Metabolomics or metabonomics, defined as the application of unbiased global, or holistic, analysis of biological samples (e.g. animal/human biofluids, tissue and cell extracts, in vitro incubation media etc., or plant/food extracts), are increasing exponentially as the potential of this approach to discover new biomarkers is becoming more widely appreciated. Analytical strategies for metabolomics research aim to characterize the whole metabolite complement of the samples under study and then relate their concentrations to features or properties of the sample (disease, stress, nutrition, ageing, environmental exposure or several other application areas). Metabolomics typically employs multidisciplinary research and collaborative efforts by scientists from different fields of expertise including advanced analytical chemistry and statistical analysis, biochemistry, medicine/life sciences, nutritional, agricultural or environmental sciences. These holistic methods of profiling represent a hypothesis-free research strategy for the detection of potential biomarkers.
Typically two strategies can be followed in MS-based metabolite profiling analysis:
- In “untargeted metabolomics” unbiased holistic the profiling aims toward the detection of as many features as possible without prior knowledge of their identity. Multivariate statistical analysis is apllied where data guides the analyst to recognize the important features (metabolites) that contribute to the differentiation of the groups/samples. Next effort is put towards the identification of these discriminating molecules (peak annotation or structure elucidation).
- In “targeted metabolomics” a limited number of known metabolites or compound classes are measured. Subsequently multivariate statistical analysis uses only this ‘‘thinned’’ data set.
The BIOMIC_AUTh metabolomics group has long experience developing methods in both approaches, both in research field along with applications in life and agricultural sciences as shown by several highly cited publications.