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
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.