Deciphering dynamic signals in control of cell fate decisions

项目概要 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以产生不可逆的 诸如是否增殖、分化或死亡等决定。炎症因子参与许多正常的活动 患病细胞的命运决定通过动态重组细胞内的蛋白质来启动信号。对于 例如，配体结合的 TNF 受体会在质膜附近短暂地组织大型蛋白质复合物， 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之间易位 细胞区室，例如细胞质和细胞核。动态属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）对于每个细胞对刺激的反应至关重要。鉴于有 即使在克隆细胞系中，细胞间也存在很大的异质性，固定时间点的静态测量无法 揭示动态信息处理的机制。我们假设相同的组件 在单个细胞中，信号通路是确定性地相互关联的，尽管存在大量的信号通路 细胞之间的异质性。在这里，我们建议对活细胞荧光报告基因进行多重表达，以实现上行- 和同一细胞中同一信号通路的下游成分，并关联时变信号 来自活细胞显微镜数据。结合使用定量成像、微流体和计算 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 将它们在几种不同的细胞系中进行比较。我们还将比较不同环境下的细胞反应 共享信号模块并汇聚于 NF-κB 转录系统的炎症因子，例如 TNF、LPS 或 IL-1 等。使用丰富的单细胞数据集，我们将使用转移熵来测量相互的 同一通路中时变信号特征之间的信息，并推断信号机制 转导以及与细胞命运的相关性。来自活细胞实验的数据将被纳入 机制模型正式化我们对信息如何通过信号网络传递的理解 转化为转录，并提出扰动来测试预测的机制。我们预计越来越多 准确的模型可能会导致非直观的策略来操纵单细胞的决策。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 理解疾病中根深蒂固的生物学问题的潜力，并利用这些知识来合理地解决问题 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.