Hunter, Morag Rose, Cui, Lili, Porebski, Benjamin Thomas, Pereira, Sara, Sonzini, Silvia, Odunze, Uchechukwu, Iyer, Preeti, Engkvist, Ola, Lloyd, Rebecca Louise, Peel, Samantha, Sabirsh, Alan, Ross‐Thriepland, Douglas, Jones, Arwyn Tomos ORCID: https://orcid.org/0000-0003-2781-8905 and Desai, Arpan Shailesh 2023. Understanding Intracellular Biology to Improve mRNA Delivery by Lipid Nanoparticles. small methods , 2201695. 10.1002/smtd.202201695 |
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Abstract
Poor understanding of intracellular delivery and targeting hinders development of nucleic acid‐based therapeutics transported by nanoparticles. Utilizing a siRNA‐targeting and small molecule profiling approach with advanced imaging and machine learning biological insights is generated into the mechanism of lipid nanoparticle (MC3‐LNP) delivery of mRNA. This workflow is termed Advanced Cellular and Endocytic profiling for Intracellular Delivery (ACE‐ID). A cell‐based imaging assay and perturbation of 178 targets relevant to intracellular trafficking is used to identify corresponding effects on functional mRNA delivery. Targets improving delivery are analyzed by extracting data‐rich phenotypic fingerprints from images using advanced image analysis algorithms. Machine learning is used to determine key features correlating with enhanced delivery, identifying fluid‐phase endocytosis as a productive cellular entry route. With this new knowledge, MC3‐LNP is re‐engineered to target macropinocytosis, and this significantly improves mRNA delivery in vitro and in vivo. The ACE‐ID approach can be broadly applicable for optimizing nanomedicine‐based intracellular delivery systems and has the potential to accelerate the development of delivery systems for nucleic acid‐based therapeutics.
Item Type: | Article |
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Date Type: | Published Online |
Status: | Published |
Schools: | Pharmacy |
Additional Information: | License information from Publisher: LICENSE 1: URL: http://creativecommons.org/licenses/by/4.0/ |
Publisher: | Wiley |
ISSN: | 2366-9608 |
Date of Acceptance: | 6 April 2023 |
Last Modified: | 17 Jun 2023 07:20 |
URI: | https://orca.cardiff.ac.uk/id/eprint/160411 |
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