Mycobacterium kansasii Infection in a Farmed White-Tailed Deer (Odocoileus virginianus) in Florida, USA.

A 7-year-old farmed white-tailed deer doe was transported to a Levy County, Florida property and began to decline in health, exhibiting weight loss and pelvic limb weakness. The doe prematurely delivered live twin fawns, both of which later died. The doe was treated with corticosteroids, antibiotics, gastric cytoprotectants, and B vitamins but showed no improvement. The doe was euthanized, and a post mortem examination was performed under the University of Florida's Cervidae Health Research Initiative. We collected lung tissue after the animal was euthanized and performed histological evaluation, using H&E and Ziehl-Neelsen (ZN) staining, and molecular evaluation, using conventional PCR, followed by Sanger sequencing. The microscopic observations of the H&E-stained lung showed multifocal granuloma, while the ZN-stained tissue revealed low numbers of beaded, magenta-staining rod bacteria inside the granuloma formation. Molecular analysis identified the presence of Mycobacterium kansasii. This isolation of a non-tuberculous Mycobacterium in a white-tailed deer emphasizes the importance of specific pathogen identification in cases of tuberculosis-like disease in farmed and free-ranging cervids. We report the first case of M. kansasii infection in a farmed white-tailed deer (Odocoileus virginianus) in Florida. Although M. kansasii cases are sporadic in white-tailed deer, it is important to maintain farm biosecurity and prevent farmed cervids from contacting wildlife to prevent disease transmission.

Recent advances in data collection for Cryo-EM methods.

Methods of transmission electron microscopy (TEM) are typically used to resolve structures of vitrified biological specimens using both single particle analysis (SPA) and tomographic methods and use both conventional as well as scanning transmission modes of data collection. Automation of data collection for each method has been developed to different levels of convenience for the users. Automation of methods using the conventional TEM mode has progressed the furthest. Beam-image shift strategies first used in data collection for SPA were shown to be equally valuable for cryo-electron tomography (cryo-ET). Machine learning methods have been applied for target selection and for planning optimal paths of data collection for SPA. These methods also enabled automated screening. Apertures matching the square shape of cameras have been recently described. Some progress has also been made in the automation of cryo applications of scanning TEM, promising an increase of throughput and potential for further improvement.

A novel bluetongue virus serotype 2 strain isolated from a farmed Florida white-tailed deer (Odocoileus virginianus) arose from reassortment of gene segments derived from co-circulating serotypes in the Southeastern USA.

Bluetongue disease is a reportable animal disease that affects wild and farmed ruminants, including white-tailed deer (WTD). This report documents the clinical findings, ancillary diagnostics, and genomic characterization of a novel reassortant bluetongue virus serotype 2 (BTV-2) strain isolated from a dead Florida farmed WTD in 2022. Our analyses support that this BTV-2 strain likely stemmed from the acquisition of genome segments from co-circulating BTV strains in Florida and Louisiana. In addition, our analyses also indicate that genetically uncharacterized BTV strains may be circulating in the Southeastern USA; however, the identity and reassortant status of these BTV strains cannot be determined based on the VP2 and VP5 genome sequences. Hence, continued surveillance based on complete genome characterization is needed to understand the genetic diversity of BTV strains in this region and the potential threat they may pose to the health of deer and other ruminants.

Fully automated multi-grid cryoEM screening using Smart Leginon.

Single-particle cryo-electron microscopy (cryoEM) is a swiftly growing method for understanding protein structure. With increasing demand for high-throughput, high-resolution cryoEM services comes greater demand for rapid and automated cryoEM grid and sample screening. During screening, optimal grids and sample conditions are identified for subsequent high-resolution data collection. Screening is a major bottleneck for new cryoEM projects because grids must be optimized for several factors, including grid type, grid hole size, sample concentration, buffer conditions, ice thickness and particle behavior. Even for mature projects, multiple grids are commonly screened to select a subset for high-resolution data collection. Here, machine learning and novel purpose-built image-processing and microscope-handling algorithms are incorporated into the automated data-collection software Leginon, to provide an open-source solution for fully automated high-throughput grid screening. This new version, broadly called Smart Leginon, emulates the actions of an operator in identifying areas on the grid to explore as potentially useful for data collection. Smart Leginon Autoscreen sequentially loads and examines grids from an automated specimen-exchange system to provide completely unattended grid screening across a set of grids. Comparisons between a multi-grid autoscreen session and conventional manual screening by 5 expert microscope operators are presented. On average, Autoscreen reduces operator time from ∼6 h to <10 min and provides a percentage of suitable images for evaluation comparable to the best operator. The ability of Smart Leginon to target holes that are particularly difficult to identify is analyzed. Finally, the utility of Smart Leginon is illustrated with three real-world multi-grid user screening/collection sessions, demonstrating the efficiency and flexibility of the software package. The fully automated functionality of Smart Leginon significantly reduces the burden on operator screening time, improves the throughput of screening and recovers idle microscope time, thereby improving availability of cryoEM services.

Learning to automate cryo-electron microscopy data collection with Ptolemy.

Over the past decade, cryo-electron microscopy (cryoEM) has emerged as an important method for determining near-native, near-atomic resolution 3D structures of biological macromolecules. To meet the increasing demand for cryoEM, automated methods that improve throughput and efficiency of microscope operation are needed. Currently, the targeting algorithms provided by most data-collection software require time-consuming manual tuning of parameters for each grid, and, in some cases, operators must select targets completely manually. However, the development of fully automated targeting algorithms is non-trivial, because images often have low signal-to-noise ratios and optimal targeting strategies depend on a range of experimental parameters and macromolecule behaviors that vary between projects and collection sessions. To address this, Ptolemy provides a pipeline to automate low- and medium-magnification targeting using a suite of purpose-built computer vision and machine-learning algorithms, including mixture models, convolutional neural networks and U-Nets. Learned models in this pipeline are trained on a large set of images from real-world cryoEM data-collection sessions, labeled with locations selected by human operators. These models accurately detect and classify regions of interest in low- and medium-magnification images, and generalize to unseen sessions, as well as to images collected on different microscopes at another facility. This open-source, modular pipeline can be integrated with existing microscope control software to enable automation of cryoEM data collection and can serve as a foundation for future cryoEM automation software.

Smart data collection for CryoEM.

This report provides an overview of the discussions, presentations, and consensus thinking from the Workshop on Smart Data Collection for CryoEM held at the New York Structural Biology Center on April 6-7, 2022. The goal of the workshop was to address next generation data collection strategies that integrate machine learning and real-time processing into the workflow to reduce or eliminate the need for operator intervention.

Better, Faster, Cheaper: Recent Advances in Cryo-Electron Microscopy.

Cryo-electron microscopy (cryo-EM) continues its remarkable growth as a method for visualizing biological objects, which has been driven by advances across the entire pipeline. Developments in both single-particle analysis and in situ tomography have enabled more structures to be imaged and determined to better resolutions, at faster speeds, and with more scientists having improved access. This review highlights recent advances at each stageof the cryo-EM pipeline and provides examples of how these techniques have been used to investigate real-world problems, including antibody development against the SARS-CoV-2 spike during the recent COVID-19 pandemic.

Small Molecule Microcrystal Electron Diffraction for the Pharmaceutical Industry-Lessons Learned From Examining Over Fifty Samples.

The emerging field of microcrystal electron diffraction (MicroED) is of great interest to industrial researchers working in the drug discovery and drug development space. The promise of being able to routinely solve high-resolution crystal structures without the need to grow large crystals is very appealing. Despite MicroED's exciting potential, adoption across the pharmaceutical industry has been slow, primarily owing to a lack of access to specialized equipment and expertise. Here we present our experience building a small molecule MicroED service pipeline for members of the pharmaceutical industry. In the past year, we have examined more than fifty small molecule samples submitted by our clients, the majority of which have yielded data suitable for structure solution. We also detail our experience determining small molecule MicroED structures of pharmaceutical interest and offer some insights into the typical experimental outcomes. This experience has led us to conclude that small molecule MicroED adoption will continue to grow within the pharmaceutical industry where it is able to rapidly provide structures inaccessible by other methods.

Leginon: New features and applications.

Leginon is a system for automated data acquisition from a transmission electron microscope. Here we provide an updated summary of the overall Leginon architecture and an update of the current state of the package. We also highlight a few recent developments to provide some concrete examples and use cases.

Body-size Scaling is Related to Gut Microbial Diversity, Metabolism and Dietary Niche of Arboreal Folivorous Flying Squirrels.

Thermal homeostasis of mammals is constrained by body-size scaling. Consequently, small mammals require considerable energy to maintain a high mass-specific metabolic rate (MSMR) and sustain target body temperature. In association with gut microbiota, mammalian hosts acquire absorbable molecules and fulfill their metabolic requirements. Our objective was to characterize gut microbes in wild mammals and relate those findings to host body-size scaling. Two large (Petaurista philippensis grandis and P. alborufus lena), one medium (Trogopterus xanthipes) and one small (Pteromys volans orii) species of flying squirrels (FS) were studied. Using 16S rRNA genes, 1,104 OTUs were detected from four FS, with 1.99% of OTUs shared among all FS. Although all FS gut microbiota were dominated by Firmicutes, they were constituted by different bacterial families. Moreover, Bacteroidetes accounted for up to 19% of gut microbiota in small FS, but was absent in large FS. Finally, based on metagenome predictions, carbohydrate and amino acid metabolism genes were enriched in small body-size FS. In conclusion, gut microbiota compositions and predictive metabolic functions were characteristic of body-size in FS, consistent with their adaptations to folivorous dietary niches.

Variations in Gut Microbiota of Siberian Flying Squirrels Correspond to Seasonal Phenological Changes in Their Hokkaido Subarctic Forest Ecosystem.

Gut microbial communities of animals are influenced by diet and seasonal weather changes. Since foraging strategies of wild animals are affected by phenological changes, gut microbial communities would differ among seasons. However, interactions of plant-animal-microbiota with seasonal changes have not been well characterized. Here, we surveyed gut microbial diversity of Siberian flying squirrels (Pteromys volans orii) from a natural forest in Hokkaido during spring and summer of 2013 and 2014. Additionally, we compared microbial diversity to temperature changes and normalized difference vegetation index (NDVI). Changes in both seasonal temperature and phenology were significantly associated with alterations in gut microbiota. There were two clusters of OTUs, below and above 20 °C that were significantly correlated with low and high temperatures, respectively. Low-temperature cluster OTUs belonged to various phyla, whereas the high-temperature cluster was only constituted by Firmicutes. In conclusion, gut microbiota of Siberian flying squirrels varied with environmental changes on an ecological scale.

High resolution single particle cryo-electron microscopy using beam-image shift.

Automated data acquisition is used widely for single-particle reconstruction of three-dimensional (3D) volumes of biological complexes preserved in vitreous ice and imaged in a transmission electron microscope. Automation has become integral to this method because of the very large number of particle images required in order to overcome the typically low signal-to-noise ratio of these images. For optimal efficiency, automated data acquisition software packages typically employ some beam-image shift targeting as this method is both fast and accurate (±0.1 µm). In contrast, using only stage movement, relocation to a targeted area under low-dose conditions can only be achieved in combination with multiple iterations or long relaxation times, both reducing efficiency. Nevertheless it is well known that applying beam-image shift induces beam-tilt and with it a potential structure phase error with a phase error π/4 the highest acceptable value. This theory has been used as an argument against beam-image shift for high resolution data collection. Nevertheless, in practice many small beam-image shift datasets have resulted in 3D reconstructions beyond the π/4 phase error limit. To address this apparent contradiction, we performed cryo-EM single-particle reconstructions on a T20S proteasome sample using applied beam-image shifts corresponding to beam tilts from 0 to 10 mrad. To evaluate the results we compared the FSC values, and examined the water density peaks in the 3D map. We conclude that the phase error does not limit the validity of the 3D reconstruction from single-particle averaging beyond the π/4 resolution limit.

Routine determination of ice thickness for cryo-EM grids.

Recent advances in instrumentation and automation have made cryo-EM a popular method for producing near-atomic resolution structures of a variety of proteins and complexes. Sample preparation is still a limiting factor in collecting high quality data. Thickness of the vitreous ice in which the particles are embedded is one of the many variables that need to be optimized for collection of the highest quality data. Here we present two methods, using either an energy filter or scattering outside the objective aperture, to measure ice thickness for potentially every image collected. Unlike geometrical or tomographic methods, these can be implemented directly in the single particle collection workflow without interrupting or significantly slowing down data collection. We describe the methods as implemented into the Leginon/Appion data collection workflow, along with some examples from test cases. Routine monitoring of ice thickness should prove helpful for optimizing sample preparation, data collection, and data processing.

Benchmarking cryo-EM Single Particle Analysis Workflow.

Cryo electron microscopy facilities running multiple instruments and serving users with varying skill levels need a robust and reliable method for benchmarking both the hardware and software components of their single particle analysis workflow. The workflow is complex, with many bottlenecks existing at the specimen preparation, data collection and image analysis steps; the samples and grid preparation can be of unpredictable quality, there are many different protocols for microscope and camera settings, and there is a myriad of software programs for analysis that can depend on dozens of settings chosen by the user. For this reason, we believe it is important to benchmark the entire workflow, using a standard sample and standard operating procedures, on a regular basis. This provides confidence that all aspects of the pipeline are capable of producing maps to high resolution. Here we describe benchmarking procedures using a test sample, rabbit muscle aldolase.

Routine single particle CryoEM sample and grid characterization by tomography.

Single particle cryo-electron microscopy (cryoEM) is often performed under the assumption that particles are not adsorbed to the air-water interfaces and in thin, vitreous ice. In this study, we performed fiducial-less tomography on over 50 different cryoEM grid/sample preparations to determine the particle distribution within the ice and the overall geometry of the ice in grid holes. Surprisingly, by studying particles in holes in 3D from over 1000 tomograms, we have determined that the vast majority of particles (approximately 90%) are adsorbed to an air-water interface. The implications of this observation are wide-ranging, with potential ramifications regarding protein denaturation, conformational change, and preferred orientation. We also show that fiducial-less cryo-electron tomography on single particle grids may be used to determine ice thickness, optimal single particle collection areas and strategies, particle heterogeneity, and de novo models for template picking and single particle alignment.

Best practices for managing large CryoEM facilities.

This paper provides an overview of the discussion and presentations from the Workshop on the Management of Large CryoEM Facilities held at the New York Structural Biology Center, New York, NY on February 6-7, 2017. A major objective of the workshop was to discuss best practices for managing cryoEM facilities. The discussions were largely focused on supporting single-particle methods for cryoEM and topics included: user access, assessing projects, workflow, sample handling, microscopy, data management and processing, and user training.

Automated data collection in single particle electron microscopy.

Automated data collection is an integral part of modern workflows in single particle electron microscopy (EM) research. This review surveys the software packages available for automated single particle EM data collection. The degree of automation at each stage of data collection is evaluated, and the capabilities of the software packages are described. Finally, future trends in automation are discussed.

Multiplexed TEM Specimen Preparation and Analysis of Plasmonic Nanoparticles.

We describe a system for rapidly screening hundreds of nanoparticle samples using transmission electron microscopy (TEM). The system uses a liquid handling robot to place up to 96 individual samples onto a single standard TEM grid at separate locations. The grid is then transferred into the TEM and automated software is used to acquire multiscale images of each sample. The images are then analyzed to extract metrics on the size, shape, and morphology of the nanoparticles. The system has been used to characterize plasmonically active nanomaterials.

CTF Challenge: Result summary.

Image formation in bright field electron microscopy can be described with the help of the contrast transfer function (CTF). In this work the authors describe the "CTF Estimation Challenge", called by the Madrid Instruct Image Processing Center (I2PC) in collaboration with the National Center for Macromolecular Imaging (NCMI) at Houston. Correcting for the effects of the CTF requires accurate knowledge of the CTF parameters, but these have often been difficult to determine. In this challenge, researchers have had the opportunity to test their ability in estimating some of the key parameters of the electron microscope CTF on a large micrograph data set produced by well-known laboratories on a wide set of experimental conditions. This work presents the first analysis of the results of the CTF Estimation Challenge, including an assessment of the performance of the different software packages under different conditions, so as to identify those areas of research where further developments would be desirable in order to achieve high-resolution structural information.

MRC2014: Extensions to the MRC format header for electron cryo-microscopy and tomography.

The MRC binary file format is widely used in the three-dimensional electron microscopy field for storing image and volume data. Files contain a header which describes the kind of data held, together with other important metadata. In response to advances in electron microscopy techniques, a number of variants to the file format have emerged which contain useful additional data, but which limit interoperability between different software packages. Following extensive discussions, the authors, who represent leading software packages in the field, propose a set of extensions to the MRC format standard designed to accommodate these variants, while restoring interoperability. The MRC format is equivalent to the map format used in the CCP4 suite for macromolecular crystallography, and the proposal also maintains interoperability with crystallography software. This Technical Note describes the proposed extensions, and serves as a reference for the standard.