are negligible, 0.0114Z1.46!10K8, whereas chances are intermediate, 0.704Z0.24, or high, 0.994Z0.96, at 70% and 99% isotope enrichment, respectively. By comparison, the chances of a fully labeled 30-carbon mass isotopomer at 99% enrichment are clearly reduced, 0.9930Z0.74. Matrix effect: The matrix effect is a long-standing observation in chemical and enzymatic analyses of complex biological samples. Namely, the nature or composition of complex samples can influence the apparent amount of metabolites and thus might lead to false quantitative results. Matrix effects can either stabilize labile compounds (matrix stabilization) or suppress compound measurements (matrix suppression). Matrix effects can occur at any step during chemical analysis, from extraction through clean-up, to final instrumental analysis. Well known examples are the matrix suppression effects of electrospray ionization–mass spectrometry [28] or matrix-assisted laser desorption–time of flight–mass spectrometry [29] technologies or the oxidation of labile metabolites, such as vitamin C. Recovery: Recovery measurements are the analytical means to control and standardize metabolite measurements for matrix effects. Recovery is routinely expressed as a percentage or ratio. The comparison made is between equal amounts of metabolites either supplied as a pure reference sample or added to the biological sample under scrutiny. Recovery analyses are most elegantly performed by using chemically synthesized mass isotopomers, which can be distinguished from the respective naturally occurring counterparts by highresolution mass analysis. Metabolic phenotype: The qualitative and quantitative inventory of all metabolites in a biological sample [2–4].
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from low molecular weight volatiles to storage polymers, such as starch or triacylglycerols. The diversity of required methods is in stark contrast to genome, transcriptome and proteome profiling technologies, which monitor molecules of highly similar chemical properties, such as DNA, RNA and proteins, respectively. Metabolome analyses not only need to accommodate the high diversity of biomolecules but also need to cover the vast dynamic range (see Glossary) of metabolite concentrations. These encompass highly abundant nutrients or primary metabolites and equally important trace compounds that might carry biological signals. In addition, the metabolome is formed by a complex network of reactions, which are subject to rapid enzymatic turnover [17–21]. Extreme care and fast inactivation of all biochemical reactions during sampling is therefore vital [2,12,22,23], which is not the case in proteome and transcriptome analyses. Although the sequence information embedded within protein and RNA structure enables unequivocal identification of the source organism, metabolites per se do not carry information on their respective origins. Thus, metabolite measurements need to be controlled for artifact chemical contamination that might arise during biological experimentation or chemical analysis.
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Even though there appear to be considerable technical obstacles, metabolome analyses are in high demand and have been widely proposed for studies in molecular physiology [2,16,24], functional genomics [1,3,7,15], clinical chemistry [8], biomarker discovery, research on the mode of drug action and monitoring of drug therapy [15,25,26]. This interest necessitates a short discussion of the properties of novel metabolomic approaches compared with classical chemical analytics. Analytical approaches of metabolome analysis: general variants, properties and applications Four major variants of analytical approaches are currently conceivable, fingerprinting, profiling, absolute quantification of pool sizes and, finally, flux analysis, recently suggested as an ‘-omics’ approach in its own right (‘fluxomics’ [27]). Table 1 gives a short overview of the typical characteristics of these variants. We are aware that all shades of intermediate analytical set-up might exist and that single analytical technologies, such as GC-MS or LC-MS, might enable all four levels of information to be obtained, depending on the chosen experimental set-up. Quantification of concentrations predates the ‘-omic’ era and was the first means to characterize the metabolic
Table 1. Overview of the four general variants in the toolbox of metabolome analyses. Properties of fingerprinting, profiling, pool size and flux analysis are described for typical analyses Major field of application Major result
Sample composition
Fingerprinting Functional genomics, diagnostics
Profiling Functional genomics, molecular physiology
Sample classification based on apparent metabolite pattern
Relative quantification of changes in metabolite pool size, identification and discovery of novel metabolites High complexity (minimal pre-purification)
Pool size analysis Biochemistry, biotechnology, molecular physiology Absolute quantification of metabolite pools
Flux analysis Biotechnology, modeling
Quantification of metabolite flux
Sample throughput
High
Analytical technology Metabolite coverage
Nonhyphenated technologies Hyphenated technologies possible required Limited only by choice of metabolite extraction and analytical technology
Low complexity (partial High complexity (minimal prepurification) or highly selective purification) possible Low (might be extremely Medium–low high when dedicated to a single metabolite) Combination of hyphenated or nonhyphenated technologies (dependent on the means of prepurification) Preconceived, that is, limited to a predefined set of targeted metabolites
Metabolite identification
Fingerprinting Identification of metabolites not required
Pool size analysis Unambiguous metabolite identification required
Metabolite concentrations
Required control experiments
Analytical trade-off
High-medium
Profiling Identification of as many metabolites as possible
The concentration of the most abundant metabolite determines the highest possible sample load. The dynamic range of the instrument defines the detection limit of coanalyzed minor metabolites In addition, analysis of Detector response is recovery, detection limits corrected for the initial and linearity of detector amount of sample and total response of all known losses of material during metabolites sample preparation and handling Absolute quantification is The precision of substituted for relative metabolite identification quantification in exchange and quantification is for full metabolite sacrificed for optimised coverage and medium to sample through-put. high sample throughput
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Flux analysis Unambiguous metabolite and mass isotopomer identification required Prepurification enables concentration of trace metabolites and thus adaptation to the sensitivity range of the analytical instrument. The dynamic range of instrumental analysis is thus nonlimiting. In addition, tracer In addition, experiments with quantitative radioactive or stable calibration of the isotope-labeled detector response by metabolites dilution of a series of pure metabolites The number of analyzed The number of analyzed metabolites is restricted metabolites is in exchange for precise restricted in exchange quantification of for precise metabolite mass quantification isotopomers
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Box 1. Experimental set-up of mass isotopomer ratio profiling (a)
'Non-sample' control (internal standarda) (b) (c)
Labelling control (reference sample) Samples of interest (treatments) (strains) (...) TRENDS in Biotechnology
Figure I. (a) A yeast parent strain is grown on pure U-13C-glucose (99 atom %)a in synthetic defined media (red). In (b), an identical culture is prepared with unlabeled glucose (blue). In (c), experiments on different strains or treatments are performed with unlabeled carbon sources (blue). Equal amounts of culture (a) are combined with samples of (b) or (c). Labeled samples serve as analytical internal standards and are typically monitored by ‘non-sample’ controls. The labeling control (b) checks for inherent changes owing to 13C-labeling (see Box 3b). Relative changes in metabolite pool size are determined by mass isotopomer ratio, as exemplified in Box 3a.aThe vitamin and auxotrophic supplements can be non-labelled (Box 2, point 3).
make-up of biological samples via metabolite pool sizes, that is, the metabolic phenotype [2,4]. The availability of pure metabolites is a prerequisite for quantitative analytical methods. Thus, quantification of concentrations might be considered as preconceived: only information on the metabolite under scrutiny is retrieved. The reason for this immanent bias is obvious – quantitative analytics requires calibration of detector signals to metabolite amount. Unequivocal identification and determination of detection limit, linear range, upper loading limit and inherent method variability are only possible when pure reference metabolites are available. In addition, recovery analyses are necessary to check the influence of the sample composition on quantitative detection, the so-called matrix effect [16,28,29]. The discovery of radioactive and stable isotopes and development of specific detectors for radioactive decay and the single unit mass differences of isotopes sparked investigations of metabolite flux [18,19,27,30–35]. Flux analysis is again targeted to the number of preconceived metabolites, and requires application of an isotope tracer that leads to a partial incorporation of isotopes into metabolite pools [30,32–35]. Demands on subsequent analytical resolution are high. Resolution is necessary not only for each metabolite but also for each isotope variant, the mass isotopomers. By contrast, profiling and fingerprinting technologies are aimed at detection of all metabolites that fall within the range of the chosen technologies [1–16,24,25]. Accordingly, the concept of metabolite purification before analysis is reverted into combinatorial approaches that aim www.sciencedirect.com
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to combine as many metabolites as possible into one analysis. Thus, profiling and fingerprinting could be termed ‘non-biased’, that is, limited only by the scope of the chosen means of metabolite extraction and analytical technology [4]. The major appeal of these approaches is the potential of discovery. Novel or unexpected metabolites can now be linked to physiological processes or gene function and used as biomarkers [25,26]. The nonbiased approach, however, comes at a cost. Typical fingerprinting analyses use noncalibrated detector readings obtained from complex metabolite mixtures for sample classification and biomarker screening. Analysis is, as a rule, thoroughly checked by nonsample controls and calibrated to reference samples, so that relative changes in signals, as compared with the reference sample, can be calculated. This set-up, although highly efficient in screening and classifying high numbers of samples, could be deemed to be insufficient for four main reasons. Firstly, little or no effort is put into assigning metabolite identity to detector signals. Applications are thus restricted to sample classification, without the potential to unravel the underlying metabolic and physiological cause. Secondly, single metabolites could be represented by multiple detector readings, such as diverse NMR signals or wavelengths. Therefore, it is conceivable that a single or few abundant metabolites might unknowingly dominate sample classification. Thirdly, artifacts caused by laboratory contamination cannot be completely ruled out and will have an impact on fingerprints. Fourthly, and most importantly, metabolite recovery is not controlled in fingerprints. Thus, observed apparent changes do not necessarily reflect direct metabolic changes within the sample but might actually be caused by matrix effects. For the above reasons, metabolite profiling was suggested; this aims to identify as many metabolites as possible. This concept has become feasible with the advent of hyphenated technologies that enable joined measurement of multiple chemical properties and exploit these properties, for example mass and chromatographic retention, for separation and metabolite identification. This increase in resolution and chemical information represents a substantial improvement because values that were obtained from hyphenated technologies and that describe compound properties that allow metabolite identification can now be exchanged between laboratories. As a testing ground, an open exchange of metabolite identification based on GC-MS mass spectra and chromatographic retention was envisioned [36], has now been initiated (mass spectral libraries at CSB.DB, http://csbdb.mpimpgolm.mpg.de/csbdb/gmd/gmd.html) and will hopefully be joined by efforts on other technology platforms. The fundamental advantage of profiling is the opportunity to meet quantitative standards for all identified metabolites within profiles, especially the highly important aspect of metabolite recovery. The technological means for control of matrix effects in metabolite profiles have been suggested previously, namely the use of stable isotope labeled internal standards [3,37]. However, the high costs of chemical synthesis and the apparent lack of availability of standard synthesis for as-yet unidentified metabolites
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Box 2. Head to tail comparison of gas chromatography–mass spectrometry (GC-MS) spectra from separate 12 C-metabolite preparations
C-labeled and
Succinic acid (C4H6O4) (2TMSa)
Ion current %
12
14 7b
100
C
247
73b
50 0 50 73
100
14 7
50
100
10 x
150
200
25 1
13C
250
300
350
400
Glycine (C2H5NO2) (3TMS) 73
100 Ion current %
In Figure II mass spectra show the number of carbon atoms in all those mass fragments which originate from metabolites. 1: High labeling efficiency is essential because the chances of obtaining a fully labeled mass isotopomer decrease when atom numbers increase (see Glossary). Up to C28, we found unambiguous mass isotopomer distribution in metabolites from yeast grown on pure U-13C-glucose (99 atom %). 2: Incomplete labeling, although insufficient for the determination of high carbon numbers, still enables quantification by mass isotopomer ratios. For high molecular weight metabolites, in vivo labelling of less abundant elements, for example N, chemical tagging or analysis of low molecular weight constituents is advisable, such as are employed in proteome analysis [41,42]. 3: Addition of unlabeled essential vitamins and auxotrophic supplements to microbial cultures causes respective products to be unlabeled. For example, we found NADC to be fully labeled at the 15 carbon atoms which are ultimately synthesized from glucose. The residual six carbon atoms resulting from the nicotinic acid vitamin supplement were unlabeled. a The GC-MS metabolite profiling requires chemical derivatization by N-methyl-N-(trimethylsilyl)-trifluoroacetamide (MSTFA). This reagent introduces a specific number of trimethylsilyl moieties (TMS) to each metabolite molecule, as is indicated in brackets. b Mass fragments at 73 and 147 mass units are generated exclusively from TMS moieties.
13
12C
24 8
17 4
27 6
14 7
50 0 50
14 7
100 73 50
27 8 17 5
100
150
10 x
200
13C
24 9 250
300
350
400
Glutamic acid (C5H9NO4) (3TMS)
Ion current %
100
12
C
73 24 6
12 8 14 7
50
34 8 36 3
0 50
14 7 13 1
100
36 8 35 3
25 0
73 50
13C
10 x 100
150
200
250
300
350
400
Mass to charge ratio (m/z) TRENDS in Biotechnology
Figure II.
made this suggestion appear unfeasible for general application. Recent advances in in vivo labeling of microorganisms, specifically yeast, that use and modify the experimental concepts of flux analysis, open up novel perspectives for avoiding the analytical pitfalls of metabolite profiling [38]. Quantitative metabolite profiling by mass isotopomer ratios Efforts towards quantitative profiling technologies were essential for the general acceptance of transcriptomics and proteomics as semiquantitative methods [39–44]. Approaches that introduce a differential label in the course of chemical analysis currently prevail in quantitative transcriptome [39,40] and proteome [41–44] analyses, for example isotope-coded protein-tagging techniques [41,42], protein fluorescence labeling [43,44] and two-color nucleotide labeling by fluorescent probes [39,40]. Although all RNA or protein molecules have common chemical www.sciencedirect.com
moieties that can be exploited for directed chemical labeling, in metabolome analyses comprehensive chemical tagging technologies are impossible, not least because of the high chemical diversity of metabolites. The most elegant solution to this problem is the introduction of label at atomar level through in vivo labeling of the biological reference sample [38]. The two elements found most abundantly in living organisms are carbon and hydrogen 13C carbon can be easily supplied in the form of pure carbon sources to synthetic defined media of microbes or as carbon dioxide to photosynthetic organisms. By contrast, complete hydrogen replacement requires deuterated water and nutrients. For this reason, 13 C carbon labeling appears most promising. In vivo labeling can be performed in a similar way to a typical flux experiment but must be directed towards complete labeling, for example by feeding pure U-13C-glucose (99 atom %), starting with colony plating (see Figure I in Box 1). Replacement of other elements essential to life
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appears feasible but is either experimentally more difficult or restricted to limited parts of the metabolome. Novel applications of 13C-saturated microbial metabolomes Several fascinating uses of 13C-saturated microbial metabolomes are envisioned and some have already been pursued. The most essential application is internal standardization of metabolite profiling experiments (see Figure I in Box 1 and Figure III in Box 3) by addition of standardized extracts from 13C-saturated microbial metabolomes. This procedure enables correction for the recovery of each metabolite [38]. Moreover, when the same metabolite is profiled using different MS-based technology platforms, the isotope mass ratio will be identical and independent of suppression effects, as occurs for example in ESI-MS [28] or matrix-assisted laser desorption–time of flight (MALDI-TOF)-MS [29,33] experiments. Thus, isotope mass ratio profiling has the potential to finally unify measurements obtained from the multitude of relevant profiling technologies. Although this issue alone justifies efforts to establish 13 C isotope mass ratio metabolite profiling, having isotope mass ratios at our disposal opens the door for the use of MS-based methods for the purpose of quantification of pool size, which absolutely require standardization by stable isotope techniques, such as MALDI-TOF-MS [29,31,45]. In addition, the enrichment of trace compounds or
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unstable metabolites that is typically accompanied by high and highly variable metabolite losses is now a feasible procedure in metabolite profiling. The mass spectra of 13C-labeled molecules provide information concerning the number of carbon atoms in each fragment when mass shifts of highly labeled and unlabeled metabolites are compared (see Figure II in Box 2). This knowledge enhances interpretation of mass spectral fragmentation and elucidation of molecular sum formulas, both essential means to narrow down possible chemical structures of yet unidentified compounds [37]. A variant of typical flux analyses and tracing experiments for pathway identification can be pursued. A stable isotope-labeled metabolome enables analysis of the fate of unlabeled chemicals. Thus, the multitude of cheap and commercially available unlabeled compounds can now be used for tracer and pulse experiments within a 13 C-saturated metabolome. This approach appears feasible because we have demonstrated that only minor changes in metabolite levels occur upon 13C labeling (see Figure IIIb in Box 3). A final, and possibly trivial, but highly effective advantage of in vivo stable isotope labeling is the very fact that a metabolite is labeled in vivo. This fact is direct proof that the compound is indeed a metabolite and is not one of the possible laboratory contaminants, which have hitherto been tedious to detect and avoid.
Box 3. Quantification by gas chromatography–mass spectrometry mass isotopomer ratio profiling (a)
(b) 109
2
Glycine
278a 1 276
Ion current
5 247 a 4 251 3 2 1 0 240 250 260 270
13C
Ion current %
Ion current %
Succinic acid
Glycine (m/z 278 a:276)
107
Glutamic acid (m/z 353 a:348)
105
0 270 280 290 300
Succinic acid (m/z 251 a:247)
Ion current %
Glutamic acidb 5 353a 4 348 3 368b 363 2 1 0 345 355 365 375 m/z
103
103
105 12C
107
109
Ion current TRENDS in Biotechnology
Figure III. (a) shows fragment pairs of labeled and unlabeled mass isotopomers representing the same metabolite. Ion currents reflect the relative changes in metabolite abundance. (b) Plot of labeled over unlabeled metabolite fragments from a mass isotopomer ratio profile, demonstrating that yeast cultures – in this case overnight batch cultures – exhibit small but perceptible changes in metabolite levels upon in vivo 13C labeling (This plot represents the labeling control experiment shown in in Box 1). a Mass fragments which represent the 13C-labeled mass isotopomer, that is, the specific internal standard for this metabolite. b Metabolites can be monitored by one or multiple mass isotopomer pairs for quantification and confirmation. c Labeled mass isotopomers, especially those with fewer than three carbon atoms, are best corrected for natural stable mass isotopes. www.sciencedirect.com
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In conclusion, we are convinced that mass isotopomer ratio metabolite profiling will not only enhance accurate and quantitative monitoring of the metabolome but also enable comparison of quantitative results from diverse analytical sources and thus take into account the fact that metabolome data need to be generated through a set of diverse analytical platforms.
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TRENDS in Biotechnology
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Metabolome analysis: the potential of in vivo labeling with stable isotopes for metabolite profiling Claudia Birkemeyer, Alexander Luedemann, Cornelia Wagner, Alexander Erban and Joachim Kopka Department Willmitzer, Max Planck Institute of Molecular Plant Physiology, Am Mu¨hlenberg 1, D-14467 Golm, Germany
Metabolome analysis technologies are still in early development because, unlike genome, transcriptome and proteome analyses, metabolome analysis has to deal with a highly diverse range of biomolecules. Combinations of different analytical platforms are therefore required for comprehensive metabolomic studies. Each of these platforms covers only part of the metabolome. To establish multiparallel technologies, thorough standardization of each measured metabolite is required. Standardization is best achieved by addition of a specific stable isotope-labeled compound, a mass isotopomer, for each metabolite. This suggestion, at first glance, seems unrealistic because of cost and time constraints. A possible solution to this problem is discussed in this article. Saturation in vivo labeling with stable isotopes enables the biosynthesis of differentially mass-labeled metabolite mixtures, which can be exploited for highly standardized metabolite profiling by mass isotopomer ratios. Introduction The field of analytical biochemistry has recently received a novel extension: metabolomics, a concept that has been defined as the science of the comprehensive monitoring of the metabolic complement in biological systems [1–6]. Metabolomics provides information about biological systems which cannot be obtained by the classical ‘–omics’ approaches: genomics, transcriptomics and proteomics. Metabolomics could be viewed, therefore, as the missing fourth Rosetta stone [7], which fills the metabolic gap within the previously developed systems-wide approaches towards the global analysis of biological processes. Metabolomic approaches are under dynamic development and several synonyms have been suggested, such as metabonomics [8], metabolite profiling [9] or fingerprinting [4,6]. A multitude of analytical platforms has been introduced [10], including spectroscopy fingerprints at infrared (IR), near infrared (NIR) or ultraviolet (UV) wavelength ranges, gas chromatography–mass spectrometry (GC-MS) [2,3,9], liquid chromatography–electrospray ionization–mass spectrometry (LC-ESI-MS) [11–13], capillary electrophoresis coupled to mass spectrometry (CE-MS) [14] or liquid chromatography with nuclear Corresponding author: Kopka, J. ([email protected]).
magnetic resonance spectroscopy (LC-NMR) [8,15], just to mention a few. There is no single analytical platform currently conceivable that would enable the multiparallel analysis of the complete metabolome [10,16], which comprises the full range of chemically diverse biomolecules Glossary Dynamic range: The range of concentrations, between detection limit and maximum amount of a substance to be quantified by one analytical technology. Hyphenated technologies: Hyphenation stands for the combination of at least two principles of chemical separation in a single instrument, such as gas chromatography–mass spectrometry (GC-MS), capillary electrophoresis– electrospray mass spectrometry (CE-ESI-MS) or high-performance liquid chromatography–nuclear magnetic resonance spectroscopy (HPLC-NMR). Exploiting two chemical properties for compound separation is prerequisite for in-parallel analysis of multiple compounds from a complex mixture. Mass isotopomer: Chemical substances are composed of naturally occurring (or technically enriched) mixtures of atomic isotopes, which have, as a rule, the same chemical properties but exhibit different mass. A modern mass spectrometer can resolve the mass differences of a single isotope substitution within a molecule. Each mass variant of a chemical substance is called a mass isotopomer. Mass isotopomer distribution: The mass isotopomer distribution of molecules can be precisely calculated and is dependent: (i) on the number of atoms present in a molecule and (ii) on the natural or technically enriched isotope abundances of each element [46]. High isotope enrichment and low atom numbers result in highly abundant, fully labeled mass isotopomers. Low enrichment and high atom numbers favor partial labeling and cause a broad distribution. For example, a four-carbon molecule might carry a 12C- or 13C-label at either of the positions. The chances for a fully 13C-labeled four-carbon mass isotopomer at ambient 1.1% enrichment are negligible, 0.0114Z1.46!10K8, whereas chances are intermediate, 0.704Z0.24, or high, 0.994Z0.96, at 70% and 99% isotope enrichment, respectively. By comparison, the chances of a fully labeled 30-carbon mass isotopomer at 99% enrichment are clearly reduced, 0.9930Z0.74. Matrix effect: The matrix effect is a long-standing observation in chemical and enzymatic analyses of complex biological samples. Namely, the nature or composition of complex samples can influence the apparent amount of metabolites and thus might lead to false quantitative results. Matrix effects can either stabilize labile compounds (matrix stabilization) or suppress compound measurements (matrix suppression). Matrix effects can occur at any step during chemical analysis, from extraction through clean-up, to final instrumental analysis. Well known examples are the matrix suppression effects of electrospray ionization–mass spectrometry [28] or matrix-assisted laser desorption–time of flight–mass spectrometry [29] technologies or the oxidation of labile metabolites, such as vitamin C. Recovery: Recovery measurements are the analytical means to control and standardize metabolite measurements for matrix effects. Recovery is routinely expressed as a percentage or ratio. The comparison made is between equal amounts of metabolites either supplied as a pure reference sample or added to the biological sample under scrutiny. Recovery analyses are most elegantly performed by using chemically synthesized mass isotopomers, which can be distinguished from the respective naturally occurring counterparts by highresolution mass analysis. Metabolic phenotype: The qualitative and quantitative inventory of all metabolites in a biological sample [2–4].
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from low molecular weight volatiles to storage polymers, such as starch or triacylglycerols. The diversity of required methods is in stark contrast to genome, transcriptome and proteome profiling technologies, which monitor molecules of highly similar chemical properties, such as DNA, RNA and proteins, respectively. Metabolome analyses not only need to accommodate the high diversity of biomolecules but also need to cover the vast dynamic range (see Glossary) of metabolite concentrations. These encompass highly abundant nutrients or primary metabolites and equally important trace compounds that might carry biological signals. In addition, the metabolome is formed by a complex network of reactions, which are subject to rapid enzymatic turnover [17–21]. Extreme care and fast inactivation of all biochemical reactions during sampling is therefore vital [2,12,22,23], which is not the case in proteome and transcriptome analyses. Although the sequence information embedded within protein and RNA structure enables unequivocal identification of the source organism, metabolites per se do not carry information on their respective origins. Thus, metabolite measurements need to be controlled for artifact chemical contamination that might arise during biological experimentation or chemical analysis.
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Even though there appear to be considerable technical obstacles, metabolome analyses are in high demand and have been widely proposed for studies in molecular physiology [2,16,24], functional genomics [1,3,7,15], clinical chemistry [8], biomarker discovery, research on the mode of drug action and monitoring of drug therapy [15,25,26]. This interest necessitates a short discussion of the properties of novel metabolomic approaches compared with classical chemical analytics. Analytical approaches of metabolome analysis: general variants, properties and applications Four major variants of analytical approaches are currently conceivable, fingerprinting, profiling, absolute quantification of pool sizes and, finally, flux analysis, recently suggested as an ‘-omics’ approach in its own right (‘fluxomics’ [27]). Table 1 gives a short overview of the typical characteristics of these variants. We are aware that all shades of intermediate analytical set-up might exist and that single analytical technologies, such as GC-MS or LC-MS, might enable all four levels of information to be obtained, depending on the chosen experimental set-up. Quantification of concentrations predates the ‘-omic’ era and was the first means to characterize the metabolic
Table 1. Overview of the four general variants in the toolbox of metabolome analyses. Properties of fingerprinting, profiling, pool size and flux analysis are described for typical analyses Major field of application Major result
Sample composition
Fingerprinting Functional genomics, diagnostics
Profiling Functional genomics, molecular physiology
Sample classification based on apparent metabolite pattern
Relative quantification of changes in metabolite pool size, identification and discovery of novel metabolites High complexity (minimal pre-purification)
Pool size analysis Biochemistry, biotechnology, molecular physiology Absolute quantification of metabolite pools
Flux analysis Biotechnology, modeling
Quantification of metabolite flux
Sample throughput
High
Analytical technology Metabolite coverage
Nonhyphenated technologies Hyphenated technologies possible required Limited only by choice of metabolite extraction and analytical technology
Low complexity (partial High complexity (minimal prepurification) or highly selective purification) possible Low (might be extremely Medium–low high when dedicated to a single metabolite) Combination of hyphenated or nonhyphenated technologies (dependent on the means of prepurification) Preconceived, that is, limited to a predefined set of targeted metabolites
Metabolite identification
Fingerprinting Identification of metabolites not required
Pool size analysis Unambiguous metabolite identification required
Metabolite concentrations
Required control experiments
Analytical trade-off
High-medium
Profiling Identification of as many metabolites as possible
The concentration of the most abundant metabolite determines the highest possible sample load. The dynamic range of the instrument defines the detection limit of coanalyzed minor metabolites In addition, analysis of Detector response is recovery, detection limits corrected for the initial and linearity of detector amount of sample and total response of all known losses of material during metabolites sample preparation and handling Absolute quantification is The precision of substituted for relative metabolite identification quantification in exchange and quantification is for full metabolite sacrificed for optimised coverage and medium to sample through-put. high sample throughput
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Flux analysis Unambiguous metabolite and mass isotopomer identification required Prepurification enables concentration of trace metabolites and thus adaptation to the sensitivity range of the analytical instrument. The dynamic range of instrumental analysis is thus nonlimiting. In addition, tracer In addition, experiments with quantitative radioactive or stable calibration of the isotope-labeled detector response by metabolites dilution of a series of pure metabolites The number of analyzed The number of analyzed metabolites is restricted metabolites is in exchange for precise restricted in exchange quantification of for precise metabolite mass quantification isotopomers
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Box 1. Experimental set-up of mass isotopomer ratio profiling (a)
'Non-sample' control (internal standarda) (b) (c)
Labelling control (reference sample) Samples of interest (treatments) (strains) (...) TRENDS in Biotechnology
Figure I. (a) A yeast parent strain is grown on pure U-13C-glucose (99 atom %)a in synthetic defined media (red). In (b), an identical culture is prepared with unlabeled glucose (blue). In (c), experiments on different strains or treatments are performed with unlabeled carbon sources (blue). Equal amounts of culture (a) are combined with samples of (b) or (c). Labeled samples serve as analytical internal standards and are typically monitored by ‘non-sample’ controls. The labeling control (b) checks for inherent changes owing to 13C-labeling (see Box 3b). Relative changes in metabolite pool size are determined by mass isotopomer ratio, as exemplified in Box 3a.aThe vitamin and auxotrophic supplements can be non-labelled (Box 2, point 3).
make-up of biological samples via metabolite pool sizes, that is, the metabolic phenotype [2,4]. The availability of pure metabolites is a prerequisite for quantitative analytical methods. Thus, quantification of concentrations might be considered as preconceived: only information on the metabolite under scrutiny is retrieved. The reason for this immanent bias is obvious – quantitative analytics requires calibration of detector signals to metabolite amount. Unequivocal identification and determination of detection limit, linear range, upper loading limit and inherent method variability are only possible when pure reference metabolites are available. In addition, recovery analyses are necessary to check the influence of the sample composition on quantitative detection, the so-called matrix effect [16,28,29]. The discovery of radioactive and stable isotopes and development of specific detectors for radioactive decay and the single unit mass differences of isotopes sparked investigations of metabolite flux [18,19,27,30–35]. Flux analysis is again targeted to the number of preconceived metabolites, and requires application of an isotope tracer that leads to a partial incorporation of isotopes into metabolite pools [30,32–35]. Demands on subsequent analytical resolution are high. Resolution is necessary not only for each metabolite but also for each isotope variant, the mass isotopomers. By contrast, profiling and fingerprinting technologies are aimed at detection of all metabolites that fall within the range of the chosen technologies [1–16,24,25]. Accordingly, the concept of metabolite purification before analysis is reverted into combinatorial approaches that aim www.sciencedirect.com
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to combine as many metabolites as possible into one analysis. Thus, profiling and fingerprinting could be termed ‘non-biased’, that is, limited only by the scope of the chosen means of metabolite extraction and analytical technology [4]. The major appeal of these approaches is the potential of discovery. Novel or unexpected metabolites can now be linked to physiological processes or gene function and used as biomarkers [25,26]. The nonbiased approach, however, comes at a cost. Typical fingerprinting analyses use noncalibrated detector readings obtained from complex metabolite mixtures for sample classification and biomarker screening. Analysis is, as a rule, thoroughly checked by nonsample controls and calibrated to reference samples, so that relative changes in signals, as compared with the reference sample, can be calculated. This set-up, although highly efficient in screening and classifying high numbers of samples, could be deemed to be insufficient for four main reasons. Firstly, little or no effort is put into assigning metabolite identity to detector signals. Applications are thus restricted to sample classification, without the potential to unravel the underlying metabolic and physiological cause. Secondly, single metabolites could be represented by multiple detector readings, such as diverse NMR signals or wavelengths. Therefore, it is conceivable that a single or few abundant metabolites might unknowingly dominate sample classification. Thirdly, artifacts caused by laboratory contamination cannot be completely ruled out and will have an impact on fingerprints. Fourthly, and most importantly, metabolite recovery is not controlled in fingerprints. Thus, observed apparent changes do not necessarily reflect direct metabolic changes within the sample but might actually be caused by matrix effects. For the above reasons, metabolite profiling was suggested; this aims to identify as many metabolites as possible. This concept has become feasible with the advent of hyphenated technologies that enable joined measurement of multiple chemical properties and exploit these properties, for example mass and chromatographic retention, for separation and metabolite identification. This increase in resolution and chemical information represents a substantial improvement because values that were obtained from hyphenated technologies and that describe compound properties that allow metabolite identification can now be exchanged between laboratories. As a testing ground, an open exchange of metabolite identification based on GC-MS mass spectra and chromatographic retention was envisioned [36], has now been initiated (mass spectral libraries at CSB.DB, http://csbdb.mpimpgolm.mpg.de/csbdb/gmd/gmd.html) and will hopefully be joined by efforts on other technology platforms. The fundamental advantage of profiling is the opportunity to meet quantitative standards for all identified metabolites within profiles, especially the highly important aspect of metabolite recovery. The technological means for control of matrix effects in metabolite profiles have been suggested previously, namely the use of stable isotope labeled internal standards [3,37]. However, the high costs of chemical synthesis and the apparent lack of availability of standard synthesis for as-yet unidentified metabolites
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Box 2. Head to tail comparison of gas chromatography–mass spectrometry (GC-MS) spectra from separate 12 C-metabolite preparations
C-labeled and
Succinic acid (C4H6O4) (2TMSa)
Ion current %
12
14 7b
100
C
247
73b
50 0 50 73
100
14 7
50
100
10 x
150
200
25 1
13C
250
300
350
400
Glycine (C2H5NO2) (3TMS) 73
100 Ion current %
In Figure II mass spectra show the number of carbon atoms in all those mass fragments which originate from metabolites. 1: High labeling efficiency is essential because the chances of obtaining a fully labeled mass isotopomer decrease when atom numbers increase (see Glossary). Up to C28, we found unambiguous mass isotopomer distribution in metabolites from yeast grown on pure U-13C-glucose (99 atom %). 2: Incomplete labeling, although insufficient for the determination of high carbon numbers, still enables quantification by mass isotopomer ratios. For high molecular weight metabolites, in vivo labelling of less abundant elements, for example N, chemical tagging or analysis of low molecular weight constituents is advisable, such as are employed in proteome analysis [41,42]. 3: Addition of unlabeled essential vitamins and auxotrophic supplements to microbial cultures causes respective products to be unlabeled. For example, we found NADC to be fully labeled at the 15 carbon atoms which are ultimately synthesized from glucose. The residual six carbon atoms resulting from the nicotinic acid vitamin supplement were unlabeled. a The GC-MS metabolite profiling requires chemical derivatization by N-methyl-N-(trimethylsilyl)-trifluoroacetamide (MSTFA). This reagent introduces a specific number of trimethylsilyl moieties (TMS) to each metabolite molecule, as is indicated in brackets. b Mass fragments at 73 and 147 mass units are generated exclusively from TMS moieties.
13
12C
24 8
17 4
27 6
14 7
50 0 50
14 7
100 73 50
27 8 17 5
100
150
10 x
200
13C
24 9 250
300
350
400
Glutamic acid (C5H9NO4) (3TMS)
Ion current %
100
12
C
73 24 6
12 8 14 7
50
34 8 36 3
0 50
14 7 13 1
100
36 8 35 3
25 0
73 50
13C
10 x 100
150
200
250
300
350
400
Mass to charge ratio (m/z) TRENDS in Biotechnology
Figure II.
made this suggestion appear unfeasible for general application. Recent advances in in vivo labeling of microorganisms, specifically yeast, that use and modify the experimental concepts of flux analysis, open up novel perspectives for avoiding the analytical pitfalls of metabolite profiling [38]. Quantitative metabolite profiling by mass isotopomer ratios Efforts towards quantitative profiling technologies were essential for the general acceptance of transcriptomics and proteomics as semiquantitative methods [39–44]. Approaches that introduce a differential label in the course of chemical analysis currently prevail in quantitative transcriptome [39,40] and proteome [41–44] analyses, for example isotope-coded protein-tagging techniques [41,42], protein fluorescence labeling [43,44] and two-color nucleotide labeling by fluorescent probes [39,40]. Although all RNA or protein molecules have common chemical www.sciencedirect.com
moieties that can be exploited for directed chemical labeling, in metabolome analyses comprehensive chemical tagging technologies are impossible, not least because of the high chemical diversity of metabolites. The most elegant solution to this problem is the introduction of label at atomar level through in vivo labeling of the biological reference sample [38]. The two elements found most abundantly in living organisms are carbon and hydrogen 13C carbon can be easily supplied in the form of pure carbon sources to synthetic defined media of microbes or as carbon dioxide to photosynthetic organisms. By contrast, complete hydrogen replacement requires deuterated water and nutrients. For this reason, 13 C carbon labeling appears most promising. In vivo labeling can be performed in a similar way to a typical flux experiment but must be directed towards complete labeling, for example by feeding pure U-13C-glucose (99 atom %), starting with colony plating (see Figure I in Box 1). Replacement of other elements essential to life
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appears feasible but is either experimentally more difficult or restricted to limited parts of the metabolome. Novel applications of 13C-saturated microbial metabolomes Several fascinating uses of 13C-saturated microbial metabolomes are envisioned and some have already been pursued. The most essential application is internal standardization of metabolite profiling experiments (see Figure I in Box 1 and Figure III in Box 3) by addition of standardized extracts from 13C-saturated microbial metabolomes. This procedure enables correction for the recovery of each metabolite [38]. Moreover, when the same metabolite is profiled using different MS-based technology platforms, the isotope mass ratio will be identical and independent of suppression effects, as occurs for example in ESI-MS [28] or matrix-assisted laser desorption–time of flight (MALDI-TOF)-MS [29,33] experiments. Thus, isotope mass ratio profiling has the potential to finally unify measurements obtained from the multitude of relevant profiling technologies. Although this issue alone justifies efforts to establish 13 C isotope mass ratio metabolite profiling, having isotope mass ratios at our disposal opens the door for the use of MS-based methods for the purpose of quantification of pool size, which absolutely require standardization by stable isotope techniques, such as MALDI-TOF-MS [29,31,45]. In addition, the enrichment of trace compounds or
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unstable metabolites that is typically accompanied by high and highly variable metabolite losses is now a feasible procedure in metabolite profiling. The mass spectra of 13C-labeled molecules provide information concerning the number of carbon atoms in each fragment when mass shifts of highly labeled and unlabeled metabolites are compared (see Figure II in Box 2). This knowledge enhances interpretation of mass spectral fragmentation and elucidation of molecular sum formulas, both essential means to narrow down possible chemical structures of yet unidentified compounds [37]. A variant of typical flux analyses and tracing experiments for pathway identification can be pursued. A stable isotope-labeled metabolome enables analysis of the fate of unlabeled chemicals. Thus, the multitude of cheap and commercially available unlabeled compounds can now be used for tracer and pulse experiments within a 13 C-saturated metabolome. This approach appears feasible because we have demonstrated that only minor changes in metabolite levels occur upon 13C labeling (see Figure IIIb in Box 3). A final, and possibly trivial, but highly effective advantage of in vivo stable isotope labeling is the very fact that a metabolite is labeled in vivo. This fact is direct proof that the compound is indeed a metabolite and is not one of the possible laboratory contaminants, which have hitherto been tedious to detect and avoid.
Box 3. Quantification by gas chromatography–mass spectrometry mass isotopomer ratio profiling (a)
(b) 109
2
Glycine
278a 1 276
Ion current
5 247 a 4 251 3 2 1 0 240 250 260 270
13C
Ion current %
Ion current %
Succinic acid
Glycine (m/z 278 a:276)
107
Glutamic acid (m/z 353 a:348)
105
0 270 280 290 300
Succinic acid (m/z 251 a:247)
Ion current %
Glutamic acidb 5 353a 4 348 3 368b 363 2 1 0 345 355 365 375 m/z
103
103
105 12C
107
109
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Figure III. (a) shows fragment pairs of labeled and unlabeled mass isotopomers representing the same metabolite. Ion currents reflect the relative changes in metabolite abundance. (b) Plot of labeled over unlabeled metabolite fragments from a mass isotopomer ratio profile, demonstrating that yeast cultures – in this case overnight batch cultures – exhibit small but perceptible changes in metabolite levels upon in vivo 13C labeling (This plot represents the labeling control experiment shown in in Box 1). a Mass fragments which represent the 13C-labeled mass isotopomer, that is, the specific internal standard for this metabolite. b Metabolites can be monitored by one or multiple mass isotopomer pairs for quantification and confirmation. c Labeled mass isotopomers, especially those with fewer than three carbon atoms, are best corrected for natural stable mass isotopes. www.sciencedirect.com
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In conclusion, we are convinced that mass isotopomer ratio metabolite profiling will not only enhance accurate and quantitative monitoring of the metabolome but also enable comparison of quantitative results from diverse analytical sources and thus take into account the fact that metabolome data need to be generated through a set of diverse analytical platforms.
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