Rapid Generation of High-Affinity Rabbit Anti-Mouse IgG Monoclonal Antibodies by High-Throughput Single-B-Cell Sorting and Recombinant Expression.

Rabbit monoclonal antibodies (RabMAbs) are valuable for biomedical research and diagnostic applications because of their high affinity, specificity, and broad epitope recognition; here, we established an integrated phenotype-linked workflow for rapid RabMAb discovery using serum-derived polyclonal mouse IgG as a proof-of-concept model antigen. Three Big-Eared White rabbits were immunized in parallel, and the rabbit exhibiting the highest serum endpoint titer was selected for the complete downstream single-B-cell discovery workflow. Approximately 5 × 105 activated B cells were subjected to polydisperse oblate dispersion system (POD)-based screening, yielding 13,266 antigen-positive POD events. Following recovery and 10× Genomics single-cell V(D)J sequencing, 4294 B-cell barcodes yielded valid/interpretable V(D)J data, of which 2099 contained at least one complete, productive, and translatable heavy-chain/light-chain pair, generating 2199 functional VH/VL pairing records. AbFinder™-assisted prioritization generated a computationally recommended pool of 158 candidates, and the five highest-ranked VH/VL pairs within this pool were selected for recombinant expression and validation. All five yielded antigen-reactive RabMAbs with an endpoint ELISA titer of 1:256,000 and BLI-derived apparent KD values ranging from 4.19 × 10-10 to 9.79 × 10-9 M. The antibodies showed differential concentration-dependent reactivity toward mouse IgG1, IgG2a, IgG2b, and IgG3 preparations, weak reactivity toward human IgG, and clone-dependent reactivity toward rat IgG. The workflow from spleen collection to functional validation was completed within approximately three weeks. Because only five prioritized candidates from one selected responder rabbit were evaluated, the 5/5 validation outcome should not be interpreted as an overall platform hit rate or definitive validation of the prioritization algorithm. These findings support the feasibility of this workflow for research-grade and diagnostic antibody discovery, while broader evaluation will require larger candidate cohorts, independent biological validation, and additional antigen classes.