STRAND: Sequence-Conditioned Transport for Single-Cell Perturbations

Predicting how genetic perturbations change cellular state is a core problem for building controllable models of gene regulation. Perturbations at the same gene can produce different transcriptional responses depending on their genomic locus, including different transcription start sites and regulatory elements; gene-level perturbation models collapse these distinct interventions into a single representation. We introduce STRAND, a generative model that predicts single-cell transcriptional responses by encoding regulatory DNA sequence at the perturbation locus and using it to parameterize a conditional transport process from control to perturbed cell states. Sequence conditioning supports zero-shot inference at perturbation targets unseen during training and locus-resolved inputs across a 524\,kb window at 128\,bp model resolution. On CRISPRi datasets in K562, Jurkat, and RPE1 cells, STRAND improves few-shot perturbation discrimination by up to 33\%, achieves the best average rank on unseen-gene benchmarks, and improves held-out-cell-line transfer by up to 0.12 in $\Delta$Pearson; ablations attribute these gains to both sequence conditioning and conditional transport. At the locus level, STRAND distinguishes alternative transcription start sites and validated regulatory elements that gene-identifier models treat as identical perturbations