Subcellular Imaging of the Entire Protein Coding Human Transcriptome on FFPE Tissue Using Spatial Molecular Imaging

Subcellular Imaging of the Entire Protein Coding Human Transcriptome on FFPE Tissue Using Spatial Molecular Imaging | 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 Article Subcellular Imaging of the Entire Protein Coding Human Transcriptome on FFPE Tissue Using Spatial Molecular Imaging Joseph Beechem, Rustem Khafizov, Erin Piazza, Yi Cui, Michael Patrick, and 14 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8210178/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract For the very first time, subcellular imaging of the entire human protein coding transcriptome has been accomplished. Using a fluorescence in situ hybridization approach, we developed 37,968 imaging probes to detect 18,934 mRNA transcripts without use of enzyme-amplification and achieve subcellular resolution (38 nm +/- 11 nm) of transcripts. Data is presented for formalin-fixed paraffin-embedded (FFPE) tissues (hippocampus region of brain, skin, colon, breast, pancreas, kidney) that average (across all tissues) over 2,000 transcripts per cell and over 1,100 unique genes per cell (highest transcript number/diversity ever imaged). A total of 5.5 billion transcripts measured in 2.7 million single cells. From one 5 µm thick FFPE section, this method can measure all subcellular RNA localizations, explore every protein-based biological pathway, every ligand-receptor interaction, analyze pseudotime RNA trajectories, and predict spatial copy number variations. Unbiased spatial molecular discovery on archival human tissue empowers biological discovery via reverse translation, and new capabilities for drug development, companion diagnostics, and therapeutic intervention. Whole transcriptome sub-cellular imaging establishes a new genomic standard for single cell and spatial analysis. Biological sciences/Biological techniques/Imaging/Molecular imaging Biological sciences/Biological techniques Full Text Additional Declarations Yes there is potential Competing Interest. An employee of Bruker Spatial Biology Supplementary Files WTXTargetListTableS1Ver8jmbfinalwithnegatives.xlsx Supplemental Table 1 NGA71137SupplementalTableassentJan302026.xlsx Supplementary Table Cite Share Download PDF Status: Under Review 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. 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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-8210178","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":590376461,"identity":"9408b5d3-1cce-4b29-8323-d011b9597d17","order_by":0,"name":"Joseph 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