Colocalization of gene expression and microscale tissue strains in live tendon explants using barcoded biosensors

使用条形码生物传感器对活体肌腱外植体中的基因表达和微型组织菌株进行共定位

基本信息

项目摘要

Project Summary Tendinopathy is a chronic degenerative disease that occurs in response to tendon overuse (i.e., fatigue loading). In addition to mechanical damage, tendon fatigue loading induces the expression of catabolic proteases, inflammatory cytokines, and the accumulation of abnormal matrix components (e.g., cartilaginous, fat, and calcium deposits), which further weaken the tissue and drive the progression of degeneration. Understanding why tendon cells fail to restore the tissue structure and instead exacerbate degeneration is critical for preventing tendinopathy. The prevailing hypothesis is that the counterproductive cellular response to tendon overuse results from mechanotransduction of altered biophysical stimuli resulting from local tissue damage. However, there is a fundamental lack of knowledge regarding tenocyte mechanotransduction in the native tissue environment and, therefore, whether altered biophysical stimuli are responsible for tendon degeneration. To address this knowledge gap, the objective of this proposal is to develop a live tissue explant model enabling simultaneous spatiotemporal measurement of local mechanical stimuli and cellular gene expression during tendon fatigue loading. By colocalizing the tenocyte response with the mechanical stimuli present in the native cellular microenvironment, we can directly test whether mechanotransduction is responsible for the negative cellular response observed with tendon degeneration. The critical technological obstacle to achieving this goal is the development of a biosensor that can dynamically detect the spatial distribution of cellular gene expression in tendon explants. Endogenous fluorescent reporters are limited by the time and cost of generating a transgenic mouse for specific genes of interest. Alternative biotechnologies require cell transfection, which is inefficient in tissues with a dense extracellular matrix. Therefore, we will investigate whether gold nanorods, which are internalized via endocytosis, can deliver fluorescently labeled oligonucleotide probes to detect cellular gene expression in tendon explants. In Aim 1, we will optimize the design of gold nanorod-locked nucleic acid biosensors and validate their sensitivity and spatial accuracy in measuring mechanosensitive genes in live tendon explants. Additionally, we will establish a barcode strategy with FRET-pair probes and a spectral unmixing algorithm to simultaneously measure up to ten target genes. In Aim 2, we will use this technology to determine whether the degenerative cellular response to tendon fatigue is spatially associated with changes in local mechanical stimuli (i.e., strains). This project is innovative because our results will identify the stimuli mediating the negative cellular response to tendon overuse and help develop novel therapies for preventing degeneration. Additionally, we will establish the ability to measure spatiotemporal distributions of gene expression in live tissue explants with a dense extracellular matrix (e.g., tendon). This technology will be ground- breaking for musculoskeletal research since explant models are commonly used to study musculoskeletal tissues and existing techniques requiring cell transfection are not effective due to the dense extracellular matrix.
项目摘要 肌腱病是一种慢性退行性疾病,其发生于肌腱过度使用(即,疲劳载荷)。 除了机械损伤,肌腱疲劳负荷诱导分解代谢蛋白酶的表达, 炎性细胞因子和异常基质成分的积累(例如,软骨脂肪 钙沉积物),这进一步削弱了组织并促使退化的进展。理解 为什么肌腱细胞不能恢复组织结构,反而加剧了退化, 肌腱病流行的假设是,细胞对肌腱过度使用的反应适得其反, 由局部组织损伤引起的改变的生物物理刺激的机械转导。不过有 缺乏关于天然组织中腱细胞机械传导的基本知识 环境,因此,是否改变生物物理刺激负责肌腱退变。到 为了解决这一知识差距,本提案的目标是开发一种活组织外植体模型, 局部机械刺激和细胞基因表达的同时时空测量 肌腱疲劳载荷通过将腱细胞反应与天然肌肉中存在的机械刺激共定位, 细胞微环境,我们可以直接测试是否机械转导是负责任的 观察到肌腱变性的细胞反应。实现这一目标的关键技术障碍是 目的是研制一种能动态检测细胞基因空间分布的生物传感器 在肌腱外植体中的表达。内源性荧光报告基因受到产生时间和成本的限制, 转基因小鼠的特定基因的兴趣。替代的生物技术需要细胞转染,这是 在细胞外基质密集的组织中效率低下。因此,我们将研究金纳米棒, 其通过内吞作用内化,可以递送荧光标记的寡核苷酸探针, 肌腱外植体中的基因表达。在目标1中,我们将优化金纳米棒锁核酸的设计 生物传感器,并验证其灵敏度和空间精度测量机械敏感基因在生活中 肌腱外植体。此外,我们将建立一个条码策略与FRET对探针和光谱 解混算法,同时测量多达10个靶基因。在Aim 2中,我们将使用该技术 确定对肌腱疲劳的退行性细胞反应是否与以下变化空间相关: 局部机械刺激(即,菌株)。这个项目是创新的,因为我们的结果将确定刺激 介导肌腱过度使用的负细胞反应,并帮助开发新的治疗方法, 退化此外,我们将建立测量基因时空分布的能力, 在具有致密细胞外基质的活组织外植体中的表达(例如,肌腱)。这项技术将被- 由于外植体模型通常用于研究肌肉骨骼, 组织和需要细胞转染的现有技术由于致密的细胞外基质而无效。

项目成果

期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ monograph.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ sciAawards.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ conferencePapers.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ patent.updateTime }}

Spencer Szczesny其他文献

Spencer Szczesny的其他文献

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

{{ truncateString('Spencer Szczesny', 18)}}的其他基金

Colocalization of gene expression and microscale tissue strains in live tendon explants using barcoded biosensors
使用条形码生物传感器对活体肌腱外植体中的基因表达和微型组织菌株进行共定位
  • 批准号:
    10558584
  • 财政年份:
    2022
  • 资助金额:
    $ 19.91万
  • 项目类别:
Studying Mechanotransduction in Late Embryonic Development to Inform Tendon Tissue Engineering
研究胚胎发育晚期的力转导为肌腱组织工程提供信息
  • 批准号:
    9808374
  • 财政年份:
    2019
  • 资助金额:
    $ 19.91万
  • 项目类别:
Role of Mechanical Loading and Stem Cell Mechanotransduction in Tendon Degeneration
机械负荷和干细胞力转导在肌腱退变中的作用
  • 批准号:
    9320001
  • 财政年份:
    2016
  • 资助金额:
    $ 19.91万
  • 项目类别:

相似海外基金

DMS-EPSRC: Asymptotic Analysis of Online Training Algorithms in Machine Learning: Recurrent, Graphical, and Deep Neural Networks
DMS-EPSRC:机器学习中在线训练算法的渐近分析:循环、图形和深度神经网络
  • 批准号:
    EP/Y029089/1
  • 财政年份:
    2024
  • 资助金额:
    $ 19.91万
  • 项目类别:
    Research Grant
CAREER: Blessing of Nonconvexity in Machine Learning - Landscape Analysis and Efficient Algorithms
职业:机器学习中非凸性的祝福 - 景观分析和高效算法
  • 批准号:
    2337776
  • 财政年份:
    2024
  • 资助金额:
    $ 19.91万
  • 项目类别:
    Continuing Grant
CAREER: From Dynamic Algorithms to Fast Optimization and Back
职业:从动态算法到快速优化并返回
  • 批准号:
    2338816
  • 财政年份:
    2024
  • 资助金额:
    $ 19.91万
  • 项目类别:
    Continuing Grant
CAREER: Structured Minimax Optimization: Theory, Algorithms, and Applications in Robust Learning
职业:结构化极小极大优化:稳健学习中的理论、算法和应用
  • 批准号:
    2338846
  • 财政年份:
    2024
  • 资助金额:
    $ 19.91万
  • 项目类别:
    Continuing Grant
CRII: SaTC: Reliable Hardware Architectures Against Side-Channel Attacks for Post-Quantum Cryptographic Algorithms
CRII:SaTC:针对后量子密码算法的侧通道攻击的可靠硬件架构
  • 批准号:
    2348261
  • 财政年份:
    2024
  • 资助金额:
    $ 19.91万
  • 项目类别:
    Standard Grant
CRII: AF: The Impact of Knowledge on the Performance of Distributed Algorithms
CRII:AF:知识对分布式算法性能的影响
  • 批准号:
    2348346
  • 财政年份:
    2024
  • 资助金额:
    $ 19.91万
  • 项目类别:
    Standard Grant
CRII: CSR: From Bloom Filters to Noise Reduction Streaming Algorithms
CRII:CSR:从布隆过滤器到降噪流算法
  • 批准号:
    2348457
  • 财政年份:
    2024
  • 资助金额:
    $ 19.91万
  • 项目类别:
    Standard Grant
EAGER: Search-Accelerated Markov Chain Monte Carlo Algorithms for Bayesian Neural Networks and Trillion-Dimensional Problems
EAGER:贝叶斯神经网络和万亿维问题的搜索加速马尔可夫链蒙特卡罗算法
  • 批准号:
    2404989
  • 财政年份:
    2024
  • 资助金额:
    $ 19.91万
  • 项目类别:
    Standard Grant
CAREER: Efficient Algorithms for Modern Computer Architecture
职业:现代计算机架构的高效算法
  • 批准号:
    2339310
  • 财政年份:
    2024
  • 资助金额:
    $ 19.91万
  • 项目类别:
    Continuing Grant
CAREER: Improving Real-world Performance of AI Biosignal Algorithms
职业:提高人工智能生物信号算法的实际性能
  • 批准号:
    2339669
  • 财政年份:
    2024
  • 资助金额:
    $ 19.91万
  • 项目类别:
    Continuing Grant
{{ showInfoDetail.title }}

作者:{{ showInfoDetail.author }}

知道了