{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Zhang D"],"funding":["U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI)","U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences","U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (NIGMS)","NIA NIH HHS","U.S. Department of Health & Human Services | NIH | National Institute on Aging","NHLBI NIH HHS","U.S. Department of Health & Human Services | NIH | National Heart, Lung, and Blood Institute","U.S. Department of Health & Human Services | NIH | National Eye Institute","NEI NIH HHS","U.S. Department of Health & Human Services | NIH | National Cancer Institute","U.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 HHS","NCI NIH HHS","U.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 Institute","U.S. Department of Health & Human Services | NIH | National Heart, Lung, and Blood Institute (NHLBI)","NIGMS NIH HHS"],"pagination":["1372-1377"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11260191"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["42(9)"],"pubmed_abstract":["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."],"journal":["Nature biotechnology"],"pubmed_title":["Inferring super-resolution tissue architecture by integrating spatial transcriptomics with histology."],"pmcid":["PMC11260191"],"funding_grant_id":["R01CA266280","U01CA264583","P01AG066597","R01EY030192","R01 CA266280","R01HL150359","R01 HL150359","R01 HG013185","R01GM125301","R01 EY030192","R01HG013158","P01 AG066597","R01 GM125301"],"pubmed_authors":["Yang H","Zhang D","Susztak K","Lee MYY","Li M","Furth EE","Wang L","Hu J","Feldman MD","Xu GX","Lee EB","Yan H","Schroeder A","Cho KS"],"additional_accession":[]},"is_claimable":false,"name":"Inferring super-resolution tissue architecture by integrating spatial transcriptomics with histology.","description":"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.","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Sep","modification":"2025-04-03T23:35:49.249Z","creation":"2025-04-03T23:35:49.249Z"},"accession":"S-EPMC11260191","cross_references":{"pubmed":["38168986"],"doi":["10.1038/s41587-023-02019-9"]}}