MAFG-driven astrocytes promote CNS inflammation


Multiple sclerosis is a chronic inflammatory disease of the CNS1. Astrocytes contribute to the pathogenesis of multiple sclerosis2, but little is known about the heterogeneity of astrocytes and its regulation. Here we report the analysis of astrocytes in multiple sclerosis and its preclinical model experimental autoimmune encephalomyelitis (EAE) by single-cell RNA sequencing in combination with cell-specific Ribotag RNA profiling, assay for transposase-accessible chromatin with sequencing (ATAC–seq), chromatin immunoprecipitation with sequencing (ChIP–seq), genome-wide analysis of DNA methylation and in vivo CRISPR–Cas9-based genetic perturbations. We identified astrocytes in EAE and multiple sclerosis that were characterized by decreased expression of NRF2 and increased expression of MAFG, which cooperates with MAT2α to promote DNA methylation and represses antioxidant and anti-inflammatory transcriptional programs. Granulocyte–macrophage colony-stimulating factor (GM-CSF) signalling in astrocytes drives the expression of MAFG and MAT2α and pro-inflammatory transcriptional modules, contributing to CNS pathology in EAE and, potentially, multiple sclerosis. Our results identify candidate therapeutic targets in multiple sclerosis.

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Fig. 1: scRNA-seq analysis of astrocytes in EAE.
Fig. 2: NRF2 suppresses pro-inflammatory astrocyte responses in EAE.
Fig. 3: MAFG in astrocytes promotes CNS inflammation.
Fig. 4: MAFG and MAT2α cooperate to promote pathogenic activity of astrocytes.
Fig. 5: GM-CSF signalling promotes pathogenic activity in astrocytes.
Fig. 6: MAFG activation characterizes an astrocyte population associated with MS.

Data availability

Sequencing data have been deposited into the Gene Expression Omnibus (GEO) under the SuperSeries accession number GSE130119. Clinical data for patient samples can be found in Supplementary Table 7. All other data and code that support the findings of this study are available from the corresponding author on reasonable request. Data from Schirmer et al.20 were accessed at PRJNA544731 and Data from Jäkel et al.17 were accessed at GSE118257. Data from Lake et al.40 were accessed at GSE97942.


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This work was supported by grants NS102807, NS087867, ES02530, AI126880, and AI093903 from the NIH; RSG-14-198-01-LIB from the American Cancer Society; and RG4111A1 and JF2161-A-5 from the National Multiple Sclerosis Society (to F.J.Q.) and the Canada Foundation for Innovation (CGEn and CFI-LOF 32557 to J.R.). F.J.Q., A.P. and J.P.A. received support from the International Progressive MS Alliance (PA-1604-08459). A.P. holds the T1 Canada Research Chair in Multiple Sclerosis and is funded by the Canada Institute of Health Research, the National Multiple Sclerosis Society, and Foundation of Canada. M.A.W. was supported by a training grant from the NIH and Dana-Farber Cancer Institute (T32CA207201), a postdoctoral fellowship from the NIH (F32NS101790), and a traveling neuroscience fellowship from the Program in Interdisciplinary Neuroscience at Brigham and Women’s Hospital. M.A.W. and I.C.C. received funding from the Women’s Brain Initiative at Brigham and Women’s Hospital. Sanger sequencing was carried out at the DNA Resource Core of Dana-Farber/Harvard Cancer Center (funded in part by NCI Cancer Center support grant 2P30CA006516- 48). We thank S. Jung and L. Chappell-Maor (Weizmann Institute of Science) for help in sequencing the Ribotag dataset; S. Young, T. Mason, and S. Garamszegi for assistance with coordinating deep sequencing; all members of the Quintana laboratory for helpful advice and discussions; P. Hewson for technical assistance; D. Kozoriz, A. Chicoine, and R. Krishnan for technical assistance with flow cytometry studies; Y. C. Wang for human control scRNA-seq library preparation; and the patients and their families that agreed to participate in this study.

Author information

M.A.W. performed most in vitro, in vivo, and sequencing experiments. M.A.W., I.C.C., A.R., J.A.H., and S.T. assisted with and/or performed Drop-seq encapsulation experiments. M.A.W., E.C.T., and Z.L. performed bioinformatic analyses. S.E.J.Z. performed immunostaining of MS patient tissue. C.P.C. generated some human healthy control patient scRNA-seq data. B.R.W. performed multiplexed FISH experiments with assistance from M.A.W. G.S., S.A., V.R., and L.M.S. performed some in vitro, in vivo, or sequencing experiments. S.E.J.Z. and V.R. assisted with human sample isolation for scRNA-seq. J.R., D.A.W., K.P., J.R.M., B.B., J.P.A., and A.P. provided unique reagents and discussed and/or interpreted findings. M.A.W. and F.J.Q. wrote the manuscript with input from coauthors. F.J.Q. designed and supervised the study and edited the manuscript.

Correspondence to Francisco J. Quintana.

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The authors declare no competing interests.

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Extended data figures and tables

Extended Data Fig. 1 Control analyses for scRNA-seq of B6 EAE mice.

a, Cell type marker expression in mice with EAE. n = 24,275 cells. b, Unsupervised clustering tSNE plots of CNS cells from mice with EAE. n = 6 per group, n = 4 priming, n = 3 CFA. n = 24,275 cells. c, Analysis of cluster occupation by cells across EAE time points. d, Significantly enriched genes by cell type cluster. e, PCs used in study. n = 24,275 cells. f, Cluster distribution by replicates. g, Principal componentsC used in astrocyte subclustering. n = 2,079 cells. h, Gene scatterplots of astrocyte markers in the astrocyte tSNE analysis. n = 2,079 cells. i, Astrocyte marker gene expression by time point during EAE. n = 2,079 cells. j, Correlation of NRF2 target gene expression during priming and peak EAE phases compared to in naive mice. NRF2 target genes are marked in red. In total, 88 out of 123 genes were decreased at at least one time point, whereas 40 out of 123 were decreased at both time points. n = 69 cells from cluster 4. Source data

Extended Data Fig. 2 Sorting of TdTomatoGfap astrocytes.

The forward versus side scatter (FSC versus SSC) gating strategy, followed by exclusion of FSC and SSC doublets, and TdTomato fluorescence in the phycoerythrin (PE) channel.

Extended Data Fig. 3 Expression of Mafg and Nfe2l2 in astrocytes.

a, EAE in TdTomatoGfap mice used for scRNA-seq. n = 4 mice per time point. b, Cluster composition by replicate. c, Cluster composition by EAE time point. d, Unsupervised clustering tSNE plot of TdTomatoGfap astrocytes from mice with EAE. n = 4 per group (peak, day 20 post-induction; remission, day 42 post-induction). e, Scatterplots of astrocyte markers. f, Scatterplots of genes of interest in this study. g, Principal components used in this analysis. n = 24,963 cells for all scRNA-seq experiments. Source data

Extended Data Fig. 4 Nfe2l2 knockdown in astrocytes.

a, RNA-seq analysis of astrocytes following intracerebroventricular injection of IL-1β/TNF, EAE induction, or no treatment. n = 3 per group, n = 2 naive. b, siRNA-based knockdown of Nfe2l2 in primary astrocytes. n = 6 biologically independent samples per condition. Experiment repeated twice. Unpaired two-tailed t-test. c, Nfe2l2 expression determined by qPCR in flow cytometry-sorted astrocytes. n = 3 mice per group. One-sample t-test. d, Flow cytometry sorting strategy for astrocytes, microglia and pro-inflammatory monocytes. e, Quantification of astrocytes, microglia and pro-inflammatory monocytes. n = 3 mice per condition. One-way ANOVA, Tukey post-test. Data shown as mean ± s.e.m. ***P < 0.001, NS (not significant) P > 0.05. Source data

Extended Data Fig. 5 Analysis of Nfe2l2Mafg signature by Ribotag and scRNA-seq.

a, EAE in mice used in RibotagGfap studies. n = 3 mice per time point. b, RINs for Ribotag preparation. IP, immunoprecipitated HA-tagged ribosomes. mRNA direct, enrichment of polyadenylated mRNA using mRNA direct kit (Thermo Fisher, #61011). n = 4 biologically independent samples per condition. c, K-means clustering of RibotagGfap RNA-seq data for five CNS regions. d, ENRICHR analysis of upregulated genes in EAE (top). Analysis of gene expression associated with the altered glutathione metabolism KEGG pathway by CNS region (bottom). Number of independent mouse samples studied: n = 7 cortex, n = 8 spinal cord, n = 7 parenchyma, n = 9 cerebellum, n = 8 cranial nerves. e, Gene expression scatterplots of genes of interest in B6 EAE scRNA-seq studies. n = 24,275 cells. Data shown as mean ± s.e.m. Source data

Extended Data Fig. 6 Mafg knockdown in astrocytes.

a, Quantification of GFAP immunoreactivity in CNS samples from naive or EAE mice. Cortex: n = 8 naive, n = 9 EAE; spinal cord: n = 13 naive, n = 9 EAE. Kolmogorov–Smirnov test. b, Immunostaining (left) and quantification (right) of MAFG+ GFAP+ astrocytes in mice targeted with sgMafg-delivering or sgScrmbl-delivering lentiviruses. n = 6 images per group from n = 3 mice. Two-tailed Mann–Whitney test. c, T cell subsets, astrocytes, microglia and pro-inflammatory monocytes in sgMafg-targeted versus sgScrmbl-targeted mice. n = 3 per condition for T cells; n = 2 for sgScrmbl IL-10+ group. Unpaired two-tailed t-test. n = 6 per condition for astrocytes, microglia, and monocytes. Experiment repeated twice. Unpaired two-tailed t-test. Data shown as mean ± s.e.m. Source data

Extended Data Fig. 7 Mat2a and Csf2rb knockdown in astrocytes.

a, Validation of Mat2a knockdown by western blot. n = 3 biologically independent samples per group. Single sample t-test. b, Quantification of Mafg expression by scRNA-seq in Csf2rb conditional knockout mice. n = 3 per condition. Unpaired two-tailed t-test. c, Quantification of cell populations in Csf2rb conditional knockout mice. n = 3 per condition. Unpaired two-tailed t-test. d, Large area scan of down-sampled stitched multiplexed FISH images. Arrowheads indicate T cells. Representative images from three independent experiments with n = 3 mice per group. Data shown as mean ± s.e.m. Source data

Extended Data Fig. 8 Control analyses of scRNA-seq on human samples.

a, tSNE plots of MS and control cells. b, Cluster occupation by disease state and patient. c, RINs and scRNA-seq data quality. n = 9 per analysis. Pearson’s correlation. d, Age and sex corresponding to samples analysed. n = 5 control, n = 4 MS. Unpaired two-tailed t-test (age), Fisher’s exact test (sex). e, Principal components used in this analysis. f, Expression scatterplots of genes of interest. n = 43,670 cells. g, Cell type classification based on significantly enriched genes by cluster. h, Cell type marker scatterplots. n = 43,670 cells. Data shown as mean ± s.e.m. Source data

Extended Data Fig. 9 Validation of GM-CSF–MAFG–NRF2 transcriptional signature in multiple human scRNA-seq datasets.

a, Analysis of regionally matched cortical astrocytes derived from patients with MS and control individuals analysed by Schirmer et al.20. n = 9 controls, n = 12 patients. b, Analysis of regionally matched white matter cortical astrocytes derived from patients with MS and control individuals analysed by Jäkel et al.17. n = 5 controls, n = 4 patients. c, Analysis of regionally matched cerebellar astrocytes derived from patients with MS analysed in this study and control individuals analysed by Lake et al.40. n = 9 controls, n = 2 patients. Source data

Extended Data Fig. 10 Control analyses of human astrocytes.

a, Gene scatterplots for astrocyte-specific markers in dataset representing control individuals and patients with MS compiled using data from Schirmer et al.20, Jäkel et al.17, Lake et al.40, and this study. n = 28 controls, n = 20 patients. n = 9,673 cells. b, Principal component analysis of all cells and astrocytes in each study. Number of cells analysed: Schirmer et al.20: 48,919 (all), 5,831 (astrocytes); Jäkel et al.17: 17,799 (all), 1,422 (astrocytes); this study: 43,670 cells (all), 2,332 (astrocytes). c, Principal components used in this study. d, Fraction occupation by cluster by patient. e, tSNE plot by condition and study (n = 9,673 cells). f, Analysis of IL-1β/TNF signalling in cluster 1 astrocytes from n = 28 controls and n = 20 patients with MS from the four studies. g, Canonical correlation analysis of mouse (Fig. 1, B6 EAE) and human (Fig. 6) astrocyte clusters and IPA analysis. Source data

Supplementary information

Reporting Summary

Supplementary Table

Supplementary Table 1: Cluster marker analysis in B6 EAE scRNA-seq. n=24,275 cells. Statistics by Benjamini-Hochberg test (adjusted p-value FDR) and MAST (p-value).

Supplementary Table

Supplementary Table 2: Differential gene expression by cluster of astrocytes during EAE in WT B6 mice by scRNA-seq. n=2,079 cells. Statistics by Benjamini-Hochberg test (adjusted p-value FDR) and MAST (p-value).

Supplementary Table

Supplementary Table 3: Differential gene expression by cluster of astrocytes during EAE in TdTomatoGfap mice by scRNA-seq. n=24,963 cells. Statistics by Benjamini-Hochberg test (adjusted p-value FDR) and MAST (p-value).

Supplementary Table

Supplementary Table 4: K-means clustering and differential gene expression of RNA-seq of RibotagGfap mice in EAE.

Supplementary Table

Supplementary Table 5: Differential gene expression of sgScrmbl- versus sgMafg-targeted astrocytes during EAE. n=3 mice per group. Statistics by Wald test (p-value).

Supplementary Table

Supplementary Table 6: Differential gene expression of Csf2rbAldh1l1-creERT2 versus Csf2rbf/f mice during EAE. n=3 mice per group. Statistics by Wald test (p-value).

Supplementary Table

Supplementary Table 7: Clinical information of samples analyzed.

Supplementary Table

Supplementary Table 8: Cluster marker analysis of human CNS samples by scRNA-seq from this study. n=43,670 cells. Statistics by Benjamini-Hochberg test (adjusted p-value FDR) and MAST (p-value).

Supplementary Table

Supplementary Table 9: Differential gene expression by cluster of cortical astrocytes in MS versus healthy control samples in the Schirmer et al. dataset. n=5,381 cells. Statistics by Benjamini-Hochberg test (adjusted p-value FDR) and MAST (p-value).

Supplementary Table

Supplementary Table 10: Differential gene expression by cluster of cortical white matter astrocytes in MS versus control samples in the Jäkel et al. dataset. n=1,422 cells. Statistics by Benjamini-Hochberg test (adjusted p-value FDR) and MAST (p-value).

Supplementary Table

Supplementary Table 11: Differential gene expression by cluster of cerebellar astrocytes in MS versus control samples in this study and Lake et al. n=823 cells. Statistics by Benjamini-Hochberg test (adjusted p-value FDR) and MAST (p-value).

Supplementary Table

Supplementary Table 12: Differential gene expression by cluster of astrocytes in MS versus control samples from Schirmer et al., Jäkel et al., Lake et al., and this study. n=9,673 cells. Statistics by Benjamini-Hochberg test (adjusted p-value FDR) and MAST (p-value).

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Wheeler, M.A., Clark, I.C., Tjon, E.C. et al. MAFG-driven astrocytes promote CNS inflammation. Nature 578, 593–599 (2020).

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