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.
Keywords: drug delivery; intracellular trafficking; lipid nanoparticles; machine learning; nucleic acid therapeutics.
© 2023 The Authors. Small Methods published by Wiley-VCH GmbH.