Modelling intercellular signaling events during cellular differentiation from single-cell transcriptomics data

Communication between cells is central to the normal functioning of multicellular organisms. Both during development and homeostasis, it is clear that cells require some cues from other cells in their environment for their normal functioning. Cellular differentiation is one of the processes that are heavily influenced by extracellular factors: the micro-environment of a cell can provide unique instructions that sway a cell towards a particular differentiation path. The goal of this project is to computationally model the cell-cell communication networks and their intracellular effects, during a differentiation process. While unbiased methods have already been developed to predict the intracellular networks underlying cellular differentiation, predicting regulatory networks between interacting cells has remained largely unexplored. In this project, I will develop a computational method that uses transcriptomics data to predict what external signals are send between two cells, and how they impact the cellular differentiation. The method will be applied on a model system of liver macrophage differentiation, for which bulk transcriptomics data is currently available (in-house) and single-cell transcriptomics data will be generated by other scientists of the collaborating research group of Prof. Martin Guilliams. Predictions made by the model will in turn be validated in this research group and will lead to a better fundamental understanding of macrophage differentiation.