Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

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

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.

Your institute does not have access to this article

Access options

Buy article

Get time limited or full article access on ReadCube.

$32.00

All prices are NET prices.

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.

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.

References

  1. Aspelund, A. et al. A dural lymphatic vascular system that drains brain interstitial fluid and macromolecules. J. Exp. Med. 212, 991–999 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  2. Louveau, A. et al. Structural and functional features of central nervous system lymphatic vessels. Nature 523, 337–341 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  3. Ahn, J. H. et al. Meningeal lymphatic vessels at the skull base drain cerebrospinal fluid. Nature 572, 62–66 (2019).

    CAS  PubMed  Google Scholar 

  4. Hsu, M. et al. Neuroinflammation-induced lymphangiogenesis near the cribriform plate contributes to drainage of CNS-derived antigens and immune cells. Nat. Commun. 10, 229 (2019).

    PubMed  PubMed Central  Google Scholar 

  5. Louveau, A. et al. CNS lymphatic drainage and neuroinflammation are regulated by meningeal lymphatic vasculature. Nat. Neurosci. 21, 1380–1391 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  6. De Mesquita, S. et al. Functional aspects of meningeal lymphatics in ageing and Alzheimer’s disease. Nature 560, 185–191 (2018).

    Google Scholar 

  7. Bolte, A. C. et al. Meningeal lymphatic dysfunction exacerbates traumatic brain injury pathogenesis. Nat. Commun. 11, 4524 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Hu, X. et al. Meningeal lymphatic vessels regulate brain tumor drainage and immunity. Cell Res. 30, 229–243 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. Song, E. et al. VEGF-C-driven lymphatic drainage enables immunosurveillance of brain tumours. Nature 577, 689–694 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  10. Xiang, M. et al. A single-cell transcriptional roadmap of the mouse and human lymph node lymphatic vasculature. Front. Cardiovasc. Med. 7, 52 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. Takeda, A. et al. Single-cell survey of human lymphatics unveils marked endothelial cell heterogeneity and mechanisms of homing for neutrophils. Immunity 51, 561–572.e5 (2019).

    CAS  PubMed  Google Scholar 

  12. Tewalt, E. F. et al. Lymphatic endothelial cells induce tolerance via PD-L1 and lack of costimulation leading to high-level PD-1 expression on CD8 T cells. Blood 120, 4772–4782 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. Tewalt, E. F., Cohen, J. N., Rouhani, S. J. & Engelhard, V. H. Lymphatic endothelial cells—key players in regulation of tolerance and immunity. Front. Immunol. 3, 305 (2012).

    PubMed  PubMed Central  Google Scholar 

  14. Tamburini, B. A., Burchill, M. A. & Kedl, R. M. Antigen capture and archiving by lymphatic endothelial cells following vaccination or viral infection. Nat. Commun. 5, 3989 (2014).

    CAS  PubMed  Google Scholar 

  15. Lucas, E. D. et al. Type 1 IFN and PD-L1 coordinate lymphatic endothelial cell expansion and contraction during an inflammatory immune response. J. Immunol. 201, 1735–1747 (2018).

    CAS  PubMed  Google Scholar 

  16. Rouhani, S. J. et al. Roles of lymphatic endothelial cells expressing peripheral tissue antigens in CD4 T-cell tolerance induction. Nat. Commun. 6, 6771 (2015).

    CAS  PubMed  Google Scholar 

  17. Lucas, E. D. & Tamburini, B. A. J. Lymph node lymphatic endothelial cell expansion and contraction and the programming of the immune response. Front. Immunol. 10, 36 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Santambrogio, L., Berendam, S. J. & Engelhard, V. H. The antigen processing and presentation machinery in lymphatic endothelial cells. Front. Immunol. 10, 1033 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. Herz, J., Louveau, A., Da Mesquita, S. & Kipnis, J. Morphological and functional analysis of CNS-associated lymphatics. Methods Mol. Biol. 1846, 141–151 (2018).

    CAS  PubMed  Google Scholar 

  20. Kretschmer, S. et al. Autofluorescence multiphoton microscopy for visualization of tissue morphology and cellular dynamics in murine and human airways. Lab. Invest. 96, 918–931 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. Maruyama, K. et al. Inflammation-induced lymphangiogenesis in the cornea arises from CD11b-positive macrophages. J. Clin. Invest. 115, 2363–2372 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. Zumsteg, A. et al. Myeloid cells contribute to tumor lymphangiogenesis. PLoS ONE 4, e7067 (2009).

    PubMed  PubMed Central  Google Scholar 

  23. Kerjaschki, D. The crucial role of macrophages in lymphangiogenesis. J. Clin. Invest. 115, 2316–2319 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. Trapnell, C. et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat. Biotechnol. 32, 381–386 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. Qiu, X. et al. Single-cell mRNA quantification and differential analysis with Census. Nat. Methods 14, 309–315 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. Qiu, X. et al. Reversed graph embedding resolves complex single-cell trajectories. Nat. Methods 14, 979–982 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. Volk-Draper, L. D., Hall, K. L., Wilber, A. C. & Ran, S. Lymphatic endothelial progenitors originate from plastic myeloid cells activated by Toll-like receptor-4. PLoS ONE 12, e0179257 (2017).

    PubMed  PubMed Central  Google Scholar 

  28. Ran, S. & Volk-Draper, L. Lymphatic endothelial cell progenitors in the tumor microenvironment. Adv. Exp. Med. Biol. 1234, 87–105 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. Giladi, A. et al. Dissecting cellular crosstalk by sequencing physically interacting cells. Nat. Biotechnol. 38, 629–637 (2020).

    CAS  PubMed  Google Scholar 

  30. Bendall, S. C. Diamonds in the doublets. Nat. Biotechnol. 38, 559–561 (2020).

    CAS  PubMed  Google Scholar 

  31. Paddock, C., Zhou, D., Lertkiatmongkol, P., Newman, P. J. & Zhu, J. Structural basis for PECAM-1 homophilic binding. Blood 127, 1052–1061 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. Harris, M. G. et al. Immune privilege of the CNS is not the consequence of limited antigen sampling. Sci. Rep. 4, 4422 (2014).

    PubMed  PubMed Central  Google Scholar 

  33. Rayasam, A. et al. Regional distribution of CNS antigens differentially determines T-cell mediated neuroinflammation in a CX3CR1-dependent manner. J. Neurosci. 38, 7058–7071 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. Marelli-Berg, F. M., Clement, M., Mauro, C. & Caligiuri, G. An immunologist’s guide to CD31 function in T-cells. J. Cell Sci. 126, 2343–2352 (2013).

    CAS  PubMed  Google Scholar 

  35. Acton, S. E. et al. Podoplanin-rich stromal networks induce dendritic cell motility via activation of the C-type lectin receptor CLEC-2. Immunity 37, 276–289 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. Johnson, L. A. et al. Dendritic cells enter lymph vessels by hyaluronan-mediated docking to the endothelial receptor LYVE-1. Nat. Immunol. 18, 762–770 (2017).

    CAS  PubMed  Google Scholar 

  37. Torzicky, M. et al. Platelet endothelial cell adhesion molecule-1 (PECAM-1/CD31) and CD99 are critical in lymphatic transmigration of human dendritic cells. J. Invest. Dermatol. 132, 1149–1157 (2012).

    CAS  PubMed  Google Scholar 

  38. Fletcher, J. M., Lalor, S. J., Sweeney, C. M., Tubridy, N. & Mills, K. H. G. T cells in multiple sclerosis and experimental autoimmune encephalomyelitis. Clin. Exp. Immunol. 162, 1–11 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Astarita, J. L., Acton, S. E. & Turley, S. J. Podoplanin: emerging functions in development, the immune system, and cancer. Front. Immunol. 3, 283 (2012).

    PubMed  PubMed Central  Google Scholar 

  40. Lane, R. S. et al. IFNγ-activated dermal lymphatic vessels inhibit cytotoxic T cells in melanoma and inflamed skin. J. Exp. Med. 215, 3057–3074 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. Sosa, R. A., Murphey, C., Robinson, R. R. & Forsthuber, T. G. IFN-γ ameliorates autoimmune encephalomyelitis by limiting myelin lipid peroxidation. Proc. Natl Acad. Sci. USA 112, E5038–E5047 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. Arellano, G., Ottum, P. A., Reyes, L. I., Burgos, P. I. & Naves, R. Stage-specific role of interferon-gamma in experimental autoimmune encephalomyelitis and multiple sclerosis. Front. Immunol. 6, 492 (2015).

    PubMed  PubMed Central  Google Scholar 

  43. Yao, S. et al. PD-1 on dendritic cells impedes innate immunity against bacterial infection. Blood 113, 5811–5818 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. Proulx, S. T. Cerebrospinal fluid outflow: a review of the historical and contemporary evidence for arachnoid villi, perineural routes, and dural lymphatics. Cell. Mol. Life Sci. 78, 2429–2457 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. Weller, R. O., Sharp, M. M., Christodoulides, M., Carare, R. O. & Møllgård, K. The meninges as barriers and facilitators for the movement of fluid, cells and pathogens related to the rodent and human CNS. Acta Neuropathol. 135, 363–385 (2018).

    CAS  PubMed  Google Scholar 

  46. Norwood, J. N. et al. Anatomical basis and physiological role of cerebrospinal fluid transport through the murine cribriform plate. eLife 8, e44278 (2019).

    Google Scholar 

  47. Rustenhoven, J. et al. Functional characterization of the dural sinuses as a neuroimmune interface. Cell 184, 1000–1016.e27 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  48. Russo, E., Nitschké, M. & Halin, C. Dendritic cell interactions with lymphatic endothelium. Lymphat. Res. Biol. 11, 172–182 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  49. Clarkson, B. D. et al. CCR7 deficient inflammatory dendritic cells are retained in the central nervous system. Sci. Rep. 7, 42856 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. Bianchi, R. et al. Postnatal deletion of podoplanin in lymphatic endothelium results in blood filling of the lymphatic system and impairs dendritic cell migration to lymph nodes. Arterioscler. Thromb. Vasc. Biol. 37, 108–117 (2017).

    CAS  PubMed  Google Scholar 

  51. Johnson, L. A. et al. An inflammation-induced mechanism for leukocyte transmigration across lymphatic vessel endothelium. J. Exp. Med. 203, 2763–2777 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. Rinaldi, E. & Baggi, F. LYVE-1 is ‘on stage’ now: an emerging player in dendritic cell docking to lymphatic endothelial cells. Cell. Mol. Immunol. 15, 663–665 (2018).

    CAS  PubMed  Google Scholar 

  53. Keir, M. E., Butte, M. J., Freeman, G. J. & Sharpe, A. H. PD-1 and its ligands in tolerance and immunity. Annu. Rev. Immunol. 26, 677–704 (2008).

    CAS  PubMed  Google Scholar 

  54. Park, S. J. et al. Negative role of inducible PD-1 on survival of activated dendritic cells. J. Leukoc. Biol. 95, 621–629 (2014).

    PubMed  Google Scholar 

  55. Krempski, J. et al. Tumor-infiltrating programmed death receptor-1+ dendritic cells mediate immune suppression in ovarian cancer. J. Immunol. 186, 6905–6913 (2011).

    CAS  PubMed  Google Scholar 

  56. Lim, T. S. et al. PD-1 expression on dendritic cells suppresses CD8+ T cell function and antitumor immunity. Oncoimmunology 5, e1085146 (2016).

    PubMed  Google Scholar 

  57. Palle, P., Monaghan, K. L., Milne, S. M. & Wan, E. C. K. Cytokine signaling in multiple sclerosis and its therapeutic applications. Med. Sci. (Basel) 5, 23 (2017).

    Google Scholar 

  58. Clarkson, B. D. et al. CCR2-dependent dendritic cell accumulation in the central nervous system during early effector experimental autoimmune encephalomyelitis is essential for effector T cell restimulation in situ and disease progression. J. Immunol. 194, 531–541 (2015).

    CAS  PubMed  Google Scholar 

  59. Hauglund, N. L., Kusk, P., Kornum, B. R. & Nedergaard, M. Meningeal lymphangiogenesis and enhanced glymphatic activity in mice with chronically implanted EEG electrodes. J. Neurosci. 40, 2371–2380 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  60. Hsu, M., Sandor, M. & Fabry, Z. Current concepts on communication between the central nervous system and peripheral immunity via lymphatics: what roles do lymphatics play in brain and spinal cord disease pathogenesis? Biol. Futur. 72, 45–60 (2021).

    PubMed  Google Scholar 

  61. Hsu, M., Laaker, C., Sandor, M. & Fabry, Z. Neuroinflammation-driven lymphangiogenesis in CNS diseases. Front. Cell. Neurosci. 15, 683676 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  62. Raghunandan, A. et al. Bulk flow of cerebrospinal fluid observed in periarterial spaces is not an artifact of injection. eLife 10, e65958 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  63. Smith, T., Heger, A. & Sudbery, I. UMI-tools: modeling sequencing errors in unique molecular identifiers to improve quantification accuracy. Genome Res. 27, 491–499 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  64. Yu, G., Wang, L.-G., Han, Y. & He, Q.-Y. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 16, 284–287 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

Download references

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.

Author information

Authors and Affiliations

Authors

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.

Corresponding author

Correspondence to Zsuzsanna Fabry.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

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.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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.

Source data

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.

Source data

Supplementary information

Source data

Source Data Fig. 2

Statistical source data.

Source Data Fig. 4

Statistical source data.

Source Data Fig. 5

Statistical source data.

Source Data Fig. 7

Statistical source data.

Source Data Extended Data Fig. 1

Statistical source data.

Source Data Extended Data Fig. 4

Statistical source data.

Source Data Extended Data Fig. 5

Statistical source data.

Source Data Extended Data Fig. 6

Statistical source data.

Source Data Extended Data Fig. 7

Statistical source data.

Source Data Extended Data Fig. 8

Statistical source data.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41590-022-01158-6

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing