Single-cell multiomics revealed the dynamics of antigen presentation, immune response and T cell activation in the COVID-19 positive and recovered individuals

Abstract

Introduction: Despite numerous efforts to describe COVID-19's immunological landscape, there is still a gap in our understanding of the virus's infections after-effects, especially in the recovered patients. This would be important to understand as we now have huge number of global populations infected by the SARS-CoV-2 as well as variables inclusive of VOCs, reinfections, and vaccination breakthroughs. Furthermore, single-cell transcriptome alone is often insufficient to understand the complex human host immune landscape underlying differential disease severity and clinical outcome.

Methods: By combining single-cell multi-omics (Whole Transcriptome Analysis plus Antibody-seq) and machine learning-based analysis, we aim to better understand the functional aspects of cellular and immunological heterogeneity in the COVID-19 positive, recovered and the healthy individuals.

Results: Based on single-cell transcriptome and surface marker study of 163,197 cells (124,726 cells after data QC) from the 33 individuals (healthy=4, COVID-19 positive=16, and COVID-19 recovered=13), we observed a reduced MHC Class-I-mediated antigen presentation and dysregulated MHC Class-II-mediated antigen presentation in the COVID-19 patients, with restoration of the process in the recovered individuals. B-cell maturation process was also impaired in the positive and the recovered individuals. Importantly, we discovered that a subset of the naive T-cells from the healthy individuals were absent from the recovered individuals, suggesting a post-infection inflammatory stage. Both COVID-19 positive patients and the recovered individuals exhibited a CD40-CD40LG-mediated inflammatory response in the monocytes and T-cell subsets. T-cells, NK-cells, and monocyte-mediated elevation of immunological, stress and antiviral responses were also seen in the COVID-19 positive and the recovered individuals, along with an abnormal T-cell activation, inflammatory response, and faster cellular transition of T cell subtypes in the COVID-19 patients. Importantly, above immune findings were used for a Bayesian network model, which significantly revealed FOS, CXCL8, IL1β, CST3, PSAP, CD45 and CD74 as COVID-19 severity predictors.

Discussion: In conclusion, COVID-19 recovered individuals exhibited a hyper-activated inflammatory response with the loss of B cell maturation, suggesting an impeded post-infection stage, necessitating further research to delineate the dynamic immune response associated with the COVID-19. To our knowledge this is first multi-omic study trying to understand the differential and dynamic immune response underlying the sample subtypes.

Keywords: COVID-19; T-cell activation; bayesian network model; immune response; recovered COVID-19 individuals; single cell multi-omics.

Figure 1

Cellular Heterogeneity across Healthy, Infected and Recovered COVID-19 Individuals. (A) Sample distribution and schematic workflow for the scRNA-seq, followed by analysis for the cellular heterogeneity and differential expression. (B) UMAP visualization of the 124726 cells across the healthy, active COVID-19 and the recovered individuals. (C) Frequency of cell types across the three groups, normalized to the total number of cells. (D, E) Cell type specific expression of (D) Surface markers, and (E) RNA level. Color scale denotes the relative expression whereas circle size denotes the percent of cells expressing the marker. (F) Expression of cell type specific markers across the three groups. (G) UMAP visualization of healthy vs recovered comparison group showing no batch effect between the two groups, the box highlights the cluster absent in the recovered individuals. (H) UMAP visualization of healthy vs recovered comparison group with cell type annotation, the box highlights the unidentified cluster. (I) UMAP visualization of the unidentified cluster after machine learning-based cell type annotation. (J) GSEA of differentially expressed genes between novel subset of the Naive CD4+ T cell and existing Naive CD4+/CD8+ T cells.

Figure 2

CD40-CD40LG mediated Inflammatory response in the COVID-19 patients. (A) CD40-CD40LG expression at surface level across the three groups of COVID-19 active infection, healthy and the recovered individuals. Color scale denotes the relative expression whereas circle size denotes the percentage of cells expressing the marker. (B, C) Cell- type specific surface expression of CD40-CD40LG across the 3 groups. (D) Surface level expression of FAS in the B and T cells. (E, F) Chemokine and Chemokine receptor expression at the surface level across the three groups. (G–J) RNA level expression of (G) Cytokines, (H) Chemokines, (I) Interleukins and (J) TNF Receptor Superfamily across all the cell types. (K–M) PPI level interaction network at (K) Activated CD4+ T cells, (L) CD8+ TCM, and (M) monocytes. [NS represents non-significant, *represents p-value < 0.05, **represents p-value < 0.01, **** represents p-value < 0.0001].

Figure 3

Immune, stress and antiviral Response during the SARS-CoV-2 infection. (A) G SEA at the single cell resolution across the three groups (COVID-19 patients, healthy and recovered), the row dendrograms distinguish the immune and stress response pathways. The cell types and the groups were highlighted using different color bars. Data is expressed as a relative enrichment score for each pathway. (B, C) Expression of (B) ADAM, and (C) APOBEC3 genes. (D) Expression of antiviral genes in the COVID-19 patients and correlation with HRCT score. (E) Expression of IFN family genes across all the cell types across the three groups. Cell types and groups were highlighted using different color bars. (F) Cumulative expression of type I and type II IFN receptors between the Classical monocyte and CD4+ TCM across the three groups.

Figure 4

T Cell specific surface marker expression and Pseudotime analysis across the healthy, COVID-19 positive and recovered individuals. (A) Expression of the T cell activation markers at the surface marker level. (B) Expression of T cell exhaustion markers at the surface marker level. (C) Expression of TCR αβ and γδ chain at the surface marker level. Color scale denotes the relative expression whereas circle size denotes the percentage of cells expressing the marker. (D) UMAP visualization of T cell subtypes. (E) UMAP visualization of T cell subtypes with respect to pseudotime. (F–H) Distribution of cells against pseudotime for T cell subtypes across (F) healthy, (G) COVID-19 positive, and (H) recovered individuals. (I–L) GO enrichment of genes from (I) Module 1, (J) Module 2, (K) Module 3, and (L) Module 4.

Figure 5

Similarity Network Fusion and Bayesian Network Model for Biomarker discovery. (A) SNF clustering of cells based on the gene expression, surface marker expression and clinical details of the individuals. (B) Distribution of the identified cell types across the healthy, COVID-19 positive and recovered individuals. (C) Bayesian Network Model built on the SNF clusters, gene expression, surface marker expression, and clinical data including the HRCT score of the individuals. (D–F) Specific highlights from the Bayesian Network Model showing the association of HRCT score with (D) FOS, CST3, PSAP, (E) CD74, and (F) CD45.

Figure 6

Summary of the key findings from the study. It highlights the observed T-cell dynamics within the COVID-19 patients, recovered individuals and healthy, as well as key immune findings harnessing strength of machine learning.