A Computational Approach for Cell Characterization Without Prior Isolation: Advances in scRNA-seq

A Computational Approach for Cell Characterization Without Prior Isolation: Advances in scRNA-seq | 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 A Computational Approach for Cell Characterization Without Prior Isolation: Advances in scRNA-seq Bruno Ferreira Nunes, Marco Zanata Alves, Tarcio Braga This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7094457/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 Single-cell RNA sequencing (scRNA-seq) enables high-resolution analysis of cellular heterogeneity, but traditional cell isolation methods like flow cytometry and laser microdissection often suffer from limitations in efficiency, viability, and bias. To overcome these challenges, computational tissue deconvolution approaches have emerged as effective alternatives. In this work, we introduce a high-performance computational pipeline for scRNA-seq data analysis that identifies and segregates cell populations based on marker gene expression. Our method incorporates advanced preprocessing, normalization, and clustering techniques, optimized for scalability and reproducibility in high-performance computing (HPC) environments. Compared to related tools, our pipeline offers enhanced adaptability across diverse datasets and experimental settings. We validated its performance using zebrafish ventricular tissue post-injury, effectively identifying key regenerative cell types such as immune cells, including macrophages. This approach supports in-depth biological discovery without prior physical cell separation and expands the potential of scRNA-seq applications in regenerative biology, immunology, and single-cell transcriptomics. Biological sciences/Immunology/Cell death and immune response Biological sciences/Genetics/Gene expression Biological sciences/Genetics/Genetic markers Biological sciences/Genetics/Gene regulation Full Text Additional Declarations There is NO Competing Interest. 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-7094457","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":483696050,"identity":"0170837f-f0ee-4989-a5d0-1e099b593056","order_by":0,"name":"Bruno Ferreira Nunes","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvUlEQVRIiWNgGAWjYBACAwglwcDA3sDwAchKIEELzwHGGaRoAelKIFKLufQZswcf/lhE80u+MWwu3MOQZ95AQItlX4654cw2idyZs3MMm2c8YyiWOUDIYWd4zKR5GyRyN9zOMX/Mc4AhcQYhh4G18PyRyN1/84xhMwla2IC2SPAQqcWyh61MEuSXGWfSCptnHJAoliCkxZyHeZvEhz91uf3thzc2FxywySOoBQUwg+OUVC2jYBSMglEwCjABACTHO/uzyOrKAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0009-0007-3240-2345","institution":"Federal University of Paraná","correspondingAuthor":true,"prefix":"","firstName":"Bruno","middleName":"Ferreira","lastName":"Nunes","suffix":""},{"id":483696051,"identity":"65655d61-fc46-4a0a-a5fb-37f269245d45","order_by":1,"name":"Marco Zanata Alves","email":"","orcid":"","institution":"Federal University of Paraná","correspondingAuthor":false,"prefix":"","firstName":"Marco","middleName":"Zanata","lastName":"Alves","suffix":""},{"id":483696052,"identity":"6992a5ed-5d57-411e-967c-bcd3726a38a9","order_by":2,"name":"Tarcio Braga","email":"","orcid":"","institution":"Federal University of Paraná","correspondingAuthor":false,"prefix":"","firstName":"Tarcio","middleName":"","lastName":"Braga","suffix":""}],"badges":[],"createdAt":"2025-07-10 15:22:10","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-7094457/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7094457/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":86937027,"identity":"4627d054-0fae-43fe-9bee-961c54b6f5e0","added_by":"auto","created_at":"2025-07-17 11:03:18","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":445393,"visible":true,"origin":"","legend":"Article File","description":"","filename":"paper4.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7094457/v1_covered_acc6f9f9-3610-4098-a698-8fd72db873fc.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"A Computational Approach for Cell Characterization Without Prior Isolation: Advances in scRNA-seq","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7094457/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7094457/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Single-cell RNA sequencing (scRNA-seq) enables high-resolution analysis of cellular heterogeneity, but traditional cell isolation methods like flow cytometry and laser microdissection often suffer from limitations in efficiency, viability, and bias. 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