AbDrop-a scalable microfluidics-enabled platform for rapid discovery and functional analysis of plasma cell-derived antibodies
Year: 2025
Type: article
Abstract: Conventional antibody discovery methods, such as hybridoma and phage display, face inherent limitations. Hybridoma technology relies on labor-intensive cell fusion and clone screening, often taking several weeks to obtain stable clones. Phage display allows in vitro selection but disrupts natural heavy- and light-chain pairing, potentially affecting antibody stability, safety, and developability. Single-cell approaches provide direct access to naturally paired sequences, enabling faster identification of functional candidates. Among B cell subsets, plasma cells, as terminally differentiated antibody-secreting cells, produce higher-affinity and more mature antibodies than memory B cells, yet their efficient antigen-specific enrichment at industrially relevant throughput remains challenging. Here, we present AbDrop, a microfluidics-enabled platform integrating high-throughput plasma cell capture, repertoire-level bioinformatics, and scalable antibody expression, with optional epitope binning. This workflow can process and enrich 1-2 million plasma cells per run, enabling recovery of hundreds to thousands of unique antibody sequences within a week and rapid functional validation - including binding specificity and, when performed, epitope classification - within three to four weeks. Compared with existing plasma cell - focused platforms, such as Beacon, AbDrop achieves substantially higher throughput while maintaining transparent sequence recovery and rapid downstream expression. As a proof-of-concept, we applied AbDrop to the PD-1 immune repertoire, identifying multiple functional antibodies with diverse activities, including blockers and agonists. These results demonstrate that AbDrop provides an industrially compatible, high-throughput framework for accelerated discovery and functional characterization of therapeutic antibodies.
Source: mAbs
Authors: Meng Yu, Liang Wang, Yan Yang, Su Liang, Yiyang Ge, Huanlei Xi, Shipin Lin, Ying Chen, Wei Liu, Weihua Wang, Changchun Zha, Haisong Lu
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $3,575
OpenAlex ID: W4416989103