Zhang, DaiweiSchroeder, AmeliaYan, HanyingYang, HaochenHu, JianLee, Michelle Y. Y.Cho, Kyung S.Susztak, KatalinXu, George X.Feldman, Michael D.Lee, Edward B.Furth, Emma E.Wang, LinghuaLi, Mingyao2025-04-182025-04-182024Zhang D, Schroeder A, Yan H, et al. Inferring super-resolution tissue architecture by integrating spatial transcriptomics with histology. Nat Biotechnol. 2024;42(9):1372-1377. doi:10.1038/s41587-023-02019-9https://hdl.handle.net/1805/47163Spatial transcriptomics (ST) has demonstrated enormous potential for generating intricate molecular maps of cells within tissues. Here we present iStar, a method based on hierarchical image feature extraction that integrates ST data and high-resolution histology images to predict spatial gene expression with super-resolution. Our method enhances gene expression resolution to near-single-cell levels in ST and enables gene expression prediction in tissue sections where only histology images are available.en-USPublisher PolicySpatial transcriptomicsSuper-resolutionHistologyMachine learningArticle