New methods for monitoring the immune system, in individual cells and in vivo

描述（由申请人提供）：了解单细胞中先天免疫信号网络的动态是免疫学的基本目标。使用结合活细胞成像，高通量图像分析，微流体细胞培养和计算网络建模的最新技术的方法，Covert Lab通过测量NFkB对刺激和时间依赖性刺激组合的单细胞反应来研究细胞如何解码复杂的环境信息（Nature，2010; Science Signaling 2009）。虽然这些方法和类似的方法在表征细胞群体内的表型异质性方面非常有用（例如，也在研究p53中），但从它们中得出的结论受到可测量输出数量相对较少的限制，以及到目前为止，几乎所有此类研究都是在培养细胞中进行的。我们建议大幅扩大免疫系统活细胞动态成像的范围，开发新技术以大幅增加可测量输出的数量，并实现体内测量。我们的具体目标是：（1）创建构建体和细胞的文库，其将使得能够在单个细胞中同时监测多种因子，包括多个平行信号传导途径和内源表达水平。(2)为了了解网络动力学如何控制基因表达，我们建议开发方法，将转录因子的动力学与单细胞中内源性基因表达的动力学相关联，通过将最近开发的RNA FISH技术与我们的活细胞成像技术相结合。这将是首次将动态转录因子活性与单个细胞中的基因表达进行直接比较。(3)Covert Lab将与加州大学圣地亚哥分校的Tannishtha Reya合作，将我们用于成像和定量单细胞中蛋白质定位的方法与她用于监测体内细胞运动的开创性工具相结合。通过结合这些方法，我们将是第一个观察单个细胞中转录因子在完整动物骨髓中移动时的动态。在实现这些目标的过程中，我们期望实现对环境信息如何在信号网络动态中编码的更详细和系统级的理解，并为科学界提供一些首个此类技术。

DESCRIPTION (provided by applicant): To understand the dynamics of the innate immune signaling network in single cells is a fundamental goal of immunology. Using an approach that combines the latest technologies for live-cell imaging, high-throughput image analysis, microfluidic cell culture and computational network modeling, the Covert Lab studies how cells decode complex environmental information by measuring the single-cell responses of NFkB to combinations of stimuli and time-dependent stimuli (Nature, 2010; Science Signaling 2009). Although these and similar approaches have been extremely useful in characterizing phenotypic heterogeneity within a population of cells (also in studying p53, for example), the conclusions that can be drawn from them are limited by the relatively low number of measureable outputs as well as the fact that until now, virtually all of this kind of research has been performed in cultued cells. We propose to dramatically expand the scope of live-cell dynamic imaging of the immune system, developing new technologies to dramatically increase the number of measureable outputs, and enable in vivo measurements. Our Specific Aims are: (1) to create a library of constructs and cells that will enable monitoring of a variety of factors, encompassing multiple parallel signaling pathways and at endogenous expression levels, simultaneously in individual cells. (2) To understand how network dynamics control gene expression, we propose to develop methods to correlate the dynamics of transcription factors with the dynamics of endogenous gene expression in single cells, by integrating recently developed techniques for RNA FISH with our live cell imaging technology. This will be the first time that dynamic transcription factor activity has ever been directly compared with gene expression in individual cells. (3) The Covert Lab will partner with Tannishtha Reya at UCSD to integrate our methods for imaging and quantifying protein localization in single cells with her pioneering tools for monitoring cellular movement in vivo. By combining these approaches, we will be the first to observe the dynamics of transcription factors in individual cells as they move through the bone marrow of intact animals. In achieving these goals, we expect to achieve a significantly more detailed and system-level understanding of how environmental information is encoded in signaling network dynamics, and to have produced some first-of-its-kind technology for the scientific community.