more likely to be represented in MHC-II+ sub-cluster 0, whereas CD83-deficient cells had a higher likelihood to be present in MHC-II+ sub-cluster 2 (Fig. 5d). These subclusters also differed significantly in their gene expression pattern: MHC-II+ sub-cluster 0 retained more genes of the homeostatic Gpr34+ cluster (e.g., Siglech, P2ry12, Gpr34), while MHC-II+ sub-cluster 2 contained genes like Apoe, Cd63, Ctsb, Cst7, and Cd74 (Fig. 5e), all of which have been recently assigned to a type of highly activated microglia associated with age-dependent inflammation39. Due to the disparity between both MHC-II+ subclusters and the distinct distribution of CD83cKO versus WT cells among them, we reasoned that inflammatory conditions uncover differences between microglia from CD83ΔMG and WT mice, which were less pronounced under homeostasis. Therefore, we analyzed microglia from EAE samples for differentially expressed genes (DEGs) between both genotypes as well as associated biological processes and pathways. We discovered that microglia from CD83ΔMG mice exhibited significantly higher expression of Apoe, Cst7, and Ctsb, which are associated with the highly activated phenotype (Fig. 5f), whereas control cells rather expressed genes implicated in early inflammatory activation (Adamts1, Btg1, Egr1, Large1)40,41. When evaluating the Gene Ontology Biological Process terms, we discovered that gene expression pattern
