Scalable Generation of Spatial Transcriptomics from Histology Images via Whole-Slide Flow Matching

Spatial transcriptomics (ST) has emerged as a powerful technology for bridging histology imaging with gene expression profiling. However, its application has been limited by low throughput and the need for specialized experimental facilities. Prior works sought to predict ST from whole-slide histology images to accelerate this process, but they suffer from two major limitations. First, they do not explicitly model cell-cell interaction as they factorize the joint distribution of whole-slide ST data and predict the gene expression of each spot independently. Second, their encoders struggle with memory constraints due to the large number of spots (often exceeding 10,000) in typical ST datasets. Herein, we propose STFlow, a flow matching generative model that considers cell-cell interaction by modeling the joint distribution of gene expression of an entire slide. It also employs an efficient slide-level encoder with local spatial attention, enabling whole-slide processing without excessive memory overhead. On the recently curated HEST-1k and STImage-1K4M benchmarks, STFlow substantially outperforms state-of-the-art baselines and achieves over 18% relative improvements over the pathology foundation models.

Understanding how genes are active in different parts of a tissue is important for learning more about diseases like cancer. In this study, we developed a new computational model, STFlow, that can predict gene activity using images of tissue samples. The model works step by step, gradually improving its predictions to be more accurate. This approach is both efficient and effective, helping researchers better identify important biological markers that may guide diagnosis or treatment.