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
基本信息
- 批准号:8738687
- 负责人:
- 金额:$ 33.25万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 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:
- 关键词:AffectAutomobile DrivingBehaviorBindingBiochemical GeneticsBiological AssayBiological ModelsCancer PatientCaspaseCell CycleCell DeathCell DensityCell LineCellsCessation of lifeCommitComplexComputer SimulationCuesDNA DamageDataDrug resistanceDrug usageERG geneEarly Gene TranscriptionsEndocytosisEnvironmentGene TargetingGenesGenetic TranscriptionGenetic VariationGoalsImageImage AnalysisIndividualInflammationIschemiaLigandsLogicMalignant NeoplasmsMembraneMessenger RNAModelingMolecularNF-kappa BNuclearNuclear TranslocationOutcomeOutputPathway interactionsPatternPharmaceutical PreparationsPhasePhosphotransferasesPhysiologic pulsePopulationProcessProliferatingProtein BindingProteinsRegression AnalysisResearchSideSignal PathwaySignal TransductionSignal Transduction PathwaySisterSourceStressStructureTNFRSF5 geneTestingTherapeuticTimeTranscriptional ActivationTumor Necrosis Factor ReceptorTumor Necrosis Factor-alphaViralWorkarmbasecancer cellcancer therapycaspase-8cell growthcellular imagingextracellularfeedingimprovedinsightinterestkillingsmigrationnon-geneticnovelnovel strategiesp65public health relevanceresearch studyresponsesenescencesingle cell analysissingle moleculestem cell differentiationtherapy design
项目摘要
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):接受细胞如何“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。信号转导网络是庞大的、相互关联的和高度动态的;我们仍然需要了解细胞是如何在时间和空间上整合信号的。肿瘤坏死因子(TNF)是一种炎症调节剂,是一个特别有趣的信号转导模型系统,因为它是一种配体,诱导相反的促生存和促死亡信号通路。尽管TNF受体1 (TNFR1)的表达普遍存在,但一些细胞通过分化和增殖对TNF作出反应,而另一些细胞则导致细胞死亡。引人注目的是,即使是用高浓度TNF处理的克隆癌细胞也表现出可变性:一些细胞死亡,而另一些细胞存活。是什么决定了tnf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。使用同一细胞追踪NF-?B和caspase信号动力学与自动图像分析来量化NF-?B和caspase信号动力学以及TNF-诱导的细胞命运决定的细胞背景。利用这些数据,我们将检验以下假设:i) NF-?B激活动力学影响caspase激活动力学和ii)信号动力学和细胞外环境都是tnf诱导的细胞命运的重要贡献者。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。利用Aim 1数据的多变量回归分析,我们将建立连接细胞外信号和细胞内信号与tnf诱导的细胞命运的模型。通过比较tnf诱导的细胞决策过程的竞争模型,我们将获得驱动tnf诱导细胞命运的调节电路的机制见解。3. 关注TNF信号网络的转录臂,我们将测试NF-kB核易位动力学是否可以定量预测转录输出。利用一种新颖的工作流程,通过单分子FISH对NF-kB动态和mRNA计数进行同细胞成像,我们将直接建立NF-kB易位动态与其转录活性之间的关系。最后,我们将使用蛋白质结合
项目成果
期刊论文数量(0)
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Suzanne Gaudet其他文献
Suzanne Gaudet的其他文献
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{{ truncateString('Suzanne Gaudet', 18)}}的其他基金
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 - 财政年份:2013
- 资助金额:
$ 33.25万 - 项目类别:
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