ClusterAlign: A fiducial tracking and tilt series alignment tool for thick sample tomography

Optimizing Contrast in Automated 4D STEM Cryotomography

4D STEM is an emerging approach to electron microscopy. While it was developed principally for high-resolution studies in materials science, the possibility to collect the entire transmitted flux makes it attractive for cryomicroscopy in application to life science and radiation-sensitive materials where dose efficiency is of utmost importance. We present a workflow to acquire tomographic tilt series of 4D STEM data sets using a segmented diode and an ultrafast pixelated detector, demonstrating the methods using a specimen of a T4 bacteriophage. Full integration with the SerialEM platform conveniently provides all the tools for grid navigation and automation of the data collection. Scripts are provided to convert the raw data to mrc format files and further to generate a variety of modes representing both scattering and phase contrasts, including incoherent and annular bright field, integrated center of mass, and parallax decomposition of a simulated integrated differential phase contrast. Principal component analysis of virtual annular detectors proves particularly useful, and axial contrast is improved by 3D deconvolution with an optimized point spread function. Contrast optimization enables visualization of irregular features such as DNA strands and thin filaments of the phage tails, which would be lost upon averaging or imposition of an inappropriate symmetry.

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

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.

A deep learning approach to the automatic detection of alignment errors in cryo-electron tomographic reconstructions

Electron tomography is an imaging technique that allows for the elucidation of three-dimensional structural information of biological specimens in a very general context, including cellular in situ observations. The approach starts by collecting a set of images at different projection directions by tilting the specimen stage inside the microscope. Therefore, a crucial preliminary step is to precisely define the acquisition geometry by aligning all the tilt images to a common reference. Errors introduced in this step will lead to the appearance of artifacts in the tomographic reconstruction, rendering them unsuitable for the sample study. Focusing on fiducial-based acquisition strategies, this work proposes a deep-learning algorithm to detect misalignment artifacts in tomographic reconstructions by analyzing the characteristics of these fiducial markers in the tomogram. In addition, we propose an algorithm designed to detect fiducial markers in the tomogram with which to feed the classification algorithm in case the alignment algorithm does not provide the location of the markers. This open-source software is available as part of the Xmipp software package inside of the Scipion framework, and also through the command-line in the standalone version of Xmipp.

Computational methods for in situ structural studies with cryogenic electron tomography

Cryo-electron tomography (cryo-ET) plays a critical role in imaging microorganisms in situ in terms of further analyzing the working mechanisms of viruses and drug exploitation, among others. A data processing workflow for cryo-ET has been developed to reconstruct three-dimensional density maps and further build atomic models from a tilt series of two-dimensional projections. Low signal-to-noise ratio (SNR) and missing wedge are two major factors that make the reconstruction procedure challenging. Because only few near-atomic resolution structures have been reconstructed in cryo-ET, there is still much room to design new approaches to improve universal reconstruction resolutions. This review summarizes classical mathematical models and deep learning methods among general reconstruction steps. Moreover, we also discuss current limitations and prospects. This review can provide software and methods for each step of the entire procedure from tilt series by cryo-ET to 3D atomic structures. In addition, it can also help more experts in various fields comprehend a recent research trend in cryo-ET. Furthermore, we hope that more researchers can collaborate in developing computational methods and mathematical models for high-resolution three-dimensional structures from cryo-ET datasets.

Synchronization of scanning probe and pixelated sensor for image-guided diffraction microscopy

A 4-dimensional modality of a scanning transmission electron microscope (4D-STEM) acquires diffraction images formed by a coherent and focused electron beam scanning the specimen. Newly developed ultrafast detectors offer a possibility to acquire high throughput diffraction patterns at each pixel of the scan, enabling rapid tilt series acquisition for 4D-STEM tomography. Here we present a solution to the problem of synchronizing the electron probe scan with the diffraction image acquisition, and demonstrate on a fast hybrid-pixel detector camera (ARINA, DECTRIS). Image-guided tracking and autofocus corrections are handled by the freely-available microscope-control software SerialEM, in conjunction with a high angle annular dark field (HAADF) image acquired simultaneously. The open source SavvyScan system offers a versatile set of scanning patterns, operated by commercially available multi-channel acquisition and signal generator computer cards (Spectrum Instrumentation GmbH). Images are recorded only within a sub-region of the total field, so as to avoid spurious data collection during flyback and/or acceleration periods in the scan. Hence, the trigger of the fast camera follows selected pulses from the scan generator clock gated according to the chosen scan pattern. Software and protocol are provided for gating the trigger pulses via a microcontroller (ST Microelectronics ARM Cortex). We demonstrate the system on a standard replica grating and by diffraction imaging of a ferritin specimen.