Identification of miRNA Reference Genes in Extracellular Vesicles from Adipose Derived Mesenchymal Stem Cells for Studying Osteoarthritis.
Enrico RagniCarlotta Perucca OrfeiPaola De LucaAlessandra ColombiniMarco ViganoGaia LuganoValentina BollatiLaura de GirolamoPublished in: International journal of molecular sciences (2019)
Osteoarthritis (OA) leads to chronic pain and disability, and traditional conservative treatments are not effective in the long term. The intra-articular injection of mesenchymal stem cells (MSCs) is considered a novel therapy for OA whose efficacy mainly relies on the adaptive release of paracrine molecules which are either soluble or extracellular vesicles (EVs) embedded. The correct quantification of EV-miRNAs using reliable reference genes (RGs) is a crucial step in optimizing this future therapeutic cell-free approach. The purpose of this study is to rate the stabilities of literature-selected proposed RGs for EV-miRNAs in adipose derived-MSCs (ASCs). EVs were isolated by ultracentrifugation from ASCs cultured with or without inflammatory priming mimicking OA synovial fluid condition. Expression of putative RGs (let-7a-5p, miR-16-5p, miR-23a-3p, miR-26a-5p, miR-101-3p, miR-103a-3p, miR-221-3p, miR-423-5p, miR-425-5p, U6 snRNA) was scored by using the algorithms geNorm, NormFinder, BestKeeper and ΔCt method. miR-16a-5p/miR-23a-3p yielded the most stable RGs, whereas let-7a-5p/miR-425-5p performed poorly. Outcomes were validated by qRT-PCR on miR-146a-5p, reported to be ASC-EVs enriched and involved in OA. Incorrect RG selection affected the evaluation of miR-146a-5p abundance and modulation by inflammation, with both values resulting strongly donor-dependent. Our findings demonstrated that an integrated approach of multiple algorithms is necessary to identify reliable, stable RGs for ASC-EVs miRNAs evaluation. A correct approach would increase the accuracy of embedded molecule assessments aimed to develop therapeutic strategies for the treatment of OA based on EVs.
Keyphrases
- mesenchymal stem cells
- knee osteoarthritis
- umbilical cord
- chronic pain
- cell free
- machine learning
- bioinformatics analysis
- oxidative stress
- bone marrow
- genome wide
- deep learning
- systematic review
- poor prognosis
- rheumatoid arthritis
- computed tomography
- nlrp inflammasome
- magnetic resonance imaging
- endothelial cells
- dna methylation
- stem cells
- ultrasound guided
- current status
- binding protein
- genome wide identification
- transcription factor
- dual energy
- combination therapy
- antibiotic resistance genes
- insulin resistance
- smoking cessation
- glycemic control
- circulating tumor cells