Detalles del proyecto
Descripción
Light microscopy is an essential technique used by biomedical researchers; however, its use is limited by the diffraction barrier of ~200 nm, which poses difficulties when studying subcellular structures and molecular complexes using conventional fluorescence microscopy methods. The past decade has seen the development of a number of super-resolution fluorescent microscopy techniques that overcome the diffraction limit of light. The performer proposes to build a stochastic optical resolution microscopy (STORM) which is based on the high-accuracy localization of individual fluorophores. STORM has achieved a resolution of 20 nm in the lateral direction and 50 nm in the axial direction, approximately one order of magnitude better than conventional optical microscopy techniques. The proposed microscope can be constructed from readily available components for a fraction of the cost of a commercial system without loss of functionality. The STORM system is relatively simple to set up compared to other super-resolution apparatus, is applicable to a wide variety of biological samples, and can be used with multiple colors to localize several different molecules in one sample. Furthermore, STORM can be used for 3D analysis and the visualization of living cells. These imaging capabilities combined with the availability of open-source data analysis software make this type of microscopy highly attractive. Researchers at Duke and the University of North Carolina will take immediate advantage of this equipment for specific Navy, Army, and Air Force-funded research projects, including examining tunneling nanotube formation, identifying stellate cell synapse formation with neurons, assessing basement membrane adhesions, visualizing mitotic spindle assembly, and understanding cytoskeletal reorganization.
Estado | Activo |
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Fecha de inicio/Fecha fin | 22/6/16 → … |
Enlaces | https://publicaccess.dtic.mil/search/#/grants/advancedSearch |
Financiación
- U.S. Navy: USD250,579.00
!!!ASJC Scopus Subject Areas
- Estadística y probabilidad
- Ciencias sociales (todo)