Viruses. We generated SARS-CoV-2-Venus, in which the fluorescent reporter gene Venus was inserted, by using reverse genetics. For construction of SARS-CoV-2-Venus, we replaced the ORF 8 genes of pBAC SARS2 wk521 with the Venus gene by use of recombination and designated the infectious cDNA clone pBAC SARS-CoV-2-Venus. Mouse-adapted (MA)-SARS-CoV-2 and MA-SARS-CoV-2-Venus were also generated by using reverse genetics are previously described18. Virus strains were propagated in VeroE6/TMPRSS2 (JCRB 1819) cells41. All experiments with SARS-CoV-2-Venus were performed in enhanced biosafety level 3 (BSL3) containment laboratories at the University of Tokyo, which are approved for such use by the Ministry of Agriculture, Forestry, and Fisheries, Japan.
Cells. VeroE6/TMPRSS2 (JCRB 1819) cells41 were propagated in 1 mg/ml geneticin (G418; Invitrogen) and 5 µg/ml plasmocin prophylactic (Invitrogen) in Dulbecco's modified Eagle's medium (DMEM) containing 10% Fetal Calf Serum (FCS). The cells were regularly tested for mycoplasma contamination by using PCR, and confirmed to be mycoplasma-free.
Mice. Eight-week-old hemizygous K18-hACE2 C57BL/6J mice (strain 2B6.Cg-Tg(K18-ACE2)2Prlmn/J), 24-week-old C57BL/6 HamSlc-ob/ob mice, and five-week-old Streptozotocin (STZ)-induced diabetic C57BL/6J mice were purchased from the Jackson Laboratory Japan. In addition, 24-week-old B6-NASH mice, B6.KOR/StmSlc-Apoeshl mice, SAMR1/TaSlc [Senescence-Accelerated Mouse (SAM); senescence-Resistant inbred strains (R)] mice, SAMP8/TaSlc [Senescence-Accelerated Mouse (SAM); senescence-Prone inbred strains (P)] mice, and SAMP10-ΔSglt2 mice were purchased from Japan SLC Inc. KK-Ay/Ta Jcl mice (24-week-old) were purchased from CLEA Japan Inc. Age- and sex-matched C57BL/6 mice, which served as controls, were purchased from the same vendor as each disease model mouse strain.
Experimental infection of mice. Mice were intranasally inoculated with 101–105 PFU of SARS-CoV-2-Venus, MA-SARS-CoV-2, or MA-SARS-CoV-2-Venus. Body weights were measured before infection and then daily. The protocols for the animal studies were approved by the University of Tokyo (approval numbers PA19-72 and PA21-07).
Pathological examination. Excised lung tissues were fixed in 4% paraformaldehyde phosphate buffer solution, and processed for paraffin embedding. The paraffin blocks were cut into 3-µm-thick sections and then the sections were stained using a standard hematoxylin and eosin procedure. In addition, tissue sections were stained with a rabbit polyclonal antibody for SARS-CoV nucleocapsid protein (ProSpec; ANT-180, 1:500 dilution, Rehovot) for immunohistochemical analyses. Specific antigen-antibody reactions were visualized by means of 3,3’-diaminobenzidine tetrahydrochloride staining using the Dako Envision system (Dako Cytomation; K4001, 1:1 dilution).
Virus titration assay. C57BL/6 mice and HamSlc-ob/ob mice were intranasally inoculated with 103 PFU of MA-SARS-CoV-2. Two and five days post-infection (dpi), the animals were euthanized and their organs (lungs, nasal turbinate, brain, heart, liver, spleen, kidneys, and intestine) were collected. Confluent VeroE6/TMPRSS2 cells in 12-well plates were infected with 100 µl of a dilution of the organ homogenate. The virus inoculum was removed after incubation for 1 h at 37°C, and then 1% agarose solution in DMEM was overlaid on the cells. After incubation for 48 h, the agar-covered cells were fixed with 10% neutral buffered formalin. The plaques were counted after removal of the agar.
Micro-CT imaging. C57BL/6 mice and HamSlc-ob/ob mice were inoculated intranasally with 103 PFU of MA-SARS-CoV-2. Lungs of infected mice were imaged by using an in vivo micro-CT scanner (CosmoScan FX; Rigaku). Under ketamine-xylazine anesthesia, the animals were placed in the image chamber and scanned for 2 min at 90 kV, 88 µA, FOV 45 mm, and pixel size 90.0 µm. After scanning, the lung images were reconstructed by using the CosmoScan Database software of the micro-CT (Rigaku Corporation) and analyzed using the manufacturer-supplied software as described previously42.
In vivo imaging of mouse lung. The in vivo imaging was performed by using an LSM 980 NLO (Carl Zeiss) equipped with an infrared laser (Chameleon Vision II; Coherent) as described previously10,11. K18-hACE2 mice, C57BL/6J mice, and C57BL/6 HamSlc-ob/ob mice were infected with 105 PFU of SARS-CoV-2-Venus or 104 PFU of MA-SARS-CoV-2-Venus. The infected mice were intubated under anesthesia and ventilated at a respiratory rate of 120 breaths per minute. Isoflurane was continuously delivered at 2% to maintain anesthesia. The left lung lobe of the mice was exposed and gently immobilized with a custom-made thoracic suction window. In all experiments, Texas red dextran (70,000 Da; Invitorogen), Phycoerythrin (PE)-conjugated rat anti-mouse CD41 antibody (MWReg30; BD Biosciences), and Alexa Fluor 594-conjugated rat anti-mouse Ly-6G antibody (1A8; Biolegend) were injected i.v. before imaging to visualize the lung vascular structures, platelets, and vascular neutrophils, respectively. For the analyses of pulmonary perfusion, mice infected with SARS-CoV-2-Venus or MA-SARS-CoV-2-Venus were i.v. inoculated with Dio-labeled erythrocytes. A maximal intensity projection of the indicated frames (0–10 min) was generated to show the functional capillary perfused by the erythrocytes, as described previously43. To acquire images in spectral imaging mode, lasers at wavelengths of 488 nm, 543 nm, and 910 nm were used for simultaneous excitation of fluorochromes and Venus. All emitted light between 490- and 695-nm wavelengths was detected by using a 20× water-immersion lens (Carl Zeiss). Spectral separation of the acquired lambda stacks was achieved by using the linear unmixing function of the LSM software ZEN blue (Carl Zeiss). Processing, assays, and data visualization were performed using CellProfiler (Broad Institute), Imaris (Carl Zeiss), and in-house MATLAB scripts (MathWorks). Tracking of the neutrophils in the denoised movies was performed by TrackMate (ImageJ; NIH).
Flow cytometry. Mouse lungs were dissociated using a Lung Dissociation Kit (Miltenyi) and gentleMACS Dissociator (Miltenyi) according to the manufacturer's instructions for flow cytometry (FCM). Samples were then filtered through a 70-µm filter (Miltenyi) after red blood cell lysis and resuspended for subsequent FCM staining. For experiments staining intravascular neutrophils, mice were injected i.v. with PE–conjugated rat anti-mouse Ly-6G antibody (1A8; Biolegend) 5 min before lung collection. For surface staining, cells were stained for 10 min with antibodies in PBS containing 0.5% BSA and 2 mM EDTA. The following antibody clones were used in this studies: Vio-Green-CD45 (REA737, Miltenyi), PE-NK1.1 (REA1162, Miltenyi), PE-Vio615-CD4 (REA604, Miltenyi), PE-Vio770-B220 (REA755, Miltenyi), APC-CD3 (REA641, Miltenyi), APC-Vio770-CD8a (REA601, Miltenyi), Vio-Blue-MHC class II (REA813, Miltenyi), FITC-Ly6C (REA796, Miltenyi), PE-Vio615-Ly-6G (REA526, Miltenyi), PE-Vio770-CD11c (REA754, Miltenyi), APC-Siglec-F (REA798, Miltenyi), APC-Vio770-CD11b (REA592, Miltenyi), APC-CD44 (IM7 Biolegend), APC- Pecam1 (W18222B, Biolegend), APC-CD62L (MEL-14, Proteintech), APC-CD62E (P2H3, Invitorogen), and APC-CD162 (4RA10, Elabscience). APC conjugation to the anti-CD62E mouse IgG1 antibody was performed using the APC Labeling Kit-NH2 (Wako) according to the manufacturer's protocol.
Populations of immune cells were defined as follows: B cells (CD45+ CD3− B220+), NK cells (CD45+ CD3− B220− NK1.1+), CD4 T cells (CD45+ CD3+ B220− CD4+ CD8−), CD8 T cells (CD45+ CD3+ B220− CD4− CD8+), alveolar macrophages (CD45+ CD11bdim Siglec-F+ CD11c+ MHC class II+), dendritic cells (CD45+ CD11b− Siglec-F− CD11c+), neutrophils (CD45+ CD11bhigh Ly-6G+), eosinophils (CD45+ CD11bhigh Siglec-F+ Ly-6G− CD11c−), and monocytes (CD45+ CD11bhigh Siglec-F− Ly-6G− MHC class II− Ly-6Chigh). Samples were analyzed on a flow cytometer (MACSQuant Tyto, Miltenyi).
Neutrophil motility analysis. To track the movement of neutrophils, Alexa Fluor 594-conjugated rat anti-mouse Ly-6G antibody was injected i.v. into the mice. Neutrophils were imaged at approximately 4 fps for 230 s. All movies were corrected for respiratory motion artifacts and denoised as described previously10. Single object tracking was performed by using TrackMate (ImageJ; NIH) to obtain the trajectories of individual neutrophils. For each neutrophil, speeds were measured for individual steps in its trajectory and subsequently defined as slow (≤ 50 µm/s) or rapid (> 50 µm/s) as described previously10. We then examined whether a neutrophil performed rapid movement, and calculated the durations it engaged in continuous slow movements without being interrupted by the rapid movement.
Quantification of platelet aggregates. CD41 signals were detected in a semi-automated manner by using CellProfiler (Broad Institute), and then divided into three populations according to their sizes: signals covering < 8.57 µm2 (50 pixels) were defined as a “single platelet”, signals ≥ 8.57 µm2 and < 34.28 µm2 (200 pixels) as “aggregated platelets”, and signals ≥ 34.28 µm2 as “thrombocytes”. The frequency analysis of the CD41 signals was conducted using in-house MATLAB scripts (MathWorks).
In vivo depletion of neutrophils. C57BL/6 HamSlc-ob/ob were administered 100 µg of anti-rat Kappa immunoglobulin (clone MAR 18.5, #BE0122) daily for two days prior to infection as described previously44. In addition, 50 µg of Anti-Ly6G (clone 1A8, #BP0075-1) and corresponding isotype control (#BP0089) were administered every other day from one day prior to infection. When mice were sequentially injected with two antibodies, an interval of more than 2 hours was set between injections.
scRNA-seq data re-analysis. We reanalyzed two published PBMC scRNA-seq datasets for healthy and SARS-CoV-2-infected humans9,21. For the published data from Wilk et al.9, pre-processed scRNA-seq count data with embedding, clustering, and cell type assignment from the previous study9 were obtained as an RDS file from the COVID-19 Cell Atlas (https://www.covid19cellatlas.org/#wilk20) hosted by the Wellcome Sanger Institute. For the published data from Xu et al.21, we downloaded transcript-by-cell matrices output by Cell Ranger from NCBI (GSE216020) and preprocessed using the Seurat 45 package and DoubletFinder46, which identifies and removes potential doublets, as described in a previous study21. Cells with less than 500 UMI counts, 200 detected genes, and more than 20% mitochondrial gene counts were removed as low-quality cells, as well as potential doublets. Data integration of all samples was performed using the FindIntegrationAnchors and IntegrateData functions in Seurat with the top 3000 most variable genes selected by FindVariableFeatures function. Cell-type annotation was based on marker genes of each cluster defined by FindAllMarkers functions. The subset corresponding to neutrophils in both datasets (Wilk et al. and Xu et al.) was used for differential gene expression analysis related to cell adhesion22 and neutrophil extracellular trap formation (KEGG: hsa04613), respectively.
Statistical analysis. GraphPad Prism was used to analyze all data. Student’s t test, log-rank (Mantel-Cox) tests, and an ANOVA with a multiple corrections post-test were performed, and differences were considered to be statistically significant when the p-value was less than 0.05. For the differential gene expression analysis of the scRNA-seq data, the Wilcoxon signed rank test was used and the p-values were corrected by using the Benjamini-Hochberg Procedure.