Deciphering dynamic signals in control of cell fate decisions

项目总结 从长远来看，我们的目标是了解单细胞如何整合和处理信息，使之不可逆转 决定是增殖、分化还是消亡。炎症因子参与了许多正常的 而疾病的细胞命运决定通过动态重新组织细胞内的蛋白质来启动信号。为 例如，配体结合的肿瘤坏死因子受体瞬间在质膜附近形成大的蛋白质复合体， 这些在细胞内可见为离散的点状结构，而其他蛋白质则在 细胞室，如细胞质和细胞核。动态特性是一个新兴的原理 信号转导电路内的分子提供时间代码(包括变化率，幅度， 持续时间或频率等)，这对每个细胞对刺激的反应是关键的。鉴于有一种 即使在克隆细胞系中，细胞间的显著异质性，固定时间点的静态测量也不能 揭示了动态信息加工的机制。我们假设相同的组件 在单个细胞中，信号通路彼此确定地联系在一起，即使有大量的 细胞间的异质性。在这里，我们建议将活细胞荧光记者的多重表达用于UP-1。 以及同一信令通路的下行分量，并将时变信号关联起来 从活细胞显微镜数据。使用定量成像、微流体和计算的混合技术 我们将从每个实验条件下的100-1000个单细胞中提取随时间变化的数据，以及 在几个不同的细胞系中比较它们。我们还将比较不同类型的细胞反应 共享信号模块并聚集在NF-κB转录系统上的炎症因子，如 肿瘤坏死因子、内毒素或白介素1等。使用一个丰富的单细胞数据集，我们将使用传递熵来衡量相互 同一路径中时变信号的特征之间的信息，并推断信号的机制 转导除了与细胞命运相关外。来自活细胞实验的数据将被合并到 机械模型，使我们对如何通过信令网络传递信息的理解形式化 转录，并建议扰动，以测试预测的机制。我们预计这一点会越来越多 准确的模型可能会导致在单个细胞中操纵决策的非直观策略。通过详细的 了解动态分子信号如何编码、处理和解码信息，我们有 有可能理解根植于疾病的生物学问题，并利用这些知识理性地 设计影响细胞命运决定的疗法。

PROJECT SUMMARY In the long-term, our goal is to understand how single cells integrate and process information to make irreversible decisions such as whether to proliferate, differentiate or die. Inflammatory factors that participate in many normal and diseased cell fate decisions initiate signals by dynamically re-organizing proteins within the cell. For example, ligand-bound TNF receptors transiently organize large protein complexes near the plasma membrane, and these are visible within the cell as discrete punctate structures, whereas other proteins translocate between cellular compartments such as the cytoplasm and the nucleus. It is an emerging principle that dynamic properties of molecules within signal transduction circuits provide temporal codes (including rate of change, amplitude, duration or frequency among others) that are critical to each cell’s response to stimulus. Given that there is substantial cell-to-cell heterogeneity, even in clonal cell lines, static measurements at fixed time points cannot reveal the mechanisms of dynamic information processing. We hypothesize that components of the same signaling pathway are deterministically linked to one another in a single cell, even though there is substantial heterogeneity between cells. Here, we propose to multiplex expression of live-cell fluorescent reporters for up- and down-stream components of the same signaling pathway in the same cell, and correlate time-varying signals from live-cell microscopy data. Using a hybrid of quantitative imaging, microfluidics and computational techniques we will extract time-varying data from 100s-1000s of single cells in each experimental condition, and compare them across several different cell lines. We will also compare cellular responses across different inflammatory factors that share signaling modules and converge on the NF-κB transcriptional system, such as TNF, LPS or IL-1 among others. Using a rich single-cell dataset, we will use transfer entropy to measure mutual information between features of time-varying signals in the same pathway, and infer mechanisms of signal transduction in addition to correlations with cell fate. Data from live-cell experiments will be incorporated into mechanistic models to formalize our understanding of how information is relayed through the signaling network into transcription, and suggest perturbations to test predicted mechanisms. We anticipate that increasingly accurate models may lead to non-intuitive strategies to manipulate decisions in single cells. Through a detailed understanding of how dynamic molecular signals encode, process, and decode information, we have the potential to understand biological problems that are deeply rooted in disease, and use this knowledge to rationally design therapies that impact cell fate decisions.