Electron tomography | EPFL Graph Search

Electron tomography (ET) is a tomography technique for obtaining detailed 3D structures of sub-cellular, macro-molecular, or materials specimens. Electron tomography is an extension of traditional transmission electron microscopy and uses a transmission electron microscope to collect the data. In the process, a beam of electrons is passed through the sample at incremental degrees of rotation around the center of the target sample. This information is collected and used to assemble a three-dimensional image of the target. For biological applications, the typical resolution of ET systems are in the 5–20 nm range, suitable for examining supra-molecular multi-protein structures, although not the secondary and tertiary structure of an individual protein or polypeptide. Recently, atomic resolution in 3D electron tomography reconstructions has been demonstrated. BF-TEM and ADF-STEM tomography In the field of biology, bright-field transmission electron microscopy (BF-TEM) and high-r

Structure-aware Multi-view 3D Reconstruction of Dislocations in TEM with Message Passing Neural Networks

Okan Altingövde, Pascal Fua, Gulnaz Ganeeva, Cécile Hébert, Anastasiia Mishchuk, Emadeddin Oveisi

Efficient analysis of the three-dimensional (3D) shape and distribution of curvilinear crystal defects, namely dislocations, is an open research topic in material science and computer vision. In order to determine the structural and opto-electrical characteristics of the material, accurate 3D reconstructions of dislocations are required. In its current state, an established way to obtain 3D information of dislocations is electron tomography which relies on acquiring dozens of images from tilted sample for wide range of angles. Although tomography yields sufficient results in many cases, it is manually demanding to acquire, register and process dozens of images. In previous works [1, 2], it is shown that a classical stereo reconstruction approach may be applied as an alternative in order to increase reconstruction throughput while providing good performance. A later work [3] employing modern data-driven computer vision models showed that this stereo approach can be largely automatized and be used in various imaging conditions. In this work, we further improve the performance and memory consumption of previous convolutional neural network (CNN) based stereo reconstruction approach and extend it to be applicable in cases where more than two images are available. Our proposed multi-view approach directly incorporates the structural prior of dislocations to the model"s architecture and estimates 3D line segments approximating 3D shape of dislocations. In its core, our method has two stages which are structural connectivity estimation and depth estimation. In first stage, we employ a CNN to estimate affinity of sampled image locations on dislocation cores. This affinity information is used in the later depth estimation stage to eliminate the depth discontinuities on dislocations (Figure1). Unlike previous deep data-driven method, our new approach uses structural knowledge to separate depth estimations of different dislocation segments and yields more accurate reconstructions. In Figure 2, an example affinity estimation is shown for 4 different image locations on the same dislocation. It may be seen that dislocation points that are connected by dislocation segment in 3D results in higher affinity. This information is crucial to connect 3D point estimations and obtain linear structures in 3D.

2021

Insight in the 3D morphology of silica-based nanotubes using electron microscopy

Teresa Katharina Dennenwaldt

Amorphous silica-based nanotubes (SBNTs) were synthesized from phosphoryl triamide, OP(NH2)(3), thiophosphoryl triamide, SP(NH2)(3), and silicon tetrachloride, SiCl4, at different temperatures and with varying amount of the starting material SiCl4 using a recently developed template-free synthesis approach. Diameter and length of the SBNTs are tunable by varying the synthesis parameters. The 3D mesocrystals of the SBNTs were analyzed with focused ion beam sectioning and electron tomography in the transmission electron microscope showing the hollow tubular structure of the SBNTs. The reconstruction of a small SBNT assembly was achieved from a high-angle annular-dark field scanning transmission electron microscopy tilt series containing only thirteen images allowing analyzing beam sensitive material without altering the structure. The reconstruction revealed that the individual nanotubes are forming an interconnected array with an open channel structure. (C) 2016 Elsevier Ltd. All rights reserved.

Pergamon-Elsevier Science Ltd2016

Visualization of proteins in intact cells with a clonable tag for electron microscopy

Paul Guichard

Identification of proteins in 3D maps of cells is a main challenge in structural cell biology. For light microscopy (LM) clonable reagents such as green fluorescent protein represented a real revolution and equivalent reagents for transmission electron microscopy (TEM) have been pursued for a long time. To test the viability of the metal-binding protein metallothionein (MT) as a tag for TEM in cells we have studied three MT-fusion proteins in Escherichia coli: AmiC, a component of the division ring, RecA, a DNA-binding protein, and a truncated cytoplasmic form of maltose-binding protein (MBP). Proteins fused to MT were expressed in E. coli. live cells treated with gold salts were processed by fast-freezing and freeze-substitution. Small electron-dense particles were detected in sections of bacteria expressing the MT-fusion proteins and immunogold labelling confirmed that these particles were associated to the fusion proteins. The distribution of the particles correlated with the functional locations of these proteins: MBP-MT3 concentrated in the cytoplasm, AmiC-MT1 in the bacterial division ring and RecA-MT1 in the nucleoid. The electron-dense tag was easily visualized by electron tomography and in frozen-hydrated cells.

2008

Structure-aware Multi-view 3D Reconstruction of Dislocations in TEM with Message Passing Neural Networks

Okan Altingövde, Pascal Fua, Gulnaz Ganeeva, Cécile Hébert, Anastasiia Mishchuk, Emadeddin Oveisi

2021