Unlocking single-cell level and whole-slide insights in spatial transcriptomics with PanoSpace

Unlocking single-cell level and whole-slide insights in spatial transcriptomics with PanoSpace | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Unlocking single-cell level and whole-slide insights in spatial transcriptomics with PanoSpace Hui-Feng He, Pai Peng, Shi-Tong Yang, Meng-Guo Wang, Luonan Chen, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5674707/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Spatial transcriptomics has significantly advanced our ability to map gene expression within native tissue contexts. However, current low-resolution technologies are constrained by limited spatial resolution and tissue coverage. We present PanoSpace, a novel computational framework that integrates low-resolution spatial transcriptomics data with high-resolution histological images and matched single-cell RNA sequencing references. PanoSpace achieves comprehensive single-cell level and whole-tissue analysis by accurately inferring spatial localization, cell type, and gene expression for all cells across entire tissue slides. It also facilitates exploration of intra-cell-type heterogeneity and cell-cell interactions within spatial contexts. Application of PanoSpace to breast, prostate, and cervical cancer tissues reveals detailed cell-type distributions and gene expression patterns with unprecedented resolution and coverage. Furthermore, through analysis of interactions with cancer-associated fibroblasts, PanoSpace uncovers intra-cell-type heterogeneity and provides novel insights into tumor microenvironment dynamics. These findings highlight PanoSpace as a powerful tool for offering insights beyond the reach of existing technologies and computational methods. Bioinformatics Computational Biology Artificial Intelligence and Machine Learning Full Text Additional Declarations The authors declare no competing interests. Supplementary Files supplementary.pdf Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5674707","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":392309037,"identity":"92cf699d-240d-4675-a245-fb57b35f1c06","order_by":0,"name":"Hui-Feng He","email":"","orcid":"https://orcid.org/0009-0005-7700-2615","institution":"Central China Normal University","correspondingAuthor":false,"prefix":"","firstName":"Hui-Feng","middleName":"","lastName":"He","suffix":""},{"id":392309038,"identity":"4c3e58be-ebc1-4de2-9a81-1a8df855312b","order_by":1,"name":"Pai Peng","email":"","orcid":"","institution":"Jianghan 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