Impaired local intrinsic immunity to SARS-CoV-2 infection in severe COVID-19

doi:10.1016/j.cell.2021.07.023

. 2021 Sep 2;184(18):4713-4733.e22.

doi: 10.1016/j.cell.2021.07.023. Epub 2021 Jul 23.

Impaired local intrinsic immunity to SARS-CoV-2 infection in severe COVID-19

Carly G K Ziegler¹, Vincent N Miao², Anna H Owings³, Andrew W Navia⁴, Ying Tang⁵, Joshua D Bromley⁶, Peter Lotfy⁷, Meredith Sloan³, Hannah Laird⁸, Haley B Williams⁸, Micayla George⁹, Riley S Drake⁹, Taylor Christian³, Adam Parker⁸, Campbell B Sindel¹⁰, Molly W Burger¹¹, Yilianys Pride⁸, Mohammad Hasan⁸, George E Abraham 3rd¹⁰, Michal Senitko¹⁰, Tanya O Robinson⁸, Alex K Shalek¹², Sarah C Glover¹³, Bruce H Horwitz¹⁴, Jose Ordovas-Montanes¹⁵

Affiliations

¹ Program in Health Sciences & Technology, Harvard Medical School & MIT, Boston, MA 02115, USA; Ragon Institute of MGH, MIT, and Harvard, Cambridge, MA 02139, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Harvard Graduate Program in Biophysics, Harvard University, Cambridge, MA 02138, USA; Institute for Medical Engineering & Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
² Program in Health Sciences & Technology, Harvard Medical School & MIT, Boston, MA 02115, USA; Ragon Institute of MGH, MIT, and Harvard, Cambridge, MA 02139, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Institute for Medical Engineering & Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
³ Department of Medicine, University of Mississippi Medical Center, Jackson, MS 39216, USA.
⁴ Ragon Institute of MGH, MIT, and Harvard, Cambridge, MA 02139, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Institute for Medical Engineering & Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
⁵ Division of Gastroenterology, Hepatology, and Nutrition, Boston Children's Hospital, Boston, MA 02115, USA.
⁶ Ragon Institute of MGH, MIT, and Harvard, Cambridge, MA 02139, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Institute for Medical Engineering & Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Microbiology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
⁷ Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Division of Gastroenterology, Hepatology, and Nutrition, Boston Children's Hospital, Boston, MA 02115, USA.
⁸ Division of Digestive Diseases, University of Mississippi Medical Center, Jackson, MS 39216, USA.
⁹ Ragon Institute of MGH, MIT, and Harvard, Cambridge, MA 02139, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Institute for Medical Engineering & Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
¹⁰ Division of Pulmonary, Critical Care, and Sleep Medicine, University of Mississippi Medical Center, Jackson, MS 39216, USA.
¹¹ Obstetrics and Gynecology, University of Mississippi Medical Center, Jackson, MS 39216, USA.
¹² Program in Health Sciences & Technology, Harvard Medical School & MIT, Boston, MA 02115, USA; Ragon Institute of MGH, MIT, and Harvard, Cambridge, MA 02139, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Harvard Graduate Program in Biophysics, Harvard University, Cambridge, MA 02138, USA; Institute for Medical Engineering & Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Program in Immunology, Harvard Medical School, Boston, MA 02115, USA; Harvard Stem Cell Institute, Cambridge, MA 02138, USA. Electronic address: shalek@mit.edu.
¹³ Division of Digestive Diseases, University of Mississippi Medical Center, Jackson, MS 39216, USA. Electronic address: scglover@umc.edu.
¹⁴ Division of Gastroenterology, Hepatology, and Nutrition, Boston Children's Hospital, Boston, MA 02115, USA; Division of Emergency Medicine, Boston Children's Hospital, Boston, MA 02115, USA; Program in Immunology, Harvard Medical School, Boston, MA 02115, USA. Electronic address: bruce.horwitz@childrens.harvard.edu.
¹⁵ Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Division of Gastroenterology, Hepatology, and Nutrition, Boston Children's Hospital, Boston, MA 02115, USA; Program in Immunology, Harvard Medical School, Boston, MA 02115, USA; Harvard Stem Cell Institute, Cambridge, MA 02138, USA. Electronic address: jose.ordovas-montanes@childrens.harvard.edu.

PMID: 34352228
PMCID: PMC8299217
DOI: 10.1016/j.cell.2021.07.023

Impaired local intrinsic immunity to SARS-CoV-2 infection in severe COVID-19

Carly G K Ziegler et al. Cell. 2021.

. 2021 Sep 2;184(18):4713-4733.e22.

doi: 10.1016/j.cell.2021.07.023. Epub 2021 Jul 23.

Authors

Affiliations

¹ Program in Health Sciences & Technology, Harvard Medical School & MIT, Boston, MA 02115, USA; Ragon Institute of MGH, MIT, and Harvard, Cambridge, MA 02139, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Harvard Graduate Program in Biophysics, Harvard University, Cambridge, MA 02138, USA; Institute for Medical Engineering & Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
² Program in Health Sciences & Technology, Harvard Medical School & MIT, Boston, MA 02115, USA; Ragon Institute of MGH, MIT, and Harvard, Cambridge, MA 02139, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Institute for Medical Engineering & Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
³ Department of Medicine, University of Mississippi Medical Center, Jackson, MS 39216, USA.
⁴ Ragon Institute of MGH, MIT, and Harvard, Cambridge, MA 02139, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Institute for Medical Engineering & Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
⁵ Division of Gastroenterology, Hepatology, and Nutrition, Boston Children's Hospital, Boston, MA 02115, USA.
⁶ Ragon Institute of MGH, MIT, and Harvard, Cambridge, MA 02139, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Institute for Medical Engineering & Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Microbiology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
⁷ Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Division of Gastroenterology, Hepatology, and Nutrition, Boston Children's Hospital, Boston, MA 02115, USA.
⁸ Division of Digestive Diseases, University of Mississippi Medical Center, Jackson, MS 39216, USA.
⁹ Ragon Institute of MGH, MIT, and Harvard, Cambridge, MA 02139, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Institute for Medical Engineering & Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
¹⁰ Division of Pulmonary, Critical Care, and Sleep Medicine, University of Mississippi Medical Center, Jackson, MS 39216, USA.
¹¹ Obstetrics and Gynecology, University of Mississippi Medical Center, Jackson, MS 39216, USA.
¹² Program in Health Sciences & Technology, Harvard Medical School & MIT, Boston, MA 02115, USA; Ragon Institute of MGH, MIT, and Harvard, Cambridge, MA 02139, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Harvard Graduate Program in Biophysics, Harvard University, Cambridge, MA 02138, USA; Institute for Medical Engineering & Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Program in Immunology, Harvard Medical School, Boston, MA 02115, USA; Harvard Stem Cell Institute, Cambridge, MA 02138, USA. Electronic address: shalek@mit.edu.
¹³ Division of Digestive Diseases, University of Mississippi Medical Center, Jackson, MS 39216, USA. Electronic address: scglover@umc.edu.
¹⁴ Division of Gastroenterology, Hepatology, and Nutrition, Boston Children's Hospital, Boston, MA 02115, USA; Division of Emergency Medicine, Boston Children's Hospital, Boston, MA 02115, USA; Program in Immunology, Harvard Medical School, Boston, MA 02115, USA. Electronic address: bruce.horwitz@childrens.harvard.edu.
¹⁵ Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Division of Gastroenterology, Hepatology, and Nutrition, Boston Children's Hospital, Boston, MA 02115, USA; Program in Immunology, Harvard Medical School, Boston, MA 02115, USA; Harvard Stem Cell Institute, Cambridge, MA 02138, USA. Electronic address: jose.ordovas-montanes@childrens.harvard.edu.

PMID: 34352228
PMCID: PMC8299217
DOI: 10.1016/j.cell.2021.07.023

Abstract

SARS-CoV-2 infection can cause severe respiratory COVID-19. However, many individuals present with isolated upper respiratory symptoms, suggesting potential to constrain viral pathology to the nasopharynx. Which cells SARS-CoV-2 primarily targets and how infection influences the respiratory epithelium remains incompletely understood. We performed scRNA-seq on nasopharyngeal swabs from 58 healthy and COVID-19 participants. During COVID-19, we observe expansion of secretory, loss of ciliated, and epithelial cell repopulation via deuterosomal cell expansion. In mild and moderate COVID-19, epithelial cells express anti-viral/interferon-responsive genes, while cells in severe COVID-19 have muted anti-viral responses despite equivalent viral loads. SARS-CoV-2 RNA⁺ host-target cells are highly heterogenous, including developing ciliated, interferon-responsive ciliated, AZGP1^high goblet, and KRT13⁺ "hillock"-like cells, and we identify genes associated with susceptibility, resistance, or infection response. Our study defines protective and detrimental responses to SARS-CoV-2, the direct viral targets of infection, and suggests that failed nasal epithelial anti-viral immunity may underlie and precede severe COVID-19.

Keywords: COVID-19; SARS-CoV-2; anti-viral; correlates of immunity; epithelial immunity; human; interferon; nasal mucosa; scRNA-seq.

PubMed Disclaimer

Conflict of interest statement

Declaration of interests A.K.S. reports compensation for consulting and/or SAB membership from Merck, Honeycomb Biotechnologies, Cellarity, Repertoire Immune Medicines, Hovione, Ochre Bio, Third Rock Ventures, Relation Therapeutics, and Dahlia Biosciences. J.O.-M. reports compensation for consulting services with Cellarity and Hovione. C.G.K.Z., V.N.M., A.H.O., A.W.N., Y.T., J.D.B., A.K.S., S.C.G., B.H.H., and J.O.-M. are co-inventors on a provisional patent application relating to methods of stratifying and treating viral infections.

Figures

**Figure 1**
Cellular composition of human nasopharyngeal mucosa (A) Schematic: viable cryopreservation of nasopharyngeal swabs, cellular isolation, and scRNA-seq using Seq-Well S³ (created with BioRender.com). (B–E) UMAP of 32,588 cells from all participants, colored by cell type (following iterative Louvain clustering) (B), participant’s COVID-19 status by viral PCR (C), peak level of respiratory support (WHO severity score) (D), and participant (E). (F) Violin plots of cluster marker genes (FDR < 0.01) for coarse cell type annotations (as in B). (G) Proportional abundance of coarse cell types by participant. (H) Proportional abundance of participants by coarse cell types. Red, COVID-19; blue, control. (I) Expression of entry factors for SARS-CoV-2 and other common upper respiratory viruses. Dot size represents fraction of cell type (rows) expressing a given gene (columns). Dot hue represents scaled average expression by gene column. (J–N) Proportion of ciliated cells (J), developing ciliated cells (K), deuterosomal cells (L), secretory cells (M), and goblet cells (N) by sample, separated by peak level of respiratory support. Statistical test above graph represents Kruskal-Wallis test results across all groups (following FDR correction across cell types). Statistical significance asterisks within box represent results from Dunn’s post hoc testing. ^∗p < 0.05, ^∗∗p < 0.01, ^∗∗∗p < 0.001. (O) Simpson’s Diversity index (plotted as 1-D, increasing values represent higher diversity) across epithelial cell types in COVID-19 versus control. Significance by Student’s t test. Lines represent mean ± SEM. See also Figure S1, Table S1.

**Figure S2**
Epithelial diversity and differentiation in the nasopharyngeal mucosa during COVID-19, related to Figure 2 (A) Flow cytometry and gating scheme of immune cells from a fresh nasopharyngeal (NP) swab. Representative healthy participant. Bottom right: quantification of cellular proportions. (B) Quality metrics for matched fresh versus frozen NP swabs from two healthy participants (P1 and P2). (C) Percent composition of each cell type by processing type: fresh (gray circles) or frozen (black squares). (D and E) UMAP of cells from P1, colored by cell types (D) and fresh (gray) versus frozen (black) (E). (F and G) UMAP of cells from P2, colored by cell types (F) and fresh (gray) versus frozen (black) (G). (H) Flow cytometry and gating scheme of epithelial cells from an NP swab. Representative data from a participant with severe COVID-19. (I) Secretory cell proportion of live, CD45- cells from NP swabs. Healthy donors (Control WHO 0): n = 7. Severe COVID-19 (COVID-19 WHO 6-8): n = 7. Secretory cells identified as Live, CD45^-ATubulin^-CD271^-CD49f^-CD66c⁺ cells. Statistical testing: Wilcoxon signed-rank test: ^∗∗p = 0.0047. (J) Proportional abundance of detailed epithelial cell types by participant. Ordered within group by developing ciliated cell proportion. (K) Expression of entry factors for SARS-CoV-2 and other common upper respiratory viruses among detailed epithelial cell types. Dot size represents fraction of cell type (rows) expressing a given gene (columns). Dot hue represents scaled average expression by gene column. (L) Plot of gene expression by epithelial cell velocity pseudotime (over all epithelial cells). Select genes significantly associated with ciliated cell pseudotime (FDR < 0.01). Points colored by coarse cell type annotations. Top: alignment to unspliced (intronic) regions. Bottom: alignment to spliced (exonic) regions. (M) Proportion of goblet cell subtypes (detailed annotation) by sample, normalized to all epithelial cells. Statistical test above graph represents Kruskal-Wallis test results across all groups (following FDR correction).

**Figure 2**
Altered epithelial cell composition in the nasopharynx during COVID-19 (A–D) UMAP of 28,948 cells colored by coarse cell types (A), participant’s COVID-19 status by viral PCR (B), peak level of respiratory support (WHO severity score) (C), and detailed cell types (D). Arrows represent smoothed estimate of cellular differentiation trajectories inferred by RNA Velocity. (E) Violin plots of marker genes for detailed epithelial cell types (as in D). (F–H) UMAP of 9,209 basal, goblet, and secretory cells, following sub-clustering and colored by detailed cell types (F), participant’s COVID-19 status by viral PCR (G), and inferred velocity pseudotime (darker blue shades: precursor cells, intense yellow shades: more terminally differentiated cell types) (H). (I) Gene expression by basal, goblet, and secretory cell velocity pseudotime for select genes. Points colored by detailed cell type annotations. (J) Proportion of secretory cell subtypes by sample, normalized to all epithelial cells. Statistical test above graph represents Kruskal-Wallis test results across all groups (following FDR correction). Statistical significance asterisks within box represent results from Dunn’s post hoc testing. ^∗p < 0.05, ^∗∗p < 0.01, ^∗∗∗p < 0.001. Lines represent mean and SEM. (K–M) UMAP of 13,913 ciliated cells, following sub-clustering and colored by detailed cell types (K), participant’s COVID-19 status by viral PCR (L), and inferred velocity pseudotime (darker blue shades, precursor cells; intense yellow shades, more terminally differentiated cell types) (M). (N) Gene expression by ciliated cell velocity pseudotime for select genes. Points colored by detailed cell type annotations. (O) Proportion of ciliated cell subtypes by sample, normalized to all epithelial cells. (P and Q) UMAP as in (A), separated by only control participants (P, 13,210 epithelial cells) or COVID-19 participants (Q, 15,738 epithelial cells). See also Figure S2, Table S1.

**Figure S3**
Immune cell diversity in the nasopharyngeal mucosa during COVID-19, related to Figures 1 and 3 (A–E) UMAP of 3,640 immune cells following re-clustering, colored by coarse cell types (A), detailed cell annotations (B), peak level of respiratory support (WHO severity score) (C), participant’s COVID-19 status by viral PCR (D), and participant (E). (F) Violin plots (log(1+normalized UMI per 10k)) of cluster marker genes (FDR < 0.01) for detailed immune cell type annotations (as in B). (G) Proportional abundance of detailed immune cell types by participant. (H) Proportion of immune cell subtypes by sample and disease group, normalized to all immune cells. Statistical test above graph represents Kruskal-Wallis test results across all cell types (following FDR correction). (I) Proportion of interferon responsive macrophages versus proportion of interferon responsive cytotoxic CD8 T cells per sample, normalized to total immune cells. Including all samples, Control and COVID-19 groups. (J and K) Heatmap of significantly DE genes between macrophages (all, coarse annotation) (J) and T cells (all, coarse annotation) (K) from different disease groups. Values represent row(gene)-scaled digital gene expression (DGE) following log(1+UMI per 10K) normalization. (L) Top: Dot plot of *IFNGR1, IFNGR2*, *IFNAR1,* and *IFNAR2* gene expression among all detailed immune subtypes. Bottom: Violin plots of module scores, split by Control WHO 0 (blue), COVID-19 WHO 1-5 (red), and COVID-19 WHO 6-8 (pink). Gene modules represent transcriptional responses of human basal cells from the nasal epithelium following *in vitro* treatment with IFNα or IFNγ. Significance by Wilcoxon signed-rank test. P values following Bonferroni-correction: ^∗p < 0.05, ^∗∗p < 0.01, ^∗∗∗p < 0.001.

**Figure S4**
Cell-type-specific and shared transcriptional Responses to SARS-CoV-2 infection, related to Figure 3 (A) Abundance of significant DE genes by coarse cell type between Control WHO 0 and COVID-19 WHO 1-5 samples (left), Control WHO 0 and COVID-19 WHO 6-8 samples (middle) and COVID-19 WHO 1-5 versus COVID-19 WHO 6-8 samples (right). Gene significance cutoffs: FDR-corrected p < 0.001, log2 fold change > 0.25. (B) Heatmap of significantly DE genes between ciliated cells (all, coarse annotation) from different disease groups. Values represent row(gene)-scaled digital gene expression (DGE) following log(1+UMI per 10K) normalization. (C) Top: Dot plot of *IFNGR1, IFNGR2*, *IFNAR1,* and *IFNAR2* gene expression among all detailed epithelial subtypes. Bottom: Violin plots of module scores, split by Control WHO 0 (blue), COVID-19 WHO 1-5 (red), and COVID-19 WHO 6-8 (pink). Gene modules represent transcriptional responses of human basal cells from the nasal epithelium following *in vitro* treatment with IFNα or IFNγ. Significance by Wilcoxon signed-rank test. P values following Bonferroni-correction: ^∗p < 0.05, ^∗∗p < 0.01, ^∗∗∗p < 0.001. (D) Dot plot of *ACE2* expression across select epithelial cell types and subsets. (E) Venn diagram of significantly upregulated genes among ciliated cells between COVID-19 WHO 1-5 versus Control WHO 0 (red) and COVID-19 WHO 6-8 versus Control WHO 0 (pink). Asterisk: genes impacted by corticosteroid treatment within each group. (F) Violin plots of select genes upregulated among ciliated cells in COVID-19 WHO 1-5 participants compared to Control WHO 0 (*PARP14, ISG15*) and in COVID-19 WHO 6-8 participants compared to Control WHO 0 (*FKBP5*). Cells separated by participant treatment with corticosteroids. ^∗∗∗ FDR-corrected p < 0.001. (G) Dot plot of interferon and cytokine expression among detailed epithelial and immune cell types. (H) Dot plot of type I and type III interferons among ciliated, goblet, and squamous cells. Left: healthy versus influenza A/B virus infected participants from Cao et al., 2020. Right: Control WHO 0 versus COVID-19 WHO 1-5, versus COVID-19 WHO 6-8 participants. Datasets processed and scaled identically.

**Figure 3**
Cell-type-specific and shared transcriptional responses during COVID-19 (A) Abundance of significantly DE genes by detailed cell types between disease groups. FDR-corrected p < 0.001, log2 fold change > 0.25. ø = comparison not tested, too few cells. (B) Top: volcano plots of average log fold change (FC) versus -log₁₀(FDR-adjusted p value) for ciliated cells (all, coarse annotation) between disease groups. Horizontal red dashed line: FDR-adjusted p value = 0.05. Bottom: GSEA plots across shared, type I interferon-specific, and type II interferon-specific stimulated genes. Genes ranked by their average log FC between each comparison. Black lines represent the ranked location of genes belonging to the annotated gene set. Bar height represents running enrichment score (NES, normalized enrichment score). p values following Bonferroni-correction: ^∗p < 0.05, ^∗∗p < 0.01, ^∗∗∗p < 0.001. (C) Heatmap of significantly DE genes between interferon responsive ciliated cells from different disease groups. Row(gene)-scaled digital gene expression (DGE) following log(1+UMI per 10K) normalization. (D) Top: Volcano plots related to C for interferon-responsive ciliated cells. Horizontal red dashed line: FDR-adjusted p value = 0.05. Bottom: GSEA plots across shared, type I, and type II interferon-stimulated genes. (E) Heatmap of significantly DE genes between *MUC5AC*^high goblet cells from different disease groups. Row(gene)-scaled digital gene expression (DGE) following log(1+UMI per 10K) normalization. (F) Top: Volcano plots related to (E) for *MUC5AC*^high goblet cells. Horizontal red dashed line: FDR-adjusted p value = 0.05. Bottom: GSEA plots across shared, type I, and type II interferon-stimulated genes. (G) Top: Dot plot of *IFNGR1, IFNGR2*, *IFNAR1,* and *IFNAR2* gene. Bottom: Violin plots of module scores, split by control WHO 0 (blue), COVID-19 WHO 1–5 (red), and COVID-19 WHO 6–8 (pink). Significance by Wilcoxon signed-rank test. p values following Bonferroni-correction: ^∗p < 0.05, ^∗∗p < 0.01, ^∗∗∗p < 0.001. (H) Common DE genes across detailed cell types. Left (red): COVID-19 WHO 1–5 versus control WHO 0. Right (pink), COVID-19 WHO 6–8 versus control WHO 0. (I) Relative abundances of IgG autoantibodies for human type I, II, and III interferons via multiplexed human antigen microarray (see STAR Methods). Blue circles, control WHO 0, n = 5; red circles, COVID-19 WHO 1–5, n = 12; pink squares, COVID-19 WHO 6–8, n = 8. Large pink squares, autoantibodies against 12 type I interferons from a single donor:,COVID-19 participant 27 (peak WHO severity score: 8, swab WHO severity score: 5). (J) Average expression of *STAT1, STAT2, IRF1,* and *IRF9* among ciliated cells by participant. For each gene: left: participants separated by disease group, determined by participants’ peak WHO severity score. Statistical testing by Kruskal-Wallis test across disease groups (^∗∗p = 0.0018) with Dunn’s post hoc testing: ^∗p < 0.05, ^∗∗p < 0.01, ^∗∗∗p < 0.001. Right: participants in COVID-19 WHO 6–8 group, separated by level of severity at time of nasal swab. Statistical testing by Wilcoxon signed-rank test, n.s. non-significant, p > 0.05. See also Figures S3 and S4, Tables S2, S3, and S4.

**Figure 4**
Co-detection of human and SARS-CoV-2 RNA (A) Metatranscriptomic classification of all scRNA-seq reads using Kraken2 (STAR Methods). Results shown from selected respiratory viruses (threshold > 5 reads). (B) Normalized abundance of SARS-CoV-2 aligning UMI from all scRNA-seq reads (including those derived from ambient cell barcodes). p < 0.0001 by Kruskal-Wallis test. Pairwise comparisons using Dunn’s post hoc testing. ^∗∗p < 0.01, ^∗∗∗p < 0.001. (C) SARS-CoV-2 UMIs per high-complexity single-cell transcriptome (following correction for ambient viral reads). (D) Proportional abundance of secretory cells (all, coarse annotation) versus total SARS-CoV-2 UMIs (normalized to M total UMIs). (E) Proportional abundance of *FOXJ1*^high ciliated cells versus total SARS-CoV-2 UMIs (normalized to M total UMIs). (F) Schematic: SARS-CoV-2 genome and subgenomic RNA species. (G) Schematic: SARS-CoV-2 genomic features annotated in the custom reference genome. (H) Heatmap of SARS-CoV-2 gene expression among SARS-CoV-2 RNA⁺ single cells (following correction for ambient viral reads). Disease group color bar: red, COVID-19 WHO 1–5; pink, COVID-19 WHO 6–8; black, COVID-19 convalescent; blue, control WHO 0. Top heatmap: SARS-CoV-2 genes and regions organized from 5′ to 3′. Bottom heatmap: alignment to 70-mer regions directly surrounding viral TRS sites. See also Figures S5 and S6.

**Figure S5**
Detection of SARS-CoV-2 RNA from single-cell RNA-seq data, related to Figures 4 and 5 (A–C) Metatranscriptomic classification of all scRNA-seq reads using Kraken2. Reads per sample annotated as unclassified (A), *Homo sapiens* (B), SARS-related coronaviruses (C). (D) Total recovered cells per sample versus normalized abundance of SARS-CoV-2 aligning UMI from all scRNA-seq UMI (including those derived from ambient/low-complexity cell barcodes). (E) Normalized abundance of SARS-CoV-2 aligning UMI across all COVID-19 participants. Dashed line represents partition between “Viral High” versus “Viral Low” samples (1,000 SARS-CoV-2 UMI/million (M) UMI). (F) Proportional abundance of selected cell types according to total SARS-CoV-2 abundance among COVID-19 samples, stratified by cutoffs in panel E. Statistical test above graph represents FDR-corrected Kruskal-Wallis test statistic across all groups. Statistical significance asterisks within box represent significant results from Dunn’s post hoc testing. ^∗p < 0.05, ^∗∗p < 0.01, ^∗∗∗p < 0.001. (G) Normalized abundance of SARS-CoV-2 aligning UMI versus anti-SARS-CoV-2 IgM (left) or IgG titers (right). Plasma samples taken on same day of nasopharyngeal swab. Subset of Control WHO 0 (blue circles, n = 13) and COVID-19 (red circles, mild/moderate: n = 8; pink squares, severe: n = 15) participants. Dashed lines: lower limit of detection: 100; upper limit of detection: 100,000; positive threshold: 5,000. Pearson’s correlation of COVID-19 samples: IgM: r = −0.59, ^∗∗p = 0.0028; IgG: r = −0.60, ^∗∗p = 0.0025. (H) Abundance of SARS-CoV-2 aligning UMI/cell by participant prior to (top) and following (bottom) ambient viral RNA correction (see STAR Methods). (I) Quality metrics among 415 SARS-CoV-2 RNA⁺ cells (associated with high-quality cell barcodes and following ambient viral RNA correction). Left: abundance of SARS-CoV-2 aligning UMI versus percent of all SARS-CoV-2 aligned reads (per cell barcode). Middle: abundance of human (GRCh38)-aligning UMI versus abundance of SARS-CoV-2 aligning UMI. Right: abundance of human (GRCh38) aligning UMI versus percent of all human aligned reads (per cell barcode). (J) Percent SARS-CoV-2 RNA⁺ cells (associated with high-quality cell barcodes and following ambient viral RNA correction) per donor, separated by disease group. Statistical test above graph represents Kruskal-Wallis test statistic across all groups. Statistical significance asterisks within box represent significant results from Dunn’s post hoc testing. ^∗p < 0.05, ^∗∗p < 0.01.

**Figure 5**
Cellular targets of SARS-CoV-2 in the nasopharynx (A) Summary schematic of top SARS-CoV-2 RNA⁺ cells. (Adapted from “Coronavirus Replication Cycle (Simplified) by BioRender.com (2021). Retrieved from https://app.biorender.com/biorender-templates.) (B) SARS-CoV-2 RNA⁺ cell number (top) and percent (bottom) per participant. (C) Abundance of SARS-CoV-2 RNA⁺ cells by detailed cell type, bars colored by participant. (D) Dot plot of SARS-CoV-2 RNA presence by sample (columns) and detailed cell types (rows). Dot size reflects fraction of a given participant and cell type containing SARS-CoV-2 RNA. Dot color reflects fraction of aligned reads corresponding to the SARS-CoV-2-positive strand (yellow) versus negative strand (black). Top dot plot across columns: alignment of viral reads by participant, separated by RNA species type. Right dot plot across rows: alignment of viral reads by detailed cell type. (E) Percent *ACE2*⁺ cells versus percent SARS-CoV-2 RNA⁺ cells by coarse cell type (left) and detailed cell type (right). See also Figures S5 and S6.

**Figure S6**
SARS-CoV-2 RNA and cell types containing viral reads, related to Figures 4 and 5 (A) Schematic of method to distinguish unspliced from spliced SARS-CoV-2 RNA species by searching for reads which align across a spliced or genomic Transcription Regulatory Sequence (TRS). (B) Abundance of SARS-CoV-2 aligning UMI/Cell per detailed cell type (following ambient viral RNA correction), split by UMI aligning to the viral positive strand, negative strand, 70-mer region across an unspliced TRS, and 70-mer region across a spliced TRS. (C) Abundance of SARS-CoV-2 aligning UMI/Cell per participant (following ambient viral RNA correction), split by UMI aligning to the viral positive strand, negative strand, 70-mer region across an unspliced TRS, and 70-mer region across a spliced TRS. (D and E) Dot plot of SARS-CoV-2 unspliced TRS aligning UMI (D) and spliced TRS aligning UMI (E) by participant (columns) and detailed cell type (rows). Dot size corresponds to the percent of cells within each sample/cell type containing unspliced (D) or spliced (E) TRS UMI. (F and G) Abundance of SARS-CoV-2 negative strand aligning reads by coarse epithelial cell types (F) and detailed ciliated cell types (G). Statistical significance by Kruskal-Wallis test (p value outside box). Asterisks within box: pairwise Wilcoxon rank sum test, Bonferroni-corrected: ^∗∗∗p < 0.001, ^∗∗p < 0.01, ^∗p < 0.05

**Figure 6**
Intrinsic and bystander responses to SARS-CoV-2 infection (A) Violin plots of selected genes upregulated in SARS-CoV-2 RNA⁺ cells in at least three individual cell type comparisons. Blue, control participants; red, bystander cells from COVID-19 participants; dark red, SARS-CoV-2 RNA⁺ cells. (B) Enriched gene ontologies among genes consistently up- or downregulated among SARS-CoV-2 RNA⁺ cells across cell types. (C and D) Heatmap of genes consistently higher in SARS-CoV-2 RNA⁺ cells (C) and higher in bystander cells (D) across multiple cell types. Colors represent log fold changes between SARS-CoV-2 RNA⁺ cells and bystander cells. Yellow, upregulated among SARS-CoV-2 RNA⁺ cells; blue, bystander cells. (E) Top: Violin plots of SARS-CoV-2 aligning reads among SARS-CoV-2 RNA⁺ cells. Statistical significance by Wilcoxon rank sum test. Bottom: select differentially expressed genes between SARS-CoV-2 RNA⁺ cells from participants with mild or moderate COVID-19 (red) versus severe COVID-19 (pink). Statistical significance by likelihood ratio test assuming an underlying negative binomial distribution. ^∗∗∗ FDR-corrected p < 0.001, ^∗∗p < 0.01, ^∗p < 0.05. (F) Percent *ACE2*⁺ cells versus percent SARS-CoV-2 RNA⁺ cells by detailed cell type. Left: cells from participants with mild or moderate COVID-19. Right: cells from participants with severe COVID-19. Point size reflects average type I interferon-specific module score among SARS-CoV-2 RNA⁺ cells. See also Figure S7 and Table S5.

**Figure S7**
Intrinsic and bystander responses to SARS-CoV-2 infection, related to Figure 6 (A) Heatmaps of log fold changes between SARS-CoV-2 RNA⁺ cells and bystander cells by cell type. Gene sets derived from four CRISPR screens for important host factors in the SARS-CoV-2 viral life cycle. Restricted to cell types with at least 5 SARS-CoV-2 RNA⁺ cells. Yellow: upregulated among SARS-CoV-2 RNA⁺ cells, blue: upregulated among bystander cells. (B) Violin plots of select genes upregulated in SARS-CoV-2 RNA⁺ cells when compared to matched bystanders. Plotting only SARS-CoV-2 RNA⁺ cells from COVID-19 WHO 1-5 participants (red) and COVID-19 WHO 6-8 participants (pink). Statistical significance by likelihood ratio test assuming an underlying negative binomial distribution. ^∗∗∗ FDR-corrected p < 0.001, ^∗∗p < 0.01, ^∗p < 0.05. (C) Heatmap of Spearman’s correlation between 73 clinical parameters, demographic data, or results from scRNA-seq. Includes individuals from healthy (Control WHO 0), COVID-19 mild/moderate (COVID-19 WHO 1-5) and COVID-19 severe (COVID-19 WHO 6-8) groups. Colored squares represent statistically significant associations by permutation test (p < 0.01; red: positive Spearman’s rho; blue: negative Spearman’s rho).

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Impaired local intrinsic immunity to SARS-CoV-2 infection in severe COVID-19.
Ziegler CGK, Miao VN, Owings AH, Navia AW, Tang Y, Bromley JD, Lotfy P, Sloan M, Laird H, Williams HB, George M, Drake RS, Christian T, Parker A, Sindel CB, Burger MW, Pride Y, Hasan M, Abraham GE 3rd, Senitko M, Robinson TO, Shalek AK, Glover SC, Horwitz BH, Ordovas-Montanes J. Ziegler CGK, et al. bioRxiv [Preprint]. 2021 Feb 20:2021.02.20.431155. doi: 10.1101/2021.02.20.431155. bioRxiv. 2021. Update in: Cell. 2021 Sep 2;184(18):4713-4733.e22. doi: 10.1016/j.cell.2021.07.023. PMID: 33619488 Free PMC article. Updated. Preprint.

Comment in

Poor nasal immunity can lead to severe COVID-19.
Flemming A. Flemming A. Nat Rev Immunol. 2021 Sep;21(9):547. doi: 10.1038/s41577-021-00610-2. Nat Rev Immunol. 2021. PMID: 34376831 Free PMC article. No abstract available.

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References

1. Ackermann M., Verleden S.E., Kuehnel M., Haverich A., Welte T., Laenger F., Vanstapel A., Werlein C., Stark H., Tzankov A. Pulmonary Vascular Endothelialitis, Thrombosis, and Angiogenesis in Covid-19. N. Engl. J. Med. 2020;383:120–128. - PMC - PubMed
1. Aicher T.P., Carroll S., Raddi G., Gierahn T., Wadsworth M.H., 2nd, Hughes T.K., Love C., Shalek A.K. Seq-Well: A sample-efficient, portable picowell platform for massively parallel single-cell RNA sequencing. Methods Mol. Biol. 2019;1979:111–132. - PMC - PubMed
1. Arons M.M., Hatfield K.M., Reddy S.C., Kimball A., James A., Jacobs J.R., Taylor J., Spicer K., Bardossy A.C., Oakley L.P., Public Health–Seattle and King County and CDC COVID-19 Investigation Team Presymptomatic SARS-CoV-2 Infections and Transmission in a Skilled Nursing Facility. N. Engl. J. Med. 2020;382:2081–2090. - PMC - PubMed
1. Ballestar E., Farber D.L., Glover S., Horwitz B., Meyer K., Nikolić M., Ordovas-Montanes J., Sims P., Shalek A., Vandamme N. medRxiv; 2020. Single cell profiling of COVID-19 patients: An international data resource from multiple tissues. - DOI
1. Banerjee A.K., Blanco M.R., Bruce E.A., Honson D.D., Chen L.M., Chow A., Bhat P., Ollikainen N., Quinodoz S.A., Loney C. SARS-CoV-2 Disrupts Splicing, Translation, and Protein Trafficking to Suppress Host Defenses. Cell. 2020;183:1325–1339.e21. - PMC - PubMed

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[1] Ackermann M., Verleden S.E., Kuehnel M., Haverich A., Welte T., Laenger F., Vanstapel A., Werlein C., Stark H., Tzankov A. Pulmonary Vascular Endothelialitis, Thrombosis, and Angiogenesis in Covid-19. N. Engl. J. Med. 2020;383:120–128. - PMC - PubMed

[2] Ackermann M., Verleden S.E., Kuehnel M., Haverich A., Welte T., Laenger F., Vanstapel A., Werlein C., Stark H., Tzankov A. Pulmonary Vascular Endothelialitis, Thrombosis, and Angiogenesis in Covid-19. N. Engl. J. Med. 2020;383:120–128. - PMC - PubMed

[3] Aicher T.P., Carroll S., Raddi G., Gierahn T., Wadsworth M.H., 2nd, Hughes T.K., Love C., Shalek A.K. Seq-Well: A sample-efficient, portable picowell platform for massively parallel single-cell RNA sequencing. Methods Mol. Biol. 2019;1979:111–132. - PMC - PubMed

[4] Aicher T.P., Carroll S., Raddi G., Gierahn T., Wadsworth M.H., 2nd, Hughes T.K., Love C., Shalek A.K. Seq-Well: A sample-efficient, portable picowell platform for massively parallel single-cell RNA sequencing. Methods Mol. Biol. 2019;1979:111–132. - PMC - PubMed

[5] Arons M.M., Hatfield K.M., Reddy S.C., Kimball A., James A., Jacobs J.R., Taylor J., Spicer K., Bardossy A.C., Oakley L.P., Public Health–Seattle and King County and CDC COVID-19 Investigation Team Presymptomatic SARS-CoV-2 Infections and Transmission in a Skilled Nursing Facility. N. Engl. J. Med. 2020;382:2081–2090. - PMC - PubMed

[6] Arons M.M., Hatfield K.M., Reddy S.C., Kimball A., James A., Jacobs J.R., Taylor J., Spicer K., Bardossy A.C., Oakley L.P., Public Health–Seattle and King County and CDC COVID-19 Investigation Team Presymptomatic SARS-CoV-2 Infections and Transmission in a Skilled Nursing Facility. N. Engl. J. Med. 2020;382:2081–2090. - PMC - PubMed

[7] Ballestar E., Farber D.L., Glover S., Horwitz B., Meyer K., Nikolić M., Ordovas-Montanes J., Sims P., Shalek A., Vandamme N. medRxiv; 2020. Single cell profiling of COVID-19 patients: An international data resource from multiple tissues. - DOI

[8] Ballestar E., Farber D.L., Glover S., Horwitz B., Meyer K., Nikolić M., Ordovas-Montanes J., Sims P., Shalek A., Vandamme N. medRxiv; 2020. Single cell profiling of COVID-19 patients: An international data resource from multiple tissues. - DOI

[9] Banerjee A.K., Blanco M.R., Bruce E.A., Honson D.D., Chen L.M., Chow A., Bhat P., Ollikainen N., Quinodoz S.A., Loney C. SARS-CoV-2 Disrupts Splicing, Translation, and Protein Trafficking to Suppress Host Defenses. Cell. 2020;183:1325–1339.e21. - PMC - PubMed

[10] Banerjee A.K., Blanco M.R., Bruce E.A., Honson D.D., Chen L.M., Chow A., Bhat P., Ollikainen N., Quinodoz S.A., Loney C. SARS-CoV-2 Disrupts Splicing, Translation, and Protein Trafficking to Suppress Host Defenses. Cell. 2020;183:1325–1339.e21. - PMC - PubMed

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Impaired local intrinsic immunity to SARS-CoV-2 infection in severe COVID-19

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