Our groups interest lies in understanding the basis of biological heterogeneity using computational approaches applied to ‘omics data in a disease context. We employ artificial intelligence (AI) and data science (DS) methods, not only to address the underlying causes of this heterogeneity, but also the interactions between different molecular ‘omics layers. We have a strong specialisation in the application area of oncology, but are also very interested in immunology and neurodegenerative diseases.
We believe that by better understanding global and local patterns in biological data, we can better differentiate the mechanistics behind biological and molecular processes from technical noise. As such, we rely on establishing close ties with experimental collaborators who are able to provide us with large scale and high dimensional datasets profiled using state of the art and exotic profiling techniques. We aim to take advantage of recent technological advances that are able to resolve single cell, spatial, temporal and genetic heterogeneity.
Cancer multiomics and precision oncology
The group has a strong core expertise in analysis and interpretation of cancer multiomics data, with exposure to a wide number of omics layers and tumour entities. We are in particular very interested in understanding the heterogeneous patient response to drugs and are interested in accurate classification of tumour entities, patient specific molecular drivers, tumour microenvironment and TILs, tumour evolution and heterogeneity, and germline predisposing factors.
Immunology and allergies
Regulation of innate and adaptive immunity is the primary factor distinguishing healthy and diseased states. Attenuation leaves one susceptible to infections and over activity can lead to autoimmune responses. We have a strong history of collaborating with the group of Irina Lehmann who heads the Molecular Epidemiology department at BIH, and Tobias Boettler and Maike Hoffmann heading the Liver Immunology Lab at Freiburg University Clinic.
Spatially resolved transcriptomics
Advances in single cell sequencing has led to better understanding of heterogeneous cell types and cell states. Even more recent advances in in situ transcriptomics allows one to resolve not only cell type and state, but also spatial location. With this information we can, for example, identify the location of different tumour infiltration immune cells in relation to the tumour mass. In the framework of the HCA and with help from the spaceTX consortium, we have recently published a tool called SSAM - the first computational framework for segmentation free cell-type and tissue domain analysis of in situ transcriptomics data.
*these authors contributed equally