3d virtual pathohistology of lung tissue from COVID-19 patients based on phase contrast x-ray tomography
Abstract
We present a three-dimensional (3d) approach for virtual histology and histopathology based on multi-scale phase contrast x-ray tomography, and use this to investigate the parenchymal architecture of unstained lung tissue from patients who succumbed to Covid-19. Based on this first proof-of-concept study, we propose multi-scale phase contrast x-ray tomography as a tool to unravel the pathophysiology of Covid-19, extending conventional histology by a third dimension and allowing for full quantification of tissue remodeling. By combining parallel and cone beam geometry, autopsy samples with a maximum cross section of 4mm are scanned and reconstructed at a resolution and image quality which allows for the segmentation of individual cells. Using the zoom capability of the cone beam geometry, regions-of-interest are reconstructed with a minimum voxel size of 167 nm. We exemplify the capability of this approach by 3d visualisation of the DAD with its prominent hyaline membrane formation, by mapping the 3d distribution and density of lymphocytes infiltrating the tissue, and by providing histograms of characteristic distances from tissue interior to the closest air compartment.
Data availability
All datasets were uploaded to zenodo: 10.5281/zenodo.3892637
Article and author information
Author details
Funding
Bundesministerium für Bildung und Forschung (05K19MG2)
- Tim Salditt
H2020 European Research Council (771883)
- Danny Jonigk
Max-Planck School (Matter to Life)
- Marius Reichardt
- Tim Salditt
Deutsche Forschungsgemeinschaft (-EXC 2067/1-390729940)
- Tim Salditt
Botnar Research Center of Child Health (BRCCH)
- Alexandar Tzankov
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Ethics
Human subjects: The study was approved by and conducted according to requirements of the ethics committees at the Hannover Medical School (vote Nr. 9022 BO K 2020).
Copyright
© 2020, Eckermann 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.
Metrics
-
- 609
- downloads
Views, downloads and citations are aggregated across all versions of this paper published by eLife.