Tracking the dynamics of the macrophage response to interferon-gamma at a single-cell level

项目摘要 在不损害健康的情况下清除感染需要对炎症反应进行精确的时间调节 组织。以往阐明这一调控机制的工作主要集中在单细胞 细胞的时间点或大样本。然而，体内的免疫细胞会收到复杂的时间组合 免疫反应中的刺激，以随时间变化的基因表达模式做出反应，并显示 种群内的异质性。干扰素γ是一种促炎细胞因子 在免疫反应中的作用。巨噬细胞是免疫细胞，是干扰素γ的主要应答细胞之一。 在感染过程中，巨噬细胞可能经历多个时期的干扰素γ刺激，并利用信号和 基因表达网络将这些不同的刺激解码为基因表达反应和多样化 功能。Gbp1和nos2是两个干扰素γ应答基因，在宿主防御中发挥重要作用。 微生物并受不同的网络结构和染色质调节机制的调节。 结核分枝杆菌(Mtb)感染是一个紧迫的全球健康问题，也严重依赖于 巨噬细胞对干扰素γ的反应。结核分枝杆菌感染的结果在细胞和 生物(人)水平。先前的工作表明，干扰素γ信号对巨噬细胞的杀伤是必不可少的。 这种杀死结核分枝杆菌的能力在不同的细胞中是不同的。这项提案使用的系统 将巨噬细胞系中的内源性荧光基因报告与长期活细胞成像结合在一起 同时跟踪多个基因动态表达动力学的微流控装置 随着时间的推移，在相同的单个细胞中，刺激以及结核分枝杆菌感染的结果。这可以用来获得 对动态基因表达反应和功能的机制的定量理解 异质性。在目标1中，该系统用于对单个巨噬细胞基因的表达进行量化和建模。 动态干扰素γ刺激后的动力学，并阐明信号解码的机制。此操作由以下人员完成 对巨噬细胞施加不同幅度和持续时间的干扰素γ刺激并同时跟踪 GBP1和NOS2网络的三个组分在同一单个细胞中随时间的表达动力学。一个 将开发数学模型来描述这些响应，预测对扰动的响应，以及 将使用可诱导的启动子和染色质调节剂的抑制剂来干扰解码。目标2 研究基因表达动力学中的细胞间变异性与异质性结核分枝杆菌之间的联系 感染后果。这是通过用mtb感染荧光报告巨噬细胞系来实现的，mtb标记为 活性报告和分析基因表达动力学和在单细胞中的感染结果。这个 这些目标的完成将提供对巨噬细胞机制的定量了解 将动态干扰素γ刺激解码为时变基因表达模式，以及对 结核分枝杆菌感染结局的异质性来源。

Project Summary Precise temporal regulation of inflammatory responses is required to clear infection without damaging healthy tissue. Previous work elucidating the mechanisms involved in this regulation have focused mainly on single time points or bulk samples of cells. However, immune cells in vivo receive complex temporal combinations of stimuli during an immune response, respond with gene expression patterns that vary over time, and display heterogeneity within the population. Interferon gamma (IFNγ) is a pro-inflammatory cytokine that plays key roles in immune responses. Macrophages are immune cells that are one of the primary responders to IFNγ. During infection, macrophages may experience multiple periods of IFNγ stimulation, and employ signaling and gene expression networks to decode these varying stimuli into gene expression responses and diverse functions. GBP1 and NOS2 are two IFNγ-responsive genes that have important roles in host defense against microbes and are regulated by different network architectures and chromatin regulatory mechanisms. Mycobacterium tuberculosis (Mtb) infection is a pressing global health issue that also depends critically on macrophage responses to IFNγ. Mtb infection outcomes are heterogeneous on the cellular as well as the organismal (human) level. Previous work has shown that IFNγ signaling is essential for macrophages to kill intracellular Mtb and that this ability to kill Mtb varies between cells. This proposal uses a system that combines endogenous fluorescent gene reporters in macrophage cell lines with long-term live-cell imaging in a microfluidic device to simultaneously track expression kinetics of multiple genes in response to dynamic stimuli, as well as the outcomes of Mtb infection, in the same single cells over time. This can be used to obtain a quantitative understanding of the mechanisms underlying kinetic gene expression responses and functional heterogeneity. In Aim 1, this system is used to quantify and model single macrophage gene expression kinetics following dynamic IFNγ stimulus and to elucidate the mechanism of signal decoding. This is done by applying IFNγ stimulus of varying amplitude and duration to the macrophages and simultaneously tracking expression kinetics of three components of the GBP1 and NOS2 networks in the same single cells over time. A mathematical model will be developed to describe these responses, predict the response to perturbation, and will be tested using inducible promoters and inhibitors of chromatin regulators to perturb the decoding. Aim 2 investigates the connection between cell-to-cell variability in gene expression kinetics and heterogeneous Mtb infection outcomes. This is done by infecting fluorescent reporter macrophage cell lines with Mtb marked by a viability reporter and assaying both gene expression kinetics and infection outcomes in single cells. The completion of these aims will provide a quantitative understanding of the mechanisms by which macrophages decode dynamic IFNγ stimuli into time-variant gene expression patterns, as well as mechanistic insight into the sources of heterogeneity in the outcomes of Mtb infection.