Identification of B cell subsets based on antigen receptor sequences using deep learning

Identification of B cell subsets based on antigen receptor sequences using deep learning

Blog Article

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 MELATONIN 5MG GUMMIES 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 resistance wheel 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.