Laboratory Dominic Grün
Cell fate decision is essential for the emergence of a complex multicellular organism starting from a single pluripotent zygote, and perpetually occurs during adult life to maintain organ tissues. The robustness of cell fate decision is remarkable given the stochastic interaction of hundreds of thousands of molecules in each cell. It has remained a central open question how signals from the microenvironment are integrated with stochastic processes in a cell to control cell fate decision in normal tissues and upon perturbations due to disease or tissue damage. Our lab investigates this question by combining single-cell resolution experimental methods with computational methods involving machine learning and mathematical modeling.
In our lab we are focusing on tissue development and homeostatic turnover of organ tissues to understand the driving forces and control mechanisms of cell fate decision. We investigate a variety of systems which prominently show constant turnover, lineage plasticity, and response to perturbations in terms of cell differentiation dynamics. For example, we investigate the role of the hematopoietic niche, i.e., the effect of a varying microenvironments, such as fetal liver and bone marrow, on blood cell differentiation, and compare to aberrant cell fate decision upon diseases such as leukemia.
As a complementary system we are studying plasticity and turnover of epithelial cells in the liver, the central metabolic organ of the human body exhibiting high regenerative capacity, driven by complex interactions with the immune, endothelial, and mesenchymal cell compartment. In our studies we are aiming to address basic research questions, such as the identification of the molecular mechanisms underpinning robust cell differentiation, but also aim at translating our findings into clinically relevant novel therapeutic approaches.
With the recent emergence of powerful single-cell resolution experimental methods, including single-cell RNA-seq and ATAC-seq, it has become possible to profile the composition of tissues at the level of individual cells. By quantifying the genome-wide transcriptional profiles and the state of the chromatin, cell states and types can be annotated and differentiation trajectories as well as entire lineage trees can be inferred. Moreover, spatial methods based on in situ sequencing or highly multiplexed microscopic imaging permit the quantification of the expression of hundreds of genes in tissue sections, enabling the reconstruction of spatial tissue architecture and the inference of molecular interactions between co-localized cells. Paired with computational machine learning techniques tailored to such multimodal, large datasets, these approaches facilitate the integrative analysis of systematic and stochastic gene expression variability (i.e., biological noise) within cells, which represent intrinsic cell fate determinants, as well as signaling pathways mediating interaction of neighboring cells and representing extrinsic cell fate determinants.