Deep Learning-Driven TCRβ Repertoire Analysis Enhances Diagnosis and Enables Mining of Immunological Biomarkers in Systemic Lupus Erythematosus

Deep Learning-Driven TCRβ Repertoire Analysis Enhances Diagnosis and Enables Mining of Immunological Biomarkers in Systemic Lupus Erythematosus | 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 Deep Learning-Driven TCR β Repertoire Analysis Enhances Diagnosis and Enables Mining of Immunological Biomarkers in Systemic Lupus Erythematosus Tongfei SHEN, Yifei Sheng, Wan Nie, Shuo Yang, Kaiqi Li, Ziwei Ma, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6327711/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 31 Oct, 2025 Read the published version in BioData Mining → Version 1 posted 11 You are reading this latest preprint version Abstract Background Systemic Lupus Erythematosus (SLE) is a complex autoimmune disorder characterized by abnormal T-cell responses, which significantly contribute to the disease’s immune pathology. The Complementarity Determining Region 3 (CDR3) of the TCRβ chain is pivotal for T-cell specificity, thereby positioning it as a promising target for enhancing diagnostic accuracy and gaining deeper mechanistic insights into SLE. To address these diagnostic limitations in SLE, our team developed DeepTAPE, a deep learning-based diagnostic framework that utilizes CDR3 sequences to achieve robust diagnostic performance for SLE. Results Building upon the foundation established by DeepTAPE, we devised a novel diagnostic approach that effectively integrates a TCR classifier to quantify SLE disease activity. Furthermore, this methodology employs advanced deep learning models for the bio-mining of valuable and efficient preliminary diagnostic biomarkers. As a result, this approach generates an autoimmune risk score (ARS) indicative of SLE probability. Notably, this ARS metric exhibited a strong correlation with disease activity, functioning as a quantitative clinical marker that complements traditional indices such as the SLE Disease Activity Index (SLEDAI). In addition, through a comprehensive analysis of immune repertoire data, we identified SLE-specific amino acid motifs within the CDR3 sequences, including critical 3-mer and gapped-mer oligopeptides. These motifs facilitated rapid and accurate preliminary screening for SLE, achieving an area under the curve (AUC) of 0.908, thereby significantly outperforming other candidate biomarkers. Moreover, our model revealed potential SLE-associated antigens and genes, such as CD109 and INS, which provide new insights into the immunological mechanisms underlying the disease. Conclusion This study highlights the potential of DeepTAPE not only as a diagnostic tool but also as a platform for biomarker discovery in SLE. By deepening our understanding of the immunological characteristics and mechanisms associated with SLE, this work lays a solid foundation for advancing targeted therapies and personalized medicine in autoimmune diseases. Consequently, our findings may pave the way for improved patient outcomes and more effective treatment strategies in the management of SLE. systemic lupus erythematosus deep learning TCRβ CDR3 sequence diagnosis of autoimmune diseases Full Text Additional Declarations No competing interests reported. Supplementary Files Supplementary.pdf Cite Share Download PDF Status: Published Journal Publication published 31 Oct, 2025 Read the published version in BioData Mining → Version 1 posted Editorial decision: Revision requested 08 Jul, 2025 Reviews received at journal 05 Jul, 2025 Reviews received at journal 25 Jun, 2025 Reviewers agreed at journal 25 Jun, 2025 Reviews received at journal 25 Jun, 2025 Reviewers agreed at journal 23 Jun, 2025 Reviewers agreed at journal 19 Jun, 2025 Reviewers invited by journal 19 Jun, 2025 Editor assigned by journal 01 Apr, 2025 Submission checks completed at journal 01 Apr, 2025 First submitted to journal 28 Mar, 2025 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. 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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-6327711","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":447788954,"identity":"58ea27fb-e274-4a79-aaad-c41a1af112a8","order_by":0,"name":"Tongfei SHEN","email":"","orcid":"","institution":"City University of Hong Kong","correspondingAuthor":false,"prefix":"","firstName":"Tongfei","middleName":"","lastName":"SHEN","suffix":""},{"id":447788955,"identity":"3c7c244b-4ef4-4d8a-833d-9e28f74b7db5","order_by":1,"name":"Yifei Sheng","email":"","orcid":"","institution":"City University of Hong 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Erythematosus\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"biodata-mining","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bidm","sideBox":"Learn more about [BioData Mining](http://biodatamining.biomedcentral.com/)","snPcode":"13040","submissionUrl":"https://submission.nature.com/new-submission/13040/3","title":"BioData Mining","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"systemic lupus erythematosus, deep learning, TCRβ CDR3 sequence, diagnosis of autoimmune diseases","lastPublishedDoi":"10.21203/rs.3.rs-6327711/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6327711/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground Systemic Lupus Erythematosus (SLE) is a complex autoimmune disorder characterized by abnormal T-cell responses, which significantly contribute to the disease’s immune pathology. The Complementarity Determining Region 3 (CDR3) of the TCRβ chain is pivotal for T-cell specificity, thereby positioning it as a promising target for enhancing diagnostic accuracy and gaining deeper mechanistic insights into SLE. To address these diagnostic limitations in SLE, our team developed DeepTAPE, a deep learning-based diagnostic framework that utilizes CDR3 sequences to achieve robust diagnostic performance for SLE.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eResults Building upon the foundation established by DeepTAPE, we devised a novel diagnostic approach that effectively integrates a TCR classifier to quantify SLE disease activity. Furthermore, this methodology employs advanced deep learning models for the bio-mining of valuable and efficient preliminary diagnostic biomarkers. As a result, this approach generates an autoimmune risk score (ARS) indicative of SLE probability. Notably, this ARS metric exhibited a strong correlation with disease activity, functioning as a quantitative clinical marker that complements traditional indices such as the SLE Disease Activity Index (SLEDAI).\u0026nbsp;In addition, through a comprehensive analysis of immune repertoire data, we identified SLE-specific amino acid motifs within the CDR3 sequences, including critical 3-mer and gapped-mer oligopeptides. These motifs facilitated rapid and accurate preliminary screening for SLE, achieving an area under the curve (AUC) of 0.908, thereby significantly outperforming other candidate biomarkers. Moreover, our model revealed potential SLE-associated antigens and genes, such as CD109 and INS, which provide new insights into the immunological mechanisms underlying the disease.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConclusion This study highlights the potential of DeepTAPE not only as a diagnostic tool but also as a platform for biomarker discovery in SLE. By deepening our understanding of the immunological characteristics and mechanisms associated with SLE, this work lays a solid foundation for advancing targeted therapies and personalized medicine in autoimmune diseases. Consequently, our findings may pave the way for improved patient outcomes and more effective treatment strategies in the management of SLE.\u003c/p\u003e","manuscriptTitle":"Deep Learning-Driven TCRβ Repertoire Analysis Enhances Diagnosis and Enables Mining of Immunological Biomarkers in Systemic Lupus Erythematosus","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-02 12:36:41","doi":"10.21203/rs.3.rs-6327711/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-07-08T20:07:19+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-05T05:45:15+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-25T20:31:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"323236140804782052644175635725595388436","date":"2025-06-25T15:17:10+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-25T12:13:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"177604557224751300640502713402050152212","date":"2025-06-23T09:26:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"254235438945628302123391506233418573263","date":"2025-06-19T16:13:32+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-19T16:05:00+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-01T04:37:33+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-01T04:34:13+00:00","index":"","fulltext":""},{"type":"submitted","content":"BioData Mining","date":"2025-03-28T11:15:11+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"biodata-mining","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bidm","sideBox":"Learn more about [BioData Mining](http://biodatamining.biomedcentral.com/)","snPcode":"13040","submissionUrl":"https://submission.nature.com/new-submission/13040/3","title":"BioData Mining","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d53bdb55-cdff-46c7-86bf-589072adc3b7","owner":[],"postedDate":"May 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-11-03T16:06:34+00:00","versionOfRecord":{"articleIdentity":"rs-6327711","link":"https://doi.org/10.1186/s13040-025-00490-5","journal":{"identity":"biodata-mining","isVorOnly":false,"title":"BioData Mining"},"publishedOn":"2025-10-31 15:57:46","publishedOnDateReadable":"October 31st, 2025"},"versionCreatedAt":"2025-05-02 12:36:41","video":"","vorDoi":"10.1186/s13040-025-00490-5","vorDoiUrl":"https://doi.org/10.1186/s13040-025-00490-5","workflowStages":[]},"version":"v1","identity":"rs-6327711","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6327711","identity":"rs-6327711","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}