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.
项目总结

项目成果

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Spencer Szczesny其他文献

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{{ 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:

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