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Here, we present BiGCN, a novel method that explicitly integrates transcriptomic and BCR data at single\u2010cell resolution. By uniting these two modalities within a unified embedding using a graph convolutional network (GCN) framework, BiGCN enables high\u2010fidelity characterization of B cell states and clonal relationships. Applying BiGCN across a wide spectrum of paired single \u2010 cell RNA sequencing (scRNA\u2010seq) and single \u2010cell BCR sequencing (scBCR\u2010seq) datasets, including infectious disease, autoimmunity, and a comprehensive pan\u2010cancer atlas, we demonstrate that BiGCN markedly outperforms existing approaches in information integration, functional annotation, and biological insight. BiGCN not only recapitulates the trajectory from B cell maturation to immunoglobulin class switching but also discovers intermediate B cell subtypes and reveals functional states that are otherwise obscured by conventional analyses. Our results establish BiGCN as a versatile and robust platform for B cells research, accelerating discoveries in immunology, cancer biology, and systems medicine.<\/jats:p>","DOI":"10.1002\/smtd.202501919","type":"journal-article","created":{"date-parts":[[2026,2,23]],"date-time":"2026-02-23T06:53:59Z","timestamp":1771829639000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["BiGCN Learns B Cell Functional States by Integrating Single\u2010Cell Transcriptomes and BCR Repertoires"],"prefix":"10.1002","volume":"10","author":[{"given":"Xiao","family":"Liu","sequence":"first","affiliation":[{"name":"State Key Laboratory of Systems Medicine for Cancer Shanghai Cancer Institute  Shanghai China"},{"name":"Shanghai Key Laboratory of Cancer System Regulation and Clinical Translation Shanghai Jiading District Central Hospital  Shanghai 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