Entropy Regularized Deconvolution of Cellular Cryo-Transmission Electron Tomograms

Abstract

Cryo-electron tomography (cryo-ET) allows for the high resolution visualization of biological macromolecules. However, the technique is limited by a low signal-to-noise ratio and variance in contrast at different frequencies, as well as reduced Z resolution. Here, we applied entropy regularized deconvolution to cryo-ET data generated from transmission electron microscopy and reconstructed using weighted back projection. We applied DC to several in situ cryo-ET data sets, and assess the results by Fourier analysis and subtomogram analysis.

The advent of preventive high-resolution structural histopathology by artificial-intelligence-powered cryogenic electron tomography

“…Furthermore, simulations and supervised machine learning approaches training on existing data could be leveraged to create predictive models that facilitate experimental parameter optimization, thereby saving time and computational resources. Recent algorithms have demonstrated rapid, automated, even onthe-fly tomographic reconstruction (Zheng et al, 2022), including accurate and detailed determination of the contrast transfer function and astigmatism for tilted specimens (Mastronarde, 2024), as well as missing wedge restoration by CTF deconvolution (Croxford et al, 2021) and other methods. Indeed, multiple increasingly automated pipelines have emerged to expedite cryoET workflows (Morado et al, 2016;Böhning and Bharat, 2021), including subtomogram averaging, tomographic annotation, and software interoperability (Jiménez de la Morena et al, 2022).…”

Perspective: Emerging strategies for determining atomic-resolution structures of macromolecular complexes within cells

“…In light of this simplification, it may be reasonable to further investigate the adoption of tools that have proven to be so successful in the context of fluorescence microscopy. An entropy-regularized approach to 3-D deconvolution, like that used by ( Cro x ford et al, 2021 ) in an attempt to fill in the missing wedge associated with tilt-series electron tomography, seems a promising one to also try with focal series data. While a focal series has no “missing wedge problem” as such, it is expected that regularization will help to make its inverse transform more robust in the presence of low SNR.…”