Neural stem cell fate decisions: clonal and spatially-resolved approaches.

大脑是一个高度复杂的组织，由数十亿个细胞和数万亿个连接组成。在大脑中发现的干细胞，被称为神经干细胞，其任务是在正确的时间和正确的数量产生所有适当的细胞类型（如神经元），以确保大脑在发育过程中正确组装。了解胚胎大脑中的神经干细胞如何分化为构成大脑的所有细胞一直受到阻碍，因为我们以前无法一次跟踪这些干细胞。虽然在神经干细胞内发现的分子在这些细胞向其后代细胞类型的分化中具有无可争议的作用，但关于神经干细胞与相邻细胞之间的相互作用以及这可能如何影响神经干细胞的分化的知之甚少。细胞之间的这种相互作用（称为细胞-细胞相互作用）已知会激活神经干细胞内部的分子级联，导致基因的表达，这些基因可以控制神经干细胞可以产生的子细胞的类型和数量。我的研究计划旨在建立一个详细的模型，说明胚胎脑中的单个神经干细胞如何在妊娠后期产生所有的子代细胞类型，并揭示细胞间相互作用在这些过程中所起的作用。该模型不仅可以及时捕捉神经干细胞的行为（随着大脑的发育），还可以将这些事件置于大脑中精确位置的空间背景中。为了做到这一点，我们将开发出一种方法，该方法将允许以高度平行的方式跟踪发育过程中由单个神经干细胞产生的子代细胞。然后，我们将使用这种方法产生的数据来建立发育过程中神经干细胞分化的详细模型。随后，我们将使用新的和现有的方法来理解细胞间的相互作用，在空间上解决的方式，并评估这些相互作用在神经干细胞的分化的功能。总的来说，这些研究将使我们能够了解单个干细胞如何产生发育过程中构建组织所需的所有子代细胞类型。

The brain is a highly complex tissue that is made up of billions of cells and trillions of connections. Stem cells found in the brain, referred to as neural stem cells, are tasked with producing all of the appropriate cell types (such as neurons), at the right times and in the right numbers to ensure the brain is assembled correctly during development. Understanding how neural stem cells in the embryonic brain differentiate into all of the cells which make up the brain has been hampered by our previous inability to follow these stem cells one cell at a time. While molecules found inside neural stem cells have indisputable roles in the differentiation of these cell towards their progeny cell types, much less is know with respect to the interactions between neural stem cells and neighboring cells and how this may impact the differentiation of neural stem cells. Such interactions between cells (known as cell-cell interactions) are known to activate molecular cascades insides neural stem cells that lead to the expression of genes which could control the types and number of daughter cells that neural stem cells can produce. My research program aims to build a detailed model of how single neural stem cells in the embryonic brain give rise to all of their progeny cell types late in gestation and reveal the roles played by cell-cell interactions in these processes. This model will not only capture neural stem cell behavior in time (as the brain develops) but also place these events in a spatial context in precise locations in the brain. To do this, we will develop methods which will allow use to track, in a highly parallel manner, the progeny cells produced from single neural stem cells during development. We will then use the data produced from this approach to build a detailed model of neural stem cell differentiation during development. Subsequently, we will use new and existing methods to understand cell-cell interactions in a spatially-resolved manner and assess the functions of these interactions in the differentiation of neural stem cells. Collectively, these studies will enable us to understand how single stem cells produce all of the progeny cell types needed to build a tissue during development.