Self-Supervised Editable Transport for Counterfactual Spatiotemporal Gene Perturbations

Predicting spatiotemporal tissue responses to localized gene edits from destructive, unpaired spatial snapshots is crucial for virtual perturbation screening. However, existing methods isolate temporal dynamics from interventional capacity. Bridging this dichotomy requires targeted edits that remain physically coherent and on-manifold throughout temporal propagation. We introduce **STEP** (**S**elf-supervised **T**ransport of **E**ditable **P**erturbations), a generative framework trained exclusively on unperturbed snapshots to forecast counterfactual spatial dynamics. STEP formalizes gene knockouts as *decoder-consistent proximal projections* within an editable, mask-supervised latent manifold. These interventional states propagate continuously across unpaired snapshots via a shared wild-type path law, governed by mass-calibrated flow matching and unbalanced optimal transport. Extensive benchmarks across static and dynamic spatial CRISPR assays confirm that STEP outperforms existing baselines in counterfactual forecasting and response-gene prioritization. Mechanistic analyses further validate that decoder-consistent proximal projections are crucial for maintaining manifold integrity during latent transport. Applying STEP to a zebrafish embryogenesis atlas uncovers stage-dependent *in silico* knockout heterogeneity, dissecting dynamic developmental mechanisms.