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Coupled analysis of transcriptome and BCR mutations reveals role of OXPHOS in affinity maturation

Nature Immunology volume 22, pages 904–913 (2021)Cite this article

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Abstract

Antigen-activated B cells diversify variable regions of B cell antigen receptors by somatic hypermutation in germinal centers (GCs). The positive selection of GC B cells that acquire high-affinity mutations enables antibody affinity maturation. In spite of considerable progress, the genomic states underlying this process remain to be elucidated. Single-cell RNA sequencing and topic modeling revealed increased expression of the oxidative phosphorylation (OXPHOS) module in GC B cells undergoing mitoses. Coupled analysis of somatic hypermutation in immunoglobulin heavy chain (Igh) variable gene regions showed that GC B cells acquiring higher-affinity mutations had further elevated expression of OXPHOS genes. Deletion of mitochondrial Cox10 in GC B cells resulted in reduced cell division and impaired positive selection. Correspondingly, augmentation of OXPHOS activity with oltipraz promoted affinity maturation. We propose that elevated OXPHOS activity promotes B cell clonal expansion and also positive selection by tuning cell division times.

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Fig. 1: Experimental design and scRNA-seq analysis of GC B cell response.
Fig. 2: Topic modeling uncovers prominent biological programs and reveals distinctive expression dynamics of OXPHOS and glycolysis in GC B cells.
Fig. 3: Coupled analysis of SHM and transcriptomes of NP-specific GC B cells implicates increased OXPHOS in positive selection.
Fig. 4: Coupled scRNA-seq analysis of OVA response reveals increased OXPHOS programming in positively selected GC B cell clones.
Fig. 5: Cox10 deficiency compromises cell division and positive selection of GC B cells.
Fig. 6: Oltipraz enhancement of OXPHOS activity during a GC response facilitates positive selection.

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Data availability

The scRNA-seq and bulk BCR-seq data have been deposited in the Gene Expression Omnibus database under accession number GSE154634. Source data are provided with this paper. All other data that support the findings of this study are available from the corresponding authors upon reasonable request.

Code availability

Code used in this study is available at Github: https://github.com/chendianyu/2021_NI_scGCB/.

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Acknowledgements

We thank C. Huang for discussions and technical advice; W. Zhu for mouse genotyping; the Laboratory Animal Resources Center, the High-Performance Computing Center, the Flow Cytometry Core and the Genomics Core in Westlake University; and the Division of Laboratory Animal Resources at the University of Pittsburgh. Research was supported by the National Natural Science Foundation of China (grant nos. 31970842 and U20A20346; H.X.), the National Key R&D Program of China (grant nos. 2020YFA0804200 and 2019YFA0802900; H.X.) and the Education Foundation of Westlake University (H.X.). H.S. acknowledges support from the UPMC ITTC fund and NIH grant no. U01AI141990.

Author information

Author notes

  1. These authors contributed equally: Dianyu Chen, Yan Wang, Godhev K. Manakkat Vijay.

Authors and Affiliations

  1. College of Life Sciences, School of Medicine, Zhejiang University, Hangzhou, China

    Dianyu Chen & Yan Wang

  2. Key Laboratory of Growth Regulation and Translational Research of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, China

    Dianyu Chen, Yan Wang, Shujie Fu, Di Xu, Danyang He & Heping Xu

  3. Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China

    Dianyu Chen, Yan Wang, Shujie Fu, Di Xu, Danyang He & Heping Xu

  4. Laboratory of Systems Immunology, Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, China

    Dianyu Chen, Yan Wang, Shujie Fu, Di Xu, Danyang He & Heping Xu

  5. Center for Systems Immunology, Departments of Immunology and Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA

    Godhev K. Manakkat Vijay, Colt W. Nash & Harinder Singh

  6. Division of Biomedical Informatics, Department of Pediatrics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA

    Nathan Salomonis

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Contributions

H.X. and H.S. conceived and supervised this study. D.C. performed computational analysis with guidance from H.X. and N.S. Y.W. and G.K.M.V. performed the experiments with help from S.F., D.X. and C.W.N. D.H., H.X. and H.S. wrote the manuscript with input from all authors.

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Correspondence to Harinder Singh or Heping Xu.

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Peer review information Nature Immunology thanks Laurence Morel and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. L. A. Dempsey was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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Extended data

Extended Data Fig. 1 Experimental design, mitotic and spatial gene signature scores of GC B cells.

a, Experimental design. WT C57BL/6 mice were immunized with NP-KLH plus LPS and Alum. B220+FAShiGL7hi NP-specific GC B cells were sorted for scRNA-seq analysis on day 13 after immunization. b, A 2D embedding as in Fig. 1c, colored by batches. c, Correlation plot of DZ and LZ signature scores in GC B cells. Shown is the mitotic (top row) and spatial (bottom row) gene signature scores of individual cells in different clusters. d, Violin plots of the distribution of gray zone signature scores (Supplementary Table 2) of GC B cells in different clusters. Clusters enriched for cells with high mitotic activity are highlighted with grey background. Diamond indicates the mean; lines, first and third quartiles. Each symbol represents a cell (b,c).

Extended Data Fig. 2 Topic modeling, pseudotime and 2-NBDG uptake analysis of GC B cells.

a, Shown are 2D embedding as in Fig. 1c, colored by the topic’s weight in the cell. Biologically interested programs are highlighted. b, Violin plots of the distribution of CD40 signaling-responded gene signature score (Supplementary Table 2) of GC B cells. c,d, Pseudotime trajectory of GC B cells constructed by Monocle (c). Violin plots of the distribution of pseudotime scores of cells (d). e, Violin plots of the distribution of topic 1 weights of GC B cells. f, A representative flow plot showing the gating strategy for DZ versus LZ GC B cells (above) (Supplementary Gating Strategy). A representative flow histogram showing the intracellular ATP5A expression in DZ and LZ GC B cells (bottom). g, A representative flow histogram showing intracellular TOMM20 expression in DZ and LZ GC B cells. h, Violin plots of the distribution of fatty acid beta oxidation signature scores of GC B cells. i, A representative flow plot showing the gating strategy for analyzing its uptake in GC (red) and resting (black) B cells (top) (Supplementary Gating Strategy). The 2-NBDG labeling of GC and resting B cell pairs from individual mice (n = 8) are displayed, connected by lines (bottom). j, The 2-NBDG labeling of NP+ DZ and NP+ LZ GC B cell pairs from individual mice (n = 8) are displayed, connected by lines. Data are pooled from two independent experiments. Clusters enriched for cells with high mitotic activity are highlighted with grey background and diamond indicates the mean and lines indicate first and third quartiles (b,d,e,h). Data are representative of two (f,g) or the pool from two (i,j) independent experiments. Statistical significance was tested by one-way ANOVA followed by Tukey’s multiple comparisons test (b,d,e,h) or two-tailed paired t test (i,j).

Source data

Extended Data Fig. 3 Coupled analysis of Igh variable sequences with transcriptional states of NP-specific GC B cells.

a, Violin plots of the distribution of mutation frequencies of GC B cells in different clusters. b, Violin plots of the distribution of weights of different topics (Extended Data Fig. 2a) in scRNA-seq data of IgHV1-72*01-expressing GC B cells with or without HFMs. c-e, Violin plots of the distribution of OXPHOS signature scores in scRNA-seq data of IgHV1-72*01-expressing GC B cells in designated clusters (c), isotypes (d) or mutation numbers (e). Each dot represents a cell. Diamond indicates the mean. f, Violin plot of the distribution of signature score of glutamine metabolism (Supplementary Table 2) in scRNA-seq data of IgHV1-72*01-expressing GC B cells with or without indicated high affinity mutations. g, Differentially expressed genes in IgHV1-72*01-encoding GC B cells with or without indicated high affinity mutations. Top10 differentially expressed genes (dots) are labeled. Diamond indicates the mean and lines indicate first and third quartiles (a,b,f). Statistical significance was tested by two-tailed t test (b,f) or two-way ANOVA followed by Tukey’s multiple comparisons test (c-e).

Extended Data Fig. 4 Coupled analysis of Igh variable sequences and transcriptional states of GC B cells in response to OVA immunization.

a, Experimental design. WT C57BL/6 mice were immunized with OVA plus LPS and Alum. B220+FAShiGL7hi NP-specific GC B cells were sorted on day 13 after immunization for scRNA-seq analysis. b, Proportions of GC B cells (FashiGL-7hi) in splenic B cell (CD19+) compartments (Supplementary Gating Strategy) of mice on day 13 after immunization with adjuvants only (n = 3) or adjuvants plus OVA (n = 5). Each symbol represents an individual mouse. c, Bar plot showing the counts of top 40 assigned Igh variable genes (y-axis) in GC B cells analyzed on day 13 of the response to OVA immunization. d,e, Frequency of amino-acid substitutions across IgHV1-26*01 (d) and IgHV2-5*01 (e) sequences. The most frequent mutated positions are highlighted in red. f, Pie chart displays the proportions of IgHV2-5*01 sequences (166) with indicated amino acid substitutions at position 56. Each color represents designated amino acid, and the white sector indicates germline sequences (S). g, Violin plots of the distribution of OXPHOS signature scores (Methods, Supplementary Table 2) in scRNA-seq data of IgHV2-5*01-expressing GC B cells containing or lacking the dominant mutation. Each dot represents a cell, line indicates the mean. Data are pooled from three independent experiments shown as the mean±s.e.m. (b). Statistical significance was tested by two-tailed t test (b,g).

Source data

Extended Data Fig. 5 Seahorse, Flow cytometry and ELISA analysis of Cox10 deficient B cells.

a,b, Representative trace (a) and quantitative analyses of basal (b, left) or maximum OCR (b, left) of indicated cell lines with Cox10 gene edited via CRISPR-Cas9 in the Seahorse XFe96 flux analyzer (n = 9 cell cultures per experiment). c, Proportions of NP-specific GC B cells (CD38–GL7hiNP+) in splenic B cell (B220+) compartments of Cox10+/+Aicda+/cre (n = 10 on dpi 7, n = 9 on dpi13, n = 6 on dpi21) and Cox10fl/flAicda+/cre (n = 5 on dpi 7, n = 6 on dpi13, n = 6 on dpi21) mice. d,e, ELISPOT analysis of splenocytes from Cox10+/+Aicda+/cre (n = 2 on dpi 7, n = 9 on dpi13, n = 6 on dpi21) and Cox10fl/flAicda+/cre (n = 3 on dpi 7, n = 6 on dpi13, n = 6 on dpi21) mice. Quantitative analysis at indicated dpi (d), representative images of NP-specific IgG1+ ASCs on dpi21 (e). f, Numbers of naive B cells (B220+CD38+GL7–CD138–) in Cox10+/+Aicda+/cre (n = 22) and Cox10fl/flAicda+/cre (n = 15) mice. g, Proportions of EdU+ cells in NP-specific GC B cell compartment (FashiGL7hiNP+) of Cox10+/+Aicda+/cre (n = 5) and Cox10fl/flAicda+/cre (n = 6) mice on dpi13, 2 hours post EdU administration. h, NP-specific IgG1 titers of Cox10+/+Aicda+/cre (n = 6) and Cox10fl/flAicda+/cre (n = 5) mice on dpi21. Shown are serial dilutions of serum samples for binding to NP5 (left) and NP30 (right). Data are representative of two (a,b) or five (e) or the pool from five (c,d,f) or two (g,h) independent experiments shown as the mean ± s.e.m. Each symbol represents an individual well (b) or mouse (f,g). Statistical significance was tested by two-tailed t test (b,f,g) or two-way ANOVA followed by Tukey’s multiple comparisons test (c,d).

Source data

Extended Data Fig. 6 Flow cytometry analysis of iCG B cells and TFH cells.

a, Experimental design. b, Representative flow plots showing the expression FAS and GL-7 of primary GC (left) and iGC (right) B cells cultured as in (a). Cells were gated on B220+ population (Supplementary Gating Strategy). c,d, Representative trace (c) and quantitative analyses of basal (d, left) or maximum OCR (d, right) of indicated cells in the Seahorse XFe96 flux analyzer (n = 6 cell cultures per experiment). e-i, Proportions of total and NP-specific GC B cells (e, f, n = 7 mice per group in e, n = 5 for DMSO and n = 6 for oltipraz groups in f), total and NP-specific plasma cells (g, h, n = 7 mice per group in g, n = 5 for DMSO and n = 6 for oltipraz groups in h), as well as NP-specific IgG1+ memory B cells (i, n = 7 mice per group on dpi13, n = 5 for DMSO and n = 6 on dpi21) of mice treated as in Fig. 6a at indicated time points after immunization. j, Proportions of TFH cells (CXCR5hiPD-1hi) in splenic activated CD4+ T cell (B220–CD4+CD44+, Supplementary Gating Strategy) compartments of mice treated with oltipraz (n = 6) or DMSO (n = 5) as in Fig. 6a on dpi21. k, The expression of Il21 (left) and Il4 (right) in TFH cells of mice treated with oltipraz (n = 7) or DMSO (n = 7) as in Fig. 6a on dpi13. l, m, NP-specific IgG1 titers of mice treated as in Fig. 6a on dpi13 (l) and dpi21 (m). Shown are serial dilutions of serum samples for binding to NP2 (left) and NP27 (right). Data are representative of three independent experiments (b,c,d) or the pool from two (e-k) or four (l,m) independent experiments. Each symbol represents an individual well (d) mouse (e-k). Statistical significance was tested by two-tailed t test (d-k).

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Supplementary information

Supplementary Information

Gating strategies for total and NP-specific GC B cells, DZ and LZ GC B cells, plasma cells, IgG1+ NP-specific memory B cells and TFH cells.

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Supplementary Tables 1–5.

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Chen, D., Wang, Y., Manakkat Vijay, G.K. et al. Coupled analysis of transcriptome and BCR mutations reveals role of OXPHOS in affinity maturation. Nat Immunol 22, 904–913 (2021). https://doi.org/10.1038/s41590-021-00936-y

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