A Road Map to the Neocortex

描述 摘要： 大脑的一个非常复杂的部分，新皮层，被认为赋予我们产生有意识思维，发展语言和执行复杂感知和空间推理任务的能力。了解新皮层是如何构建的是了解我们大脑功能的关键。在这里，我们建议开发新的计算和实验工具，以帮助我们了解电活动和遗传电路是如何耦合在这个复杂的器官中产生不同类型的细胞的。虽然人们可能会想象一个异常复杂的基因网络产生了大脑，但几个重编程实验表明，只有少数几个对发育重要的关键因素控制着特定的命运选择。我们的目标是发现这些因素，以建立一个粗略的路线图的关键基因表达事件导致新皮层，并在此地图上的所有其他基因的表达模式。我们将使用具有小鼠大脑从妊娠中期到成年的发育过程中每个小鼠基因的空间和时间表达模式的数据来这样做。我们将开发一种新的计算范式来分析这些数据，并提取控制新皮层结构的规则。我们将在体外定向分化系统中直接测试这些规则，重点是锥体神经元。为了实现这样的测试，我们正在开发突破性的成像技术，以测量和干扰数千个单细胞中的基因表达和电活动，因为它们在体外从干细胞分化为有丝分裂后的锥体神经元。我们将测量我们的计算分析预测的候选因子的动态，以及通过使用多个荧光报告分子在单细胞中的钙和电活性。通过使用贝叶斯统计分析来分析这些单细胞时间序列表达和活性数据，并直接干扰特定基因表达的动态，

DESCRIPTION Abstract: An enormously complex part of the brain, the neocortex, is thought to give us the ability to generate conscious thought, develop language and perform complicated perception and spatial reasoning tasks. Understanding how the neocortex is built is key to understanding how our brain functions. Here, we propose to develop novel computational and experimental tools to help us understand how electrical activity and genetic circuits are coupled to generate the different cell types in this complex organ. While one might imagine an exceptionally elaborate network of genes giving rise to the brain, several reprogramming experiments suggest that just a handful developmentally important key factors control specific fate choices. We aim to discover these sets of factors to build a coarse road map of the key gene expression events leading to the neocortex, and over lay on this map the expression patterns of all the other genes. We will do so using the data that has the spatial and temporal expression pattern of every mouse gene during the course of the development of the mouse brain from mid gestation to adult. We will develop a novel computational paradigm to analyze this data and extract the rules governing the construction of the neocortex. We will test these rules directly in an in vitro directed differentiation system, focusing on the pyramidal neurons. To enable such tests, we are developing ground-breaking imaging technologies to both measure and perturb gene expression and electrical activity in thousands of single cells as they differentiate in vitro from stem cells to post-mitotic pyramidal neurons. We will measure dynamics of candidate factors predicted by our computational analysis as well as calcium and electrical activity in single cells by using multiple fluorescent reporters. By analyzing these single cell time-series expression and activity data using a Bayesian statistical analysis and directly perturbing the dynamics of expression of specific genes and e