SARS-CoV-2 Assembly and Egress Pathway Revealed by Correlative Multi-modal Multi-scale Cryo-imaging

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Abstract

Since the outbreak of the SARS-CoV-2 pandemic, there have been intense structural studies on purified recombinant viral components and inactivated viruses. However, investigation of the SARS-CoV-2 infection in the native cellular context is scarce, and there is a lack of comprehensive knowledge on SARS-CoV-2 replicative cycle. Understanding the genome replication, assembly and egress of SARS-CoV-2, a multistage process that involves different cellular compartments and the activity of many viral and cellular proteins, is critically important as it bears the means of medical intervention to stop infection. Here, we investigated SARS-CoV-2 replication in Vero cells under the near-native frozen-hydrated condition using a unique correlative multi-modal, multi-scale cryo-imaging approach combining soft X-ray cryo-tomography and serial cryoFIB/SEM volume imaging of the entire SARS-CoV-2 infected cell with cryo-electron tomography (cryoET) of cellular lamellae and cell periphery, as well as structure determination of viral components by subtomogram averaging. Our results reveal at the whole cell level profound cytopathic effects of SARS-CoV-2 infection, exemplified by a large amount of heterogeneous vesicles in the cytoplasm for RNA synthesis and virus assembly, formation of membrane tunnels through which viruses exit, and drastic cytoplasm invasion into nucleus. Furthermore, cryoET of cell lamellae reveals how viral RNAs are transported from double-membrane vesicles where they are synthesized to viral assembly sites; how viral spikes and RNPs assist in virus assembly and budding; and how fully assembled virus particles exit the cell, thus stablishing a model of SARS-CoV-2 genome replication, virus assembly and egress pathways.

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  1. SciScore for 10.1101/2020.11.05.370239: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Experimental Models: Cell Lines
    SentencesResources
    Media was removed from the Vero Ccl-81 cells (ATCC) and replaced with an appropriate amount of virus diluted in 0.5 mL of Dulbecco’s modified Eagle medium (Merck) with 1% FCS, 10 units/mL penicillin (Gibco), 10 μg/mL streptomycin (Gibco), and 2mM l-glutamine.
    Ccl-81
    suggested: None
    Software and Algorithms
    SentencesResources
    Tilt series were recorded using SerialEM tilt series controller with pixel sizes of 1.63 Å, 2.13 Å and 4.58 Å for intact cells and 2.13 Å and 7.58 Å on lamella.
    SerialEM
    suggested: (SerialEM, RRID:SCR_017293)
    CryoET image processing: The frames in each tilt angle in a tilt series were processed to correct drift using MotionCor2 (Zheng et al., 2017).
    MotionCor2
    suggested: (MotionCor2, RRID:SCR_016499)
    For the intact cells dataset, all tilt series were aligned using the default parameters in IMOD version 4.10.22 with the eTomo interface, using gold-fiducial markers (Kremer et al., 1996).
    IMOD
    suggested: (IMOD, RRID:SCR_003297)
    The CTF estimation for each tilt was performed by using emClarity version 1.4.3, and the subvolumes were selected by using automatic template matching function within emClarity using reference derived from EMDB-21452 (Walls et al., 2020) that was low-pass filtered to 30-Å resolution in emClarity.
    emClarity
    suggested: None
    Serial cryoFIB/SEM Segmentation: Cell structures were manually segmented from stacks of images using ImageJ (Koppensteiner et al., 2012) and Microscopy Image Browser (MIB) software (Belevich et al., 2016) on a Windows computer with 32GB RAM and Wacom Cintiq Pro display tablet with pen.
    ImageJ
    suggested: (ImageJ, RRID:SCR_003070)
    Microscopy Image Browser
    suggested: (Microscopy Image Browser, RRID:SCR_016560)
    CryoET segmentation and 3D visualization: Transport vesicles, Viral membrane, Nuclear membrane, Double membrane vesicles (DMV), and single membrane vesicles (SMV) were segmented using Convolutional Neural Networks based tomogram annotation in the EMAN2.2 software package (Chen et al., 2017).
    EMAN2.2
    suggested: None

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).


    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: No clinical trial numbers were referenced.


    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.


    Results from JetFighter: We did not find any issues relating to colormaps.


    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

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