Meta-analysis of single-cell RNA-seq data reveals phenotypic switching of immune cells in severe COVID-19 patients

Abstract

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has resulted in the global coronavirus disease 2019 (COVID-19) pandemic. Despite several single-cell RNA sequencing (RNA-seq) studies, conclusions cannot be reached owing to the small number of available samples and the differences in technology and tissue types used in the studies. To better understand the cellular landscape and disease severity in COVID-19, we performed a meta-analysis of publicly available single-cell RNA-seq data from peripheral blood and lung samples of COVID-19 patients with varying degrees of severity. Patients with severe disease showed increased numbers of M1 macrophages in lung tissue, while the number of M2 macrophages was depleted. Cellular profiling of the peripheral blood showed a marked increase of CD14⁺, CD16⁺ monocytes and a concomitant depletion of overall B cells and CD4⁺, CD8⁺ T cells in severe patients when compared with moderate patients. Our analysis indicates the presence of faulty innate-to-adaptive switching, marked by a prolonged innate immune response and a dysregulated adaptive immune response in severe COVID-19 patients. Furthermore, we identified cell types with a transcriptome signature that can be used as a prognostic biomarker for disease state prediction and the effective therapeutic management of COVID-19 patients.

Keywords: COVID-19 patients; Immune cell landscape; Meta-analysis; Phenotypic switching; Single-cell RNA-Seq.

Graphical abstract

Fig. 1

Schematic flow diagram of the dataset curation and meta-analysis process. N denotes the number of GEO datasets, n denotes the number of patients.

Fig. 2

Cross-sectional analysis of single-cell RNA-seq blood datasets. (A) Uniform manifold approximation and projection (UMAP) of blood cells obtained from control, moderate, severe, and deceased subjects. Cells were clustered based on similar gene expression levels and colored by cell type. (B) Stacked bar chart showing the cell type proportion based on disease severity. Significantly altered cell types (CD4⁺ T cells, CD14⁺ monocytes, CD16⁺ monocytes) are presented in separate graphs along with their respective numbers in the different COVID-19 severities.

Fig. 3

Cross-sectional analysis of single-cell RNA-seq lung datasets. (A) Uniform manifold approximation and projection (UMAP) of lung cells obtained from control, moderate, severe, and deceased subjects. Cells were clustered based on similar gene expression levels and colored by cell type. (B) Stacked bar chart showing the cell type proportion based on disease severity. Significantly altered cell types (airway secretory cells, M1 macrophages, M2 macrophages) are presented in separate graphs along with their respective numbers in the different COVID-19 severities.

Fig. 4

Hierarchical clustering of immune cells in blood based on gene set enrichment analysis. (A) Hierarchical clustering of the regulated pathways of significantly modulated genes in CD4⁺ T cells from different disease states. (B) Hierarchical clustering of the regulated pathways of significantly modulated genes in CD8⁺ T cells from different disease states. (C) Hierarchical clustering of the regulated pathways of significantly modulated genes in CD14⁺ monocytes from different disease states. (D) Hierarchical clustering of the regulated pathways of significantly modulated genes in CD16⁺ monocytes from different disease states. The dendrograms are colored according to the p values; gray cells indicate a lack of significant enrichment.

Fig. 5

Hierarchical clustering of lung cells based on gene set enrichment analysis. (A) Hierarchical clustering of the regulated pathways of significantly modulated genes in airway secretory cells from different disease states. (B) Hierarchical clustering of the regulated pathways of significantly modulated genes in M1 macrophages from different disease states. (C) Hierarchical clustering of the regulated pathways of significantly modulated genes in M2 macrophages from different disease states. (D) Hierarchical clustering of the regulated pathways of significantly modulated genes in macrophages from different disease states. The dendrograms are colored according to the p values; gray cells indicate a lack of significant enrichment.

Fig. 6

Principal component analysis (PCA) and heatmap of blood immune cells. (A) PCA and heatmap of significantly modulated genes along with their expression in CD4⁺ T cells from different disease states. (B) PCA and heatmap of significantly modulated genes along with their expression in CD8⁺ T cells from different disease states. (C) PCA and heatmap of significantly modulated genes along with their expression in CD14⁺ monocytes from different disease states. (D) PCA and heatmap of significantly modulated genes along with their expression in CD16⁺ monocytes from different disease states. The heatmaps are colored according to the gene expression values.

Fig. 7

Principal component analysis (PCA) and heatmap of lung cells. (A) PCA and heatmap of significantly modulated genes along with their expression in airway secretory cells from different disease states. (B) PCA and heatmap of significantly modulated genes along with their expression in M1 macrophages from different disease states. (C) PCA and heatmap of significantly modulated genes along with their expression in M2 macrophages from different disease states. (D) PCA and heatmap of significantly modulated genes along with their expression in macrophages from different disease states. The heatmaps are colored according to the gene expression values.