Quantification of protein cargo loading into engineered extracellular vesicles at single-vesicle and single-molecule resolution

Silva, Andreia M.; Lazaro-Ibanez, Elisa; Gunnarsson, Anders; Dhande, Aditya; Daaboul, George; Peacock, Ben; Osteikoetxea, Xabier [Osteikoetxea, Xabier (immunológia, gene...), szerző] Genetikai, Sejt- és Immunbiológiai Intézet (SE / AOK / I); HCEMM-SE Extracelluláris Vezikula Kutatócsoport (SE / AOK / I / GSII); Salmond, Nikki; Friis, Kristina Pagh; Shatnyeva, Olga ✉; Dekker, Niek ✉

Angol nyelvű Szakcikk (Folyóiratcikk) Tudományos
Megjelent: JOURNAL OF EXTRACELLULAR VESICLES 2001-3078 10 (10) Paper: e12130 , 20 p. 2021
  • SJR Scopus - Cell Biology: D1
Extracellular Vesicles (EVs) have been intensively explored for therapeutic delivery of proteins. However, methods to quantify cargo proteins loaded into engineered EVs are lacking. Here, we describe a workflow for EV analysis at the single-vesicle and single-molecule level to accurately quantify the efficiency of different EV-sorting proteins in promoting cargo loading into EVs. Expi293F cells were engineered to express EV-sorting proteins fused to green fluorescent protein (GFP). High levels of GFP loading into secreted EVs was confirmed by Western blotting for specific EV-sorting domains, but quantitative single-vesicle analysis by Nanoflow cytometry detected GFP in less than half of the particles analysed, reflecting EV heterogeneity. Anti-tetraspanin EV immunostaining in ExoView confirmed a heterogeneous GFP distribution in distinct subpopulations of CD63(+), CD81(+), or CD9(+) EVs. Loading of GFP into individual vesicles was quantified by Single-Molecule Localization Microscopy. The combined results demonstrated TSPAN14, CD63 and CD63/CD81 fused to the PDGFR beta transmembrane domain as the most efficient EV-sorting proteins, accumulating on average 50-170 single GFP molecules per vesicle. In conclusion, we validated a set of complementary techniques suitable for high-resolution analysis of EV preparations that reliably capture their heterogeneity, and propose highly efficient EV-sorting proteins to be used in EV engineering applications.
Hivatkozás stílusok: IEEEACMAPAChicagoHarvardCSLMásolásNyomtatás
2024-07-17 20:41