Integration of multiple single cell microglial datasets reveals radial differentiation into long‐lived substates associated with Alzheimer’s pathology

Abstract

AbstractBackgroundUnsupervised classification of brain cell types can be readily achieved through analysis of gene expression patterns in multidimensional space. However, the small transcriptional differences between microglia subtypes require more principled, robust, and reproducible approaches to expose and distinguish biological differences from noise.MethodHere we leverage Fokker‐Planck diffusion maps [1,2] to examine the dynamics of microglia subtypes by finding a low dimensional representation of the underlying stochastic dynamical system responsible for generating these transcriptional states. Clustering directly on this projection allows us to reduce noise, assign time scales to these substates, and generate a hierarchy of microglial cell states. Once these subtypes have been defined, we apply pseudobulk differential expression analysis to identify marker genes, and use those genes to build a boosted tree model to classify cells states. We then apply Shapley additive explanations (SHAP) to identify which genes are most important for distinguishing between cell states.ResultWe observe a radial transition pattern emanating, with a subtype that appears homeostatic, inactivated, and poised for transition. This is consistent with the observation that microglia dynamics are sensitive to their microenvironment. Deconvolution of an independent bulk microglia dataset (n = 130) showed depletion in subtypes linked to protein misfolding and myelin phagocytosis, as well as enrichment in immunoreactive microglia in Alzheimer’s patients. SHAP reveals DUSP1, CD83, PLCG2, OLR1,CCL2, and JUN among the most important genes for differentiating into these AD‐associated clusters.ConclusionOur approach enables us to reproducibly identify microglial subtypes across sets of single cell‐resolution high dimensional molecular profile data, identifying likely drivers of subtype differentiation. With several of our driver genes already associated with Alzheimer’s via GWAS and other analyses, our results will enable us to elucidate the role of these genes in determining microglial subtypes.