Enhanced Angiogenic Potential of Electrically Stimulated Human Adipose-Derived MSCs for Ischemic Tissue Regeneration

Effective treatment of ischemic disease requires the reconstruction of blood vessels through the delivery of angiogenic factors, such as chemicals, proteins, and cells. In particular, substantial efforts have focused on enhancing the therapeutic potential of mesenchymal stem cells (MSCs) for treating ischemic diseases. In this study, we investigated the use of electrical stimulation (ES) to potentiate the pro-angiogenic properties of human adipose-derived MSCs (hAD-MSCs). Electrically potentiated MSCs (epMSCs) were generated by applying optimized ES parameters (0.3 V, 100 Hz). EpMSCs exhibited significantly enhanced angiogenic potential, including upregulated expression of pro-angiogenic factors (e.g., VEGF-A and HGF) and improved endothelial cell migration and tube formation in vitro. Transcriptomic and proteomic analyses revealed activation of key angiogenic pathways, particularly VEGFA-VEGFR2 signaling, which plays a critical role in enhancing the functionality of epMSCs. In vivo studies using a murine hindlimb ischemia model demonstrated that epMSCs enhanced blood flow recovery, induced angiogenesis, and reduced muscle atrophy more effectively than unstimulated MSCs. Overall, these findings suggest that electrical potentiation of MSCs is a promising strategy for effectively enhancing their angiogenic capabilities for treating ischemic diseases. Overall design: Total RNA was extracted from 1 × 106 cells and subjected to Quantitative RNA sequencing (Quant-Seq) analysis (Ebiogen, Seoul, Republic of Korea). Raw read counts were normalized using the Trimmed Mean of M-values method and transformed to count-per-million. The results were analyzed using ExDEGA software (Ebiogen, Seoul, Republic of Korea). DEGs (fold-changes = 1.5, p = 0.05) were visualized as a volcano plot. Pathway enrichment analysis was performed using the DAVID (http://david.abcc.ncifcrf.gov/) and QuickGO (https://www.ebi.ac.uk/QuickGO/) databases. Ninety-nine genes were represented as a heatmap using the MultiExperiment Viewer software (J. Craig Venter Institute, Rockville, MD, USA, version 4.9.0). Ten key GO biological processes were identified. Protein-protein interactions among DEGs were analyzed using the STRING v12.0, web tool (http://string-db.org/), with k-means clustering (three clusters).