We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
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
- 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.
- Subjects
B cells; ANTIGEN receptors; B cell receptors; DEEP learning; IMMUNOLOGIC memory
- Publication
Frontiers in Immunology, 2024, p01
- ISSN
1664-3224
- Publication type
Article
- DOI
10.3389/fimmu.2024.1342285