A Single-Cell Atlas of Lymphocyte Adaptive Immune Repertoires and Transcriptomes Reveals Age-Related Differences in Convalescent COVID-19 Patients

Abstract

COVID-19 disease outcome is highly dependent on adaptive immunity from T and B lymphocytes, which play a critical role in the control, clearance and long-term protection against SARS-CoV-2. To date, there is limited knowledge on the composition of the T and B cell immune receptor repertoires [T cell receptors (TCRs) and B cell receptors (BCRs)] and transcriptomes in convalescent COVID-19 patients of different age groups. Here, we utilize single-cell sequencing (scSeq) of lymphocyte immune repertoires and transcriptomes to quantitatively profile the adaptive immune response in COVID-19 patients of varying age. We discovered highly expanded T and B cells in multiple patients, with the most expanded clonotypes coming from the effector CD8⁺ T cell population. Highly expanded CD8⁺ and CD4⁺ T cell clones show elevated markers of cytotoxicity (CD8: PRF1, GZMH, GNLY; CD4: GZMA), whereas clonally expanded B cells show markers of transition into the plasma cell state and activation across patients. By comparing young and old convalescent COVID-19 patients (mean ages = 31 and 66.8 years, respectively), we found that clonally expanded B cells in young patients were predominantly of the IgA isotype and their BCRs had incurred higher levels of somatic hypermutation than elderly patients. In conclusion, our scSeq analysis defines the adaptive immune repertoire and transcriptome in convalescent COVID-19 patients and shows important age-related differences implicated in immunity against SARS-CoV-2.

Keywords: B cell; COVID-19; SARS-CoV-2; T cell; VDJ repertoire; antibody; single-cell.

Figure 1

Overview of single-cell transcriptome and immune receptor profiling of convalescent COVID-19 patient lymphocytes. Convalescent COVID-19 patients enrolled in the SERO-BL-COVID-19 study were selected according to their age for single-cell sequencing analysis of their T cells and B cells. (A) Timeline illustrates symptom onset, symptom resolution and collection of blood samples from individual patients relative to the time of positive SARS-CoV-2 RT-PCR test (day 0). (B) Graph displays the ages and duration of COVID-19 symptoms in individual patients. Dotted lines show the mean duration of symptoms in the young (y = 6.75 days) and old (y = 12.25 days) groups. A significant difference in symptom duration between groups is indicated with an asterisk (p = 0.0127; unpaired t-test). (C) Single-cell sequencing protocol. Whole blood was collected following the resolution of COVID-19 symptoms and subjected to density gradient separation for isolation of PBMC. T cells and B cells from individual patients were purified from PBMC using negative immunomagnetic enrichment, pooled (intra-patient) and prepared for droplet generation using the 10x Genomics Chromium system. Single cells were emulsified with DNA-barcoded gel beads and mRNA transcripts were reverse-transcribed within droplets, resulting in the generation of first-strand cDNA molecules labelled with cell-specific barcodes at their 3’ ends (added by template switching). Emulsions were disrupted and cDNA was amplified by means of PCR for further processing of transcriptome libraries. Transcriptome libraries from individual patients were indexed and multiplexed for deep sequencing using the Illumina NovaSeq platform. Targeted enrichment of recombined V(D)J transcripts was performed by PCR and the resulting products were processed for the generation of BCR and TCR libraries, which were then indexed, multiplexed and deep-sequenced.

Figure 2

Single-cell transcriptomic analysis delineates major T and B cell subsets. (A–C), Uniform manifold approximation and projection (UMAP) plots of major cellular subsets identified within the CD8+ T cell (A), CD4+ T cell (B) and B cell (C) populations. Cells from all patients are displayed in each plot.9 (D–F), UMAP plots showing the expression levels of selected genes used to delineate cellular subsets within the CD8+ T cell (D) CD4+ T cell (E) and B cell (F) populations. Cells from all patients are displayed in each plot. g-i, Graphs display pseudotime and trajectory inference analysis applied to CD8+T cell (G), CD4+ T cell (H) and B cell (I) clusters. (J–L), Bar graphs show the proportions of identified cellular subsets within the CD8+ T cell (J), CD4+ T cell (K) and B cell (L) populations in each patient. CCL5, C-C Motif Chemokine Ligand 5; CD27, TNFRSF7; CD40LG, CD40 ligand; FCER2, Fc Fragment of IgE Receptor II (also: CD23a); FCRL3, Fc Receptor Like 3; FOXP3, Forkhead Box Protein P3; GZMB, Granzyme B; GZMK, Granzyme K; IL7R, Interleukin-7 Receptor; LEF1, Lymphoid Enhancer Binding Factor 1; NKG7 , Natural Killer Cell Granule Protein 7; S100A4, S100 Calcium Binding Protein A4; SELL, Selectin L; TCF7, Transcription Factor 7; TNFRSF13B, TNF Receptor Superfamily Member 13B.

Figure 3

Single-cell profiling of immune repertoires highlights differential levels of inter-patient T cell and B cell clonal expansion. (A, B), Analysis of T cell clonal expansion in convalescent COVID-19 patients. (A) Bar graphs show T cell clonal expansion, as determined by the number of cells identified per TCR clonotype. Each box represents the size of individual TCR clonotypes. TCR clonotypes present in more than one cell are shown. (B) Circos plots display V-gene usage in the top ten most expanded TCR clonotypes for each patient. The size and colour (dark to light) of outer bars reflect the relative abundance of T cells expressing specific V-genes on a per class basis (top: TCRα chain, bottom: TCRβ chain). (C–E), Analysis of B cell clonal expansion in convalescent COVID-19 patients. (C) Bar graphs show B cell clonal expansion, as determined by the number of cells identified per BCR clonotype. Each box represents the size of individual BCR clonotypes. BCR clonotypes present in more than one cell are shown. (D) Circos plots display V-gene usage in the top ten most expanded BCR clonotypes for each patient. The size and colour (dark to light) of outer bars reflect the relative abundance of B cells expressing specific V-genes on a per class basis (top: Ig light chain, bottom: Ig heavy chain). (E) Graph displays the levels of somatic hypermutation (SHM) in unexpanded (1 cell), expanded (2-4 cells) and highly expanded (5 cells) BCR clonotypes across patients. SHM levels are based on the percentage similarity between Ig heavy chain V-genes and their corresponding germlines. Data are displayed as median ± IQR. (F), Graph displays SHM levels in highly expanded BCR clones (≥5 cells) of old (n = 24 clones) and young (n = 29 clones) patients. Asterisks indicate a significant difference in SHM levels between groups (p = 0.0085; unpaired t-test). Data are displayed as median ± IQR. (G), Bar graphs show the Ig isotype distribution in unexpanded (1 cell), expanded (2-4 cells) and highly expanded (≥5 cells) BCR clonotypes across patients.

Figure 4

Single-cell transcriptome and TCR sequencing reveals preferential clonal expansion in effector T cells. (A, B), UMAP plots display CD8+ (A) and CD4+ (B) T cells from specific patients according to their clonal expansion levels. T cells from other patients in each individual plot are shown in grey. (C, D), Heatmaps show differential gene expression (DGE) in unexpanded, expanded and highly expanded CD8+ (C) or CD4+ (D) T cells. Genes were filtered to include those with detectable expression in at least 50% of cells and that had a minimum 50% fold-change in expression level between groups.

Figure 5

Single-cell transcriptome and BCR profiling reveals elevated class-switching and somatic hypermutation levels in memory B cells. (A) UMAP plots display B cells from specific patients according to their clonal expansion levels. B cells from other patients in each individual plot are shown in grey. (B) Heatmap shows differential gene expression (DGE) in unexpanded, expanded and highly expanded B cells. Genes were filtered to include those with detectable expression in at least 50% of cells and that had a minimum 50% fold-change in expression level between groups. (C) Graph displays the levels of somatic hypermutation (SHM) in the BCRs of naïve, activated and memory B cells across patients. SHM levels are based on the percentage similarity between BCR heavy chain V-gene and its corresponding germline. Data are displayed as median ± IQR. (D) Graph shows the distribution of B cells expressing specific Ig isotypes relative to their location in transcriptome UMAP plots. B cells from all patients are shown. (E) Bar graphs show Ig isotype distribution of BCRs found in naïve, activated and memory B cells across patients. CALR, Calreticulin; CD74, HLA class II Histocompatibility Antigen Gamma Chain; DUSP1, Dual Specificity Phosphatase 1; HLA-D, Major Histocompatibility Complex, Class II; HSP90B1, Heat Shock Protein 90 Beta Family Member 1; PPIB, Peptidylprolyl Isomerase B; VIM, Vimentin.

Figure 6

GLIPH2 analysis of single-cell paired TCR repertoires reveals candidate SARS-CoV-2-specific TCRs. (A) Heatmap shows the proportion of TCR specificity groups containing sequences from specific pairs of patients, as determined by GLIPH2 analysis (total TCR clusters = 552). (B) Bar plot displays the proportions of predicted HLA class I alleles in HLA-attributed TCR specificity groups (total TCR clusters = 552). (C) Graph displays the proportions and numbers of candidate SARS-CoV-2-specific TCRs derived from HLA-A*0201-positive patients, as determined by GLIPH2 clustering with known SARS-CoV-2-specific TCR sequences.