Metabolomics and Analytics Centre

The ambition of the Metabolomics and Analytics Centre led by Thomas Hankemeier is to develop innovative analytical strategies for metabolomics-driven health monitoring and systems biology studies. Understanding the intricate balance between health, disease and adaptation to challenges relies on the identification of systematic changes during time. The ability to stratify risk and predict disease will enable us to tailor therapies and improve prognosis as envisioned in personalized medicine. We believe that continuous health monitoring and personalized medicine approaches can be achieved with large-cohort studies involving multiple patient samples, or in-vitro/ex-vivo samples such as stem cell derived in-vitro models. We are convinced that personalized health care can be possible only with cross-disciplinary efforts and translational collaboration, as well as standardized procedures and revolutionary analytical approaches.

Clinical metabolomics typically involves large- cohort studies with multiple patient samples or in-vitro/ex-vivo cellular models. High-quality data can only be gathered by using cutting-edge and standardized analytical approaches. This will allow for the pinpointing of metabolic differences and the deciphering of novel biomolecular pathways or biomarkers candidates.

Our research focuses on the development of high-performance metabolomics platforms aiming for high-throughput analysis and comprehensive metabolite coverage. This will provide the most complete information on the metabolome in less time and with less costs. We are implementing standardized analytical workflows for the analysis of 3D cell culture models, enabling for highly-sensitive analysis of volume-limited samples and that aim to extract the intracellular and extracellular metabolic information from a small number of cells.

Our ambition is to use these state-of-the-art and standardized technologies to unravel the biomolecular mechanisms involved in major diseases, notably cardiovascular diseases and neurological disorders. Using systems pharmacology-based approaches, we not only aim for a better diagnosis of such diseases, but also an individual prediction of the physiological response to a specific treatment.