Clonal dynamics of SARS-CoV-2-specific T cells in children and adults with COVID-19

Children infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) develop less severe coronavirus disease 2019 (COVID-19) than adults. The mechanisms for the age-specific differences and the implications for infection-induced immunity are beginning to be uncovered. We show by longitudinal multimodal analysis that SARS-CoV-2 leaves a small footprint in the circulating T cell compartment in children with mild/asymptomatic COVID-19 compared to adult household contacts with the same disease severity who had more evidence of systemic T cell interferon activation, cytotoxicity and exhaustion. Children harbored diverse polyclonal SARS-CoV- 2-specific naive T cells whereas adults harbored clonally expanded SARS-CoV-2-specific memory T cells. More naive interferon-activated CD4+ T cells were recruited into the memory compartment and recovery was associated with the development of robust CD4+ memory T cell responses in adults but not children. These data suggest that rapid clearance of SARS-CoV-2 in children may compromise their cellular immunity and ability to resist reinfection. HIGHLIGHTSO_LIChildren have diverse polyclonal SARS-CoV-2-specific naive T cells C_LIO_LIAdults have clonally expanded exhausted SARS-CoV-2-specific memory T cells C_LIO_LIInterferon-activated naive T cells differentiate into memory T cells in adults but not children C_LIO_LIAdults but not children develop robust memory T cell responses to SARS-CoV-2 C_LI O_FIG O_LINKSMALLFIG WIDTH=177 HEIGHT=200 SRC="FIGDIR/small/478400v1_ufig1.gif" ALT="Figure 1"> View larger version (44K): org.highwire.dtl.DTLVardef@e9586org.highwire.dtl.DTLVardef@17aaf37org.highwire.dtl.DTLVardef@18575e0org.highwire.dtl.DTLVardef@fde4ae_HPS_FORMAT_FIGEXP M_FIG C_FIG

Interactive Visualization for Patient-to-Patient Comparison

A visual analysis approach and the developed supporting technology provide a comprehensive solution for analyzing large and complex integrated genomic and biomedical data. This paper presents a methodology that is implemented as an interactive visual analysis technology for extracting knowledge from complex genetic and clinical data and then visualizing it in a meaningful and interpretable way. By synergizing the domain knowledge into development and analysis processes, we have developed a comprehensive tool that supports a seamless patient-to-patient analysis, from an overview of the patient population in the similarity space to the detailed views of genes. The system consists of multiple components enabling the complete analysis process, including data mining, interactive visualization, analytical views, and gene comparison. We demonstrate our approach with medical scientists on a case study of childhood cancer patients on how they use the tool to confirm existing hypotheses and to discover new scientific insights.