Towards a Scalable Production of Staphylococcus epidermidis Extracellular Vesicles: Integration of Stirred-Tank Bioreactor and Ultrafiltration-Based Recovery
Aug 2026· International Journal of Molecular Sciences· Vol 27· 0 citations· 66 references
Medicine
TL;DR
This work provides an integrated framework for Gram-positive EV manufacturing and supports future translational applications by suggesting that controlled STBR fermentation combined with UF enables a reproducible, scalable production of S. epidermidis EVs without compromising vesicle integrity.
Abstract
Extracellular vesicles (EVs) from Gram-positive bacteria are gaining more interest as potential biotechnological systems for vaccines and drug delivery. However, scalable production protocols remain poorly defined. Here, we evaluated upstream and downstream processes for the controlled production of extracellular vesicles from Staphylococcus epidermidis. Bacterial growth and vesicle release were compared between shake-flask cultures and a controlled stirred-tank bioreactor (STBR), and vesicles were isolated using either ultracentrifugation (UC) or ultrafiltration (UF). STBR cultivation significantly increased biomass accumulation during the exponential phase while preserving the same growth observed in shake flasks. Nanoparticle tracking analysis (NTA) showed time-dependent particle accumulation in both systems, with UF consistently yielding higher particle recovery than UC. Despite differences in yield, vesicles isolated by both methods displayed comparable size distributions and TEM imaging confirmed the spherical morphology. Protein quantification revealed greater protein recovery in UF preparations, whereas particle-to-protein ratios indicated similar sample composition between isolation methods. Together, these results suggest that controlled STBR fermentation combined with UF enables a reproducible, scalable production of S. epidermidis EVs without compromising vesicle integrity. This work provides an integrated framework for Gram-positive EV manufacturing and supports future translational applications.
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