High resolution neuronal lineage tracing

项目摘要 神经前体细胞是如何产生大量不同类型的神经元和神经胶质细胞的 这个话题在很大程度上仍然没有得到解决。果蝇是研究这些复合体的关键模型系统 神经发生的问题和神经系统的研究促成了几个重要的概念 这适用于哺乳动物。这些包括时间和空间模式、细胞死亡、神经和/或神经胶质指定 和不对称的细胞分裂。然而，对其神经元谱系的完全分辨仍然难以捉摸。在这 我们将利用来自合成生物学的强大基因工具来重建整个 单细胞分辨率下的多个脑结构的神经元谱系。我们将开发转基因苍蝇，在其中 血统可以通过单细胞转录组自动记录和分析。我们假设 我们对成人大脑发育的结构和分子性质的知识将为我们提供一个独特的 优势不仅是重建果蝇大脑的整个谱系树，而且还可以形成新的假设 关于每个大脑结构如何非常忠实和统一地由前体细胞形成。 目的1研制果蝇进行性血统记录仪。目前还没有建立起 果蝇神经系统中基于CRISPR的谱系方法。我们将使果蝇中的格式塔适应 ‘伤痕’DNA在血统发展过程中，并逐步记录这一血统的发展。我们会 使用遗传工具获得对我们血统测量的时空控制，并用于计算机模拟 条码结构，以优化系统的活跃性。我们将产生具有足够目标位置的苍蝇 捕捉在神经元发育过程中产生的整个神经元多样性。我们将进行实证评估 不同版本的技术，并选择最好的一种进行单细胞谱系追踪。 目的2定义成人大脑中神经元的出生顺序和克隆关系。我们会追踪血统 通过神经元发育的同时对单细胞的转录组进行测序。这应该是 使我们能够使用我们的单细胞图谱来识别不同的神经亚型。我们将描述这种血统的特征 每个细胞的信息，并将其与已公布的能够重建多树谱系的方法相结合 在我们的数据中识别不同的血统关系。我们将建立在神经元的刻板模式上 完善这一结构和重建神经元谱系的进展 目的3对重建的神经元谱系进行实验验证。我们将利用我们的先验知识 神经元发育结合后验证，作为我们血统重建的基准。我们会 首先，将我们对果蝇局部运动检测器的谱系重建与它们已知的简单和 明确的血统。我们将使用区域特定的Gal4线来追踪不同的子区域 神经上皮细胞，然后流式细胞仪和单细胞测序，以确定这些地区出生的神经元。 在我们重建的树中评估这些关系将为我们的谱系树提供进一步的验证。

Project Summary How neuronal progenitor cells produce an enormous diversity of neuronal and glial cell types is a fundamental topic that remains largely unresolved. Drosophila has been a pivotal model system to study these complex questions of neurogenesis and research in its nervous system has contributed to several important concepts that apply to mammals. These include temporal and spatial patterning, cell death, neural and/or glial specification and asymmetric cell division. Nevertheless, the full resolution of its neuronal lineages remains elusive. In this proposal we will take advantage of powerful genetic tools derived from synthetic biology to reconstruct the entire neuronal lineage of multiple brain structures at single cell resolution. We will develop transgenic flies in which lineages can be autonomously recorded and analyzed through single cell transcriptomics. We hypothesize that our knowledge on the structure and molecular nature of adult brain development will provide us with a unique advantage to not only reconstruct the entire lineage tree of the Drosophila brain, but also to form new hypotheses on how each of the brain structures form very faithfully and uniformly from precursor cells. Aim 1 Development of a progressive lineage recorder in Drosophila. There are currently no established CRISPR-based lineage methods in the Drosophila nervous system. We will adapt GESTALT in Drosophila to `scar' the DNA during lineage progression and progressively record this lineage through development. We will use genetic tools to gain spatio-temporal control over our lineage measurements and use in silico modeling of the barcode structure to optimize the activity of the system. We will generate flies with enough target sites to capture the entire neuronal diversity generated during neuronal development. We will empirically evaluate different versions of the technology and select the best one for single cell lineage tracing. Aim 2 Defining neuronal birth order and clonal relationships in the adult brain. We will lineage trace through the neuronal development while simultaneous sequencing the transcriptome of single cells. This should allow us to identify the different neural subtypes using our single cell atlas. We will characterize the lineage information per cell and combine this with the published methods capable of reconstructing multi-tree lineages to identify different lineage relationships in our data. We will build on the stereotypical mode of neuronal development to refine this structure and reconstruct the neuronal lineages Aim 3 Experimental validation of the reconstructed neuronal lineages. We will use our prior knowledge of neuronal development in combination with post hoc validation to benchmark our lineage reconstruction. We will first compare our lineage reconstruction of the local motion detectors in Drosophila to their known simple and well-defined lineage. We will use region-specific Gal4 lines to lineage trace different subregions of the neuroepithelium followed by FACS and single cell sequencing to identify the neurons born in these regions. Evaluation of those relationships in our reconstructed tree will provide further validation for our lineage trees.