Nanoengineered Platforms for Neuronal Monitoring and Regeneration
Orit Shefi a b
a Faculty of Engineering
b The institute of Nanotechnologies and Advanced Materials
Proceedings of Neuronics Conference (Neuronics)
València, Spain, 2024 February 21st - 23rd
Organizers: Sabina Spiga and Juan Bisquert
Invited Speaker, Orit Shefi, presentation 031
DOI: https://doi.org/10.29363/nanoge.neuronics.2024.031
Publication date: 18th December 2023

The ability to manipulate and direct neuronal growth has great implications in basic science and tissue engineering. Physical mechanical forces, contact guidance cues and chemical cues play key roles in neuronal morphogenesis and network formation. In this talk I will present our recent studies of 2D and 3D nanostructured scaffolds as platforms for controlling neuronal growth. We grow neurons on substrates patterned with nanotopographic cues of different shapes and materials and study the effects on neuronal geometry, dynamics and function. We compare neurons interfacing the nano-patterned substrates to neurons interfacing other neurons to reveal mechanisms translating interactions into neuronal growth behavior. In-vivo, neurons grow in a 3-dimensional (3D) extracellular matrix (ECM). Imitating the 3D environment resembling the in-vivo conditions is important for effective regeneration post trauma. We have chosen a collagen hydrogel system as the 3D ECM analog to best mimic the natural environment and develop methods to orient the collagen fibers and use them as leading cues for neurons [1]. Current efforts to implant these modified gels to bridge gaps in injured PNS models effectively will be presented.

We also used magnetic manipulations, via MNPS, as mediators to apply forces locally on neurons and their environment, as drug carriers and as a method to organize cells remotely. We transform cells into magnetic units by the uptake of magnetic nanoparticles [2,3], as well as by coating the cells with magnetic particles[4,5]. Our system opens new possibilities for improved neuronal interfaces and neural network analysis for long time periods.

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