Mapping cell-type-specific spatial gene programs uncovers tissue architecture and microenvironment organization

The complex organization of tissues is shaped by the spatial arrangement of diverse cell types executing specialized gene programs. Cell-type-specific spatial gene programs (SGPs) are transcriptional modules that capture spatial expression co-variation within cell types. However, identifying these SGPs remains challenging in spatial transcriptomics. Here we present MacSGP, a scalable framework that integrates deep graph neural networks with probabilistic modeling to identify cell-type-specific SGPs through low-rank structures while accounting for cell type mixtures. Through comprehensive simulations and real data analyses spanning diverse tissues and technologies, we demonstrate that MacSGP effectively distinguishes cell-type-specific SGPs from cell-type marker gene expression patterns while uncovering tissue architecture and cellular microenvironments. In a mouse brain dataset, we identify replicable SGPs reflecting subregional neuronal specializations, with these genes significantly enriched for heritabilities associated with brain-related phenotypes. Across colorectal cancer datasets from various platforms, MacSGP identifies consistent SGPs that define distinct cellular niches within the tumor microenvironment. MacSGP identifies cell-type-specific spatial gene programs, capturing coordinated gene activity that varies across locations within cell types. This study dissects gene programs that reveal patterns of tissue organisation and cellular environments within brain and tumour contexts.