Evaluating predictive patterns of antigen-specific B cells by single-cell transcriptome and antibody repertoire sequencing
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Date
2024
Publication Type
Journal Article
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yes
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Abstract
The field of antibody discovery typically involves extensive experimental screening of B cells from immunized animals. Machine learning (ML)-guided prediction of antigen-specific B cells could accelerate this process but requires sufficient training data with antigen-specificity labeling. Here, we introduce a dataset of single-cell transcriptome and antibody repertoire sequencing of B cells from immunized mice, which are labeled as antigen specific or non-specific through experimental selections. We identify gene expression patterns associated with antigen specificity by differential gene expression analysis and assess their antibody sequence diversity. Subsequently, we benchmark various ML models, both linear and non-linear, trained on different combinations of gene expression and antibody repertoire features. Additionally, we assess transfer learning using features from general and antibody-specific protein language models (PLMs). Our findings show that gene expression-based models outperform sequence-based models for antigen-specificity predictions, highlighting a promising avenue for computationally guided antibody discovery.
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published
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Journal / series
Volume
15 (12)
Pages / Article No.
1295 - 130300000
Publisher
Cell Press
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Edition / version
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Software
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Date collected
Date created
Subject
B cell immune response; antibody repertoire sequencing; single-cell transcriptome sequencing; machine learning for antibody discovery; single-cell sequencing dataset; antigen-specificity prediction; antigen-specific B cells
Organisational unit
03952 - Reddy, Sai / Reddy, Sai
Notes
Funding
197941 - Single-cell profiling of antibody repertoires and transcriptomes from B cells to determine the relationship with antigen-specificity and aging (SNF)