High-content study of axonal guidance using optical protein patterning

在胚胎环境中，发育中的轴突遇到化学指示剂，这些化学指示剂在被生长锥加工后导致定向生长，从而允许与特定突触靶点连接。长距离和短距离的指导线索提供有吸引力的或抑制性的信息，生长锥，导致细胞骨架重组和转向事件。这些线索以其他细胞分泌的可扩散因子或位于细胞表面或细胞外基质（ECM）中的蛋白质的形式呈现空间分布，这些空间分布由轴突以一种仍有待完全破译的方式解释。 在过去的几年里，开发新的微加工策略来复制蛋白质梯度，用于体外研究轴突导向，受到了广泛的关注。这些技术被用来设计人工环境，其中可以精确控制介导神经元寻路的不同化学成分，从而允许对生物分子对细胞生长方向性的影响进行筛选测定。在这些新技术中，只有LAPAP能够制造任意形状的蛋白质图案。此外，与替代方法相反，LAPAP允许将指导分子组织成高度精细的模式。在这里，我们利用这种新的方法来探测细胞解释化学指标的空间分布的精确方式。 我的研究项目旨在了解轴突对准离散，噪声和不规则蛋白质分布的反应。我设计了模式和高内容的实验来评估一系列随机机制和信号通路，在非均匀环境中，如何导致一个令人惊讶的强大算法来建立神经元回路。导致生长锥转向的快速化学反应的复杂网络表现出对网络的各个组成部分的可变性的可靠耐受性，以及对找到到预先确定的突触伙伴的正确路线的高分辨率灵敏度。我们的主要目标是开创性地描述这种宽容。

In the embryonic environment, developing axons encounter chemical indicators that, upon processing by the growth cone, result in directed outgrowth allowing connections with specific synaptic targets. Long- and short-range guidance cues provide either attractive or inhibitory information to growth cones, leading to cytoskeletal reorganization and steering events. These cues, in the form of either diffusible factors secreted by other cells, or of proteins positioned on cell surfaces or in the extracellular matrix (ECM), take on spatial distributions that are interpreted by axons in a manner that remains to be fully deciphered. The development of novel microfabrication strategies to reproduce protein gradients for studying axonal guidance in vitro has received extensive attention over the last few years. These technologies are used to engineer artificial environments where the different chemical components mediating neuronal pathfinding can be precisely controlled, thereby permitting screening assays on the effects of biomolecules on the directionality of cellular outgrowth. Among these new technologies, LAPAP alone is able to fabricate protein patterns of arbitrary shapes. Moreover, as opposed to alternative approaches, LAPAP allows the organization of guidance molecules into highly elaborate patterns. Here we exploit this new method for probing the precise manner in which cells interpret the spatial distribution of chemical indicators. My research program aims to understand the response of axons to quasi-discrete, noisy and irregular protein distributions. I designed patterns and high-content experiments to evaluate how a series of stochastic mechanisms and signaling pathways, in non-homogeneous environments, lead to a surprisingly robust algorithm to establish neuronal circuitry. The complex network of rapid chemical reactions that lead to growth cone steering demonstrates both a solid tolerance to variability of the individual components of the network, and a high-resolution sensitivity towards finding the correct route to pre-identified synaptic partners. A pioneering characterization of such tolerance is our main goal.