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Novel specialized cell state and spatial compartments within the germinal center

Nature Immunology volume 21, pages 660–670 (2020)Cite this article

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Abstract

Within germinal centers (GCs), complex and highly orchestrated molecular programs must balance proliferation, somatic hypermutation and selection to both provide effective humoral immunity and to protect against genomic instability and neoplastic transformation. In contrast to this complexity, GC B cells are canonically divided into two principal populations, dark zone (DZ) and light zone (LZ) cells. We now demonstrate that, following selection in the LZ, B cells migrated to specialized sites within the canonical DZ that contained tingible body macrophages and were sites of ongoing cell division. Proliferating DZ (DZp) cells then transited into the larger DZ to become differentiating DZ (DZd) cells before re-entering the LZ. Multidimensional analysis revealed distinct molecular programs in each population commensurate with observed compartmentalization of noncompatible functions. These data provide a new three-cell population model that both orders critical GC functions and reveals essential molecular programs of humoral adaptive immunity.

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Fig. 1: Transcriptional differences between the LZ, GZ and DZ.
Fig. 2: LZ, DZ and GZ B cells have unique genome accessibility profiles.
Fig. 3: Proteomic analysis of GCBC subsets.
Fig. 4: Histological identification of GZ B cells within GCs.
Fig. 5: GZ B cells are the main dividing population in the GC.
Fig. 6: GCBCs progress from LZ to GZ to DZ.
Fig. 7: Molecular regulation of GCBC progression.
Fig. 8: GZ clusters are sites of ongoing cell division.

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Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request. Bulk RNA-seq, scRNA-seq, ATAC–seq and ChIP–seq data have been deposited in the Gene Expression Omnibus database under accession code GSE133743. Proteome and phosphoproteome data have been uploaded to ProteomeXchange via the PRIDE database. The project name is ‘Proteome profiling of mouse GCBCs’, and the project accession code is PXD015524. Data from GSE100738 were also analyzed.

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Acknowledgements

We thank D. Leclerc and the UChicago flow cytometry core for providing the latest flow cytometry technologies. We also thank Y. Wang for assistance in proteomics experiments. This work is supported by the US National Institutes of Health grant nos. R01AI143778 and R21AI128785 (to M.R.C.), T32 HL007605 and F32AI143120 (to D.E.K.), T32HD007009 (to M.K.O.), R01AG047928 (to J.P.) and the Cancer Center Support grant (no. P30CA014599) for the UChicago flow cytometry core.

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Authors and Affiliations

  1. Department of Medicine, Section of Rheumatology and Gwen Knapp Center for Lupus and Immunology Research, University of Chicago, Chicago, IL, USA

    Domenick E. Kennedy, Michael K. Okoreeh, Junting Ai, Margaret Veselits, Kaitlin C. McLean, Malay Mandal & Marcus R. Clark

  2. Core for Research Informatics, University of Illinois at Chicago, Chicago, IL, USA

    Mark Maienschein-Cline

  3. Department of Immunology, St Jude Children’s Research Hospital, Memphis, TN, USA

    Yogesh Dhungana & Hongbo Chi

  4. Departments of Structural Biology and Developmental Neurobiology, St Jude Children’s Research Hospital, Memphis, TN, USA

    Hong Wang & Junmin Peng

  5. Center for Proteomics and Metabolomics, St Jude Children’s Research Hospital, Memphis, TN, USA

    Hong Wang & Junmin Peng

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  1. Domenick E. Kennedy
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Contributions

D.E.K. and M.R.C. conceived and designed experiments. D.E.K. performed and analyzed most of the experiments. D.E.K. and M.M.-C. analyzed the high throughput sequencing data. M.K.O assisted in scRNA-seq experiments. J.A. assisted with imaging experiments. M.V. assisted in influenza experiments. Y.D., H.W., J.P. and H.C. assisted with proteomics experiments and analyses. M.M. and K.C.M. assisted with some experiments. M.M. provided valuable insights into the study design. D.E.K. and M.R.C. oversaw the entire project and edited the final manuscript.

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Correspondence to Marcus R. Clark.

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Extended data

Extended Data Fig. 1 Transcriptional analysis of GCBC subsets in support of Fig. 1.

a, Schematic of immunization strategy. Mice were immunized intraperitoneally with sheep red blood cells (SRBCs), boosted on day 5, followed by isolation of GCBC subsets from spleens. b, RNA-Seq volcano plot displaying the -Log10 FDR vs Log2 fold-change of genes differentially expressed between DZ and LZ cells isolated by the Classical GC strategy. c, RNA-Seq heatmap and volcano plot displaying head-to-head differentially expressed genes between DZ vs LZ B cells isolated using the New Strategy in Fig. 1c. d and e, RNA-Seq heatmaps and volcano plots displaying head-to-head differentially expressed genes between GZ vs LZ (d) and DZ vs GZ (e) B cells isolated using the New Strategy in Fig. 1c. For RNA-Seq, n = 2 per cell type. Each n represents cells pooled from 20 mice. q values were generated with edgeR (see Methods).

Extended Data Fig. 2 Epigenetic differences between GCBC subsets in support of Fig. 2.

a, Genome accessibility and enhancer tracks aligned at the Otub2 locus. b, mRNA expression of Otub2. c, mRNA expression of the indicated TF in LZ, GZ and DZ B cells. d, TF motifs enriched in accessible regions for the indicated genome accessibility cluster and associated GCBC subset. p values were generated using HOMER (see Methods). For each cluster, n= the number of accessibility peaks indicated in Fig. 2f. e and f, Genome accessibility and enhancer tracks aligned at the Foxn2 (e) and Ccnb1 (f) locus for GCBCs isolated by the Classic GC method. For ATAC-Seq data, n = 2 per cell type. Each n represents cells pooled from 20 mice. (b,c) Each dot corresponds to an independent biological sample.

Extended Data Fig. 3 Phosphoproteomic analysis of GCBC subsets in support of Fig. 3.

a, Schematic of phosphoproteome differential expression analysis on GC populations isolated with the New gating strategy. b, Phosphoproteome cell distance plot of the indicated cell populations. Scale represents Euclidian distance. c, Box plots of phosphoproteomic clustering analysis (left). Boxes represent interquartile ranges (IQRs; Q1–Q3 percentile) and black vertical lines represent median values. Maximum and minimum values (ends of whiskers) are defined as Q3 + 1.5× the IQR and Q1 − 1.5× the IQR, respectively. Pathway analysis associated with the indicated cluster (right). Numbers correspond to -log10 p value. For each cluster, n= the number of phosphoproteins indicated above boxplot. P values were generated by Metascape using an established hypergeometric test coupled with Benjamini-Hochberg p-value correction algorithm. Light blue, gray, and dark blue (left side of heatmap) correspond to the LZ, GZ, or DZ subsets respectively. d, Total protein expression of Ki67. e, Relative levels of the indicated Ki67 phosphopeptides from cluster 1. f, List of GZ upregulated phosphopeptides for Ki67 from cluster 1. For phosphoproteome data, n = 2 per cell type. Cells were isolated from a total of 120 mice. Each n was generated from 5-6 million purified B cell subsets. See also Supplementary Data 5.

Extended Data Fig. 4 Histological analysis of the GZ in support of Fig. 4.

a and b, Immunofluorescence microscopy of GCs 14 days post SRBC immunization. Single panels and merged image displaying GL7, CD35, and Cyclin B1 in two distinct GCs. (n > 7 mice) c, Expression of Lars2 (mitochondrial leucyl-tRNA synthetase) in LZ, GZ and DZ. Each dot corresponds to an independent biological sample. d, Immunofluorescence microscopy of GC 14 days post SRBC immunization examining colocalization of Cyclin B1 and LARS2. (a) is related to Fig. 4 panel a.

Extended Data Fig. 5 Analysis of single GCBC transcription in support of Fig. 6.

scRNA-Seq UMAP plots generated with Monocle3 displaying the enrichment for gene expression signatures derived from bulk RNA-Seq clusters 1-8. Bulk RNA-Seq gene expression trend is displayed to the side of each scRNA-Seq UMAP plot. Boxes represent interquartile ranges (IQRs; Q1–Q3 percentile) and black vertical lines represent median values. Maximum and minimum values (ends of whiskers) are defined as Q3 + 1.5× the IQR and Q1 − 1.5× the IQR, respectively.

Extended Data Fig. 6 Integration of transcriptional and proteomic analysis in support of Fig. 7.

a-c, Heatmap of gene expressions (left) and protein expressions (right) for the indicated genes. Full lists related to Fig. 7a, c, e. d-i, Heatmaps of head-to-head GCBC subset comparisons for IPA upstream regulator analysis. For factors indicated. d-f, Analysis of total proteome dataset. g-i, Analysis of phosphoproteome dataset. j, Summary of relative activation status for panels (d-i), for the indicated activated factors and cell types. For head-to-head comparisons, increased activation for the indicated factors corresponds to increased color intensity GZ (green), DZ (magenta), and LZ (blue).

Extended Data Fig. 7 Visualization of GZ proliferative clusters in support of Fig. 8.

a, Immunofluorescence microscopy 3D z stacks displaying whole GCs and focusing on GZ clusters within the GC. GL7 (green), Cyclin B1 (red), DAPI (blue). (n = 4) Right panel is related to GZ displayed in Fig. 7m. b, Schematic of EdU and BrdU injection experimental design. c and d, Immunofluorescence microscopy of whole GCs for the indicated markers 5.5 hr post BrdU injection (n≥3 mice per timepoint indicated in panel b). e and f, Immunofluorescent microscopy of GCs for the indicated markers (e) and quantification of EdU intensity within the GZ (f) 5.5 hr post BrdU injection. Each dot represents a cell. g, Schematic representing dilution of EdU during cell division (5.5 hr). h, Immunofluorescent microscopy of GCs for the indicated markers visualizing cells in multiple stages of active cell division within a GZ cluster 5.5 hr post BrdU injection (n > 3 mice).

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Kennedy, D.E., Okoreeh, M.K., Maienschein-Cline, M. et al. Novel specialized cell state and spatial compartments within the germinal center. Nat Immunol 21, 660–670 (2020). https://doi.org/10.1038/s41590-020-0660-2

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