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Neuroinflammation creates an immune regulatory niche at the meningeal lymphatic vasculature near the cribriform plate

Nature Immunology volume 23, pages 581–593 (2022)Cite this article

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Abstract

Meningeal lymphatics near the cribriform plate undergo lymphangiogenesis during neuroinflammation to drain excess fluid. Here, we hypothesized that lymphangiogenic vessels may acquire an altered phenotype to regulate immunity. Using single-cell RNA sequencing of meningeal lymphatics near the cribriform plate from healthy and experimental autoimmune encephalomyelitis in the C57BL/6 model, we report that neuroinflammation induces the upregulation of genes involved in antigen presentation such as major histocompatibility complex class II, adhesion molecules including vascular cell adhesion protein 1 and immunoregulatory molecules such as programmed cell death 1 ligand 1, where many of these changes are mediated by interferon-γ. The inflamed lymphatics retain CD11c+ cells and CD4 T cells where they capture and present antigen, creating an immunoregulatory niche that represents an underappreciated interface in the regulation of neuroinflammation. We also found discontinuity of the arachnoid membrane near the cribriform plate, which provides unrestricted access to the cerebrospinal fluid. These findings highlight a previously unknown function of local meningeal lymphatics in regulating immunity that has only previously been characterized in draining lymph nodes.

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Fig. 1: cpLECs display a unique phenotype during neuroinflammation.
Fig. 2: Neuroinflammation increases leukocyte binding to cpLECs.
Fig. 3: cpLECs capture and present CNS-derived antigen.
Fig. 4: Inflamed cpLECs activate naive 2D2 T cells.
Fig. 5: Upregulation of podoplanin and PD-L1 is mediated by IFN-γ.
Fig. 6: cpLECs are in a prime position to access the CSF.
Fig. 7: cpLECs have direct access to the SAS.

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Data availability

The data generated for this study are deposited at the Gene Expression Omnibus under GSE175802. Source data are are provided with this paper.

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Acknowledgements

We thank K. Maclivay for his expertise in flow cytometry, L. Schmitt-Brunold for her expertise in molecular biology and all members of our laboratory for insightful discussions and constructive criticisms of this work. We thank members of the University of Wisconsin Flow Core Facility for their assistance and expertise in FACS sorting, T. Duellman and S. Splinter BonDurant for their expertise and assistance with scRNA-seq and B. Rauch and E. Meyerand at the UW Small Animal Imaging Facility supported by a UWCCC grant no. P30 CA014520 for the use of its facilities and services. This work was supported by National Institutes of Health grant nos. NS108497 and NS103506 awarded to Z.F., grant no. HL128778 awarded to M.S., a Neuroscience Training Program grant no. T32-GM007507 to M.H. and C.L. and a 10X Genomics Pilot grant to Z.F.

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Author notes

  1. These authors jointly supervised this work: Matyas Sandor, Zsuzsanna Fabry.

Authors and Affiliations

  1. Neuroscience Training Program, University of Wisconsin–Madison, Madison, WI, USA

    Martin Hsu, Collin Laaker & Andy Madrid

  2. Department of Pathology and Laboratory Medicine, School of Medicine and Public Health, University of Wisconsin–Madison, Madison, WI, USA

    Melinda Herbath, Matyas Sandor & Zsuzsanna Fabry

  3. School of Pharmacy, University of Wisconsin–Madison, Madison, WI, USA

    Yun Hwa Choi

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Contributions

M.H., M.S. and Z.F. conceptualized the experiments. M.H. performed the experiments, generated the figures, analyzed the data and wrote the manuscript. A.M. performed the analysis and assisted in generating the figures from the scRNA-seq data. C.L. assisted with FACS for scRNA-seq and manuscript writing. C.L. and ME.H. (Melinda Herbath) assisted with the in vitro coculture experiments. Y.H.C. assisted with the MRI experiments. All authors edited the manuscript.

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Correspondence to Zsuzsanna Fabry.

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Nature Immunology thanks Britta Engelhardt and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Laurie Dempsey was the primary editor(s) on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 Characterization of cribriform plate cell suspension.

(a): Experimental design for generating a single cell suspension of the cribriform plate. (b – d): Gating strategy used to identify cell types from the cpLEC cell suspension between healthy (c) and EAE score 3.0 (d). Microglia are identified as CD45intermediate CD11b+, macrophages as CD45+ CD11b+ CD11c−, dendritic cells as CD45+ CD11b+ CD11c+, CD4 T cells as CD45+ CD11b− CD11c− CD4+, CD8 T cells as CD45+ CD11b− CD11c− CD8+, B cells as CD45+ CD11b− CD11c− CD4− CD8- B220+, blood endothelial cells as CD45low CD31+ Lyve-1- Podoplanin−, and lymphatic endothelial cells as CD45low CD31+ Lyve-1+ Podoplanin+. (e – l): Quantitation of the average cell numbers of microglia (e), macrophages (f), dendritic cells (g), CD4 T cells (h), CD8 T cells (i), B cells (j), blood endothelial cells (k), and lymphatic endothelial cells (l) between healthy and EAE score 3.0. n = 5 healthy mice, 4 EAE mice; data are represented as mean ± standard error of the mean. For microglia, p = 0.0001; for macrophages, p = 0.0091; for dendritic cells, p = 0.0141; for CD4 T cells, p = 0.0031; for CD8 T cells, p = 0.0011; for B cells, p = 0.1059; for blood endothelial cells, p = 0.2523; for lymphatic endothelial cells, p = 0.0045; unpaired Student’s t-test.

Source data

Extended Data Fig. 2 Volcano and CNET plots of scRNAseq.

(a): Volcano plot showing the top 50 most up-regulated and down-regulated genes in Cluster 1. (b): Cnet plot detailing the strength of association between representative GO enrichment terms for Cluster 1 for regulation of angiogenesis, regulation of protein catabolic process, regulation of cell-cell adhesion, and response to nutrient levels along with their associated genes. (c): Volcano plot showing the top 50 most up-regulated and down-regulated genes in Cluster 2. (d): Cnet plot detailing the strength of association between representative GO enrichment terms for Cluster 2 for antigen processing and presentation, response to interferon-gamma, leukocyte cell-cell adhesion, leukocyte chemotaxis, and leukocyte activation involved in immune response along with their associated genes. (e): Volcano plot showing the top 50 most up-regulated genes in Cluster 3. (f): Cnet plot detailing the strength of association between representative GO enrichment terms for Cluster 3 for nuclear division, positive regulation of cell cycle, DNA replication, and cell cycle checkpoint along with their associated genes.

Extended Data Fig. 3 Visualizing cpLEC trajectories.

(a): Single cell data was clustered using UMAP methodology and trajectories learned using default parameters by monacle3. Trajectories through pseudotime are shown for the three clusters; note that cluster 3 lies in between clusters 1 and 2 through pseudotime, and there are no direct connections between clusters 1 and 2. (b): 5 representative genes associated with the GO enrichment term adhesion/chemotaxis that is enriched in cluster 2 are shown through pseudotime. (c): 4 representative genes associated with the GO enrichment term response to IFN-γ that is enriched in cluster 2 are shown through pseudotime. (d): 3 representative genes associated with the GO enrichment term antigen processing/presentation that is enriched in cluster 2 are shown through pseudotime. (e): 4 representative genes associated with the GO enrichment term leukocyte activation that is enriched in cluster 2 are shown through pseudotime. Note that these genes tend to be up-regulated later through pseudotime relative to genes associated with the other 3 enrichment terms.

Extended Data Fig. 4 Characterizing cpLEC-leukocyte doublets.

(a): Gating strategy used to visualize leukocyte – LEC binding. Live Ghost UV450− doublets were gated for, and a leukocyte bound to a LEC were gated for both the leukocyte marker CD45 and LEC markers Podoplanin, Lyve-1, and CD31. Leukocytes were further gated for CD11b+ macrophages, CD11b+ CD11c+ dendritic cells, CD4+ T cells, CD8+ T cells, and B220+ B cells. (b-e): Representative confocal images taken of a healthy cribriform plate section immunolabeled with OVA-GFP (b), Podoplanin (c), CD11b (d), and merged (e). Scale bar = 50 µm. (f-i): Representative confocal images taken of an EAE score 3.0 cribriform plate section immunolabeled with OVA-GFP (f), Podoplanin (g), CD11b (h), and merged (i). Scale bar = 50 µm. (j): Quantitation of the average percent area of OVA-GFP within Podoplanin+ cpLECs after excluding CD11b+ area. n = 4 healthy mice, 6 EAE mice; data are represented as mean ± standard error of the mean. For percent area of OVA-GFP, p = 0.9079; unpaired Student’s t-test. (k): Quantitation of the absolute area of OVA-GFP within Podoplanin+ cpLECs. n = 4 healthy mice, 6 EAE mice; data are represented as mean ± standard error of the mean. For absolute area of OVA-GFP, p = 0.0380; unpaired Student’s t-test. (l): Quantitation of the average number of CD11b+ cells containing intracellular OVA-GFP within Podoplanin+ cpLECs. n = 4 healthy mice, 6 EAE mice; data are represented as mean ± standard error of the mean. For number of OVA-GFP+ CD11b+ cells, p = 0.0022; unpaired Student’s t-test. (m – o): Representative confocal images taken of the cribriform plate from healthy (m) or EAE score 3.0 at 15 days post-immunization (n) immunolabeled with VCAM-1 and Podoplanin. Scale bar = 200um. (o): Quantitation of the average percent area of Podoplanin+ meningeal lymphatic vessels near the cribriform plate that express Vcam-1. n = 6 mice per group; data are represented as mean ± standard error of the mean. For percent area of Vcam-1+ labeling within Podoplanin+ cells, p = 0.0152; unpaired Student’s t-test. (p – u): Representative confocal images taken of an EAE score 3.0 cribriform plate section immunolabeled with CD11c (p), F4/80 (q), Ly6G (r), Lyve-1 (s), and merged (t). Quantitation reveals that during EAE, the majority of CD11c+ cells in contact with Lyve-1+ cpLECs are F4/80− and Ly6G−, with a relatively minor subset of CD11c+ cells identified as F4/80+ macrophages or Ly6G+ neutrophils (u). Scale bar = 50 µm.

Source data

Extended Data Fig. 5 cpLECs can present OVA323-339 to OT-II T cells.

(a – f): Healthy or EAE score 3.0 cpLECs were FACS sorted as CD31+ Podoplanin+ after excluding CD45intermediate microglia, CD45+ leukocytes, Ghost+ dead cells, and doublets. Sorted cpLECs were then co-cultured with magnetically purified CD4 OT-II T cells from splenocytes using negative selection after Cell Trace Violet labeling in the presence of 100 µg/mL of OVA323-339 for either 24 hours (a – c) or 72 hours (d – f). (a – b): Gating strategy for measuring the early T cell activation marker CD69 by OT-II T cells after 24 hours of co-culture. (c): Quantitation of the average percentage of 2D2 T cells expressing CD69 after 24 hours of co-culture. n = 3 replicates per group, pooled from 4 healthy and 4 EAE mice; data are represented as mean ± standard error of the mean. For percent of CD69 OT-II cells, p < 0.0001. (d – e): Gating strategy for measuring T cell proliferation by the dilution of Cell Trace Violet by OT-II T cell after 72 hours of co-culture. (f): Quantitation of the average percent of proliferated OT-II T cells after 72 hours of co-culture. n = 3 replicates per group, pooled from 4 healthy and 4 EAE mice; data are represented as mean ± standard error of the mean. For percent of proliferated OT-II cells, p = 0.0012.

Source data

Extended Data Fig. 6 cpLECs upregulate CD31, Podoplanin, Lyve-1, and PD-L1 during EAE.

(a): Gating strategy used to confirm the up-regulation of CD31, Podoplanin, Lyve-1, and PDL-1 at the protein level during EAE score 3.0. (b – q): After gating for cpLECs as either Podoplanin+ CD31+ (b – e, n – q), Podoplanin+ Lyve-1+ (f – i), or Lyve-1+ CD31+ (j – m), the median fluorescence intensity (MFI) of Lyve-1 (b – e), CD31 (f – i), Podoplanin (j – m), and PDL-1 (n – q) by both singlet cpLECs and doublets in which a cpLEC is bound to a CD45+ leukocyte. n = 5 healthy cpLECs, 4 EAE cpLECs, and 4 EAE cpLECs + leukocytes; data are represented as mean ± standard error of the mean. For Lyve-1 MFI, healthy cpLECs vs. EAE cpLECs, p = 0.0003, healthy cpLECs vs. EAE cpLECs + leukocytes, p = 0.0001, EAE cpLECs vs. EAE cpLECs + leukocytes, p = 0.6590; for CD31 MFI, healthy cpLECs vs. EAE cpLECs, p = 0.0043, healthy cpLECs vs. EAE cpLECs + leukocytes, p < 0.0001, EAE cpLECs vs. EAE cpLECs + leukocytes, p < 0.0001; for Podoplanin MFI, healthy cpLECs vs. EAE cpLECs, p = 0.0112, healthy cpLECs vs. EAE cpLECs + leukocytes, p < 0.0001, EAE cpLECs + EAE cpLECs + leukocytes, p = 0.0028; for PD-L1 MFI, healthy cpLECs vs. EAE cpLECs, p = 0.0027, healthy cpLECs vs. EAE cpLECs + leukocytes, p < 0.0001, EAE cpLECs + EAE cpLECs + leukocytes, p < 0.0001; one-way ANOVA using Tukey’s multiple comparisons test.

Source data

Extended Data Fig. 7 Background doublet MFI is negligible compared to protein expression.

(a – d): FMO controls showing the increase in MFI of Podoplanin (a), CD31 (b), Lyve-1 (c), and PDL-1 (d) of doublets relative to singlets due to background is negligible relative to actual protein expression. n = 4 mice per group; data are represented as mean ± standard error of the mean. For Podoplanin MFI (a), singlet FMO vs. doublet FMO, p > 0.9999, singlet FMO vs. EAE singlets, p = 0.0055, singlet FMO vs. EAE doublets, p < 0.0001, doublet FMO vs. EAE singlets, p = 0.0057, doublet FMO vs. EAE doublets, p < 0.0001, EAE singlets vs. EAE doublets, p = 0.0016; for CD31 MFI (b), singlet FMO vs. doublet FMO, p = 0.0130, singlet FMO vs. EAE singlets, p < 0.0001, singlet FMO vs. EAE doublets, p < 0.0001, doublet FMO vs. EAE singlets, p < 0.0001, doublet FMO vs. EAE doublets, p < 0.0001, EAE singlets vs. EAE doublets, p < 0.0001; for Lyve-1 MFI (c), singlet FMO vs. doublet FMO, p = 0.0182, singlet FMO vs. EAE singlets, p < 0.0001, singlet FMO vs. EAE doublets, p < 0.0001, doublet FMO vs. EAE singlets, p < 0.0001, doublet FMO vs. EAE doublets, p < 0.0001, EAE singlets vs. EAE doublets, p = 0.7736; for PD-L1 MFI (d), singlet FMO vs. doublet FMO, p = 0.9828, singlet FMO vs. EAE singlets, p = 0.0003, singlet FMO vs. EAE doublets, p < 0.0001, doublet FMO vs. EAE singlets, p = 0.0005, doublet FMO vs. EAE doublets, p < 0.0001, EAE singlets vs. EAE doublets, p < 0.0001; one-way ANOVA with Tukey’s post-hoc multiple comparisons test.

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Extended Data Fig. 8 Lymphangiogenesis does not require IFN-γ.

(a): Representative gating strategy used to characterize IFN-γ dependent regulation of Podoplanin and PDL-1. EAE was induced, and the expression of IFN-γ mediated Podoplanin, PDL-1, and CD31 was analyzed by flow cytometry at day 15 post-immunization at score 3.0 of EAE. Gating strategy taken from a representative wild-type EAE sample. (b – d): Gating strategy for identifying cpLECs between wild-type healthy (b), wild-type EAE score 3.0 (c), and IFN-γ-/- EAE score 3.0 (d). (e): Quantitation of the average number of cpLECs between wild-type healthy (b), wild-type EAE (c), and IFN-γ-/- EAE (d) reveal lymphangiogenesis in IFN-γ deficient transgenic mice. n = 5 wild-type healthy mice, 5 wild-type EAE mice, and 4 IFN-γ-/- mice; data are represented as mean ± standard error of the mean; for wild-type healthy vs. wild-type EAE, p = 0.0248, wild-type healthy vs. IFN-γ-/- EAE, p = 0.0062, wild-type EAE vs. IFN-γ-/- EAE, p = 0.5964; one-way ANOVA with Tukey’s post-hoc multiple comparisons test.

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Hsu, M., Laaker, C., Madrid, A. et al. Neuroinflammation creates an immune regulatory niche at the meningeal lymphatic vasculature near the cribriform plate. Nat Immunol 23, 581–593 (2022). https://doi.org/10.1038/s41590-022-01158-6

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