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Main Menu - Block
- Overview
- Anatomy and Histology
- Cryo-Electron Microscopy
- Electron Microscopy
- Flow Cytometry
- Gene Targeting and Transgenics
- Immortalized Cell Line Culture
- Integrative Imaging
- Invertebrate Shared Resource
- Janelia Experimental Technology
- Mass Spectrometry
- Media Prep
- Molecular Genomics
- Primary & iPS Cell Culture
- Project Pipeline Support
- Project Technical Resources
- Quantitative Genomics
- Scientific Computing Software
- Scientific Computing Systems
- Viral Tools
- Vivarium
GCIB-SEM
GCIB-SEM (Gas Cluster Ion Beam - Scanning Electron Microscopy)
Although multiple electron microscopic techniques can achieve ~10 nm isotropic resolution, none are well-suited to image volumes at or above 1 mm3. Structural imaging at this scale opens new experimental achievements, like imaging large sections of vertebrate brains or an entire invertebrate nervous system. Unfortunately, current methods don’t meet the automatic resolution and throughput requirements needed to trace volumes spanning many cubic millimeters.
Janelia researchers solved this problem with a novel atomic cluster milling and an imaging technique called gas cluster ion beam SEM (GCIB-SEM). In addition, a hybrid of focused ion beam SEM (FIB-SEM) and diamond knife section-based techniques improves sectioning reliability while improving z resolution and maintaining compatibility with MultiSEM imaging.
So far, the instrument and technique have effectively contributed to landmark studies in the Connectome of the adult Drosophila Central Brain and Behavioral Studies in Zebrafish.
The system is the subject of pending US and PCT patent applications and is available for licensing and development into a commercial system.
Advantages
- Images at an isotropic resolution near 10nm.
- Automatically runs in sequence to achieve massive volume imaging of over 1mm3 sections.
- Incorporates engineering leaps for imaging systems to outperform all related parent methods.
Intellectual Property:
US Patent 11,177,110
Inventors:
Kenneth Jeffrey Hayworth
Harald Hess
David Peale
C. Shan Xu
Tech ID: 2018-017