Back

Humanized Extracellular Vesicles for Efficient RNA Delivery

Ma, X.; Zhao, S. R.; Cepko, C. L.

2025-12-17 bioengineering
10.64898/2025.12.15.694436 bioRxiv
Show abstract

Engineered extracellular vesicles (EVs) are a class of non-viral delivery vectors for RNA-based vaccines and gene therapies. A specialized form of engineered EVs, known as enveloped protein nanocages (EPNs), has been developed to enhance cargo loading and delivery. When EPNs are equipped with a viral fusogen, such as vesicular stomatitis virus glycoprotein (VSV-G), they have been shown to deliver proteins or RNA efficiently into recipient cells. Comparisons across different EPN types and optimization of their different features have been difficult, as assays for their activity have not been reported for single, active units. As we were interested in optimizing EVs, we first developed a biological titration assay inspired by the methods used for infectious viral particles. With this assay, we optimized EVs using a modular platform, creating EVs composed predominantly of human-derived protein components. This system achieved efficient RNA delivery, with functional titers comparable to those of lentiviral vectors. The optimized chimeric proteins comprising the EV particles integrate domains from human epsin 1, human citramalyl-CoA lyase beta-like protein (CLYBL), and human CEP55. The constructs also include a short 21-amino-acid peptide from a non-human source for RNA packaging, resulting in an EV-based RNA delivery system with reduced immunogenicity compared with EPNs and retroviral virus-like particles (VLPs). Significance StatementWe developed engineered extracellular vesicles (EVs) as RNA delivery vehicles to address limitations of virus-like particles (VLPs) and lipid nanoparticles (LNPs) in gene therapies and vaccines. We first developed an assay for individual active particles, using methods typically employed for viral titrations. This approach allowed iterative optimization of a modular EV platform. Our optimized particles comprise primarily human proteins and reach titers that comparable to those of lentiviral vectors.

Published in Proceedings of the National Academy of Sciences (predicted rank #2) · training set

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.