Coordinators: Christos Samakovlis und Herbert Schiller
Modern single-cell analysis technologies rapidly generate a growing number of cell type and cell state atlases in the healthy and the diseased lung. Emerging spatial methods further position these cell types in the context of tissue architecture and enable systematic deciphering of local cell communication programs. Under disease conditions, ectopic or aberrant cell types or states regularly emerge; their significance, however, is often unknown.
An outstanding challenge for medical research is to 1) define early deviation points of the lifelong cellular state trajectories from normal to early disease, 2) explain causative cellular and molecular aberrations driving the disease, 3) identify suitable intervention junctions to reprogram altered tissue states back to homeostasis, and 4) analyze cellular modes of responsiveness to therapeutic intervention. This research program will address such outstanding questions by elucidating how multicellular gene programs are controlled in time and space (cell-intrinsic vs. cell-extrinsic programming), both in lung development as well as upon lung disease inception and progression. We will aim at the integration of all available genetic data (e.g., GWAS) with the multicellular gene programs to generate mechanistic models of these genetic disease associations. We will employ a combination of spatially resolved single-cell genomics and proteomics tools as well as bioinformatic and AI-driven data analysis to study organotypic disease models and clinical cohorts with careful disease staging or longitudinal design. Finally, to address the underlying causes of disease and predict patient responses to treatment, we propose single-cell analysis of intervention experiments in patient-derived organoid cultures and precision-cut lung slices, which will accelerate preclinical translation.
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