scholarly journals To be or not to be? Examples of incorrect identification of autophagic compartments in conventional transmission electron microscopy of mammalian cells

Autophagy ◽  
2008 ◽  
Vol 4 (2) ◽  
pp. 257-260 ◽  
Author(s):  
Eeva-Liisa Eskelinen



1992 ◽  
Vol 00 (7) ◽  
pp. 9-9
Author(s):  
Janet L. Burns ◽  
Richard J. Spontak

Traditional methods of sample preparation and analysis in conventional transmission electron microscopy (TEM) are not readily applicable to multicomponent complex liquids which may contain a wealth of microstructural information. Two techniques which facilitate the study of structure in such liquids are freeze-fracture (FF) TEM and cryo-TEM.





2010 ◽  
Vol 16 (1) ◽  
pp. 54-63 ◽  
Author(s):  
Niels de Jonge ◽  
Rachid Sougrat ◽  
Brian M. Northan ◽  
Stephen J. Pennycook

AbstractA three-dimensional (3D) reconstruction of the cytoskeleton and a clathrin-coated pit in mammalian cells has been achieved from a focal-series of images recorded in an aberration-corrected scanning transmission electron microscope (STEM). The specimen was a metallic replica of the biological structure comprising Pt nanoparticles 2–3 nm in diameter, with a high stability under electron beam radiation. The 3D dataset was processed by an automated deconvolution procedure. The lateral resolution was 1.1 nm, set by pixel size. Particles differing by only 10 nm in vertical position were identified as separate objects with greater than 20% dip in contrast between them. We refer to this value as the axial resolution of the deconvolution or reconstruction, the ability to recognize two objects, which were unresolved in the original dataset. The resolution of the reconstruction is comparable to that achieved by tilt-series transmission electron microscopy. However, the focal-series method does not require mechanical tilting and is therefore much faster. 3D STEM images were also recorded of the Golgi ribbon in conventional thin sections containing 3T3 cells with a comparable axial resolution in the deconvolved dataset.



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