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. 2021 May;11(5):e430.
doi: 10.1002/ctm2.430.

Identification of potential vaccine targets for COVID-19 by combining single-cell and bulk TCR sequencing

Pingping Wang  1 , Zhaochun Xu  1 , Wenyang Zhou  1 , Xiyun Jin  1 , Chang Xu  1 , Meng Luo  1 , Kexin Ma  1 , Huimin Cao  1 , Yan Huang  1 , Xiaoyu Lin  1 , Fenglan Pang  1 , Yiqun Li  1 , Qinghua Jiang  1   2
Affiliations

Identification of potential vaccine targets for COVID-19 by combining single-cell and bulk TCR sequencing

Pingping Wang et al. Clin Transl Med. 2021 May.
No abstract available

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

FIGURE 1
FIGURE 1
Single cell transcriptome profiling of T cells of patients with COVID‐19 and controls. (A) An overview of experimental design. PBMCs from 16 patients with COVID‐19 were divided to perform scRNA‐seq, scTCR‐seq, deep TCR‐seq and HLA genotyping. (B) UMAP plot of T cells from patients with COVID‐19 and controls. Clustering was based on unsupervised k‐means using the normalized gene expression values after batch effect removal. (C) Bar plots showing the distribution of T cell types in patients with COVID‐19 and healthy controls. (D) Dot plot shows the average log‐normalized gene expression of marker genes for cell types discussed in the main text. The size of the dot represents the percentage of cells that express the gene in each cluster, and the color represents the average level of expression after scaling
FIGURE 2
FIGURE 2
Clonally expanded T cells in COVID‐19 patients. (A) Clonal distribution of T cell receptors in COVID‐19 patients. (B) UMAP plot shows the distribution of clonally expanded T cells. (C) Residual plot for the Pearson's chi‐squared test of clone expansion from cell types, using the corrplot package in R. Red circles indicate an overrepresentation, and blue circles indicate an underrepresentation. A Pearson's chi‐squared test shows this difference is statistically significant (χ 2 = 17605, df = 24, simulated P < 2.2E‐16). (D) Bar plot shows the distribution of clonotypes by size (NA = 1, ≥ 2, ≥ 10 ,and ≥ 20 cells, NA represents cells with no αβTCR sequence detected). (E) Pie charts show the cell type composition of clonotypes from each sample stratified by clone size
FIGURE 3
FIGURE 3
Antigen‐specific T cells in COVID‐19 patients. (A) Comparison of D50 TCR diversity between patients with COVID‐19 and healthy controls. Low D50 value indicates low diversity and high clonal expansion. (B) Heatmap plot for clustering results of shared TCR groups among all samples. Each entry of the pairwise sharing matrix documents the number of shared TCR groups between two individuals. Unsupervised hierarchical clustering was applied to organize the columns and rows of the matrix. (C) The distribution of T cells of 916 COVID‐19 TCR groups overlaid on top of all the single cell data in the UMAP plot of T cells. (D) Residual plot for the Pearson's chi‐squared test of COVID‐19 specific TCR groups from cell types, using the corrplot package in R. Red circles indicate an overrepresentation, and blue circles indicate an underrepresentation. A Pearson's chi‐squared test shows this difference is statistically significant (χ 2 = 2658.5, df = 12, simulated P < 2.2E‐16)
FIGURE 4
FIGURE 4
Identification of clonally expanded TCR groups and potential virus epitopes. (A) Diagram showing perfectly matched TCR groups and peptides presented by MHC I alleles of COVID‐19 patients (FDR < 0.001). (B) The distribution of MHC I presentation regions in each protein from the SARS‐CoV‐2 genome. Peptides computationally predicted to bind MHC‐I with high affinity are colored as dark blue. Those peptides presented by MHC‐I with high affinity, significantly cooccurring with a COVID‐19 TCR group are colored as red
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