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.

Public Health Relevance

Mitochondrial dysfunction is a factor in numerous chronic diseases and environmental exposures, but early deficits in MT function are difficult to detect. To address this problem, we have developed and applied a new technology for single cell mass spectrometry, called NIMS. NIMS has both single cell resolution and the high sensitivity needed to detect early biomarkers of MT dysfunction, and will be used to define signatures of early MT dysfunction in single cells. Thus, NIMS technology reduces system heterogeneity, has the high sensitivity needed for detecting meaningful metabolic markers of early MT decline, and the ability to spatially assign signatures in distinct cell types. We apply NIMS identify the effects of oxidative damage in the brain, blood and urine in animal models.

Agency
National Institute of Health (NIH)
Institute
National Institute of Environmental Health Sciences (NIEHS)
Type
Research Project (R01)
Project #
5R01ES020766-02
Application #
8335450
Study Section
Special Emphasis Panel (ZES1-LWJ-J (MI))
Program Officer
Shaughnessy, Daniel
Project Start
2011-09-20
Project End
2016-06-30
Budget Start
2012-07-01
Budget End
2013-06-30
Support Year
2
Fiscal Year
2012
Total Cost
$440,807
Indirect Cost
$171,891
Name
Lawrence Berkeley National Laboratory
Department
Genetics
Type
Organized Research Units
DUNS #
078576738
City
Berkeley
State
CA
Country
United States
Zip Code
94720
Lee, Do-Yup; McMurray, Cynthia T (2014) Trinucleotide expansion in disease: why is there a length threshold? Curr Opin Genet Dev 26:131-40
Trushina, Eugenia; Canaria, Christie A; Lee, Do-Yup et al. (2014) Loss of caveolin-1 expression in knock-in mouse model of Huntington's disease suppresses pathophysiology in vivo. Hum Mol Genet 23:129-44
Hura, Greg L; Budworth, Helen; Dyer, Kevin N et al. (2013) Comprehensive macromolecular conformations mapped by quantitative SAXS analyses. Nat Methods 10:453-4
Xun, Zhiyin; Lee, Do-Yup; Lim, James et al. (2012) Retinoic acid-induced differentiation increases the rate of oxygen consumption and enhances the spare respiratory capacity of mitochondria in SH-SY5Y cells. Mech Ageing Dev 133:176-85