Physical understanding of axonal growth patterns on grooved substrates: groove ridge crossing versus longitudinal alignm
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RESEARCH ARTICLE
Physical understanding of axonal growth patterns on grooved substrates: groove ridge crossing versus longitudinal alignment Deming Zhang1,2 · Hairui Suo3 · Jin Qian4 · Jun Yin1,2 · Jianzhong Fu1,2 · Yong Huang5 Received: 29 April 2020 / Accepted: 29 July 2020 © Zhejiang University Press 2020
Abstract Surface topographies such as micrometric edges and grooves have been widely used to improve neuron outgrowth. However, finding the mechanism of neuron–surface interactions on grooved substrates remains a challenge. In this work, PC12 cells and chick forebrain neurons (CFNs) were cultured on grooved and smooth polyacrylonitrile substrates. It was found that CFNs showed a tendency of growing across groove ridges; while PC12 cells were only observed to grow in the longitudinal direction of grooves. To further investigate these observations, a 3D physical model of axonal outgrowth was developed. In this model, axon shafts are simulated as elastic 3D beams, accounting for the axon outgrowth as well as the focal contacts between axons and substrates. Moreover, the bending direction of axon tips during groove ridge crossing is governed by the energy minimization principle. Our physical model predicts that axonal groove ridge crossing is contributed by the bending compliance of axons, caused by lower Young’s modulus and smaller diameters. This work will aid the understanding of the mechanisms involved in axonal alignment and elongation of neurons guided by grooved substrates, and the obtained insights can be used to enhance the design of instructive scaffolds for nerve tissue engineering and regeneration applications. Keywords Grooved substrates · Neuron outgrowth · Axonal outgrowth model · Axonal crossing
Introduction
Electronic supplementary material The online version of this article (https://doi.org/10.1007/s42242-020-00089-1) contains supplementary material, which is available to authorized users. * Jun Yin [email protected] 1
The State Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou 310028, China
2
Key Laboratory of 3D Printing Process and Equipment of Zhejiang Province, School of Mechanical Engineering, Zhejiang University, Hangzhou 310028, China
3
School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China
4
Key Laboratory of Soft Machines and Smart Devices of Zhejiang Province, Department of Engineering Mechanics, Zhejiang University, Hangzhou 310027, China
5
Department of Mechanical and Aerospace Engineering, University of Florida, Gainesville, FL 32611, USA
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