Deciphering dynamic signals in control of cell fate decisions

破译控制细胞命运决定的动态信号

• 批准号: 10165183
• 负责人: Robin E. C. Lee
• 金额: $ 39.41万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2026-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10165183
• 关键词: Biological; Cell Culture Techniques; Cell Death; Cell Fate Control; Cell Line; Cell Nucleus; Cell membrane; Cells; Cellular Structures; Clone Cells; Coculture Techniques; Code; Computational Technique; Cytoplasm; Data; Data Set; Decision Making; Disease; Frequencies; Genetic Transcription; Goals; Heterogeneity; Hybrids; Immune; Inflammation; Inflammatory; Knowledge; Lead; Learning; Ligands; Link; Measurement; Mediating; Modeling; Molecular; Pathway interactions; Plant Roots; Process; Proliferating; Property; Proteins; Reporter; Robot; Signal Pathway; Signal Transduction; Stimulus; Stream; Structure; System; Time; Tumor Necrosis Factor Receptor; cancer cell; computer framework; experimental study; high dimensionality; in vivo; information processing; live cell microscopy; predictive test; protein complex; quantitative imaging; rate of change; response; therapy design

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.

项目摘要 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， 决定是否扩散，分化或死亡。炎症因子参与许多正常的 并且患病细胞命运决定通过动态重组细胞内的蛋白质来启动信号。为 例如，配体结合的TNF受体在质膜附近瞬时组织大的蛋白质复合物， 这些蛋白质在细胞内以离散的点状结构可见，而其他蛋白质则在 细胞隔室，例如细胞质和细胞核。这是一个新兴的原则， 信号转导电路内的分子的变化提供时间代码（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至关重要。鉴于有 即使在克隆细胞系中，细胞间的异质性也很大，固定时间点的静态测量不能 揭示动态信息加工的机制。我们假设相同的成分 信号通路在单个细胞中确定性地相互联系，即使存在大量的 细胞间的异质性。在这里，我们提出了活细胞荧光报告基因的多重表达， 和同一细胞中同一信号通路的下游成分，并将时变信号 活细胞显微镜数据。我们还将在预测的途径之间多重表达报告基因， 有crosstalk。使用定量成像，机器人控制的细胞培养和计算机的混合 技术，我们将在广泛的实验条件下从单细胞中提取时变数据， 反映了细胞在体内可能遇到的情况。我们还将比较不同炎症反应的细胞反应， 这些因子共享信号传导模块并汇聚在NF-κB转录系统上，我们将学习如何 免疫细胞和癌细胞在共培养物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，我们将确定信号转导的紧急属性，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 纳入机械模型，以正式确定我们对信息如何通过 信号网络转录，并建议扰动测试预测的机制。我们预计 越来越精确的模型可能导致非直观的策略来操纵单个细胞中的决策。通过 详细了解动态分子信号如何编码，处理和解码信息，我们有 了解深深植根于疾病的生物学问题的潜力，并利用这些知识， 合理设计影响细胞命运决定的疗法。