Neuroprotective Potential of Adipose Mesenchymal Stem Cell-derived Extracellular Vesicles on Damaged Motoneurons
Year: 2025
Type: article
Abstract: Intranasal delivery represents a non-invasive and promising route to directly target the central nervous system (CNS), as it enables therapeutic agents to bypass the blood–brain barrier and allows repeated administration with low invasiveness. In this context, extracellular vesicles derived from adipose mesenchymal stem cells (ASC-EVs) are gaining increasing attention for their intrinsic neuroprotective and immunomodulatory properties, making them attractive candidates for the treatment of neurodegenerative diseases such as amyotrophic lateral sclerosis (ALS). In this study, we established an in vitro epithelial barrier model using RPMI 2650 cells (derived from nasal epithelium carcinoma) cultured on transwell system and coupled with motor neuron-like NSC-34 cells treated with hydrogen peroxide to mimic oxidative stress-induced injury. This setup was designed to investigate the ability of ASC-EVs to cross the epithelial layer, representing the first step of access to the CNS following intranasal administration, and to reach damaged motor neurons. The study also explores the mechanisms underlying epithelial transport by evaluating both transcellular and paracellular routes through the use of selective pathway inhibitors and by monitoring changes in transepithelial electrical resistance (TEER) and junctional integrity. Our findings demonstrate that ASC-EVs are able to traverse the epithelial barrier and reach damaged motor neurons, where they are internalized and exert significant neuroprotective effects. These results provide a mechanistic basis supporting the potential of intranasally delivered ASC-EVs as a non-invasive therapeutic approach for ALS and other neurodegenerative disorders.
Author: Movement Sciences
Institution: University of Verona
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $1,024
OpenAlex ID: W4417277416
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