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Identification of B cell subsets based on antigen receptor sequences using deep learning

Authors
 Hyunho Lee  ;  Kyoungseob Shin  ;  Yongju Lee  ;  Soobin Lee  ;  Seungyoun Lee  ;  Eunjae Lee  ;  Seung Woo Kim  ;  Ha Young Shin  ;  Jong Hoon Kim  ;  Junho Chung  ;  Sunghoon Kwon 
Citation
 FRONTIERS IN IMMUNOLOGY, Vol.15 : 1342285, 2024-03 
Journal Title
FRONTIERS IN IMMUNOLOGY
Issue Date
2024-03
MeSH
B-Lymphocyte Subsets* ; COVID-19 Vaccines ; Deep Learning* ; Humans ; Phylogeny ; Receptors, Antigen, B-Cell / genetics
Keywords
B cell phylogenetic inference ; B cell receptor ; B cell subset ; antibody repertoire ; deep learning ; integrated gradients ; next-generation sequencing ; somatic hypermutation
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.
Files in This Item:
T202402404.pdf Download
DOI
10.3389/fimmu.2024.1342285
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Neurology (신경과학교실) > 1. Journal Papers
Yonsei Authors
Kim, Seung Woo(김승우) ORCID logo https://orcid.org/0000-0002-5621-0811
Shin, Ha Young(신하영) ORCID logo https://orcid.org/0000-0002-4408-8265
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/199120
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