Setting a Successful Sorting for Extracellular Vesicle Isolation
Extracellular vesicles (EVs) released by mesenchymal stromal cells (MSCs) contain a set of microRNAs with regenerative and anti-inflammatory roles. Therefore, purified MSC-EVs are envisioned as a next-generation therapeutic option for a wide array of diseases. In this protocol, we report the strategy for successfully sorting EVs from the supernatant of adipose-derived MSCs (ASCs), often used in orthopedics regenerative medicine applications. First, we described the sample preparation, focusing on EV isolation and labeling steps with carboxyfluorescein succinimidyl ester (CFSE) for fluorescence detection; subsequently, we detailed the sorting process, which constitutes the main part of the protocol. In addition to the rules defined by MISEV 2023 and MIFlowCyt EV guidelines, we applied specific experimental conditions concerning nozzle size, frequency, and sheath pressure. Morphological parameters are established using beads of diameters selected to cover the theoretical range of EV size. After ASC-EVs sorting, we performed a purity check of the sorted fraction by re-analyzing it with the sorter and verifying the EV size distribution with the nanoparticle tracking analysis technique. Due to the increasing importance of EVs, having a pure population to study and characterize is becoming crucial. Here, we demonstrate a winner strategy to set up sorting to achieve this goal.
Authors
- Laura de Girolamo (ORCID: https://orcid.org/0000-0002-9979-3092)
- Chiara Villa (ORCID: https://orcid.org/0000-0002-1667-5892)
- Daniela Boselli (ORCID: https://orcid.org/0000-0002-9356-5099)
- Enrico Ragni (ORCID: https://orcid.org/0000-0003-0272-7417)
- Monica Romanò
Institutions
- IRCCS Ospedale Galeazzi - Sant'Ambrogio (IT)
- IRCCS Ospedale San Raffaele (IT)
Publication Details
- Journal
- Journal of Visualized Experiments
- Published
- 2024-10-11
- DOI
- https://doi.org/10.3791/67232
- Citations
- 2
- Primary Topic
- Extracellular vesicles in disease
- Type
- article
- Field-Weighted Citation Impact
- 0.47