Abstract Quantifying how biological and chemical exposures reshape spatial gene regulation across tissues remains challenging due to technical and statistical constraints. Moreover, spatial transcriptomic comparisons are often hindered by tissue misalignment between conditions and the pervasive zero inflation of single-cell gene expression data. Existing differential expression approaches typically ignore spatial dependencies and fail to capture differential gene activation. We present Spatial-ZEDNet, a hierarchical Gaussian random field framework that jointly detects spatially differentially expressed genes (DEGs) and differentially activated genes (DAGs) while explicitly modeling zero inflation. Unlike previous tools, Spatial-ZEDNet aligns biological signals across conditions without requiring spatial coordinate matching, improving spatial inference robustness. In both simulations and real biological applications, Spatial-ZEDNet demonstrates superior power and specificity relative to standard methods and is robust in distinguishing DEGs from spatially variable genes. Applied to colitis and Plasmodium infection datasets, the method identified spatially localized expression and activation of immune genes, including Mmp7, Olr1, Ifitm3, and Gbp3, several of which correspond to known inflammatory disease loci, highlighting coordinated tissue-specific responses often missed by conventional methods. These findings demonstrate that explicitly modeling excess zeros improves the detection of spatially regulated activation states. Spatial-ZEDNet provides a statistically rigorous, interpretable framework for integrating spatial transcriptomic data across environmental and therapeutic exposures, advancing mechanistic understanding of exposure-induced tissue remodeling.
Egbon et al. (Sun,) studied this question.