The ‘omics’ sciences of systems biology approaches, including genomics, transcriptomics, proteomics, and metabolomics, have been studied for decades. Whereas much attention has been focused on the developments and applications of the first three, metabolomics, the study of all metabolites (includes flora and drug metabolites) produced in the body, is the relative newcomer in the ‘omics’ family that can be considered most closely related to patients’ phenotype.
The exact number of human metabolites is unknown, with estimation ranges from the thousands to tens of thousands. With these many small molecules targets, metabolomics has a great and largely untapped potential in the fields of oncology, microbiology and other health care related research areas. It can be used to identify diagnostic disease biomarkers, to discover novel therapeutic targets, and also can be used for treatment monitoring, as well as prediction of therapeutic responses.
Technologies for metabolomics analysis must allow separating, detecting, quantifying, and identifying many individual metabolites present within a complicated biological matrix (such as tissue, cell, biofluids or breath), a changeling task given the approximately thousands of human metabolites, plus drugs, food components, and bacterial metabolites. Due to this large number of analytes to be detected, it is clear that not one analytical technique will cover the entire metabolome. In the current analytical chemistry field, standardized techniques for metabolites analysis and identification of small molecules include nuclear magnetic resonance (NMR) and mass spectrometry (MS). The NMR techniques can yield helpful structural information, but are relatively slow therefore is not suitable for high throughput clinical diagnostic purpose. On the other hand, the mass spectrometry-based techniques offer an excellent combination of sensitivity and selectivity, and can be used for high throughput analysis which fits perfectly in a fast pace clinical diagnostic laboratory setting.
Quantitative metabolite measurement