bioRxivImmunologyPreprint
Existence of blood circulating immune-cell clusters (CICs) comprising antigen-presenting cells and B cells
S. Bae, Y. Lee, M. Kang, J. Noh, D. Seo, H. Seo, S. Lee, S. Kwon
Abstract
Cell-to-cell physical interactions are involved in almost every physiological processes in multicellular organisms. Although the dynamics of these interactions could be highly diverse and complex in many circumstances, certain cell-to-cell interactions among immune cells have been well studied due importance in understanding disease pathogenesis and immune therapy development1. Dendritic cells (DCs) and B cells are directly involved in adaptive immune response against pathogens. Interaction mechanism between these two celltypes is well-known to occur in germinal centers either indirectly via helper T (Th) cells or directly via cell contact. However, there are animal in vitro and in vivo evidence that such direct DC-to-B cell contact can occur outside germinal centers like in peripheral blood or collagen matrix and display antiviral immune-related activity2,3. Here, we provide evidence that certain types of antigen presenting cells (APCs) can form robust cell clusters with B cells and circulate in blood. From healthy human blood immune single cell RNA-seq datasets, we detected APC subpopulations (0.34{+/-}0.19% of total PBMCs) that were also enriched with well-known naive B cell markers. We visually observed DC:B doublets and multiplets ([~]0.69% of total live PBMCs) in wildtype mouse blood using flow cytometry and microscopic imaging, thus proving the existence of circulating immune-cell clusters (CICs) composed of APCs and B cells. BCR repertoire of these healthy mouse CICs were similar to circulating B cells. Noticeably, frequency of these APC:B CICs were higher COVID-19 patients than healthy donors and their B cell subtype composition (e.g. naive, plasmablast, IgM+, IgG+) varied with disease severity.
- organism: human
- organism: mouse
- assay: scRNA-seq
- research focus: cell type annotation
- research focus: differential expression
- approach: statistical model
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