From Histology to Cell Sentences: Visual Soft-Prompts for Long-Horizon Ranked Gene Expression

Spatial transcriptomics (ST) measures gene expression in tissue but remains costly and sparse, whereas histology images (H&E) are abundant. Existing virtual-ST methods typically regress a small highly variable gene (HVG) panel or retrieve expression from a reference atlas. We instead cast H&E$\to$expression as ranked sequence generation in the spirit of LLaVA-style visual conditioning of an LLM, adapted to a domain where that recipe does not directly transfer. A lightweight adapter couples two domain-specific foundation models trained on massive corpora, a pathology expert and a single-cell language model, mapping frozen visual features into Visual Soft-Prompts that elicit cell sentences: gene symbols ranked by relative expression over a $\sim$27k-gene vocabulary. Prediction length is a central challenge: short-context models give the strongest early-rank fidelity but emit only a handful of genes, while naively extending the autoregressive horizon with a frozen decoder yields longer lists that eventually derail into duplicate loops and invalid tokens. A failure mode analysis shows that standard decoding fixes such as nucleus sampling or constrained decoding do not repair this. With attention LoRA and a larger training budget, derailing is largely prevented, producing near-complete cell sentences while retaining early-rank precision. Scored against the full ground-truth cell sentence at every depth, dense panel regressors and averaging retrieval methods (ST-Net, BLEEP) lead briefly but collapse once they exhaust the panel they were built and evaluated on: they cannot, by construction, name genes outside it, whereas our model continues informatively over the full expressed set. On a frozen 1k HVG panel used only as a secondary calibration, a mid-length LoRA model (2048 tokens) approaches ST-Net and BLEEP on panel correlation (within $\sim$0.03 PCC). The result is a generative path from H&E to ranked cell sentences that needs no pre-specified gene panel and can compose with external predictions rather than only replace them.