Combined Tilt- and Focal-Series Tomography for HAADF-STEM

Dahmen, Tim; Kohr, Holger; Lupini, Andrew R.; Baudoin, Jean-Pierre; Kübel, Christian; Trampert, Patrick; Slusallek, Philipp; Jonge, Niels de

doi:10.1017/s1551929516000328

Cited by 1 publication

(2 citation statements)

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“…HAADF-STEM has emerged as a powerful imaging technique that provides nanoscale-level structural detail. , It is, however, sensitive to environmental and instrumental noise during image acquisition that introduces extraneous signals not associated with the scattering of the sample. ,, For example, images are acquired at different projection angles by tilting the sample stage, at high tilt angles; however, focusing becomes more difficult, which leads to image blurring . In addition, limited beam penetration and focal depth coupled with the restricted tilt range results in a lower set of projections which also introduces artifacts (i.e., “missing cone” artifacts). , Beam damage and environmental noise (e.g., airflow, sound, temperature, etc.) also deteriorate image quality and limit the accuracy of HAADF-STEM tomographic reconstruction. ,, Due to the particle nature of electrons and the collection method, Poisson noise remains the dominant form of noise in STEM imaging. , To account for these effects within our simulated data, we apply several HAADF-STEM-related noise conditions including Gaussian noise, Poisson noise, and tip-blurring effects to each simulated image similar to the approach implemented by Schwenker et al − Parameters such as broadening effects, counts, and additive background noise were adjusted to account for the different levels of noise that may be encountered during image acquisition.…”

Section: Methodsmentioning

confidence: 99%

“…31 In addition, limited beam penetration and focal depth coupled with the restricted tilt range results in a lower set of projections which also introduces artifacts (i.e., "missing cone" artifacts). 32,33 Beam damage and environmental noise (e.g., airflow, sound, temperature, etc.) also deteriorate image quality and limit the accuracy of HAADF-STEM tomographic reconstruction.…”

Section: Coarse-grained Molecular Dynamics Simulations Andmentioning

confidence: 99%

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Denoising Autoencoder Trained on Simulation-Derived Structures for Noise Reduction in Chromatin Scanning Transmission Electron Microscopy

Alvarado

Agrawal

et al. 2023

ACS Cent. Sci.

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Scanning transmission electron microscopy tomography with ChromEM staining (ChromSTEM), has allowed for the three-dimensional study of genome organization. By leveraging convolutional neural networks and molecular dynamics simulations, we have developed a denoising autoencoder (DAE) capable of postprocessing experimental ChromSTEM images to provide nucleosome-level resolution. Our DAE is trained on synthetic images generated from simulations of the chromatin fiber using the 1-cylinder per nucleosome (1CPN) model of chromatin. We find that our DAE is capable of removing noise commonly found in high-angle annular dark field (HAADF) STEM experiments and is able to learn structural features driven by the physics of chromatin folding. The DAE outperforms other well-known denoising algorithms without degradation of structural features and permits the resolution of α-tetrahedron tetranucleosome motifs that induce local chromatin compaction and mediate DNA accessibility. Notably, we find no evidence for the 30 nm fiber, which has been suggested to serve as the higher-order structure of the chromatin fiber. This approach provides high-resolution STEM images that allow for the resolution of single nucleosomes and organized domains within chromatin dense regions comprising of folding motifs that modulate the accessibility of DNA to external biological machinery.

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