AUTHOR=Lee Hyunho , Shin Kyoungseob , Lee Yongju , Lee Soobin , Lee Seungyoun , Lee Eunjae , Kim Seung Woo , Shin Ha Young , Kim Jong Hoon , Chung Junho , Kwon Sunghoon TITLE=Identification of B cell subsets based on antigen receptor sequences using deep learning JOURNAL=Frontiers in Immunology VOLUME=15 YEAR=2024 URL=https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2024.1342285 DOI=10.3389/fimmu.2024.1342285 ISSN=1664-3224 ABSTRACT=

B cell receptors (BCRs) denote antigen specificity, while corresponding cell subsets indicate B cell functionality. Since each B cell uniquely encodes this combination, physical isolation and subsequent processing of individual B cells become indispensable to identify both attributes. However, this approach accompanies high costs and inevitable information loss, hindering high-throughput investigation of B cell populations. Here, we present BCR-SORT, a deep learning model that predicts cell subsets from their corresponding BCR sequences by leveraging B cell activation and maturation signatures encoded within BCR sequences. Subsequently, BCR-SORT is demonstrated to improve reconstruction of BCR phylogenetic trees, and reproduce results consistent with those verified using physical isolation-based methods or prior knowledge. Notably, when applied to BCR sequences from COVID-19 vaccine recipients, it revealed inter-individual heterogeneity of evolutionary trajectories towards Omicron-binding memory B cells. Overall, BCR-SORT offers great potential to improve our understanding of B cell responses.