NF-κB inhibitor alpha controls SARS-CoV-2 infection in ACE2-overexpressing human airway organoids

As SARS-CoV-2 continues to spread worldwide, tractable primary airway cell models that recapitulate the cell-intrinsic response to arising viral variants are needed. Here we describe an adult stem cell-derived human airway organoid model overexpressing the ACE2 receptor (ACE2-OE) that supports robust viral replication while maintaining 3D architecture and cellular diversity of the airway epithelium. ACE2-OE organoids were infected with SARS-CoV-2 variants and subjected to single-cell RNA-sequencing. Interferon-lambda was upregulated in cells with low-level infection while the NF-kB inhibitor alpha gene (encoding IkBa) was consistently upregulated in infected cells, and its expression positively correlated with infection levels. Confocal microscopy showed more IkBa expression in infected than bystander cells, but found concurrent nuclear translocation of NF-kB that IkBa usually prevents. Overexpressing a nondegradable IkBa mutant reduced NF-kB translocation and increased viral infection. These data demonstrate the functionality of ACE2-OE organoids in SARS-CoV-2 research and underscore that the strength of the NF-kB feedback loop in infected cells controls viral replication.

www.nature.com/scientificreports/thereby forming a liquid layer (termed mucus) to protect the underlying epithelium.(4) Ciliated cells are located across the apical surface and facilitate the movement of mucus across the airway tract 4 .In the lungs, alveolar epithelial cells (ATI and II cells) line the alveoli where gas exchange takes place.
The viral spike protein has been found in the airway epithelium and lung tissue of deceased COVID-19 patients 5 .In ex vivo infections, ciliated cells were identified as natural targets of SARS-CoV-2 6 .However, evidence of infection was also noted in secretory and basal cells, in both in vivo and ex vivo infections 7 .This includes viral budding in cells with secretory vesicles in infected human bronchial epithelial cells 8 ; others found goblet cells infected in in vitro human airway epithelial cell cultures and, as a response to infection, showed increased mucus production 9 .
To recapitulate the complex cellularity of the airway epithelium and provide a physiological, yet workable model of the infection in humans, multiple stem-cell derived 3D organoid models have been applied to virology 7,[10][11][12] .Organoids consists of multiple cell types grown in a 3D structure to mimic organ structure.They can be derived from embryonic stem cells (ESCs), pluripotent stem cells (iPSCs) or progenitor cells in adult tissues.SARS-CoV-2 replicates in iPSC-or ESC-derived human airway organoids (HAOs) 13,14 and adult stemcell HAOs that encompass both airway and alveolar cells 15,16 .In the latter model, donor lungs are digested and cultured in Matrigel and a medium that allows for the outgrowth of basal, club and goblet cells, which can then include ciliated cells with additional ex vivo differentiation steps 4,17 .
Differentiation, often performed at the air-liquid interface, produces a pseudostratified configuration that most closely resembles the in vivo situation, but is lengthy, requires large volumes of cells, and loses 3D structure.The undifferentiated organoids provide an intermediate model system that is more structured and heterogeneous than a cell line, yet less rigid than the fully differentiated model.These undifferentiated HAOs are tractable and easy to work with, while not being cancerously transformed and maintaining cellular diversity that resembles the airway epithelium.The undifferentiated organoid model has been used to study respiratory syncytial virus 17 and SARS-CoV-2 6,[18][19][20] .However, infection rates in this model have been variable and donor-dependent [18][19][20] .
To address these challenges and opportunities for studying SARS-CoV-2 infection, we generated and explored an adult stem cell-derived undifferentiated HAO model.Our model was genetically modified with ACE2 overexpression to overcome obstacles with low infection rates and long and variable differentiation times.We studied the effects of infection in this system using a genetically diverse set of variants, and applied scRNA-seq to understand the cell-intrinsic response to SARS-CoV-2 in this primary cell model.We identified NFKBIA, a critical regulator of the NF-κB pathway, as a universal gene that marks highly infected cells across all viral variants, underscoring the recognized link between inflammation and SARS-CoV-2 infection that dominates acute respiratory distress syndrome and severe disease in the lung.

Results
HAOs were grown from digested uninfected donor lungs, according to a published protocol (Fig. 1A) 17 .As expected and confirmed by light sheet microscopy, the majority of cells (~ 80%) in one organoid expressed P63, the basal cell marker, but a minority (~ 20%) represented secretory cells, including MUC5AC + goblet and CC10 + club cells, which aligns with published reports of undifferentiated HAOs (Fig. 1B) 17 .Due to the absence of cell differentiation, very few FOXJ1 + ciliated cells were observed (not shown).Compared to primary lung epithelium, markers for basal cells (P63, KRT5) were statistically overrepresented in quantitative RT-PCR analysis of organoids, and those for ciliated cells (FOXJ1), goblet cells (MUC5AC), and club cells (CC10) were underrepresented (Fig. 1C).Expression of ACE2 mRNA was also higher in primary lung tissue than the undifferentiated organoids, and no significant difference was noted in TMPRSS2 mRNA levels (Fig. 1D,E).No ACE2 protein was detected in the organoids by western blotting, which explains their poor infection efficiency in previous studies (Fig. 1F,G, Fig. S1) 18 .
To overcome these issues, undifferentiated organoids were transduced with a lentiviral vector expressing the ACE2 open reading frame.ACE2-overexpressing organoids (ACE2-OE) were selected and maintained robust ACE2 levels over at least nine culture passages as shown by western blotting (Fig. 1F,G).This was also confirmed by confocal immunofluorescence microscopy where ACE2 protein expression was visible on the surface of most cells in the ACE2-OE organoids (Fig. 1H).Organoid differentiation at the air-liquid interface was unaffected by ACE2 overexpression, as differentiation to all four cell types was similar between wildtype (WT) and ACE2-OE organoids as assessed by RT-qPCR for epithelial cell markers (Fig. 1I).
Next, WT and ACE2-OE undifferentiated HAOs were infected with the SARS-CoV-2 WA1 ancestral strain at a multiplicity of infection (MOI) of 1. Culture supernatants were collected at 24 and 72 h and subjected to plaque assays.ACE2 overexpression resulted in approximately two log higher infectious particle production at 24 h and a log difference at 72 h (Fig. 2A-C, Fig. S2).As a second measure of active viral infection, infected organoids were subjected to confocal immunofluorescence microscopy after staining with an antibody against doublestranded (ds) RNA.This antibody specifically stains the RNA replication centers in infected cells containing positive-negative strand RNA hybrids [21][22][23][24][25] .dsRNA + cells in ACE2-OE organoids had two-to threefold greater ACE2 expression than globally observed in WT organoids (Fig. 2D,E).These results show a robust increase in infected cell numbers with ACE2 overexpression that may now allow downstream analysis of SARS-CoV-2 infection at the single cell level.
To test this, WT and ACE2-OE, organoids were subjected to 10× scRNA-seq after infection with the SARS-CoV-2 WA1 strain.Raw sequencing data were aligned to the human genome with the SARS-CoV-2 genome appended, run through scVI 26,27 , and represented in a joint (batch-corrected) low dimensional space (see "Methods").In that space, the WT infected (WT_I), ACE2 uninfected (ACE2_U) and WT uninfected (WT_U) conditions clustered together, and a large subset of the ACE2 infected (ACE2_I) cells clustered separately (Fig. 3A,B, Fig. S4).To further validate the presence of cell types in the organoids identified by microscopy, the Lung Cell Atlas 28 was used as a reference to assign cell types to our scRNA-seq data.In agreement with our previous results (Fig. 1), most cells were basal cells, and the remainder were respiratory goblet cells-a secretory cell type present in the lungs (Fig. 3E).Only a small number were identified as ciliated cells (Fig. 3E).Furthermore, this annotation revealed no significant difference in proportion of each cell type when ACE2 and WT organoids were compared, indicating that ACE2 overexpression does not change the cellular composition of organoids (Supp.Fig. 4A).
To further confirm that overexpression of ACE2 did not change the biology of the organoids, a differential gene expression analysis was conducted between WT_U and ACE2_U samples (Supp.Fig. 4B).ACE2 was identified as the highest differentially expressed gene, and only four other genes (C20orf85, BPIFA1, TMEM190, UTP14C) were differentially expressed with a q-value less than 0.01.Considering SARS-CoV-2 RNA, we found expression primarily in cells from the ACE2_I condition (Fig. 3C, Supp.Fig. 4C-F).These results are consistent with our previous data (Fig. 2), showing that SARS-CoV-2 infection levels are higher in the ACE2 condition than the WT.They further suggest that the difference in infection levels between the organoids is most likely due to ACE2 overexpression and not altered abundance of other receptors or cofactors.
Considering only cells from the infected conditions (ACE2_I, WT_I), we observed a broad cell-to-cell variation in the abundance of viral products, presumably reflecting different levels of infection.To distinguish cells that had productive SARS-CoV-2 infections from those that had little to no infection, we fitted a Gaussian Mixture Model (GMM) to the overall amount of viral product in each cell, so as to classify cells from the infected samples into high, low and no infection groups (accounting for possible ambient viral product; see "Methods" and Fig. 3D).As expected, most of successfully infected cells (low and high infection groups in our model) came from the ACE2_I condition with 8.24% and 15.01%vs 1.47% and 0.04%, for high and low, ACE2-OE vs WT respectively.Conversely, a subset of ACE2_I cells from the "no infection" group of cells clustered together with cells from the uninfected samples (ACE2_U, WT_U), suggesting that some cells in the infected organoid were not only uninfected but also not influenced by the infection of other cells.
To determine whether the successfully infected cells were detecting the viral replication, we assessed their levels of interferon gene expression.We found no expression of IFN-α, -β, or -γ in our scRNA-seq data.This result is similar to data published from COVID-19 patient samples in which IFN-λ is the primary response 29 .Interestingly, while we found IFN-λ expression only in infected samples, the highest amounts were observed in cells with low levels of infection, with substantially lower expression in cells from the no-infection or high-infection groups www.nature.com/scientificreports/(Fig. 3F,G).While these data help to demonstrate that these organoids recapitulate the response of lungs during infection, they also suggest that the anti-viral IFN-λ response is only active in cells with low-level infections.Unlike the ACE2_I sample, we found comparably little expression of interferon genes in the WT_I sample (Fig. 3G).To further interrogate this, we looked at the levels of interferon-stimulated genes (ISGs) in the different groups of cells.Consistently, we found that ISGs were primarily expressed in ACE2_I cells with low levels of viral RNA and not in the other infection groups or in the WT cells (Fig. 3H,I).Finally, for an unbiased analysis, we examined the differential expression in infected and uninfected samples in each of our genetic backgrounds (ACE2 OE and WT).Using gene set enrichment analysis, we found strong enrichment for interferon and antiviral response program in the ACE2-overexpressing background, and much less so when comparing the WT samples (Supp.Fig. 5A,B).Our results therefore suggest that the lack of viral replication in WT cells is not due to an immune response, but more likely a result of limited viral entry and replication.
Next, we compared transcriptome responses at the single-cell level in a second set of ACE2-OE organoids, either uninfected or infected with various viral variants.Besides WA1 (using similar conditions as above), we tested the Alpha variant (B.1.1.7),two separate isolates of the Beta variant (B.1.351),and the California-resident Epsilon variant (B.1.429).Irrespective of cell type, the cells from the infected organoids were well mixed, and most uninfected cells clustered separately (Fig. 4A), indicating no clear difference or outlier among the infections (Fig. 4B).Basal cells remained the overwhelming cell type in this experiment, with a smaller representation of goblet cells (Fig. 4C).We again used the GMM approach for classifying the level of infection within each cell (Fig. 4D).As in Fig. 3, we saw activation of INF-λ and ISGs across all samples infected by the variants but not in the uninfected sample (Fig. 4E,F).
In addition, all variants had similar infection levels with one of the Beta strains having a slightly higher level (Fig. 4J).However, a comparison of the replication dynamics over time of Beta B, Beta A and WA-1 showed no significant differences, suggesting that the higher levels of virus are more likely due to the variability of the assay and not to an intrinsic difference in these viruses (Supp.Fig. 6).These results show the functionality of the organoid system across viral variants.
To determine which genes were most consistently upregulated with infection across the variants, we computed the correlation between gene expression and viral product (over cells) for each variant.The genes were then ranked by their level of correlation, separately for each variant (Fig. 4G; Table S1).Interestingly, we found a high level of consistency among the resulting lists of top ranked genes, with CXCL3 being the top ranked in four out of the five variants, TNFAIP3 in the top three in four of the variants, and NFKBIA in the top five in all variants.NFKBIA had the highest expression of the top-correlated genes, and had high expression in the high infection cluster (Fig. 4H).This cluster also had the highest levels of SARS-COV-2 viral products (Fg.4I).NFKBIA is a quintessential NF-κB response gene and encodes the IκBα protein, which in turn dampens NF-κB activity by preventing its translocation into the nucleus 30 .This usually generates a negative feedback loop where NF-κB activity is terminated or temporarily restrained by the upregulation of IκBα.The fact that NFKBIA is the top-upregulated gene in the analysis indicates strong activation of the NF-κB signaling pathway in infected cells across variants.
To investigate this finding in greater depth, we confirmed upregulation of the IκBα protein by confocal microscopy in A549 epithelial cells overexpressing ACE2 as they form a homogenous monolayer on the cover slips.Cells were infected with SARS-CoV-2 WA-1 and co-stained for dsRNA and IκBα to differentiate between infected and uninfected cells.Cells were imaged, and the mean fluorescence intensities (MFI) of IκBα were compared in dsRNA + and dsRNA − cells.IκBα protein expression was enhanced in infected (dsRNA + ) versus noninfected bystander (dsRNA − ) cells, mirroring the induction of NFKBIA transcript levels in the scRNA-Seq analysis (Fig. 5A).In a parallel experiment, we co-stained infected A549-ACE2 cells with antibodies against dsRNA and the p65/RELA subunit of NF-κB and quantified p65 nuclear localization.75% of dsRNA + cells showed p65 nuclear localization, but only a few dsRNA − cells did (Fig. 5B,C).This confirms a strong activation of the NF-κB signaling pathway only in infected, and not in uninfected, cells, which explains the consequent upregulation of NFKBIA as a response gene.However, the resultant increase in IκBα activity appears insufficient to fully downregulate NF-κB signaling in cells infected with SARS-CoV-2, as the majority of infected cells localize p65 in the cell nucleus.
The IκBα protein can be phosphorylated by upstream kinases in a process that destabilizes the inhibitor and as a consequence facilitates p65 nuclear translocation and transcriptional activity 31 .To further strengthen IκBα function in the infectious process, we stably overexpressed an IκBα construct in A549-ACE2 cells with the serine residues at positions 32 and 36 mutated to alanine to prevent phosphorylation 32 .This generates a "superinhibitor" that is unresponsive to cell signals and is highly active in retaining p65 in the cytoplasm, preventing its transcriptional activity in the nucleus 32 .First, we confirmed the proper function of the mutated inhibitor by stimulating A549-ACE2 cells transfected with the inhibitor with TNFα, normally leading to phosphorylation and degradation of IκBα and consequent nuclear NF-κB translocation 33 .In nontransfected cells, 100% of cells had nuclear translocation of p65 in response to TNFα (Fig. 5D,E).However, in cells expressing nondegradable IκBα, only 25% of cells showed p65 nuclear translocation, confirming that the super-inhibitor was active, keeping p65 in the cytoplasm.Co-staining of p65 and the HA-tagged IκBα mutant revealed that the remaining p65 translocation occurred in cells with low expression of the IκBα mutant (Fig. 5E, Fig. S7).
When cells expressing the IκBα mutant were infected with SARS-CoV-2 WA1, infected cultures showed twofold more dsRNA + cells than the parent cell line, supporting the model that host IκBα promotes viral infection by restraining the NF-κB response (Fig. 5F).

Discussion
The organoid model presented here adds another tool to the studies of SARS-CoV-2 infection in primary airway cells.It overcomes the hurdle of donor-dependent variable infection rates 18 and may present a tractable intermediate between cell line studies and fully differentiated primary cell models.We acknowledge that ACE2-OE may have more rapid virally-induced cell death, a potential limitation to this study.However, the organoids are able to restrict viral infection over the 72 h timepoint, suggesting that despite higher infection in the ACE2-OE HAOs, the cells are not intrinsically overwhelmed by virus (Fig. 2A-B).Our data showing that ACE2-OE organoids recapitulate transcriptional changes seen in vivo, such as the induction of IFN-λ, also support the validity of the model.
We found infection in three cell types: stem-like basal cells and secretory goblet and club cells, with high viral replication associated with infection level.Transcriptional profiling in all three cell types yielded common www.nature.com/scientificreports/transcriptional changes as indicated by clustering according to infection status by different viral variants.NFKBIA stood out as a gene of interest in controlling SARS-CoV-2 infection across variants and a highly induced gene whose mRNA levels positively correlated with viral RNA levels.These data are consistent with published reports showing high levels of expression of NF-κB signaling-related genes in SARS-CoV-2-infected cells 34 .The overexpression of a mutant IκBα protein that cannot be phosphorylated and consequently degraded and the resulting five-fold increase in dsRNA + cells support this model and identify IκBα as a bona fide proviral factor.This is corroborated by studies by Sunshine et al., who identified NFKBIA in a Perturb-Seq study as a host protein supporting viral replication 35 .
It remains to be determined what the relevant upstream and downstream signals of this proviral phenotype are and whether the continuous presence of NF-κB in the cell nucleus provides benefits for the SARS-CoV-2 lifecycle.Our findings indicate that after overexpression of the mutant IκBα protein, nuclear p65 levels were lower in infected cells, supporting a model in which IκBα promotes viral infection by constraining, albeit not completely, the well-known antiviral activities of NF-κB.However, knockdown studies of p65 have pointed to a positive role of the factor in SARS-CoV-2 infection that needs further mechanistic exploration 36 .The fact that nuclear p65 is a striking differentiator of infected from bystander cells in our confocal microscopy studies underscores a possible role of nuclear p65 in SARS-CoV-2 infection.

Cell lines
A549 cells were cultured in complete DMEM (DMEM with 10% FBS, 1% penicillin-streptomycin, 1% glutamine).A549-ACE2 cells were made as described 37 and cultured in complete DMEM supplemented with 10 μg/mL blasticidin.Vero-TMPRSS2 cells were a gift of Dr. Raul Andino and A549 cells were a gift of Dr. Andreas Puschnik.

Creation of HAO-ACE2 lines
HAO-ACE2 lines stably expressing hACE2 were generated by lentiviral transduction with plasmid LV-ACE2 39 , and selected with 2 µg/mL of blasticidin, as described in a study from our group 40 .

Differentiation of HAOs at the air-liquid interface
HAOs were cultured at the air-liquid interface (ALI) for differentiation.Organoids were collected in cold basal medium and dissociated as described above.After spin and medium aspiration, cells were resuspended in warm basal medium with 2% (v/v) BME2 and seeded onto pre-coated 2% BME2 in 6.5-mm insert of a 24-well plate Transwell Permeable Support (Costar).Cells were incubated for 1 h at 37 °C and then 500 µL of warm HAO of 1 for 2 h, and then then the virus-containing medium was removed, the cells were washed, fresh medium was added, and the cells were incubated until post-infection processing.

Propagation of SARS-CoV-2 variants
All SARS-CoV-2 variants were propagated on Vero-E6 cells expressing human TMPRSS2 in an aerosol biosafety level-3 lab (aBSL3) lab.Stocks were titered via plaque assay on Vero-E6 TMPRSS2 cells as described 25 and sequenced to confirm no novel cell-culture mutations.

Single-cell RNA sequencing
HAOs were infected with SARS-CoV-2 as described above for 24 h.Organoids were removed from the plate by dissolving the geltrax with cold basal medium and spun down.A single-cell suspension was made by digesting the organoids with 10× TrypLE for 15 min and passing them through a 40-μM filter.Library prep was done according to the standard 10× protocol with 6000 cells loaded into the 10× Chromium Controller.

Analysis of SARS-CoV-2 sequencing
After alignment, cells that had > 20% Unique Molecular Identifiers (UMIs) coming from mitochondrial genes were removed.Cells were also removed if they had fewer than 200 UMIs or greater than 300,000 UMIs.After filtering, the single-cell RNA-seq dataset contained 38,462 cells and 36,611 genes.

scRNA-seq analysis with scVI
We used the Seurat v3 method 43 as implemented in Scanpy v.1.8.1 44 to select the top 4000 highly variable genes from the dataset, excluding the SARS-CoV-2 genes and ACE2.Then we ran scVI 26 as implemented in scvi-tools v.0.13.0 27 with default parameters and with each sequencing round treated a batch covariate.This resulted in a single latent space for all the data.We visualized the data by running the Scanpy function scanpy.pp.neighbor, followed by scanpy.tl.umap for each round of sequencing independently on the scVI latent space.

Cell-type annotation
We first used seven different automated cell-type annotation methods to get a consensus prediction using the Lung Cell Atlas as a reference dataset 28 .The seven methods were: (1) OnClass 45 , (2) SVM 46 , (3) Random Forest (as implemented in sklearn) 47 , (4) KNN after batch correction with BBKNN 48 , (5) KNN after batch correction with Scanorama 49 , (6) KNN trained on the Lung Cell Atlas 28 scVI latent space 26 , (7) scANVI 50 .The resulting prediction was the majority prediction over the seven methods.We also derived a consensus score, based on the number of methods that agreed with the consensus prediction.After manual inspection, we determined that the only cell types present in the dataset were either: "Basal Cells", "Goblet Cells", and "Lung Ciliated Cells".For all cells with a consensus score less than 5 or were not predicted as a basal/goblet/ciliated cell, we reclassified with a KNN trained on cells with consensus score greater than 5 and predicted as a basal/goblet/ciliated cell.

Correlation analysis
To identify the top genes correlated with increased SCV2 levels, we computed the Pearson correlation as implemented in SciPy v1.4.1 (scipy.stats.pearsonr).We calculated the correlation for each gene in each sample individually between the log-normalized gene counts as implemented in Scanpy 44 v.1.8.1 (scanpy.pp.normalize_ total(adata, target_sum = 1e4) followed by sc.pp.log1p) with the sum of all SCV2 RNA transcripts per sample.Then for each sample, we filtered for genes expressed in at least 20% of cells in the sample.

DE testing and GO analysis
We used DESeq2 (v1.34.0) to identify genes differentially expressed between WT HAOs and ACE2-overexpressed HAOs by pseudobulking the transcript counts of each sample 51 .We used Scanpy v.1.8.1 to identify differentially expressed genes between cells in the infected vs uninfected conditions 44 .Metascape (v 3.5) with default Express Analysis settings were used to identify enriched Gene Ontology Terms 52 .

Classifying infection with Gaussian mixture models
To classify whether a cell was actually infected with SCV2 (not just exposed to the virus), we used a Gaussian Mixture Model (GMM) to classify whether the SCV2 mRNA UMIs were due to background viral mRNA or from replicating virus within a cell.We used the scikit-learn v.1.0.1 GMM implementation (sklearn.mixture.GaussianMixture) with default parameters.Bayesian information criterion (BIC) 53 as implemented in scikitlearn was used for model selection; defined as BIC = − 2log(L) + log(N)d, where L is the maximum likelihood of the GMM, N is the number of samples, and d is the degrees of freedom.For each experimental condition, we tested the optimal GMM for two vs three components as well as whether a single model should be trained on the log (sum of viral mRNA per cell + 1) or a separate model for each individual log(viral mRNA counts + 1) per viral gene.When training a separate model individually, we trained a GMM for each viral gene, summed the posterior probability of each component for each model, and for each cell assigned the component with the greatest total posterior probability.The BIC was then calculated as the mean BIC for each individual model.For each experimental condition, we selected the model with the lowest BIC.In all the uninfected experimental conditions (cells not exposed to virus), the optimal GMM model was two-component GMM trained on the

Figure 1 .
Figure 1.Characterization of human airway organoids (HAOs) and overexpression of ACE2.(A) Schematic detailing the establishment of organoid culture and ACE2 overexpression.(B) Light sheet microscopy of HAOs with staining for the cell markers CC10 (club cells) MUC5AC (goblet cells) and p63 (basal cells) with a brightfield image of a single organoid in the lower right-hand corner.(C) RT-qPCR comparison of cellmarker gene expression between digested primary lung tissue and organoids, (N = 3, organoids, N = 4, input, ± SD shown).(D) RT-qPCR comparing ACE2 expression level between organoids and digested lung tissue.(E) RT-qPCR comparing TMPRSS2 expression level between organoids and digested lung tissue (N = 3, organoids, N = 4, input, ± SD shown).(F) Representative western blot showing ACE2 protein levels in organoids at baseline and with ACE2 overexpression.(G) Quantification of ACE2 protein levels in wildtype organoids and organoids overexpressing ACE2.(H) Confocal imaging comparing ACE2 expression levels in wildtype and ACE2overexpressing organoids.(I) RT-qPCR comparison of cell-marker genes after differentiation at the air-liquid interface of wildtype and ACE2 overexpressing organoids (N = 2, ± SD shown).

Figure 2 .
Figure 2. Infection of HAOs with SARS-CoV-2.Organoids were infected at an MOI of 1 for 2 h, and then washed and fresh medium added.Organoids were incubated for the length of time indicated (A) Plaque assay comparing wildtype and ACE2 overexpressing organoids at 24 h post-infection.(B) Plaque assay comparing wildtype and ACE2 overexpressing organoids at 72 h post-infection.(C) Representative images of plaque assays performed on supernatant from SARS-CoV-2 infection of wildtype and ACE2 overexpressing organoids.(D) Representative images of infected HAOs at 24 and 72 h stained for dsRNA (green).(E) Quantification of percent of total DAPI + cells expression dsRNA across three experiments at 24 and 72 h.

Figure 3 .
Figure 3. Single-cell RNA-sequencing of WT and ACE2 OE organoids infected with SARS-CoV-2.(A) UMAP colored by overexpression condition of organoids.(B) UMAP colored by overexpression condition and infection condition.(C) UMAP colored by log expression of SARS-CoV-2 RNA.(D) UMAP colored by GMM infection classification.(E) UMAP colored by cell type identification.(F) Dot plot showing levels of interferon genes and IL-6 RNA grouped by infection level.(G) Dot plot showing levels of interferon genes and IL-6 grouped by overexpression condition.(H) Dot plot showing levels of top differentially expressed ISGs grouped by infection level.ISGs were selected from the list of top 50 differentially expressed genes between the high-infected and uninfected cells.(I) Dot plot showing levels of top differentially expressed ISGs grouped by overexpression background.

Figure 4 .
Figure 4. Single-cell RNA-sequencing of airway organoids infected with SARS-CoV-2 variants.(A) UMAP showing distribution of cells from each variant infection.(B) UMAP colored by infected and uninfected conditions.(C) UMAP colored by cell type (D) UMAP colored by SARS-CoV-2 infection level (via GMM classification).(E) Dot plot showing levels of interferon genes and IL-6 grouped by variant.(F) Dot plot showing levels of ISG expression grouped by variant.(G) Heatmap showing top five genes correlated to SARS-CoV-2 RNA level grouped by variant.The number in the box shows the ranking of the gene based on correlation for the variant and coloring shows the mean expression of the gene listed on the left.Heatmap was made using Python package Matplotlib version 3.5.2(https:// matpl otlib.org/ stable/).(H) UMAP colored by log expression of NFKBIA RNA.(I) UMAP colored by log expression of SARS-CoV-2 RNA.(J) Infection level proportions per variant.