Back

Bottom-up engineering of EV-mimetic vesicles reveals selective protein loading and modulates cellular uptake and biodistribution

Chen, Y.; Benslimane, Y.; Douanne, N.; Dickinson, K.; Tsering, T.; Ghasemi, M.; Kienzle, L.; Frutos-Grilo, E.; Juncker, D.; Larroque, A.-L.; Serrano, L.; Nerguizian, V.; Burnier, J.

2026-10-06 bioengineering
10.64898/2026.10.05.756895 bioRxiv
Show abstract

Biological systems such as extracellular vesicles (EVs) are highly sophisticated and functionally diverse. Despite growing recognition of EVs' roles in health and disease through mediating intercellular communication, their inherent heterogeneity and challenges in purity and yield hinder their mechanistic research and clinical translation. To address this gap, we sought to translate essential EV properties into a controllable and simplified platform by engineering biomimetic synthetic vesicles (SVs) incorporating defined proteins and lipids. Using microfluidics, we generated SVs loaded with bulk membrane and cytosolic proteins from cancer cells, as well as individual organotropism-associated integrins. Bulk membrane protein incorporation into SVs reduced surface charge, modified lipid profiles, and enhanced cellular uptake, while integrin incorporation altered biodistribution. To investigate protein loading, we profiled input materials and SVs by mass spectrometry, identifying features linked to high incorporation, such as low molecular weight, positive charge, high isoelectric point, and predicted stability, globularity, lipidation, and amphipathic helices. This work presents a synthetic platform for dissecting the roles of EV-associated features and cargo, while providing insights into protein properties relevant to the optimization of delivery systems.

Matching journals

The top 8 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.