STAN, a computational framework for inferring spatially informed transcription factor activity

Transcription factors (TFs) orchestrate cellular responses to environmental signals and intercellular communication. The activity of TFs is influenced by neighboring cells, impacting cellular fate and function. Spatial transcriptomics (ST) allows for the mapping of mRNA expression across tissue samples, providing insights into the local microenvironment. However, the potential of ST data to systematically infer TF activity and its role in cell identity has not been fully exploited. We introduce STAN (Spatially informed Transcription factor Activity Network), a linear mixed-effects computational approach that predicts spatially informed, spot-specific TF activities by integrating curated TF-target gene priors, mRNA expression, spatial coordinates, and histological features. We demonstrate the utility of STAN on lymph node, dorsolateral prefrontal cortex, breast cancer, and glioblastoma ST datasets, identifying TFs associated with specific cell types, spatial regions, pathological zones, and ligand-receptor pairs. STAN enhances the utility of ST data, revealing the intricate interplay between TFs and spatial organization in diverse biological contexts.