Cell type-specific differential expression in Alzheimer’s disease and cancer using spatial transcriptomics

Abstract

Abstract 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.