Kinetic modeling and single-cell molecular mechanisms of early T-cell commitment

描述(申请人提供)：成人组织多能干细胞有双重调节程序：一种是保留其分化为各种细胞类型的能力，另一种是允许它们在阻止或推迟分化的同时自我更新。要将单元格 区分，这两个程序组件之间的联系必须被打破。分化与自我更新的决定必须涉及基因调控网络中不同回路的激活和交叉抑制，但这些网络结构还没有被很好地理解。目前尚不清楚单个多能干细胞如何在分子水平上控制它们决定分化的时机，或者它们相对于克隆扩张到给定命运的分化速度。这就是我们建议在一个特别容易处理的实验系统中，使用单细胞实时成像、基因网络分析以及群体动力学和基因网络动力学的计算建模相结合来解决的问题。在造血方面，这个更广泛的问题可以解决，因为有很好的短期和长期多能干细胞的特征，加上各种部分受限的祖细胞，它们具有不同的发育潜力，但仍然推迟了直线承诺，保持了多种选择。T淋巴细胞发育途径是造血学的一个分支，在这个一般问题中可能会不同寻常地被解决。它基于一系列定义明确的中间状态，具有可重复的发育和基因表达特征，并且T细胞程序可以在体外实验中在造血祖细胞中触发和引导。与此同时，T细胞谱系承诺的途径涉及广泛的增殖，提出了如何区分促进分化的增殖和自我更新的问题，并提供了一个系统来测试控制这种区别的因素。我们已经组织了一个多学科的系统生物学合作，通过计算建模和实验分析的综合策略，剖析了选择进入T细胞途径的基础。我们的初步工作表明，T细胞前体细胞最初经历了一个自我更新的类似增殖的阶段，在这个阶段分化能力被推迟，而在单个细胞中分化的准备最终是由内在调节变化提供的。这种转换背后的调节电路现在可以通过利用单细胞实时跟踪和基因表达分析方法的新途径来访问，同时利用我们最近对早期T细胞前体的全基因组转录组分析的结果。在这里，我们建议：通过对单个细胞中T细胞系分化选择与增殖相关的动力学建模，建立一个框架来解决这个问题；通过实时成像来追踪单个细胞的命运；通过实时成像来确定单个细胞的基因表达特征，以最好地预测它们的发育行为；并以交错、迭代的方式使用计算和实验方法来推断控制进入T细胞途径的基因网络结构，并验证其关键联系。

DESCRIPTION (provided by applicant): Adult tissue multipotent stem cells have a double regulatory program: one that preserves their ability to differentiate into various cell types, and one that allows them to self-renew while preventing or postponing differentiation. For the cells to differentiate, the link between these two program components must be broken. Differentiation vs. self-renewal decisions must involve activation and cross-inhibition of different circuits in a gene regulatory network, but these network architectures are not well understood. It is not known at a molecular level how individual multipotent stem cells control the timing of their decisions to differentiate, or the speed with which they will differentiate relative to clonal expansion to a given fate. This is the problem that we propose to address using a combination of single-cell live imaging, gene network analysis, and computational modeling of population dynamics and gene network dynamics in a particularly tractable experimental system. In hematopoiesis, this wider problem can be addressed because there is excellent characterization of short- term and long-term multipotent stem cells, plus a variety of partially restricted progenitor cells which have different repertoires of developmental potential but still defer lineag commitment, keeping multiple options open. The T-lymphocyte developmental pathway is one branch of hematopoiesis in which this general question may be unusually accessible. It is based on a well-defined series of intermediate states with reproducible developmental and gene expression characteristics, and the T cell program can be triggered and guided in hematopoietic precursors experimentally in vitro. At the same time, the path to T-cell lineage commitment involves extensive proliferation, raising the question of how proliferation that advances differentiation may be distinguished from self-renewal, and providing a system in which to test factors that control this distinction. We have assembled a multidisciplinary systems biology collaboration to dissect the basis of the choice to enter the T-cell pathway, by an integrated strategy of computational modeling and experimental analysis. Our initial work has shown that T cell precursors initially go through a phase of self-renewal-like proliferation in which differentiation competence is delayed, and that readiness to differentiate in single cells is finaly provided by intrinsic regulatory changes. The regulatory circuitry underlying this switch is now accessible by exploiting new access to single-cell live tracking and gene expression analysis methods while leveraging the results of our recent genome-wide transcriptome analyses of early T-cell precursors. Here we propose: to establish a framework for the problem by modeling the kinetics of T-lineage differentiation choices relative to proliferation in individual cells; to trak individual cell fates forward and backward through the T-cell developmental process by live imaging; to determine gene expression features of individual cells that best predict their developmental behavior; and to use computational and experimental approaches in an interlaced, iterative way to deduce the gene network architecture that controls entry into the T-cell pathway, and to validate its key linkages.