Zhenyi Zhang

Time-series spatial transcriptomics measures each tissue section once, so how cells migrate, proliferate and influence one another can only be inferred indirectly. CytoBridge learns continuous population evolution in a joint space of transcriptomic state and spatial coordinates, splitting velocity into an interaction term estimated from spatial neighborhoods and ligand–receptor priors and an intrinsic-context term, while allowing cell numbers to grow or shrink. Across five datasets on three spatial transcriptomics platforms it generates unmeasured intermediate stages; in a leave-one-stage-out benchmark it had the lowest Sliced-W2 in 12 of 33 combinations, more than any other method or control.

Time-series spatial transcriptomics measures each tissue section once, so how cells migrate, proliferate and influence one another can only be inferred indirectly. CytoBridge learns continuous population evolution in a joint space of transcriptomic state and spatial coordinates, splitting velocity into an interaction term estimated from spatial neighborhoods and ligand–receptor priors and an intrinsic-context term, while allowing cell numbers to grow or shrink. Across five datasets on three spatial transcriptomics platforms it generates unmeasured intermediate stages; in a leave-one-stage-out benchmark it had the lowest Sliced-W2 in 12 of 33 combinations, more than any other method or control.