Advanced analytics

Identifying and characterizing the source

Analyzing complex biological samples presents a real challenge; a bacterial extract can contain several tens of thousands of compounds, which makes its detailed characterization or the precise identification of an active molecule extremely complex. The advanced analytics hub takes on this challenge by finding solutions to various analytical needs, from simple detection and quantification of a known molecule in bacterial extracts to extensive physicochemical characterization of a complex extract or to the identification of a new bioactive compound.

Core activities

Dereplication and structure elucidation

In collaboration with the activity testing and data science units, the advanced analytics unit has implemented a compound dereplication strategy that couples biological assays, analytics and data processing to identify the chemical entity that is responsible for a certain biological activity. Depending on the sample type, the advanced analytics unit determines the most appropriate separation and detection techniques: liquid-chromatography (LC) followed by UV detection (LC-UV), LC followed by high resolution mass spectrometry (LC-HRMS), gas chromatography followed by mass spectrometry (GC-MS), or by nuclear magnetic resonance (NMR). The analytics results are then combined with genomics data obtained either from whole genome sequencing of the original strain of interest - performed by the biodiversity farming unit - or from public databases. It is the combined analysis of this data, which is performed in collaboration with the data science unit, that will ultimately identify the metabolite responsible for an activity and shed light on its chemical structure.

Metabolomics for extract characterization

Compounds that present an antimicrobial or biological activity of interest will need to be produced in the laboratory. To this aim, it is important to understand the metabolic pathways that lead to its biosynthesis and that are utilized in vivo by the strain that carries the activity of interest. This is achieved using metabolomics, an analytical strategy for comprehensive analysis of the metabolites present in a biological specimen (metabolome). Because metabolomics aim to measure molecules with very disparate physical properties (e.g. water soluble organic acids versus nonpolar lipids), the bioprocess engineering team first separates the metabolome into subsets of metabolites with similar properties using various extraction methods. Characterization of the composition of each subset can then be carried out by coupling different detectors to a chromatographic system (LC-UV, LC-HRMS, GC-MS or NMR), followed by a statistical analysis performed in collaboration with the data science team. The data obtained is then used to guide genetic engineering in the synthetic biology unit aiming to reconstitute the metabolic pathways of interest in a bacterial micro-factory and optimize production of a given compound.

Support activities

  • Based on metabolomics analysis, guide genetic engineering led by the synthetic biology  team to direct optimization of compound production.

  • Detect and quantify known molecules in bacterial extracts at each successive genetic engineering  round to direct small and then large scale production of a compound.


Arora, N., & Banerjee, A. K. (2019). Dereplication in Natural Product Discovery. Current Topics in Medicinal Chemistry, 19(2), 101–102.

Ito, T., & Masubuchi, M. (2014). Dereplication of microbial extracts and related analytical technologies. The Journal of Antibiotics, 67(5), 353–360.

Watrous, J., Roach, P., Alexandrov, T., Heath, B. S., Yang, J. Y., Kersten, R. D., van der Voort, M., Pogliano, K., Gross, H., Raaijmakers, J. M., Moore, B. S., Laskin, J., Bandeira, N., & Dorrestein, P. C. (2012). Mass spectral molecular networking of living microbial colonies. Proceedings of the National Academy of Sciences, 109(26), E1743–E1752.

van der Hooft, J. J. J., Mohimani, H., Bauermeister, A., Dorrestein, P. C., Duncan, K. R., & Medema, M. H. (2020). Linking genomics and metabolomics to chart specialized metabolic diversity. Chemical Society Reviews, 49(11), 3297–3314.

Plainfossé, H., Trinel, M., Verger-Dubois, G., Azoulay, S., Burger, P., & Fernandez, X. (2020). Valorisation of Ribes nigrum L. Pomace, an Agri-Food By-Product to Design a New Cosmetic Active. Cosmetics, 7(3), 56.

Nicholson, J. K., Lindon, J. C., & Holmes, E. (1999). “Metabonomics”: understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data. Xenobiotica, 29(11), 1181–1189.