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Bottom-up assembly of biomedical relevant fully synthetic extracellular vesicles

MPS-Authors
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Staufer,  Oskar
Cellular Biophysics, Max Planck Institute for Medical Research, Max Planck Society;

/persons/resource/persons133081

Dietrich,  Franziska
Cellular Biophysics, Max Planck Institute for Medical Research, Max Planck Society;

/persons/resource/persons224532

Schröter,  Martin
Cellular Biophysics, Max Planck Institute for Medical Research, Max Planck Society;

/persons/resource/persons225871

Fabritz,  Sebastian
Chemical Biology, Max Planck Institute for Medical Research, Max Planck Society;

/persons/resource/persons75304

Boehm,  Heike
Cellular Biophysics, Max Planck Institute for Medical Research, Max Planck Society;

/persons/resource/persons84351

Platzman,  Ilia
Cellular Biophysics, Max Planck Institute for Medical Research, Max Planck Society;

/persons/resource/persons76135

Spatz,  Joachim Pius
Cellular Biophysics, Max Planck Institute for Medical Research, Max Planck Society;

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Citation

Staufer, O., Dietrich, F., Rimal, R., Schröter, M., Fabritz, S., Boehm, H., et al. (2021). Bottom-up assembly of biomedical relevant fully synthetic extracellular vesicles. Science Advances, 7(36): eabg6666, pp. 1-12. doi:10.1126/sciadv.abg6666.


Cite as: http://hdl.handle.net/21.11116/0000-0009-21DE-8
Abstract
Extracellular vesicles (EVs) are fundamental for intercellular communication and influence nearly every process in cell physiology. However, because of their intricate molecular complexity, quantitative knowledge on their signaling mechanisms is missing, particularly impeding their therapeutic application. We used a complementary and quantitative engineering approach based on sequential synthetic bottom-up assembly of fully functional EVs with precisely controlled lipid, protein, and RNA composition. We show that the functionalities of synthetic EVs are analogous to natural EVs and demonstrate their programmable therapeutic administration for wound healing and neovascularization therapy. We apply transcriptome profiling to systematically decode synergistic effects between individual EV constituents, enabling analytical dissection and a fundamental understanding of EV signaling.