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Myeloid cell replacement is neuroprotective in chronic experimental autoimmune encephalomyelitis

Nature Neuroscience (2024)Cite this article

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Abstract

Multiple sclerosis (MS) is an autoimmune disease characterized by demyelination of the central nervous system (CNS). Autologous hematopoietic cell transplantation (HCT) shows promising benefits for relapsing–remitting MS in open-label clinical studies, but the cellular mechanisms underlying its therapeutic effects remain unclear. Using single-nucleus RNA sequencing, we identify a reactive myeloid cell state in chronic experimental autoimmune encephalitis (EAE) associated with neuroprotection and immune suppression. HCT in EAE mice results in an increase of the neuroprotective myeloid state, improvement of neurological deficits, reduced number of demyelinated lesions, decreased number of effector T cells and amelioration of reactive astrogliosis. Enhancing myeloid cell incorporation after a modified HCT further improved these neuroprotective effects. These data suggest that myeloid cell manipulation or replacement may be an effective therapeutic strategy for chronic inflammatory conditions of the CNS.

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Fig. 1: Myeloid expansion and transcriptional activation are features of chronic EAE.
Fig. 2: BMT changes the density and morphology of CNS myeloid cells in chronic EAE.
Fig. 3: BMT and microglia replacement enhance the myeloid transcriptional response and improve clinical outcome.
Fig. 4: BMT reduces spinal cord demyelination and reactive astrogliosis.
Fig. 5: BMT modulates the heterogeneity of CNS immune cells in chronic EAE with persistence of DAM.
Fig. 6: Graft-derived Iqgap1+ cells are a main driver of the altered transcriptional state in the CNS myeloid population after BMT.

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Data availability

The murine sequencing datasets generated and analyzed in the current study are available in the Gene Expression Omnibus repository under accession numbers GSE217529 and GSE242512. The human spinal cord dataset29 was accessed as six fastq files (three control and three MS samples) through Sequence Read Archive (accession number PRJNA726991). Data from previously published human brain NucSeq datasets ((three control and five MS samples; Cell Browser dataset ID, chronic-ms)30; (five control and four MS samples; Cell Browser dataset ID, oligodendrocyte-ms)32; and (nine control and twelve MS samples; Cell Browser dataset ID, ms)31) were obtained as expression matrices from the UCSC Cell Browser (https://cells.ucsc.edu)67. Reference genomes (refdata-gex-mm10-2020-A and refdata-gex-GRCh38-2020-A) were accessed at www.10xgenomics.com. Source data are provided with this paper.

Code availability

Computational analyses were performed using freely available software packages as described in the Methods. Central to the analysis pipeline of NucSeq data in this study are the software packages 10× Genomics Cell Ranger (v.6.1.2; https://www.10xgenomics.com/support/software/cell-ranger)70 and R packages Seurat (v.4.2.0)68, sctransform (v.0.3.5)61, fgsea (v.1.24.0)56, MAST (v.1.20.0)66, VISION (v.3.0.0)26 and biomaRt (v.2.50.3)69. Additional information regarding the computational methodology of this project is available from the corresponding author upon reasonable request.

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Acknowledgements

We would like to thank all members of the Wernig laboratory, G. Wang, C. Bennett, B. Ajami, J. Blum and N. Or-Geva for helpful discussions throughout the project; A. Lang and M. Vangipuram for administrative support; and the FACS core at the Institute for Stem Cell Biology and Regenerative Medicine and Stanford Neuroscience Microscopy Service (supported by the National Institutes of Health, NS069375) for technical support. We thank the scientists at Plexxikon Inc. for their generous gift of PLX5622. Funding was provided by a Howard Hughes Medical Institute Faculty Scholar Award (to M.W.); the German Research Foundation (Deutsche Forschungsgemeinschaft; MA 8492/1-1 to M.M.D.M.); the National Multiple Sclerosis Society and the American Brain Foundation (FAN-2207-39823 to D.W.); and the New York Stem Cell Foundation Druckenmiller Fellowship (NYSCF-D-F74 to Y.Y.).

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  1. These authors contributed equally: Alan Napole, Danwei Wu

Authors and Affiliations

  1. Institute for Stem Cell Biology and Regenerative Medicine and Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA

    Marius Marc-Daniel Mader, Alan Napole, Danwei Wu, Alexa Scavetti, Yohei Shibuya, Yongjin Yoo, Ron Danziger, Christina Tan & Marius Wernig

  2. Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA

    Marius Marc-Daniel Mader, Alan Napole, Danwei Wu, Alexa Scavetti, Yohei Shibuya, Yongjin Yoo, Ron Danziger, Christina Tan & Marius Wernig

  3. Department of Neurology and Neurosciences, Division of Neuroimmunology and Multiple Sclerosis Center, Stanford University of Medicine, Stanford, CA, USA

    Danwei Wu & Lawrence Steinman

  4. Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA

    Micaiah Atkins, Aulden Foltz, Oliver Hahn & Tony Wyss-Coray

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Contributions

M.M.D.M., M.W., L.S., R.D. and C.T. designed and conceived the study. M.M.D.M., A.N., Y.S., A.S., D.W., Y.Y., R.D. and C.T. worked with the study animals. M.M.D.M., M.A., A.F., O.H. and T.W.C. performed the NucSeq experiments and analysis. D.W., A.S., M.M.D.M. and A.N. performed immunostaining, microscopy and image analysis. M.M.D.M., M.W., O.H., A.N., D.W. and L.S. performed data analysis and interpretation. M.M.D.M. and M.W. drafted and edited the original manuscript; all authors reviewed, revised and approved the final version of the paper.

Corresponding author

Correspondence to Marius Wernig.

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The authors declare that they have no conflict of interest related to this study. PLX5622 was provided by Plexxikon Inc. under a material transfer agreement between Stanford University and Plexxikon Inc.

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Nature Neuroscience thanks Jessica Williams and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Characterization of chronic EAE via spinal cord NucSeq.

a, Experimental design and groups of the NucSeq main cohort. b, Absolute number of nuclei per library. c, Number of features and counts per library. d, Annotated UMAP plot of 68,593 nuclei integrated from all 8 libraries. Clustering based on the first 20 principal components and a resolution of 0.6. e, Annotation and canonical marker genes of clusters. f, Significant DEGs (padj < 0.05, based on MAST) for main cell types between the Control and EAE condition. The top 15 intersections are illustrated.

Source data

Extended Data Fig. 2 Flowcytometric analysis of murine spinal cord nuclei reveals an increase in the NeuN- Olig2- fraction in EAE.

a, Gating strategy of fluorescence activated nuclei sorting and representative gates for each animal. b, Quantification of gated populations. Two-sided Welch t-test. Total n = 14 animals. Box plot elements represent median (center line), first and third quartiles (lower and upper hinges) and smallest/highest value with at most 1.5*IQR (inter-quartile range) from the hinge (whiskers).

Source data

Extended Data Fig. 3 EAE alters the transcriptional program in both astrocytes and myeloid cells.

a, GSEA-derived top 20 significantly enriched pathways (Control vs EAE) of the Gene Ontology biological process term for the myeloid and astrocyte clusters (p values based on GSEA). b, c, Representative genes of selected enriched pathways.

Source data

Extended Data Fig. 4 EAE induces a proinflammatory gene set enrichment in oligodendrocytes.

a, GSEA-derived top 16 significantly enriched pathways (Control vs EAE) of the Gene Ontology biological process term for oligodendrocyte lineage clusters (p values based on GSEA). b, Representative genes of selected enriched pathways. c, Immunofluorescent stain for Olig2 and complement component 4 (C4) in the spinal cord white matter. White arrows demonstrate spatial association between C4 and oligodendrocytes. Stain was evaluated in three independent EAE and three independent Control spinal cords. Scale bar is 20 µm.

Source data

Extended Data Fig. 5 EAE-specific transcriptional changes are not induced by CFA injection without MOG-immunization.

a, Experimental design of the control cohort. Animals with Complete Freund’s Adjuvant (CFA) were sacrificed and analyzed 25 days after injection. Animals injected with phosphate buffered saline (PBS) were age matched to the animals of the main cohort at the timepoint of analysis. b, Absolute number of nuclei per animal sample. c, Number of features and counts per animal sample. d, Annotation and canonical marker genes of cell clusters. Clustering based on the first 20 principal components and a resolution of 0.6. e, UMAP plot of 17,127 nuclei integrated from the murine control cohort. f, Distribution of main cell clusters of the PBS and CFA groups. g, Fraction of myeloid nuclei. Dots represent individual animals (n = 8 animals total). Two-sided Welch t-test. h, Absolute number and top five intersections of significant DEGs (padj < 0.05) between PBS and CFA group. i, DEGs of the myeloid cluster between PBS and CFA group. Significant (padj < 0.05) genes are color coded if >1.5 fold up (red) or down (blue) regulated. j, Expression of EAE Scores in Control and EAE animals of the main cohort (left panels), and PBS and CFA animals of the control cohort (right panels). Box plot elements represent median (center line), first and third quartiles (lower and upper hinges) and smallest/highest value with at most 1.5*IQR (inter-quartile range) from the hinge (whiskers).

Source data

Extended Data Fig. 6 Integration of NucSeq data derived from MS patients.

a, Absolute number of nuclei per original human dataset. b, Number of features and counts per original human dataset. c, UMAP plot of 132,425 nuclei integrated from the original human brain datasets. Lesion-dependent conditions of the original datasets are shown (A_, Absinta; J_, Jäkel; S_, Schirmer; A, active; A/CA, acute/chronic active; CA, chronic active; CI, chronic inactive; remyel, remyelinating; wm, white matter), which were combined into 4 groups: control, active/chronic active (A/CA), chronic inactive (CI), and MS white matter (WM). d, Annotation and canonical marker genes of brain clusters. Clustering based on the first 12 principal components and a resolution of 0.4. e, UMAP plot of 5419 nuclei integrated from the original individual libraries derived from human spinal cord samples. f, Annotation and canonical marker genes of spinal cord clusters. Clustering based on the first 12 principal components and a resolution of 0.4.

Source data

Extended Data Fig. 7 Active MS brain lesions demonstrate myeloid cell expansion and activation.

a, Relative distribution of main cluster groups between different original datasets and control/MS conditions. b, Relative proportion of the myeloid and lymphocyte cell clusters. Dots represent proportions per individual original sample (n = 54 total samples derived from brain and 6 from spinal cord). A/CA, active / chronic active lesion; CI, inactive lesion; WM, white matter. Two-sided Welch t-test. c, Number of significant DEGs (padj < 0.05, based on MAST) per main cell type between the Control and MS condition. Only cell types with significant DEGs are shown. d, Expression of the human orthologs of the cell type specific EAE Scores in the myeloid, oligodendrocyte, and astrocyte subclusters (p values based on two-sided Mann–Whitney U test with Holm method adjustment). e, UMAP plots of the subclusters of the immune cells of the human brain (left panel, 7822 nuclei) and spinal cord (right panel, 794 nuclei) datasets. f, Annotation and marker genes of brain (upper panel) and spinal cord (lower panel) immune subclusters. g, Distribution of cell populations between Control and MS samples for the human brain (left panel) and spinal cord (right panel). Notably, the category ‘Remyel’ consists of only 2 nuclei. h, Expression of the human ortholog of the EAE Myeloid Score in myeloid subclusters of the human brain (upper panel) and spinal cord (lower panel) datasets. i, Expression of IQGAP1 in the brain myeloid cluster. Box plot elements represent median (center line), first and third quartiles (lower and upper hinges) and smallest/highest value with at most 1.5*IQR (inter-quartile range) from the hinge (whiskers).

Source data

Extended Data Fig. 8 BMT-induced morphological changes in myeloid cells are present in both spinal cord and brain.

a, Representative FACS gating strategy to quantify GFP chimerism in peripheral blood. b, Representative images of GFP+ chimerism in brain myeloid cells. Scale bars are 100 µm. c, Quantification of GFP positive and negative Iba1+ cells. Dots represent the average of multiple measurements per animal. A total of 17 animals or 26 animals were analyzed for spinal cord or brain regions, respectively. d, Pearson correlation coefficient (r) and scatter plot demonstrate the correlation between brain and spinal cord GFP+ chimerism. Regression line based on linear model. Two-sided test. e, A gene signature for CDMCs was based on previously published 1296 DEGs between CDMCs and endogenous microglia25 and a score was calculated with the VISION pipeline. Expression in the myeloid cluster is demonstrated. f, The fractions of nuclei in the myeloid cluster with a CDMC score expression of >−0.05 are shown (total n = 8 sequencing libraries derived from 14 spinal cords). g, Quantification of maximum branch length of ramified myeloid cells of the spinal cord in different conditions. Each dot represents one cell (total n = 210), dot colors represent different animals (n = 12). Two-sided Mann–Whitney U test. h, Representative immunofluorescent images of ramified myeloid cells in the brain. Brightness/contrast was adjusted individually per image for morphological assessment due to differing Iba1 intensity between conditions. Scale bars are 20 µm. i, Morphological analysis of ramified myeloid cells of the cortex in different conditions. Each dot represents one cell (total n = 162), dot colors represent different animals (n = 15). Two-sided Mann–Whitney U test. Box plot elements represent median (center line), first and third quartiles (lower and upper hinges) and smallest/highest value with at most 1.5*IQR (inter-quartile range) from the hinge (whiskers).

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Extended Data Fig. 9 Transcriptional changes in astrocytes and oligodendrocytes in the context of EAE and BMT.

a, Venn diagrams of DEGs (padj < 0.05, based on MAST with Bonferroni correction) between selected conditions for main oligodendrocyte and astrocyte clusters. b, Scatterplots show the relationship of significant DEGs (padj < 0.05 for both conditions, based on MAST with Bonferroni correction) of disease-associated (Control vs EAE) and treatment-associated (EAE vs EAE-BMT or EAE vs EAE-BMT-PLX) conditions of different oligodendrocyte and astrocyte clusters. Purple numbers represent the DEGs per quadrant. c, Gene set enrichment analysis based on differentially expressed genes between the EAE and EAE-BMT groups in selected clusters. Shown are the top six Gene Ontology (GO) Biological Process (BP) pathways based on GSEA-derived p values. Terms are abbreviated to fit the format. A complete list can be found in Supplementary Table 3. d, Correlation between white matter demyelination and clinical score. Regression lines based on linear models; r = Pearson correlation coefficient. Two-sided test. e, Expression of marker genes and EAE Astro Score in astrocyte subclusters. f, Expression profile of DEGs shared with genes of a multiple sclerosis genome wide association study (GWAS) for astrocyte subclusters. Expression values (average log2 fold-change) are normalized by columns. If no significant DEG was present for a cell type, the value 0 was applied.

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Extended Data Fig. 10 Transcriptional changes in myeloid cells in the context of EAE and BMT.

a, Marker gene expression of different T cell populations. NK, natural killer cells. Teff, effector T cells. Treg, regulatory T cells. b, Expression of GFP in nuclei of the myeloid cluster. Ordered towards the front based on expression. c, Expression of selected microglia marker genes in clusters M.1, M.2, and M.5 in different conditions. Adjusted p values based on DEG analysis (MAST with Bonferroni correction), compare also Supplementary Table 16. d, Venn diagram showing significant DEGs (padj <0.05, based on MAST with Bonferroni correction) of the myeloid cluster between different conditions. e, Scatterplots show the relationship of significant DEGs (padj < 0.05 for both conditions, MAST with Bonferroni correction) of disease-associated (Control vs EAE) and treatment-associated (EAE vs EAE-BMT or EAE vs EAE-BMT-PLX) conditions of the myeloid cluster. Purple numbers represent the DEGs per quadrant. f, Expression profile of DEGs shared with genes of a multiple sclerosis genome wide association study (GWAS) for myeloid subclusters. Expression values (average log2 fold-change) are normalized by columns. If no significant DEG was present for a cell type, the value 0 was applied.

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Supplementary information

Supplementary Information

Supplementary File 1

Reporting Summary

Supplementary Table 1

Marker genes of main cell clusters of the main EAE NucSeq dataset. Based on Seurat’s FindAllMarkers function (Wilcoxon Rank Sum test; adjusted P value based on Bonferroni correction). ‘pct.1’ represents the percentage of cells in the cluster of interest, in which the gene is expressed. ‘pct.2’ represents the percentage of cells in all other clusters, in which the gene is expressed.

Supplementary Table 2

Differentially expressed genes of main cell clusters of the main EAE NucSeq dataset. Calculation was based on the MAST algorithm with a log-fold-change threshold of 0.25, minimum detection fraction of 0.1 and Bonferroni correction of P values. ‘pct.1’ and ‘pct.2’ represent the percentages of cells in the cluster of interest (celltype) in condition 1 (cond1) and 2 (cond2), respectively, in which the gene is expressed.

Supplementary Table 3

Enriched pathways of main cell clusters of the main EAE NucSeq dataset (GSEA). Organized per pathway type (pathway.category), compared conditions (cond1 and cond2), and cell cluster (celltype).

Supplementary Table 4

Gene signatures and mouse orthologous GWAS genes used in this study (as indicated in the ‘category’ column).

Supplementary Table 5

Marker genes of main cell clusters of the control cohort NucSeq dataset. Based on Seurat’s FindAllMarkers function (Wilcoxon Rank Sum test; adjusted P value based on Bonferroni correction). ‘pct.1’ represents the percentage of cells in the cluster of interest, in which the gene is expressed. ‘pct.2’ represents the percentage of cells in all other clusters, in which the gene is expressed.

Supplementary Table 6

Differentially expressed genes of main cell clusters of the control cohort NucSeq dataset. Calculation was based on the MAST algorithm with a log-fold-change threshold of 0.25, minimum detection fraction of 0.1 and Bonferroni correction of P values. ‘pct.1’ and ‘pct.2’ represent the percentages of cells in the cluster of interest (celltype) in condition 1 (cond1) and 2 (cond2), respectively, in which the gene is expressed.

Supplementary Table 7

Marker genes of main cell clusters of the human brain NucSeq dataset. Based on Seurat’s FindAllMarkers function (Wilcoxon Rank Sum test; adjusted P value based on Bonferroni correction). ‘pct.1’ represents the percentage of cells in the cluster of interest, in which the gene is expressed. ‘pct.2’ represents the percentage of cells in all other clusters, in which the gene is expressed.

Supplementary Table 8

Marker genes of main cell clusters of the human spinal cord NucSeq dataset. Based on Seurat’s FindAllMarkers function (Wilcoxon Rank Sum test; adjusted P value based on Bonferroni correction). ‘pct.1’ represents the percentage of cells in the cluster of interest, in which the gene is expressed. ‘pct.2’ represents the percentage of cells in all other clusters, in which the gene is expressed.

Supplementary Table 9

Differentially expressed genes (control vs MS) of main cell clusters of the human brain NucSeq dataset. Calculation was based on the MAST algorithm with a log-fold-change threshold of 0.25, minimum detection fraction of 0.1 and Bonferroni correction of P values. ‘pct.1’ and ‘pct.2’ represent the percentages of cells in the cluster of interest (celltype) in condition 1 (cond1) and 2 (cond2), respectively, in which the gene is expressed.

Supplementary Table 10

Differentially expressed genes (control vs MS) of main cell clusters of the human spinal cord NucSeq dataset. Calculation was based on the MAST algorithm with a log-fold-change threshold of 0.25, minimum detection fraction of 0.1 and Bonferroni correction of P values. ‘pct.1’ and ‘pct.2’ represent the percentages of cells in the cluster of interest (celltype) in condition 1 (cond1) and 2 (cond2), respectively, in which the gene is expressed.

Supplementary Table 11

Marker genes of immune subclusters of the human brain NucSeq dataset. Based on Seurat’s FindAllMarkers function (Wilcoxon Rank Sum test; adjusted P value based on Bonferroni correction). ‘pct.1’ represents the percentage of cells in the cluster of interest, in which the gene is expressed. ‘pct.2’ represents the percentage of cells in all other clusters, in which the gene is expressed.

Supplementary Table 12

Marker genes of immune subclusters of the human spinal cord NucSeq dataset. Based on Seurat’s FindAllMarkers function (Wilcoxon Rank Sum test; adjusted P value based on Bonferroni correction). ‘pct.1’ represents the percentage of cells in the cluster of interest, in which the gene is expressed. ‘pct.2’ represents the percentage of cells in all other clusters, in which the gene is expressed.

Supplementary Table 13

Marker genes of astrocyte subcluster of the EAE NucSeq dataset. Based on Seurat’s FindAllMarkers function (Wilcoxon Rank Sum test; adjusted P value based on Bonferroni correction). ‘pct.1’ represents the percentage of cells in the cluster of interest, in which the gene is expressed. ‘pct.2’ represents the percentage of cells in all other clusters, in which the gene is expressed.

Supplementary Table 14

Marker genes of immune subclusters of the EAE NucSeq dataset. Based on Seurat’s FindAllMarkers function (Wilcoxon Rank Sum test; adjusted P value based on Bonferroni correction). ‘pct.1’ represents the percentage of cells in the cluster of interest, in which the gene is expressed. ‘pct.2’ represents the percentage of cells in all other clusters, in which the gene is expressed.

Supplementary Table 15

Marker genes of TC&NK subclusters of the EAE NucSeq dataset. Based on Seurat’s FindAllMarkers function (Wilcoxon Rank Sum test; adjusted P value based on Bonferroni correction). ‘pct.1’ represents the percentage of cells in the cluster of interest, in which the gene is expressed. ‘pct.2’ represents the percentage of cells in all other clusters, in which the gene is expressed.

Supplementary Table 16

Differentially expressed genes of immune cell subclusters of the EAE NucSeq dataset. Calculation was based on the MAST algorithm with a log-fold-change threshold of 0.25, minimum detection fraction of 0.1 and Bonferroni correction of P values. ‘pct.1’ and ‘pct.2’ represent the percentages of cells in the cluster of interest (celltype) in condition 1 (cond1) and 2 (cond2), respectively, in which the gene is expressed.

Supplementary Table 17

Enriched pathways of myeloid subcluster marker genes of the EAE NucSeq dataset (GSEA). Organized per pathway type (pathway.category) and cell subcluster (cluster).

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Mader, M.MD., Napole, A., Wu, D. et al. Myeloid cell replacement is neuroprotective in chronic experimental autoimmune encephalomyelitis. Nat Neurosci (2024). https://doi.org/10.1038/s41593-024-01609-3

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