| Autor: |
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 |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
Frontiers in Immunology, Vol 15 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
1664-3224 |
| DOI: |
10.3389/fimmu.2024.1342285 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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