FRESCO: FLOW MATCHING FOR RENDERING EXPRESSION-SPECIFIC CELLULAR MORPHOLOGY

Predicting how cells respond to unseen perturbations is central to phenotypic drug discovery and functional genomics. High-throughput screens now measure both cell morphology and single-cell gene expression, but typically in different cells, paired only by treatment. Generating morphologies consistent with a treatment's transcriptional response therefore requires learning a distribution over cells from this group-level pairing. We introduce FRESCO, a conditional rectified-flow model that generates multi-channel single-cell images from gene expression resampled across a treatment's cells. FRESCO represents expression as transcriptional program tokens, conditions on a batch-matched control cell to account for acquisition effects, and factorizes attention across spatial positions and channels. Because standard image metrics overlook perturbation-specific responses, we also introduce metrics of response direction and retrieval. On two transcriptome–morphology screens (28 compounds; 1,000 gene knockouts), FRESCO reaches a median response cosine of 0.57 and 25.9% top-1 retrieval on 56 held-out knockouts, against 0.28 and 11.1% for the strongest baseline, and roughly halves FID relative to CellFlux on three morphology-only benchmarks.