Metabolite profiling in plant biology: platforms and destinations

Genes and genomes can be routinely sequenced, the resulting information stored, accessed and analyzed, and organisms with altered gene expression produced. Use of these resources requires powerful phenotyping platforms, including approaches for the systematic analysis of metabolite composition. Whereas the chemistry of nucleic acids is relatively simple and uniform, there are tens of thousands of metabolites, with an immense range of types of structure. This has led to a plethora of different extraction, separation and detection systems for different groups of metabolically important compounds. Researchers have typically measured a handful of metabolites, chosen on the basis of assumptions about what was relevant and the technical capacity of their laboratory. But now, in parallel with the development of genome-wide gene-expression arrays, there has been a shift to an 'unbiased' approach to metabolite analysis.It is helpful to distinguish between metabolite fingerprinting, metabolite profiling and metabolomics. Metabolic fingerprinting is the application of a broad analytic technology to discover some big differences between two samples, for example two different genotypes. It provides information that helps to orientate a research project. Metabolite profiling is the measurement of hundreds or potentially thousands of metabolites. It requires a streamlined pipeline for extraction, separation and analysis, so that large numbers of metabolites can be measured in a robust and quantitative manner while in the presence of the extraordinarily complex mixture of chemicals ('matrix') that is found in cellular extracts. Metabolomics, in the strict sense, is the measurement of all metabolites in a given system. It is not yet technically possible, and will probably require a platform of complementary technologies, because no single technique is comprehensive, selective, and sensitive enough to measure them all [1]. This article provides an overview of technologies for metabol