CBRCell Biology ResearchTBA2529-7627WHIOCE PUBLISHING PTE. LTD.10.18063/CBR.v7i2.1919ArticleVirtualST: Morphology- and Cell-Composition-Conditioned Diffusion for Predicting Spatial Gene Expression from H&E Imageshttps://artdesignp.com/journal/CBR/7/2/10.18063/CBR.v7i2.1919LiangYuping,XuSiwen2026722026-06-26Spatial transcriptomics (ST) measures gene expression while preserving tissue spatial information, but its high cost and limited throughput restrict large-scale application. In contrast, hematoxylin and eosin (H&E)-stained histology images are widely available in routine pathology. We propose VirtualST, a conditional diffusion model for predicting spot-level spatial gene expression from H&E images. The model integrates histological features extracted by a pathology foundation model, local cell-type composition derived from nuclei segmentation, and spatial information from neighboring spots. VirtualST was evaluated on multiple cancer cohorts from HEST-bench and compared with representative histology-to-expression prediction methods. The results showed that VirtualST achieved competitive performance across different cancer types and performed well for representative colorectal cancer marker genes. These findings suggest that VirtualST provides an effective approach for spatial gene-expression prediction from routine histology images.spatial transcriptomics,spatial gene expression prediction,computational pathology,conditional diffusion,cell composition,spatial graph refinement

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