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Droplet-based single-cell pairing for high-throughput interaction mapping of antigen-receptor combinations

  • Lang Nan (Co-first Author)
  • , Tianjiao Mao (Co-first Author)
  • , Charles W. F. Chan (Co-first Author)
  • , Bei Wang
  • , Ziyu Han
  • , Gigi C. G. Choi
  • , Xueyong Wei*
  • , Alan S. L. Wong*
  • , Ho Cheung Shum*
  • *Corresponding author for this work

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

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Abstract

Mapping the interaction potential of different variant combinations of viral antigens and human cell receptors and understanding how viral antigen mutations interact with human genetic polymorphisms are critical for predicting infection susceptibility and informing precision public health strategies. Here, we develop a droplet-based single-cell pairing and library-on-library interaction screening (SPLIS) system for high-throughput profiling of the syncytium-formation landscapes of various spike-angiotensin-converting enzyme 2 (ACE2) variant combinations. This system uses combined droplet sorting and merging to deterministically encapsulate one antigen-presenting sender cell and one receptor-expressing receiver cell into each drop, followed by selection and sequencing of the fused DNA readouts to characterize the syncytium-formation potential of each combination. We applied SPLIS to characterize both fusion-enhancing and -inhibiting variant pairs, comprehensively profiling how ACE2 single-nucleotide polymorphisms modulate susceptibility to emerging severe acute respiratory syndrome coronavirus 2 spike mutations. Our system emerges as a powerful tool to interrogate the interactions between two libraries of variants, offering valuable insights into host susceptibility patterns and viral infectivity trends. © 2025 the Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a creative commons Attribution Non commercial license 4.0 (cc BY-Nc).
Original languageEnglish
Article numbereaeb1515
JournalScience Advances
Volume11
Issue number50
Online published12 Dec 2025
DOIs
Publication statusPublished - Dec 2025

Funding

This work was supported by Collaborative Research Fund (C7165-20GF to H.C.S.), General Research Fund (17303123 and 17307919 to H.C.S.), Theme-Based Research Schemes (T11-705/21-N to H.C.S.), RGC Senior Research Fellow (SRFS2425-7S04 to H.C.S.), Young Talent Support Plan of Xi’an Jiaotong University (YQ6J001 to L.N.), Shaanxi Provincial Key Research and Development Program (2025GH-YBXM-031 to L.N.), Shaanxi Sanqin Talent Program (2024SYJ20 to L.N.), and Health@InnoHK Innovation and Technology Commission program of the Hong Kong SAR Government (to A.S.L.W.).

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Publisher's Copyright Statement

  • This full text is made available under CC-BY-NC 4.0. https://creativecommons.org/licenses/by-nc/4.0/

RGC Funding Information

  • RGC-funded

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