Identification of B cell subsets based on antigen receptor sequences using deep learning
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..
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Preprint| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
bioRxiv.org - (2024) vom: 06. Mai Zur Gesamtaufnahme - year:2024 |
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| Sprache: |
Englisch |
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| Beteiligte Personen: |
Lee, Hyunho [VerfasserIn] |
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| doi: |
10.1101/2024.02.06.579098 |
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| PPN (Katalog-ID): |
XBI042485355 |
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| 245 | 1 | 0 | |a Identification of B cell subsets based on antigen receptor sequences using deep learning |
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| 520 | |a 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. | ||
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| 700 | 1 | |a Lee, Yongju |e verfasserin |4 aut | |
| 700 | 1 | |a Lee, Soobin |e verfasserin |4 aut | |
| 700 | 1 | |a Lee, Seungyoun |e verfasserin |4 aut | |
| 700 | 1 | |a Lee, Eunjae |e verfasserin |4 aut | |
| 700 | 1 | |a Kim, Seung Woo |e verfasserin |4 aut | |
| 700 | 1 | |a Shin, Ha Young |e verfasserin |4 aut | |
| 700 | 1 | |a Kim, Jong Hoon |e verfasserin |4 aut | |
| 700 | 1 | |a Chung, Junho |e verfasserin |4 aut | |
| 700 | 1 | |a Kwon, Sunghoon |e verfasserin |4 aut | |
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