Mesenchymal stromal cells (MSCs) possess broad immunoregulatory capabilities, positioning them as promising candidates for treating autoimmune diseases. Their immunoregulatory properties are primarily mediated by the release of paracrine factors, including extracellular vesicles (EVs). We previously demonstrated that the glycolytic reprogramming of human umbilical cord MSCs (UC-MSCs) enhances their ability to inhibit proinflammatory T cell proliferation and reduce the secretion of pro-inflammatory cytokines. However, no attention has yet been given to the underlying mechanisms. By comparing EVs from control and glycolytic UC-MSCs, this study seeks to define their immunoregulatory impact on activated immune cells and in experimental arthritis, and to elucidate the functional contribution of their microRNA cargo. EVs were isolated from control and glycolytic UC-MSCs by ultracentrifugation and characterized using nanoparticle tracking analysis (NTA) and flow cytometry (FACS). Their effects on memory CD4+ T cells and B cells isolated from healthy donors were assessed in vitro. EV internalization was measured on memory CD4+ T cells by FACS, confocal microscopy, and qPCR. In vivo, the effects were evaluated in the delayed-type hypersensitivity (DTH) and collagen-induced arthritis (CIA) models. Finally, the miRNA cargo and its potential immunoregulatory role were determined through miRNA sequencing comparing EVs derived from control and glycolytic UC-MSCs. EVs derived from glycolytic UC-MSCs potently suppress the pro-inflammatory T cell response and promote the generation of Tr1 cells, primarily via their miRNA cargo, unveiling a distinct mechanism of action that underpins their superior therapeutic efficacy in inflammatory and autoimmune diseases such as arthritis.

For Edge-Seq Data, 4 replicate EVs of untreated UC-MSCs, the controls, were generated: 109-YH1_F2203236 (Ctrl_1), 109-YH5_F2203240 (Ctrl_2), 109-YH9_F2203244 (Ctrl_3), and 109-YH13_F2203248 (Ctrl_4). And 4 replicate EVs of glycolytic UC-MSCs, treated with oligomycin: 109-YH3_F2203238 (Olig_1), 109-YH6_F2203241 (Olig_2), 109-YH10_F2203245 (Olig_3), and 109-YH14_F2203249 (Olig_4), were generated. Sample Ctrl_2 had to be removed from the analysis for this case, as it deviated from normalization. Raw data corresponds to microRNA_exosomas.xlsx and processed data (ready to input in Python/R) corresponds to main.csv. All scripts to reproduce further analysis of the main manuscript can be found here: https://github.com/cfarkas/miRNA_exosomes/tree/main. In the repository, miRNA-counts_example.csv located in /data/ is identical to main.csv from here, and both can be used as inputs for the GitHub scripts to reproduce all analyses. . ├── README.md ├── LICENSE ├── CITATION.cff ├── .gitignore ├── env/ │ ├── RUVSeq_env.yml │ └── requirements.txt ├── scripts/ │ ├── run_mirna_pipeline.R │ ├── tf_immune_analysis.py │ ├── tf_immune_heatmaps_ranked_table.py │ └── cross_species_mouse_seed_enrichment.R ├── resources/ │ └── miR_Family_Info.txt.zip ├── data/ │ └── miRNA-counts_example.csv └── docs/ ├── Commands_2026.txt └── miRNA_without_sample2_report.docx```results.zip``` corresponds to the main output of the GitHub pipeline, ```mirna_pipeline_github.zip``` corresponds to the GitHub repository itself.