| Pacific Rim Symposium on Surfaces, Coatings and Interfaces (PacSurf 2018) | |
| Biomaterial Surfaces & Interfaces | Wednesday Sessions |
| Session BI-WeM |
| Session: | Soft Surfaces and Biofunctional Coatings |
| Presenter: | Hideaki Yamamoto, Tohoku University, Japan |
| Authors: | H. Yamamoto, Tohoku University, Japan A. Hirano-Iwata, Tohoku University, Japan |
| Correspondent: | Click to Email |
Nerve cells in culture take irreplaceable roles in molecular and cellular neuroscience. However, the fact that neurons form random connections, which are substantially different from the actual brain, has limited the wide application of cell culture in systems-level studies.
Surface modification combined with microfabrication has a high potential to circumvent this limitation of cell culture technology in neuroscience [1-2]. By patterning biomolecules that scaffolds cellular growth, a glass coverslip can be functionalized so that growth of primary neurons can be controlled extrinsically, at the level of both individual cells [3-5] and cell populations [6]. Taking advantage of the cell micropatterning technology, we reconstitute functional neuronal networks of rat cortical neurons and investigate how meso-scale connectivity among neurons determines network dynamics. We focus on the modular organization of brain networks, characterized by the presence of densely-connected subsystems, i.e., modules, that are weakly interacting with each other [7]. Analysis of spontaneous neural activity by fluorescence calcium imaging shows that an atypical dynamics of the cultured networks, characterized by a bursting activity that is highly-synchronized across the whole network, is suppressed by the induction of modular organization in the networks. Increasing the degree of modularization causes the networks to generate activity patterns that are spatiotemporally more complex. Our results demonstrate that surface micropatterning expands the cell culture system as a unique tool to model and study the structure-function relationships in living neuronal networks.
References: [1] Offenhaeusser et al., Soft Matter 3, 290-298 (2007). [2] Wheeler et al., Proc. IEEE 98, 398-406 (2010). [3] Yamamoto et al., Appl. Phys. Lett. 109, 043703 (2016). [4] Kono et al., PLoS ONE 11, e0160987 (2016). [5] Matsumura et al., Sci. Rep. 8, 9905 (2018). [6] Yamamoto et al., Phys. Rev. E 94, 012407 (2016). [7] Yamamoto et al., Front. Comput. Neurosci. 12, 17 (2018).