Analytical Platforms Used In TheField Of Metabonomics: A Mini Review

Mainak Mal *

*Research Fellow, National University of Singapore, 21 Lower Kent Ridge Road, Singapore 119077.



Metabonomics involves metabolic profiling of various biomatrices in response to any diseased condition or genetic modification or due to effect of environment or lifestyle related factors With its advent, the field of metabonomics has made valuable contributions and insight to system biology research. Metabonomicsprovides a powerful tool complementary to genomics and proteomics and can be used to obtain valuable information into functional biology, toxicology, pharmacology and diagnosis of diseases. This mini-review briefly describes the advantages, disadvantage and applications of the various analytical platforms used in metabonomics such as nuclear magnetic resonance (NMR) spectroscopy,Fourier transform infrared (FTIR) spectroscopy, LC with ultraviolet or coulometric detection, capillary electrophoresis (CE) with ultraviolet detection and mass spectrometry (MS) based techniques like direct infusion MS, gas chromatography mass spectrometry (GC/MS), liquid chromatography mass spectrometry (LC/MS) or capillary electrophoresis mass spectrometry (CE/MS).

Keywords: metabonomics, analytical techniques, NMR, GC/MS, LC/MS


Since its inception the field of metabonomicshas grown remarkably in terms of its applications and contributions to system biology research. Metabonomicsprovides a powerful tool for gaining valuable insight into functional biology, toxicology, pharmacology and diagnosis of diseases. Metabonomics involves determination of changes in metabolic profiles of living organisms in response to any diseased condition or genetic modification or due to effect of environment or lifestyle related factors [1]. Metabonomicsis complementary to genomics and proteomics as it measures the perturbed metabolic end-points due to environmental, pharmacological or pathological influences while in genomics and proteomics, more upstream biological events are typically profiled and studied [2]. It involves the analysis of various biological matrices such as plasma, urine and tissues using suitable analytical platforms. Metabonomics can be carried out with a global non-targeted approach as well as with a targeted approach. In targeted metabonomics, alterations in the levels of a specific class of metabolites or metabolites belonging to a specific metabolic pathway are ascertained using an appropriate analytical technique [3]. In global non-targeted metabonomics, metabolites belonging to diverse metabolic pathways are profiled. The metabolites that are determined in non- targeted approach belong to various chemical classes such as organic acids, amino acids, fatty acids, amines, sugars, sugar alcohols, steroids, nucleic acid bases and other miscellaneous substances. So, multiple complementary analytical techniques are often utilized for non-targeted metabonomics of biological matrices, in order to cover as much of metabolic space as possible [4].In this mini-review, the different analytical platforms used in metabonomics as well as their advantages, disadvantage and applications have been described in a succinct manner.


In an ideal world, an analytical platform for metabonomics should allow analysis with minimal or no sample preparation, should be high throughput, highly sensitive and should exhibit high degree of robustness and reproducibility. Moreover for non-targeted metabonomics, comprehensive coverage of metabolic space and ease of identification of profiled metabolites are additional desirable properties of an analytical platform. Analytical platforms that are commonly used for metabonomics include nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS) based techniques like direct infusion MS, gas chromatography mass spectrometry (GC/MS), liquid chromatography mass spectrometry (LC/MS) or capillary electrophoresis mass spectrometry (CE/MS). In addition to these techniques other methods like Fourier transform infrared (FTIR) spectroscopy, LC with ultraviolet or coulometric detection and CE with ultraviolet detection have also been used for metabonomics. In this section a brief overview of the different analytical methods used for metabonomics is provided.

1.Nuclear magnetic resonance (NMR) spectroscopy

NMR spectroscopy possesses many attributes of an ideal platform for metabonomics such as minimal sample pretreatment, high reproducibility, robustness, rapid analysis time, non-selectivity (in terms of metabolic space) and capability of providing detailed structural information about profiled metabolites. Although NMR spectroscopy is comparatively less sensitive than MS-based techniques, the availability of cryogenic NMR probes has improved the sensitivity and throughput of NMR spectroscopy [5]. An estimate of NMR sensitivity in terms of number of metabolites measured can be obtained from a recent study in which NMR was able to measure 49 metabolites in human serum as compared to 96 by LC/MS and99 by GC/MS [6]. NMR spectroscopy has been utilized extensively for the metabonomics of liquid biomatrices like body fluids and tissue extracts. The introduction of high resolution magic angle spinning NMR (HR-MAS NMR) spectroscopy has extended the applicability of NMR spectroscopy for metabonomics of solid and semisolid biomatrices like intact tissue specimens. Proton (1H) NMR spectroscopy is the dominant technique used for metabonomics. Spectral assignment and metabolite identification in 1H NMR is quite complicated and dependent on chemical shifts, relative intensities, signal multiplicities of the 1H resonances and coupling constants. Two dimensional 1H NMR spectroscopic methods like correlation spectroscopy (COSY) and total correlation spectroscopy (TOCSY) are often utilized for spectral assignment and identification of metabolites. Apart from 1H, other types of nuclei such as 15N, 13C or 31P can also be exploited to aid spectral assignment in certain cases. Various NMR pulse sequences can be utilized to differentiate spectral contributions of macromolecules (such as proteins and lipoproteins) from those obtained from low molecular weight metabolites [7].

2. Mass spectrometry (MS) based techniques

MS can be used alone as direct infusion MS or in conjunction with separation techniques for metabonomics. Direct infusion of liquid biomatrices or tissue extracts into MS has been used for metabonomics in some cases. Although it is a high throughput technique, it suffers from the disadvantage of high matrix effects, as proper sample preparation steps and chromatographic separation are not involved. Matrix effect can be defined as the indirect or direct changes or interference in response due to the presence of unintended analytes (for analysis) or other interfering substances in the sample. The limitations become more pronounced in the case of complex and variable biomatrices like urine and also in the case of isobaric analytes [8].

High resolution, high sensitivity and availability of commercial libraries for metabolite identification render GC/MS an excellent and robust platform for the global non-targeted metabonomics of complex biomatrices. However, GC/MS analysis involves tedious sample preparation steps as it is necessary to derivatize analytes to reduce their polarity and increase volatility. This shortcoming is often tolerated in metabonomic research where the demand for chromatographic resolution takes priority over the need for the assay to be high throughput. The advent of two dimensional gas chromatography time of flight mass spectrometry (GC×GC/TOFMS) has comprehensively enhanced the metabolic space coverage of conventional GC/MS. Moreover, the development of softwares packages with deconvolution feature to differentiate co-eluting chromatographic peaks has facilitated shorter GC/MS analysis thus improving the throughput of the technique [9].

LC/MS has certain advantages over GC/MS in terms of ease of sample pretreatment and flexibility in throughput. The applicability of LC/MS in non-targeted metabonomics is comparatively restricted due to the constraint in the number of metabolites amenable to analysis. However, developments in diverse LC column chemistries and chemical derivatization strategies have enhanced the metabolic space coverage of LC/MS [10]. Although LC/MS is considered as a suitable analytical platform for both targeted and non-targeted metabonomics, its applicability is more established in the case of targeted profiling of metabolites. This is due the fact that LC/MS can be operated in highly selective and sensitive mode if desired for targeted analysis. For non-targeted profiling, LC/MS is generally operated in both positive and negative electrospray ionization (ESI) modes for the comprehensive coverage of metabolic space. The emergence of microbore LC/MS and ultra performance LC (UPLC) systems has improved the resolving capacity, sensitivity and separation speed of conventional LC/MS [9].

CE/MS has also been used as a platform for metabonomics especially for profiling low abundance metabolites. The separation mechanism of CE/MS makes it a suitable platform for analysis of polar, ionisable metabolites. Another advantage of CE/MS is the small sample volume needed. Moreover, liquid biomatrices like urine requires minimal sample preparation steps prior to analysis [11].

3. LC/NMR/MS hybrid techniques

For metabonomics, LC/NMR/MS hybrid platforms can also be utilized. In such systems the LC eluent is split into two parts and subjected to concomitant analysis by both NMR and MS. The resulting NMR- and MS-based data provide in-depth molecular information and aids in metabolite identification [12]. Emergence of highly sophisticated data analysis techniques has further facilitated the analysis and interpretation of LC/NMR/MS hybrid platforms [13]. LC/NMR/MS methods have shown promise in the discovery of metabolite based markers of xenobiotic induced renal toxicity and characterization of lipoproteins in human blood serum. LC/NMR/MS has also been utilized for urinary metabonomics of pediatric metabolic disorders such as methylmalonaciduria. It has also been used for the metabonomics of human amniotic fluid with an aim to diagnose disorders in the mother or developing fetus [14].

4. Other analytical techniques

Although LC with ultraviolet [15] or coulometric detection [16]and CE with ultraviolet detection [17] have been explored for metabonomics, their usage is limited by their inability to identify metabolites directly. However, in the case of LC with coulometric detection, libraries of standard compounds can be created on the basis of LC retention times and redox properties for metabolite identification [16]. Although FTIR spectroscopy has been explored for metabonomics, its applicability is very limited as it does not provide sufficient information to identify metabolites.However, the short analysis time required per sample (about 5 to 10 s) enables its usage as an optional tool for screening or group classification or as an adjunct method to other commonly used analytical platforms [18]. The advantages, disadvantages and applications of different analytical platforms used for metabonomics have been summarized in Table 1.

Table 1. Advantages, disadvantages and applications of different analytical platforms used for metabonomics

Analytical Technique




NMR spectroscopy

Involves minimal sample pretreatment

Highly reproducible and robust

High throughput

Non-selective (in terms of metabolic space)

Provides detailed structural information about profiled metabolites

Less sensitive than MS-based techniques

Spectral assignment and metabolite identification is complicated

Metabonomics for xenobiotic toxicity assessment, different forms of cancer, neurological disorders, metabolic disorders, aging etc [19].

Intact tissue based metabonomics using solid state MAS-NMR [5].

Direct infusion MS

High throughput

Suffers from high matrix effect

and isobaric interference

Microbial metabonomics studies [8]


High resolution and sensitivity Availability of EI spectra-based commercial libraries for metabolite identification

Not susceptible to matrix effect

Involves tedious sample preparation

Comparatively low throughput

Profiling of volatile metabolites in lung cancer [20] and skin emissions [21] using headspace GC/MS.

Metabonomics for validating animal models of diseases [22], xenobiotic toxicity assessment [23], different forms of cancer [24], metabolic disorders [25], neurological disorders [26].

Table 1. Advantages, disadvantages and applications of different analytical platforms used for metabonomics(Continued).

Analytical Technique





Ease of sample pretreatment as compared to GC/MS

Allows flexibility in throughput

Highly suitable for targeted profiling

Susceptible to matrix effect

Non-availability of EI spectra-based commercial libraries for easy metabolite identification

Metabonomics for xenobiotic metabolism and toxicity assessment [20], different forms of cancer [27], metabolic disorders [28], neurological disorders [29].


Liquid biomatrices like urine requires minimal sample preparation

Requires small sample volume

Metabolic space coverage restricted to polar, ionisable metabolites

Microbial metabonomics and as a complementary platform to GC/MS or LC/MS for metabonomics of diseases [9].


Provides in-depth molecular information

Combination of NMR and MS aids in metabolite identification

Highly expensive instrumentation

Metabonomics for metabolic disorders [30] and human amniotic fluid [14].

FTIR spectroscopy

High throughput

Applicability is limited as it does not provide sufficient information to identify metabolites

Microbial metabonomics, metabonomics for cancer and other diseases [8].

CE or LC with UV or coulometric detection

Comparatively inexpensive

Application limited due to inability to identify metabolites readily

Less sensitive and less specific than MS-based techniques

Metabonomics for evaluation of food impact [15], animal model of diabetes [31], targeted profiling of exogenous metabolites [16].


[1] J.K. Nicholson, J.C. Lindon, E. Holmes, 'Metabonomics': understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data, Xenobiotica 29 (1999) 1181-1189.

[2] O. Fiehn, Combining genomics, metabolome analysis, and biochemical modelling to understand metabolic networks, Comp.Funct. Genomics 2 (2001) 155-168.

[3] M. Morris, S.M. Watkins, Focused metabolomic profiling in the drug development process: advances from lipid profiling,Curr.Opin. Chem. Biol. 9 (2005) 407-412.

[4] J.C. Lindon, J.K. Nicholson, E. Holmes, The handbook of metabonomics and metabolomics, Elsevier, The Netherlands, 2007.

[5] H.C. Keun, O. Beckonert, J.L. Griffin, C. Richter, D. Moskau, J.C. Lindon, J.K. Nicholson, Cryogenic probe 13C NMR spectroscopy of urine for metabonomic studies, Anal. Chem. 74 (2002) 4588-4593.

[6] N. Psychogios, D.D. Hau,J. Peng, A.C. Guo, R. Mandal, S.Bouatra, I. Sinelnikov, R. Krishnamurthy, R Eisner, B. Gautam, N. Young, J. Xia, C. Knox, E. Dong, P. Huang,Z. Hollander, T.L. Pedersen, S.R. Smith, F. Bamforth, R. Greiner, B. McManus, J.W. Newman, T. Goodfriend, D.S. Wishart, The human serum metabolome,PLoS One 6(2011)e16957.

[7] J.C. Lindon, E. Holmes, J.K. Nicholson, Metabonomics in pharmaceutical R&D, FEBS J 274(2007)1140-1151.

[8] W.B. Dunn, N.J. Bailey, H.E. Johnson, Measuring the metabolome: current analytical technologies, Analyst 130(2005) 606-625.

[9] E.M. Lenz,I.D. Wilson, Analytical strategies in metabonomics. J Proteome Res. 6(2007)443-458.

[10] J.M. Halket, D. Waterman, A.M. Przyborowska,R.K. Patel,P.D. Fraser, P.M. Bramley, Chemical derivatization and mass spectral libraries in metabonomics by GC/MS and LC/MS/MS, J Exp. Bot. 56(2005) 219-243.

[11] R. Lee,A.S. Ptolemy,L. Niewczas,P. Britz-McKibbin, Integrative metabolomics for characterizing unknown low-abundance metabolites by capillary electrophoresis-mass spectrometry with computer simulations, Anal. Chem. 79(2007)403-415.

[12] J.C. Lindon, E. Holmes, M.E. Bollard, E.G. Stanley, J.K. Nicholson,Metabonomics technologies and their applications in physiological monitoring, drug safety assessment and disease diagnosis, Biomarkers 9(2004) 1-31.

[13] O. Cloarec, A. Campbell, L.H. Tseng, U. Braumann, M. Spraul, G. Scarfe, R. Weaver, J.K. Nicholson. Virtual chromatographic resolution enhancement in cryoflow LC-NMR experiments via statistical total correlation spectroscopy, Anal. Chem. 79(2007) 3304-3311.

[14] G. Graca, I.F. Duarte,I.M. Carreira, A.B. Couceiro, R. DominguesMdo, M. Spraul, L.H. Tseng, A.M. Gil, Metabolite profiling of human amniotic fluid by hyphenated nuclear magnetic resonance spectroscopy, Anal. Chem. 80(2008)6085-6092.

[15] H. Pham-Tuan, L. Kaskavelis, C.A. Daykin, H.G. Janssen, Method development in high-performance liquid chromatography for high-throughput profiling and metabonomic studies of biofluid samples, J Chromatogr. B Analyt. Technol. Biomed. Life Sci. 789(2003)283-301.

[16] P.H. Gamache, D.F. Meyer, M.C. Granger, I.N.Acworth, Metabolomic applications of electrochemistry/mass spectrometry, J Am. Soc. Mass. Spectrom. 15 (2004) 1717-1726.

[17] S. Zomer, C. Guillo, R.G. Brereton, M. Hanna-Brown, Toxicological classification of urine samples using pattern recognition techniques and capillary electrophoresis, Anal.Bioanal. Chem. 378(2004) 2008-2020.

[18] C. Leon, I. Rodriguez-Meizoso, M. Lucio, V. Garcia-Canas, E. Ibanez, P. Schmitt-Kopplin, A. Cifuentes. Metabolomics of transgenic maize combining Fourier transform-ion cyclotron resonance-mass spectrometry, capillary electrophoresis-mass spectrometry and pressurized liquid extraction, J Chromatogr. A 1216(2009)7314-7323.

[19] M. Coen, E. Holmes, J.C. Lindon, J.K. Nicholson, NMR-based metabonomics and metabonomic approaches to problems in molecular toxicology, Chem. Res.Toxicol. 21(2008) 9-27.

[20] C. Chen, F.J. Gonzalez, J.R. Idle, LC-MS-based metabolomics in drug metabolism, Drug.Metab. Rev. 39(2007) 581-597.

[21] M. Gallagher, C.J. Wysocki, J.J. Leyden, A.I. Spielman, X. Sun, G. Preti, Analyses of volatile organic compounds from human skin, Br. J Dermatol. 159(2008) 780-791.

[22] K.L. Chang, L.S. New, M. Mal, C.W. Goh, C.C. Aw, E.R. Browne, E.C. Chan,Metabonomics of 3-nitropropionic acid early-stage Huntington's disease rat model using gas chromatography time-of-flight mass spectrometry, J Proteome Res. 10(2011) 2079-2087.

[23] K.J. Boudonck, M.W. Mitchell, L. Nemet, L. Keresztes, A. Nyska, D. Shinar, M. Rosenstock, Discovery of metabolomics biomarkers for early detection of nephrotoxicity, Toxicol. Pathol. 37 (2009) 280-292.

[24] C. Denkert, J.Budczies, W. Weichert, G. Wohlgemuth, M. Scholz, T. Kind, S. Niesporek, A. Noske, A. Buckendahl, M. Dietel, O. Fiehn, Metabolite profiling of human colon carcinoma--deregulation of TCA cycle and amino acid turnover, Mol. Cancer 7 (2008) 72.

[25] Y. Bao, T. Zhao, X. Wang, Y.Qiu, M. Su, W. Jia, Metabonomic variations in the drug-treated type 2 diabetes mellitus patients and healthy volunteers, J Proteome Res. 8(2009)1623-1630.

[26] B.R. Underwood, D. Broadhurst, W.B. Dunn, D.I. Ellis, A.W. Michell, C. Vacher, D.E. Mosedale, D.B. Kell, R.A. Barker, D.J. Grainger, D.C. Rubinsztein, Huntington disease patients and transgenic mice have similar pro-catabolic serum metabolite profiles, Brain 129(2006)877-886.

[27] E.S. Ong, L. Zou, S. Li, P.Y. Cheah,K.W. Eu, C.N. Ong,Metabonomics in colorectal cancer reveals signature metabolic shifts during tumorigenesis, Mol. Cell Proteomics (2010).

[28] C. Wang, H. Kong, Y. Guan, J. Yang, J. Gu, S. Yang, G. Xu, Plasma phospholipid metabonomics and biomarkers of type 2 diabetes mellitus based on high-performance liquid chromatography/electrospray mass spectrometry and multivariate statistical analysis, Anal. Chem. 77(2005) 4108-4116.

[29] N.J. Li, W.T. Liu, W. Li, S.Q. Li, X.H. Chen, K.S. Bi, P. He, Plasma metabonomics of Alzheimer's disease by liquid chromatography/mass spectrometry, Clin.Biochem. 43(2010)992-997.

[30] G. Zurek, B. Schneider, T. Zey, J. Shockcor, M. Spraul, C. Baessmann, Hyphenated LC-NMR/MS for the characterization of complex metabolic profiles and biomarker discovery in biofluids, 8th Conference of the Israel Analytical Chemistry Society, Jan 11-12 (2005).

[31] C. Barbas, E.P. Moraes, A. Villasenor. Capillary electrophoresis as a metabolomics tool for non-targeted fingerprinting of biological samples, J Pharm. Biomed. Anal. 55(2011)823-831.

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