Use of mass spectrometry for metabolite profiling and metabolomics

The metabolome of a biological system refers to the complement of all low molecular weight (<1,500 daltons) metabolites in that system (Fig. 1). As biological changes in a system are thought to be amplified at the level of the metabolome, metabolites have been coined ‘the canaries of the genome’. Metabolomics refers to the quantitative analysis of the metabolome. Whilst the measurement and quantification of individual or small numbers of metabolites is well established in biochemistry, metabolomics differs from more targeted analyses in the number of classes of metabolites being detected, the range of analytical techniques being employed and the need for advanced signal processing and bioinformatics tools. \n \nDifferent organisms are likely to contain variable numbers of metabolites. For example, well-characterised prokaryotic systems, such as E. coli, are estimated to contain approximately 750 metabolites (1). On the other hand, individual eukaryotic cells may contain between 4,000 and 20,000 metabolites (2), while estimates of all metabolites in the plant and fungal kingdoms, which are characterised by having complex secondary metabolism, range into the hundreds of thousands (3). The number of metabolites in specific cell, tissue and biofluid samples of metazoan organisms may also vary markedly. For example, the Human Metabolome Project (http://www.hmdb.ca/) has identified and quantified 6,826 metabolites in human tissues and biofluids. Of these, 3,970 have been identified in serum, while other biofluids, such as urine and cerebrospinal fluid, contain a comparatively simpler composition (472 and 360 metabolites, respectively) (4). \n \nIn common with some other ‘-omics’ approaches, metabolomics employs and is highly dependent on diverse analytical approaches (summarised in Fig. 2), including mass spectrometry (MS), nuclear magnetic resonance spectroscopy (NMR) and Fourier Transform infrared spectroscopy. Of these approaches, MS-based techniques have developed most rapidly and are increasingly being deployed in metabolomics analyses (Table 1). This article provides a short overview of MS-based metabolomics and provides a starting point for scientists considering exploiting this rapidly emerging field.