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

TNF 诱导的信号传导、转录和命运决定的单细胞分析

• 批准号: 8578770
• 负责人: Suzanne Gaudet
• 金额: $ 33.25万
• 依托单位: DANA-FARBER CANCER INST
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-23 至 2018-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8578770
• 关键词: Affect; Automobile Driving; Behavior; Binding; Biochemical Genetics; Biological Assay; Biological Models; Cancer Patient; Caspase; Cell Cycle; Cell Death; Cell Density; Cell Line; Cells; Cessation of life; Commit; Complex; Computer Simulation; Cues; DNA Damage; Data; Drug resistance; Drug usage; ERG gene; Early Gene Transcriptions; Endocytosis; Environment; Gene Targeting; Genes; Genetic Transcription; Genetic Variation; Goals; Image; Image Analysis; Individual; Inflammation; Ischemia; Ligands; Logic; Malignant Neoplasms; Membrane; Messenger RNA; Modeling; Molecular; NF-kappa B; Nuclear; Nuclear Translocation; Outcome; Output; Pathway interactions; Pattern; Pharmaceutical Preparations; Phase; Phosphotransferases; Physiologic pulse; Population; Process; Proliferating; Protein Binding; Proteins; Regression Analysis; Research; Side; Signal Pathway; Signal Transduction; Signal Transduction Pathway; Sister; Source; Stress; Structure; TNFRSF5 gene; Testing; Therapeutic; Time; Transcriptional Activation; Tumor Necrosis Factor Receptor; Tumor Necrosis Factor-alpha; Viral; Work; arm; base; cancer cell; cancer therapy; caspase-8; cell growth; cellular imaging; extracellular; feeding; improved; insight; interest; killings; migration; non-genetic; novel; novel strategies; p65; public health relevance; research study; response; senescence; single cell analysis; single molecule; stem cell differentiation; therapy design

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.

描述（由申请人提供）：受体细胞如何“计算”其对配体的反应？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。信号转导网络是巨大的、相互关联的、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（TNF）是一种炎症调节因子，是一种特别令人感兴趣的信号转导模型系统，因为它是一种诱导相反的促存活和促死亡信号传导途径的配体。虽然TNF受体1（TNFR 1）的表达是普遍存在的，一些细胞响应TNF的分化和增殖，而其他人致力于细胞死亡。引人注目的是，即使是用高浓度TNF处理的克隆癌细胞也显示出变异性：一些细胞死亡，但其他细胞存活。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NF-？的同细胞追踪B和半胱天冬酶信号动力学与自动图像分析，以量化各自的贡献NF-？B和半胱天冬酶信号动力学以及TNF诱导的细胞命运决定的细胞背景。使用这些数据，我们将测试的假设：i）NF-？B活化动力学影响半胱天冬酶活化动力学，和ii）信号传导动力学和细胞外环境都是TNF诱导的细胞命运的强贡献者。2.解码细胞整合细胞内信号和外部环境以承诺TNF诱导的细胞命运的逻辑。使用我们的目标1的数据的多变量回归分析，我们将建立模型连接细胞外线索和细胞内信号TNF诱导的细胞命运。通过比较TNF诱导的细胞决策过程的竞争模型，我们将获得对驱动TNF诱导的细胞命运的调节电路的机械见解。3.聚焦于TNF信号网络的转录臂，我们将测试NF-kB核转位动力学是否可以定量预测转录输出。使用一种新的工作流程，通过单分子FISH进行NF-κ B动力学和mRNA计数的同细胞成像，我们将直接建立NF-κ B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异源二聚体和p50-p50同源二聚体之间的竞争如何有助于解码NF-kB激活和响应TNF的细胞系间变异性。通过这项工作，我们采取了一种新的方法来研究信号转导，利用细胞间的变异性来定量了解细胞如何整合信息以确定它们对配体的反应行为。这些方法将有助于理解TNF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。