High-throughput single-cell screening of viable hybridomas and patient-derived antibody-secreting cells using punchable microwells.

Monoclonal antibodies (mAbs) hold significant potential as therapeutic agents and are invaluable tools in biomedical research. However, the lack of efficient high-throughput screening methods for single antibody-secreting cells (ASCs) has limited the diversity of available antibodies. Here, we introduce a novel, integrated workflow employing self-seeding microwells and an automated microscope-puncher system for the swift, high-throughput screening and isolation of single ASCs. The system allows for the individual screening and isolation of up to 6,400 cells within approximately one day, with the opportunity for parallelization and efficient upscaling. We successfully applied this workflow to both hybridomas and human patient-derived B cells, enabling subsequent clonal expansion or antibody sequence analysis through an optimized, single-cell nested reverse transcription-polymerase chain reaction (RT-PCR) procedure. By providing a time-efficient and more streamlined single ASC screening and isolation process, our workflow holds promise for driving forward progress in mAb development.

Integrating artificial intelligence-based epitope prediction in a SARS-CoV-2 antibody discovery pipeline: caution is warranted.

BACKGROUND: SARS-CoV-2-neutralizing antibodies (nABs) showed great promise in the early phases of the COVID-19 pandemic. The emergence of resistant strains, however, quickly rendered the majority of clinically approved nABs ineffective. This underscored the imperative to develop nAB cocktails targeting non-overlapping epitopes. METHODS: Undertaking a nAB discovery program, we employed a classical workflow, while integrating artificial intelligence (AI)-based prediction to select non-competing nABs very early in the pipeline. We identified and in vivo validated (in female Syrian hamsters) two highly potent nABs. FINDINGS: Despite the promising results, in depth cryo-EM structural analysis demonstrated that the AI-based prediction employed with the intention to ensure non-overlapping epitopes was inaccurate. The two nABs in fact bound to the same receptor-binding epitope in a remarkably similar manner. INTERPRETATION: Our findings indicate that, even in the Alphafold era, AI-based predictions of paratope-epitope interactions are rough and experimental validation of epitopes remains an essential cornerstone of a successful nAB lead selection. FUNDING: Full list of funders is provided at the end of the manuscript.