Development and application of nbLIBRA-seq for high-throughput discovery of antigen-specific nanobodies

ABSTRACT Nanobodies are of high interest in many fields of medicine and biotechnology due to their high stability, tissue penetration, and engineering adaptability compared to monoclonal antibodies. However, nanobody discovery has been limited by technologies that rely on laborious library generation, panning, and clone screening techniques. Here, we demonstrate the successful adaptation of Linking B-Cell Receptor to Antigen Specificity through Sequencing (LIBRA-seq) to immunized alpacas for the rapid identification of antigen-specific nanobodies, derived from heavy-chain antibodies. We validated for nanobody discovery (nbLIBRA-seq) in two different disease settings. First, we identified over 300 antigen-specific heavy chain antibodies against human Transferrin Receptor (TfR1), also known as CD71, from a single alpaca blood sample. Experimental validation showed nbLIBRA-seq was able to identify nanobodies that exhibit specific binding to CD71, with two nanobodies also showing receptor internalization on human T cells. In a separate experiment, we tested the ability of nbLIBRA-seq to perform nanobody discovery with multiple antigens in the antigen screening library. Using fusion glycoproteins from the related respiratory syncytial virus (RSV) and human metapneumovirus (hMPV), 1,125 antigen-specific heavy-chain expressing B cells were recovered via nbLIBRA-seq. A subset of these nanobodies was validated experimentally to possess the target antigen specificity. Together, our results illustrate the potential of nbLIBRA-seq to rapidly identify antigen-specific heavy chain antibodies for a range of diverse targets, a capability that will be of critical significance for the effective and efficient development of novel nanobody-based therapeutics against targets of biomedical significance. Full Text Availability The license terms selected by the author(s) for this preprint version do not permit archiving in PMC. The full text is available from the preprint server.