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- Advancement of clinical proteomics for systems medicine
- Bridging from the single cell to the cell population – Epo-induced cellular responses and erythroleukemia
- Deciphering tumor microenvironment interactions determining lung cancer development
- Mechanisms controlling the compensation of liver injury and towards model-based biomarkers for early detection of liver cancer
- Application of dynamic pathway modelling for personalized medicine
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Junior Research Group Systems Immunology and Single Cell Biology
Dr. Felix Hartmann
The progression and clinical outcome of human cancer are influenced by cellular interactions in the tissue microenvironment. Our lab strives to understand how such interactions influence the behavior of the human immune system. Specifically, we are studying how metabolic interactions between T cells, macrophages, and tumor cells determine immune function and tumor progression.
We employ a novel proteomic imaging technology termed multiplexed ion beam imaging (MIBI) to address these questions. MIBI enables the simultaneous quantification of more than 40 proteins and their spatial localization directly in human tissues. We have established the MIBI technology at the DKFZ and have set up all necessary experimental and analytical workflows. Now, we are using these workflows to analyze the metabolic regulation of human immune cells in clinical tissues. In addition, we are conducting functional studies on the relevance of metabolic pathways using genetic manipulation and human organoid cultures. In short, our research combines:
Systems Immunology: Analysis of the immune system in human tissues via multiplexed imaging. Together with clinicians, we profile human tissue samples from clinical trials focusing on cancer, inflammation/autoimmunity, and cardiovascular disease.
Single-Cell Biology: We develop experimental tools to study novel aspects of cell biology on the single-cell level, for example, the connection between metabolic reprogramming and epigenetic modifications.
Computational Biology: In collaboration with computational scientists, we apply machine learning approaches that utilize the single-cell and subcellular nature of our high-dimensional imaging datasets.
Overall, our lab aims to advance our understanding of the interactions between the immune system and the local tissue structure and thus contribute to future improvements in human cancer immunotherapy.