Axon guidance of rat cortical neurons by microcontact printed gradients
Axon guidance of rat cortical neurons by microcontact printed gradients
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DOI：
10.1016/j.biomaterials.2010.11.036
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发表时间：
2011-03-01
期刊：
影响因子：
14
通讯作者：
Meffert, Simone H.
中科院分区：
文献类型：
--
作者：
Fricke, Rita;Zentis, Peter D.;Meffert, Simone H.
基底结合梯度以多种时空模式表达，在复杂神经回路的发育过程中起着至关重要的作用。通过研究一个定义的基质约束提示对一个受限神经网络的影响，可以更深入地理解轴突的引导机制。在这项研究中，我们构建了一个不连续的底物结合梯度来控制神经元细胞的位置、神经突生长的路径和轴突的方向性。利用层粘连蛋白/聚l -赖氨酸（PLL）或单独使用PLL进行微接触印刷，设计和制造了各种坡度、宽度和长度略有变化的梯度图案。研究了这些梯度对大鼠胚胎皮质神经元的神经突生长及其对轴突导向的影响。用Tau-1免疫染色法测定神经突长度，评价轴突。我们发现层粘连蛋白/PLL和PLL的微梯度指导神经元的粘附，不同程度地控制神经突的生长，并引导高达84%的轴突。蛋白质微模式对轴突引导和神经突生长的影响取决于所使用的蛋白质和几何参数。我们的方法在单个多极神经元的轴突定向方面非常成功，并且具有很高的效率。因此，它可以用于工程定义神经网络来分析功能电路的信号处理，以及揭示轴突引导机制的基本问题。(C) 2010 Elsevier Ltd.版权所有。
Substrate-bound gradients expressed in numerous spatio-temporal patterns play a crucial role during the development of complex neural circuits. A deeper understanding of the axon guidance mechanism is provided by studying the effect of a defined substrate-bound cue on a confined neural network. In this study, we constructed a discontinuous substrate-bound gradient to control neuronal cell position, the path of neurite growth, and axon directionality. A variety of gradient patterns, with slight changes in slope, width, and length were designed and fabricated by microcontact printing using laminin/poly-L-lysine (PLL) or PLL alone. The gradients were tested for neurite growth and their impact on axon guidance of embryonic rat cortical neurons. The neurite length was determined and the axon was evaluated by Tau-1 immuno-staining. We found that the microgradients of laminin/PLL and PLL directed neurons' adhesion, differentially controlled the neurite growth, and guided up to 84% of the axons. The effect of the protein micropattern on axon guidance and neurite growth depended on the protein and geometric parameters used. Our approach proved to be very successful in guiding axons of single multipolar neurons with very high efficiency. It could thereby be useful to engineer defined neural networks for analyzing signal processing of functional circuits, as well as to unravel fundamental questions of the axon guidance mechanism. (C) 2010 Elsevier Ltd. All rights reserved.