Research ArticleCORONAVIRUS

Immunophenotyping of COVID-19 and influenza highlights the role of type I interferons in development of severe COVID-19

See allHide authors and affiliations

Science Immunology  10 Jul 2020:
Vol. 5, Issue 49, eabd1554
DOI: 10.1126/sciimmunol.abd1554

Cytokine drivers of COVID-19

By carrying out single-cell RNA sequencing analyses on immune cells in the blood, Lee et al. have compared immune responses in patients with mild and severe COVID-19 with immune responses in severe cases of influenza. They report TNF/IL-1β–driven inflammation as defining characteristics of COVID-19 that was relatively weak in the response to influenza. By further comparing the transcriptomes of mild and severe cases of COVID-19, they found type I interferons to be key drivers of inflammation in severe COVID-19. By profiling the immune responses to SARS-CoV-2 and flu in parallel, Lee et al. have shed new light on the similarities and differences between the immune responses to these two distinct respiratory viruses.

Abstract

Although most severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)–infected individuals experience mild coronavirus disease 2019 (COVID-19), some patients suffer from severe COVID-19, which is accompanied by acute respiratory distress syndrome and systemic inflammation. To identify factors driving severe progression of COVID-19, we performed single-cell RNA sequencing using peripheral blood mononuclear cells (PBMCs) obtained from healthy donors, patients with mild or severe COVID-19, and patients with severe influenza. Patients with COVID-19 exhibited hyperinflammatory signatures across all types of cells among PBMCs, particularly up-regulation of the tumor necrosis factor/interleukin-1β (TNF/IL-1β)–driven inflammatory response as compared with severe influenza. In classical monocytes from patients with severe COVID-19, type I interferon (IFN) response coexisted with the TNF/IL-1β–driven inflammation, and this was not seen in patients with milder COVID-19. We documented type I IFN–driven inflammatory features in patients with severe influenza as well. On the basis of this, we propose that the type I IFN response plays a pivotal role in exacerbating inflammation in severe COVID-19.

INTRODUCTION

Currently, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes coronavirus disease 2019 (COVID-19), is spreading globally (1, 2), and the World Health Organization has declared it a pandemic. As of 2 June 2020, more than 6.1 million confirmed cases and more than 376,000 deaths have been reported worldwide (3).

SARS-CoV-2 infection usually results in a mild disease course with spontaneous resolution in the majority of infected individuals (4). However, some patients, particularly elderly patients, develop severe COVID-19 infection that requires intensive care with mechanical ventilation (4, 5). The mortality rate for COVID-19 in Wuhan, China is estimated to be 1.4% (5). Although this rate is lower than that of severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome, which are caused by other human pathogenic coronaviruses (6), it is much higher than that of influenza, a common respiratory viral disease requiring hospitalization and intensive care in severe cases.

In severe cases of COVID-19, a hyperinflammatory response, also called a cytokine storm, has been observed and is suspected of causing the detrimental progression of COVID-19 (7). Circulating levels of proinflammatory cytokines, including tumor necrosis factor (TNF) and interleukin-6 (IL-6), are increased in severe cases (8). Gene expression analyses have also shown that IL-1–related proinflammatory pathways are highly up-regulated in severe cases (9). In a murine model of SARS-CoV infection, a delayed but considerable type I interferon (IFN-I) response promotes the accumulation of monocytes-macrophages and the production of proinflammatory cytokines, resulting in lethal pneumonia with vascular leakage and impaired virus-specific T cell responses (10).

Immune dysfunction is also observed in patients with COVID-19. In severe cases, the absolute number of T cells is reduced (8, 11), and the T cells exhibit functional exhaustion with the expression of inhibitory receptors (12, 13). However, hyperactivation of T cells as reflected in the up-regulation of CD38, human leukocyte antigen (HLA)–DR, and cytotoxic molecules was also reported in a lethal case of COVID-19 (14). Immune dysfunction in patients with severe COVID-19 has been attributed to proinflammatory cytokines (15).

In the present study, we performed single-cell RNA sequencing (scRNA-seq) using peripheral blood mononuclear cells (PBMCs) to identify factors associated with the development of severe COVID-19 infection. By comparing COVID-19 and severe influenza, we report that the TNF/IL-1β–driven inflammatory response was dominant in COVID-19 across all types of cells among PBMCs, whereas the up-regulation of various IFN-stimulated genes (ISGs) was prominent in severe influenza. When we compared the immune responses from patients with mild and severe COVID-19 infections, we found that classical monocytes from severe COVID-19 exhibit IFN-I–driven signatures in addition to TNF/IL-1β–driven inflammation.

RESULTS

Single-cell transcriptomes of PBMCs from patients with COVID-19 and influenza

PBMCs were collected from healthy donors (n = 4), hospitalized patients with severe influenza (n = 5), and patients with COVID-19 of varying clinical severity, including severe, mild, and asymptomatic (n = 8). PBMCs were obtained twice from three (the patients C3, C6, and C7) of the eight patients with COVID-19 at different time points during hospitalization. PBMC specimens from patients with COVID-19 were assigned to severe or mild COVID-19 groups according to the National Early Warning Score (NEWS; mild < 5, severe ≥ 5) evaluated on the day of whole blood sampling (16). In NEWS scoring, respiratory rate, oxygen saturation, oxygen supplement, body temperature, systolic blood pressure, heart rate, and consciousness were evaluated (16). Severe influenza was defined when hospitalization was required irrespective of NEWS score. Patients with severe influenza were enrolled from December 2015 to April 2016, before the emergence of COVID-19. The severe COVID-19 group was characterized by significantly lower lymphocyte count and higher serum level of C-reactive protein than the mild COVID-19 group on the day of blood sampling (fig. S1A). Multiplex real-time polymerase chain reaction (PCR) for N, RNA-dependent RNA polymerase (RdRP), and E genes of SARS-CoV-2 was performed, and there was no statistical difference in Ct values for all three genes between two groups (fig. S1B). Demographic information is provided with experimental batch of scRNA-seq in table S1 and clinical data in tables S2 and S3.

Using the 10× Genomics scRNA-seq platform, we analyzed a total of 59,572 cells in all patients after filtering the data with stringent high quality, yielding a mean of 6900 unique molecular identifiers (UMIs) per cell and detecting 1900 genes per cell on average (table S4). The transcriptome profiles of biological replicates (PBMC specimens in the same group) were highly reproducible (fig. S1C), ensuring the high quality of the scRNA-seq data generated in this study.

To examine the host immune responses in a cell type–specific manner, we subjected 59,572 cells to t-distributed stochastic neighbor embedding (tSNE) based on highly variable genes using the Seurat package (17) and identified 22 different clusters unbiased by patients or experimental batches of scRNA-seq (Fig. 1A and fig. S1D). These clusters were assigned to 13 different cell types based on well-known marker genes and two uncategorized clusters (Fig. 1, B and C, and table S5). In downstream analysis, we only focused on 11 different immune cell types, including immunoglobulin G (IgG) B cell, IgG+ B cell, effector memory (EM)–like CD4+ T cell, non–EM-like CD4+ T cell, EM-like CD8+ T cell, non–EM-like CD8+ T cell, natural killer (NK) cell, classical monocyte, intermediate monocyte, nonclassical monocyte, and dendritic cell (DC) after excluding platelets, red blood cells (RBCs), and two uncategorized clusters. The individual C8 (asymptomatic case) was also excluded due to a lack of replicates. In hierarchical clustering, most transcriptome profiles from the same cell type tended to cluster together, followed by disease groups, suggesting that both immune cell type and disease biology, rather than technical artifacts, are the main drivers of the variable immune transcriptome (fig. S1E).

Fig. 1 Single-cell transcriptomes of PBMCs from COVID-19 and influenza patients.

(A) tSNE projections of 59,572 PBMCs from healthy donors (HDs) (four samples, 17,590 cells), patients with severe influenza (FLU) (five samples, 10,519 cells), patients with COVID-19 (asymptomatic: one sample, 4425 cells; mild COVID-19: four samples, 16,742 cells; severe COVID-19: six samples, 10,296 cells) colored by group information. (B) Normalized expression of known marker genes on a tSNE plot. (C) tSNE plot colored by annotated cell types. (D) Proportion of cell types in each group excluding Uncategorized 1, Uncategorized 2, RBC, and platelet. The colors indicate cell type information. (E) Boxplots showing the fold enrichment in cell type proportions from patients with mild COVID-19 (n = 4), severe COVID-19 (n = 6), and FLU (n = 5) compared with the HD group (mild COVID-19 versus HD: n = 16; severe COVID-19 versus HD: n = 24; FLU versus HD: n = 20). For the boxplots, the box represents the interquartile range (IQR), and the whiskers correspond to the highest and lowest points within 1.5 × IQR. Uncategorized 1 (relatively high UMIs per cell and the presence of multiple marker genes), Uncategorized 2 (B cell like and high expression of ribosomal protein genes), RBC, and platelet were excluded. Two-sided KS tests were conducted for each cell type between the disease and HD groups. *P < 0.05, **P < 0.01, and ***P < 0.001.

As a feature of immunological changes, we investigated the relative proportions of immune cells among PBMCs in the disease groups compared with the healthy donor group (Fig. 1, D and E, and fig. S1F). Unlike the limited changes in mild COVID-19, significant changes were observed in both influenza and severe COVID-19 across multiple cell types among PBMCs. In severe COVID-19, the proportion of classical monocytes significantly increased whereas those of DCs, nonclassical monocytes, intermediate monocytes, NK cells, EM-like CD8+ T cells, and EM-like CD4+ T cells significantly decreased (Fig. 1E). In severe influenza, the proportion of classical monocytes significantly increased whereas those of DCs, non–EM-like CD4+ T cells, EM-like CD4+ T cells, IgG+ B cells, and IgG B cells significantly decreased. We validated the proportions of immune cell subsets from scRNA-seq by flow cytometry analysis. The relative proportions of total lymphocytes, B cells, CD4+ T cells, CD8+ T cells, NK cells, and total monocytes from scRNA-seq significantly correlated with those from flow cytometry analysis (fig. S1G).

Transcriptional signatures associated with COVID-19

To compare the effect of infection between diseases, we performed hierarchical clustering based on relative gene expression changes against the healthy donor group. Unexpectedly, all types of cells among PBMCs were clustered together according to the disease groups instead of cell types (Fig. 2A). Further investigation of the variable genes based on K-means clustering supported COVID-19–specific up- or down-regulated gene expression patterns across all types of cells among PBMCs (fig. S2A). These results indicate that, in COVID-19, peripheral blood immune cells may be influenced by common inflammatory mediators regardless of cell type. Despite distinct transcriptional signatures between COVID-19 and influenza, severe COVID-19 and influenza shared transcriptional signatures in all types of monocytes and DCs (black boxed region in Fig. 2A), possibly reflecting common mechanisms underlying the innate immune responses in severe influenza and severe COVID-19.

Fig. 2 Immune landscape of COVID-19.

(A) Hierarchical clustering using the PCC of a normalized transcriptome between diseases in cell type resolution (n = 33). The color intensity of the heatmap indicates the PCC values. The color bars above the heatmap indicate the cell type and disease group. The black box indicates the cell types that highly correlate between the severe COVID-19 and FLU groups. (B) Illustration of the enrichment P values for the select GO biological pathways (n = 49) of DEGs in COVID-19 and FLU patients (left, six columns: DEGs for COVID-19 and FLU groups compared with HD; right, two columns: DEGs between COVID-19 and FLU groups). MHC, major histocompatibility complex. (C) tSNE plot of representative gene expression patterns for GBP1 (FLU specific), CREM (COVID-19 specific), and CCL3 (COVID-19/FLU common). (D) Top: Dendrogram from WGCNA analysis performed using relative normalized gene expression between the COVID-19 and FLU groups for the genes belonging to the select biological pathways in (B) (n = 316). Bottom: Heat map of relative normalized gene expression between the COVID-19 and FLU groups. The color bar (left) indicates cell type information clustered by hierarchical clustering based on the PCC for relative normalized gene expression. Modularized gene expression patterns by WGCNA are shown together (G1, n = 10; G2, n = 147; G3, n = 27; G4, n = 17; G5, n = 12; G6, n = 64; G7, n = 34; G8, n = 5).

Next, we sought to identify relevant biological functions in disease-specific up- or down-regulated genes in terms of the Gene Ontology (GO) biological pathways. First, we combined both mild and severe COVID-19 as a COVID-19 group and identified disease-specific changes in genes for each cell type compared with the healthy donor group using model-based analysis of single-cell transcriptomics (MAST) (18). NFKB1, NFKB2, IRF1, and CXCR3 were specifically up-regulated in COVID-19, and CXCL10, STAT1, TLR4, and genes for class II HLA and immunoproteasome subunits were specifically up-regulated in influenza (table S6). TNF, TGFB1, IL1B, and IFNG were commonly up-regulated. When we directly compared COVID-19 and influenza, NFKB1, NFKB2, and TNF were up-regulated in COVID-19, whereas STAT1, TLR4, and genes for immunoproteasome subunits were up-regulated in influenza. For each group of differentially expressed genes (DEGs), we identified the top 10 enriched GO biological pathways and collected them to demonstrate P value enrichment in each group of DEGs (Fig. 2B). Both distinct and common biological functions were identified as illustrated by inflammatory response genes being highly active in both COVID-19 and influenza, but genes for transcription factors, including inflammatory factors (i.e., NFKB1/2 and STAT4), were up-regulated in COVID-19. In contrast, a limited response in genes associated with the IFN-I and IFN-II signaling pathways, T cell receptor pathways, and adaptive immune response was observed in COVID-19 compared with influenza. Such disease-specific gene expression patterns were exemplified at single-cell resolution by GBP1 (IFN-γ–mediated signaling pathway) being specifically up-regulated in influenza, CREM (positive regulation of transcription) being specifically up-regulated in COVID-19, and CCL3 (inflammatory response) being commonly up-regulated (Fig. 2C and table S7).

We expanded our analysis in a cell type–specific manner by conducting weighted gene correlation network analysis (WGCNA) (19) for the collected genes associated with Fig. 2B. We identified several modular expression patterns (Fig. 2D and table S8). In the COVID-19 group, NFKB1/2, JUN, and TNF were modularized in CD8+ T and NK cells (G6 and G7 in Fig. 2D), and IL1B, NFKBID, and OSM were modularized in all types of monocytes and DCs (G3 in Fig. 2D). In the influenza group, GBP1, TAP1, STAT1, IFITM3, OAS1, IRF3, and IFNG were modularized in all types of T cells and NK cells (G2 in Fig. 2D), and CXCL10 and TLR4 were modularized in all types of monocytes and DCs (G5 and part of G6 in Fig. 2D). Consistently, the DEGs between COVID-19 and influenza were dominant in CD8+ T cells and all types of monocytes (fig. S2B).

Distinct subpopulations of CD8+ T cells in COVID-19 and influenza

To uncover disease-specific transcriptional signatures in CD8+ T cells, we performed subclustering analysis from EM-like and non–EM-like CD8+ T cell clusters using Seurat (17). Each disease group specifically enriched subclusters compared with the two other groups were identified in the non–EM-like CD8+ T cell cluster (Fig. 3A). Of the six subclusters from the non–EM-like CD8+ T cell cluster, cluster 1 and cluster 3 were significantly enriched in the influenza and COVID-19 groups, respectively (Fig. 3, B and C, and fig. S3A). Clusters with the high expression of PPBP, a marker of platelets, were excluded in following analysis (e.g., cluster 6 in fig. S3A). Up-regulated genes in cluster 1 and cluster 3 were associated with previously defined gene sets for “influenza A virus infection” and “SARS-CoV infection,” respectively (fig. S3B) (20). We also found that the cluster 3–specific up-regulated genes reflect activation of immune response, including CD27, RGS1, CCL5, SELL, and RGS10 (fig. S3C and table S9). Protein interaction network analysis of selected top 30 up-regulated genes in each cluster based on STRING v11 (21) revealed the up-regulation of PRF1, GNLY, GZMB, and GZMH in cluster 1 and the up-regulation of GZMK, GZMA, CXCR3, and CCL5 in cluster 3 (Fig. 3D, green). STAT1, TAP1, PSMB9, and PSME2, which are up-regulated preferentially by IFN-γ, were overexpressed only in influenza-specific cluster 1 (Fig. 3D, blue). We validated these data by intracellular staining for granzyme B and phorbol 12-myristate 13-acetate (PMA)/ionomycin-stimulated intracellular cytokine staining for IFN-γ. The percentages of granzyme B+ and IFN-γ+ cells among CD8+ T cells were significantly higher in the influenza group than in the COVID-19 group (fig. S3D). Of the seven representative GO biological pathways for the proinflammatory and IFN responses, pathways for responses to IFN-I and IFN-II were more associated with influenza-specific cluster 1, whereas pathways for the response to TNF or IL-1β were more prominent in COVID-19–specific cluster 3 (Fig. 3E).

Fig. 3 Subpopulation analysis of CD8+ T cells.

(A) tSNE plot of the non–EM-like CD8+ T cell subpopulations in all groups (left, n = 6253), COVID-19 (top right, n = 2653), FLU (middle right, n = 1452), and HD (bottom right, n = 2148) colored by cluster information. (B and C) Boxplots showing the proportion of individual subclusters from the non–EM-like CD8+ T cell cluster within each group (COVID-19, n = 10; FLU, n = 5; HD, n = 4). The proportions follow normal distribution as tested by the Shapiro-Wilk normality test except the proportion of cluster 3 in the COVID-19 group (P = 0.04). Cluster 1 and cluster 3 were highly enriched in the FLU and COVID-19 group, respectively. Two-sided Welch’s t test P values were 4.4 × 10−3 between COVID-19 and FLU in cluster 1, 3.5 × 10−2 between FLU and HD donor in cluster 1, 8.6 × 10−3 between COVID-19 and FLU in cluster 3, and 5.8 × 10−3 between COVID-19 and HD in cluster 3. *P < 0.05 and **P < 0.01. (D) STRING analysis using the top 30 up-regulated genes in cluster 1 (left) and cluster 3 (right). (E) Bar plots showing enrichment P values of eight representative GO biological pathways for proinflammation and IFN in cluster 1– or cluster 3–specific up-regulated genes (cluster 1, n = 66; cluster 3, n = 183).

Transcriptional signatures of classical monocytes in COVID-19

We performed subclustering analysis from all three types of monocyte clusters to find COVID-19–specific subclusters. However, there was no COVID-19–specifically enriched subcluster (fig. S4, A and B). Next, we further focused on classical monocytes considering their crucial roles for inflammatory responses. We investigated DEGs between influenza and COVID-19 to seek COVID-19–specific transcriptional signatures in classical monocytes (Fig. 4A). TNF and IL1B, major genes in the inflammatory response, were identified as COVID-19–specific and commonly up-regulated genes, respectively. To better characterize the transcriptional signatures in classical monocytes, we performed K-means clustering of up-regulated genes in at least one disease group compared with the healthy donor group. We identified five different clusters of up-regulation (Fig. 4B and table S10): Genes in cluster 1 are commonly up-regulated in all disease groups, cluster 2 is influenza specific, cluster 3 is associated with mild/severe COVID-19, cluster 4 is associated with influenza and severe COVID-19, and cluster 5 is severe COVID-19 specific.

Fig. 4 Transcriptome of classical monocytes in patients with COVID-19.

(A) Venn diagram of DEGs in COVID-19 and FLU compared with HD. The representative genes are shown together. (B) K-means clustering of DEGs between all pairs of FLU, mild COVID-19, and severe COVID-19 (n = 499). The color indicates the relative gene expression between the diseases and HD. The representative genes are shown together. (C) Bar plots showing the average −log10(P value) values in enrichment analysis using the perturbed genes of four different cell lines listed in L1000 LINCS for up-regulated genes in cluster 2 (C2, left) and cluster 3 (C3, right). Error bars indicate SD. (D) Combined enrichment scores were compared between C2 and C3 for the gene sets of the type I IFN response (left; GSE26104) and TNF response (right; GSE2638 and GSE2639). **P < 0.01. Each dot indicates an individual. (E) Bar plots showing the average −log10(P value) values in the enrichment analysis using the perturbed genes listed of four different cell lines in L1000 LINCS for up-regulated genes in cluster 4 (C4, left) and cluster 5 (C5, right). Error bars indicate SD (C and E).

We examined each cluster-specific genes by gene set enrichment analysis (GSEA) using cytokine-responsive gene sets originated from each cytokine-treated cells (LINCS L1000 ligand perturbation analysis in Enrichr) (22). COVID-19–specific cluster 3 genes were enriched by TNF/IL-1β–responsive genes, whereas influenza-specific cluster 2 genes were enriched by IFN-I–responsive genes in addition to TNF/IL-1β–responsive genes (Fig. 4C), indicating that the IFN-I response is dominant in influenza compared with COVID-19. We confirmed this result by analyzing cluster-specific genes with cytokine-responsive gene sets originated from other sources (Fig. 4D). Unexpectedly, clusters 4 and 5 exhibited strong associations with IFN-I–responsive genes, in addition to TNF/IL-1β–responsive genes (Fig. 4E), indicating that severe COVID-19 acquires IFN-I–responsive features in addition to TNF/IL-1β–inflammatory features.

IFN-I response in addition to TNF/IL-1β inflammatory response in severe COVID-19

Next, we directly compared classical monocytes between mild and severe COVID-19. When we analyzed DEGs, severe COVID-19 was characterized by up-regulation of various ISGs, including ISG15, IFITM1/2/3, and ISG20 (Fig. 5A). Both TNF/IL-1β–responsive genes and IFN-I–responsive genes were enriched in severe COVID-19–specific up-regulated genes (Fig. 5B). We measured plasma concentrations of TNF, IL-1β, IL-6, IFN-β, IFN-γ, and IL-18 in a larger cohort of patients with COVID-19. Among these cytokines, IL-6 and IL-18 were significantly increased in severe COVID-19 compared with mild COVID-19, whereas there was no difference in plasma concentrations of the other cytokines between the two groups (fig. S5A). These results indicate that cytokine-responsive gene signatures cannot be simply explained by a few cytokines because of overlapped effects of cytokines.

Fig. 5 Trajectory analysis of classical monocytes.

(A) Volcano plot showing DEGs between mild and severe COVID-19 groups. Each dot indicates individual gene, colored by red when a gene is significant DEG. (B) Bar plot showing the average −log10(P value) values in enrichment analysis using the perturbed genes of four different cell lines listed in L1000 LINCS for up-regulated genes in the severe COVID-19 group. Error bars indicate SD. (C) Trajectory analysis of classical monocytes from specimens obtained at two different time points in a single COVID-19 patient (mild: C7-2, 1,197 cells; severe: C7-1, 631 cells). The color indicates cluster information (left) or the severity of COVID-19 (right). (D) Relative expression patterns of representative genes in the trajectory analysis are plotted along the pseudotime. The color indicates the relative gene expression calculated by Monocle 2. (E) Bar plots showing the average −log10(P value) values in the enrichment analysis using the perturbed genes of four different cell lines in L1000 LINCS for up-regulated genes in cluster 3 (left) and cluster 1 (right). Error bars indicate SD. (F) Comparison of combined enrichment scores between cluster 3 and cluster 1 for the gene sets from systemic lupus erythematosus (SLE) (n = 16) and rheumatoid arthritis (RA) (n = 5). ***P < 0.001; ns, not significant. (G) GSEA of up-regulated genes in cluster 3 (left) and cluster 1 (right) to the class 1 gene module of monocyte-derived macrophages by Park et al. (24). NES, normalized enrichment score.

To further investigate the characteristics of severe COVID-19, we performed a trajectory analysis with Monocle 2 (23) using two internally well-controlled specimens (one severe and one mild) in which both PBMC samples were collected from a single patient (the individual C7) with COVID-19. Trajectory analysis aligned classical monocytes along the disease severity with cluster 1 and cluster 3 corresponding to later and earlier pseudotime, respectively (Fig. 5C). Representative genes in cluster 1 were enriched in the severe stage and highly associated with the both IFN-I and TNF/IL-1β–associated inflammatory response (Fig. 5D, fig. S5B, and table S11). GSEA confirmed that both the IFN-I response and TNF/IL-1β inflammatory response were prominent in cluster 1 but not in cluster 3 (Fig. 5E). Cluster 1 exhibited a significantly higher association with a gene set from systemic lupus erythematosus, which is a representative inflammatory disease with IFN-I features, than cluster 3 (Fig. 5F, left) but was not significantly associated with a gene set from rheumatoid arthritis (Fig. 5F, right).

We obtained additional evidence of the IFN-I–potentiated TNF inflammatory response in severe COVID-19 by analyzing a gene module that is not responsive to IFN-I but associated with TNF-induced tolerance to TLR stimulation. Park et al. (24) previously demonstrated that TNF tolerizes TLR-induced gene expression in monocytes, although TNF itself is an inflammatory cytokine. They also showed that IFN-I induces a hyperinflammatory response by abolishing the tolerance effects of TNF and defined a gene module responsible for the IFN-I–potentiated TNF–nuclear factor κB (NF-κB) inflammatory response as “class 1” (24). This gene module was significantly enriched in cluster 1 but not in cluster 3 (Fig. 5G), which suggests that the IFN-I response may exacerbate hyper-inflammation by abolishing a negative feedback mechanism.

Validation of hyperinflammatory features combined with IFN-I response in lung tissues from a lethal case of COVID-19

Last, we validated IFN-I response and inflammatory features using bulk RNA-seq data obtained using postmortem lung tissues from patients with lethal COVID-19 (25). Although the analysis was limited to only two patients without individual cell-type resolution, in genome browser, up-regulation of IFITM1, ISG15, and JAK3 and down-regulation of RPS18 were observed commonly in postmortem COVID-19 lung tissues and classical monocytes of severe COVID-19 (Fig. 6A). In the analysis with cytokine-responsive gene sets, both the IFN-I response and TNF/IL-1β–inflammatory response were prominent in the lung tissues (Fig. 6B). DEGs in the lung tissues were significantly associated with cluster 4, which is commonly up-regulated in both influenza and severe COVID-19, and cluster 5, which is specific to severe COVID-19 in Fig. 4B (Fig. 6C). These genes were also significantly associated with the cluster 1 identified in the trajectory analysis but not with cluster 3 (Fig. 6D). When gene sets were defined by DEGs between mild and severe COVID-19, the DEGs in postmortem lung tissues were significantly associated with genes up-regulated specifically in severe COVID-19 (Fig. 6E).

Fig. 6 Validation of the combined IFN-I and inflammatory responses in the transcriptome of postmortem lung tissues from lethal COVID-19.

(A) UCSC Genome Browser snapshots of representative genes. (B) Bar plot showing the average −log10(P value) values from the enrichment analysis using the perturbed genes of four different cell lines in L1000 LINCS for up-regulated genes (n = 386) in postmortem lung tissues compared with biopsied healthy lung tissue. Error bars indicate SD. (C) GSEA of significantly up- and down-regulated genes in postmortem lung tissues for gene sets originated from up-regulated genes in C2 (n = 96), C3 (n = 143), C4 (n = 218), and C5 (n = 30) of Fig. 4B. (D and E) GSEA of significantly up- and down-regulated genes in postmortem lung tissues for gene sets originated from the top 200 up-regulated genes in cluster 3 (left) and cluster 1 (right) from the trajectory analysis in Fig. 5C (D) and from gene sets originated from the top 200 up-regulated genes in classical monocytes of mild (left) and severe (right) COVID-19 (E).

DISCUSSION

Severe COVID-19 has been shown to be caused by a hyperinflammatory response (7). In particular, inflammatory cytokines secreted by classical monocytes and macrophages are considered to play a crucial role in severe progression of COVID-19 (26). In the current study, we confirmed the results from previous studies by showing that the TNF/IL-1β inflammatory response is dominant in COVID-19, although a small number of patients were enrolled. However, we also found that severe COVID-19 is accompanied by the IFN-I response in addition to the TNF/IL-1β response. These results indicate that the IFN-I response might contribute to the hyperinflammatory response by potentiating TNF/IL-1β–driven inflammation in severe progression of COVID-19.

In the current study, we carried out scRNA-seq using PBMCs instead of specimens from the site of infection, e.g., lung tissues or bronchoalveolar lavage (BAL) fluids. However, hierarchical clustering based on relative changes to the healthy donor group showed that all types of cells among PBMCs were clustered together according to the disease groups as shown in Fig. 2A, indicating that there is disease-specific global impact across all types of cells among PBMCs. This finding suggests that peripheral blood immune cells are influenced by common inflammatory mediators regardless of cell type. However, we could not examine granulocytes in the current study because we used PBMCs, not whole blood samples, for scRNA-seq.

In transcriptome studies for cytokine responses, we often analyze cytokine-responsive genes rather than cytokine genes themselves. However, we cannot exactly specify responsible cytokine(s) from the list of up-regulated genes because of overlapped effects of cytokines. For example, up-regulation of NF-κB–regulated genes can be driven by TNF, IL-1β, or other cytokines, and up-regulation of IFN-responsive genes can be driven by IFN-I or other IFNs. In the current study, we designated the IFN-I response because many up-regulated IFN-responsive genes were typical ISGs.

Recently, Wilk et al. (27) also performed scRNA-seq using PBMCs from patients with COVID-19 and healthy controls. Similar to our study, they found IFN-I–driven inflammatory signatures in monocytes from patients with COVID-19. However, they did not find substantial expression of proinflammatory cytokine genes such as TNF, IL6, IL1B, CCL3, CCL4, and CXCL2 in peripheral monocytes from patients with COVID-19, whereas we detected the up-regulation of TNF, IL1B, CCL3, CCL4, and CXCL2 in the current study. Moreover, they found a developing neutrophil population in patients with COVID-19 that was not detected in our study. These discrepant results might be due to different platforms for scRNA-seq. Wilk et al. (27) used the Seq-Well platform, whereas we used the 10× Genomics platform that is more generally used. We also note that recent scRNA-seq analyses of COVID-19 sometimes lead to unrelated or contradictory conclusions to each other despite the same platform (28, 29). Although it often occurs in unsupervised analysis of highly multidimensional data, more caution will be required in designing scRNA-seq analysis of COVID-19, including definition of the severity and sampling time points.

Recently, Blanco-Melo et al. (25) examined the transcriptional response to SARS-CoV-2 in in vitro–infected cells, infected ferrets, and postmortem lung samples from patients with lethal COVID-19 and reported that IFN-I and IFN-III responses are attenuated. However, we noted that the IFN-I signaling pathway and innate immune response genes were relatively up-regulated in postmortem lung samples from patients with lethal COVID-19 compared with SARS-CoV-2–infected ferrets in their paper. Given that SARS-CoV-2 induces only mild disease without severe progression in ferrets (30), we interpret that IFN-I response is up-regulated in severe COVID-19 (e.g., postmortem lung samples from patients with lethal COVID-19) but not in mild COVID-19 (e.g., SARS-CoV-2–infected ferrets). Severe COVID-19–specific signatures found in our current study were significantly enriched in the publically available data of postmortem lung tissues from the Blanco-Melo et al.’s study, although the analysis was limited to only two patients without individual cell type resolution (Fig. 6). In a recent study, Zhou et al. (31) also found a robust IFN-I response in addition to proinflammatory response in BAL fluid of patients with COVID-19. Moreover, up-regulation of IFN-I–responsive genes has been demonstrated in SARS-CoV-2–infected intestinal organoids (32).

Although IFN-I has direct antiviral activity, their immunopathological role was also reported previously (33). In particular, the detrimental role of the IFN-I response was elegantly demonstrated in a murine model of SARS (10). In SARS-CoV–infected BALB/c mice, the IFN-I response induced the accumulation of pathogenic inflammatory monocytes-macrophages and vascular leakage, leading to death. It was proposed that a delayed but considerable IFN-I response is critical for the development of acute respiratory distress syndrome and increased lethality during pathogenic coronavirus infection (6, 34).

Currently, the management of patients with severe COVID-19 relies on intensive care and mechanical ventilation without a specific treatment because the pathogenic mechanisms of severe COVID-19 have not yet been clearly elucidated. In the current study, we demonstrated that severe COVID-19 is characterized by TNF/IL-1β–inflammatory features combined with the IFN-I response. In a murine model of SARS-CoV infection, timing of the IFN-I response is a critical factor determining outcomes of infection (6, 10). Delayed IFN-I response contributes to pathological inflammation, whereas early IFN-I response controls viral replication. Therefore, we propose that anti-inflammatory strategies targeting not only inflammatory cytokines, including TNF, IL-1β, and IL-6, but also pathological IFN-I response need to be investigated for the treatment of patients with severe COVID-19.

MATERIALS AND METHODS

Patients

Patients diagnosed with COVID-19 were enrolled from Asan Medical Center, Severance Hospital, and Chungbuk National University Hospital. SARS-CoV-2 RNA was detected in patients’ nasopharyngeal swab and sputum specimens by multiplex real-time reverse-transcriptase PCR using the Allplex 2019-nCoV Assay kit (Seegene, Seoul, Republic of Korea). In this assay, N, RdRP, and E genes of SARS-CoV-2 were amplified, and Ct values were obtained for each gene. SARS-CoV-2–specific antibodies were examined using the SARS-CoV-2 Neutralization Antibody Detection kit (GenScript, Piscataway, NJ) and were positive in all patients with COVID-19 in convalescent plasma samples or the last plasma sample in a lethal case. Hospitalized patients diagnosed with influenza A virus infection by a rapid antigen test of a nasopharyngeal swab were also enrolled from Asan Medical Center and Chungbuk National University Hospital from December 2015 to April 2016, before the emergence of COVID-19. Patients’ clinical features, laboratory findings, and chest radiographs were collected from their electronic medical records at each hospital. This study protocol was reviewed and approved by the institutional review boards of all participating institutions. Written informed consent was obtained from all patients.

Single-cell RNA sequencing

PBMCs were isolated from peripheral venous blood via standard Ficoll-Paque (GE Healthcare, Uppsala, Sweden) density gradient centrifugation, frozen in freezing media, and stored in liquid nitrogen until use. All samples showed a high viability of about 90% on average after thawing. Single-cell RNA-seq libraries were generated using the Chromium Single Cell 3′ Library & Gel Bead Kit v3 (10× Genomics, Pleasanton, CA) following the manufacturer’s instructions. Briefly, thousands of cells were separated into nanoliter-scale droplets. In each droplet, complementary DNA (cDNA) was generated through reverse transcription. As a result, a cell barcoding sequence and UMI were added to each cDNA molecule. Libraries were constructed and sequenced as a depth of approximately 50,000 reads per cell using the Nextseq 550 or Novaseq 6000 platform (Illumina, San Diego, CA).

Single-cell RNA-seq data processing

The sequenced data were demultiplexed using mkfastq (Cell Ranger 10× Genomics, v3.0.2) to generate fastq files. After demultiplexing, the reads were aligned to the human reference genome (GRCh38; 10× Cell Ranger reference GRCh38 v3.0.0), feature-barcode matrices were generated using the cellranger count and then aggregated by cellranger aggr using default parameters. The following analysis was performed using Seurat R package v3.1.5 (17). After generating the feature-barcode matrix, we discarded cells that expressed <200 genes and genes not expressed in any cells. To exclude low-quality cells from our data, we filtered out the cells that express mitochondrial genes in >15% of their total gene expression as described in previous studies (29, 35, 36). Doublets were also excluded, which were dominant in the cluster “Uncategorized 1.” Although there was a high variability in the number of UMIs detected per cell, most of the cells (90.5%) were enriched in a reasonable range of the UMIs (1000 to 25,000), and 59% of cells with less than 1000 UMIs were platelet or RBC excluded in downstream analysis. In each cell, the gene expression was normalized on the basis of the total read count and log transformed. To align the cells originating from different samples, 2000 highly variable genes from each sample were identified by the vst method in Seurat R package v3.1.5 (17). Using the canonical correlation analysis, we found anchors and aligned the samples based on the top 15 canonical correlation vectors. The aligned samples were scaled, and principal components analysis (PCA) was conducted. Last, the cells were clustered by unsupervised clustering (0.5 resolution) and visualized by tSNE using the top 15 principal components (PCs).

Cell type annotation through marker gene identification in each cluster

To identify marker genes, up-regulated genes in each cluster relative to the other clusters were selected on the basis of the Wilcoxon rank sum test in Seurat’s implementation with >0.25 log fold change compared with the other clusters and a Bonferroni-adjusted P < 0.05 (table S4). By manual inspection, among the 22 different clusters, 20 were assigned to 11 known immune cell types, RBCs which are characterized by HBA1, HBA2, and HBB, and platelets. The clusters characterized by similar marker genes were manually combined as one cell type. The two remaining clusters were assigned to Uncategorized 1 and “Uncategorized 2” because they had no distinct features of known cell types. On the basis of the distribution of UMI counts, the cluster Uncategorized 1 was featured by relatively high UMIs per cell compared with other clusters and the presence of higher expression of multiple cell type marker genes. The cluster Uncategorized 2 was featured by a B cell–like signatures and high expression of ribosomal protein genes, not recommended to be further analyzed according to the 10× platform guideline. In these aspects, RBCs, platelets, Uncategorized 1, and Uncategorized 2 were excluded in downstream analysis.

Reproducibility of biological replicates

To check the reproducibility of biological replicates (individuals within a same group), we calculated the Spearman’s rank correlation coefficient for UMI counts that were merged according to each individual. The correlation coefficients of all individual pairs within the same group were visualized by a boxplot (COVID-19, n = 45; FLU, n = 10; HD, n = 6).

Hierarchical clustering of the transcriptomes at cell type resolution

In fig. S1E, to investigate the similarity of the transcriptomes between cell types across diseases, we merged the UMI counts of each cell type according to healthy donor, influenza, mild COVID-19, and severe COVID-19. Next, the UMI counts for each gene were divided by the total UMI count in each cell type and multiplied by 100,000 as the normalized gene expression. On the basis of a median expression value of >0.5, we calculated the relative changes in gene expression divided by the median value for each gene. Hierarchical clustering analysis was performed on the basis of the Pearson correlation coefficient (PCC) of the relative change in gene expression.

Hierarchical clustering of variable gene expression among disease groups at cell type resolution

In Fig. 2A and fig. S2A, to compare the highly variable gene expression among mild and severe COVID-19 and influenza relative to healthy donors, the normalized gene expression used in fig. S1E was divided by the values in the healthy donor group. We selected the highly variable genes in terms of the top 25% SD followed by log2 transformation (pseudocount = 1). In Fig. 2A, hierarchical clustering analysis was performed on the basis of the PCCs of the selected highly variable genes. For fig. S2A, to investigate the expression patterns of the selected highly variable genes (n = 6052), K-means clustering (k = 50) was performed on the basis of Euclidean distance. We manually ordered the clusters and visualized them as a heatmap, revealing four distinct patterns: influenza specific (n = 1046), COVID-19 specific (n = 1215), influenza/COVID-19 common (n = 1483), and cell type specific (n = 2308).

Analysis of dynamic changes in cell type composition compared with healthy donors

To investigate the dynamic changes in cell type composition, we calculated the proportion of cell types in each individual. As a control, we calculated the relative variation in each cell type composition between all pairs of healthy donors. Similarly, for each disease group, we calculated the relative variation in each cell type by dividing the fraction of the cell type in individual patient by that of individual healthy donor. After log2 transformation, we conducted statistical analysis using the relative variation in composition between the control and disease groups using a two-sided Kolmogorov-Smirnov (KS) test.

Identification of DEGs using MAST

For any two transcriptome profiles, to identify DEGs, we used the MAST algorithm in Seurat’s implementation based on a Bonferroni-adjusted P < 0.05 and a log2 fold change > 0.25.

GO analysis for biological pathways

In Fig. 2B, the DEGs in COVID-19 and influenza compared with healthy donors or COVID-19 compared with influenza were identified at cell type resolution. All DEGs were combined according to the disease groups for further analysis. The overlapping up- or down-regulated DEGs between COVID-19 and influenza compared with healthy donors were defined as “common up” or “common down.” The specific DEGs in COVID-19 or influenza were assigned as “COVID-19 up/down” or “FLU up/down,” respectively. In addition, COVID-19–specific up- or down-regulated genes compared with influenza were assigned as “COVID-19 > FLU” or “FLU > COVID-19,” respectively. The GO analysis was performed by DAVID. For each group of DEGs, the top 10 enriched GO biological pathways were selected, resulting in 49 unique GO biological pathways across all groups. The −log10(P values) are shown as a heatmap in Fig. 2B.

WGCNA analysis to identify modular gene expression patterns

The WGCNA was conducted with the genes listed in the top 10 GO biological pathways of “COVID-19 up,” “FLU up,” and Common up defined in Fig. 2B. The normalized gene expression values of the genes in COVID-19 were divided by the values in influenza and log2 transformed (pseudocount = 1). We used default parameters with the exception of soft threshold = 10 and networkType = “signed” when we constructed a topological overlap matrix. The modular gene expression patterns were defined using cutreeDynamic with a minClusterSize of 5. We visualized the modular gene expression pattern as a heatmap in which the cell types were ordered according to hierarchical clustering with the default parameters of hcluster in R.

Subclustering analysis

To find disease-specific subpopulations, each immune cell type was subjected to the subclustering analysis using Seurat. Briefly, the highly variable genes (n = 1000) were selected on the basis of vst and then scaled by ScaleData in Seurat with the vars.to.regress option to eliminate variation between individuals. The subpopulations were identified by FindClusters with default parameters, except resolution (non–EM-like CD8+ T cells, 0.3; classical monocytes, 0.2); the inputs were the top eight PCs obtained from PCA of the scaled expression of the highly variable genes. The subpopulations were visualized by tSNE using the top eight PCs.

Trajectory analysis

The trajectory analysis was performed with 2000 highly variable genes in classical monocytes across mild (C7-2) and severe (C7-1) COVID-19 as defined by the vst method in Seurat. The following analysis was performed using Monocle2. Briefly, the input was created from the UMI count matrix of the highly variable genes using the newCellDataSet function with default parameters, except expressionFamily = “negbinomial.size”. The size factors and dispersion of gene expression were estimated. The dimension of the normalized data was reduced on the basis of DDRTree using reduceDimension with default parameters, except scaling = FALSE, which aligned the cells to the trajectory with three distinct clusters.

To determine genes that gradually changed along the trajectory, we identified the DEGs using MAST between clusters 1 and 3, which represent the severe stage and mild stage, respectively. The expression patterns of representative DEGs were visualized along the pseudotime after correction with estimated size factors and dispersion for all genes.

K-means clustering analysis of monocytes

In Fig. 4B, we performed K-means clustering of DEGs among all pairs of mild COVID-19, severe COVID-19, and influenza. The log2-transformed relative gene expression of DEGs compared with healthy donors was subjected to K-means clustering (k = 10). Here, we used up-regulated DEGs in at least one disease group compared with the healthy donor group. We manually assigned five clusters based on gene expression patterns.

Data analysis of the transcriptome profiles of postmortem lung tissues

The transcriptome profiles of postmortem lung tissues from two lethal cases of COVID-19 and biopsied heathy lung tissues from two donors were downloaded from a public database (GSE147507). The DEGs were identified using DESeq2 based on a Bonferroni-adjusted P < s0.05 and a log2 fold change > 1.

Enrichment analysis using Enrichr and GSEA 4.0.3

Enrichr, the web-based software for GSEA was used for LINCS L1000 ligand perturbation analysis (22), virus perturbation analysis, and disease perturbation analysis from the GEO database. “Combined score” was calculated as a parameter of enrichment as the log(P value) multiplied by the z-score from the Fisher’s exact test. GSEA 4.0.3 software was used to conduct the GSEA when a ranked list of genes was available (Figs. 5G and 6, C to E) (37). Results for IFN-γ–responsive genes were not presented because those were considerably overlapped with IFN-α–responsive genes, which are typical ISGs. The normalized enrichment score and false discovery rate (FDR) q value were calculated to present the degree and significance of enrichment.

Flow cytometry analysis

Cryopreserved PBMCs were thawed, and dead cells were stained using the Live/Dead Fixable Cell Stain kit (Invitrogen, Carlsbad, CA). Cells were stained with fluorochrome-conjugated antibodies, including anti-CD3 (BV605; BD Biosciences), anti-CD4 (BV510; BD Biosciences), anti-CD8 (BV421; BD Biosciences), anti-CD14 (PE-Cy7; BD Biosciences), anti-CD19 (Alexa Fluor 700; BD Biosciences), and anti-CD56 (VioBright FITC; Miltenyi Biotec). For staining with anti–granzyme B (BD Biosciences), cells were permeabilized using a Foxp3 staining buffer kit (eBioscience).

For intracellular cytokine staining of IFN-γ, PBMCs were stimulated with PMA (50 ng/ml) (Sigma-Aldrich) and ionomycin (1 μg/ml) (Sigma-Aldrich). Brefeldin A (GolgiPlug, BD Biosciences) and monesin (GolgiStop, BD Biosciences) were added 1 hour later. After another 5 hours of incubation, cells were harvested for staining with the Live/Dead Fixable Cell Stain kit, anti-CD3, anti-CD4, and anti-CD8. After cell permeabilization, cells were further stained with anti–IFN-γ (Alexa Fluor 488; eBioscience). Flow cytometry was performed on an LSR II instrument using FACSDiva software (BD Biosciences), and the data were analyzed using FlowJo software (Treestar, San Carlos, CA).

Enzyme-linked immunosorbent assay and cytometric bead arrays

Cytokines were measured in plasma samples, including IFN-β, IL-18 (enzyme-linked immunosorbent assay, R&D Systems, Minneapolis, MN), IL-1β (Cytometric bead array flex kit, BD Biosciences, San Jose, CA), TNF, IL-6, and IFN-γ (LEGENDplex bead-based immunoassay kit, BioLegend, San Diego, CA).

Statistical analysis

We performed the KS test to compare the distributions of two groups without assuming that the distributions follow normality. Welch’s t test was conducted to compare the two distributions after confirming the normality of the distributions using the Shapiro-Wilk normality test. A Wilcoxon signed rank test was conducted to compare the differences between two groups with paired subjects. The Mann-Whitney test was performed to compare the means of two groups. Statistical analyses were performed using Prism software version 5.0 (GraphPad, La Jolla, CA). P < 0.05 was considered significant.

SUPPLEMENTARY MATERIALS

immunology.sciencemag.org/cgi/content/full/5/49/eabd1554/DC1

Fig. S1. Clinical characteristics and assessment of the quality of scRNA-seq results.

Fig. S2. Transcriptome features of highly variable genes.

Fig. S3. Characterization of disease-specific CD8+ T cell subpopulations.

Fig. S4. Subpopulation analysis of classical monocytes.

Fig. S5. Serum cytokine concentration of COVID-19 patients (A), and STRING analysis of up-regulated genes in cluster 1 obtained from the trajectory analysis of classical monocytes (B).

Table S1. Experimental batches of scRNA-seq.

Table S2. Clinical characteristics of patients with severe influenza.

Table S3. Clinical characteristics of patients with COVID-19.

Table S4. The scRNA-seq results.

Table S5. A list of marker genes for each cluster.

Table S6. A list of DEGs and associated biological pathways in Fig. 2B.

Table S7. Cell types in which the GBP1, CREM, and CCL3 were up-regulated in Fig. 2C.

Table S8. A list of genes in each module obtained from WGCNA in Fig. 2D.

Table S9. A list of up-regulated genes in non–EM-like CD8+ T cell subpopulations.

Table S10. A list of genes included in each cluster defined by K-mean clustering of classical monocytes.

Table S11. A list of genes up-regulated in early and late pseudotime.

https://creativecommons.org/licenses/by/4.0/

This is an open-access article distributed under the terms of the Creative Commons Attribution license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

REFERENCES AND NOTES

Acknowledgments: We thank our laboratory members for support and critical suggestions throughout the course of this work. Funding: This work was funded by Samsung Science and Technology Foundation under Project Number SSTF-BA1402-51 (to E.-C.S.) and by the SUHF Fellowship (to I.J.). Author contributions: Conceptualization: J.S.L., I.J., and E.-C.S. Methodology: J.S.L., S.P., B.C., S.K.N., and I.J. Investigation: S.J.C., H.L., M.S., and J.-S.K. Resources: H.W.J., J.Y.A., S.J.J., J.Y.C., and S.-H.K. Writing: J.S.L., S.P., I.J., and E.-C.S. Review and editing: H.W.J., J.Y.A., S.J.J., H.K.L., S.-H.P., S.H.P., J.Y.C., and S.-H.K. Supervision: J.Y.C., S.-H.K., I.J., and E.-C.S. Funding: I.J. and E.-C.S. Competing interests: The authors declare that they have no competing financial interests. Data and materials availability: Correspondence and requests for materials should be addressed to J.Y.C. (seran{at}yuhs.ac), S.-H.K. (shkimmd{at}amc.seoul.kr), I.J. (ijung{at}kaist.ac.kr), or E.-C.S. (ecshin{at}kaist.ac.kr). All raw and processed data have been deposited in the GEO database under accession number GSE149689. For all data analyses, we used publicly available software. This work is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. This license does not apply to figures/photos/artwork or other content included in the article that is credited to a third party; obtain authorization from the rights holder before using such material.
View Abstract

Stay Connected to Science Immunology

Navigate This Article