A new high-volume antibody testing platform developed by researchers at the University of Illinois Urbana-Champaign could significantly speed up the discovery of antibody-based treatments and vaccines. Dubbed oPool+ display, the system combines advanced synthesis and binding analysis technologies to create and evaluate hundreds of antibodies in a matter of days—tasks that traditionally take weeks or months per antibody. Led by biochemistry professor Nicholas Wu, the team used oPool+ to study how antibodies bind to hemagglutinin, a key protein on the surface of the influenza virus. By analyzing about 300 diverse antibody variants from multiple individuals, they identified common structural features that enable effective binding across different influenza strains. Graduate student Wenhao "Owen" Ouyang, the study’s first author, emphasized the platform’s transformative potential. “With a rapid, high-throughput method like ours, if we can quickly find a promising antibody candidate, it has great potential to become an effective therapeutic,” he said. Traditional antibody research is slow and costly, with each antibody requiring extensive manual work. The oPool+ display system dramatically reduces both time and expense—cutting material and labor costs by 80 to 90%. The platform enables researchers to test thousands of antibody-antigen interactions in just a few days, making large-scale immune response analysis feasible. The team’s findings revealed shared antibody binding characteristics across diverse individuals, a critical insight for developing universal influenza vaccines. Since immune responses vary widely between people, identifying common targets is essential for broad protection. Looking ahead, the researchers aim to scale oPool+ to handle thousands or even tens of thousands of antibodies. The platform could be applied to a wide range of diseases, including other viruses, bacterial infections, and cancer. Ouyang also highlighted its role in advancing artificial intelligence in biomedicine. “We can generate many AI-predicted antibody structures, but we lack ways to validate them at scale,” he said. “With oPool+, we can test AI predictions in real time and feed the results back to improve the models—creating a powerful feedback loop for better drug discovery.” The platform’s speed and efficiency make it a promising tool for responding to future emerging pathogens, much like the rapid development needed during the COVID-19 pandemic. By quickly characterizing immune responses to new threats, oPool+ could accelerate the creation of treatments and vaccines when time is critical. The findings were published in Science Translational Medicine, showcasing a major step forward in antibody research and its applications in medicine.