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High-throughput barcoding of nanoparticles identifies cationic, degradable lipid-like materials for mRNA delivery to the lungs in female preclinical models
Xue, Lulu Hamilton, Alex G. Zhao, Gan Xiao, Zebin El-Mayta, Rakan Han, Xuexiang Gong, Ningqiang Xiong, Xinhong Xu, Junchao Figueroa-Espada, Christian G.
Xue, Lulu
Hamilton, Alex G.
Zhao, Gan
Xiao, Zebin
El-Mayta, Rakan
Han, Xuexiang
Gong, Ningqiang
Xiong, Xinhong
Xu, Junchao
Figueroa-Espada, Christian G.
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Journal article
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2024-02-29
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Bioengineering
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https://doi.org/10.1038/s41467-024-45422-9
Abstract
Lipid nanoparticles for delivering mRNA therapeutics hold immense promise for the treatment of a wide range of lung-associated diseases. However, the lack of effective methodologies capable of identifying the pulmonary delivery profile of chemically distinct lipid libraries poses a significant obstacle to the advancement of mRNA therapeutics. Here we report the implementation of a barcoded high-throughput screening system as a means to identify the lung-targeting efficacy of cationic, degradable lipid-like materials. We combinatorially synthesize 180 cationic, degradable lipids which are initially screened in vitro. We then use barcoding technology to quantify how the selected 96 distinct lipid nanoparticles deliver DNA barcodes in vivo. The top-performing nanoparticle formulation delivering Cas9-based genetic editors exhibits therapeutic potential for antiangiogenic cancer therapy within a lung tumor model in female mice. These data demonstrate that employing high-throughput barcoding technology as a screening tool for identifying nanoparticles with lung tropism holds potential for the development of next-generation extrahepatic delivery platforms.
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Xue, L., Hamilton, A.G., Zhao, G. et al. High-throughput barcoding of nanoparticles identifies cationic, degradable lipid-like materials for mRNA delivery to the lungs in female preclinical models. Nat Commun 15, 1884 (2024). https://doi.org/10.1038/s41467-024-45422-9
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Springer Science and Business Media
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Nature Communications, Vol. 15, Iss. 1
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Attribution CC BY