System dynamics and gene network architecture of early T-cell development

项目摘要 发育进程的时间在早期胚胎中得到了很好的研究，但细胞谱系是在 在生命的后期阶段，干细胞前体的stochketin，相对而言，对该基因的了解很少 控制单个细胞启动发育的概率或其发育速率的网络。 发展进程从多能血液前体发育小鼠T细胞是有利的。 揭示这类系统的机制的模型。T细胞通路中的各个阶段 在基因表达模式中定义，并且可以跟踪从沿着通路的特定阶段开始的细胞 通过体外发育有效地实现。来自早期或晚期胚胎的T细胞祖细胞的不同群组 和出生后的生命在细胞分化的速度上有内在的差异。我们假设 这一通路中最早的细胞开始具有正稳定的“阶段1”基因调控网络状态 这本质上反对分化，直到累积的信号反应可以诱导一个新的翻转。 网络状态不同T细胞群之间内在分化速度的差异，以及 它们在分化完成之前所经历的增殖程度与细胞周期的持续性相关。 第一阶段监管。然而，到目前为止，很难批判性地剖析这些网络 因为处于T细胞发育的最早阶段的细胞是罕见的，并且可能具有各种各样的异质性。 这项提议是由新的技术进步推动的，这些技术进步为我们提供了一个令人兴奋的机会， 这是第一次在单细胞中功能性地研究这种机制，并通过一项新的系统生物学合作， 上级分析的单细胞转录反应的调控扰动，无论是在基因和 细胞水平。新的计算方法被优化以揭示基因网络改变如何转移 介于正常或异常发育状态之间的细胞亚群。实验工具包括最近 用荧光报告分子开发小鼠，报告单个细胞的谱系定型状态;成像 条件，允许跟踪生活，通过整个承诺过程的个人克隆;和一个有效的 Cas9转基因小鼠系统允许我们有效地删除原代T细胞前体中的基因， 可以确定扰动对基因表达和发育动力学的影响。我们都能 定义起始群体中的分子亚状态，并监测特定调节因子的影响 干扰使用单细胞RNA-seq（10个基因组学）和一个新的高度多重单分子荧光 用于低丰度转录物高灵敏度定量的原位杂交技术。关键词预测 网络调节器将直接测试这里的扰动和克隆分化的延时成像 从单个细胞。最后，所需的小细胞数量使我们能够定义单个克隆内的差异， 来研究最早的个体发育波的前兆。我们建议应用这些新工具来确定 在早期T细胞的不同波中维持或破坏未定型状态的基因网络电路。

PROJECT SUMMARY Timing of developmental progression is well-studied in early embryos, but cell lineages are generated stochastically from stem-cell precursors in later stages of life, and relatively little is known about the gene networks that control the probabilities that individual cells will initiate development or their rates of developmental progression. Mouse T cell development from multipotent blood precursors is an advantageous model for revealing mechanisms of these kinds of systems. The stages within the T-cell pathway are well defined in gene expression patterns, and cells starting from specific stages along the pathway can be tracked efficiently through development in vitro. Different cohorts of T cell progenitors from earlier or later embryonic and postnatal life have cell-intrinsic differences in the speeds with which they can differentiate. We hypothesize that the earliest cells in this pathway begin with a positively stabilized “Phase 1” gene regulatory network state that intrinsically opposes differentiation, until cumulative responses to signaling can induce a flip to a new network state. The differences in intrinsic differentiation speeds between different T-cell cohorts, and the extents of proliferation they undergo before differentiation is complete, are correlated with the persistence of the phase 1 regulatory state. However, until now it has been difficult to dissect these networks critically because cells in the earliest stages of T-cell development are rare and may have varied kinds of heterogeneity. This proposal is driven by new technological advances that open an exciting opportunity to dissect this mechanism functionally in single cells for the first time, and by a new systems biology collaboration that offers superior analyses of single-cell transcriptional responses to regulatory perturbation, both at the gene and at the cell levels. The new computational methods are optimized for revealing how gene network alterations shift subsets of cells between normal or abnormal developmental states. The experimental tools include recently developed mice with fluorescent reporters that report lineage commitment status of individual cells; imaging conditions that allow tracking living, individual clones through the whole commitment process; and an effective Cas9 transgenic mouse system that allows us to delete genes efficiently in primary T-cell precursors, so that impacts of perturbations on both gene expression and developmental kinetics can be defined. We can both define molecular sub-states in the starting population and monitor the impacts of specific regulatory factor perturbations using single-cell RNA-seq (10Genomics) and a new highly multiplex single-molecule fluorescent in situ hybridization technology for high sensitivity quantitation of low-abundance transcripts. Predictions of key network regulators will be directly tested here by perturbations and time-lapse imaging of clones differentiating from single cells. Finally, the small cell numbers needed allow us to define variances within single clones and to study the earliest ontogenic waves of precursors. We propose to apply these new tools to determine the gene network circuitries that sustain or destabilize the uncommitted state in different waves of early T cells.