biostudies-literatureZhang DU.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI)U.S. Department of Health & Human Services | NIH | National Institute of General Medical SciencesU.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (NIGMS)NIA NIH HHSU.S. Department of Health & Human Services | NIH | National Institute on AgingNHLBI NIH HHSU.S. Department of Health & Human Services | NIH | National Heart, Lung, and Blood InstituteU.S. Department of Health & Human Services | NIH | National Eye InstituteNEI NIH HHSU.S. Department of Health & Human Services | NIH | National Cancer InstituteU.S. Department of Health & Human Services | NIH | National Human Genome Research Institute (NHGRI)U.S. Department of Health & Human Services | NIH | National Eye Institute (NEI)NHGRI NIH HHSNCI NIH HHSU.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging)U.S. Department of Health & Human Services | NIH | National Human Genome Research InstituteU.S. Department of Health & Human Services | NIH | National Heart, Lung, and Blood Institute (NHLBI)NIGMS NIH HHS1372-1377https://www.ebi.ac.uk/biostudies/studies/S-EPMC11260191biostudies-literatureUnknown42(9)Spatial 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.Nature biotechnologyInferring super-resolution tissue architecture by integrating spatial transcriptomics with histology.PMC11260191R01CA266280U01CA264583P01AG066597R01EY030192R01 CA266280R01HL150359R01 HL150359R01 HG013185R01GM125301R01 EY030192R01HG013158P01 AG066597R01 GM125301Yang HZhang DSusztak KLee MYYLi MFurth EEWang LHu JFeldman MDXu GXLee EBYan HSchroeder ACho KSfalseInferring super-resolution tissue architecture by integrating spatial transcriptomics with histology.Spatial 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.2024-01-01T00:00:00Z2024 Sep2025-04-03T23:35:49.249Z2025-04-03T23:35:49.249ZS-EPMC112601913816898610.1038/s41587-023-02019-9