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Altered human oligodendrocyte heterogeneity in multiple sclerosis

Nature volume 566, pages 543–547 (2019)Cite this article

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Abstract

Oligodendrocyte pathology is increasingly implicated in neurodegenerative diseases as oligodendrocytes both myelinate and provide metabolic support to axons. In multiple sclerosis (MS), demyelination in the central nervous system thus leads to neurodegeneration, but the severity of MS between patients is very variable. Disability does not correlate well with the extent of demyelination1, which suggests that other factors contribute to this variability. One such factor may be oligodendrocyte heterogeneity. Not all oligodendrocytes are the same—those from the mouse spinal cord inherently produce longer myelin sheaths than those from the cortex2, and single-cell analysis of the mouse central nervous system identified further differences3,4. However, the extent of human oligodendrocyte heterogeneity and its possible contribution to MS pathology remain unknown. Here we performed single-nucleus RNA sequencing from white matter areas of post-mortem human brain from patients with MS and from unaffected controls. We identified subclusters of oligodendroglia in control human white matter, some with similarities to mouse, and defined new markers for these cell states. Notably, some subclusters were underrepresented in MS tissue, whereas others were more prevalent. These differences in mature oligodendrocyte subclusters may indicate different functional states of oligodendrocytes in MS lesions. We found similar changes in normal-appearing white matter, showing that MS is a more diffuse disease than its focal demyelination suggests. Our findings of an altered oligodendroglial heterogeneity in MS may be important for understanding disease progression and developing therapeutic approaches.

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Fig. 1: snRNA-seq reveals oligodendroglia heterogeneity in the human brain.
Fig. 2: Altered oligodendroglia heterogeneity in human MS brain.
Fig. 3: Depletion of specific oligodendrocyte subclusters and increased expression of myelination genes in mature oligodendrocytes in human MS brain.
Fig. 4: Differential gene expression analysis of MS lesions reveals potential specific markers.

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Data availability

Sequence data have been deposited at the European Genome-phenome Archive (EGA), which is hosted by the EBI and the CRG, under accession number EGAS00001003412. UMI expression and cell-type annotation tables have been deposited in the Gene Expression Omnibus (GEO) under accession number GSE118257. A browsable webresource is available at https://ki.se/en/mbb/oligointernode.

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Acknowledgements

We thank the MS Society UK Tissue Bank and the MRC Sudden Death and MS brain banks for post-mortem brain tissue, Advanced Cell Diagnostics for their help with BaseScope, T. Jimenez-Beristain, A. Nanni and A. Moshref for support, Eukaryotic Single Cell Genomics Facility (ESCGF) at Science for Life Laboratory, L. Wigge (Wallenberg Advanced Bioinformatics Infrastructure (WABI) Long Term Bioinformatic Support at SciLifeLab), the National Genomics Infrastructure, and M. Prasad and F. Koechl at F. Hoffmann-La Roche for providing assistance with snRNA-seq, and B. Vernay, E. O’Duibhir and M. Vermeren (CRM) for imaging support. The bioinformatics computations were performed at Swedish National Infrastructure for Computing (SNIC) at UPPMAX, Uppsala University. Funding: S.J.: European Union, Horizon 2020, Marie-Skłodowska Curie Actions EC no. 789492; C.ff.-C.: Wellcome Trust Investigator award; A.W.: UK Multiple Sclerosis Society, F. Hoffmann-La Roche; E.A.: European Union, Horizon 2020, Marie-Skłodowska Curie Actions, grant SOLO no. 794689; A.M.F.: European Committee for Treatment and Research of Multiple Sclerosis; G.C.-B.: European Union Horizon 2020/European Research Council Consolidator Grant EPIScOPE no. 681893, Swedish Research Council (no. 2015-03558), Swedish Brain Foundation (no. FO2017-0075), Swedish Cancer Society (Cancerfonden, CAN2016/555), Stockholm City Council (grant 20170397), Ming Wai Lau Centre for Reparative Medicine, F. Hoffmann-La Roche.

Reviewer information

Nature thanks Ed Lein, Klaus-Armin Nave and the other anonymous reviewer(s) for their contribution to the peer review of this work.

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Author notes

  1. These authors contributed equally: Sarah Jäkel, Eneritz Agirre

  2. These authors jointly supervised the work: Dheeraj Malhotra, Charles ffrench-Constant, Anna Williams, Gonçalo Castelo-Branco

Authors and Affiliations

  1. MRC Centre for Regenerative Medicine and MS Society Edinburgh Centre, Edinburgh bioQuarter, University of Edinburgh, Edinburgh, UK

    Sarah Jäkel, Charles ffrench-Constant & Anna Williams

  2. Laboratory of Molecular Neurobiology, Department of Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden

    Eneritz Agirre, Ana Mendanha Falcão, David van Bruggen, Ka Wai Lee & Gonçalo Castelo-Branco

  3. Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel, Basel, Switzerland

    Irene Knuesel & Dheeraj Malhotra

  4. Ming Wai Lau Centre for Reparative Medicine, Stockholm node, Karolinska Institutet, Stockholm, Sweden

    Gonçalo Castelo-Branco

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Contributions

S.J., E.A., A.M.F., I.K., D.M., C.ff.-C., A.W. and G.C.-B. designed all experiments. S.J. and A.M.F. performed snRNA-seq sample preparation. E.A., D.v.B. and K.W.L. performed the computational analysis of the snRNA-seq data; S.J. performed validation experiments, with assistance of A.W. and I.K.; S.J., E.A., C.ff.-C., A.W. and G.C.-B. wrote the manuscript with input from the co-authors. C.ff.-C., A.W. and G.C-.B. oversaw all aspects of the study.

Corresponding authors

Correspondence to Charles ffrench-Constant, Anna Williams or Gonçalo Castelo-Branco.

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D.M. and I.K. are employees at F. Hoffmann-La Roche.

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Extended data figures and tables

Extended Data Fig. 1 snRNA-seq of human post-mortem brain tissue.

a, Schematic overview of the methodology and workflow used to isolate single nuclei from human white matter, and RNA-seq analysis using Chromium 10x Genomics and Illumina next-generation sequencing (scheme was created with BioRender). b, Luxol Fast Blue (LFB) staining of human control (left) and MS (right) brain sections; white matter is outlined with a dotted line. MS brains were divided into NAWM (1) and different lesion types (2–4). c, Violin plots of additional markers enriched in specific oligodendrocyte subpopulations showing normalized gene expression (OPC n = 352, COP n = 242, ImOLG n = 207, Oligo1 n = 1,129, Oligo2 n = 1,839, Oligo3 n = 775, Oligo4 n = 1,579, Oligo5 n = 1,167, Oligo6 n = 1,484). Violin plots are centred on the median with interquartile ranges, and the shape represents cell distribution. d, Double in situ hybridization (BaseScope) of human control white matter counterstained with haematoxylin. e, Correlation between RNA integrity number (RIN) values and the number of genes per nucleus or number of nuclei recovered in individual samples. f, Quality control parameters of different human brain oligodendrocyte snRNA-seq datasets showing the individual number of genes (top) and the number of UMIs (bottom) per nucleus (n = 1,161 nuclei from Habib et al.10, n = 3,998 control nuclei from this dataset, and n = 4,873 nuclei from Lake et al.9). g, t-SNE projections of known cellular markers for the identification of all brain cell clusters in control samples (n = 6,591 nuclei).

Extended Data Fig. 2 Combination with other human brain snRNA-seq datasets identifies the Oligo6 subcluster as an intermediate oligodendrocyte state.

a, t-SNE projections representing oligodendrocyte lineage clusters when performing clustering analysis with the combination of the three datasets (left) and assigning cell identity according to the clusters identified in Fig. 1 (right, in parentheses, the numerical cluster identity with the dataset combination, as indicated in the left t-SNE analysis) (n = number of nuclei; nCluster0 = 1,445; nCluster1 = 1,406; nCluster2 = 1,355; nCluster3 = 1,299; nCluster4 = 1,150; nCluster5 = 1,068; nCluster6 = 828; nCluster7 = 605; nCluster8 = 59; nCluster9 = 250; and nCluster10 = 28). b, t-SNE projections indicating the cell origin when combining the current snRNA-seq dataset with datasets from Habib et al.10 and Lake et al.9 snRNA-seq datasets sorted by different individuals (top), different datasets (middle) and different regions (bottom) (n = 9,493 nuclei). c, d, Heat maps representing expression of genes associated with intermediate states across the oligodendroglial lineage (as defined by Lake et al.9) at a cluster (c) and individual nuclei (d) level. e, Frequency distribution of identified oligodendroglia between different datasets.

Extended Data Fig. 3 Seurat CCA clustering of snRNA-seq dataset at different clustering resolutions.

a, b, Seurat clustering at a lower (a) and higher (b) resolution than the clustering resolution in Fig. 1 (n = 17,799 nuclei derived from 5 control individuals and 4 patients with MS).

Extended Data Fig. 4 Validation of oligodendrocyte subcluster markers and regional oligodendrocyte subpopulation distribution in human control brain.

a, Violin plots showing normalized expression counts of SOX6, RTN4 (encoding NOGO) and OLIG2 counts in different oligodendrocyte subpopulations (n = number of nuclei; control OPC n = 273; COP n = 153; ImOLG n = 81; Oligo1 n = 952; Oligo2 n = 388; Oligo3 n = 82; Oligo4 n = 724; Oligo5 n = 393; Oligo6 n = 991). Violin plots are centred on the median with interquartile ranges, and the shapes represent cell distribution. b, Colocalization of SOX6 and OLIG2 as a marker for OPCs. Scale bar, 20 µm. c, Colocalization of OPALIN and OLIG2 as a marker for Oligo6. Scale bar, 20 µm. d, Colocalization of KLK6 and OLIG1/OLIG2 as a marker for Oligo5. e, Colocalization of SOX6, NOGO and OLIG2. SOX6+OLIG2+NOGO− cells (top) are OPCs, NOGO+OLIG2+SOX6− cells are mature oligodendrocytes. Scale bar, 10 µm. f, OPALIN staining of a control brain section. Scale bars, 5 mm and 300 µm (inset). g, OPALIN+ Oligo6 nuclei in different bins of 300-µm increments from the grey–white matter border. Scale bar, 50 µm. n = 3 different control and MS individuals with NAWM and lesions. P values determined by ANOVA. Data are mean ± s.e.m. h, Combined OPALIN and KLK6 staining of another human control brain block. Scale bars, 5 mm and 50 µm (inset). In b–e, experiments were independently performed in two batches. i, Validation of oligodendrocyte mRNA markers in combination with OLIG1 and OLIG2 IHC. BCAN (top left), CLDND1 (top right), KLK6 (bottom left) and CDH20 (bottom right). Red arrowheads denote marker+ OLIG1/2+ oligodendrocytes; blue arrowheads denote marker− OLIG1/2+ oligodendrocytes. Scale bars, 10 µm.

Extended Data Fig. 5 Comparison of human control and MS oligodendrocyte snRNA-seq and mouse EAE oligodendroglia scRNA-seq datasets shows similarities and differences in oligodendrocyte heterogeneity.

Heat map of the mean AUROC values (see Methods) from the unsupervised classification of cell-type-to-cell-type comparisons between human (current dataset) and mouse4 oligodendroglia.

Extended Data Fig. 6 GO analysis reveals functional differences between human oligodendrocyte subclusters.

The most significantly differentially expressed genes from the snRNA-seq experiment of each oligodendrocyte subcluster were selected, and GO and pathway analysis was performed with the ClueGO plug-in in Cytoscape on each individual cluster. Individual doughnut charts present the percentage of identified genes associated with the term and depict the most significant biological categories.

Extended Data Fig. 7 Clustering of snRNA-seq dataset by different origins.

a, t-SNE projections representing human control and MS white matter nuclei after dimensionality reduction with PCA at different resolutions. b–d, Clustering of snRNA-seq datasets by sample after dimensionality reduction with PCA (left) and canonical correlation analysis (CCA) (right), highlighting control/MS individual and lesion type combined (b), control/MS individual (c) and lesion type (d) separately. e, Frequency distributions of oligodendrocyte subclusters by control (left) and MS (right) individuals. n = 17,799 cells derived from 5 control individuals and 4 patients with MS.

Extended Data Fig. 8 Validation of skewed MS heterogeneity and oligodendrocyte gene expression profiling in control and NAWM tissues.

a, Validation of BCAN-expressing OPCs in combination with OLIG1 and OLIG2 IHC. Red arrowhead denotes BCAN+OLIG1/2+ OPC; blue arrowhead denotes BCAN−OLIG1/2+ oligodendrocyte. Scale bar, 20 µm. t-SNE overlay of BCAN expression in the snRNA-seq dataset in control and MS samples. Scale bar, 20 µm. Data are mean ± s.e.m. n = 4 samples from different control individuals, n = 6 NAWM samples and n = 5 MS lesion samples from different patients with MS. P values determined by ANOVA. b, KLK6-expressing oligodendrocytes in control white matter, NAWM and MS lesions. Scale bar, 50 µm. Data are mean ± s.e.m. n = 4 samples from different control individuals; n = 5 different MS individuals. P values determined by ANOVA. t-SNE overlay of KLK6 expression in the control and MS snRNA-seq dataset. c, Violin plots showing the normalized expression counts of genes enriched in ImOLG in the snRNA-seq dataset (OPC n = 352; COP n = 242; ImOLG n = 207; Oligo1n = 1,129; Oligo2 n = 1,839; Oligo3 n = 775; Oligo4 n = 1,579; Oligo5 n = 1,167; Oligo6 n = 1,484). Violin plots are centred on the median with interquartile ranges, and the shape represents cell distribution. d, MRF IHC in control white matter, NAWM and MS lesions. Scale bar, 50 µm. Data are mean ± s.e.m. n = 6 samples from different control individuals, and n = 7 different patients with MS. P values determined by ANOVA. t-SNE overlay of MRF expression in the snRNA-seq dataset. e, t-SNE overlay of MBP expression in the control and the MS snRNA-seq dataset (n = 4,037 oligodendrocytes in control; n = 4,737 oligodendrocytes in MS). f, Western blot of the MRF antibody on human brain lysate to validate the specificity of the antibody. For gel source data, see Supplementary Fig. 1. g, Combination of MRF mRNA and protein labelling to confirm the specificity of the MRF antibody in control white matter. Scale bar, 10 µm. h, Heat maps representing the average gene expression of a subset of genes, including myelin-related genes, in control versus MS samples in OPCs (control versus MS and control versus NAWM) and mature oligodendrocytes (control versus NAWM). In a, b and d, each experiment was performed in two (three for d) independent batches, and P values are only displayed compared to control; in f and g, each experiment was performed twice on independent samples.

Extended Data Fig. 9 Validations of altered oligodendrocyte heterogeneity in MS and mRNA expression differences in lesions.

a, Quantification of BaseScope in situ hybridization of CDH20 in individual patients with MS (corresponds to Fig. 4c) shows an enrichment in chronic inactive lesions in each individual (n = individual number of quantified fields per patient (n = 7): MS235: n = 10 for active and chronic inactive lesions, MS200: n = 4 for active, and chronic inactive and chronic active lesions, MS249: n = 4 for active and n = 8 for chronic inactive lesions, MS361: n = 7 for active and n = 10 for chronic inactive lesions, MS106: n = 11 for chronic active and chronic inactive lesions, MS161: n = 6 for chronic active and n = 10 for chronic inactive lesions, MS300: n = 7 for active and n = 10 for chronic inactive lesions. Data are mean ± s.e.m. b, c, BaseScope in situ hybridization of WWOX mRNA shows depletion of detected mRNA in chronic active lesions on average (b) and in individual patients with MS (c). Scale bars, 2 mm (left panel) and 20 µm (middle/right panel). In b, n = 2 for active lesions and n = 4 for chronic inactive and chronic active lesions, in c, dots display the individual number of quantified fields per patient (n = 5), MS245: n = 8 for active, n = 10 for chronic inactive and n = 9 for chronic active lesions, MS361: n = 6 for active and n = 10 for chronic inactive lesions, MS101: n = 6 for chronic inactive and n = 11 for chronic active lesions, MS161: n = 10 for chronic inactive and n = 7 for chronic active lesions, MS296: n = 11 for chronic active and n = 6 for chronic inactive lesions. Data are mean ± s.e.m. P values determined by ANOVA. d, Dot plot of the total normalized RNA UMI counts found within the lesions, NAWM and controls, in which both size and colour indicate z-scores. Blue and large denote low scores; red and large denote high scores; small denotes intermediate scores. e, Density histograms showing the difference in distribution of normalized counts observed between control and remyelinated lesions.

Supplementary information

Supplementary Figure 1

Raw image file of MRF Western Blot for Extended Data Fig. 8f. Left lane: protein ladder. Middle and right lane: duplicates of human protein lysates. Dashed box indicates cropped region. Loading control was not used as no quantitative measures were taken.

Reporting Summary

Supplementary Table 1

A list of human donor tissue, including frozen samples used in snRNA-Seq and paraffin samples used in ISH and IHC validations, with sex, age at death, cause of death, MS type and disease duration, when available. Abbreviations: M=male, F=female, SP=secondary progressive, PP=primary progressive, NA=not available.

Supplementary Table 2

Sequencing statistics of the 20 samples used for the study, quantification of number of nuclei positive for OL marker combinations (relative to Fig. 1c right) and QC stats of the available published snRNA-seq dataset (relative to Extended Data Figure 2). n=17,799 nuclei derived from 5 control and 4 MS patients.

Supplementary Table 3

A list of differentially expressed genes for each of the 23 nuclei clusters. (Bonferroni corrected Wilcoxon Rank Sum two tailed test, adjusted p-val < 0.05). n= number of individual nuclei, nAstrocytes=1046, nAstrocytes2=196, nCOPs=242, nEndothelial_cells1=452, nEndothelial_cells2=384, nMacrophages=368, nImmune_cells=423, nMicroglia_Macrophages=428, nNeuron1=1507, nNeuron2=1438, nNeuron3=543, nNeuron4=948, nNeuron5=595, nImOLGs=207, nOligo2=1839, nOligo4=1579, nOligo6=1484, nOligo5=1167, nOligo1=1129, nOligo3=775, nOPCs=352, nPericytes=585 and nVasc_smooth_muscle=112.

Supplementary Table 4

A list of differentially expressed genes for the 9 OL clusters, when considering only the OL lineage, and selection of OL markers. n= number of individual nuclei, nOPC=352, nCOP=242, nImOLG=207, nOligo1=1129, nOligo2=1839, nOligo3=775, nOligo4=1579, nOligo5=1167, nOligo6=1484. Bonferroni corrected Wilcoxon Rank Sum two tailed test, adjusted p-val < 0.05.

Supplementary Table 5

Comparison of human control and MS OL snRNA-seq and mouse EAE oligodendroglia scRNA-seq datasets shows similarities and differences in OL heterogeneity (related to Extended Data Fig. 5). Selected top hits with mean AUROC values >= 0.5 for unsupervised classification of cell type pairs between human (current dataset) and mouse OLs (ref.[4]).

Supplementary Table 6

A list of differentially expressed genes in control Vs. MS nuclei in OL clusters (related to Figure 3e and Extended Figure 8h). (Bonferroni corrected Wilcoxon Rank Sum two tailed test, adjusted p-val < 0.05).

Supplementary Table 7

A list of differentially expressed genes between different types of lesions within OL clusters. (Bonferroni corrected Wilcoxon Rank Sum two tailed test, adjusted p-val < 0.05). (nOPC=79, nCOP=89, nImOLG=126, nOligo1=177, nOligo2=1451, nOligo3=693, nOligo4=855, nOligo5=774, nOligo6= 493).

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Jäkel, S., Agirre, E., Mendanha Falcão, A. et al. Altered human oligodendrocyte heterogeneity in multiple sclerosis. Nature 566, 543–547 (2019). https://doi.org/10.1038/s41586-019-0903-2

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