Single-cell analysis of TNF-induced signaling, transcription and fate decisions

描述(申请人提供)：感受性细胞如何“计算”它对配体的反应？尽管积累了大量关于信号转导途径的数据，但我们无法准确预测细胞对配体或药物的反应，这表明我们对这个问题的答案仍然不完整。信号转导网络是庞大的、相互连接的、高度动态的；我们仍然需要了解细胞如何在时间和空间上整合信号。肿瘤坏死因子是一种炎症调节因子，是一种特别有趣的信号转导模型系统，因为它是一种配体，可以诱导反对支持生存和支持死亡的信号通路。尽管肿瘤坏死因子受体1(TNFR1)的表达普遍存在，但一些细胞对肿瘤坏死因子的反应是通过分化和增殖，而另一些细胞则致力于细胞死亡。引人注目的是，即使是用高浓度的肿瘤坏死因子处理的克隆性癌细胞也表现出变异性：一些细胞死亡，而另一些细胞存活。是什么决定了肿瘤坏死因子治疗的癌细胞是存活还是死亡？为了解决这个问题，我们将：1.使用核因子-β和caspase信号动力学的同细胞跟踪和自动图像分析来量化核因子-β和半胱氨酸天冬氨酸酶信号动力学以及细胞环境在肿瘤坏死因子诱导的细胞命运决定中的作用。利用这些数据，我们将检验假设：i)核因子-β激活动力学影响caspase激活动力学，ii)信号动力学和细胞外环境都对肿瘤坏死因子诱导的细胞命运有很大贡献。2.破译细胞整合细胞内信号和外部环境以决定肿瘤坏死因子诱导的细胞命运的逻辑。通过对来自目标1的数据进行多变量回归分析，我们将建立将细胞外信号和细胞内信号与肿瘤坏死因子诱导的细胞命运联系起来的模型。通过比较肿瘤坏死因子诱导的细胞决策过程的相互竞争的模型，我们将获得对驱动肿瘤坏死因子诱导的细胞命运的调控电路的机械性见解。3.针对肿瘤坏死因子信号网络的转录臂，我们将检验核转录因子-kB核转位动力学是否能定量预测转录输出。使用一种新的工作流程进行同一细胞的核因子-kB动态成像和单分子FISH的mRNA计数，我们将直接建立核因子-kB易位动力学与其转录活性之间的关系。最后，我们将使用蛋白质结合 P65-p50异二聚体和p50-p50同源二聚体之间的竞争如何有助于解码核因子-kB的激活和细胞系对细胞系对肿瘤坏死因子的反应的变异性。通过这项工作，我们采用了一种新的方法来研究信号转导，利用细胞间的可变性来定量地了解细胞如何整合信息来确定它们对配体的反应行为。这些方法将有助于理解肿瘤坏死因子诱导的细胞死亡的多因素控制，并将广泛适用于其他信号转导网络的研究。

DESCRIPTION (provided by applicant): How does a receptive cell "compute" its response to a ligand? Despite large amounts of accumulated data on signal transduction pathways, our inability to accurately predict the response of cells to ligands or drugs indicates that our answer to this question are still incomplete. Signal transduction networks are large, interconnected and highly dynamic; we still need to understand how cells integrate signals in time and space. Tumor Necrosis Factor (TNF), a regulator of inflammation, is a particularly interesting model system for signal transduction because it is a ligand that induces opposing pro-survival and pro-death signaling pathways. Although TNF receptor 1 (TNFR1) expression is ubiquitous, some cells respond to TNF by differentiating and proliferating, while others commit to cell death. Strikingly, even clonal cancer cells treated with high TNF concentrations show variability: some cells die but others survive. What determines whether a TNF-treated cancer cell survives or dies? To tackle this question, we will: 1. Use same-cell tracking of NF-?B and caspase signaling dynamics with automated image analysis to quantify the respective contributions of NF-?B and caspase signaling dynamics as well as cellular context to TNF- induced cell fate decision. Using these data we will test the hypotheses that: i) NF-?B activation dynamics influence caspase activation dynamics and ii) both signaling dynamics and extracellular context are strong contributors to TNF-induced cell fate. 2. Decode the logic by which cells integrate intracellular signals and external context to commit to a TNF- induced cell fate. Using multivariable regression analysis of our data from Aim 1 we will build models connecting both extracellular cues and intracellular signals to TNF-induced cell fate. By comparing competing models of TNF-induced cell decision processes, we will derive mechanistic insights into the regulatory circuitry driving TNF-induced cell fate. 3. Focusing on the transcriptional arm of the TNF signaling network, we will test whether NF-kB nuclear translocation dynamics can quantitatively predict transcriptional output. Using a novel workflow to perform same-cell imaging of NF-kB dynamics and mRNA counting by single-molecule FISH, we will directly establish the relationship between NF-kB translocation dynamics and its transcriptional activity. Finally, we will use protein-binding microarrays to ask how competition between p65-p50 heterodimers and p50-p50 homodimers contributes to decoding NF-kB activation and to cell line-to-cell line variability in response to TNF. With this work, we take a new approach to studies of signal transduction, harnessing cell-to-cell variability to gain a quantitative understanding of how cells integrate information to determine their behavior in response to a ligand. These approaches will contribute to understanding the multi-factorial control of TNF-induced cell death and will be broadly applicable to the study of other signal transduction networks.