Metabolomics involves the comparison of metabolomes (the full metabolite complement of an organism) between control and test groups to find differences in their profiles. Those differences may be correlated to the disease being studied in biomarker discovery or to changes in metabolic output in toxicology studies when a drug candidate is introduced to a test subject. Environmental metabolomics is also growing in importance where studies are performed to assess chemical risks to wildlife and the environment or to monitor the maintenance of healthy livestock in intensive farming with respect to disease.
Unlike gene-expression studies or proteomics analyses, which only reveal part of what might be happening in a cell, metabolomic profiling can give an instantaneous snapshot of the entire physiology of that cell. More importantly, if data from proteomics, transcriptomics, and metabolomics can be integrated, a more complete picture of a living organism’s biology can be obtained.
There are usually several steps involved in metabolomics analysis:
• Profiling (also known as differential expression analysis) involves finding the interesting metabolites with statistically significant variations in abundance within a set of experimental and control samples.
• Identification is the determination of the chemical structure of these metabolites after profiling.
• Validation uses much larger numbers of samples to account for the effect of natural or biological variations to validate the previously identified metabolites. It is quantitative and requires analytical standards.
• Interpretation, the last step in the workflow, makes connections between the metabolites discovered and the biological processes or conditions.
Because of the vast chemical diversity of metabolites and their wide variation in abundance, metabolomics research usually requires multiple techniques; certain classes of samples are more amenable to one analysis technique than others (Figure 1).
The two most commonly used techniques are GC/MS and LC/MS. Comprehensive metabolomics labs frequently incorporate both of these approaches.
A major challenge of metabolomics involves data processing and analysis; a full range of software programs is needed to turn raw metabolomics data into useful biological results. A typical metabolomics experiment requires large numbers of samples to generate results that are statistically rigorous. Aside from the need for highly sensitive and accurate instrumentation, powerful software tools are essential to address the vast amounts of data generated by these experiments. Analytical capabilities include deconvolution programs for processing GC/MS and LC/MS files, an array of statistical analysis tools to find significant metabolites, a metabolite database to identify metabolites, and finally, bioinformatics software for visualizing molecular interaction networks.