Deep metabolomics for environmental & holobiont studies

We leverage advanced analytical chemistry and artificial intelligence to decode the intricate metabolic interplay between hosts and their associated microbiota. Our approach enables the sensitive detection of low-abundance metabolites, including microbially derived metabolites that are inaccessible by conventional methods. By integrating robotics, chromatographic and mass-spectrometric technologies, and coupling these with computational methods for instrument control and spectral annotation, we generate comprehensive, highly detailed molecular profiles of environmental and holobiont systems.

Research aims

Our mission is to illuminate the metabolic and functional dynamics within complex environmental and holobiont communities, driving novel therapeutic and ecological breakthroughs. By identifying key molecular interactions and elucidating the microbial communication mechanisms that underpin host biology and environmental adaptation, we support discoveries with potential transformative applications in precision medicine, agronomy, pollution monitoring, and ecosystem-resilience enhancement.

Current projects

• Junior Chair Professor (CNRS Chemistry, 2023–2027). Combining state-of-the-art mass-spectrometry acquisition with advanced computational methods to provide a holistic view of molecular landscapes in marine holobionts — focusing on sea-anemone models (representative of cnidarians such as corals) and their responses to environmental stress. Co-funded by the CNRS (€200k) and the IdEx of Université Côte d'Azur (€120k).
• Automated MS acquisition for Orbitrap (Lucas Pradi, PhD). Developing an innovative automated mass-spectrometry acquisition framework to enhance the quality and coverage of fragmentation spectra, overcoming the limitations of conventional acquisition strategies while democratizing advanced metabolomics analysis. Funded by FNR Luxembourg (€200k, 2024–2028).

Selected publications

• Hoffmann, M. A., Nothias, L.-F., Ludwig, M., et al. (2022). High-confidence structural annotation of metabolites absent from spectral libraries. Nature Biotechnology, 40, 411–421. 10.1038/s41587-021-01045-9
• Dührkop, K., Nothias, L.-F., Fleischauer, M., et al. (2021). Systematic classification of unknown metabolites using high-resolution fragmentation mass spectra. Nature Biotechnology, 39, 462–471. 10.1038/s41587-020-0740-8
• Zuo, Z., Cao, L., Nothias, L.-F., & Mohimani, H. (2021). MS2Planner: improved fragmentation spectra coverage in untargeted mass spectrometry by iterative optimized data acquisition. Bioinformatics, 37(Suppl. 1), i231–i236. 10.1093/bioinformatics/btab279