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。我们还将在预测的途径之间为记者多路复用 有串扰。使用定量成像，机器人控制的细胞培养和计算的混合体 技术，我们将在各种实验条件下从单个细胞中提取时间变化的数据， 反映在体内可能遇到的细胞。我们还将比较不同炎症的细胞反应 共享信号模块并在NF-κB转录系统上收敛的因素，我们将学习如何 免疫细胞和癌细胞在共培养和高维细胞结构中传达这些信号。 使用丰富的单细胞数据集，我们将确定信号传输的新兴特性，并推断传递 连接信号机制并与细胞命运相关的功能。实验实验的数据将是 并入机械模型，以形式化我们对信息如何通过 信号网络进入转录，并建议扰动以测试预测的机制。我们预料到这一点 越来越精确的模型可能导致非直觉策略来操纵单个细胞的决策。通过 对动态分子信号如何编码，过程和解码信息的详细理解，我们有 了解深层根植疾病的生物学问题的潜力，并利用这些知识来 理性设计疗法会影响细胞脂肪的决策。