Intracellular processing of DNA-lipid nanoparticles: A quantitative assessment by image segmentation
Alessandra Cavegn, Samuel Waldner, David C. Wang, Jarosław Sędzicki, Evrim Ümit Kuzucu, Michael Zogg, Claudia Lotter, Jörg Huwyler
Abstract
Carriers for efficient delivery of nucleic acids, such as lipid nanoparticles (LNPs), have gained much attention for gene therapy applications. Intracellular processing of such nanocarriers is a complex mechanism comprising cellular internalization by endocytosis pathways, endosomal release into the cytosol, lysosomal degradation, and recycling. The endosomal escape rates of current formulations are considered low, and methods to reliably quantify endocytic events are not readily available. To address this shortcoming and to support the optimization of LNP formulations, the current study presents an automated live-cell imaging-based analysis method. Engineered HuH7 hepatic cell lines overexpressing fluorescent Galectin and Rab reporters together with lysosomal co-staining enabled qualitative and quantitative tracking of DNA-loaded LNPs. The use of two fluorescently labeled DNA-LNP formulations containing either SM-102 or ALC-0315 ionizable lipids revealed significant differences in endosomal escape rates and intracellular processing. Upon treatment, only subpopulations of the HuH7 target cells could be activated with respect to escape or recycling. Recycling inhibitors were therefore used to promote endosomal escape. These findings provide valuable insights into the timing and regulation of endocytic events, which will be instrumental to optimize therapeutic LNP formulations. • A live cell confocal live-cell imaging method was developed quantify DNA-LNP uptake, endosomal escape, recycling, and degradation. • HuH7 cells expressing fluorescent Gal9 and Rab4a were used to track intracellular trafficking of DNA-LNPs. • LNPs containing SM-102 or ALC-0315 showed distinct endosomal processing and activated different cell subpopulations. • A custom automated image segmentation algorithm enables high-throughput optimization of LNPs.