Back

Ultrafiltration Segregates Tissue Regenerative Stimuli Harboured Within and Independent of Extracellular Vesicles

Cooper, T. T.; Sherman, S. E.; Dayarathna, T.; Bell, G. I.; Ma, J.; McRae, D. M.; Lagugne-Labarthet, F.; Pasternak, S. H.; Lajoie, G. A.; Hess, D. A.

2020-01-29 cell biology
10.1101/2020.01.28.923037 bioRxiv
Show abstract

The release of extracellular vesicles (EVs) from human multipotent stromal cells (MSC) has been proposed as a mechanism by which MSC mediate regenerative functions in vivo. Our recent work has characterized MSC derived from human pancreatic tissues (Panc-MSC) that generated a tissue regenerative secretome. Despite these advancements, it remains unknown whether regenerative stimuli are released independent or within extracellular vesicles. Herein, this study demonstrates ultrafiltration is a simple method to enrich for EVs which can be injected in murine models of tissue regeneration. The enrichment of EVs from Panc-MSC conditioned media (CM) was validated using nanoscale flow cytometry and atomic force microscopy; in addition to the exclusive detection of classical EV-markers CD9, CD81, CD63 using label-free mass spectrometry. Additionally, we identified several pro-regenerative stimuli, such as WNT5A or ANGPT1, exclusive to EV-enriched CM. Endothelial cell tubule formation was enhanced in response to both Panc-MSC CM fractions in vitro yet only intramuscular injection of EV-enriched CM demonstrated vascular regenerative functions in NOD/SCID mice with unilateral hind-limb ischemia (*<p<0.05). Furthermore, both EV-depleted and EV-enriched CM reduced hyperglycemia following intrapancreatic injection in hyperglycemic mice (**p<0.01). Collectively, understanding the functional synergy between compartments of the secretome is required to advance cell-free biotherapeutics into applications of regenerative medicine.

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.