3D virtual histopathology of cardiac tissue from Covid-19 patients based on phase-contrast X-ray tomography

  1. Marius Reichardt
  2. Patrick Moller Jensen
  3. Vedrana Andersen Dahl
  4. Anders Bjorholm Dahl
  5. Maximilian Ackermann
  6. Harshit Shah
  7. Florian Länger
  8. Christopher Werlein
  9. Mark P Kuehnel
  10. Danny Jonigk  Is a corresponding author
  11. Tim Salditt  Is a corresponding author
  1. Georg-August-Universität Göttingen, Germany
  2. Technical University of Denmark, Denmark
  3. University Medical Center of the Johannes Gutenberg University Mainz, Germany
  4. Medical University Hannover, Germany

Abstract

We have used phase-contrast X-ray tomography to characterize the three-dimensional (3d) structure of cardiac tissue from patients who succumbed to Covid-19. By extending conventional histopathological examination by a third dimension, the delicate pathological changes of the vascular system of severe Covid-19 progressions can be analyzed, fully quantified and compared to other types of viral myocarditis and controls. To this end, cardiac samples with a cross section of 3:5mm were scanned at a laboratory setup as well as at a parallel beam setup at a synchrotron radiation facility. The vascular network was segmented by a deep learning architecture suitable for 3d datasets (V-net), trained by sparse manual annotations. Pathological alterations of vessels, concerning the variation of diameters and the amount of small holes, were observed, indicative of elevated occurrence of intussusceptive angiogenesis, also confirmed by high resolution cone beam X-ray tomography and scanning electron microscopy. Furthermore, we implemented a fully automated analysis of the tissue structure in form of shape measures based on the structure tensor. The corresponding distributions show that the histopathology of Covid-19 differs from both influenza and typical coxsackie virus myocarditis.

Data availability

The tomographic datasets recorded in WG configuration as well as the PB datasets used for the segmentation of the vascular system and the respective laboratory datasets were uploaded to https://doi.org/10.5281/zenodo.4905971.Additional data (raw data, PB and laboratory reconstructions, structure tensor analysis) is curated here at University ofGöttingen and at DESY can be obtained upon request from the corresponding author (tsaldit@gwdg.de); due to the extremely large size >15TB it cannot presently be uploaded easily to a public repository.The implementation of the structure tensor analysis is provided in https://lab.compute.dtu.dk/patmjen/structure-tensor.The neural network code used for the segmentation of the vasculature was uploaded to GitHub (github.com/patmjen/blood-vessel-segmentation)

The following data sets were generated

Article and author information

Author details

  1. Marius Reichardt

    Institut für Röntgenphysik, Georg-August-Universität Göttingen, Göttingen, Germany
    Competing interests
    The authors declare that no competing interests exist.
  2. Patrick Moller Jensen

    Technical University of Denmark, Kopenhagen, Denmark
    Competing interests
    The authors declare that no competing interests exist.
  3. Vedrana Andersen Dahl

    Technical University of Denmark, Kopenhagen, Denmark
    Competing interests
    The authors declare that no competing interests exist.
  4. Anders Bjorholm Dahl

    Technical University of Denmark, Kopenhagen, Denmark
    Competing interests
    The authors declare that no competing interests exist.
  5. Maximilian Ackermann

    Institute of Anatomy and Cell Biology, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-9996-2477
  6. Harshit Shah

    Medical University Hannover, Hannover, Germany
    Competing interests
    The authors declare that no competing interests exist.
  7. Florian Länger

    Medical University Hannover, Hannover, Germany
    Competing interests
    The authors declare that no competing interests exist.
  8. Christopher Werlein

    Medical University Hannover, Hannover, Germany
    Competing interests
    The authors declare that no competing interests exist.
  9. Mark P Kuehnel

    Medical University Hannover, Hannover, Germany
    Competing interests
    The authors declare that no competing interests exist.
  10. Danny Jonigk

    Medical University Hannover, Hannover, Germany
    For correspondence
    Jonigk.Danny@mh-hannover.de
    Competing interests
    The authors declare that no competing interests exist.
  11. Tim Salditt

    Institut für Röntgenphysik, Georg-August-Universität Göttingen, Göttingen, Germany
    For correspondence
    tsaldit@gwdg.de
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-4636-0813

Funding

Bundesministerium für Bildung und Forschung (Max Planck School Matter to Life)

  • Marius Reichardt
  • Tim Salditt

Bundesministerium für Bildung und Forschung (05K19MG2)

  • Tim Salditt

Deutsche Forschungsgemeinschaft (EXC 2067/1-390729940)

  • Tim Salditt

H2020 European Research Council (XHale,771883)

  • Danny Jonigk

Deutsche Forschungsgemeinschaft (KFO311 (project Z2))

  • Danny Jonigk

Hanseatic League of Science

  • Patrick Moller Jensen

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Reviewing Editor

  1. Hina Chaudhry, Harvard University, United States

Ethics

Human subjects: Formalin-fixed paraffin-embedded tissue blocks of control hearts, influenza and coxsackie virus myocarditis hearts were retrieved from archived material from the Institute of Pathology at Hannover Medical School in accordance with the local ethics committee (ethics vote number: 1741-2013 and 2893-2015). Formalin-fixed paraffin-embedded tissue blocks of COVID-19 autopsy cases were retrieved after written consent in accordance with the local ethics committee at Hannover medical school (ethics vote number: 9022 BO K 2020)

Version history

  1. Received: June 17, 2021
  2. Preprint posted: September 18, 2021 (view preprint)
  3. Accepted: December 10, 2021
  4. Accepted Manuscript published: December 21, 2021 (version 1)
  5. Version of Record published: January 10, 2022 (version 2)

Copyright

© 2021, Reichardt et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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  1. Marius Reichardt
  2. Patrick Moller Jensen
  3. Vedrana Andersen Dahl
  4. Anders Bjorholm Dahl
  5. Maximilian Ackermann
  6. Harshit Shah
  7. Florian Länger
  8. Christopher Werlein
  9. Mark P Kuehnel
  10. Danny Jonigk
  11. Tim Salditt
(2021)
3D virtual histopathology of cardiac tissue from Covid-19 patients based on phase-contrast X-ray tomography
eLife 10:e71359.
https://doi.org/10.7554/eLife.71359

Share this article

https://doi.org/10.7554/eLife.71359

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