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Nonetheless, a myriad of methods, especially those utilized in platforms like Visium, often relinquish spatial details owing to intrinsic resolution limitations. In response, we introduce TransformerST, an innovative, unsupervised model anchored in the Transformer architecture, which operates independently of references, thereby ensuring cost-efficiency by circumventing the need for single-cell RNA sequencing. TransformerST not only elevates Visium data from a multicellular level to a single-cell granularity but also showcases adaptability across diverse spatial transcriptomics platforms. By employing a vision transformer-based encoder, it discerns latent image-gene expression co-representations and is further enhanced by spatial correlations, derived from an adaptive graph Transformer module. The sophisticated cross-scale graph network, utilized in super-resolution, significantly boosts the model\u2019s accuracy, unveiling complex structure\u2013functional relationships within histology images. Empirical evaluations validate its adeptness in revealing tissue subtleties at the single-cell scale. Crucially, TransformerST adeptly navigates through image-gene co-representation, maximizing the synergistic utility of gene expression and histology images, thereby emerging as a pioneering tool in spatial transcriptomics. It not only enhances resolution to a single-cell level but also introduces a novel approach that optimally utilizes histology images alongside gene expression, providing a refined lens for investigating spatial transcriptomics.<\/jats:p>","DOI":"10.1093\/bib\/bbae052","type":"journal-article","created":{"date-parts":[[2024,3,4]],"date-time":"2024-03-04T13:34:25Z","timestamp":1709559265000},"source":"Crossref","is-referenced-by-count":38,"title":["Innovative super-resolution in spatial transcriptomics: a transformer model exploiting histology images and spatial gene expression"],"prefix":"10.1093","volume":"25","author":[{"given":"Chongyue","family":"Zhao","sequence":"first","affiliation":[{"name":"Department of Pediatrics, University of Pittsburgh , Pittsburgh, 15224, Pennsylvania , USA"}]},{"given":"Zhongli","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Pediatrics, University of Pittsburgh , Pittsburgh, 15224, Pennsylvania , USA"},{"name":"School of Medicine, Tsinghua University , Beijing, 100084, Beijing , China"}]},{"given":"Xinjun","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center , New York, 10065, New York , USA"}]},{"given":"Shiyue","family":"Tao","sequence":"additional","affiliation":[{"name":"Department of Pediatrics, University of Pittsburgh , Pittsburgh, 15224, Pennsylvania , USA"},{"name":"Department of Biostatistics, University of Pittsburgh , Pittsburgh, 15261, Pennsylvania , USA"}]},{"given":"William A","family":"MacDonald","sequence":"additional","affiliation":[{"name":"Health Sciences Sequencing Core at UPMC Children\u2019s Hospital of Pittsburgh , Department of Pediatrics , , Pittsburgh, 15224, Pennsylvania , USA"},{"name":"University of Pittsburgh , Department of Pediatrics , , Pittsburgh, 15224, Pennsylvania , USA"}]},{"given":"Kun","family":"He","sequence":"additional","affiliation":[{"name":"Division of Pediatric Rheumatology , Department of Pediatrics , , Pittsburgh, 15224, Pennsylvania , USA"},{"name":"University of Pittsburgh , Department of Pediatrics , , Pittsburgh, 15224, Pennsylvania , USA"}]},{"given":"Amanda C","family":"Poholek","sequence":"additional","affiliation":[{"name":"Division of Pediatric Rheumatology , Department of Pediatrics , , Pittsburgh, 15224, Pennsylvania , USA"},{"name":"University of Pittsburgh , Department of Pediatrics , , Pittsburgh, 15224, Pennsylvania , USA"},{"name":"Department of Immunology , University of Pittsburgh , Pittsburgh, 15224, Pennsylvania , USA"},{"name":"Health Sciences Sequencing Core at UPMC Children\u2019s Hospital of Pittsburgh , Department of Pediatrics , , Pittsburgh, 15224, Pennsylvania , USA"},{"name":"University of Pittsburgh , Department of Pediatrics , , Pittsburgh, 15224, Pennsylvania , USA"}]},{"given":"Kong","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Medicine, University of Pittsburgh , Pittsburgh, 15213, Pennsylvania , USA"}]},{"given":"Heng","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Maryland , College Park, 20742, Maryland , USA"}]},{"given":"Wei","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Pediatrics, University of Pittsburgh , Pittsburgh, 15224, Pennsylvania , USA"},{"name":"Department of Biostatistics, University of Pittsburgh , Pittsburgh, 15261, Pennsylvania , USA"}]}],"member":"286","published-online":{"date-parts":[[2024,3,2]]},"reference":[{"issue":"3","key":"2024030414142792800_ref1","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1038\/s41587-019-0392-8","article-title":"Integrating microarray-based spatial transcriptomics and single-cell rna-seq reveals tissue architecture in pancreatic ductal adenocarcinomas","volume":"38","author":"Moncada","year":"2020","journal-title":"Nat 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