key: cord-0282479-nkx4oyax
authors: Reichardt, Marius; Jensen, Patrick Møller; Dahl, Vedrana Andersen; Dahl, Anders Bjorholm; Ackermann, Maximilian; Shah, Harshit; Länger, Florian; Werlein, Christopher; Kühnel, Mark; Jonigk, Danny; Salditt, Tim
title: 3D virtual Histopathology of Cardiac Tissue from Covid-19 Patients based on Phase-Contrast X-ray Tomography
date: 2021-09-17
journal: bioRxiv
DOI: 10.1101/2021.09.16.460594
sha: 59d344856ae314d6a94d75d98abbe152d2eb15ec
doc_id: 282479
cord_uid: nkx4oyax

For the first time, 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 histopatholocigal 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 the synchrotron in a parallel beam configuration. 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 scanning electron microscopy. Further, 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.

The coronavirus disease 2019 (Covid-19) is caused by the serve acute respiratory syndrome coron-30 avirus (SARS-CoV-2), predominantly entering the body via the respiratory tract. SARS-CoV-2 infects 31 cells by binding its spike protein to the surface protein angiotensin-converting enzyme 2 (ACE2) of 32 the host cell (Hoffmann et al., 2020) . Severe cases are most frequently affected by viral pneumonia 33 and acute respiratory distress syndrome (ARDS), with a pathophysiology distinctly different from e.g. 34 influenza infection (Ackermann et al., 2020b) . Mediated by a distinct inflammatory microenviron-35 ment, an uncontrolled infection can develop and result in massive tissue damage, again primarily 36 reported in the lung. Apart from diffuse alveolar damage, the main histological hallmark of ARDS, 37 specific findings in the lung histopathology are high prevalence of micro-thrombi and high levels 38 of intussusceptive angiogenesis (IA) (Ackermann et al., 2020b,a; Bois et al., 2021) . The latter is a 39 rapid process of intravascular septation that produces two lumens from a single vessel. It is distinct 78 In this work, we now focus on the 3d cytoarchitecture of cardiac tissue. We have scanned 79 unstained, paraffin embedded tissue, prepared by a biopsy punch from paraffin embedded tissue 80 blocks, collected from patients which have succumbed to . For comparison, we have 81 scanned tissue from influenza (Inf) and myocarditis (Myo) patients as well as from a control group 82 (Ctr). In total, we have scanned 26 samples, all wihch had undergone routine histopathological 83 assessment beforehand. We used both a synchrotron holo-tomography setup and a laboratory CT 84 with custom designed instrumentation and reconstruction workflow, as described in (Eckermann 85 et al., 2020). Based on the reconstructed volume data, we then determined structural parameters, 86 such as the orientation of the cardiomyocytes and the degree of anisotropy, as well as a set of shape 87 measures defined from the structure tensor analysis. This procedure is already well established for 88 Murine heart models (Dejea et al., 2019) . Segmentation of the vascular network enabled by deep 89 learning methods is used to quantify the architecture of the vasculature. 90 Following this introduction, we describe the methodology, which is already summarized in Fig.1 . 91 We then describe the reconstructed tissue data, based on visual impression, and compare the 92 different groups. We then apply automated image processing for classification and quantification 93 of tissue pathologies. Finally, we segment and quantify the vasculature, both from voxel-based 94 measures and from extracted graph representations of the segmented vessel network. From the 95 generalized shape measures, as well as the inspection of particular vessel architectures exhibiting 96 the IA phenomenon, distinct changes of Cov with respect to the other pathologies and to Ctr are 97 observed. The paper closes with a short conclusions and outlook section. µ ). In total, 26 post mortem heart tissue samples were investigated: 11 from Covid-19 patients, 4 from influenza patients, 5 from patients who died with myocarditis and 6 control samples. (B) From each of the samples a biopsy punch with a diameter of . was taken and transferred onto a holder for the tomography acquisition. After tomographic scans at the laboratory and parallel beam setup at the synchrotron, one punch with a diameter of was taken from one of the control and Covid-19 samples. (C) Sketch of the laboratory micro-CT setup. Tomographic scans of all samples were recorded in cone beam geometry with an effective pixel size of eff = µ using a liquid metal jet source (EXCILLUM, Sweden). (D) Sketch of the parallel beam configuration GINIX endstation (P10 beamline, DESY, Hamburg). In this geometry, datasets of Covid-19 and control samples were acquired at an effective voxel size of . For each sample a plane of 3×3 tomographic acquisitions was recorded. (E) Cone beam configuration of the GINIX setup. After the investigation in parallel geometry, a biopsy with a diameter of was taken from a control sample and a high resolution scan in cone beam geometry was recorded. This configuration is based on a coherent illumination by a wave guide and allows for high geometric magnification and effective voxel sizes below . Figure 1 illustrates the sample preparation and the tomographic scan geometries used to assess 169 For the LJ and WG scans recorded at large cone beam geometry, the FDK-function was used, while The laboratory datasets and the stitched datasets reconstructed from the PB recordings were 

where a second convolution  is applied with length scale , thus defining the structural scale on 192 which the tissue structure is analyzed/reported. Since the reconstructed electron density ( ) along 193 a fiber is approximately constant along the fiber tangent, the vector describing the local structural 194 orientation is given by the eigenvector with the smallest eigenvalue of the symmetric matrix . 195 In this work, the size of , determining the sub-volume on which the structural analysis is performed, 196 was set to 32 pixels for PB datasets and 12 pixels for LJ acquisitions. This corresponds to ≈ 20.8µm (5)

The shape measure distribution of the exemplary slice is shown in Fig. 2C . The eccentricity of the ellipse is given by

and describes how much the ellipse deviates from being circular. The area of the ellipse is given by Ctr-I   II  III  IV  V  VI   Cov-I   II  III  IV  V  VI   II  III  IV  V  I   II  III  IV  I   VII  VIII  IX  X  XI absorption coeffient (µ/voxel) After the analysis in parallel beam geometry a biopsy with a diameter of was taken from the . biopsy punch. This configuration reveals sub-cellular structures such as nuclei of one cardiomyocytes, myofibrils and intercalated discs. (B) Slice of the reconstructed volume perpendicular to the orientation of the cardiomyocytes. The red box marks an area which is magnified and shown on the right. One cardiomyocyte is located in the center of the magnified area. In this view, the nucleus can be identified. It contains two nucleoli, which can be identified as dark spots. The myofibrils appear as round discs. (C) Orthogonal slice which oriented along the orientation of the cardiomyocytes. A magnification of the area marked with a red box. In this view, a nucleus but also the myofibrils can be identified as dark, elongated structures in the cell. Further, an intercalated disc is located at the bottom of the area. (D) Volume rendering of a tomographic reconstruction from a Covid-19 sample. Slices orthogonal (E) and along (F) to the cardiomyocyte orientation are shown on the right. In the magnified areas, a nucleus of an endothelial cell and an intraluminar pillar -the morphological hallmark of intussusceptive angiogenesis-are visible. Scale bars: orthoslices µ ; magnified areas µ . I   I  n  f  -I I   n  f  -I  I I   n  f  -I  I  I   I  n  f  -V  I   M  y  o  -I   M  y  o  -I  I   M  y  o  -I  I  I   M  y Here, the projected source upgrade foreseen for PETRA IV will provide a significant gain in resolution 501 and throughput. Robotic sample exchange will therefore be required, and a serious upscaling of 502 the data management and online reconstruction pipeline. First reconstructions of heart biopsies 503 exploiting the enhanced coherence and resolution of a waveguide holo-tomography setup already 504 indicates that this is a very promising direction. 

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