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: 9137977
• 负责人: Robin E. C. Lee
• 金额: $ 38.5万
• 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 2021-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9137977
• 关键词: Biological; Cell Death; Cell Fate Control; Cell Line; Cell Nucleus; Cell membrane; Cells; Code; Computational Technique; Cytoplasm; Data; Data Set; Decision Making; Disease; Entropy; Frequencies; Genetic Transcription; Goals; Heterogeneity; Hybrids; Inflammation; Inflammatory; Interleukin-1; Knowledge; Lead; Life; Ligand Binding; Link; Measurement; Measures; Mediating; Microfluidics; Modeling; Pathway interactions; Plant Roots; Process; Proliferating; Property; Proteins; Reporter; Signal Pathway; Signal Transduction; Stimulus; Stream; Structure; System; TNF gene; Testing; Time; Tumor Necrosis Factor Receptor; computer framework; information processing; live cell microscopy; molecular dynamics; protein complex; quantitative imaging; rate of change; research study; 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. Using a hybrid of quantitative imaging, microfluidics and computational techniques we will extract time-varying data from 100s-1000s of single cells in each experimental condition, and compare them across several different cell lines. We will also compare cellular responses across different inflammatory factors that share signaling modules and converge on the NF-κB transcriptional system, such as TNF, LPS or IL-1 among others. Using a rich single-cell dataset, we will use transfer entropy to measure mutual information between features of time-varying signals in the same pathway, and infer mechanisms of signal transduction in addition to correlations 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个单元格如何整合和处理信息以使不可逆 诸如繁殖，区分还是死亡之类的决定。参与许多正常的炎症因素 通过动态重新组织蛋白质在细胞中，解散的细胞脂肪决策启动了信号。为了 例如，结合配体的TNF受体瞬时组织大型蛋白质复合物，附近的质膜附近 这些在单元中可见为离散点状结构，而其他蛋白质之间的转移 细胞室，例如细胞质和细胞核。动态特性是新的原则 信号转导电路中的分子的分子提供临时代码（包括变化速率，放大器，，，， 持续时间或频率）对于每个细胞对刺激的响应至关重要。鉴于有 大量的细胞到细胞异质性，即使在克隆细胞系中，固定时间点的静态测量也不能 揭示动态信息处理的机制。我们假设相同的组成部分 信号通路在单个单元格中确定彼此链接，即使存在很大 细胞之间的异质性。在这里，我们建议将活细胞荧光记者的多重表达用于UP- 以及同一单元格中同一信号通路的下游组件，并将随时间变化的信号相关联 来自活细胞显微镜数据。使用定量成像，微流体和计算的混合体 技术，我们将在每个实验条件下从100s-1000s的单个单元中提取时间变化的数据，并且 在几个不同的细胞系中进行比较。我们还将比较跨不同的细胞反应 共享信号模块并在NF-κB转录系统上收敛的炎症因子，例如 TNF，LPS或IL-1等。使用丰富的单细胞数据集，我们将使用转移熵来测量互惠 在相同途径中时变化信号的特征之间的信息，并推断信号的机制 除了与细胞命运相关之外，转导。实验实验的数据将纳入 机械模型以正式了解我们如何通过信号网络中继信息的理解 转录，并建议扰动以测试预测的机制。我们预计这越来越 准确的模型可能导致非直觉策略来操纵单个细胞中的决策。通过详细的 了解动态分子信号如何编码，过程和解码信息，我们有 了解深层植根于疾病的生物学问题的潜力，并利用这些知识合理地 设计影响细胞脂肪决策的疗法。