journal article·2024-10-25Open access

Superparamagnetic Iron Oxide Nanoparticle-Labeled Extracellular Vesicles for Magnetic Resonance Imaging of Ischemic Stroke

Shannon Helsper, Xuegang Yuan, Richard Jeske, Jamini Bhagu, Colin Esmonde, Leanne Duke, Li Sun, Yan Li, Samuel C. Grant

ACS Applied Nano Materials· Volume 7· Issue 20

Abstract

Stroke is a leading cause of death and disability worldwide. Extracellular vesicles (EVs) derived from human mesenchymal stem cells (hMSCs) offer a unique and promising alternative to direct cell injection as part of a cell-based therapy for stroke treatment. The development of labeling strategies is essential to identifying the initial biodistribution and clearance of EV-based therapeutics. In this study, hMSC-EVs were labeled with ultrasmall superparamagnetic iron oxide (USPIO) nanoparticles for magnetic resonance imaging (MRI). Two methods of preparation were evaluated after EVs were sonicated in the presence of USPIO nanoparticles. The labeled EVs were purified by (1) ultracentrifugation only or (2) an extension of a harvesting approach that employs poly(ethylene glycol) (PEG) to enrich EVs. Following in vitro assessment, labeled EVs were applied to an ischemic stroke model and imaged both immediately and longitudinally using MRI. In vitro assessment showed the EV characteristics after USPIO nanoparticle labeling. The PEG method exhibited a 3.6-fold enhancement in contrast by using an equivalent USPIO concentration of 0.5 mg/mL and equivalent acquisition parameters (TE = 3.5 ms, TR = 5 s) when the dilution factor is considered. Sufficient USPIO nanoparticle labeling was achieved to visualize the initial biodistribution and assess the initial therapeutic potential. Taken together, simultaneous USPIO nanoparticle labeling and EV enrichment with PEG enhanced MRI contrast and improved outcomes with respect to delivery and ischemic stroke recovery.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Extracellular vesicles in disease · Machine Learning and ELM · Lattice Boltzmann Simulation Studies

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

National High Magnetic Field Laboratory, Florida State University, Tallahassee, Florida 32310, United States

Chemical & Biomedical Engineering, FAMU-FSU College of Engineering, Florida State University, Tallahassee, Florida 32310, United States

Department of Biomedical Sciences, College of Medicine, Florida State University, Tallahassee, Florida 32304, United States

Funding

National Institute of Neurological Disorders and Stroke

References (55)

Show source-provided references
1. 10.1083/jcb.20121113810.1083/jcb.201211138 ↗
2. 10.1016/j.biocel.2011.10.00510.1016/j.biocel.2011.10.005 ↗
3. 10.1186/s12964-024-01548-310.1186/s12964-024-01548-3 ↗
4. 10.2147/IJN.S15940410.2147/IJN.S159404 ↗
5. 10.1016/j.addr.2021.04.02710.1016/j.addr.2021.04.027 ↗
6. 10.1016/j.actbio.2019.12.02010.1016/j.actbio.2019.12.020 ↗
7. 10.5966/sctm.2015-007810.5966/sctm.2015-0078 ↗
8. 10.1177/096368971881699010.1177/0963689718816990 ↗
9. 10.3390/cells904085110.3390/cells9040851 ↗
10. 10.3390/ijms1806119010.3390/ijms18061190 ↗
11. 10.1007/s12975-018-0654-710.1007/s12975-018-0654-7 ↗
12. 10.1177/0271678X1770891710.1177/0271678X17708917 ↗
13. 10.1038/s41582-018-0126-410.1038/s41582-018-0126-4 ↗
14. 10.1021/acsnano.7b0449510.1021/acsnano.7b04495 ↗
15. 10.1021/acs.nanolett.8b0414810.1021/acs.nanolett.8b04148 ↗
16. 10.1021/nn404945r10.1021/nn404945r ↗
17. 10.1002/cpcb.2310.1002/cpcb.23 ↗
18. 10.1002/jev2.1205410.1002/jev2.12054 ↗
19. 10.1016/j.mri.2020.02.00110.1016/j.mri.2020.02.001 ↗
20. 10.1021/acsami.2c2056610.1021/acsami.2c20566 ↗
21. 10.3389/fphar.2022.81951610.3389/fphar.2022.819516 ↗
22. 10.1016/j.impact.2020.10026110.1016/j.impact.2020.100261 ↗
23. 10.1016/j.jconrel.2015.03.03310.1016/j.jconrel.2015.03.033 ↗
24. 10.1002/mrm.2071810.1002/mrm.20718 ↗
25. 10.1016/j.jcyt.2012.10.01310.1016/j.jcyt.2012.10.013 ↗
26. 10.1007/s12975-022-01057-w10.1007/s12975-022-01057-w ↗
27. 10.1007/s12975-021-00976-410.1007/s12975-021-00976-4 ↗
28. 10.1021/acsbiomaterials.0c0128610.1021/acsbiomaterials.0c01286 ↗
29. 10.2147/IJN.S10415210.2147/IJN.S104152 ↗
30. 10.1038/srep2397810.1038/srep23978 ↗
31. 10.1007/978-1-4939-7253-1_2510.1007/978-1-4939-7253-1_25 ↗
32. 10.1038/jcbfm.2014.17410.1038/jcbfm.2014.174 ↗
33. 10.1016/B978-0-12-802734-9.00018-410.1016/B978-0-12-802734-9.00018-4 ↗
34. 10.3389/fimmu.2019.0097710.3389/fimmu.2019.00977 ↗
35. 10.1002/cmmi.47010.1002/cmmi.470 ↗
36. 10.1161/01.STR.20.1.8410.1161/01.STR.20.1.84 ↗
37. 10.1016/j.jmr.2005.07.01310.1016/j.jmr.2005.07.013 ↗
38. 10.1073/pnas.152123011310.1073/pnas.1521230113 ↗
39. 10.1089/cell.2017.004710.1089/cell.2017.0047 ↗
40. 10.1371/journal.pone.016364510.1371/journal.pone.0163645 ↗
41. 10.2147/NDT.S21678410.2147/NDT.S216784 ↗
42. 10.3402/jev.v4.2950910.3402/jev.v4.29509 ↗
43. 10.1016/j.jcyt.2019.04.05510.1016/j.jcyt.2019.04.055 ↗
44. 10.3390/cells1009241310.3390/cells10092413 ↗
45. 10.1080/20013078.2018.153575010.1080/20013078.2018.1535750 ↗
46. 10.4252/wjsc.v13.i7.77610.4252/wjsc.v13.i7.776 ↗
47. 10.1002/jev2.1223510.1002/jev2.12235 ↗
48. 10.3389/fneur.2018.0093110.3389/fneur.2018.00931 ↗
49. 10.1016/j.omtn.2017.04.01010.1016/j.omtn.2017.04.010 ↗
50. 10.1016/j.actbio.2016.06.00310.1016/j.actbio.2016.06.003 ↗
51. 10.1016/j.actbio.2019.11.02710.1016/j.actbio.2019.11.027 ↗
52. 10.1016/j.pnmrs.2014.02.00110.1016/j.pnmrs.2014.02.001 ↗
53. 10.1007/s12975-012-0168-710.1007/s12975-012-0168-7 ↗
54. 10.1002/jex2.7000210.1002/jex2.70002 ↗
55. 10.1002/jev2.1245110.1002/jev2.12451 ↗