Deep profiling of B cells responding to various pathogens uncovers compartments in IgG memory B cell and antibody-secreting lineages

Abstract

Improving our understanding of B cell transition to memory B cells (MBCs) and antibody-secreting cells (ASCs) is crucial for clinical monitoring and vaccine strategies. To explore these dynamics, we compared prepandemic antigen responses (influenza hemagglutinin, respiratory syncytial virus fusion glycoprotein, and tetanus toxoid) with recently encountered severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antigen responses in convalescent COVID-19 patients using spectral flow cytometry. Our analysis revealed the CD43+CD71+IgG+ activated B cell subset, highly enriched for SARS-CoV-2 specificities, as a juncture for ASC and MBC differentiation, with CD86+ phenotypically similar to ASCs and CD86- to IgG+ MBCs. Moreover, subpopulations within IgG+ MBCs were further identified based on CD73 and CD24 expression. Activated MBCs (CD73-/CD24lo) were predominantly SARS-CoV-2-specific, while resting MBCs (CD73+/CD24hi) recognized prepandemic antigens. A CD95- subcluster within resting MBCs accounted for over 40% of prepandemic-specific cells, indicating long-lasting memory. These findings advance our understanding of IgG+ MBC and ASC development stages, shedding light on the decision-making process guiding their differentiation.

Fig. 1.. IgG+ ActBCs and MBCs are engaged in SARS-CoV-2 response.

(A) Combinatorial probe staining and gating strategy for the detection of multiple B cell specificities in a single PBMC sample. From live B cells (gating strategy, fig. S1B), antigen-reactive B cells are detected as double positive for the binding of the same antigen multimerized with two different fluorochromes. RBD-specific B cells are detected out of S-specific B cells. (B) Frequency of antigen-reactive B cells in total B cells from healthy controls and mild, moderate, severe, and critical patients. (C) Heatmap of the 26 B cell populations and 13 major populations defined after FlowSOM analysis of 23 parameters from total B cells of healthy controls and patients. CD19 median fluorescence intensity variability in this heatmap is only based on CD19-positive B cells. (D) Frequency of B cell subsets defined by FlowSOM analysis according to B cell specificity and disease severity within non-naïve B cells (non-NBC, non-TBC). (E) Frequency of IgG+ MBCs (left) or IgG+ ActBCs (right) with S specificity within the total B cell compartment according to disease severity. (F) Frequency of IgG+ ActBCs in total B cells according to disease severity. (G) Frequency of SARS-CoV-2–specific B cells (S: left; RBD: middle; NC: right) in naïve, DN2, IgM+ MBCs, IgG+ MBCs, and IgG+ ActBCs. (H) S probe median fluorescence intensity in DN2, IgA+ MBCs, IgG+ MBCs, and IgG+ ActBCs.

Fig. 2.. CD73, CD24, and CD95 expression delineates RMBC and AMBC subsets.

(A to I) Composite dataset generated from flow cytometry data of all antigen-specific B cells, in addition to 400 nonspecific B cells from each individual donor of the IgG+ MBCs and IgG+ ActBC major populations as defined by FlowSOM clustering. (A) UMAP of flow cytometry data (15 markers, see table S3A) from the composite dataset. UMAP representation of each B cell specificity (from left to right: S, RBD, NC, HA, RSV-F, TT, AND nonspecific B cells). (B) Leiden clustering identified three distinct clusters (1: IgG+ RMBC; 2: AMBC; 3: ActBCs). (C) Overlay of IgG+ memory and activated major populations captured by FlowSOM clustering on the UMAP data generated out of antigen-specific B cells. (D) Frequency of the three clusters as identified by Leiden clustering (1: IgG+ RMBC; 2: IgG+ AMBC; 3: IgG+ ActBC) according to antigen specificity. (E) Feature plots showing scaled normalized counts for 14 relevant B cell markers in all selected cells. (F) Leiden clustering of the IgG+ MBC compartment represented on UMAP (nine clusters were identified). (G) Frequency distribution of the nine clusters as identified by Leiden clustering according to antigen specificity within the IgG+ MBC population. Only data points corresponding to a minimum of 20 antigen-specific B cells were used for the analysis. (H) Comparative analysis of cluster 1 for HA, RSV-F, and TT versus nonspecific B cells for samples that encompass at least 20 cells for a given specificity. (I) Comparative analysis of clusters 2, 3, and 5 of S-specific versus NC-specific B cells for samples that encompass at least 20 cells for a given specificity.

Fig. 3.. ActBCs at the crossroad of memory cells and ASCs.

(A) Frequency of IgG+ ActBC populations (A: CD86− FcRL5−; B: CD86− FcRL5+; C: CD86+ FcRL5−) out of SARS-CoV-2–specific B cells (S, RBD, and NC). (B and C) UMAP analysis of flow cytometry data (15 markers; see table S3C) generated out of all plasmablasts and all antigen-specific B cells captured from IgG+ MBCs and IgG+ ActBCs. (B) Overlay of plasmablasts, clusters 1 to 4 (previously generated in Fig. 2 as identified by Leiden clustering out of IgG+ ActBCs and MBCs), on UMAP. (C) Overlay of activated populations (A: CD86− FcRL5−; B: CD86− FcRL5+; C: CD86+ FcRL5−), plasmablast population, and IgG+ MBCS, all captured by FlowSOM clustering on the UMAP data. (D) Comparative analysis of cell surface expression by histogram representation of 14 relevant B cell markers between populations of IgG+ ActBCs (A: CD86− FcRL5−; B: CD86− FcRL5+; C: CD86+ FcRL5−) and plasmablasts (PB_1, PB_2, and PB_3).