DeepSpot-M: a multimodal foundation model for transcriptome-wide virtual spatial transcriptomics from histology

Spatial transcriptomics remains costly and low-throughput, limiting it to a small fraction of routine histology and leaving the molecular state of disease unmeasured in most patients. Predicting spatial expression from histology could address this gap, but existing methods are restricted to predefined genes and small cohorts. We present DeepSpot-M, a multimodal foundation model that predicts spatial expression by representing genes with embeddings from foundation models spanning DNA, RNA, proteins, single cells and biomedical text. By reformulating prediction as a query over genes, DeepSpot-M spans the protein-coding transcriptome and predicts genes unseen during training. Trained on a large pan-cancer dataset, it transfers to held-out cancers, outperforming specialised models trained on them, and adapts to new cohorts and single-cell assays from one slide via test-time adaptation. Applied to TCGA, it generates a virtual atlas of 28,664 slides across 32 cancers, recovering a pan-cancer map of malignancy from histology. The same query interface further enables transcriptome restoration, cross-species non-coding RNA inference, in silico variant-effect mapping and natural-language querying. We anticipate DeepSpot-M will provide a scalable foundation for virtual spatial transcriptomics and biomarker discovery.