GoldDigger and Checkers, computational developments in cryo-scanning transmission electron tomography to improve the quality of reconstructed volumes

Abstract

In this work, we present a pair of tools to improve the fiducial tracking and reconstruction quality of cryo-scanning transmission electron tomography (STET) datasets. We then demonstrate the effectiveness of these two tools on experimental cryo-STET data. The first tool, GoldDigger, improves the tracking of fiducials in cryo-STET by accommodating the changed appearance of highly defocussed fiducial markers. Since defocus effects are much stronger in scanning transmission electron microscopy than in conventional transmission electron microscopy, existing alignment tools do not perform well without manual intervention. The second tool, Checkers, combines image inpainting and unsupervised deep learning for denoising tomograms. Existing tools for denoising cryo-tomography often rely on paired noisy image frames, which are unavailable in cryo-STET datasets, necessitating a new approach. Finally, we make the two software tools freely available for the cryo-STET community.

Keywords

• cryo-scanning transmission electron tomography
• deep-learning-based denoising
• thick biological sample
• tilt-series fiducial alignment