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Induction of bronchus-associated lymphoid tissue is an early life adaptation for promoting human B cell immunity

Nature Immunology volume 24, pages 1370–1381 (2023)Cite this article

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Abstract

Infants and young children are more susceptible to common respiratory pathogens than adults but can fare better against novel pathogens like severe acute respiratory syndrome coronavirus 2. The mechanisms by which infants and young children mount effective immune responses to respiratory pathogens are unknown. Through investigation of lungs and lung-associated lymph nodes from infant and pediatric organ donors aged 0–13 years, we show that bronchus-associated lymphoid tissue (BALT), containing B cell follicles, CD4+ T cells and functionally active germinal centers, develop during infancy. BALT structures are prevalent around lung airways during the first 3 years of life, and their numbers decline through childhood coincident with the accumulation of memory T cells. Single-cell profiling and repertoire analysis reveals that early life lung B cells undergo differentiation, somatic hypermutation and immunoglobulin class switching and exhibit a more activated profile than lymph node B cells. Moreover, B cells in the lung and lung-associated lymph nodes generate biased antibody responses to multiple respiratory pathogens compared to circulating antibodies, which are mostly specific for vaccine antigens in the early years of life. Together, our findings provide evidence for BALT as an early life adaptation for mobilizing localized immune protection to the diverse respiratory challenges during this formative life stage.

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Fig. 1: Pediatric organ donors and their characteristics.
Fig. 2: Formation and regression of BALT containing GCs during infancy and childhood.
Fig. 3: Distinct B cell subset composition and age-associated dynamics in lungs and LLNs.
Fig. 4: Analysis of helper T cell subsets in lungs and LLNs.
Fig. 5: Transcriptional signatures and clonal dynamics for lung and LLN B cells in early life.
Fig. 6: B cell clonal expansion and SHM in the lungs and LLNs during early life and childhood.
Fig. 7: Distinct antigen specificities for tissue B cells compared to circulating antibodies.

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Data availability

BCR-seq data that support the findings of this study have been deposited in the SRA with the accession code PRJNA847585. The single-cell transcriptome and CITE-seq data have been deposited in Gene Expression Omnibus with accession number GSE223646. Source data are provided with this paper.

Code availability

Custom computer code used in this study is available at https://github.com/simslab/DropSeqPipeline8 and https://github.com/arosenfeld/immunedb.

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Acknowledgements

This work was supported by NIH grants AI100119, AI106697, AI128949 and AI168634 and a grant from the Helmsley Charitable Trust awarded to D.L.F. T.J.C. was supported by K23 AI141686, and T.M.B was supported by AI42288. Flow cytometry analysis was performed in the Columbia Center for Translational Immunology Flow Cytometry Core supported by NIH S10RR027050 and S10OD020056. Acquisition of human samples from the University of Florida was supported by the Helmsley Charitable Trust (to T.M.B. and D.L.F.). E.T.L.P., U.H. and T.H. were supported by NIH grant AI106697. scRNA-seq was performed in the Sulzberger Columbia Genome Center, which is supported by the NIH/NCI Cancer Center Support Grant P30CA013696. We wish to thank the families for the gift of organ donation that made this study possible. We thank S. Kleinstein for help and guidance with the scBCR analysis.

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Author notes

  1. These authors contributed equally: Rei Matsumoto, Joshua Gray.

Authors and Affiliations

  1. Department of Surgery, Columbia University Irving Medical Center, New York, NY, USA

    Rei Matsumoto & Donna L. Farber

  2. Department of Microbiology and Immunology, Columbia University Irving Medical Center, New York, NY, USA

    Joshua Gray, Ksenia Rybkina, Rebecca S. Guyer & Donna L. Farber

  3. Department of Microbiology, Immunology and Genetics, Ben-Gurion University of the Negev, Be’er-Sheva, Israel

    Hanna Oppenheimer, Lior Levy, Lilach M. Friedman & Tomer Hertz

  4. The National Institute for Biotechnology in the Negev, Ben-Gurion University of Negev, Be’er-Sheva, Israel

    Hanna Oppenheimer, Lior Levy, Lilach M. Friedman & Tomer Hertz

  5. Department of Human Biology, University of Haifa, Haifa, Israel

    Muhammad Khamaisi & Uri Hershberg

  6. Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Wenzhao Meng, Aaron M. Rosenfeld, Marissa C. Bradley & Eline T. Luning Prak

  7. Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA

    David Chen & Peter A. Sims

  8. Department of Pathology, Immunology and Laboratory Medicine, University of Florida Diabetes Institute, Gainesville, FL, USA

    Mark A. Atkinson, Todd M. Brusko & Maigan Brusko

  9. Department of Pediatrics, Columbia University Irving Medical Center, New York, NY, USA

    Thomas J. Connors

  10. Department of Biochemistry & Molecular Biophysics, Columbia University Irving Medical Center, New York, NY, USA

    Peter A. Sims

  11. Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA

    Tomer Hertz

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  1. Rei Matsumoto
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Contributions

R.M. designed and performed experiments, analyzed data and wrote the manuscript. J.G. designed and performed the flow cytometry studies and single-cell transcriptome profiling, analyzed data and wrote the manuscript. K.R. designed and performed the flow cytometry studies for the TFH analysis. L.M.F., H.O., L.L. and T.H. performed antigen microarray assays, analyzed the data and helped write the manuscript. W.M., A.M.R. and E.T.L.P. performed the Vh chain sequencing, analyzed the data and helped write and edit the manuscript. M.K. and U.H. analyzed bulk and scBCR repertoires and helped write the manuscript. D.C. constructed libraries for single-cell transcriptome profiling. P.A.S. analyzed scRNA-seq data. T.M.B., M.A.A. and M.B. coordinated acquisition of human infant tissues. T.J.C., R.S.G., J.G., M.C.B. and K.R. coordinated acquisition and/or processed human infant tissue. D.L.F. planned experiments, analyzed data and wrote and edited the manuscript.

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Correspondence to Donna L. Farber.

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Nature Immunology thanks the anonymous reviewers for their contribution to the peer review of this work. Primary Handling Editor: L. A. Dempsey, in collaboration with the Nature Immunology team. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 Germinal Centers in pediatric lungs and associated lymph nodes contain follicular dendritic cells.

Tissue sections from lung-associated lymph nodes (LLN) and lungs were stained with anti-CD3 (green), anti-CD20 (blue), anti-CD10 (red), and anti-follicular dendritic cell (FDC) (light blue) antibodies. a, Representative image of LLN from a 1.5 yr old donor showing FDC in a germinal center (GC) structure (scale bar: 150μm) (n = 3, with 3 sections imaged per donor). b, FDC in BALT shown in representative images from 4 organ donors paired by row. FDC, and CD10 channels are shown for each donor and merged column shows staining with all four fluorochromes (n = 5, with 3 sections imaged per donor). c, LLN from a 4 yr old donor showing FDC (white arrows) in GC structures (pink) (scale bar: 200μm). Heterogeneous appearance of FDC with GC B cells across different follicles suggests multiple stages of GC formation (n = 3, with 3 sections imaged per donor). FDC=follicular dendritic cell, yr= year, LLN=lung lymph node, GC=germinal center.

Extended Data Fig. 2 Gating strategy for analysis of B cell subsets by flow cytometry.

Lymphocytes were selected based on forward (FSC) vs side scatter (SSC) height (H) properties, then single cell gating was applied based on SSC-H vs area (-A), followed by selection of CD45+ live cells. B cells (CD19 + CD3-) were selected by CD19 expression and lack of CD3 expression and three subpopulations were identified based on IgD and IgM expression: 1. IgD+ cells (second row) were delineated into subsets based on CD27 expression with IgD+CD27- cells further divided based on CD38 and CD10 expression into transitional and naive B cells and IgD+CD27+ cells to IgD+ GC and non-class switched (NCS) memory B cells, which were analyzed for CD69 expression for tissue residency. 2. IgD-IgM- cells were also delineated based on CD27 expression to GC B cells based on CD38 and CD10 expression and CD27+IgD-IgM-CD10- B cells were further delineated into IgA+ memory and IgG+ class-switched (CS) memory B cells which were further analyzed for CD69 expression for tissue residency and CD95 for activation. 3. IgD-IgM+ B cells were delineated into immature subsets based on CD38 and CD10 expression.

Extended Data Fig. 3 Gating strategy for analysis of follicular helper T cells (TFH) by flow cytometry.

Single cells were selected based on side scatter (SSC), height (H) vs area (A) properties, followed by selection of CD45+ live cells. Conventional CD4+ T cells were gated based on lack of CD25 and FoxP3 expression and further gated on CD45RA− non-naïve cells. Tfh subsets were then identified based on expression of CXCR5 and PD-1. GC = germinal center.

Extended Data Fig. 4 Determination of B cell subsets in the LLN from Leiden clustering and BCR clone identification.

a, UMAP projection of all B cells colored by original Leiden cluster and additional key markers delineating known B cell subsets (bottom rows). b, Dotplot shows top differentially expressed genes (DEGs) per Leiden cluster indicating how clusters were collapsed to delineate known subsets. Gene expression values were scaled to a log2 fold change. Dots are colored by average logFC expression and sized by the percentage of cells per cluster that expressed the particular gene. Gene lists were filtered based on a minimum logFC of >1, adjusted P < 0.01 and detected on at least 10% of cells within its cluster. Significant genes calculated using a two-sided Wilcoxon with tie correction. Indicated at the bottom of the dotplot is the Leiden clusters that make up the different B cell subsets based on gene expression and the tissue they are predominantly expressed in. c. Barplots indicating the number of clones for each individual donor in each site, stratified by subsets as identified in (b).

Source data

Extended Data Fig. 5 B cells from lung- and gut-associated lymph nodes exhibit similar transcriptional profiles.

Single cell RNA-sequencing data derived from 33,737 B cells from the lung-associated lymph node (LLN) and gut-associated mesenteric lymph nodes (MLN) of three donors aged 1–3 years was analyzed as in Fig. 5 and Extended Data Fig. 4. a, UMAP projection of all B cells colored by donor (top), tissue (middle) and subset (bottom), b, Expression of top lineage-defining genes for naïve/transitional (top row), memory (middle row) and germinal center (bottom row) B cells in the UMAP. c, Heat map showing z-score expression of the top differentially expressed genes associated with the major B cell subsets in each row: Transitional (Trans), Naïve, Non-class switched (NCS) memory, Class-switched (CS) memory, and germinal center (GC) B cells. d, Proportional composition of each B cell subset in the LLN and MLN for each donor displayed as a barplot.

Source data

Extended Data Fig. 6 Antigen specificity of antibodies derived from lungs, lung-associated lymph nodes, and plasma of individual donors.

a, Spider plots showing relative reactivities of IgG and IgA for the different antigen types (green=LLN, pink=lung, violet=plasma) from each pediatric donor (lung/LLN: n = 6, plasma: n = 6 (3 samples from organ donors and 3 samples from living blood donors) as determined by the antigen array assay (see methods). Tissue and plasma samples derived from the same donors are linked with vertical lines. b, Bar graphs showing geometric mean titers (GMT) of each Influenza strain in serum of adult (red, n = 5) and pediatric (green, n = 6) donors. Significance was determined by unpaired t test and p value generated by two-tailed test (p ≦0.05). Error bars indicate SD.

Source data

Supplementary information

Supplementary Information

Supplementary Tables 1, 2 and 7–9.

Reporting Summary

Peer review file

Supplementary Data

Supplementary Tables 3–6. Lists of differentially expressed genes (DEGs) with corresponding P values and log2 (fold change) values.

Source data

Source Data Fig. 1

Statistical source data. Donor metainformation.

Source Data Fig. 2

Statistical source data. Tab 1: percentage of airways containing BALT. Tab 2: percent cell density in and out of BALT. Tab 3: percentage of BALT with GC. Tab 4: percentage of AID+ GC B cells.

Source Data Fig. 3

Statistical source data. Proportions of B cell subsets in LLNs (tab 1) and lung (tab 2).

Source Data Fig. 4

Statistical source data. Proportions of TFH subsets, ICOS, TIGIT and BCL-2 expression in the lungs and LLNs.

Source Data Fig. 5

Statistical source data. Tab 1: Fig. 5c. B cell subset proportions. Tab 2: Fig. 5g. Proportion of expanded clones (>1). Tab 3: Fig. 5h. Fraction of mutated clones (>2%). Tab 4: Fig. 5i. Average mutations per clone. Tab 5: Fig. 5j. Ratio of FWR to CDR mutations.

Source Data Fig. 6

Statistical source data. Tab 1: Fig. 6a. Clonal proportion plots. Tab 2: Fig. 6c. Fraction mutated. Tab 3: Fig. 6d. Mutation rate in shared clones. Tab 4: Fig. 6e,f. Mutation rate of shared clones mutated in both sites or clones mutated in one site.

Source Data Fig. 7

Statistical source data. Tab 1: Mean IgG in lung, LLNs and plasma. Tab 2: Mean IgA in lungs, LLNs and plasma. Tab 3: Mean IgG/IgA across flu strains. Tab 4: Infected/healthy bar plots.

Source Data Extended Data Fig. 4

Data table. Extended Data Fig. 4c. Number of clones per subset, tissue and donor

Source Data Extended Data Fig. 5

Statistical source data. Extended Data Fig. 5d. B cell subset proportions in the mesenteric LNs and LLNs.

Source Data Extended Data Fig. 6

Statistical source data. Individual values for IgG (tab 1) and IgA (tab 2).

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Matsumoto, R., Gray, J., Rybkina, K. et al. Induction of bronchus-associated lymphoid tissue is an early life adaptation for promoting human B cell immunity. Nat Immunol 24, 1370–1381 (2023). https://doi.org/10.1038/s41590-023-01557-3

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