Beyond their deployment as COVID-19 vaccines, lipid nanoparticles (LNPs) have emerged as versatile vehicles for therapeutic nucleic acid delivery. However, achieving efficient and cell-targeted transfection in extrahepatic tissues, particularly pancreatic β cells, remains a major challenge. Here, we develop a dual-targeting LNP engineering strategy that integrates high-throughput compositional screening with surface conjugation of β cell-specific targeting ligands to enable selective gene delivery to pancreatic β cells. Compositional optimization identified LNP formulations that achieved over a 148-fold increase in β cell transfection efficiency in vitro and more than an 8-fold increase in pancreatic selectivity in vivo compared to the Moderna LNP formulation. Surface conjugation of the ZnT8-specific monoclonal antibody (mAb43), which recognizes the zinc transporter ZnT8 highly expressed on murine β cells, further increased pancreatic transgene expression by more than 2-fold and achieved over 70% β cell transfection in murine models. To improve translational potential, we conjugated a high-affinity camelid single-domain antibody (4hD29 nanobody) targeting dipeptidyl peptidase-6 (DPP6), a biomarker enriched on human β cells, to compositionally optimized LNPs to deliver human STAT2-siRNA. These dual-targeting LNPs reduced STAT2 expression in human β cells under IFN-α stimulation to below baseline levels observed in unstimulated controls and induced > 4-fold increase in PDL1 expression. Together, this integrated LNP design for β cell-directed gene delivery establishes a versatile platform for RNA therapeutics and gene-editing applications in a pro-inflammatory type 1 diabetes context.
Di Yu, Yining Zhu, A. Roca-Rivada et al.· ACS Nano· 0 citations
Lipid nanoparticles (LNPs) are the leading platform for mRNA delivery, with their in vivo performance governed by lipid composition and colloidal stability. While anionic helper lipids can bias LNP expression toward the spleen, weak RNA-lipid interactions during purification often induce nanoparticle rearrangement and reduced activity. These stability limitations effectively narrow the accessible formulation design and screening space, leaving large regions of anionic compositional space underexplored. Here, we extend our cleavable crosslinking strategy to stabilize anionic LNPs without replacing the primary lipid constituents of the parent LNP formulation. By tuning the lengths of the cholesterol-derived acid-cleavable crosslinker and PEG-diamine, we achieved balanced structural stability. The optimized crosslinked formulation exhibited a significant increase in splenic mRNA expression at 12 h compared to the uncrosslinked LNPs. Notably, 33.2% of CD45+ tdTomato+ cells in the spleen were identified as T cells. Mechanistic analyses suggest that controlled mRNA release and altered intracellular processing contribute to the improved transfection efficiency. Together, these findings define a tunable crosslinking window that expands the accessible design landscape for tissue- and cell-specific mRNA delivery.
Yunhe Su, Joseph Choy, Xiang Liu et al.· ACS Applied Materials and In...· 0 citations
Lipid nanoparticles (LNPs) are promising non-viral vectors for central nervous system (CNS) gene therapy, but effective delivery is limited by the blood-brain barrier and cell-type specificity. Here, we combined intracranial delivery with high-throughput, cluster-based screening and machine learning to identify LNP formulations optimized for functional neuronal gene-editing activity. We screened 720 LNP formulations for their ability to transfect neurons and deliver genetic payloads efficiently. Following this, cluster-mode intracranial screening enabled targeted delivery to specific brain regions, including the ventral posteromedial nucleus (VPM) and hippocampus, and efficiently narrowed hundreds of candidates to single optimized formulations. Optimized LNPs achieved approximately 20% gene editing, measured as functional activity, in targeted areas and exhibited neuron- and astrocyte-enriched transfection patterns. These results demonstrate that intracranial delivery, combined with machine-learning-guided optimization, can identify LNPs capable of precise cell- and region-preferential gene delivery, supporting the development of targeted therapies for neurological disorders.
S. S. Cai, Cody Slater, Veronica E. Farag et al.· Cell Reports Physical Scienc...· 0 citations
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