Abstract
RNA-based vaccines against SARS-CoV-2 have proven critical to limiting COVID-19 disease severity and spread. Cellular mechanisms driving antigen-specific responses to these vaccines, however, remain uncertain. Here we identify and characterize antigen-specific cells and antibody responses to the RNA vaccine BNT162b2 using multiple single-cell technologies for in depth analysis of longitudinal samples from a cohort of healthy participants. Mass cytometry and unbiased machine learning pinpoint an expanding, population of antigen-specific memory CD4 + and CD8 + T cells with characteristics of follicular or peripheral helper cells. B cell receptor sequencing suggest progression from IgM, with apparent cross-reactivity to endemic coronaviruses, to SARS-CoV-2-specific IgA and IgG memory B cells and plasmablasts. Responding lymphocyte populations correlate with eventual SARS-CoV-2 IgG, and a participant lacking these cell populations failed to sustain SARS-CoV-2-specific antibodies and experienced breakthrough infection. These integrated proteomic and genomic platforms identify an antigen-specific cellular basis of RNA vaccine-based immunity.
【초록키워드】 SARS-CoV-2, IgG, IgM, Coronaviruses, Vaccine, Immunity, Vaccines, T cells, disease severity, Antibody Response, Sequencing, memory B cells, progression, CD4, CD8, Population, memory, Antigen, COVID-19 disease, RNA, B cell, cross-reactivity, BNT162b2, Spread, lymphocyte, Cohort, T cell, Antibody responses, Characteristics, cells, IgA, plasmablasts, response, Breakthrough infection, receptor, genomic, Critical, SARS-CoV-2-specific antibodies, platform, mechanism, single-cell, SARS-CoV-2 IgG, RNA vaccine, proteomic, memory B cell, specific antibodies, cellular, Endemic, Cytometry, helper cells, SARS-CoV-2-specific antibody, Participants, mass, COVID-19 disease severity, participant, cellular mechanisms, driving, cell population, depth analysis, longitudinal samples, Cell, identify, healthy, follicular, Responding, 【제목키워드】 Vaccine, SARS-CoV-2 RNA,