Rapid On-Cell Selection of High-Performance Human Antibodies

Abstract

Phage display is a critical tool for developing antibodies. However, existing approaches require many time-consuming rounds of biopanning and screening of potential candidates due to a high rate of failure during validation. Herein, we present a rapid on-cell phage display platform which recapitulates the complex <i>in vivo</i> binding environment to produce high-performance human antibodies in a short amount of time. Selection is performed in a highly stringent heterogeneous mixture of cells to quickly remove nonspecific binders. A microfluidic platform then separates antigen-presenting cells with high throughput and specificity. An unsupervised machine learning algorithm analyzes sequences of phage from all pools to identify the structural trends that contribute to affinity and proposes ideal candidates for validation. In a proof-of-concept screen against human Frizzled-7, a key ligand in the Wnt signaling pathway, antibodies with picomolar affinity were discovered in two rounds of selection that outperformed current gold-standard reagents. This approach, termed μCellect, is low cost, high throughput, and compatible with a wide variety of cell types, enabling widespread adoption for antibody development.