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。我们还将在预测的不同路径之间为记者多路表达 来点相声。使用定量成像、机器人控制的细胞培养和计算的混合 技术，我们将在广泛的实验条件下从单个细胞提取随时间变化的数据 反映细胞在体内可能遇到的情况。我们还将比较不同炎症状态下的细胞反应 共享信号模块并汇聚在NF-κB转录系统上的因子，我们将学习如何 免疫细胞和癌细胞在共培养和更高维度的细胞结构中传递这些信号。 使用丰富的单细胞数据集，我们将识别信号转导的紧急特性，并推断转移 连接信号机制并与细胞命运相关的功能。来自活细胞实验的数据将是 结合到机械模型中，以形式化地理解信息是如何通过 信号网络进入转录，并建议扰动以测试预测的机制。我们预料到 越来越精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。穿过 对动态分子信号如何编码、处理和解码信息的详细了解，我们有 了解深深植根于疾病的生物问题并利用这些知识 合理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. We will also multiplex expression for reporters between pathways predicted to have crosstalk. Using a hybrid of quantitative imaging, robot-controlled cell cultures, and computational techniques, we will extract time-varying data from single cells in a broad range of experimental condition that reflect what cells may encounter in vivo. We will also compare cellular responses across different inflammatory factors that share signaling modules and converge on the NF-κB transcriptional system, and we will learn how immune and cancer cells communicate these signals in co-cultures and higher-dimensional cellular structures. Using a rich single-cell dataset, we will identify emergent properties of signal transduction, and infer transfer functions that connect signaling mechanisms and correlate 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.