Current challenges and developments in GC–MS based metabolite profiling technology
Introduction
Since the crystallization of the metabolomics (Oliver et al., 1998, Tweeddale et al., 1998) or metabonomics (Nicholson et al., 1999) concept gas chromatography hyphenated to mass spectrometry has developed into a widely spread basic and general metabolomics technique. From the early proposal as a key technology for metabolite profiling (e.g. Jellum et al., 1975, Jellum, 1977, Jellum, 1979) GC–MS is now applied as a routine technology for the screening of apparent or up to now hidden metabolic phenotypes in functional genomic studies of plants (e.g. Trethewey et al., 1999, Fiehn et al., 2000, Fiehn, 2002, Roessner et al., 2001, Fernie et al., 2004, Roessner-Tunali et al., 2004, Trethewey, 2004) or microbes (e.g. Barsch et al., 2004, Stephanopoulos et al., 2004, Strelkov et al., 2004). Publications utilizing GC–MS are rapidly increasing since the year 2000 and laboratories which enter the metabolomics field add GC–MS to their suite of technology platforms. This decision is facilitated by low costs compared to CE–MS, LC–MS, or LC–NMR instrumentation, unsurpassed chromatographic reproducibility and resolution, highly repeatable mass spectral fragmentation upon electron impact ionization (EI) and few, if any, matrix effects. The downside of GC–MS technology appears to be the requirement for chemical derivatization prior to quantitative analysis. However, this requirement for the chemical modification of those compounds which are not volatile per se may – in the long run – be turned into the advantage of exploiting selective chemical enrichment and fractionation for the profiling of trace compounds in the presence of bulk metabolites (e.g. Mueller et al., 2002, Birkemeyer et al., 2003, Schmelz et al., 2003, Schmelz et al., 2004).
Metabolomic studies and respective key technologies, such as GC–MS, now have fully emerged in biological science and add to our capability to describe and functionally assess biological systems with increasing resolution at the levels of the genome, transcriptome, and proteome. The vision of a fully comprehensive metabolome analysis of relative changes in metabolite pool sizes and metabolic flux, recently termed fluxome (Fischer and Sauer, 2003, Sauer, 2004), may be called the fourth significant addition to the field of “-omics” technologies. Multi-parallel metabolite analyses contribute two highly important and novel aspects to functional genomic and molecular physiology investigations. (1) Metabolites are the same molecular entity irrespective of the organism which makes use of them. Thus, the role of metabolites and their interaction with other system levels can be investigated without the typical ambiguity arising from orthologous and paralogous sequences. The function of metabolites and the phylogenetic change in the use of metabolites are now both open to be analyzed. Common or differential use of metabolic signals may be found in comparative studies across species and phylum boundaries. (2) Other than the fields of genomics, transcriptomics, and proteomics, the science of metabolomics has a rich history in flux measurement and modelling. The dynamics of transcription or translation, such as transcript turnover and protein stability, are technically accessible to studies of specific transcriptional or translational regulation. But multi-parallel measurements in these fields are far from routine. While the field of transcript regulation is currently revolutionized by the recent discovery of the function of micro-RNAs and small interfering-RNAs, the modelling and our understanding of the regulation of metabolite accumulation appears at least in microbial organisms to be much more advanced.
Despite multiple efforts at establishing diverse and competing metabolite profiling techniques, a fully comprehensive metabolome analysis of small molecules is and will – perhaps for a long time – remain a vision to be approached (Sumner et al., 2003, Bino et al., 2004, Birkemeyer et al., 2005). Metabolites are highly chemically diverse as compared to proteins, RNA, and DNA. No single common analytical technology can currently be envisioned to cover all metabolite classes. Thus, the current feasible approach is a combination of minimally overlapping and within analytical limits non-biased profiling analyses dedicated to roughly uniform compound classes (e.g. Nikiforova et al., 2005).
In conclusion the high diversity of chemical compounds, especially the specific biological use of stereo- and geometric isomers, as well as the demand for multiple analytical technologies currently poses three grand challenges to the science of metabolomics. (1) Metabolomic technologies allow multi-parallel analysis of hundreds of metabolites. However, the majority of covered metabolic components in metabolite profiles is still non-identified (Schauer et al., 2005a). Thus, the major challenge, even more so in other technologies than GC–MS, is the identification of the flood of hitherto non-identified metabolic components. (2) Well-known key metabolites and signalling compounds are still not accessible by routine multi-parallel profiling methods. Low metabolite concentration, unique chemical properties, such as chemical instability, and resulting laborious and time-consuming means of chemical analysis may be seen as the main obstacles which currently exclude these compounds from most comprehensive studies (Kopka et al., 2004). Therefore, the second challenge may be phrased as the demand for multi-parallel profiling analyses targeted to important signalling compounds and crucial but not yet accessible metabolic intermediates. (3) All of the above tasks can only be achieved through a highly cooperative and interactive metabolomics community and thus the demand and challenge emerges to establish an efficient exchange of metabolite identifications (Kopka et al., 2005, Schauer et al., 2005a) and quantitative results.
In the following the GC–MS technology will be used to exemplify major aspects of these challenges. The subsequent discussion will be biased towards GC–MS technology, but many aspects will be put under general scrutiny. Thus, a contribution to the ongoing discussion and development of common concepts within the metabolomics community is intended.
Section snippets
Compound identification: from mass fragment to metabolite
The first discovery of metabolic markers, such as the early increase of maltose during 1 h cold-response of Arabidopsis thaliana ecotype Col-0 (Kaplan et al., 2004) is initially made through observation of a statistically significant change in detector response. In mass spectral technologies this detector response is equivalent to an ion current, usually the ion current of a selected mass fragment, such as m/z = 160 (Fig. 1A), at a defined chromatographic retention time which can be standardized
Acknowledgements
I would like to thank A.R. Fernie and A. Erban, Max Planck Institute of Molecular Plant Physiology, Potsdam-Golm, Germany, and Prof. R. Trengove, Department of Biotechnology, Murdoch University, Murdoch, WA, Australia, for critically discussing this manuscript or specific aspects of the presented concept. My thanks extend to Prof. Dr. Lothar Willmitzer for his long-standing support. This work was supported by the Max-Planck society, and the Bundesministerium für Bildung und Forschung (BMBF),
References (49)
- et al.
Potential of metabolomics as a functional genomics tool
Trends Plant Sci.
(2004) - et al.
Comprehensive chemical derivatization for gas chromatography–mass spectrometry-based multi-targeted profiling of the major phytohormones
J. Chromatogr. A
(2003) - et al.
Metabolome analysis: the potential of in vivo labeling with stable isotopes for metabolite profiling
Trends Biotechnol.
(2005) - et al.
Design of experiments: an efficient strategy to identify factors influencing extraction and derivatization of Arabidopsis thaliana samples in metabolomic studies with gas chromatography/mass spectrometry
Anal. Biochem.
(2004) - et al.
Development of a computer-assisted search for anomalous compounds (CASAC)
J. Chromatogr.
(1975) Profiling of human-body fluids in healthy and diseased states using gas-chromatography and mass-spectrometry, with special reference to organic-acids
J. Chromatrogr.
(1977)- et al.
Systematic functional analysis of the yeast genome
Trends Biotechnol.
(1998) High-throughput phenomics: experimental methods for mapping fluxomes
Curr. Opin. Biotechnol.
(2004)- et al.
GC–MS libraries for the rapid identification of metabolites in complex biological samples
FEBS Lett.
(2005) - et al.
Trilinear chemometric analysis of two dimensional comprehensive gas chromatography–time-of-flight mass spectrometry data
J. Chromatogr. A
(2004)
Algorithm for locating analytes of interest based on mass spectral similarity in GC × GC–TOF-MS data: analysis of metabolites in human infant urine
J. Chromatogr. A
Plant metabolomics: large-scale phytochemistry in the functional genomics era
Phytochemistry
Metabolite profiling as an aid to metabolic engineering in plants
Curr. Opin. Plant Biol.
Metabolic profiling: a Rosetta stone for genomics?
Curr. Opin. Plant Biol.
Fluorescent two-dimensional difference gel electrophoresis unveils the potential of gel based proteomics
Curr. Opin. Biotechnol.
Construction and application of a mass spectral and retention time index database generated from plant GC/EI-TOF-MS metabolite profiles
Phytochemistry
Quantitative analysis of the microbial metabolome by isotope dilution mass spectrometry using uniformly 13C-labeled cell extracts as internal standards
Anal. Biochem.
Mass spectrometry-based proteomics
Nature
Comprehensive metabolite profiling of Sinorhizobium meliloti using gas chromatography–mass spectrometry
Funct. Integr. Genomics
Metabolite profiling: from diagnostics to systems biology
Nat. Rev. Mol. Cell Biol.
Metabolite profiling for plant functional genomics
Nat. Biotechnol.
Metabolomics—the link between genotypes and phenotypes
Plant Mol. Biol.
Metabolic flux profiling of Escherichia coli mutants in central carbon metabolism using GC–MS
Eur. J. Biochem.
Quantitative analysis of complex protein mixtures using isotope-coded affinity tags
Nat. Biotechnol.
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