Large-scale analysis of neurite growth dynamics on micropatterned substrates.
Large-scale analysis of neurite growth dynamics on micropatterned substrates.
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微图案基底上神经突生长动力学的大规模分析。
DOI：
10.1039/c0ib00058b
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发表时间：
2011
期刊：
影响因子：
--
通讯作者：
FatihYanik,Mehmet
中科院分区：
文献类型：
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作者：
Wissner-Gross,ZacharyD;Scott,MarkA;Ku,David;Ramaswamy,Priya;FatihYanik,Mehmet
在神经系统的发育和再生过程中，神经元表现出复杂的生长动力学，并且几个神经突竞争成为神经元的单个轴突。许多数学和生物物理模型已被提出来解释这种竞争，这仍然未经实验验证。神经突动力学的大规模、精确和可重复的测量一直难以执行，因为神经元具有不同数量的神经突，这些神经突本身具有复杂的形态。为了克服这些挑战，使用最少数量的原代神经元，我们产生了可重复的神经元形态在大规模上使用激光图案化的微米宽的条纹的粘附蛋白，否则高度非粘附性基板。通过分析数以千计的定量延时测量高度可重复的神经突生长动力学，我们表明，总的神经突生长加速，直到神经元突起，不成熟的神经突竞争，即使在很短的长度，神经元极性表现出明显的过渡神经突生长。提出的神经突生长模型只部分同意我们的实验观察。我们进一步表明，简单而具体的修改可以显着改善这些模型，但仍然不能完全预测复杂的神经突生长行为。我们的高内容的分析提出了显着的和不平凡的限制可能的机制模型的神经突生长和规范。这里提出的方法也可以用于大规模的化学和基于目标的屏幕上的各种复杂和微妙的表型的治疗发现使用最少数量的初级神经元。
During both development and regeneration of the nervous system, neurons display complex growth dynamics, and several neurites compete to become the neuron’s single axon. Numerous mathematical and biophysical models have been proposed to explain this competition, which remain experimentally unverified. Large-scale, precise, and repeatable measurements of neurite dynamics have been difficult to perform, since neurons have varying numbers of neurites, which themselves have complex morphologies. To overcome these challenges using a minimal number of primary neurons, we generated repeatable neuronal morphologies on a large scale using laser-patterned micron-wide stripes of adhesive proteins on an otherwise highly non-adherent substrate. By analyzing thousands of quantitative time-lapse measurements of highly reproducible neurite growth dynamics, we show that total neurite growth accelerates until neurons polarize, that immature neurites compete even at very short lengths, and that neuronal polarity exhibits a distinct transition as neurites grow. Proposed neurite growth models agree only partially with our experimental observations. We further show that simple yet specific modifications can significantly improve these models, but still do not fully predict the complex neurite growth behavior. Our high-content analysis puts significant and nontrivial constraints on possible mechanistic models of neurite growth and specification. The methodology presented here could also be employed in large-scale chemical and target-based screens on a variety of complex and subtle phenotypes for therapeutic discoveries using minimal numbers of primary neurons.