Fig 2: CD11c, CXCR3, and CD71 identify atBC, actBCs, and MBCs via flow cytometryPBMCs from 7 non-exp and 11 malaria-exp donors were isolated and analyzed by flow cytometry for expression of markers associated with different B cell populations.(A) Flow cytometry plots from representative individuals showing the CD11c, CXCR3, and CD71 expression on mature IgD- B cells.(B) Quantification of (A) showing the percentage of cells found in each cell type by country; bars represent mean ± SD.(C) The expression of surface markers on each cell type, measured by mean fluorescence intensity (MFI); analysis was performed using two-way ANOVA; bars represent mean ± SD.(D) Representative flow cytometry plots showing the expression of CD27 and CD21 per cell type.(E) Quantification of (D) showing the percentage of cells separated by expression of CD27 and CD21 found in each cell type; bars represent the mean proportion ± SD.Where the exact p value is not quoted, *p < 0.05, **p < 0.01, ***p < 0.001, and ****p < 0.0001.

Fig 3: CITE-seq analysis reveals a cryptic population of atBCs found predominantly in non-exposed individualsCITE-seq analysis to correlate expression of cell surface markers with gene expression was performed on cells from the four donors described in Figure 2.(A) Surface protein expression measured by CITE-seq projected onto UMAP plots. Color was scaled for each marker with highest and lowest centered log ratio (CLR)-normalized expression level noted.(B) Contour plots showing the CLR-normalized expression of CD27 and CD21 as measured by CITE-seq; data are concatenated from all individuals.(C) Quantification of (B); data show the mean proportion ± SD of the CD21/CD27 CLR-normalized expression per individual.(D) Histogram plots showing the CLR-normalized expression of CD11c on the different atBC, MBC, and actBC clusters; the gray histogram represents expression on naive B cells; data are concatenated from all individuals. Bar graph shows mean ± SD of the surface protein expression of CD11c for each individual, with triangles representing non-exposed and squares representing malaria-exposed individuals; analysis was performed using one-way ANOVA with each individual as a blocking factor, only comparisons to the naive population are shown.(E) Histogram plots showing the CLR-normalized expression of CXCR3 on the different atBC, MBC, and actBC clusters; the gray histogram represents expression on naive B cells; data are concatenated from all individuals. Bar graph shows the mean ± SD of the surface protein expression of CXCR3 for each individual, with triangles representing non-exposed and squares representing malaria-exposed individuals; analysis was performed using one-way ANOVA with each individual as a blocking factor, only comparisons to the naive population are shown.

Fig 4: Single-cell sequencing of hemagglutinin-specific B cells to study the aged vaccine response(A) Study design. Venepuncture performed on days 0 (just prior to 2016–2017 trivalent influenza vaccine immunization), 7, and 42. Peripheral blood mononuclear cells were isolated on the day of venepuncture and cryopreserved for later index sorting experiments.(B) Hemagglutinin inhibition (HAI) assay titers from days 0, 7, and 42 are shown for 22- to 36-year-old or 67- to 86-year-old volunteers as open or grayed circles, respectively (n = 10 in both groups). Data are from one technical replicate (of two independent experiments).(C and D) PBMC ELISpot at day 7 in both age groups for antibody-secreting cells after stimulation with HA (C) or TIV (D). Data are from eight experimental runs on separate days.(E) Recombinant biotinylated hemagglutinin multimers conjugated with streptavidin-PE or streptavidin-APC allows the identification of hemagglutinin-specific B cells. Gated on live, singlet CD19+ lymphocytes. Full gating strategy is shown in Figure S1B.(F) The proportion of hemagglutinin-binding B cells increases after vaccination, for both 22- to 36-year-olds or 67- to 86-year-olds. Proportion expressed as percentage of live B cells that did not bind free streptavidin. Data are from ten experimental runs on separate days.(G) UMAP embedding of single-cell RNA sequencing from A/Cal09-specific B cells, n = 771 cells. UMAP projection based on the first 40 principal components using the features with the top 10% variance, after removal of low-quality cells (Figure S1) and size normalization by deconvolution. Louvain clustering finds five clusters. Sorts were performed on 10 days with all four conditions present. Ten library preparations were performed and sequenced as ten lanes.(H) Cell-identity assignment based on published transcriptional profiles of 29 human immune subsets including the following B cell subsets: IgD+CD27- “naive,” IgD+CD27+ “non-switched Bmem,” IgD-CD27+ “switched Bmem,” IgD-CD27- “IgD-CD27-,” IgD-CD27+CD38+ plasmablasts “PB,” and plasmacytoid DCs. The following numbers of cells were identified: naïve, 120 cells; non-switched Bmem, 165 cells; switched Bmem, 144 cells; IgD-CD27- Bmem, 334 cells; plasmablast, 3 cells; plasmacytoid DCs, 1 cell. No cells were defined as T cells or members of other lymphoid or myeloid lineages.(I–L) UMAP embedding as in (G), showing the logicle transformed index sort surface expression of IgD (I), CD27 (J), CD21 (K), and CD38 (L) proteins.In (B), p values from paired two-tailed Wilcoxon signed-rank test (on log2 transformed data) are summarized: ns, p > 0.05; *p < 0.05. In (C) and (D), groups are compared with a two-tailed Mann-Whitney test. In (F), samples from the same individual are indicated with a gray line and p values shown are from a paired two-tailed Wilcoxon signed-rank test. In (G)–(J), the scales reflect the decimal log of the logicle transformed fluorescence intensity value.