EN / DA

Fabian Coscia

Post.doc.

Proteomics Program, Novo Nordisk Foundation Center for Protein Research | fabian.coscia@cpr.ku.dk

The main research focus of Professor Matthias Mann’s laboratory is to identify novel biomarkers that can be used for patient diagnosis and possibly for the prevention and treatment of metabolic diseases, such as diabetes and cancer. To this end, the lab is developing and using cutting-edge mass spectrometry-based proteomics; an area in which the Mann Group is world-leading. The Mann Group undertakes ambitious research projects involving proteomics of blood, plasma, cerebrospinal fluid and tissue for the phenotyping of patients. One goal is to establish robust, high-throughput proteome profiling pipelines for these materials, allowing for the proteomic screening of clinical cohorts. The group’s overarching aim is to identify biological markers for early detection of metabolic disorders, to improve diagnosis and help to develop individualized therapies. “Our eventual goal is to prevent the development of the metabolic syndrome in the first place, by targeted and personalized life style interventions,” says Professor and Group Leader, Matthias Mann. To this end, the group builds on its longstanding expertise in mass spectrometry to implement an artificial intelligence-guided platform for analyzing the proteomes of patient tissue from low amounts of formalin-fixed, paraffin-embedded samples at high accuracy and sensitivity. “Our highly sensitive methods now enable us to simultaneously profile thousands of proteins derived from only a few hundred cells, allowing us to identify the proteins that are most critical for various diseases,” Mann says. Another area of focus is the interpretation of ‘multi-omics’ data, which is still a challenge. Often, a single ‘omics’ dimension is not sufficient to capture the full complexity of a disease. To overcome these challenges, the Mann Group is developing the Clinical Knowledge Graph where multi-omics data, together with vast amounts of meta-data, is collected and harmonized – enabling analyses and providing an excellent ecosystem for machine learning.