A comparison between long non-coding RNA repertoires of exosomes derived from naïve and cytokine-activated human mesenchymal stem cells

Autor: Hui Chu Lin, Oscar K. Lee, Chien Wei Lee, Yu Hsuan Wang
Rok vydání: 2020
Předmět:
Zdroj: Cytotherapy. 22:S53
ISSN: 1465-3249
Popis: Background & Aim Mesenchymal stem cells (MSCs) are being used in the fields of regenerative medicine, especially in the treatment of musculoskeletal disorders, diabetes, autoimmune diseases, neuronal injury, and neurodegeneration. The therapeutic effects of MSCs are largely mediated by paracrine factors including exosomes, which are nanometer-sized membrane-bound vesicles with functions as mediators of cell-cell communication. Interestingly, the therapeutic potential of MSCs could be prompted by pro-inflammatory cytokines stimulation. The alternation of MSC-secreted immunomodulatory factors composition after cytokine treatment strengthen the modulation of exosomal regenerative nature. MSC-derived exosomes bypass the concern of pro-fibrogenic potential and the possibility of tumorigenicity upon in vivo transplantation of MSCs and could represent a novel cell-free therapeutics. However, the composition of exosomes, in particular long non-coding RNAs (lncRNAs), are not comprehensively investigated. In this study, we compared the expression level of long non-coding RNAs in exosomes derived from naive and cytokine-activated human adipose-derived MSCs. Methods, Results & Conclusion Human adipose-derived MSCs were incubated with or without TNF-α and IFN-γ in serum-free medium for 24 hrs, and the condition medium were harvested for exosome isolation and small RNA sequencing. Exosome isolation protocol is as previously described (Wu and Lee, Stem Cell Research & Therapy (2017) 8:117). Small RNAs in exosomes were sequenced by the Illumina HiSeq 2500 System (50SE). Twenty upregulated lncRNAs after cytokine treatment are selected for lncRNA-RNA and lncRNA-protein interaction prediction. Our results provide a reference dataset that can help to identify the therapeutic targets for future translational studies.
Databáze: OpenAIRE