Methods for generating high-resolution structural models from electron microscope tomography data.
Reconstructed volumes generated by tilt-image electron-microscope tomography offer the best spatial resolution currently available for studying cell structures in situ. Analysis is often accomplished by creating surface models that delineate grayscale contrast boundaries. Here, we introduce a specialized and convenient sequence of segmentation operations for making such models that greatly improves their reliability and spatial resolution as compared to current approaches, providing a basis for making accurate measurements. To assess the reliability of the surface models, we introduce a spatial uncertainty measurement based on grayscale gradient scale length. The model generation and measurement methods are validated by applying them to synthetic data, and their utility is demonstrated by using them to characterize macromolecular architecture of active zone material at the frog's neuromuscular junction.
Ress
David B
DB
Department of Neurobiology, Stanford University School of Medicine, Stanford, CA 94305, USA. ress@stanford.edu, grantser@stanford.edu
Harlow
Mark L
ML
Marshall
Robert M
RM
McMahan
Uel J
UJ
D003198:Computer Simulation; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008854:Microscopy, Electron; D008961:Models, Structural; D014054:Tomography