Journal Article

PUBDB-2024-01925

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png 3D imaging of SARS-CoV-2 infected hamster lungs by X-ray phase contrast tomography enables drug testing

Reichmann, J. (First author)Extern* ; Sarrazin, C. ; Schmale, S. ; Blaurock, C. ; Balkema-Buschmann, A. ; Schmitzer, B. (Corresponding author) ; Salditt, T. (Corresponding author)Extern*

2024
Macmillan Publishers Limited, part of Springer Nature [London]

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Please use a persistent id in citations: doi:10.1038/s41598-024-61746-4  doi:10.3204/PUBDB-2024-01925

Abstract: X-ray Phase Contrast Tomography (XPCT) based on wavefield propagation has been established as a high resolution three-dimensional (3D) imaging modality, suitable to reconstruct the intricate structure of soft tissues, and the corresponding pathological alterations. However, for biomedical research, more is needed than 3D visualisation and rendering of the cytoarchitecture in a few selected cases. First, the throughput needs to be increased to cover a statistically relevant number of samples. Second, the cytoarchitecture has to be quantified in terms of morphometric parameters, independent of visual impression. Third, dimensionality reduction and classification are required for identification of effects and interpretation of results. To address these challenges, we here design and implement a novel integrated and high throughput XPCT imaging and analysis workflow for 3D histology, pathohistology and drug testing. Our approach uses semi-automated data acquisition, reconstruction and statistical quantification. We demonstrate its capability for the example of lung pathohistology in Covid-19. Using a small animal model, different Covid-19 drug candidates are administered after infection and tested in view of restoration of the physiological cytoarchitecture, specifically the alveolar morphology. To this end, we then use morphometric parameter determination followed by a dimensionality reduction and classification based on optimal transport. This approach allows efficient discrimination between physiological and pathological lung structure, thereby providing quantitative insights into the pathological progression and partial recovery due to drug treatment. Finally, we stress that the XPCT image chain implemented here only used synchrotron radiation for validation, while the data used for analysis was recorded with laboratory CT radiation, more easily accessible for pre-clinical research.

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Contributing Institute(s):

1. DOOR-User (DOOR ; HAS-User)

Research Program(s):

1. 6G3 - PETRA III (DESY) (POF4-6G3) (POF4-6G3)
2. DFG project 390729940 - EXC 2067: Multiscale Bioimaging: Von molekularen Maschinen zu Netzwerken erregbarer Zellen (390729940) (390729940)
3. SFB 1456 A03 - Dimensionalitätsreduktion und Regression im Wasserstein-Raum für quantitative 3D-Histologie (A03) (456837373) (456837373)

Experiment(s):

1. PETRA Beamline P10 (PETRA III)

Appears in the scientific report 2024

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