Metabolomics is the study of global small-molecule (<1500 Da) profiles in a system (cell, tissue, or organism). Together with genomics, transcriptomics, proteomics and other -omics sciences, it contributes to the understanding of global systems biology.

The main analytical tools in metabolomics are mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy, each having different strengths and weaknesses. Metabolomic studies rely on two main strategies: i) targeted metabolomics, for hypothesis-driven experiments, that focus on a few specific metabolic pathways/sets of biomarkers expected to play a role in the biochemical problem under examination; ii) untargeted metabolomics, to measure ideally the entire set of small molecules in  a biological sample providing a global picture of the metabolome.

Like other omics, metabolomics deals with huge datasets and therefore univariate and multivariate statistics are applied for the identification of groups from molecular profiles and of characteristic metabolite changes, in line with the project objectives. Biological explanations and hypotheses for the noticed effects are searched through statistical analyses that combine analytical data on statistically relevant numbers of samples with the associated meta-data.


In current practices metabolomics is used for the prognosis, diagnosis, patient stratification, monitoring of the effect of pharmaceutical/surgical intervention as well as biomarker discovery.


The most commonly used samples for metabolomics analysis are:

  • blood: serum, plasma;

  • cells and cell lysates;

  • urine;

  • cerebrospinal fluid;

  • exhalated breath condensate;

  • saliva;

  • sweat;

  • sperm;

  • prostate fluids;

  • lymph;

  • fecal extracts;

  • gut microflora (intact or lysed);

  • fecal supernatant;

  • intact tissues;

  • tissue extracts in both hydrophilic and hydrophobic solvents.

The tremendous progress in the development of new analytical technologies in metabolomics, as well as in the various -omics, the necessity of holistic medical correlations between clinical, scientific and analytical features, the need for efficient solutions to provide transnational access to high quality human biological samples and associated medical information for academia and industry, represent major challenges for the development of medical research in the future. From all of this, an intimate link between metabolomics and biobanks emerges, as depicted below.


We are therefore founding an EXpert CEnter in METabolomics (EXCEMET) that combines all the essential analytical and informatics/computational expertise in the field with the main objective to strengthen the relationships between the metabolomics community and biobanks and to offer services to help biobanks developing increasingly higher standards for their sample quality.

EXCEMET will complement the activities of the biobanks, extending the access from high quality samples to advanced biomolecular analyses and the dissemination of curated knowledge from those analyses in open-access, long-term maintained databases.


EXCEMET foresees to contribute assessing and developing a number of critical steps of the overall workflow implemented at biobanks, in particular:

  1. to check the performances of adopted procedures for sample storage, as different conditions may influence the molecular profiles;

  2. to evaluate the effect of preanalytical steps on the sample quality and stability via metabolic profiling, to propose standard operating procedures (SOPs) to be implemented for deposition in biobanks;

  3. to make a comparative evaluation of the various sample stabilizers and to provide hints for the new stabilizers;

  4. to check quality/integrity of biobank samples, in terms of intactness of the original metabolome (good warranty of the intactness of the overall biomolecular profile);

  5. to define shelf-life in biobanks;

  6. to evaluate the minimum samples number to be tested for statistical significance;

  7. to generate a common set of reference samples to be shared among the different members of EXCEMET, and to be used as common internal standards to improve interoperability across the centers;

  8. to achieve technical developments for decreasing the minimal sample volume, increasing sensitivity/reduction of experiment time; reducing the costs, and improving reproducibility of the results, in order to propose a better service to biobanks.


EXCEMET will combine analytical competences from the two main platforms (NMR and MS) to exploit in a synergistic approach the advantages of each of them, as well as all the essential IT and computational expertise for data analysis, biomedical data interpretation, data visualization and ontology definition.