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

项目总结 发育进程的时间在早期胚胎中得到了很好的研究，但细胞谱系是产生的 是从生命后期的干细胞前体随机产生的，对该基因的了解相对较少 控制单个细胞启动发育的概率或它们的速率的网络 发育进程。从多能血液前体细胞培养小鼠T细胞是一个有利的因素 揭示这类系统机制的模型。T细胞途径中的各个阶段都很好 在基因表达模式中定义，并且可以跟踪从该途径的特定阶段开始的细胞 通过体外发育有效。胚胎早期或晚期T细胞祖细胞的不同队列 而出生后的生命在分化速度上有细胞固有的差异。我们假设 这一途径中最早的细胞开始于一个正稳定的“第一阶段”基因调控网络状态 这本质上是反对分化的，直到对信号的累积反应可以引发新的 网络状态。不同T细胞群之间内在分化速度的差异，以及 在分化完成之前，它们经历的增殖程度与 阶段1监管状态。然而，到目前为止，很难对这些网络进行批判性的剖析 因为在T细胞发育的早期阶段的细胞是罕见的，并且可能具有各种各样的异质性。 这一提议是由新的技术进步推动的，这为剖析这一点提供了一个令人兴奋的机会。 机制首次在单细胞中发挥作用，并通过一种新的系统生物学合作提供了 对调控扰动的单细胞转录反应的优势分析，无论是在基因上还是在基因上 细胞水平。新的计算方法被优化，以揭示基因网络改变是如何变化的 处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 用报告单个细胞的谱系承诺状态的荧光报告器开发的小鼠；成像 允许在整个承诺过程中跟踪活的、个体克隆的条件；以及有效的 Cas9转基因小鼠系统，允许我们有效地删除初级T细胞前体中的基因，从而 扰动对基因表达和发育动力学的影响是可以定义的。我们都可以 定义起始种群中的分子亚态，并监控特定调控因子的影响 利用单细胞核糖核酸序列(10基因组)和一种新的高度多元化的单分子荧光进行微扰 低丰度转录本高灵敏度定量的原位杂交技术。关键字的预测 网络调节器将在这里通过扰动和克隆区分的延时成像直接进行测试 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和 研究最早的前体波。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不稳定未承诺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.