Comprehensive Mapping of Immune Nanobody Repertoires with NanoMAP 2267451

Abstract

Nanobodies have recently emerged as an alternative to classical antibodies in therapeutic and diagnostic contexts, promising improved stability and simpler manufacturing. However, many labs still rely on low throughput conventional screening methods for nanobody discovery. Here we report streamlined experimental and computational tools for discovery of nanobodies, permitting deep characterization of the binding properties of immune repertoires. To improve nanobody discovery, we developed NanoMAP, an integrated experimental and computational pipeline for nanobody discovery. We immunized alpacas with a pool of antigens, and created a phage display library from circulating B-cells. We then panned this phage display library on each antigen separately, and used competitors or antigen variants to assess complex binding phenotypes of the immune repertoire. Finally, we sequenced the panned libraries and developed a clustering method that allows data to be aggregated within B-cell clonal families, improving signal-to-noise ratios and reducing the complexity of the repertoire. We tested NanoMAP on three distinct pools of targets, collecting data on close to 1M unique nanobody sequences. We found that our specialized clustering method outperformed standard sequence clustering, producing clonal families that are coherent in sequence and function. By aggregating sequencing data within clonal families, NanoMAP produced reliable and rich data on binding phenotypes for each antigen. Using this information, we discovered nanobodies recognizing functionally relevant, and evolutionarily conserved sites on each antigen, demonstrating the broad utility of our methods. NIAID R01AI25704, NIGMS 5K12GM133314-07 Computational and Systems Immunology (COMP)