Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Canada.
Toronto Recombinant Antibody Centre, University of Toronto, Toronto, Canada.
Commun Biol. 2021 May 12;4(1):561. doi: 10.1038/s42003-021-02066-5.
Synthetic antibody (Ab) technologies are efficient and cost-effective platforms for the generation of monoclonal Abs against human antigens. Yet, they typically depend on purified proteins, which exclude integral membrane proteins that require the lipid bilayers to support their native structure and function. Here, we present an Ab discovery strategy, termed CellectSeq, for targeting integral membrane proteins on native cells in complex environment. As proof of concept, we targeted three transmembrane proteins linked to cancer, tetraspanin CD151, carbonic anhydrase 9, and integrin-α11. First, we performed in situ cell-based selections to enrich phage-displayed synthetic Ab pools for antigen-specific binders. Then, we designed next-generation sequencing procedures to explore Ab diversities and abundances. Finally, we developed motif-based scoring and sequencing error-filtering algorithms for the comprehensive interrogation of next-generation sequencing pools to identify Abs with high diversities and specificities, even at extremely low abundances, which are very difficult to identify using manual sampling or sequence abundances.
合成抗体 (Ab) 技术是针对人类抗原生成单克隆 Ab 的高效且经济有效的平台。然而,它们通常依赖于纯化的蛋白质,而这些蛋白质排除了需要脂质双层来支持其天然结构和功能的整合膜蛋白。在这里,我们提出了一种 Ab 发现策略,称为 CellectSeq,用于针对复杂环境中天然细胞上的整合膜蛋白。作为概念验证,我们针对与癌症相关的三种跨膜蛋白,四跨膜蛋白 CD151、碳酸酐酶 9 和整合素-α11 进行了靶向研究。首先,我们进行了基于细胞的原位选择,以富集针对抗原的特异性结合物的噬菌体展示合成 Ab 库。然后,我们设计了下一代测序程序来探索 Ab 的多样性和丰度。最后,我们开发了基于基序的评分和测序错误过滤算法,用于全面分析下一代测序池,以识别具有高多样性和特异性的 Abs,即使在极低的丰度下,这在使用手动采样或序列丰度进行识别时非常困难。