Abstract
Antibody engineering has been widely developed to improve the affinity and activity of therapeutic monoclonal antibodies (mAbs). Conventional single-cell sorting technology is a powerful method for antibody discovery, effectively producing human mAbs; however, it remains expensive and limits the number of cells available for analysis. Furthermore, sorting hundreds of B cells may not yield cells encoding antibodies with high neutralizing potency since several unexpected antibodies, such as autoantibodies and infectivity-enhancing antibodies, can be included in antigen-specific B cells in patients with infectious diseases. The preparation of antibodies for infectious diseases, such as that caused by severe acute respiratory syndrome coronavirus 2 with rapid mutations, requires discovery technology to generate and select diverse human mAb libraries. We present a synthetic chimeric antibody (SynCA) technology that facilitates in vitro reconstitution of diverse mAbs by integrating the bulk sorting of numerous antigen-specific B cells and machine learning. We compared our method, which cloned antibody variable regions from a single cDNA extracted from ∼5000 B cells, with a single-cell sorting method in the convalescent sera of recovered patients with coronavirus disease 2019. The SynCA method enhanced the B cell receptor repertoire diversity in both heavy and light chain genes. Additionally, the random pairing of the heavy and light chain genes reconstituted antibodies in vitro, providing new insights that the nucleotide sequence information from antibody gene regions (DH and JL regions) predicts antibody reconstitution. This method is both cost-effective and rapid, and it can be employed to produce mAbs for therapeutic, diagnostic, and research purposes.