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: 10469399
• 负责人: Robin E. C. Lee
• 金额: $ 40.24万
• 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/10469399
• 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; rational design; 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 决定是否增殖、分化或死亡的炎症因素。 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。 例如，配体结合的 TNF 受体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 细胞质和细胞核等细胞区室的动态特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。 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。 信号通路在单个细胞中是确定性地相互关联的，尽管存在大量的信号通路 在这里，我们建议对活细胞荧光产生物进行多重表达。 和同一细胞中同一信号通路的下游成分，并关联时变信号 我们还将根据活细胞显微镜数据对预测的途径之间的生产者进行多重表达。 使用成像、机器人控制的定量细胞培养和计算的混合。 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 反映细胞在体内可能遇到的情况，我们还将比较不同炎症的细胞反应。 共享信号模块并汇聚于 NF-κB 转录系统的因子，我们将了解如何 免疫细胞和癌细胞在共培养物和高维细胞结构中传递这些信号。 使用丰富的单细胞数据集，我们将识别信号转导的新兴特性，并推断传输 连接信号机制并与活细胞实验数据相关的功能。 纳入机械模型中，以形式化我们对信息如何通过机械装置传递的理解 信号网络转化为转录，并提出扰动来测试预测的机制。 越来越准确的模型可能会导致在单细胞中操纵决策的非直观策略。 详细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 合理设计影响细胞命运决定的疗法。