Jiayi Wang

Understanding how local signalling shapes cell-state transitions requires linking spatial context to transcriptional dynamics. Here we present CellVocal, a framework that integrates spatial RNA velocity with cell-cell communication to decompose cell-state transitions into intrinsic and communication-associated components and further attribute their contributions to signalling modules and neighbouring sender cells. CellVocal recovered complex trajectories and communication perturbations in simulations and reconstructed coherent dynamics and spatially restricted ligand-receptor programmes across the mouse brain, gut and lung. Further analyses linked inferred NCAM1-NCAM1 signalling to lower cell-cycle-associated RNA velocity during embryonic neurogenesis. In melanoma, inferred local MIF-CD74+CD44 signalling corresponded to a predicted decline in the melanocytic programme. Analysis of acute liver failure identified hepatocyte-derived ANGPTL4-SDC1 signalling in association with acetaminophen-related communication enrichment and predicted matrix-remodelling RNA dynamics. By converting static ligand-receptor maps into velocity-constrained networks of signalling influence, CellVocal provides a general strategy for generating spatially resolved hypotheses about how intercellular communication contributes to predicted transcriptional changes.

Inferring early cell fate from single-cell RNA-sequencing data is essential for identifying cellular origins and fate plasticity in development and disease. However, existing methods often fail to exploit tree-structured lineage trajectories, limiting the accuracy and interpretability of fate mapping. Here we present DyMoTree, a computational framework that models cell fate decisions as nonlinear mappings between progenitor and terminal cell states under explicit lineage constraints. By integrating lineage graphs with a tree-structured neural architecture, DyMoTree learns lineage-resolved cell-state transition maps from single-cell transcriptomes, enabling robust inference of early fate bias and identification of fate-specific progenitor substates and driver genes. Across simulations, lineage-tracing experiments, and in vivo systems, DyMoTree outperformed existing methods in resolving early fate biases. Applications to mouse embryogenesis, lung adenocarcinoma progression, and CAR-T immunotherapy revealed regulatory programs underlying developmental and disease-associated transitions. DyMoTree provides a general framework for modeling lineage-resolved cell-state dynamics underlying development and disease progression. Abstract Figure:

Cell–cell communication coordinates tissue development, homeostasis, and immunity, yet defining signaling interactions within intact tissues remains challenging. Single-cell transcriptomics enables systematic ligand– receptor inference, but tissue dissociation removes spatial context and obscures local and region-specific signaling. Spatial transcriptomics and spatial multi-omics can recover communication in situ, although existing methods often incompletely integrate heterogeneous data or produce poorly interpretable ligand–receptor lists. Here we present CellSTIC, a framework that resolves cell–cell communication in spatial multi-omics as structured programs grounded in tissue architecture. CellSTIC integrates multimodal evidence from local neighborhoods and organizes interactions into a hierarchical semantic representation that remains traceable to underlying molecules. This enables analysis from individual ligand–receptor pairs to functional modules comparable across tissues, regions, and states. In simulations and diverse tissue datasets, CellSTIC recovered spatially coherent communication structures and domains, revealing immune, brain, developmental, and regenerative programs, and providing a general approach for generating mechanistic hypotheses in situ. Abstract Figure: