Interpreting the B-cell receptor repertoire with single-cell gene expression using Benisse

Ze Zhang, Woo Yong Chang, Kaiwen Wang, Yuqiu Yang, Xinlei Wang, Chen Yao, Tuoqi Wu, Li Wang, Tao Wang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)

Abstract

B-cell receptors (BCRs) are a crucial player in the development and activation of B cells, and their mature forms are secreted as antibodies, which execute functions such as the neutralization of invading pathogens. All current analytical approaches for BCRs solely investigate the BCR sequences and ignore their correlations with the transcriptomics of the B cells, yielding conclusions of unknown functional relevance regarding the roles of BCRs and B cells, and could generate biased interpretation. Many single-cell RNA-sequencing (scRNA-seq) techniques can now capture both the gene expression and BCR of each B cell, which could potentially address this issue. Here, we investigated 43,938 B cells from 13 scRNA-seq datasets with matched scBCR sequencing, and we observed an association between the BCRs and the B cells’ transcriptomics. Motivated by this, we developed the Benisse model (BCR embedding graphical network informed by scRNA-seq) to provide refined analyses of BCRs guided by single-cell gene expression. Benisse revealed a gradient of B-cell activation along BCR trajectories. We discovered a stronger coupling between BCRs and B-cell gene expression during COVID-19 infections. We found that BCRs form a directed pattern of continuous and linear evolution to achieve the highest antigen targeting efficiency, compared with the convergent evolution pattern of T-cell receptors. Overall, a simultaneous digestion of the BCR and gene expression of B cells, viewed through the lens of Benisse, will lead to a more insightful interpretation of the functional relevance of the BCR repertoire in different biological contexts.

Original languageEnglish
Pages (from-to)596-604
Number of pages9
JournalNature Machine Intelligence
Volume4
Issue number6
DOIs
Publication statusPublished - Jun 2022
Externally publishedYes

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