Predicting factors regulating cell fate transition in development and disease

This thesis integrates single-cell and spatial omics data to uncover regulatory networks and signaling interactions driving kidney development, addressing limitations in kidney organoid maturity. Using scRNA-seq, spatial transcriptomics, and computational tools like SCENIC and NicheNet, the study prioritizes transcription factors (TFs) and signaling pathways influencing cell fate. Spatial data refined cell-cell interaction contexts, while de novo network construction and developing a novel deep-learning-based cell communication, DeepNiche, enhanced accuracy in handling sparse data and predicting regulatory interactions. These advancements provide a robust framework for improving kidney organoid development and understanding renal cell differentiation.