Integrated single-cell analyses of affinity-tested B cells enable the identification of a gene signature to predict antibody affinity.

Advancements in single-cell technologies and deep sequencing have expanded the B cell repertoire available for antibody discovery. However, selecting the highest-affinity antibodies from many sequences remains challenging, reflecting our incomplete understanding of the mechanisms sustaining affinity maturation and associated molecular markers. Here, we generated datasets of antigen-specific B cells after mouse immunization and reanalyzed public data to identify "High Signature" (HS), a transcriptomic signature predictive of high-affinity antibodies. HS was derived through differential expression analyses and machine learning by integrating antibody sequences, gene expression, and affinity measurements of expressed antibodies. HS enabled sub-nanomolar-affinity antibody selection without prior sequence analysis in de novo immunization campaigns. HS-expressing B cells were 3 times more likely to yield high-affinity antibodies than randomly picked cells. HS demonstrated translatability to two human PBMC datasets from COVID patients, resulting in enriched high-affinity antibody selection, highlighting its antibody discovery potential across species. A record of this paper's transparent peer review process is included in the supplemental information.