ABSTRACT
T cell-B cell interaction is the key immune response to protect the host from severe viral infection. However, how T cells support B cells to exert protective humoral immunity in humans is not well understood. Here, we use COVID-19 as a model of acute viral infections and analyze CD4+ T cell subsets associated with plasmablast expansion and clinical outcome. Peripheral helper T cells (Tph cells; denoted as PD-1highCXCR5-CD4+ T cells) are significantly increased, as are plasmablasts. Tph cells exhibit "B cell help" signatures and induce plasmablast differentiation in vitro. Interestingly, expanded plasmablasts show increased CXCR3 expression, which is positively correlated with higher frequency of activated Tph cells and better clinical outcome. Mechanistically, Tph cells help B cell differentiation and produce more interferon γ (IFNγ), which induces CXCR3 expression on plasmablasts. These results elucidate a role for Tph cells in regulating protective B cell response during acute viral infection.
Subject(s)
COVID-19 , Programmed Cell Death 1 Receptor , Humans , Programmed Cell Death 1 Receptor/metabolism , CD4-Positive T-Lymphocytes , COVID-19/metabolism , T-Lymphocytes, Helper-Inducer , Plasma Cells/metabolism , Receptors, CXCR5 , Receptors, CXCR3/metabolismABSTRACT
Dysregulated immune responses against the SARS-CoV-2 virus are instrumental in severe COVID-19. However, the immune signatures associated with immunopathology are poorly understood. Here we use multi-omics single-cell analysis to probe the dynamic immune responses in hospitalized patients with stable or progressive course of COVID-19, explore V(D)J repertoires, and assess the cellular effects of tocilizumab. Coordinated profiling of gene expression and cell lineage protein markers shows that S100Ahi/HLA-DRlo classical monocytes and activated LAG-3hi T cells are hallmarks of progressive disease and highlights the abnormal MHC-II/LAG-3 interaction on myeloid and T cells, respectively. We also find skewed T cell receptor repertories in expanded effector CD8+ clones, unmutated IGHG+ B cell clones, and mutated B cell clones with stable somatic hypermutation frequency over time. In conclusion, our in-depth immune profiling reveals dyssynchrony of the innate and adaptive immune interaction in progressive COVID-19.
Subject(s)
Adaptive Immunity/immunology , COVID-19/immunology , Gene Expression Profiling/methods , Immunity, Innate/immunology , SARS-CoV-2/immunology , Single-Cell Analysis/methods , Adaptive Immunity/drug effects , Adaptive Immunity/genetics , Aged , Antibodies, Monoclonal, Humanized/therapeutic use , CD4-Positive T-Lymphocytes/drug effects , CD4-Positive T-Lymphocytes/immunology , CD4-Positive T-Lymphocytes/metabolism , CD8-Positive T-Lymphocytes/drug effects , CD8-Positive T-Lymphocytes/immunology , CD8-Positive T-Lymphocytes/metabolism , COVID-19/genetics , Cells, Cultured , Female , Gene Expression Regulation/drug effects , Gene Expression Regulation/immunology , Humans , Immunity, Innate/drug effects , Immunity, Innate/genetics , Male , RNA-Seq/methods , Receptors, Antigen, B-Cell/genetics , Receptors, Antigen, B-Cell/immunology , Receptors, Antigen, T-Cell/genetics , Receptors, Antigen, T-Cell/immunology , SARS-CoV-2/drug effects , SARS-CoV-2/physiology , COVID-19 Drug TreatmentABSTRACT
Given the heterogeneity seen in cell populations within biological systems, analysis of single cells is necessary for studying mechanisms that cannot be identified on a bulk population level. There are significant variations in the biological and physiological function of cell populations due to the functional differences within, as well as between, single species as a result of the specific proteome, transcriptome, and metabolome that are unique to each individual cell. Single-cell analysis proves crucial in providing a comprehensive understanding of the biological and physiological properties underlying human health and disease. Omics technologies can help to examine proteins (proteomics), RNA molecules (transcriptomics), and the chemical processes involving metabolites (metabolomics) in cells, in addition to genomes. In this review, we discuss the value of multiomics in drug discovery and the importance of single-cell multiomics measurements. We will provide examples of the benefits of applying single-cell omics technologies in drug discovery and development. Moreover, we intend to show how multiomics offers the opportunity to understand the detailed events which produce or prevent disease, and ways in which the separate omics disciplines complement each other to build a broader, deeper knowledge base.