International Journal of Molecular Sciences (Sep 2024)

Comparison between USPIOs and SPIOs for Multimodal Imaging of Extracellular Vesicles Extracted from Adipose Tissue-Derived Adult Stem Cells

  • Arnaud M. Capuzzo,
  • Giusi Piccolantonio,
  • Alessandro Negri,
  • Pietro Bontempi,
  • Maria A. Lacavalla,
  • Manuela Malatesta,
  • Ilaria Scambi,
  • Raffaella Mariotti,
  • Kerstin Lüdtke-Buzug,
  • Mauro Corsi,
  • Pasquina Marzola

DOI
https://doi.org/10.3390/ijms25179701
Journal volume & issue
Vol. 25, no. 17
p. 9701

Abstract

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Adipose tissue-derived adult stem (ADAS) cells and extracellular vesicle (EV) therapy offer promising avenues for treating neurodegenerative diseases due to their accessibility and potential for autologous cell transplantation. However, the clinical application of ADAS cells or EVs is limited by the challenge of precisely identifying them in specific regions of interest. This study compares two superparamagnetic iron oxide nanoparticles, differing mainly in size, to determine their efficacy for allowing non-invasive ADAS tracking via MRI/MPI and indirect labeling of EVs. We compared a USPIO (about 5 nm) with an SPIO (Resovist®, about 70 nm). A physicochemical characterization of nanoparticles was conducted using DLS, TEM, MRI, and MPI. ADAS cells were labeled with the two nanoparticles, and their viability was assessed via MTT assay. MRI detected labeled cells, while TEM and Prussian Blue staining were employed to confirm cell uptake. The results revealed that Resovist® exhibited higher transversal relaxivity value than USPIO and, consequently, allows for detection with higher sensitivity by MRI. A 200 µgFe/mL concentration was identified as optimal for ADAS labeling. MPI detected only Resovist®. The findings suggest that Resovist® may offer enhanced detection of ADAS cells and EVs, making it suitable for multimodal imaging. Preliminary results obtained by extracting EVs from ADAS cells labeled with Resovist® indicate that EVs retain the nanoparticles, paving the way to an efficient and multimodal detection of EVs.

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