Abstract
During the last two decades, mesenchymal stem cells (MSCs) gained a place of privilege in the field of regenerative medicine. Recently, extracellular vesicles (EVs) have been identified as major mediators of MSCs immunosuppressive as well as pro-regenerative activities in many disease models, including inflammatory/degenerative conditions as joint diseases and osteoarthritis. In order to shed light on EVs potential, a rigorous profiling of embedded proteins, lipids and nucleic acids (mRNA/miRNA) is mandatory. Nevertheless, reliable strategies to efficiently score miRNA cargo and modulation under diverse experimental conditions or treatments are missing. The aim of this work was to identify reliable reference genes (RGs) to analyze miRNA content in EVs secreted by adipose-derived MSCs (ASCs) and verify their consistency under inflammatory conditions that were proposed to enhance ASC-EVs immunomodulatory and regenerative potential. RefFinder algorithm, that integrates the currently available major computational programs (geNorm, NormFinder, BestKeeper, and Delta Ct method), allowed to identify miR-22-5p and miR-29a-5p as the most stable RGs. Notably, both miRNAs maintained the highest stability when EVs isolated from IFNg-treated ASCs were included in the analysis. In addition, considerable effects of suboptimal RGs choice on the reliable quantification of miRNAs involved at different levels (tissue homeostasis or macrophage polarization) in the osteoarthritis phenotype, and thus considered as promising therapeutic molecule, have clearly been demonstrated. In conclusion, a proper normalization method is not only needed for research purposes but also mandatory to characterize clinical products and predict their therapeutic potential, especially in the emerging field of MSCs derived-EVs as new tools for regenerative medicine.