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Understanding how cells respond to biological perturbations over time and across tissues is key to identifying regulators and networks that inform personalized medicine. Current methods struggle to quantify these dynamic influences in complex multicellular or multitissue systems, especially using single-cell data with spatial and temporal resolution. To address this, we introduce  Perturb-STNet , a novel framework that leverages network-based spatiotemporal models to rank spatial and temporal differentially expressed regulators due to perturbation (pSTDERs) driving developmental and disease processes.  Perturb-STNet  identifies significant pSTDERs, estimates dynamic regulatory networks, and provides detailed visualizations of regulator, cell, and neighborhood interactions critical for understanding disease progression and therapeutic responses. We validated  Perturb-STNet  using synthetic data and epithelial-to-mesenchymal transition lung cancer data, showing superior performance compared to standard methods. Additionally, we applied it to CODEX single-cell imaging temporal data from a murine melanoma model to study CD8+ T-cell therapy effects, and to MERFISH spatial transcriptomics temporal data to explore inflammation and tissue repair in colitis. In melanoma,  Perturb-STNet  uncovered regulators like KLRG1 and CD79b, along with mediating pairs and triples (IgD-H2kb, PDL1-H2kb, NKP46-CD117, and FOXP3-CD5-CD25), revealing therapeutic strategies including checkpoint inhibition by targeting PDL1-H2kb to restore CD8+ T cell function, Treg depletion through inhibition of FOXP3-CD5-CD25 axis, and NK cell activation by enhancing NKP46-CD117 interactions. In colitis,  Perturb-STNet  identified key genes (Csf1r, Col6a1, Lgr4, Myc, and Fzd5) and mediator pairs (Itga5-Flnc, Cd68-Csf1r, Csf1r-Cx3cl1, and Tnfrsf1b-Bmp1) involved in immune regulation, matrix remodeling, and epithelial repair, offering potential therapeutic targets. Overall,  Perturb-STNet  enables robust identification of spatiotemporal regulatory networks in single-cell perturbation data across diverse disease contexts.

Fusion of spatiotemporal and network models to prioritize multiscale effects in single-cell perturbations