Name: Subnanometer-Resolution Structural Determination of Hemagglutinin from Cryo-Electron Tomography of Influenza Viruses (Video) | JoVE Journal
Uploaded: 2025-11-07T12:50:00.000+00:00
Duration: 8 min 19 s
Description: Discover cryo-electron tomography for sub-nanometer structural analysis of influenza hemagglutinin. This protocol enables visualization of conformational changes during receptor binding on intact viruses.

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The authors would like to acknowledge helpful discussions with the Schiffer Lab. We would also like to thank the UMass Chan cryoEM Core facility for their help with data acquisition and for providing us with support and advice. This work was supported by the National Institute of General Medical Sciences R01GM143773 to M.S. and R35GM151996 to C.A.S.

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The authors have nothing to disclose.

Better structural understanding of critical viral proteins can accelerate the discovery of novel treatments against these viruses. In the past decade, the resolution revolution has accelerated the determination of high-resolution viral structures using single particle cryoEM, but this method is limited to either purified proteins or non-enveloped viruses with icosahedral symmetry. In contrast, cryoET is capable of visualizing morphologically diverse membranous virions in 3D, but is of limited resolution. The pipeline pre...

Cryo-electron tomography (cryoET) has been applied over the past decades to capture snapshots of protein complexes, viruses, cells, and organisms. A modality of cryo-electron microscopy (cryoEM), cryoET is a structural biology method where a biological sample is flash frozen, then imaged through a variety of orientations through tilting1,2,3. Images taken at each orientation are then computationally aligned to their common tilt axis and reconstructed into a tomogram to provide a three-dimensional view4.
Whereas X-ray crystallography and single particle cryoEM demand purified, structurally homogenous molecules, cryoET can image a molecule directly within its native context4. Therefore, one main advantage of cryoET is its ability to visualize pleomorphic samples, such as membranous viruses, including influenza5,6,7. Another promise of cryoET is its ability to image across scales. While tomograms are not typically resolved past 5-10 nm8, the integration of subtomogram averaging, where copies of the same particle are identified, aligned, and averaged, can result in near atomic resolution in some biological molecules such as ribosomes9,10. However, only limited types of molecules can reach this resolution; subtomogram averages do not typically surpass 10-15 &#197; resolution. In contrast, single particle cryoEM routinely achieves resolutions of 3-4 &#197; post the resolution revolution11. Recent advancements in both higher throughput of cryoET data acquisition and analysis software have allowed for subnanometer resolution structure determination of additional biological molecules within their native context12,13,14,15,16,17,18.
One common usage for cryoET is to visualize virus morphology, organization, and structure. Despite the lower resolution afforded by this technique compared to single particle cryoEM or X-ray crystallography, cryoET combined with subtomogram averaging can provide information on how viral proteins behave in their native environment and provide crucial details on their organization in the context of the virion. A common target for cryoET of viruses is the surface glycoproteins that are commonly used for host cell attachment and fusion, as they are often the main antigens and targets for therapeutics or vaccines. With recent advancements in cryoET processing packages, it has become increasingly feasible to achieve subnanometer resolution averages of these glycoproteins19,20,21,22. One such example is hemagglutinin (HA), the major protein on the surface of influenza virions. Not only does this protein conduct both receptor binding and membrane fusion, but it also covers the virion in an incredibly dense manner, with hundreds to thousands of HAs on a singular virion5. The protocol presented here (Figure 1) integrates several commonly used packages with in-house scripts to delineate stages from pre-processing to model refinement for a subtomogram average of influenza HA.

<table><tbody><tr><td>AMD Ryzen Threadripper PRO 5965WX</td><td>AMD</td><td>https://www.amd.com/en/support/downloads/drivers.html/processors/ryzen-threadripper-pro/ryzen-threadripper-pro-5000wx-series/amd-ryzen-threadripper-pro-5965wx.html</td><td></td></tr><tr><td>AreTomo 1.3.4</td><td>UC San Francisco</td><td>https://drive.google.com/drive/folders/1Z7pKVEdgMoNaUmd_cOFhlt-QCcfcwF3_</td><td></td></tr><tr><td>EMAN2 2.99.52</td><td>Baylor College of Medicine</td><td>https://blake.bcm.edu/emanwiki/EMAN2</td><td></td></tr><tr><td>IMOD 4.12.27</td><td>University of Colorado at Boulder</td><td>https://bio3d.colorado.edu/imod/</td><td></td></tr><tr><td>Influenza analysis scripts</td><td>UMass Chan Medical School</td><td>https://github.com/jqyhuang/influenza-analysis</td><td></td></tr><tr><td>IsoNet 0.3</td><td>UCLA</td><td>https://github.com/IsoNet-cryoET/IsoNet</td><td></td></tr><tr><td>M 2.0.0</td><td>Genentech</td><td>https://warpem.github.io/warp/home/m/</td><td></td></tr><tr><td>NVIDIA A4000</td><td>NVIDIA&amp;nbsp;</td><td>https://www.nvidia.com/en-us/products/workstations/rtx-a4000/</td><td></td></tr><tr><td>Open3D&amp;nbsp;</td><td>Intel Labs</td><td>https://www.open3d.org/</td><td></td></tr><tr><td>PHENIX 1.21-5207</td><td>Lawrence Berkeley National Laboratory</td><td>phenix-online.org</td><td></td></tr><tr><td>RELION 4.0</td><td>MRC Laboratory of Molecular Biology</td><td>https://relion.readthedocs.io/en/release-4.0/</td><td></td></tr><tr><td>Ubuntu 20.04</td><td>Ubuntu</td><td>https://releases.ubuntu.com/focal/</td><td></td></tr><tr><td>UCSF ChimeraX 1.6.1</td><td>UC San Francisco</td><td>https://www.cgl.ucsf.edu/chimerax/</td><td></td></tr><tr><td>Warp 2.0.0</td><td>Genentech</td><td>http://warpem.github.io/warp/</td><td></td></tr></tbody></table>

subnanometer-resolution structural determination of hemagglutinin from cryo-electron tomography of influenza viruses

Subnanometer-Resolution Structural Determination of Hemagglutinin from Cryo-Electron Tomography of Influenza Viruses

Cryo-electron tomography is a powerful tool to visualize heterogeneous samples, with one major application being structural characterization of pleomorphic viruses. In recent years, subtomogram averaging of viral glycoproteins has emerged as a method to directly visualize these crucial proteins on the surface of intact virions. One important target is the hemagglutinin (HA) glycoprotein of influenza virus, which densely covers the viral envelope and is responsible for influenza receptor binding and membrane fusion. While subtomogram averages of influenza HA have been reported, their resolutions have been limited due to the low signal-to-noise ratio inherent to cryoET as well as the manual effort required to analyze heterogenous influenza virions. Presented here is a cryoET analysis pipeline that integrates several software packages to analyze tomographic data of influenza virions efficiently and robustly. This protocol describes the structural determination of HA from influenza virions, through steps from initial motion correction to final model building. Following this pipeline, a HA reconstruction at 6.0 &#197; resolution was obtained from two cryoET datasets collected from the A/Puerto Rico/8/34 (PR8) influenza strain.

To demonstrate the utilization of this processing protocol (Figure 1), the previously outlined workflow was applied to two datasets of 25 tomograms combined, obtained from an H1N1 influenza A virus strain (A/Puerto Rico/8/1934). Data collection parameters are outlined in Table 1. Figure 2 illustrates a representative tomogram and zoomed-in views of pleomorphic influenza virions. Varied morphologies are captured in this tomogram, as virions range...

This article presents a protocol for data processing of influenza viruses imaged using cryo-electron tomography and subsequent subtomogram averaging of the hemagglutinin glycoprotein. This protocol covers step-by-step data processing, from image preprocessing to final model refinement.

Cryo-electron tomography is a powerful tool to visualize heterogeneous samples, with one major application being structural characterization of ...

Our lab uses structural biology to study viruses and the evolution of drug resistance. In the current work, we are characterizing how influenza hemagglutinin changes conformation upon receptor binding. We have leveraged cryo-electron tomography to visualize viral morphology organization and structure.Specifically, we have used this modality of cryo-electron microscopy to image the pleomorphic virus influenza. Our protocol allows us to study the organization and structure of influenza hemagglutinin in situ on intact viruses at sub-nanometer resolution. This higher resolution allows us to visualize formerly hidden interactions.To begin, launch a Linux terminal for activating the Conda environment. Use the terminal to activate the Conda environment with ISO net installed. Create a sub-folder named tomo folder, then move all tomogram files into that folder.Next, use the code to generate a star file in the project folder. Using a text editor, open the generated star file and input the approximate defocus value for the zero-degree-tilt images into the fourth column. Now run the CTFD convolution command in the terminal.After deconvolving activate the EMAN2 GUI environment to begin pre-processing for particle picking. Under tomography, left click the arrow next to raw data, select import tomograms from the dropdown menu, and choose the files. Then, left click the arrow next to segmentation and choose pre-processed tomograms.Use suitable default parameters. After pre-processing, an info directory with blank JSON files matching the base names of the process tomography files will be created automatically. Add angstrom pixel information to the files before proceeding.To train a convolutional neural network, or CNN to recognize the HA glycoprotein, change the working directory to the location of the tomograms to be used. Then open the CNN training interface using the terminal. Confirm that four windows are open.One containing the information on CNN parameters and tomogramss in the directory, and the other three windows containing images of good references, bad references and particles selected by the CNN. In the CNN GUI, click on the new option to initialize a CNN with default learning rate of 0.0001 and box size of eight. Under file name column, left click on a representative tomogram to open it.Hover the cursor over the open tomogram and click the mouse scroll wheel to open a new window. Left click the N number slider to traverse the Z axis. Next, on the open tomogram, use the good references panel to select 10 to 20 features corresponding to the HA.Positive images appear in the positive window.To remove one, hold shift and left click it. Now switch to the bad references panel and select 10 to 20 references corresponding to features such as empty membrane patches, the RNP density, fiducial markers, and debris. Set the number of iterations to 50 and click on the train icon to begin CNN training.After training is completed, press apply. Selected particles will be displayed on tomogram as blue circles or can be seen in the particles window. Continue refining by selecting additional positive and negative references, periodically clicking save to preserve progress.Once the CNN performs satisfactorily, click apply all to run particle picking. Then evaluate results on several tomograms and retrain as needed. To save coordinates for each tomogram, reopen the EMAN2 GUI using the command e2projectmanager.py.Click the arrow under subtomogram average and select manual boxing. Now, input the tomogram's name and click launch. Then, sequentially click on file, save box cord, and tomogram_ha.txt to save the coordinates. Navigate to the neural net sub folder and info sub folders to backup and net save. hdf, trainouts.hdf, segouts. hdf, and boxes3Dref.hdf. Train a second CNN to detect the M1 protein layer and VRNP presence by changing the training box size from eight to 14.To curate particles, first download notebooks from the GitHub repository. Then open the CNN particle cleaning the IPYNB notebook, and load the required modules. Load in the HA and M1 coordinate text files into the notebook.View them as 3D clouds using the open 3D library. Next, filter out HA coordinate outliers using open 3D statistical outlier removal method. Next, calculate the distances between HA and M1 point clouds by identifying HA points more than 20 pixels from M1 as outliers and save remaining coordinates to an output text file.Concatenate all particles and save as star files using pts2 star file.IPYNB. Using WarpTools, extract particles that bending factor four and perform initial subtomogram averaging in RELION minus four using the code. Convert the warp star file to RELION four format.Then generate an initial reference using RELION refine MPI on a subset of particles. Perform 3D auto refinement on subtomograms with RELION refine MPI. Upon convergence, repeat auto refinement with titan translation offset to eight and step to two.Leverage 2D classification to discard junk particles using RELION refine. Create star files with good classes using the code, then combine the individual star files. Representative tomograms revealed pleomorphic virions with shapes ranging from spherical to elongated forms.The final subtomogram average reached a global resolution of 6.0 angstroms with local resolution ranging from five to seven angstroms. Alpha helices, and beta sheets were resolved in the final HA reconstruction, supporting the structural quality of the refined map. Glycosylation sites were identifiable at four positions on the HA head and stem, confirming visibility of glycan density in the final reconstruction.

subnanometer-resolution-structural-determination-hemagglutinin-from

Research

JoVE Journal

Biochemistry

The protocol describes a high-throughput approach to determining structures of membrane proteins using cryo-electron tomography and 3D image processing.  It covers the details of specimen preparation, data collection, data processing and interpretation, and concludes with the production of a representative target for the approach, the HIV-1 Envelope glycoprotein.  These computational procedures are designed in a way that enables researchers and students to work remotely and contribute to data...

Determination of Molecular Structures of HIV Envelope Glycoproteins using Cryo-Electron Tomography and Automated Sub-tomogram Averaging

We describe a correlative microscopy method that combines high-speed 3D live-cell fluorescent light microscopy and high-resolution cryo-electron tomography. We demonstrate the capability of the correlative method by imaging dynamic, small HIV-1 particles interacting with host HeLa cells.

Correlative Microscopy for 3D Structural Analysis of Dynamic Interactions

We present a protocol of how to collect and process electron cryo-tomograms of whole mitochondria. The technique provides detailed insights into the structure, function, and organization of large membrane protein complexes in native biological membranes.

Visualization of ATP Synthase Dimers in Mitochondria by Electron Cryo-tomography

The attachment of virions to a surface is a requirement for single virion imaging by Super-resolution fluorescence imaging or atomic force microscopy (AFM). Here we demonstrate a sample preparation method for controlled adhesion of virions to glass surfaces suitable for use in AFM and super-resolution fluorescence imaging.

Sample Preparation for Single Virion Atomic Force Microscopy and Super-resolution Fluorescence Imaging

An approach is presented for determining structures of viral membrane glycoprotein complexes using a combination of electron cryo-tomography and sub-tomogram averaging with the computational package Jsubtomo.

Averaging of Viral Envelope Glycoprotein Spikes from Electron Cryotomography Reconstructions using Jsubtomo

This paper presents a protocol for processing cryo-EM images using the software suite SPHIRE. The present protocol can be applied for nearly all single particle EM projects that target near-atomic resolution.

High-resolution Single Particle Analysis from Electron Cryo-microscopy Images Using SPHIRE

We present a protocol for lamella preparation of plunge frozen biological specimens by cryo-focused ion beam micromachining for high-resolution structural studies of macromolecules in situ with cryo-electron tomography. The presented protocol provides guidelines for the preparation of high-quality lamellae with high reproducibility for structural characterization of macromolecules inside the Saccharomyces cerevisiae.

Preparation and Cryo-FIB micromachining of Saccharomyces cerevisiae for Cryo-Electron Tomography

Here protocols are described to prepare virus assemblies suitable for liquid-EM and cryo-EM analysis at the nanoscale using transmission electron microscopy.

Advancing High-Resolution Imaging of Virus Assemblies in Liquid and Ice

The present protocol describes high-resolution cryo-electron tomography remote data acquisition using Tomo5 and subsequent data processing and subtomogram averaging using emClarity. Apoferritin is used as an example to illustrate detailed step-by-step processes to achieve a cryo-ET structure at 2.86 Å resolution.

Cryo-Electron Tomography Remote Data Collection and Subtomogram Averaging

This video protocol illustrates how to perform scanning transmission electron microscopy tomography of virological specimens. For optimal outcomes, samples are prepared by high-pressure freezing and subsequent freeze substitution.

Scanning Transmission Electron Microscopy Tomography in Virology: 3D Imaging of High-pressure Frozen, Freeze-substituted Samples

Discover cryo-electron tomography for sub-nanometer structural analysis of influenza hemagglutinin. This protocol enables visualization of conformational changes during receptor binding on intact viruses.

Subnanometer-Resolution Structural Determination of Hemagglutinin from Cryo-Electron Tomography of Influenza Viruses

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