Deciphering cellular context for efficient and cell type-specific CRISPR-Cas13d gRNA design using in vivo RNA structure and deep learning

Deciphering cellular context for efficient and cell type-specific CRISPR-Cas13d gRNA design using in vivo RNA structure and deep learning | 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 Deciphering cellular context for efficient and cell type-specific CRISPR-Cas13d gRNA design using in vivo RNA structure and deep learning Lei Sun, Suiru Lu, Jindong Sun, Chengqian Wang, Yongkang Tang, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7184055/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 The efficacy and tissue specificity of RNA therapeutics are critical for clinical translation. Here, by large-scale profiling of the dynamic RNA structurome across four cell lines, we systematically characterized the impact of in vivo target RNA structure and RNA-protein interactions on CRISPR/Cas13d gRNA activity. We identified the structural patterns of high-efficacy gRNA targets and observed that structural differences can lead to variations in efficacy across different cellular contexts. By stabilizing single-stranded structure, RNA-binding proteins also enhanced gRNA efficacy. Leveraging this cell context information, along with approximately 290,000 RfxCas13d screening data, we developed SCALPEL, a deep learning model that predicts gRNA performance across various cellular environments. SCALPEL outperforms existing state-of-the-art models, and most importantly, it enables cell type-specific predictions of gRNA activity. Validation screens across multiple cell lines demonstrate that cellular context significantly influences gRNA performance, even for identical targeting sequences, underscoring the feasibility of cell type-specific knockdown by targeting structural dynamic regions. SCALPEL can also facilitate designing highly efficient virus-targeting gRNAs and gRNAs that robustly knockdown maternal transcripts essential for early zebrafish development. Our method offers a novel approach to develop context-specific gRNAs, with potential to advance tissue- or organ-specific RNA therapies. Biological sciences/Biotechnology Biological sciences/Computational biology and bioinformatics Full Text Additional Declarations There is NO Competing Interest. Animal Ethics Statement: All experiments adhered to the ARRIVE guidelines and were approved by the Ethics Committee for Animal Research of the School of Life Sciences at Shandong University (permit number SYDWLL-2023-069). Supplementary Files TableS2QualitycontroloficSHAPEsequencingdata.xlsx Table S2_Quality control of icSHAPE sequencing data TableS7Oligoinformation.xlsx Table S7_Oligo information TableS6ValidationscreengRNAdepletionandgenedepletion.xlsx Table S6_ Validation screen gRNA depletion and gene depletion TableS1SCALPELgRNAdatasource.xlsx Table S1_SCALPEL gRNA data source TableS4ValidationscreengRNAannotationsofA375andHelacelllines.xlsx Table S4_Validation screen gRNA annotations of A375 and Hela cell lines TableS5ValidationscreennormalizedcounttableofA375andHelacelllines.xlsx Table S5_Validation screen normalized count table of A375 and Hela cell lines Supplementaryfigures.pdf Supplementary_figures TableS3ThelocationanddifferenceofRNAstructuralvariableregionsbetweeneachtwocelllines.xlsx Table S3_The location and difference of RNA structural variable regions between each two cell lines 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-7184055","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":503118490,"identity":"3cee2024-fa18-4cf2-a28e-9ad10ca20219","order_by":0,"name":"Lei 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