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Magnetoelectric Microrobots for Spinal Cord Injury Regeneration

Ye, H.; Zang, J.; Zhu, J.; Arx, D. v.; Pustovalov, V.; Mao, M.; Tang, Q.; Veciana, A.; Torlakcik, H.; Zhang, E.; Sevim, S.; Sanchis-Gual, R.; Chen, X.-Z.; Ahmed, D.; Sanchez-Vives, M. V.; Puigmarti-Luis, J.; Nelson, B. J.; Neuhauss, S. C. F.; Pane, S.

2024-08-08 neuroscience
10.1101/2024.08.06.606378 bioRxiv
Show abstract

Regenerative medicine continually seeks effective methods to address spinal cord injuries (SCI), which are known for their limited regenerative potential. Despite advances in neural progenitor cell (NPC) transplants for spinal cord injuries, challenges related to graft survival, reliable in vivo differentiation, and neural integration significantly hinder real functional recovery and limit clinical outcomes. This study introduces NPCbots, biohybrid microrobots engineered by integrating human-induced pluripotent stem cell-derived NPCs with magnetoelectric nanoparticles composed of cobalt ferrite-barium titanate. These enable magnetic navigation and neuronal stimulation, enhancing targeted therapeutic interventions. Our lab-on-a-chip system allows for the mass production of NPCbots, ensuring their differentiation and biocompatibility. Remarkably, in a zebrafish model of SCI, NPCbots stimulated by an alternating magnetic field demonstrated rapid in vivo differentiation and integration into damaged neural pathways, significantly enhancing neural regeneration. Within three days, injured zebrafish treated with NPCbots exhibited almost normal swimming behavior and significantly improved exploratory behavior, showcasing the potential of NPCbots to swiftly repair neural structures and restore the central nervous systems functionality in spinal cord injury models through non-invasive means. Additionally, precise in vitro and in vivo manipulation of NPCbots indicates their broader application in various neurodegenerative disorders, offering a promising route for effective spinal cord and neurological recovery.

Published in Nature Materials (predicted rank #18) · training set

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