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Orchestrating cells on a chip: Employing surface acoustic waves towards the formation of neural networks
For the investigation of cell-cell interaction in general and for neural communication and future applications of neural networks, a controllable and well-defined network structure is crucial. We here propose the implementation of an acoustically driven system for tunable and deliberate stimulation...
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Published in: | Physical review. E 2018-07, Vol.98 (1-1), p.012411-012411, Article 012411 |
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Main Authors: | , , , , , |
Format: | Article |
Language: | English |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | For the investigation of cell-cell interaction in general and for neural communication and future applications of neural networks, a controllable and well-defined network structure is crucial. We here propose the implementation of an acoustically driven system for tunable and deliberate stimulation and manipulation of cell growth on a chip. This piezoelectric chip allows us to generate a checkerboard-like standing surface acoustic wave pattern coupled to a fluid layer in a microfluidic chamber on top. Such a dynamically induced patterning lattice is shown to allow for the active positioning of the neurons and subsequent guided neurite outgrowth, thus finally overcoming the limitations of static approaches. After thorough characterization of the resulting tunable potential landscape, we successfully demonstrate cell adhesion and even growth of the such positioned cells within the well-defined pressure nodes. We demonstrate neuron growth at predetermined positions and observe a subsequent neurite outgrowth, even being correlated with the artificial potential landscape. For the very delicate and sensitive primary neural cells, this is a change of paradigm! Our experimental findings give us confidence that our hybrid lab-on-a-chip system in the near future will allow researchers to study cell-cell interaction of primary neurons. If scaled to a true network level, it will enable us to control and study how neural networks connect, interact, and communicate. |
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ISSN: | 2470-0045 2470-0053 |
DOI: | 10.1103/PhysRevE.98.012411 |