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.