Multiple sclerosis (MS) is a neuroinflammatory disease with a relapsing–remitting disease course at early stages, distinct lesion characteristics in cortical grey versus subcortical white matter and neurodegeneration at chronic stages. Here we used single-nucleus RNA sequencing to assess changes in expression in multiple cell lineages in MS lesions and validated the results using multiplex in situ hybridization. We found selective vulnerability and loss of excitatory CUX2-expressing projection neurons in upper-cortical layers underlying meningeal inflammation; such MS neuron populations exhibited upregulation of stress pathway genes and long non-coding RNAs. Signatures of stressed oligodendrocytes, reactive astrocytes and activated microglia mapped most strongly to the rim of MS plaques. Notably, single-nucleus RNA sequencing identified phagocytosing microglia and/or macrophages by their ingestion and perinuclear import of myelin transcripts, confirmed by functional mouse and human culture assays. Our findings indicate lineage- and region-specific transcriptomic changes associated with selective cortical neuron damage and glial activation contributing to progression of MS lesions.
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All raw snRNA-seq data (fastq files) were deposited to the Sequence Read Archive (SRA) under accession number PRJNA544731 (NCBI Bioproject ID: 544731).
GBD 2015 Disease and Injury Incidence and Prevalence Collaborators. Global, regional, and national incidence, prevalence, and years lived with disability for 310 diseases and injuries, 1990–2015: a systematic analysis for the Global Burden of Disease Study 2015. Lancet 388, 1545–1602 (2016).
Reich, D. S., Lucchinetti, C. F. & Calabresi, P. A. Multiple sclerosis. N. Engl. J. Med. 378, 169–180 (2018).
Lassmann, H. Multiple sclerosis pathology. Cold Spring Harb. Perspect. Med. 8, a028936 (2018).
Trapp, B. D. et al. Axonal transection in the lesions of multiple sclerosis. N. Engl. J. Med. 338, 278–285 (1998).
Schirmer, L., Antel, J. P., Brück, W. & Stadelmann, C. Axonal loss and neurofilament phosphorylation changes accompany lesion development and clinical progression in multiple sclerosis. Brain Pathol. 21, 428–440 (2011).
Peterson, J. W., Bö, L., Mörk, S., Chang, A. & Trapp, B. D. Transected neurites, apoptotic neurons, and reduced inflammation in cortical multiple sclerosis lesions. Ann. Neurol. 50, 389–400 (2001).
Magliozzi, R. et al. Meningeal B-cell follicles in secondary progressive multiple sclerosis associate with early onset of disease and severe cortical pathology. Brain 130, 1089–1104 (2007).
Magliozzi, R. et al. A gradient of neuronal loss and meningeal inflammation in multiple sclerosis. Ann. Neurol. 68, 477–493 (2010).
Lucchinetti, C. F. et al. Inflammatory cortical demyelination in early multiple sclerosis. N. Engl. J. Med. 365, 2188–2197 (2011).
Trapp, B. D. et al. Cortical neuronal densities and cerebral white matter demyelination in multiple sclerosis: a retrospective study. Lancet Neurol. 17, 870–884 (2018).
Dal-Bianco, A. et al. Slow expansion of multiple sclerosis iron rim lesions: pathology and 7 T magnetic resonance imaging. Acta Neuropathol. 133, 25–42 (2017).
Mainero, C. et al. A gradient in cortical pathology in multiple sclerosis by in vivo quantitative 7 T imaging. Brain 138, 932–945 (2015).
Jäkel, S. et al. Altered human oligodendrocyte heterogeneity in multiple sclerosis. Nature 566, 543–547 (2019).
Masuda, T. et al. Spatial and temporal heterogeneity of mouse and human microglia at single-cell resolution. Nature 566, 388–392 (2019).
Lake, B. B. et al. Neuronal subtypes and diversity revealed by single-nucleus RNA sequencing of the human brain. Science 352, 1586–1590 (2016).
Lodato, S. & Arlotta, P. Generating neuronal diversity in the mammalian cerebral cortex. Annu. Rev. Cell Dev. Biol. 31, 699–720 (2015).
Lee, S. et al. Noncoding RNA NORAD regulates genomic stability by sequestering PUMILIO proteins. Cell 164, 69–80 (2016).
Mus, E., Hof, P. R. & Tiedge, H. Dendritic BC200 RNA in aging and in Alzheimer’s disease. Proc. Natl Acad. Sci. USA 104, 10679–10684 (2007).
Bayraktar, O. A. et al. Single-cell in situ transcriptomic map of astrocyte cortical layer diversity. Preprint at https://doi.org/10.1101/432104 (2018).
Machado-Santos, J. et al. The compartmentalized inflammatory response in the multiple sclerosis brain is composed of tissue-resident CD8+ T lymphocytes and B cells. Brain 141, 2066–2082 (2018).
Pasetto, L. et al. Targeting extracellular cyclophilin A reduces neuroinflammation and extends survival in a mouse model of amyotrophic lateral sclerosis. J. Neurosci. 37, 1413–1427 (2017).
Liddelow, S. A. et al. Neurotoxic reactive astrocytes are induced by activated microglia. Nature 541, 481–487 (2017).
Anderson, M. A. et al. Astrocyte scar formation aids central nervous system axon regeneration. Nature 532, 195–200 (2016).
Chang, A. et al. Cortical remyelination: a new target for repair therapies in multiple sclerosis. Ann. Neurol. 72, 918–926 (2012).
Ousman, S. S. et al. Protective and therapeutic role for αB-crystallin in autoimmune demyelination. Nature 448, 474–479 (2007).
Schirmer, L. et al. Differential loss of KIR4.1 immunoreactivity in multiple sclerosis lesions. Ann. Neurol. 75, 810–828 (2014).
Gadea, A., Schinelli, S. & Gallo, V. Endothelin-1 regulates astrocyte proliferation and reactive gliosis via a JNK/c-Jun signaling pathway. J. Neurosci. 28, 2394–2408 (2008).
Hametner, S. et al. Iron and neurodegeneration in the multiple sclerosis brain. Ann. Neurol. 74, 848–861 (2013).
Fard, M. K. et al. BCAS1 expression defines a population of early myelinating oligodendrocytes in multiple sclerosis lesions. Sci. Transl. Med. 9, eaam7816 (2017).
Brophy, P. J., Boccaccio, G. L. & Colman, D. R. The distribution of myelin basic protein mRNAs within myelinating oligodendrocytes. Trends Neurosci. 16, 515–521 (1993).
Zrzavy, T. et al. Loss of ‘homeostatic’ microglia and patterns of their activation in active multiple sclerosis. Brain 140, 1900–1913 (2017).
Lucchinetti, C. et al. Heterogeneity of multiple sclerosis lesions: implications for the pathogenesis of demyelination. Ann. Neurol. 47, 707–717 (2000).
Haider, L. et al. The topograpy of demyelination and neurodegeneration in the multiple sclerosis brain. Brain 139, 807–815 (2016).
Hauser, S. L. et al. B-cell depletion with rituximab in relapsing-remitting multiple sclerosis. N. Engl. J. Med. 358, 676–688 (2018).
Munschauer, M. et al. The NORAD lncRNA assembles a topoisomerase complex critical for genome stability. Nature 561, 132–136 (2018).
Campbell, G. R. et al. Mitochondrial DNA deletions and neurodegeneration in multiple sclerosis. Ann. Neurol. 69, 481–492 (2011).
Fischer, M. T. et al. Disease-specific molecular events in cortical multiple sclerosis lesions. Brain 136, 1799–1815 (2013).
Carassiti, D. et al. Neuronal loss, demyelination and volume change in the multiple sclerosis neocortex. Neuropathol. Appl. Neurobiol. 44, 377–390 (2018).
Absinta, M. et al. Persistent 7-tesla phase rim predicts poor outcome in new multiple sclerosis patient lesions. J. Clin. Invest. 126, 2597–2609 (2016).
Falcão, A. M. et al. Disease-specific oligodendrocyte lineage cells arise in multiple sclerosis. Nat. Med. 24, 1837–1844 (2018).
Kirby, L. et al. Oligodendrocyte precursor cells are co-opted by the immune system to cross-present antigen and mediate cytotoxicity. Preprint at https://doi.org/10.1101/461434 (2018).
Matevossian, A. & Akbarian, S. Neuronal nuclei isolation from human postmortem brain tissue. J. Vis. Exp. 20, e914 (2008).
Shekhar, K. et al. Comprehensive classification of retinal bipolar neurons by single-cell transcriptomics. Cell 166, 1308–1323 (2016).
Maaten, L. d. & Hinton, G. Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008).
Finak, G. et al. MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data. Genome Biol. 16, 278 (2015).
Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).
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).
Yu, G., Wang, L.-G., Han, Y. & He, Q.-Y. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 16, omi.2011.0118 (2014).
Aibar, S. et al. SCENIC: single-cell regulatory network inference and clustering. Nat. Methods 14, 1083–1086 (2017).
Meguro, R. et al. Nonheme-iron histochemistry for light and electron microscopy: a historical, theoretical and technical review. Arch. Histol. Cytol. 70, 1–19 (2007).
Jahn, O., Tenzer, S., Bartsch, N., Patzig, J. & Werner, H. B. in The Cytoskeleton (ed. Dermietzel, R.) 335–353 (Humana, 2013).
Werneburg, S., Mühlenhoff, M., Stangel, M. & Hildebrandt, H. Polysialic acid on SynCAM 1 in NG2 cells and on neuropilin-2 in microglia is confined to intracellular pools that are rapidly depleted upon stimulation. Glia 63, 1240–1255 (2015).
Schafer, D. P. et al. Microglia sculpt postnatal neural circuits in an activity and complement-dependent manner. Neuron 74, 691–705 (2012).
Chen, Y. et al. NS21: re-defined and modified supplement B27 for neuronal cultures. J. Neurosci. Methods 171, 239–247 (2008).
We thank J. Cyster (UCSF), D. Reich (National Institutes of Health, Bethesda) and S. Teichmann (Wellcome Sanger Institute) for advice and comments on the manuscript, I. Pshenichnaya for technical assistance and A. Hupalowska for figure illustrations. D. Gveric and A. LeFevre provided human brain samples from the UK Multiple Sclerosis Tissue Bank, funded by the Multiple Sclerosis Society of Great Britain and Northern Ireland, and the National Institutes of Health (NIH) NeuroBioBank at the University of Maryland, respectively. L.S. was supported by postdoctoral fellowships from the German Research Foundation (DFG, SCHI 1330/1-1) and the National Multiple Sclerosis Society (NMSS) funded in part by the Dave Tomlinson Research Fund (FG-1607-25111). D.V. was supported by a BOLD & BASIC fellowship from the UCSF Quantitative Biosciences Institute. S.W. was supported by a postdoctoral fellowship from the DFG (WE 6170/1-1), and S.M. was supported by EMBO (ALTF_393-2015) and DFG (MA 7374/1-1). A.B. acknowledges an NIH postdoctoral fellowship (F32NS103266). D.H.R. is a Paul G. Allen Frontiers Group Distinguished Investigator. This work was funded by the Dr. Miriam and Sheldon G. Adelson Medical Research Foundation (D.H.R., D.P.S., R.J.M.F.), the Hertie Foundation (medMS-MyLab program; P1180016 to L.S.), the National Human Genome Research Institute (4U41HG002371 to M.H.), the California Institute for Regenerative Medicine (GC1R-06673-C to M.H.), the Silicon Valley Community Foundation (2018-182809 to M.H.), the NMSS (PP-1609-25953 to D.H.R.), the NIHR Cambridge Biomedical Research Center (D.H.R.), and grants from the NIH/NINDS (NS040511 to D.H.R., R35NS097305 to A.R.K.), European Research Council and the Wellcome Trust (to D.H.R.).
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Peer review information Nature thanks Marco Prinz, Ori Staszewski and theother anonymous reviewer(s) for their contribution to the peer review of this work.
Extended data figures and tables
a, Representative images selected from nuclear suspensions (control, n = 9) after ultracentrifugation and before capturing by 10x Genomics confirming DAPI nuclear counterstaining with presence of smaller and larger DAPI+ nuclei. Note that larger nuclei are co-stained with anti-NeuN antibody confirming neuronal origin (white arrowheads). b, Coloured t-SNE plots showing numbers of genes (left) and UMIs (right) per captured nuclei from control and MS samples. c, Coloured t-SNE plot visualizing nuclei from different lesion stages based on classic pathological MS lesion staging. Acute, acute chronic-active; chronic, chronic inactive; ctrl, control. d, Coloured t-SNE plots visualizing nuclei from samples with different levels of upper- and deep-layer cortical demyelination as well as subcortical demyelination. e, Representative t-SNE plots with cell-type specific marker genes for OL progenitor cells, stromal cells including pericytes, endothelial cells and leukocytes. For t-SNE plots, data shown from 9 control and 12 MS samples and a total of 48,919 nuclei.
a, NORAD and PPIA expression patterns in cortical neurons and selected glial subtypes. Note baseline expression of NORAD and PPIA in neuronal versus glial subtypes and preferential upregulation of both NORAD and PPIA in upper-cortical-layer ENs (L2–L3 EN (EN-L2-3) and L4 EN (EN-L4)) in MS lesion tissue versus deep-cortical-layer excitatory and inhibitory neurons (L5–L6 EN (EN-L5-6) and IN-SST). For all t-SNE and violin plots, data are shown from 9 control and 12 MS samples. For t-SNE plots, data from 48,919 nuclei are shown. For L2–L3 EN, L4 EN and L5–L6 EN violin plots, data are shown from 6,120, 3,125 and 3,058 nuclei. Box plots inside violin plots represent median and standard deviation of gene expression. b, Visualization of enriched GO terms in L2–L3 EN, L4 EN and L5–L6 EN cells based on DGE analysis (linear mixed model regression). Binomial test with FDR correction was used to calculate FDR-corrected P values using genes differentially expressed in L2–L3 EN, L4 EN and L5–L6 EN nuclei (n = 428, 364 and 327, respectively).
a, t-SNE plots for neuron subtype specific expression of RORB, THY1, NRGN, SST, SV2C and PVALB (left). LAST (control, n = 5) showing layer-specific expression of neuronal RORB in intermediate cortical layer 4 and widespread expression of pyramidal neuron marker THY1 with enrichment in layer 5; note that SST-expressing INs preferentially map to deep-cortical layers. Co-expression studies (control, n = 5) with SYT1 confirm neuronal expression of RORB, THY1 and SST (black arrowheads). b, Heat map with hierarchical clustering of lymphocyte-associated transcripts allowing subclustering of lymphocytes in T cells, B cells and plasma cells based on marker gene expression (top left). t-SNE plots for typical B cell (plasma cell) and T cell marker genes enriched in lymphocyte clusters (top right). Immunohistochemistry for T cell adapter protein SKAP1 (black arrowheads mark SKAP1+ T cells) together with spatial transcriptomics for B-cell-associated IGHG1 encoding immunoglobulin G1 (IgG1) (magenta arrowheads; bottom left); note increased expression of the plasma cell-associated marker gene MZB1 (top left) and preferential enrichment of MZB1+ and IGHG1-expressing plasma cells (white arrowheads, bottom right) in inflamed meningeal tissue versus mixed T and B cell infiltration in perivascular cuffs of subcortical lesions (bottom). One caveat to these findings is the relatively small number of MS tissue samples, which limited our ability to cluster T cell populations. For t-SNE plots (a, b) and hierarchical clustering (b), data are shown from 9 control and 12 MS samples. For t-SNE plots, data shown for all 48,919 nuclei; for hierarchical clustering, data are shown from 53 nuclei in the B cell cluster. For in situ hybridization and immunohistochemistry experiments in b, representative images shown from individual tissue sections (control, n = 4; MS, n = 7).
Extended Data Fig. 4 Astrocyte and oligodendrocyte cluster analysis and spatial transcriptomics in MS lesions.
a, Differential spatial expression patterns of astroglial GFAP in subcortical versus cortical demyelination by immunohistochemistry (left); t-SNE plots visualizing astrocyte-specific genes corresponding to all (RFX4), protoplasmic (SLC1A2, GPC5) and fibrous or reactive astrocytes (GFAP, CD44). Quantification of RFX4+ in situ hybridization signals per nuclei in GM and WM of control samples validates RFX4 as a canonical astrocyte marker (control, n = 5); quantification of GPC5+ and CD44+ in situ hybridization signals per RFX4+ astrocytes validates GPC5 as protoplasmic GM and CD44 as fibrous WM marker. Two-tailed Mann–Whitney U-tests were performed. Data are mean ± s.e.m. b, Upregulation of astroglial CRYAB, MT3 (black arrowheads) and endothelin type B receptor transcript EDNRB (white arrowhead) in reactive astrocytes in subcortical lesions. c, t-SNE plots showing OL-specific expression of myelin-encoding genes MBP, CNP and transcription factor ST18; note co-expression of ST18 with PLP1 in control WM by in situ hybridization. d, Visualization of enriched GO terms in myelinating OLs based on DGE analysis. Binomial test with FDR correction was used to calculate FDR-corrected P values using 151 genes differentially expressed in OLs. e, Co-expression spatial transcriptomic studies confirming upregulation of heat-shock protein 90 transcript HSP90AA1 in both progenitor (PDGFRA-expressing) and myelinating (PLP1-expressing) OLs at lesion rims (PPWM, black arrowheads). The black asterisk indicates a blood vessel. For t-SNE and violin plots, data shown from 9 control and 12 MS samples. For astrocyte violin plots, 1,571 control and 3,810 MS nuclei are shown. Box plots inside violin plots represent median and standard deviation of gene expression. For in situ hybridization and immunohistochemistry experiments, representative images from from three control and four MS individual tissue sections are shown.
Hierarchical cluster analysis identifies several homeostatic and activated MS-specific microglia subtypes according to inflammatory lesion stages allowing transcriptomic staging of microglia subtypes. Clusters with enriched genes are marked and annotated a–f (see Supplementary Table 7 for gene list). Note that phagocytosing cells are identified by presence of OL and myelin-associated encoded genes (cluster f at the bottom of the heat map).
a, Representative Coomassie stain of brain homogenate (Hom.) and purified myelin (P.M.) from adult rat brain (left). Western blots for myelin basic protein (Mbp), Mog, synaptophysin (Syp) and neurofilament heavy molecular weight (NF-H) (centre). PCRs of myelin basic protein (Mbp) and synaptophysin (Syp) transcripts in brain homogenate and purified myelin fractions (right). b, Densitometric quantification of myelin and homogenates prepared from n = 4 independent rat hemispheres for Coomassie (total protein), western blot proteins and PCRs shown in a of purified myelin fractions normalized to their respective homogenates. Data are median ± s.e.m. of the four biological replicates. Similar results were obtained with brain homogenate and purified myelin fractions not used in this study. P values were calculated from Students’s two tailed t-test with Welch’s correction. P <0.05 was considered significant.
Supplementary Table 1: Characteristics of MS and control patient samples. Supplementary Table 2: Metadata for single nuclei profiles. Supplementary Table 3: Unbiased marker genes for each cell type. Supplementary Table 4: Raw and normalized cell numbers for each cell type. Supplementary Table 5: Trajectory-dependent EN-L2-3 genes. Supplementary Table 6: Differentially expressed genes per cell type. Supplementary Table 7: Microglial cluster genes based on hierarchical cluster analysis.
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Schirmer, L., Velmeshev, D., Holmqvist, S. et al. Neuronal vulnerability and multilineage diversity in multiple sclerosis. Nature 573, 75–82 (2019). https://doi.org/10.1038/s41586-019-1404-z
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