Systemic Tissue and Cellular Disruption from SARS-CoV-2 Infection revealed in COVID-19 Autopsies and Spatial Omics Tissue Maps

  1. Jiwoon Park
  2. Jonathan Foox
  3. Tyler Hether
  4. David Danko
  5. Sarah Warren
  6. Youngmi Kim
  7. Jason Reeves
  8. Daniel J. Butler
  9. Christopher Mozsary
  10. Joel Rosiene
  11. Alon Shaiber
  12. Ebrahim Afshinnekoo
  13. Matthew MacKay
  14. Yaron Bram
  15. Vasuretha Chandar
  16. Heather Geiger
  17. Arryn Craney
  18. Priya Velu
  19. Ari M. Melnick
  20. Iman Hajirasouliha
  21. Afshin Beheshti
  22. Deanne Taylor
  23. Amanda Saravia-Butler
  24. Urminder Singh
  25. Eve Syrkin Wurtele
  26. Jonathan Schisler
  27. Samantha Fennessey
  28. André Corvelo
  29. Michael C. Zody
  30. Soren Germer
  31. Steven Salvatore
  32. Shawn Levy
  33. Shixiu Wu
  34. Nicholas Tatonetti
  35. Sagi Shapira
  36. Mirella Salvatore
  37. Massimo Loda
  38. Lars F. Westblade
  39. Melissa Cushing
  40. Hanna Rennert
  41. Alison J. Kriegel
  42. Olivier Elemento
  43. Marcin Imielinski
  44. Alain C. Borczuk
  45. Cem Meydan
  46. Robert E. Schwartz
  47. Christopher E. Mason

Reviewed by

Read the full article See related articles

Listed in

Log in to save this article

Abstract

The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) virus has infected over 115 million people and caused over 2.5 million deaths worldwide. Yet, the molecular mechanisms underlying the clinical manifestations of COVID-19, as well as what distinguishes them from common seasonal influenza virus and other lung injury states such as Acute Respiratory Distress Syndrome (ARDS), remains poorly understood. To address these challenges, we combined transcriptional profiling of 646 clinical nasopharyngeal swabs and 39 patient autopsy tissues, matched with spatial protein and expression profiling (GeoMx) across 357 tissue sections. These results define both body-wide and tissue-specific (heart, liver, lung, kidney, and lymph nodes) damage wrought by the SARS-CoV-2 infection, evident as a function of varying viral load (high vs. low) during the course of infection and specific, transcriptional dysregulation in splicing isoforms, T cell receptor expression, and cellular expression states. In particular, cardiac and lung tissues revealed the largest degree of splicing isoform switching and cell expression state loss. Overall, these findings reveal a systemic disruption of cellular and transcriptional pathways from COVID-19 across all tissues, which can inform subsequent studies to combat the mortality of COVID-19, as well to better understand the molecular dynamics of lethal SARS-CoV-2 infection and other viruses.

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2021.03.08.434433: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementConsent: Patient sample collection: All autopsies are performed with consent of next of kin and permission for retention and research use of tissue.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Antibodies
    SentencesResources
    Samples were stained with immunofluorescent antibodies for CD68, CD45, PanCK, and DNA (Syto-13).
    CD68
    suggested: None
    CD45
    suggested: None
    Antibody Panel including (TMPRSS2, clone EPR3861; ACE2, clone EPR4436; Cathepsin L/V/K/H, clone EPR8011; DDX5, clone EPR7239; and SARS-CoV-2 spike glycoprotein, polyclonal); Abeam; ab273594, Lot# GR3347471-1 GeoMx Solid Tumor TME Morphology Kit; Nanostring Technologies, Inc.; GMX-PRO-MORPH-HST-12; Item 121300310 Alexa Fluor® 647 alpha-Smooth Muscle Actin Antibody, clone 1A4; Novus Bio; IC1420R Nanostring morphological and staining panels are pre-validated by the manufacturer: https://www.nanostring.com/download_file/view/2872/8714 Morphological markers were previously demonstrated in human tissue in https://doi.org/10.1101/2020.08.25.267336 qRT-PCR: Total RNA was extracted in TRIzol (Invitrogen) according to the manufacturer’s instructions.
    TMPRSS2
    suggested: (Thermo Fisher Scientific Cat# PA5-96019, RRID:AB_2807821)
    SARS-CoV-2 spike glycoprotein
    suggested: None
    Muscle Actin Antibody ,
    suggested: None
    Software and Algorithms
    SentencesResources
    Cell deconvolution of the GeoMx data was performed using the SpatialDecon R package38.
    SpatialDecon
    suggested: None
    Gene set enrichment analysis (GSEA)39 was performed to qualify coordinate gene expression changes quantified during differential expression analysis.
    Gene set enrichment analysis
    suggested: (Gene Set Enrichment Analysis, RRID:SCR_003199)
    Final libraries were quantified using fluorescent-based assays including PicoGreen (Life Technologies) or Qubit Fluorometer (invitrogen) and Fragment Analyzer (Advanced Analytics) and sequenced on a NovaSeq 6000 sequencer (v1 chemistry) with 2×150bp targeting 60M reads per sample.
    PicoGreen
    suggested: None
    This workflow involved quality control of the reads with FastQC42, adapter trimming using Trim Galore!
    Trim Galore
    suggested: (Trim Galore, RRID:SCR_011847)
    (https://github.com/FelixKrueger/TrimGalore), read alignment with STAR43, gene quantification with Salmon44, duplicate read marking with Picard MarkDuplicates (https://github.com/broadinstitute/picard), and transcript quantification with StringTie45.
    Picard
    suggested: (Picard, RRID:SCR_006525)
    Other quality control measures included RSeQC, Qualimap, and dupRadar.
    RSeQC
    suggested: (RSeQC, RRID:SCR_005275)
    Qualimap
    suggested: (QualiMap, RRID:SCR_001209)
    FeatureCounts reads were normalized using variance-stabilizing transform (vst) in DESeq2 package in R for visualization purposes in log-scale46.
    FeatureCounts
    suggested: (featureCounts, RRID:SCR_012919)
    Cell deconvolution was performed using MuSiC on single cell reference datasets for lung, liver, kidney, and heart47–51.
    MuSiC
    suggested: (MuSiC, RRID:SCR_008792)
    Differential expression of genes was calculated by DESeq2.
    DESeq2
    suggested: (DESeq, RRID:SCR_000154)
    Briefly, Salmon isoform count matrices from every sample were imported using importRdata59, using Gencode v33 exon annotations and nucleotide sequences.
    Gencode
    suggested: (GENCODE, RRID:SCR_014966)

    Results from OddPub: Thank you for sharing your data.

    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.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  2. Version published to 10.1101/2021.03.08.434433v1 on bioRxiv