TomoAlign: A novel approach to correcting sample motion and 3D CTF in CryoET

Automatic detection of alignment errors in cryo-electron tomography

Cryo-electron tomography is an imaging technique that allows the study of the three-dimensional structure of a wide range of biological samples, from entire cellular environments to purified specimens. This technique collects a series of images from different views of the specimen by tilting the sample stage in the microscope. Subsequently, this information is combined into a three-dimensional reconstruction. To obtain reliable representations of the specimen of study, it is mandatory to define the acquisition geometry accurately. This is achieved by aligning all tilt images to a standard reference scheme. Errors in this step introduce artifacts into the final reconstructed tomograms, leading to loss of resolution and making them unsuitable for detailed sample analysis. This publication presents algorithms for automatically assessing the alignment quality of the tilt series and their classification based on the residual errors provided by the alignment algorithms. If no alignment information is available, a set of algorithms for calculating the residual vectors focused on fiducial markers is also presented. This software is accessible as part of the Xmipp software package and the Scipion framework.

Enhancing phage therapy by coating single bacteriophage-infected bacteria with polymer to preserve phage vitality

The efficacy of bacteriophages in treating bacterial infections largely depends on the phages' vitality, which is impaired when they are naturally released from their hosts, as well as by culture media, manufacturing processes and other insults. Here, by wrapping phage-invaded bacteria individually with a polymeric nanoscale coating to preserve the microenvironment on phage-induced bacterial lysis, we show that, compared with naturally released phages, which have severely degraded proteins in their tail, the vitality of phages isolated from polymer-coated bacteria is maintained. Such latent phages could also be better amplified, and they more efficiently bound and lysed bacteria when clearing bacterial biofilms. In mice with bacterially induced enteritis and associated arthritis, latent phages released from orally administered bacteria coated with a polymer that dissolves at neutral pH had higher bioavailability and led to substantially better therapeutic outcomes than the administration of uncoated phages.

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

Advances in cryogenic electron microscopy (cryoEM) single particle analysis have revolutionized structural biology by facilitating the in vitro determination of atomic- and near-atomic-resolution structures for fully hydrated macromolecular complexes exhibiting compositional and conformational heterogeneity across a wide range of sizes. Cryogenic electron tomography (cryoET) and subtomogram averaging are rapidly progressing toward delivering similar insights for macromolecular complexes in situ, without requiring tags or harsh biochemical purification. Furthermore, cryoET enables the visualization of cellular and tissue phenotypes directly at molecular, nanometric resolution without chemical fixation or staining artifacts. This forward-looking review covers recent developments in cryoEM/ET and related technologies such as cryogenic focused ion beam milling scanning electron microscopy and correlative light microscopy, increasingly enhanced and supported by artificial intelligence algorithms. Their potential application to emerging concepts is discussed, primarily the prospect of complementing medical histopathology analysis. Machine learning solutions are poised to address current challenges posed by "big data" in cryoET of tissues, cells, and macromolecules, offering the promise of enabling novel, quantitative insights into disease processes, which may translate into the clinic and lead to improved diagnostics and targeted therapeutics.

DUAL: deep unsupervised simultaneous simulation and denoising for cryo-electron tomography

Recent biotechnological developments in cryo-electron tomography allow direct visualization of native sub-cellular structures with unprecedented details and provide essential information on protein functions/dysfunctions. Denoising can enhance the visualization of protein structures and distributions. Automatic annotation via data simulation can ameliorate the time-consuming manual labeling of large-scale datasets. Here, we combine the two major cryo-ET tasks together in DUAL, by a specific cyclic generative adversarial network with novel noise disentanglement. This enables end-to-end unsupervised learning that requires no labeled data for training. The denoising branch outperforms existing works and substantially improves downstream particle picking accuracy on benchmark datasets. The simulation branch provides learning-based cryo-ET simulation for the first time and generates synthetic tomograms indistinguishable from experimental ones. Through comprehensive evaluations, we showcase the effectiveness of DUAL in detecting macromolecular complexes across a wide range of molecular weights in experimental datasets. The versatility of DUAL is expected to empower cryo-ET researchers by improving visual interpretability, enhancing structural detection accuracy, expediting annotation processes, facilitating cross-domain model adaptability, and compensating for missing wedge artifacts. Our work represents a significant advancement in the unsupervised mining of protein structures in cryo-ET, offering a multifaceted tool that facilitates cryo-ET research.

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.

Accurate, automatic determination of astigmatism and phase with Ctfplotter in IMOD

Ctfplotter in the IMOD software package is a flexible program for determination of CTF parameters in tilt series images. It uses a novel approach to find astigmatism by measuring defocus in one-dimensional power spectra rotationally averaged over a series of restricted angular ranges. Comparisons with Ctffind, Gctf, and Warp show that Ctfplotter's estimated astigmatism is generally more reliable than that found by these programs that fit CTF parameters to two-dimensional power spectra, especially at higher tilt angles. In addition to that intrinsic advantage, Ctfplotter can reduce the variability in astigmatism estimates further by summing results over multiple tilt angles (typically 5), while still finding defocus for each individual image. Its fitting strategy also produces better phase estimates. The program now includes features for tuning the sampling of the power spectrum so that it is well-represented for analysis, and for determining an appropriate fitting range that can vary with tilt angle. It can thus be used automatically in a variety of situations, not just for fitting tilt series, and has been integrated into the SerialEM acquisition software for real-time determination of focus and astigmatism.

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.

Extensive Angular Sampling Enables the Sensitive Localization of Macromolecules in Electron Tomograms

Cryo-electron tomography provides 3D images of macromolecules in their cellular context. To detect macromolecules in tomograms, template matching (TM) is often used, which uses 3D models that are often reliable for substantial parts of the macromolecules. However, the extent of rotational searches in particle detection has not been investigated due to computational limitations. Here, we provide a GPU implementation of TM as part of the PyTOM software package, which drastically speeds up the orientational search and allows for sampling beyond the Crowther criterion within a feasible timeframe. We quantify the improvements in sensitivity and false-discovery rate for the examples of ribosome identification and detection. Sampling at the Crowther criterion, which was effectively impossible with CPU implementations due to the extensive computation times, allows for automated extraction with high sensitivity. Consequently, we also show that an extensive angular sample renders 3D TM sensitive to the local alignment of tilt series and damage induced by focused ion beam milling. With this new release of PyTOM, we focused on integration with other software packages that support more refined subtomogram-averaging workflows. The automated classification of ribosomes by TM with appropriate angular sampling on locally corrected tomograms has a sufficiently low false-discovery rate, allowing for it to be directly used for high-resolution averaging and adequate sensitivity to reveal polysome organization.

ScipionTomo: Towards cryo-electron tomography software integration, reproducibility, and validation

Image processing in cryogenic electron tomography (cryoET) is currently at a similar state as Single Particle Analysis (SPA) in cryogenic electron microscopy (cryoEM) was a few years ago. Its data processing workflows are far from being well defined and the user experience is still not smooth. Moreover, file formats of different software packages and their associated metadata are not standardized, mainly since different packages are developed by different groups, focusing on different steps of the data processing pipeline.

The Scipion framework, originally developed for SPA (de la Rosa-Trevín et al., 2016), has a generic python workflow engine that gives it the versatility to be extended to other fields, as demonstrated for model building (Martínez et al., 2020). In this article, we provide an extension of Scipion based on a set of tomography plugins (referred to as ScipionTomo hereafter), with a similar purpose: to allow users to be focused on the data processing and analysis instead of having to deal with multiple software installation issues and the inconvenience of switching from one to another, converting metadata files, managing possible incompatibilities, scripting (writing a simple program in a language that the computer must convert to machine language each time the program is run), etcetera. Additionally, having all the software available in an integrated platform allows comparing the results of different algorithms trying to solve the same problem. In this way, the commonalities and differences between estimated parameters shed light on which results can be more trusted than others. ScipionTomo is developed by a collaborative multidisciplinary team composed of Scipion team engineers, structural biologists, and in some cases, the developers whose software packages have been integrated. It is open to anyone in the field willing to contribute to this project.

The result is a framework extension that combines the acquired knowledge of Scipion developers in close collaboration with third-party developers, and the on-demand design of functionalities requested by beta testers applying this solution to actual biological problems.