Human lungs show limited permissiveness for SARS-CoV-2 due to scarce ACE2 levels but virus-induced expansion of inflammatory macrophages.

BACKGROUND: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) utilises the angiotensin-converting enzyme 2 (ACE2) transmembrane peptidase as cellular entry receptor. However, whether SARS-CoV-2 in the alveolar compartment is strictly ACE2-dependent and to what extent virus-induced tissue damage and/or direct immune activation determines early pathogenesis is still elusive. METHODS: Spectral microscopy, single-cell/-nucleus RNA sequencing or ACE2 "gain-of-function" experiments were applied to infected human lung explants and adult stem cell derived human lung organoids to correlate ACE2 and related host factors with SARS-CoV-2 tropism, propagation, virulence and immune activation compared to SARS-CoV, influenza and Middle East respiratory syndrome coronavirus (MERS-CoV). Coronavirus disease 2019 (COVID-19) autopsy material was used to validate ex vivo results. RESULTS: We provide evidence that alveolar ACE2 expression must be considered scarce, thereby limiting SARS-CoV-2 propagation and virus-induced tissue damage in the human alveolus. Instead, ex vivo infected human lungs and COVID-19 autopsy samples showed that alveolar macrophages were frequently positive for SARS-CoV-2. Single-cell/-nucleus transcriptomics further revealed nonproductive virus uptake and a related inflammatory and anti-viral activation, especially in "inflammatory alveolar macrophages", comparable to those induced by SARS-CoV and MERS-CoV, but different from NL63 or influenza virus infection. CONCLUSIONS: Collectively, our findings indicate that severe lung injury in COVID-19 probably results from a macrophage-triggered immune activation rather than direct viral damage of the alveolar compartment.

Understanding fiber mixture by simulation in 3D Polarized Light Imaging.

3D Polarized Light Imaging (3D-PLI) is a neuroimaging technique that has opened up new avenues to study the complex architecture of nerve fibers in postmortem brains. The spatial orientations of the fibers are derived from birefringence measurements of unstained histological brain sections that are interpreted by a voxel-based analysis. This, however, implies that a single fiber orientation vector is obtained for each voxel and reflects the net effect of all comprised fibers. The mixture of various fiber orientations within an individual voxel is a priori not accessible by a standard 3D-PLI measurement. In order to better understand the effects of fiber mixture on the measured 3D-PLI signal and to improve the interpretation of real data, we have developed a simulation method referred to as SimPLI. By means of SimPLI, it is possible to reproduce the entire 3D-PLI analysis starting from synthetic fiber models in user-defined arrangements and ending with measurement-like tissue images. For the simulation, each synthetic fiber is considered as an optical retarder, i.e., multiple fibers within one voxel are described by multiple retarder elements. The investigation of different synthetic crossing fiber arrangements generated with SimPLI demonstrated that the derived fiber orientations are strongly influenced by the relative mixture of crossing fibers. In case of perpendicularly crossing fibers, for example, the derived fiber direction corresponds to the predominant fiber direction. The derived fiber inclination turned out to be not only influenced by myelin density but also systematically overestimated due to signal attenuation. Similar observations were made for synthetic models of optic chiasms of a human and a hooded seal which were opposed to experimental 3D-PLI data sets obtained from the chiasms of both species. Our study showed that SimPLI is a powerful method able to test hypotheses on the underlying fiber structure of brain tissue and, therefore, to improve the reliability of the extraction of nerve fiber orientations with 3D-PLI.

Estimating Fiber Orientation Distribution Functions in 3D-Polarized Light Imaging.

Research of the human brain connectome requires multiscale approaches derived from independent imaging methods ideally applied to the same object. Hence, comprehensible strategies for data integration across modalities and across scales are essential. We have successfully established a concept to bridge the spatial scales from microscopic fiber orientation measurements based on 3D-Polarized Light Imaging (3D-PLI) to meso- or macroscopic dimensions. By creating orientation distribution functions (pliODFs) from high-resolution vector data via series expansion with spherical harmonics utilizing high performance computing and supercomputing technologies, data fusion with Diffusion Magnetic Resonance Imaging has become feasible, even for a large-scale dataset such as the human brain. Validation of our approach was done effectively by means of two types of datasets that were transferred from fiber orientation maps into pliODFs: simulated 3D-PLI data showing artificial, but clearly defined fiber patterns and real 3D-PLI data derived from sections through the human brain and the brain of a hooded seal.