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

项目总结/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全身性炎症， 慢性病虽然促炎细胞因子信号在慢性疾病中很常见，但目前还不清楚 什么样的细胞因子在疾病开始和发展的不同时间点是活跃的。也不清楚 什么样的细胞因子在从急性期到慢性期炎症信号传导的转变中起作用。电流 研究评价方法不适用于快速动力学（酶免疫测定）或定量 多路复用（分子成像技术）。我们计划使用一个基于荧光碳纳米传感器的平台 先前已经开发，修改，以快速检测促炎细胞因子在一个多路复用的方式。的 多重细胞因子纳米传感器将被封装在可注射水凝胶基质中，用于微创 植入和快速测量。我们将使用这个传感器平台来创建一个细胞因子信号检测 在啮齿类动物慢性疾病模型中的循环和原位细胞因子信号网络。纳米传感器 网络最初将使用外源性细胞因子注射在健康小鼠中进行验证。我们将确保传感器 在疾病相关浓度下同时检测多种细胞因子，可持续数月， 没有特异性问题。包封水凝胶基质将被设计成允许蛋白质通过 但是传感器的保持基于尺寸，并且将经历最小的生物污染。在技术验证之后， 我们将使用纳米传感器网络来测量至少八种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性疾病 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， 他人在每个疾病模型中，我们将结合炎症和免疫反应的传统评估， 通过每周抽血结合酶免疫测定进行评价。局部炎症也会 在处死时通过单细胞转录组测序和免疫组织化学染色进行评价。 将通过纳米传感器网络获得动力学细胞因子测量结果，该网络部署在至少7个地点， 每只动物，2只局部和2只全身，通过水凝胶注射。每天测量每种细胞的动力学细胞因子 在疾病开始之前和之后立即、慢性进展期间和终末期期间进行定量 疾病传感器测量将通过整体动物成像和简单的3秒光激发进行 非侵入性地从动物外部进行。这些传感器将提供实时、长期的细胞因子定量 在整个疾病进展过程中的浓度。我们希望了解细胞因子的动态变化， 从急性炎症反应到慢性炎症反应的转变以及多个器官的促炎作用 在疾病发展过程中。我们将研究每种疾病的促炎细胞因子特征， 在其发展的特定时期，为研究各个领域的科学家提供了难以获得的动态数据， 进一步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.