Integrating Real-Time Multi-System Cytokine Signaling in Chronic Disease

项目摘要/摘要 在接下来的五年里，我的实验室的目标是研究时间分辨的系统性炎症 慢性病。尽管促炎细胞因子信号在慢性疾病中很常见，但尚不清楚。 在疾病发生和发展的不同时间点，哪些细胞因子是活跃的。目前也不清楚 什么细胞因子在炎症信号从急性期到慢性期的转变中起作用。当前 研究评估方法不适用于快速动力学(酶免疫分析)或定量 多路传输(分子成像技术)。我们计划使用基于荧光碳纳米传感器的平台i 以前已经开发和修改，以多重方式快速检测促炎细胞因子。这个 复合细胞因子纳米传感器将被包裹在可注射水凝胶基质中，以实现微创 植入和快速测量。我们将使用这个传感器平台来创建细胞因子信号检测 慢性病啮齿动物模型中循环和原位细胞因子信号的网络。纳米传感器 该网络最初将在健康小鼠身上使用外源性细胞因子注射进行验证。我们会确保传感器 同时检测多种细胞因子，与疾病相关的浓度，几个月来一直起作用，以及 没有表现出任何特定的问题。胶囊化水凝胶基质将被设计成允许蛋白质通过 但传感器的保留基于大小，并将经历最小的生物污垢。经过技术验证后， 我们将使用纳米传感器网络来测量至少八个模型的局部和循环细胞因子水平 慢性疾病，包括：心血管疾病、癌症、神经退行性疾病和自身免疫 疾病。未来的工作将扩展到传染病，慢性肾脏疾病，肌肉骨骼疾病，以及 其他。在每个疾病模型中，我们将结合传统的炎症和免疫反应评估， 通过每周抽血和酶免疫分析进行评估。局部炎症也会 通过单细胞转录本测序和免疫组织化学染色对处死时进行评估。 动态细胞因子测量将通过部署在#年至少7个地点的纳米传感器网络获得 每只动物2只，局部和全身各2只，通过水凝胶注射。每天都会检测动态细胞因子。 在疾病开始之前和之后、在慢性进展期间和在终末期进行量化 疾病。传感器测量将通过全动物成像和简单的3秒光激发进行 非侵入性地从动物外部进入。这些传感器将提供实时、长期的细胞因子量化 在疾病发展过程中的浓度。我们希望了解动态细胞因子的变化。 从急性炎症反应到慢性炎症反应的感染和多器官的促炎作用 在疾病发展过程中。我们将研究每种疾病的促炎细胞因子信号 在其发展的特定时代，为研究每个领域的科学家提供了难以获得的动态数据和 进一步深入了解慢性病的发病机制。

Project Summary/Abstract Over the next five years, my laboratory aims to study time-resolved systemic inflammation across multiple chronic diseases. Though pro-inflammatory cytokine signaling is common amongst chronic diseases, it is unclear what cytokines are active at various timepoints throughout disease initiation and progression. It is also unclear what cytokines are functional in the inflection from acute to chronic-phase inflammatory signaling. Current methods of research evaluation are not amenable to rapid kinetics (enzymatic immunoassays) or quantitative multiplexing (molecular imaging techniques). We plan to use a fluorescent carbon nanosensor-based platform I have previously developed, modified to rapidly detect pro-inflammatory cytokines in a multiplexed manner. The multiplexed cytokine nanosensor will be encapsulated within an injectable hydrogel matrix for minimally-invasive implantation and rapid measurement. We will use this sensor platform to create a cytokine signal detection network for both circulating and in situ cytokine signals in rodent models of chronic diseases. The nanosensor network will initially be validated in healthy mice using exogenous cytokine injection. We will ensure the sensor detects multiple cytokines simultaneously, at disease-relevant concentrations, is functional for months, and exhibits no specificity issues. The encapsulating hydrogel matrix will be designed to allow passage of proteins but retention of the sensor based on size, and will undergo minimal biofouling. Following technology validation, we will use the nanosensor network to measure local and circulating cytokine levels in at least eight models of chronic disease, including: cardiovascular disease, cancer, neurodegenerative disease, and autoimmune disease. Future work will be extended to infectious disease, chronic renal disease, musculoskeletal disease, and others. In each disease model, we will couple traditional assessments of inflammation and immune response, as evaluated by weekly blood draws coupled with enzymatic immunoassays. Local inflammation will also be evaluated at the time of sacrifice via single-cell transcriptomic sequencing and immunohistochemical staining. Kinetic cytokine measurements will be obtained via the nanosensor network, deployed in at least 7 locations in each animal, 2 local and 2 systemic, via hydrogel injection. Each will be measured daily for kinetic cytokine quantification prior to and immediately after disease initiation, during chronic progression, and during end-stage disease. Sensor measurement will be performed via whole-animal imaging and simple 3-second light excitation non-invasively from outside the animal. These sensors will provide real-time, long-term quantification of cytokine concentrations throughout disease progression. We expect to understand kinetic cytokine changes in the inflection from acute to chronic inflammatory responses and the pro-inflammatory contribution of multiple organs during disease development. We will investigate pro-inflammatory cytokine signatures for each disease at specific times in its development, providing scientists studying each field difficult-to-obtain dynamic data and further insight into the pathogenesis of chronic disease.