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March 18, 2026Nano Letters3 citationsOpen Access

Balancing Multivalent Avidity and Receptor Availability Governs mRNA Delivery by Antibody-Functionalized Lipid Nanoparticles

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KHKazuki HashibaMFMasahiro FukasakaCOChisa Okuma

Key Points

  • The study aims to clarify how surface avidity and receptor availability affect mRNA delivery using antibody-functionalized lipid nanoparticles.
  • Developed a VHH-LNP platform with controlled ligand density and orientation.
  • Utilized single-particle nanoflow cytometry to analyze functional ligands.
  • Examined the relationship between ligand density and delivery efficiency in CD8-targeted LNPs.
  • Determined optimal avidity for effective mRNA delivery is approximately 0.1 VHH per 100 nm².
  • Observed that high ligand density leads to receptor degradation.
  • Achieved selective mRNA expression in CD8⁺ T cells with optimized LNPs.

Abstract

Expanding the therapeutic scope of mRNA requires delivery systems with precise cell selectivity. Antibody-functionalized lipid nanoparticles (LNPs) enable programmable targeting, but quantitative rules linking surface avidity to delivery remain unresolved. Here, we establish a single-domain antibody (VHH)-LNP platform with controlled orientation and tunable ligand density, combined with an antigen-specific ligand binding fluorescence assay using single-particle nanoflow cytometry to quantify only functional ligands. Using this system, we show that CD8-targeted LNPs exhibit a bell-shaped dependence of delivery efficiency on ligand density, revealing an optimal avidity of ∼0.1 VHH per 100 nm2. Mechanistically, excessive ligand density induces receptor degradation, while optimal avidity balances multivalent engagement with receptor preservation. Optimized LNPs achieve selective mRNA expression in CD8+ T cells and enable in vivo CAR-T generation, resulting in dose-dependent B cell depletion at 10-30 μg/kg. Surface avidity thus emerges as a quantitative design parameter, transforming antibody-decorated LNP engineering from empirical to rule-based design.

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Cite This Study

Hashiba et al. (2026) studied this question.

synapsesocial.com/papers/69ba43f74e9516ffd37a5bb4https://doi.org/10.1021/acs.nanolett.5c06525
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