Duru Jens, Küchler Joël, Ihle Stephan J, Forró Csaba, Bernardi Aeneas, Girardin Sophie, Hengsteler Julian, Wheeler Stephen, Vörös János, Ruff Tobias
Laboratory of Biosensors and Bioelectronics, Institute for Biomedical Engineering, Eidgenössische Technische Hochschule (ETH) Zürich, Zürich, Switzerland.
Cui Laboratory, Stanford University, Stanford, CA, United States.
Front Neurosci. 2022 Feb 21;16:829884. doi: 10.3389/fnins.2022.829884. eCollection 2022.
In bottom-up neuroscience, questions on neural information processing are addressed by engineering small but reproducible biological neural networks of defined network topology . The network topology can be controlled by culturing neurons within polydimethylsiloxane (PDMS) microstructures that are combined with microelectrode arrays (MEAs) for electric access to the network. However, currently used glass MEAs are limited to 256 electrodes and pose a limitation to the spatial resolution as well as the design of more complex microstructures. The use of high density complementary metal-oxide-semiconductor (CMOS) MEAs greatly increases the spatial resolution, enabling sub-cellular readout and stimulation of neurons in defined neural networks. Unfortunately, the non-planar surface of CMOS MEAs complicates the attachment of PDMS microstructures. To overcome the problem of axons escaping the microstructures through the ridges of the CMOS MEA, we stamp-transferred a thin film of hexane-diluted PDMS onto the array such that the PDMS filled the ridges at the contact surface of the microstructures without clogging the axon guidance channels. This method resulted in 23 % of structurally fully connected but sealed networks on the CMOS MEA of which about 45 % showed spiking activity in all channels. Moreover, we provide an impedance-based method to visualize the exact location of the microstructures on the MEA and show that our method can confine axonal growth within the PDMS microstructures. Finally, the high spatial resolution of the CMOS MEA enabled us to show that action potentials follow the unidirectional topology of our circular multi-node microstructure.
在自下而上的神经科学中，关于神经信息处理的问题通过构建小型但可重复的、具有特定网络拓扑结构的生物神经网络来解决。网络拓扑结构可以通过在聚二甲基硅氧烷（PDMS）微结构中培养神经元来控制，这些微结构与微电极阵列（MEA）相结合，以便对网络进行电接入。然而，目前使用的玻璃MEA仅限于256个电极，这对空间分辨率以及更复杂微结构的设计构成了限制。使用高密度互补金属氧化物半导体（CMOS）MEA大大提高了空间分辨率，能够对特定神经网络中的神经元进行亚细胞读出和刺激。不幸的是，CMOS MEA的非平面表面使PDMS微结构的附着变得复杂。为了克服轴突通过CMOS MEA的脊逃出微结构的问题，我们将己烷稀释的PDMS薄膜压印转移到阵列上，使得PDMS填充微结构接触表面的脊，而不会堵塞轴突引导通道。这种方法在CMOS MEA上产生了23%结构上完全连接但密封的网络，其中约45%在所有通道中都表现出尖峰活动。此外，我们提供了一种基于阻抗的方法来可视化微结构在MEA上的确切位置，并表明我们的方法可以将轴突生长限制在PDMS微结构内。最后，CMOS MEA的高空间分辨率使我们能够表明动作电位遵循我们的圆形多节点微结构的单向拓扑结构。
