https://orcid.org/0000-0002-1145-0862
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ABSTRACT
Extracellular vesicles (EV) convey biological information by transmitting macromolecules between cells and tissues and are of great promise as pharmaceutical nanocarriers, and as therapeutic per se. Strategies for customizing the EV surface and cargo are being developed to enable their tracking, visualization, loading with pharmaceutical agents and decoration of the surface with tissue targeting ligands. While much progress has been made in the engineering of EVs, an exhaustive comparative analysis of the most commonly exploited EV-associated proteins, as well as a quantification at the molecular level are lacking. Here, we selected 12 EV-related proteins based on MS-proteomics data for comparative quantification of their EV engineering potential. All proteins were expressed with fluorescent protein (FP) tags in EV-producing cells; both parent cells as well as the recovered vesicles were characterized biochemically and biophysically. Using Fluorescence Correlation Spectroscopy (FCS) we quantified the number of FP-tagged molecules per vesicle. We observed different loading efficiencies and specificities for the different proteins into EVs. For the candidates showing the highest loading efficiency in terms of engineering, the molecular levels in the vesicles did not exceed ca 40–60 fluorescent proteins per vesicle upon transient overexpression in the cells. Some of the GFP-tagged EV reporters showed quenched fluorescence and were either non-vesicular, despite co-purification with EVs, or comprised a significant fraction of truncated GFP. The co-expression of each target protein with CD63 was further quantified by widefield and confocal imaging of single vesicles after double transfection of parent cells. In summary, we provide a quantitative comparison for the most commonly used sorting proteins for bioengineering of EVs and introduce a set of biophysical techniques for straightforward quantitative and qualitative characterization of fluorescent EVs to link single vesicle analysis with single molecule quantification.
Acknowledgments
The authors would like to thank Dr Yiqi Seow (A*STAR, Singapore) for gifting us the plasmid expressing mouse CD63 fused to GFP (mCD63-GFP), Dr Ghulam Dar (University of Oxford) for the Lamp2b-GFP plasmid and Dr Imre Mäger (University of Oxford, UK) for the Myr-GFP plasmid. Moreover, we would like to thank the Imaging Core Facility (IMCF, University of Basel) for providing access to their microscopes.
SELA is supported by the Swedish Medical Research Council (VR-Med) and the Swedish foundation for strategical research (SSF-IRC, FormulaEx). GC is supported by the Karolinska Institutet Doctoral grant. A.Gö is an International Society for Advancement of Cytometry (ISAC) Marylou Ingram Scholar.
Author contributions
GC, WH, NMK and SEA devised the study.
GC, WH, NKM and SEA designed the experiments.
WH, ES and JV performed the MS proteomics analysis.
AGr, CG performed the Cryo-EM analysis.
AGö helped with the design and analysis of the flow cytometry experiments and performed the imaging flow cytometry experiments.
All other experiments were performed by WH and GC.
JH, YL, JZN, OPBW gave critical inputs on the experiments and discussion of results.
GC, WH and NMK wrote the paper. All authors reviewed the manuscript.
Conflict of interest
AGö, JZN, OPBW and SEA are consultants for and have equity interests in Evox Therapeutics Ltd., Oxford, UK.
Supplementary material
Supplementary data for this article can be accessed here.
* Correction Statement
These authors contributed equally.
This article has been republished with minor changes. These changes do not impact the academic content of the article.
