Back

An extracellular vesicle biogenesis-inspired engineering platform for efficient protein delivery and therapeutic base editing

Xie, S.; Yang, Q.; Ilahibaks, N.; Qu, K.; Yao, B.; Vader, P.; Brans, M. A. D.; Snijders Blok, C.; Gunnarsson, A.; Doevendans, P. A.; Xiao, J.; Schiffelers, R.; Lei, Z.; Sluijter, J.

2026-05-18 bioengineering
10.64898/2026.05.18.721905 bioRxiv
Show abstract

Efficient and controllable delivery of genome-editing proteins remains a central challenge for therapeutic translation of gene-editing technologies. Extracellular vesicles (EVs) offer an attractive non-viral delivery modality due to their biocompatibility and large capacity for cytosolic cargo delivery. Yet, rational strategies to achieve controlled and programmable protein loading are still lacking. Here, we present NEO-TOP-EVs, an EV biogenesis-guided engineering platform that systematically integrates key features of three design principles inspired by vesicle formation: 1) PI(4,5)P2-mediated plasma membrane targeting, 2) ESCRT-dependent membrane scission, and 3) self-assembly-driven cargo clustering for enabling efficient encapsulation of genome-editing ribonucleoproteins. Together, the NEO design increased cargo incorporation and enhanced functional delivery of gene editing modalities under particle-normalized conditions. Using NEO-TOP-EVs, we achieve efficient delivery of Cas9 and adenine base editor ribonucleoproteins without nucleic acid templates. In an in vitro proof-of-concept, delivery of an adenine base editor targeting proprotein convertase subtilisin/kexin type 9 (PCSK9) induces efficient splice-site disruption, resulting in reduced PCSK9 expression and enhanced LDL receptor activity. Proof-of-concept in vivo experiments provide preliminary evidence of functional Cre protein delivery to the liver. Together, these findings establish NEO-TOP-EVs as a modular platform for protein-based genome editing, demonstrating how biogenesis-informed EV engineering yields functional protein delivery at levels relevant to therapeutic development.

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

1
Advanced Science
249 papers in training set
Top 0.4%
18.4%
2
Nature Nanotechnology
30 papers in training set
Top 0.1%
14.5%
3
Advanced Materials
53 papers in training set
Top 0.2%
10.3%
4
Nature Communications
4913 papers in training set
Top 21%
9.0%
50% of probability mass above
5
Nature Biotechnology
147 papers in training set
Top 2%
6.2%
6
Angewandte Chemie International Edition
81 papers in training set
Top 0.5%
6.2%
7
Advanced Functional Materials
41 papers in training set
Top 0.7%
3.9%
8
ACS Nano
99 papers in training set
Top 1%
3.5%
9
Science Advances
1098 papers in training set
Top 10%
2.7%
10
Journal of the American Chemical Society
199 papers in training set
Top 2%
2.3%
11
Small
70 papers in training set
Top 0.3%
2.0%
12
Nano Letters
63 papers in training set
Top 1%
2.0%
13
Nature Chemical Biology
104 papers in training set
Top 2%
1.6%
14
Nature Biomedical Engineering
42 papers in training set
Top 1%
1.3%
15
ACS Central Science
66 papers in training set
Top 2%
0.9%
16
Nucleic Acids Research
1128 papers in training set
Top 15%
0.9%
17
Molecular Therapy
71 papers in training set
Top 2%
0.9%
18
Proceedings of the National Academy of Sciences
2130 papers in training set
Top 42%
0.9%
19
Cell Systems
167 papers in training set
Top 12%
0.7%
20
Journal of Controlled Release
39 papers in training set
Top 1%
0.7%
21
Advanced Healthcare Materials
71 papers in training set
Top 2%
0.7%
22
Nature Chemistry
34 papers in training set
Top 1%
0.6%