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

Chirichella, M.; Ratcliff, M.; Gu, S.; Miragaia, R.; Sammito, M.; Cutano, V.; Cohen, S.; Angeletti, D.; Romero-Ros, X.; Schofield, D. J.

doi:10.1101/2025.01.15.633143

bioRxiv · 10.1101/2025.01.15.633143

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

Chirichella, M.·Ratcliff, M.·Gu, S.·Miragaia, R.·Sammito, M.·Cutano, V.·Cohen, S.·Angeletti, D.·Romero-Ros, X.·Schofield, D. J.

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Abstract

Advancements in single-cell technologies and deep sequencing have revealed the vast B-cell repertoire arising from immunisation, enhancing the number of antibodies available for testing. However, selecting the highest affinity antibodies from many sequences is not a straightforward feat, as mechanisms sustaining affinity maturation and related markers remain under-studied. Here, we generated datasets of antigen-specific B-cells after mouse immunisation as well as re-analysed public data to identify a novel transcriptomic signature, "High Signature" (HS), with predictive power for high-affinity antibodies. HS, derived by integrating antibody sequences, gene expression, and affinity measurements, enabled sub-nanomolar affinity antibody selection without sequence pre-analysis. Notably, HS-expressing B-cells were 2.5 times more likely to yield high-affinity antibodies than randomly picked cells. Mechanistically, we identified RUVBL2, an AAA+ ATPase, as a primary HS modulator, suggesting its involvement in affinity maturation. Furthermore, HS applied to human PBMC data enriched high-affinity antibody expression, underscoring its potential in antibody discovery.

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Chirichella, M., Ratcliff, M., Gu, S., Miragaia, R., Sammito, M., Cutano, V., Cohen, S., Angeletti, D., Romero-Ros, X., Schofield, D. J.. 2025-01-17. Integrated single-cell analyses of affinity-tested B-cells enable the identification of a gene signature to predict antibody affinity.. https://doi.org/10.1101/2025.01.15.633143

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