Metabolic markers for mitochondrial function

线粒体功能的代谢标志物

基本信息

项目摘要

ABSTRACT Mitochondrial (MT) dysfunction is a factor in numerous chronic diseases and the toxicity related to environmental exposures, but early deficits in MT function are difficult to detect. Current clinical markers for mitochondrial dysfunction typically detect only advanced symptoms of tissue injury and disease, yet the sensitivity to detect mild MT dysfunction and heterogeneity within tissue has hampered robust identification of meaningful biomarkers at early stages. Mitochondrial biology is variable; and chronic, low level MT dysfunction may be below the detection sensitivity of many techniques. We need new tools to enhance the mechanistic understanding of environmentally-induced mitochondrial toxicity at early stages to enable prevention and intervention. To address this problem, we have developed and applied a new technology for single cell mass spectrometry, called Nanostructure-Initiator Mass Spectrometry (NIMS). NIMS has both the single cell resolution (1-10 ¿m) and the high sensitivity (attomolar) needed to detect early biomarkers of MT dysfunction as metabolic "signatures" in individual cells. NIMS offers a number of advantages over standard mass spectrometry, including (1) ultra-high sensitivity, (2) high selectivity, and (3) single cell resolution to reduce sample complexity. We apply NIMS to identify metabolic signatures for early MT dysfunction in the brain and blood of diseased animals or animals treated with environmental toxins at "subclinical" levels. In Aim 1, we use NIMS to generate metabolic signatures for MT decline. In Aim 2, we will functionally test whether the biomarker reflects functional changes in MT or MT within the context of the cell. NIMS can be applied to any tissue and any cell type, to quantitatively sort out complex changes that occur in dynamic cellular environments, and minimizes the inherent system heterogeneity that has confounded efforts in detecting meaningful markers of MT decline.
摘要 线粒体(MT)功能障碍是许多慢性疾病的一个因素, 环境暴露,但MT功能的早期缺陷很难检测。目前的临床标志物 线粒体功能障碍通常只能检测到组织损伤和疾病的晚期症状, 检测组织内轻度MT功能障碍和异质性的敏感性阻碍了对MT的强有力识别 有意义的生物标志物。线粒体生物学是可变的;慢性低水平MT功能障碍 可能低于许多技术的检测灵敏度。我们需要新的工具来增强 在早期阶段了解环境引起的线粒体毒性,以便能够预防和 干预为了解决这个问题,我们开发并应用了一种新的单细胞团技术, 纳米结构-引发剂质谱(Nanostructure-Initiator Mass Spectrometry,NIMS)。NIMS既有单细胞 分辨率(1-10 μ m)和检测MT功能障碍的早期生物标志物所需的高灵敏度(阿托摩尔) 作为单个细胞的代谢“特征”。NIMS提供了许多优于标准质量的优点 光谱分析,包括(1)超高灵敏度,(2)高选择性,和(3)单细胞分辨率,以减少 样本复杂度我们应用NIMS来识别大脑中早期MT功能障碍的代谢特征, 患病动物或用“亚临床”水平的环境毒素处理过的动物的血液。在目标1中,我们使用 NIMS生成MT下降的代谢特征。在目标2中,我们将在功能上测试生物标志物是否 反映MT或MT在细胞环境中的功能变化。NIMS可以应用于任何组织, 任何细胞类型,以定量分类动态细胞环境中发生的复杂变化, 最大限度地减少了固有的系统异质性,这种异质性混淆了检测有意义的 MT下降。

项目成果

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Cynthia Therese McMurray其他文献

Cynthia Therese McMurray的其他文献

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{{ truncateString('Cynthia Therese McMurray', 18)}}的其他基金

Predicting neurodegeneration in living patients by IR imaging of skin fibroblasts
通过皮肤成纤维细胞的红外成像预测活体患者的神经退行性变
  • 批准号:
    10433612
  • 财政年份:
    2022
  • 资助金额:
    $ 43.29万
  • 项目类别:
Novel Spectral Biomarkers for Alzheimer's Disease
阿尔茨海默病的新型光谱生物标志物
  • 批准号:
    10359211
  • 财政年份:
    2021
  • 资助金额:
    $ 43.29万
  • 项目类别:
DNA Expansion and Mismatch Repair
DNA 扩增和错配修复
  • 批准号:
    9403408
  • 财政年份:
    2017
  • 资助金额:
    $ 43.29万
  • 项目类别:
DNA Expansion and Mismatch Repair
DNA 扩增和错配修复
  • 批准号:
    9978826
  • 财政年份:
    2017
  • 资助金额:
    $ 43.29万
  • 项目类别:
DNA Expansion and Mismatch Repair
DNA 扩增和错配修复
  • 批准号:
    9766311
  • 财政年份:
    2017
  • 资助金额:
    $ 43.29万
  • 项目类别:
Metabolic markers for mitochondrial function
线粒体功能的代谢标志物
  • 批准号:
    8895766
  • 财政年份:
    2011
  • 资助金额:
    $ 43.29万
  • 项目类别:
Metabolic markers for mitochondrial function
线粒体功能的代谢标志物
  • 批准号:
    8335450
  • 财政年份:
    2011
  • 资助金额:
    $ 43.29万
  • 项目类别:
Metabolic markers for mitochondrial function
线粒体功能的代谢标志物
  • 批准号:
    8697051
  • 财政年份:
    2011
  • 资助金额:
    $ 43.29万
  • 项目类别:
Metabolic markers for mitochondrial function
线粒体功能的代谢标志物
  • 批准号:
    8218086
  • 财政年份:
    2011
  • 资助金额:
    $ 43.29万
  • 项目类别:
Mismatch Repair and DNA expansion
错配修复和 DNA 扩增
  • 批准号:
    7996892
  • 财政年份:
    2010
  • 资助金额:
    $ 43.29万
  • 项目类别:
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