Inflammatory bowel disease (IBD) is a chronic inflammatory condition with a significant impact on quality of life, causing pain and disability and requiring life-long medical care. The pathology is deeply affected by immune responses, intestinal microbiota, dietary and environmental triggers, and risk-associated genetic variants. In addition, the risk of colorectal cancer is increased in patients with IBD, likely due to pathological proliferation during regeneration after intestinal damage. Several animal models of IBD exist, including transfer of CD45RBhi cells to immune-deficient mice. This particular model shows high similarity to the human disease, notably in its response to pharmaceutical treatment. In order to improve therapy, through identification of new target genes and cells, we aim to describe at a single-cell resolution how the disease develops during the course of the disease. We will generate bulk, single cell and spatial transcriptomic datasets, generated at different time points in the pathological process, to identify regenerative and pathological mechanisms that can be promoted or inhibited. The cell-specific and spatial resolution enables us to better predict the functional relevance of differentially expressed candidate genes, which will be validated in vivo. Ultimately, our aim is that our reliably generated data sets will not only help us propose novel candidate genes for future personalized treatment.