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Cognate microglia–T cell interactions shape the functional regulatory T cell pool in experimental autoimmune encephalomyelitis pathology

Nature Immunology volume 23, pages 1749–1762 (2022)Cite this article

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Abstract

Microglia, the parenchymal brain macrophages of the central nervous system, have emerged as critical players in brain development and homeostasis. The immune functions of these cells, however, remain less well defined. We investigated contributions of microglia in a relapsing–remitting multiple sclerosis paradigm, experimental autoimmune encephalitis in C57BL/6 x SJL F1 mice. Fate mapping-assisted translatome profiling during the relapsing–remitting disease course revealed the potential of microglia to interact with T cells through antigen presentation, costimulation and coinhibition. Abundant microglia–T cell aggregates, as observed by histology and flow cytometry, supported the idea of functional interactions of microglia and T cells during remission, with a bias towards regulatory T cells. Finally, microglia-restricted interferon-γ receptor and major histocompatibility complex mutagenesis significantly affected the functionality of the regulatory T cell compartment in the diseased central nervous system and remission. Collectively, our data establish critical non-redundant cognate and cytokine-mediated interactions of microglia with CD4+ T cells during autoimmune neuroinflammation.

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Fig. 1: Longitudinal fate map-based translatome profiling of microglia during the RR-EAE course.
Fig. 2: Translatome analysis of microglia during the course of RR-EAE.
Fig. 3: Absence of overt microglia activation during the REL stage.
Fig. 4: Interactions of microglia with T cells in RR-EAE.
Fig. 5: Analysis of EAE pathology in animals harboring a microglial Ifn-γR deficiency.
Fig. 6: Microglial Ifn-γR deficiency affects brain T cells in RR-EAE.
Fig. 7: Transcriptome analysis of the CNS T cell compartment during RR-EAE.
Fig. 8: Microglial MHC class II deficiency affects Treg cells in RR-EAE.

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Data availability

The RNA-seq datasets reported in this study can be found at Gene Expression Omnibus under the accession code GSE214709. Other data and reagents are available from the corresponding author on request. Source data are provided with this paper.

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Acknowledgements

The Jung Laboratory was supported by a collaborative network grant of the International Progressive MS Alliance (PMSA), the Minerva Foundation with funding from the Federal German Ministry for Education, the American Brain Foundation, the Roland N. Karlen Foundation, the Blythe Brenden–Mann Foundation, the Estate of David Levinson and the Deutsche Forschungsgemeinschaft (DFG) (CRC/TRR167 ‘NeuroMac’). T.K. is supported by the DFG (SFB1054-B06 (ID 210592381), TRR128-A07 (ID 213904703), TRR128-A12 (ID 213904703), TRR128-Z02 (ID 213904703), TRR274-A01 (ID 408885537) and EXC 2145 (SyNergy, ID 390857198) and the Hertie Foundation. We thank all members of the Jung Laboratory, the staff of the Weizmann FACS facility as well as the Weizmann Veterinary Services for expert technical help. We thank B. Dassa and A. Sarusi-Portuguez a for advice with bioinformatics, and I. Milenkovic (Institute of Neurology, Medical University of Vienna) for valuable comments on the manuscript. S. Jung is the incumbent of the Henry. H. Drake Professional Chair of Immunology.

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Authors and Affiliations

  1. Department of Immunology and Regenerative Biology, Weizmann Institute of Science, Rehovot, Israel

    Zhana Haimon, Gal Ronit Frumer, Jung-Seok Kim, Sébastien Trzebanski, Louise Chappell-Maor, Sigalit Boura-Halfon & Steffen Jung

  2. Department of Veterinary Resources, Weizmann Institute of Science, Rehovot, Israel

    Rebecca Haffner-Krausz

  3. Department of Life Sciences Core Facilities, Weizmann Institute of Science, Rehovot, Israel

    Shifra Ben-Dor & Ziv Porat

  4. Institute for Experimental Neuroimmunology, Klinikum rechts der Isar, Technical University of Munich School of Medicine, Munich, Germany

    Andreas Muschaweckh & Thomas Korn

  5. Munich Cluster for Systems Neurology (SyNergy), Munich, Germany

    Thomas Korn

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Contributions

Z.H. and S.J. conceived the project and designed the experiments. Z.H., G.R.F., J.-S.K., S.T. and S.B.-H. performed experiments. Z.P. helped with the ImageStream analysis. L.C.-M. performed the RNA-seq. S.B.-D and R.H.-K. critically helped with the CRISPR–Cas9 mutagenesis design and generation of the mutant animals. A.M. and T.K. critically advised on the T cell analysis. Z.H. and S.J. wrote the manuscript. S.J. supervised the project and acquired funding.

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Correspondence to Steffen Jung.

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Extended data

Extended Data Fig. 1 Gating strategies for flow cytometric analysis.

Representative analysis and gating strategy for Fig. 1d, upper panel, isotype control; lower panel, anti-HA staining, both at MAX stage.

Extended Data Fig. 2 Translatome analysis of microglia during the RR-EAE course.

(a) Representative genes from each cluster. Light gray circles, input, dark gray triangles, IP IgG, light blue squares, IP HA. Each dot represents individual mice, lines represent mean. NI, non-immunized, INIT, initiation, MAX, maximal score, REM, remission, RCOV, clinical recovery, REL, relapse. N = 6 NI, 4 INIT, 6 MAX, 8 REM, 5 RCOV and 4 REL individual mice per group. (b) Expression of selected genes encoding proposed BAM markers during the RR-EAE course. Heatmap represents log2 values of average HA samples in each stage, alongside graphs of expression of individual genes. N = 6 NI, 4 INIT, 6 MAX, 8 REM, 5 RCOV and 4 REL individual mice per group, lines represent mean.

Extended Data Fig. 3 Total brain transcriptome analysis during the RR-EAE course.

(a) Heatmap of 1378 genes retrieved as total brain transcriptomes. Differential expression was calculated using DESeq2 package. Each column is an average of input samples. Genes were clustered using K-means clustering. NI, non-immunized, INIT, initiation, MAX, maximal score, REM, remission, RCOV, clinical recovery, REL, relapse. N = 6 NI, 4 INIT, 6 MAX, 8 REM, 5 RCOV and 4 REL individual mice per group. (b) Heatmap of genes selected according to biological relevant categories. (c) Normalized reads of selected genes that show dynamic expression in brain transcriptome (input samples) during EAE course. N = 6 NI, 4 INIT, 6 MAX, 8 REM, 5 RCOV and 4 REL individual mice per group, lines represent mean. (d)Number of DEG (abs fold change > 2, p < 0.05) detected in the total brain transcriptomes at different stages.

Extended Data Fig. 4 Probing the extent of ex-vivo Microglia-T cell doublet formation.

(a) Scheme describing the mixing experiment where brains from a whole body GFP or whole body TdTomato mice were mixed together or isolated separately. Mixed brains went through enzymatic digestion and cell isolation process together. Gating strategy showing the extent of GFP/TdTomato cells from each population. (b) Calculation of the probability to obtain spurious vs. real doublets, showing that most of the doublets are not spurious (with kind help of Shalev Itzkovitz, WIS).

Extended Data Fig. 5 ImageStream and Immunohistochemistry analysis of microglia / T cell interactions in the brain.

(a) Image stream analysis of Mg–T doublets with Foxp3+ or RORγt+ T cells, by intracellular staining. Microglia were identified as CD11b+ Ly6C/G- and CD45 low cells, T cells were identified as CD45hi CD3+ CD4+ and Foxp3+ or RORγt+ cells. (b) Immunofluorescence staining of brain sections of TAM-treated Cx3cr1creER:Rpl22HA mice at the MAX stage, staining for IBA1 (cyan), HA (green), CD3 (red), Foxp3 (blue) and MHC-II (magenta). Scale bar 20uM. The image was acquired using linear unmixing in Zeiss LSM880 confocal microscope. Representative of 3 independent experiments. (c) Enlargement of the selected window of (b).

Extended Data Fig. 6 Generation of Ifnγr receptor deficient SJL mice.

(a) Schematic of CRISPR/Cas9−based Ifnγr1 targeting strategy to delete 203 bp spanning the promoter and first exon of the Ifnγr1 gene. (b) Schematic showing the WT and mutant Ifnγr1 locus with details of the respective 203 bp deletion. (c) Genomic PCR analysis of Ifnγr1 −/− SJL mouse (~400 bp) and WT control (~600 bp). Representative out of infinite number of experiments. (d) Flow cytometric analysis of non-classical CD115+ Ly6c- blood monocytes for expression of CD119 (Ifnγr) in WT, Ifnγr1−/+ or Ifnγr1 −/− SJL mouse. e, f. Flow cytometric analysis of IFNγR (E) or MHC-II (F) expression of MoMF (gated as live CD45hi CD11b+ TMEM119-) in control or MgΔIfnγr1 mice at REM. MFI, mean fluorescent intensity. N = 5 individual mice per group, lines represent mean. Significance was calculated using two tailed t-test. Dashed line/empty circles - FMO; black histogram/black dots - control; red histogram/red triangles - MgΔIfnγr1 mice. g. Normalized MFI of Treg cells markers on Foxp3+ T cells of MgΔIfnγr1 and control brains. Data combined from 5 independent experiments n = 27 individual mice per group, lines represent mean. Significance was calculated using two tailed t-test. *** p < 0.001. h. Overlay of microglia (light blue; gated on live Ly6c/g1− CD11b+ CD45int TMEM119+), singlet T cells (red, gated on live Ly6c/g1- CD11b− CD45hi CD4+ TCRβ+) and total T cells (gray; gated on live Ly6c/g1− CD45hi CD4+ TCRβ+), related to Fig. 5g. i. Normalized MFI of non-Treg cells (CD4+TCRβ+ Foxp3−) of MgΔIfnγr1 and control brains. Data combined from 5 independent experiments n = 27 per group, lines represent mean. Significance was calculated using two tailed t-test. *p < 0.05, *** p < 0.001.

Extended Data Fig. 7 Bulk RNAseq analysis of T cells isolated from brains of MgΔIfnγr mice and littermate controls.

(a) Sorting strategy for isolation of CD25hi (Treg) and CD25− (Teff) cells from brains of MgΔIfnγr mice and littermate controls. (b) Normalized reads of selected genes from sorted CD25− (Teff) and CD25hi (Treg) cells extracted from B6/SJL MgΔIfnγr and control mice at REM stage of RR-EAE, representing Treg genes not changed between MgΔIfnγr and control. n = 6 individual mice per group, lines represent mean. (c) Normalized reads of selected genes from sorted CD25- (Teff) and CD25hi (Treg) cells extracted from B6/SJL MgΔIfnγr and control mice at REM stage of RR-EAE, representing genes significantly changed in Treg cells between MgΔIfnγr and control. n = 6 individual mice per group, lines represent mean. (d) GSEA comparison WT and MgΔIfnγr T reg cell transcriptomes with list of genes upregulated in WT CNS Treg cells vs. splenic Treg cells6. Normalized enrichment score (NES) and the false discovery rate (FDR) are shown in the enrichment plot. Normalized enrichment score = −1.04, FDR q-value = 0.34. This indicates that Treg cells from MgΔIfnγr brains do not acquire features of a CNS Treg signature.

Extended Data Fig. 8 Generation of MHC II deficient SJL mice and analysis of T cell compartment following RR-EAE challenge.

(a) Schematic of CRISPR/Cas9-based H2-Ab1s targeting strategy. (b) Schematic showing the WT and mutant H2-Ab1s locus with details of the respective 247 bp deletion. (c) Genomic PCR analysis of H2-Ab1s −/− SJL mouse (631 bp) and WT control (~900 bp). Representative out of infinite number of experiments. (d) Flow cytometric analysis of I-As on B220+ B cells in blood of WT C57BL6, WT SJL and H2-Ab1s hetero- or homozygote SJL mice establishing MHC-II deficiency of H2-Ab1s −/− SJL mouse. (e) Flow cytometric analysis of MHCII surface expression (I-Ab) on microglia of MgΔMHCII mice and controls (diseased brains). N = 20 individual mice per group, pooled from 3 independent experiments. Lines represent mean. Significance was calculated using two tailed t-test. ****p < 0.0001. (f) Flow cytometric analysis of FoRBY2 CD4+ T cells extracted from spleen and lymph nodes of 2D2 donor animals post MACS isolation, prior to adoptive transfer. (g) Flow cytometric analysis of blood of MgΔMHCII mice and control recipient mice at day 6 post immunization showing detection of engrafted 2D2 cells as CD45.1- TCRβ11+ cells, gated from GR1−CD45hi CD11b− CD4+ TCRb+ T cells. N = 11 individual mice per group, line represent mean, data are pooled from 3 independent experiments. Significance was calculated using two tailed t-test. (h) Flow cytometric analysis of Gitr, Foxp3 and CD25 expression on CD4+ T cells, showing that Foxp3+ or CD25+ Tregs can be defined by Gitr. (i) Gating strategy for detection of grafted cells and GITR Tregs. Related to Fig. 7d, e. j Normalized MFI of Gitr and Klrg1 on Gitrhi Treg population in control and MgΔMHCII mice. n = 20 individual mouse per group, lines represent mean, data pooled from 3 independent experiments. Significance was calculated using two tailed t-test.

Source data

Extended Data Fig. 9 Analysis of engrafted MOG-specific TCR transgenic cells (2D2).

(a) Workflow and gating strategy for concatenation of 2D2 cells. Related to Fig. 8h. (b) Flow cytometric analysis of brains of three representative individual control and MgΔMHCII mice to show the reporter gene expression in the 2D2 graft. (c) Gates used for concatenation shown in Fig. 8h. Upper plots represent entire 2D2 concatenated population, lower plots represent only Foxp3+ cells gated from 2D2+ cells, in control (left) or MgΔMHCII (right). Related to Fig. 8h.

Supplementary information

Supplementary Information

Supplementary note showing gene sequences for the SJL Ifngr1 locus, chromosome 10, and the SJL H2-Ab1 locus, chromosome 17.

Reporting Summary

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Source data

Source Data Fig. 1

Flow cytometry quantification.

Source Data Fig. 2

Gene lists, scores and DPI.

Source Data Fig. 4

Quantifications (for details see the figure legends).

Source Data Fig. 5

DPI and gene lists.

Source Data Fig. 7

Gene lists.

Source Data Fig. 8

Gene lists.

Source Data Extended Data Fig. 8

Clinical disease scores.

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Haimon, Z., Frumer, G.R., Kim, JS. et al. Cognate microglia–T cell interactions shape the functional regulatory T cell pool in experimental autoimmune encephalomyelitis pathology. Nat Immunol 23, 1749–1762 (2022). https://doi.org/10.1038/s41590-022-01360-6

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  • DOI: https://doi.org/10.1038/s41590-022-01360-6

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