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Spatial transcriptomics enables spatially resolved gene expression measurements at near single-cell resolution. The detection of genes that are differentially expressed across tissue context for cell types of interest is an essential challenge for dissecting pathological mechanisms. However, changes in cell type composition across space and measurement contamination from other cell types introduce complex statistical challenges. Here, we introduce a statistical method, Generalized Linear Admixture Models for Differential Expression (GLAMDE), that estimates cell type-specific patterns of differential gene expression while accounting for localization of other cell types. By modeling spatial transcriptomics gene expression as an additive mixture, across cell types, of generalized log-linear functions, we provide a unified framework for identifying gene expression changes for a wide-range of relevant contexts: changes due to pathology, anatomical regions, physical proximity to specific cell types, and cellular microenvironment. Our approach enables statistical inference across multiple samples and replicates. We demonstrate, through simulations and validation experiments on Slide-seq and MERFISH data, that our approach accurately identifies cell type-specific differential gene expression with valid uncertainty quantification. Lastly, we apply GLAMDE to characterize spatially-localized tissue changes in the context of disease. In an Alzheimer’s mouse model Slide-seq dataset, we identify plaque-dependent patterns of cellular immune activity. We also find a putative interaction between tumor cells and myeloid immune cells in a Slide-seq tumor dataset. GLAMDE is available within the spacexr R package. D.C. was supported by a Fannie and John Hertz Foundation Fellowship and an NSF Graduate Research Fellowship. This work was supported by an NIH Early Independence Award (DP5, 1DP5OD024583 to F.C.), the NHGRI (R01, R01HG010647 to F.C.), as well as the Burroughs Wellcome Fund, the Searle Scholars Award, and the Merkin Institute to F.C.. R.A.I. was supported by NIH grants R35GM131802 and R01HG005220.

At homeostasis, most Th17 cells are found in the lamina propria of the intestine, where they contribute to tissue homeostasis by inhibiting microbiota from tissue invasion and promote barrier functions. Recent studies in humans and mice have implicated intestinal Th17 cells in extra-intestinal autoimmune diseases, including multiple sclerosis, but the mechanism in which intestinal Th17 cells mediate these dichotomous functions remains unknown. Here, we combined single-cell RNA- and TCR-seq (scRNA/TCR-seq) with fate mapping of tissue Th17 cells to profile over 84,000 tissue Th17 cells and characterize their heterogeneity, plasticity, and migration at homeostasis and during CNS autoimmunity. We discovered a homeostatic Th17 cell population, that is induced by the intestinal microbiota, is present in both lymphoid organs and the intestine, and expresses IL-17. Joint scRNA- and TCR-seq showed that during EAE this homeostatic population gives rise to a pathogenic Th17 cell population, that migrates specifically through the draining lymph nodes and the spleen to the CNS, and highly expresses GM-CSF and IFNγ, but expresses little or low levels of IL-17. This conversion of homeostatic Th17 cells into encephalitogenic Th17 cells depended on IL-23R signaling. Our work characterizes Th17 heterogeneity, plasticity, and migration during homeostasis and CNS autoimmune disease, identifies a mechanism by which intestinal Th17 cells influence the generation of an autoimmune disease-inducing population, and provides further evidence for a direct relationship between the IL-17-producing and GM-CSF- and IFNγ- producing pathogenic T cells.