ChronoCCI decodes the chronological logic of cell-cell communication along branching single-cell trajectories

Cell-cell communication (CCI) drives cell-fate decisions during tissue development, yet mapping these dynamics at single-cell resolution remains a challenge. While existing single-cell tools infer static signaling networks or map linear trajectories, they fail to capture the continuous, asynchronous coordination of pathways at multilineage branching points. Consequently, identifying the precise chronological timing of interactions and specific molecular triggers remains a major challenge. To resolve this, we introduce ChronoCCI, an automated Python framework that aligns consensus ligand-receptor networks directly with multi-trajectory Markov chain dynamics from CellRank. By modeling continuous expression trends across branching lineages via fate-weighted smoothing, ChronoCCI reconstructs the chronological cascade of tissue signaling on a shared time axis, tracking active cell types at the peak of each molecular wave. Crucially, ChronoCCI incorporates snoop_py, a screening algorithm leveraging chronological Mean Absolute Error (MAE) scoring to automatically isolate highly asymmetric lineage-specific signaling gatekeepers at developmental bifurcations. Validated on a human peripheral blood mononuclear cell dataset, ChronoCCI autonomously maps the temporal dynamics of myeloid maturation. It successfully prioritizes the GNAI2 - C5AR1 phagocytic checkpoint and the late-stage SELPLG - ESAM vascular rolling axis as highly asymmetric chronological triggers selectively retained along distinct lineages. Requiring zero prior biological assumptions, ChronoCCI provides a powerful tool to dissect intercellular cross-talk in developmental, regenerative, and pathological tissue atlases.