Characterizing dynamic tissue architectures by identifying cell-type-specific spatiotemporal gene programs with stGP

Cellular gene programs unfold over biological time within spatially organized tissues. The same cell type can activate distinct programs in different spatial domains or multicellular niches. Spatiotemporal transcriptomics enables in situ measurement of these processes; however, the interplay between temporal progression and spatial organization complicates the identification of whether a gene program is influenced by temporal changes, spatial structure, or both. To overcome this challenge, we present spatiotemporal Gene Programs (stGP), a statistical framework for identifying interpretable cell-type-specific gene programs across multi-sample spatiotemporal transcriptomic studies. stGP preserves the molecular identity of each program through shared gene loadings, and decomposes individual cell activity into a temporal component that captures gene program responses over biological time, and a spatial component that characterizes variations within tissue sections. We quantify their relative contributions by estimating their variance components. Through comprehensive simulations and analyses of three spatiotemporal transcriptomic datasets across different tissues and technologies, stGP uncovers spatiotemporal gene programs that distinguish preserved tissue architecture from dynamic remodeling. Our framework delineates age-associated cellular responses and uncovers localized program deployment within anatomical regions, aging hotspots, and multicellular niches. Our results establish stGP as an effective and robust framework for dissecting dynamic tissue architecture, providing insights into how cell-type-specific gene programs are coordinated across time and spatial microenvironments.