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Heterogeneous plasma cells and long-lived subsets in response to immunization, autoantigen and microbiota

Nature Immunology volume 23, pages 1564–1576 (2022)Cite this article

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An Author Correction to this article was published on 09 December 2022

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Abstract

Durable antibody immunity depends on long-lived plasma cells (LLPCs) that primarily reside in the bone marrow (BM). However, due to LLPC rarity, it has not been possible to define their phenotypes or determine their heterogeneity. By single-cell mRNA sequencing, cytometry and a genetic pulse–chase mouse model, we show that IgG and IgM LLPCs display an EpCAMhiCXCR3– phenotype, whereas IgA LLPCs are Ly6AhiTigit–. While IgG and IgA LLPCs are mainly contributed by somatically hypermutated cells following immunization or infection, cells with innate properties and public antibodies are found in IgA and IgM LLPC compartments. Particularly, IgM LLPCs are highly enriched with public clones shared among different individual animals, differentiated in a T cell-independent manner and have affinity for self-antigens and microbial-derived antigens. Taken together, our work reveals different routes toward LLPC development and paves the way for deeper understanding of cellular and molecular underpinnings of long-term antibody immunity.

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Fig. 1: Heterogeneity of SPPCs and BMPCs revealed by scRNA-seq.
Fig. 2: Identification of LLPCs induced by NP-KLH immunization.
Fig. 3: Identification of LLPCs by a genetic pulse–chase approach.
Fig. 4: Surface phenotyping of PC subsets.
Fig. 5: Half-life analysis of EpCAMhiCXCR3− and Ly6AhiTigit– PCs.
Fig. 6: LLPC phenotypes in immunization and infection models.
Fig. 7: C7 LLPCs are enriched with public clones.
Fig. 8: C7 LLPCs are in part induced by gut microbiota.

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Data availability

The raw sequencing data have been deposited in the Genome Sequence Archive in BIG Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences (http://gsa.big.ac.cn), under the accession number CRA004574. The mouse BCR reference was downloaded from the IMGT database (https://www.imgt.org/vquest/refseqh.html#VQUEST). Source data are provided with this paper.

Code availability

A custom-written Python routine for retrieving DNA barcodes and example scripts for single-cell analysis have been deposited in GitHub (https://github.com/LF4XnC/LLPC). Analyses of scRNA-seq and BCR data were performed according to software or package manuals, with details available in the Methods. Further information on data analysis and visualization are available from the corresponding authors upon request. Source data are provided with this paper.

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Acknowledgements

We thank C. Qin from the ILAS in the Chinese Academy of Medical Sciences for providing the GF mice. We thank Y. Lu from AbCipher Bio-technology for assistance with monoclonal IgM antibody expression and purification. This work was funded in part by the National Key R&D Program of China (Ministry of Science and Technology, 2018YFE0200300 to H.Q. and 2018YFA0800200 to J.W.), the National Natural Science Foundation of China (grants 31830023, 81621002 and 81761128019 to H.Q. and grant 32070908 to X.L.), the Tsinghua University Spring Breeze Fund, the Tsinghua-Peking Center for Life Sciences, the Beijing Municipal Science & Technology Commission, the Beijing Frontier Research Center for Biological Structure, and the Changping Laboratory. This work was also funded in part by the Bill & Melinda Gates Foundation and the Howard Hughes Medical Institute. The findings and conclusions within are those of the authors and do not necessarily reflect positions or policies of the Bill & Melinda Gates Foundation or the Howard Hughes Medical Institute.

Author information

Author notes

  1. These authors contributed equally: Xin Liu, Jiacheng Yao.

Authors and Affiliations

  1. Tsinghua-Peking Center for Life Sciences, Beijing, China

    Xin Liu, Yongshan Zhao & Hai Qi

  2. Laboratory of Dynamic Immunobiology, Institute for Immunology, Tsinghua University, Beijing, China

    Xin Liu, Yongshan Zhao & Hai Qi

  3. Department of Basic Medical Sciences, School of Medicine, Tsinghua University, Beijing, China

    Xin Liu, Yongshan Zhao & Hai Qi

  4. Changping Laboratory, Beijing, China

    Xin Liu, Jianbin Wang & Hai Qi

  5. College of Chemistry and Molecular Engineering, Peking University, Beijing, China

    Jiacheng Yao

  6. Beijing Advanced Innovation Center for Genomics (ICG), Biomedical Pioneering Innovation Center (BIOPIC), Peking University, Beijing, China

    Jiacheng Yao

  7. School of Life Sciences, Tsinghua University, Beijing, China

    Jianbin Wang & Hai Qi

  8. NexVac Research Center, Tsinghua University, Beijing, China

    Jianbin Wang & Hai Qi

  9. Beijing Frontier Research Center for Biological Structure, Tsinghua University, Beijing, China

    Jianbin Wang & Hai Qi

  10. Beijing Key Laboratory for Immunological Research on Chronic Diseases, Tsinghua University, Beijing, China

    Hai Qi

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Contributions

X.L. and H.Q. conceptualized the study. J.Y. and J.W. designed the analysis workflow of the sequencing data. X.L. and J.Y. conducted the majority of the experiments. Y.Z. helped with sorting and animal work. J.Y. and X.L. conducted RNA-seq and BCR-seq data analyses under the supervision of J.W. All authors contributed to data interpretation. The paper was drafted by X.L. and J.Y., commented on by J.W. and finalized by H.Q. with input from all authors.

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Correspondence to Jianbin Wang or Hai Qi.

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Nature Immunology thanks Stephen Nutt 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 Supporting information for scRNA-seq analysis.

a, The gating strategy used for sorting PCs from Blimp1-eYFP mice. b, UMAP visualization of PCs from 4 batches of experiments before (left) and after batch correction (right). c, Violin plots showing the distribution of the number of genes (left) and UMI (right) detected per cell in the indicated clusters. d-e, UMAP visualization of the expression levels of Xbp1, Prdm1, Sdc1, Bach2 (d) and Mki67 (e) in all PCs.

Source data

Extended Data Fig. 2 Development of Blimp1-IRES-CreERT2-P2A-DTR (BICREAD) mice and validation of PC labeling and deletion.

a, Schematic of the targeting strategy to create the BICREAD allele, with approximate positions of genotyping primers shown. b-c, Representative contour plots (b) and summary statistics (c) showing the percentage of tdTomato+ PCs in mice gavaged with 2 mg of tamoxifen for four consecutive days, analyzed 48 h after the last tamoxifen treatment. d-e, Representative contour plots (d) and summary statistics (e) of CD138+BLIMP1+ PCs in the SP and BM of BICREAD mice, 24 h after intraperitoneal injection of 20 ng DT or PBS. Data were pooled from two (b-c) or three (d-e) independent experiments.

Source data

Extended Data Fig. 3 Differentially expressed genes in C0 through C14.

A heatmap showing expression levels of selected marker genes. Each row represents a gene named on the left, and each column is a cell. A total of 500 cells from each cluster of 500 or more cells are used to draw the heatmap, and for clusters with fewer than 500 cells all cells are included. Genes coding for cell surface proteins are in red.

Extended Data Fig. 4 Validation of EpCAMhiCXCR3− BMPCs.

a, The gating strategy for sorting EpCAMhiCXCR3− BMPCs from B6 mice. b, Left: UMAP visualization of total BMPCs, which are classified into different clusters by using the Fig. 1f dataset as a reference. See Methods for more technical details. Right: Isotypes of BMPC cells. c, UMAP visualization of sort-purified EpCAMhiCXCR3− cells (red) overlaid on top of total BMPCs (green). d, Distribution of the 15 clusters in total BMPCs or EpCAMhiCXCR3− BMPCs.

Source data

Extended Data Fig. 5 Similarities between C7 and C8 cells.

a, Pairwise comparison of single-cell gene expression in indicated clusters from the reference dataset. Each dot represents one gene, with differentially expressed genes labeled in red or blue. Pearson’s correlation coefficients are shown above each plot. b, Dot plots showing a series of manually curated genes that are more similarly expressed by C7 and C8 cells than by cells in other clusters, with color intensity of each dot indicating expression level and the dot size indicating percentage of expressing cells in the cluster.

Extended Data Fig. 6 Characterization of public clones.

a, A Circos plot showing all public clones across eight independent collections of mice. Linked lines represent public clones, with eight most abundant ones colored and the rest in grey. b-c, Isotype composition (b) and SHM abundance (c) in C7 and non-C7 public clones compared to other PCs. The Y-axis of (c) is pseudo log-transformed. d, UMAP visualization of non-C7 public clones superimposed on total PCs in grey, with cells grouped by their usage of VH and JH segments. The five most abundant public clones in each group are differentially colored, with the rest of non-C7 public clones in red.

Source data

Extended Data Fig. 7 Reactivities of public-clone antibodies to dead cells, live cells and gut microbes.

Antibodies from C7 and non-C7 public clones, together with an NP-binding antibody as negative control (Neg), tested for binding to dead cells, live cells or fecal microbes. a-c, Binding of public-clone antibodies to dead or live A20 cells. a, Gating strategy to identify dead and live A20 cells as targets. b, Representative contour plots showing binding to dead or live cells by a negative-control antibody, antibodies of non-C7 public clones, or antibodies of C7 public clones, as indicated. c, Summary statistics. d-f, Binding of public-clone antibodies to gut microbes. d, Gating strategy to identify fecal microbes based on size. e, Representative contour plots showing binding to fecal microbes by the same set of antibodies used in (b). f, Summary statistics. Each symbol is one independent experiment, and lines denote means.

Source data

Supplementary information

Reporting Summary

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Liu, X., Yao, J., Zhao, Y. et al. Heterogeneous plasma cells and long-lived subsets in response to immunization, autoantigen and microbiota. Nat Immunol 23, 1564–1576 (2022). https://doi.org/10.1038/s41590-022-01345-5

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