2Metabolomics Australia, School of Botany, The University of Melbourne, Victoria, Australia
The rapidly emerging field of metabolomics combines strategies to identify and quantify cellular metabolites using sophisticated analytical technologies with the application of statistical and multi-variant methods for information extraction and data interpretation. In the last two decades, huge progress was made in the sequencing of a number of different organisms. Simultaneously, large investments were made to develop analytical approaches to analyze the different cell products, such as those from gene expression (transcripts), proteins, and metabolites. All of these so-called ‘omics approaches, including genomics, transcriptomics, proteomics, and metabolomics, are considered important tools to be applied and utilized to understand the biology of an organism and its response to environmental stimuli or genetic perturbation.
Metabolites are considered to “act as spoken language, broadcasting signals from the genetic architecture and the environment” (1), and therefore, metabolomics is considered to provide a direct “functional readout of the physiological state” of an organism (2). A range of analytical technologies has been employed to analyze metabolites in different organisms, tissues, or fluids (for review see Reference 3). Mass spectrometry coupled to different chromatographic separation techniques, such as liquid or gas chromatography or NMR, are the major tools to analyze a large number of metabolites simultaneously. Although the technology is highly sophisticated and sensitive, there are still a few bottlenecks in metabolomics. Due to the huge diversity of chemical structures and the large differences in abundance, there is no single technology available to analyze the entire metabolome. Therefore, a number of complementary approaches have to be established for extraction, detection, quantification, and identification of as many metabolites as possible (3,4).
Another challenge in metabolomics is to extract the information and interpret it in a biological context from the vast amount of data produced by high-throughput analyzers. The application of sophisticated statistical and multi-variant data analysis tools, including cluster analysis, pathway mapping, comparative overlays, and heatmaps, has not only been an exciting and steep learning process for biochemists, but has also demonstrated that current thinking needs to change to deal with large data sets and distinguish between noise and real sample-related information. In addition, and still without consensus in the metabolomics community, is the question, “How do we deal with data that don't make biological sense based on literature and common knowledge?” We are only beginning to even assume where metabolomics, together with the other ‘omics technologies, is going to lead us: Will we find more answers to our questions or will it bring more questions requiring more answers?Potential and applications of metabolomics
There are four conceptual approaches in metabolomics: target analysis, metabolite profiling, metabolomics, and metabolic fingerprinting (5). Target analysis has been applied for many decades and includes the determination and quantification of a small set of known metabolites (targets) using one particular analytical technique of best performance for the compounds of interest. Metabolite profiling, on the other hand, aims at the analysis of a larger set of compounds, both identified and unknown with respect to their chemical nature. This approach has been applied for many different biological systems using GC-MS, including plants (6), microbes (7), urine (8), and plasma samples (9). Metabolomics employs complementary analytical methodologies, for example, LC-MS/MS, GC-MS, and/or NMR, in order to determine and quantify as many metabolites as possible, either identified or unknown compounds. The fourth conceptual approach is metabolic finger-printing (or footprinting for external and/or secreted metabolites). Here a metabolic “signature” or mass profile of the sample of interest is generated and then compared in a large sample population to screen for differences between the samples. When signals that can significantly discriminate between samples are detected, the metabolites are identified and the biological relevance of that compound can be elucidated, greatly reducing the analysis time.
Since metabolites are so closely linked to the phenotype of an organism, metabolomics can be used for a large range of applications, including phenotyping of genetically modified plants and substantial equivalence testing, determination of gene function, and monitoring responses to biotic and abiotic stress. Metabolomics can therefore be seen as bridging the gap between genotype and phenotype (5), providing a more comprehensive view of how cells function, as well as identifying novel or striking changes in specific metabolites. Analysis and data mining of metabolomic data sets and their metadata can also lead to new hypotheses and new targets for biotechnology.