Expanding the landscape of antibody discovery

Abstract

Library:library screening technologies hold substantial promise for paired antibody:antigen discovery, but challenges have persisted. In this issue of Cell Reports Methods, Wagner et al. introduce a method that combines antibody-ribosome-mRNA complexes, antigen cell surface display, and single-cell RNA sequencing to successfully screen diverse antibody gene libraries against a library of viral receptor proteins.

Library:library screening technologies hold substantial promise for paired antibody:antigen discovery, but challenges have persisted. In this issue of Cell Reports Methods, Wagner et al. introduce a method that combines antibody-ribosome-mRNA complexes, antigen cell surface display, and single-cell RNA sequencing to successfully screen diverse antibody gene libraries against a library of viral receptor proteins.

Main text

Affinity-based screening technologies are firmly established as effective tools to discover lifesaving medicines and reveal fundamental features of natural immune responses. Most high-throughput affinity-based antibody discovery techniques use protein display of a diverse library of antibody genes expressed on a phage, cell, or ribosome. Several rounds of panning against a target antigen are used to select or enrich for binding molecules. Success rates can vary depending on the platform, antigen, and the performance requirements for the desired antibody.^1,2

Common techniques screen for antibody binding to just a single antigen, often incorporating a large antibody gene library that can exceed 10¹⁰ variants in phage display. However, panning a library against a single target selects for specificity only to that antigen, at the potential expense of recognizing other antigens, and many applications also require understanding or selecting for binding diversity against a panel of multiple antigens. A few examples include the need to evaluate cross-reactivity against related proteins (e.g., highly evolved viral families like SARS-CoV-2, influenza, and HIV-1), engineering antibodies for multi-species recognition, and identifying drug polyspecificity risks early in therapeutic development. More complex technologies to screen antibody and antigen libraries against each other at the same time (a library:library screen) offer the ability to track and/or select for antibody recognition against multiple antigens at the same time.

Several notable technologies have been developed for library:library screening. Here, we will highlight a few developments, although due to space limitations, we unfortunately cannot list all the relevant advances. Many challenges are common across library:library screening technologies, including the potential for high background from polyspecific clones, a limited throughput that makes it difficult to sample all of the many possible interactions, difficulty in detecting low-affinity and high-affinity interactions together, and the need to express complex proteins (e.g., multi-domain protein complexes that require mammalian or human glycosylation, or other post-translational modifications). Each screening technology has addressed some or all of these challenges in various ways. In 1995, Krebber et al. reported a platform to screen antibody libraries against antigen libraries on the C and N termini of bacteriophage gIIIp, respectively, rendering the phage infective only if it encoded an antibody-antigen interacting pair.³ Another library:library screening technology based on protein fragment complementation was reported in 2001.⁴ In 2009, Bowley et al. combined yeast displaying antibodies with phages displaying antigens, sorted into 96-well plates for analysis.⁵ In recent years, protein-protein interaction screening has been performed by reprogramming orthogonal yeast mating proteins⁶ and, in other studies, by using lentivirus libraries to transduce mammalian cell libraries based on interaction affinity.^7,8 In 2023, Yang et al. co-expressed libraries of two different polypeptides as a tethered yeast display fusion protein, separated by a protease cleavage site, to evaluate protein-protein interactions at a large scale.⁹

In the current issue of Cell Reports Methods, Wagner et al.¹⁰ introduce PolyMap, an approach for antibody:antigen library screening that promises high adaptability and scalability while overcoming some of the disadvantages of previous technologies (Figure 1). They used barcoded mRNA display of scFvs as an antibody library and stained an antigen library expressed on the surface of mammalian cells. The authors then used single-cell mRNA sequencing technologies to detect the interacting pairs between expressed antigens and scFv molecules. To validate platform efficacy, Wagner et al.¹⁰ panned anti-SARS-CoV-2 antibody controls against a panel of historical SARS-CoV-2 spike (S) surface glycoproteins as a model system. After characterizing system performance with known controls, the authors then performed a cross-reactivity analysis campaign. They cloned antibody libraries from donor samples collected in 2020 and 2023 that had been pre-enriched for binding to SARS-CoV-2 receptor binding domain (RBD) as scFv gene libraries. The scFv libraries were expressed in mRNA display and subsequently panned for affinity interactions against a panel of 18 SARS-CoV-2 S protein antigens expressed on the surface of mammalian cells. An exciting use case was demonstrated in the platform’s capacity to map the epitopes of antibody and antigen interactions, which is important for viruses like SARS-CoV-2 where immune escape can be significant. Further, the platform can be adapted to other types of protein:protein interactions, such as cytokines and immune checkpoint proteins.

Figure 1.

Overview of the PolyMap workflow

PolyMap screens a ribosome-displayed scFv library against CHO cells that express a surface-displayed antigen library. Drop-seq is used to analyze scFv-antigen interactions at a single-cell level, with NGS revealing the sequences of interacting pairs. Adapted from Wagner et al.¹⁰

PolyMap addresses several key challenges inherent to library:library screening, with potential to advance a variety of translational applications in the coming years. Importantly, it is grounded in well-established techniques like mRNA display and single-cell sequencing that have already been demonstrated to be robust and scalable, offering sufficient throughput for many applications in library:library screening. Each antigen-expressing cell can potentially bind thousands of different antibodies, and single-cell mRNA sequencing techniques like Drop-seq are capable of processing 1,000–10,000 antigen-expressing cells in most studies. The high throughput will enable screening of combinatorial libraries with very large numbers of potential antibody and antigen combinations, with the only relevant screening bottleneck occurring with the 10³–10⁴ positive interacting pairs that require single-cell sequencing.

Another exciting feature of PolyMap is that it expresses antigen in mammalian cells, using CHOZN cells in the present study. PolyMap’s use of mammalian cells for antigen surface display is important because human or viral protein antigens can be expressed in a native format with appropriate mammalian glycosylation and/or post-translational modifications. mRNA display can support large libraries of scFv proteins, and thus the use of mammalian cells for antigen expression addresses a key limitation of many prior library:library screening technologies that expressed antigens in phage, mRNA, or bacteria. These advances make PolyMap a significant addition to our current antibody drug discovery tools that will enable scientists and researchers to more fully explore the expanding landscape of antibody:antigen interactions in the coming years.

Declaration of interests

S.J. is a co-inventor of antibody discovery technologies filed by Massachusetts Institute of Technology and Massachusetts General Hospital. B.J.D. is a co-inventor of antibody discovery technologies filed by Massachusetts Institute of Technology, Massachusetts General Hospital, The University of Kansas, and The University of Texas at Austin.