Multiple sclerosis (MS) is a chronic autoimmune disease of the central nervous system (CNS) characterized by a relapsing remitting and/or progressive course of demyelination, axonal injury, and neurologic dysfunction (Reich et al., 2018). Highly efficacious disease modifying therapies have revolutionized the prevention and treatment of MS relapses, but are less effective at stopping hallmarks of MS progression such as brain atrophy (Faissner et al., 2019). Accumulating evidence points to a pivotal role for leptomeningeal inflammation (LMI) in contributing to this pathology (Howell et al., 2011; Absinta et al., 2015). LMI is found in all subtypes of MS, ranging histologically from disorganized collections of leukocytes to highly organized ectopic lymphoid follicles, and correlates with the presence of cortical gray matter demyelination, neurite loss, and decreased volume (Wicken et al., 2018). Gray matter pathology (GMP) has been linked to debilitating symptoms such as cognitive impairment and depression (Geurts and Barkhof, 2008), and tends to occur in spatial relation to areas of LMI. Indeed, the most common gray matter lesion location is directly sub-pial (Bø et al., 2003a), and there is a gradient of increased pathology toward the surface of the brain in MS patients (Magliozzi et al., 2010; Mainero et al., 2015). Interestingly, GMP occurs without local blood-brain barrier disruption or robust infiltration of peripheral immune cells into the brain parenchyma (van Horssen et al., 2007; Bø et al., 2003b). LMI is therefore speculated to be a source of pro-inflammatory molecules that contribute to GMP (Pikor et al., 2015), but the pathway(s) involved remain(s) unknown.

Several putative mechanisms linking LMI to GMP have been proposed. Magliozzi and colleagues identified increased expression of numerous cytokines and chemokines, including interferon gamma (IFNγ), tumor necrosis factor (TNF), interleukin (IL)-2, IL-22, CXCL13, and CXCL10, in the meninges and CSF of post-mortem MS cases with high levels of meningeal inflammation and GM demyelination (Magliozzi et al., 2018). Microarray analysis of cortical lesions from MS cases with LMI revealed a shift in TNF signaling from TNFR1/TNFR2 and I-mediated anti-apoptotic pathways toward TNFR1- and RIPK3-mediated pro-apoptotic/pro-necroptotic pathways (Magliozzi et al., 2019). In marmoset and rat models of experimental autoimmune encephalomyelitis (EAE), sub-pial cortical lesions were found with prominent microglial activation and immunoglobulin deposition on myelin sheaths (Storch et al., 2006; Merkler et al., 2006). Interestingly, complement deposition, one possible mechanism of immunoglobulin-related cellular injury, is not found in purely cortical gray matter lesions, in contrast to white or gray/white matter lesions (Brink et al., 2005). Numerous other mediators of injury, including reactive oxygen and nitrogen species, metabolic stress, and excitotoxicity have been proposed to drive neurodegeneration in MS and may be at play in GMP.

Previous attempts to characterize the relation between LMI and GMP have been limited by the absence of spatially resolved data, which therefore lacks critical information about the anatomic relationship between LMI and the underlying brain parenchyma. Here, we present a spatial transcriptomic dataset and analysis in a mouse model of relapsing/remitting CNS autoimmunity and meningeal inflammation, Swiss Jim Lambert (SJL) mouse EAE (Magliozzi et al., 2004). Our prior work in SJL EAE demonstrated meningeal areas of gadolinium contrast enhancement on magnetic resonance imaging (MRI) that correspond histologically to collections of B cells, T cells, and myeloid cells (Bhargava et al., 2021). In the parenchyma adjacent to areas of meningeal inflammation we identified astrocytosis, activated microglia, demyelination, and evidence of axonal stress and damage (Bhargava et al., 2021). This work characterizes the spatially resolved transcriptome of LMI in the SJL EAE model system, revealing the subset of genes and gene sets active in the LMI that extend into the adjacent parenchyma and providing insights into immune pathways that could underlie sub-pial neurodegeneration.

We tracked the development of LMI during SJL EAE using contrast-enhanced serial MRI imaging. MRI data was then used to target areas of LMI for spatial transcriptomics (Figure 1A). Mice developed a characteristic relapsing pattern of neurologic impairment, and contrast-enhanced MRI was performed at weeks 6, 8, and 10 post-immunization (Figure 1B and C). In the SJL EAE model, contrast enhancing meningeal lesions are most frequently found in the intrapeduncular cistern, quadrigeminal cistern, and the cleft between the hippocampus and medial geniculate nucleus (Bhargava et al., 2021; Bedussi et al., 2017; Figure 1D). Lesion number remained stable throughout the disease course independent of EAE score (Figure 1—figure supplement 1A and B).

Figure 1 with 2 supplements see all

Download asset Open asset

Brain slices were collected from four naïve mice and four EAE mice 11 weeks post-immunization and used to prepare five and six samples for spatial transcriptomics, respectively. Hematoxylin and eosin (H&E) staining confirmed the presence of meningeal inflammation in the areas of contrast enhancement, as we had previously observed (Bhargava et al., 2021; Figure 1E). Since naïve animals were used as controls, we confirmed that CFA alone does not produce lasting glial reactivity or LMI formation. Groups of animals were given CFA only or left naïve. Neither group developed neurologic signs, and after 11 weeks the brains were processed for IHC analysis. There was no evidence of LMI development, and no difference in glial reactivity as measured by GFAP, IBA1, or CD68 intensity (Figure 1—figure supplement 1C–E).

Spatial transcriptomic data from all samples were of high quality and read depth when assessed by treatment group (Figure 1—figure supplement 2A–C) or sample (Figure 1—figure supplement 2D and E). There was expected anatomic variability in number of read counts and number of features per spot, with relatively low counts and features in white matter as compared to the cortex or hippocampus (Figure 1—figure supplement 2F and G), and spots had similar degrees of average complexity between EAE and naïve samples (Figure 1—figure supplement 2H). UMAP clustering revealed no significant independent effect of sample (Figure 1—figure supplement 2I) or slide (Figure 1—figure supplement 2J). Numerous genes were differentially expressed between EAE and naïve slices as estimated by DESeq2 on samples pseudobulked by biological replicate (Supplementary file 1; Figure 1—figure supplement 2K), including genes associated with the complement cascade, immune infiltration, antigen presentation, and astrocyte activation (Figure 1F; Figure 1—figure supplement 2L).

We next explored the activity of a broad range of pathways using the pathway responsive genes (PROGENy) method (Schubert et al., 2018; Figure 2A). Inflammatory pathways related to TNF, JAK-STAT, and NFκB signaling were upregulated in EAE compared to naïve, with peak activity around sites of meningeal inflammation (Figure 2B and C). Pathways related to Trail and PI3K were downregulated in EAE compared to naïve (Figure 2D and E), and TGFβ pathway activity was unchanged between groups (Figure 2D and E).

Figure 2

Download asset Open asset

To focus our analyses on foci of meningeal inflammation specifically, we performed unbiased UMAP clustering on the spatial transcriptomic dataset and identified 12 distinct clusters (Figure 3A). Grouping the dimensional reduction UMAP plot by EAE and naïve revealed that cluster eleven (C11) was restricted to EAE samples (Figure 3B). C11 makes up 1–5% of the total spots in those samples (Figure 3C), or about 20–120 total spots, and was significantly enriched in EAE relative to naïve samples (Figure 3D). Visualizing the clusters using spatial feature plots confirmed that most clusters map consistently to specific anatomic regions and were similar between naïve and EAE (Figure 3E; Figure 3—figure supplement 1A). C11 overlapped with previously noted areas of meningeal MRI enhancement, suggesting that C11 represents areas of meningeal inflammation (Figure 3E; Figure 3—figure supplement 1B—D). We compared the gene expression in C11 to other clusters and found 132 upregulated genes and 70 downregulated genes (p<0.05; log 2 fold change >1; Figure 3F). Inflammation-related genes were prominently represented in C11 relative to other clusters (Supplementary file 2), with top genes including Cd74, C3, and Gfap (Figure 3G; Figure 3—figure supplement 1E). The presence of glial genes such as Gfap within cluster 11 likely represents areas of sub-pial brain parenchyma included within the cluster. Immunoglobulin genes, which are highly expressed in areas of human GMP (Magliozzi et al., 2019), were also upregulated in C11 (Figure 3—figure supplement 1F). We next performed gene set enrichment analysis of C11 spots using the gene ontology (GO) database (Carbon et al., 2021; Ashburner et al., 2000), finding 538 variable gene sets (adjusted p-value<0.05; Supplementary file 3). Among the most prominently enriched gene sets were those involved in antigen processing and presentation, complement activation, lymphocyte activation, and cytokine production and response (Figure 3H).

Figure 3 with 1 supplement see all

Download asset Open asset

After establishing the defining transcriptomic features of meningeal inflammation in our model, we next sought to characterize inflammatory changes in the adjacent CNS parenchyma. We performed unbiased subclustering of each cluster in turn and found EAE-specific subclusters and differentially expressed genes within clusters 1 and 2. These subclusters were labeled 1_3, 1_4, and 2_6 (Figure 4A and B; Figure 4—figure supplement 1) and represent regions of the thalamus and hypothalamus (Figure 4C).

Figure 4 with 2 supplements see all

Download asset Open asset

Next, we assessed whether EAE-specific subclusters were physically closer to meningeal inflammation than other related subclusters. The distance between the average location in each subcluster to the nearest point of C11 was calculated. In the subclusters of cluster 1, we found no difference in proximity to C11 between EAE-specific subclusters vs. subclusters present in EAE and naïve. However, in subclusters of cluster 2, the EAE-specific subcluster was significantly closer to C11 on average compared to other subclusters (Figure 4D). To explore which pathways were activated in these subclusters, we performed gene set enrichment analysis using the GO database (Figure 4E–G; Supplementary file 4, Supplementary file 5, Supplementary file 6). All subclusters displayed enrichment of GO gene sets related to inflammation, antigen processing and presentation, and humoral immunity. Interestingly, pathways related to neuron death, cellular stress, and negative regulation of cellular processes were also enriched (Figure 4E–G; Figure 4—figure supplement 2). Interestingly, other pathways related to cell death, including positive regulation of apoptotic process or programmed cell death, were upregulated in subcluster 1_3 but downregulated in subcluster 1_4, suggesting spatially variable survival signals (Supplementary file 4, Supplementary file 5, Supplementary file 6). Overall, 31 upregulated pathways were conserved between cluster 11 and subclusters 1_3, 1_4, 2_6 (Figure 4H). We found prominent conserved upregulation of pathways related to adaptive/B-cell-mediated immunity, antigen processing and presentation, cell killing, and others (Figure 4I).

Our pathway analysis of meningeal inflammation and areas of inflamed adjacent brain parenchyma suggested that inflammatory signals increased in meningeal inflammation could have variable ‘penetration’ into the adjacent brain. We sought to test this using spatial trajectory gene/gene set expression modeling available within the SPATA2 software package (Kueckelhaus et al., 2020). Trajectories were drawn in EAE samples from the largest region of meningeal inflammation to the central thalamus (Figure 5A). Gene and gene set expression levels were then evaluated along the length of these trajectories and compared to ideal patterns of expression, as demonstrated for representative genes B2m and C3 (Figure 5B–E). The difference between gene or gene set expression and the ideal patterns, here ‘logarithmic descending’ or ‘gradient descending’, is represented by the residual line (Figure 5B and E). The area under the residual curve (residual AUC) is therefore inversely proportional to fit for the given gene or gene set and ideal pattern. C3 expression declines rapidly along the trajectory and the logarithmic descending residual AUC is lower than gradient descending residual AUC (Figure 5E), while B2m follows a less steep decline and fits the two patterns similarly (Figure 5C). Trajectory analysis of gene sets enriched in meningeal inflammation and adjacent brain parenchyma was performed in this way, and the average residual AUC was calculated for gradient descending/ascending and logarithmic descending/ascending patterns (Figure 5Fi). As expected, all gene sets fit descending patterns better than ascending ones. There was variability in fit to the gradient descending pattern of expression, representing a more gradual decline in pathway enrichment along the trajectory, with gene sets related to antigen processing and presentation, cell killing, IL-6 production, and IFNγ response having the best fit (Figure 5Fi). Enrichment score trajectory heatmap of a representative sample corroborates this, showing increased activity farther along the spatial trajectory for these gene sets (Figure 5Fii).

Figure 5 with 1 supplement see all

Download asset Open asset

We next validated this variability in pattern of expression in a separate cohort of SJL mice with EAE using RNAscope to label selected transcripts related to glial activation (Fcgr3 and Gfap) antigen presentation (B2m and Cd74) and complement (C3) (Figure 5G and I). Consistent with our spatial transcriptomics data, each of these transcripts was substantially induced in EAE as compared to naïve (Figure 5—figure supplement 1). We quantified the shortest distance of each target-positive cell from the region of LMI (Figure 5H and J). For all transcripts, the number of positive cells decreased with distance exponentially. Exponential regression was applied to find the best fit curves of the data, modeled by the equation y=a * x^b, where y is percentage of cells, x is distance from LMI. The exponential constant b reflects the rate of decrement and had higher absolute value in C3 (b=–1.13) relative to B2m (b=–0.59) (Supplementary file 7).

Meninges-restricted inflammation in MS is intimately related to sub-pial gray matter demyelination (Howell et al., 2011), atrophy, and neurocognitive symptoms, but therapeutically targeting this aspect of the disease is challenging due to poorly understood pathologic mechanisms. Here, we present spatial transcriptomics analysis in a mouse model of LMI, finding a broad swath of inflammatory pathways upregulated at foci of LMI and a subset of them upregulated in the nearby brain parenchyma. Notably, genes related to B-cell-mediated responses and antigen processing and presentation were upregulated in inflammatory parenchymal subclusters. Variable penetrance of inflammatory pathway activity was evident when analyzing the expression pattern of gene sets along a linear trajectory from LMI into the CNS parenchyma, where antigen processing and presentation, IL-6 production, and the IFNγ response pathway activity followed a more gradual decline as compared to other pathways.

An important limitation of the spatial transcriptomics method used in this study is the spatial resolution of each spot. Since each spot is ~55 µm in diameter it is likely that multiple cells are captured in each one. The simultaneous addition of single-cell analysis would facilitate deconvolution of cell types within each spot. Furthermore, some spots of the borders of anatomic structures are likely to include both regions. This is exemplified by the spots within cluster 11, which in this study mainly represent meningeal inflammation, also being enriched in glial genes such as Gfap. Other methods of spatial RNA analysis exist that allow single-cell resolution, but to date are probe-based and therefore limited by the number of genes assessed.

Prior work supports roles for pathways identified in our dataset, including B cell and IFNγ-mediated responses, in contributing to neurodegeneration in MS. LMI in MS is rich in B cells, and the critical role of B cells in MS has been underscored by the success of B cell depletion in relapsing and progressive MS. Recent studies have proposed numerous mechanisms whereby B cells could contribute to cortical pathology, including indirectly through activation and inflammatory polarization of T cells, myeloid cells, and astrocytes or directly through production of neurotoxic cytokines or antibodies (Bhargava et al., 2022). B cell culture supernatants from MS patients, but not healthy controls, are toxic to rat and human neurons and oligodendrocytes with this effect being mediated by the extracellular vesicle fraction of the supernatants (Lisak et al., 2017; Benjamins et al., 2019). B cells are also sources for inflammatory cytokines, such as IL-6 and GM-CSF, and antibodies, which are speculated to contribute to GMP (Bhargava et al., 2022).

The role of IFNγ in the pathogenesis of MS and EAE is complex, and likely has stage-specific protective and pathologic effects (Arellano et al., 2015). Its prominent upregulation in EAE was initially considered evidence of its pathogenic nature, but subsequent experiments showed that it was pathogenic during the initiation phase but protective later in the course (Arellano et al., 2015). IFNγ signaling and subsequent upregulation of antigen processing and presentation on glia has been identified as a mechanism that could lead to remyelination failure and subsequent neuronal loss (Kirby et al., 2019). Oligodendrocyte precursor cells upregulate antigen presentation and cross-presentation pathways in response to IFNγ, promoting inflammation and making them susceptible to CD8+ T cell killing (Kirby et al., 2019). Upregulation of genes for self-antigen presentation has also been noted in neurons and oligodendrocyte lineage (OL) cells in post-mortem MS single nucleus RNAseq (Schirmer et al., 2019) and OL cells in EAE (Falcão et al., 2018; Jäkel et al., 2019; Langseth et al., 2023; Kukanja et al., 2024). Recent work also assessed subcortical white matter lesions from post-mortem MS cases via spatial transcriptomics, finding prominent elevation of TNF signaling, in agreement with presented results in the SJL EAE model (Lerma-Martin et al., 2022).

Another important consideration in these experiments is our choice of naïve, rather than CFA only, controls. While often used as the control in EAE studies focused on mechanisms of autoimmunity, CFA only can independently induce systemic inflammation. Since this study seeks to describe transcriptomic changes in neuroinflammation more broadly, we chose to use a healthy comparison group to maximize our ability to find genes enriched in neuroinflammation. Ultimately, however, the choice of naïve or CFA only controls is unlikely to have affected our conclusions. SJL-EAE, unlike the more common C57Bl6-EAE, does not require pertussis toxin during the induction. The only difference between naïve and CFA only controls is the subcutaneous CFA delivered at time of immunization (11 weeks prior to experiment endpoint). Indeed, when we compared CFA only and healthy animals at 11 weeks there was no difference in glial reactivity by GFAP, IBA1, or CD68 mean fluorescence intensity (MFI). There was also no evidence of neurologic symptoms or LMI development in CFA only controls.

While SJL EAE models many features of LMI in MS (Magliozzi et al., 2004), there are important differences that limit the direct translation of these results to MS. The majority of LMI identified in mice with SJL EAE in our experiments occurred in subarachnoid cisterns, and as a result prominently affected areas of deep gray matter including thalamus and hypothalamus as opposed to cortical lesions also seen in MS. Notably, neuronal loss in the thalamus and other deep gray nuclei does occur in MS with a similar ‘surface-in’ gradient of neuronal injury, thought to also be related to toxic CSF-derived factors (Azevedo et al., 2018; Magliozzi et al., 2022). Demyelination, microglia and astrocyte activation, and neuronal loss are evident in the parenchyma adjacent to LMI in SJL EAE (Bhargava et al., 2021; Gupta et al., 2023). We also noted some variability in the extent and location of LMI, which is unavoidable in EAE. While other animal models of LMI, such as directly injecting inflammatory cytokines into the meninges/cortex, produce more predictable regions of LMI, they involve traumatic injury to the brain and typically lack follicle-like structures (Silva et al., 2021). Notably, novel rodent models of cytokine-mediated LMI have recently been developed, including virus-mediated meningeal overexpression of TNF and IFNγ or sub-arachnoid injection of recombinant lymphotoxin-α, were recently developed, and exhibit lymphoid-like structures (James Bates et al., 2022; James et al., 2020). Spatially resolved analyses of these models could provide additional insights into sub-pial pathology during neuroinflammation.

This work is the first to characterize a mouse model of LMI and gray matter injury using spatial transcriptomics, and in addition to the analysis presented here contributes a publicly available dataset for future research. We highlight the importance of antigen processing and presentation and complement signaling, which are prominently upregulated in sub-pial gray matter, in our model. Future studies should focus on spatial transcriptomics in post-mortem or biopsied human tissue. While access to appropriate samples remains a significant barrier, recent advances in RNA extraction from formalin fixed paraffin embedded tissues (Newton et al., 2020; Gracia Villacampa et al., 2021) will allow for the use of large banks of historically collected and preserved samples and could dramatically improve availability.

Spatial transcriptomics sequencing data has been deposited in the Gene Expression Omnibus (GEO) database under accession code GSE236963.

1. 1. Gadani SP
   2. Singh S
   3. Smith M
   4. Kim S
   5. Hu J
   6. Calabresi PA
   7. Bhargava P

   (2024) NCBI Gene Expression Omnibus

   ID GSE236963. Spatial Transcriptomics of Meningeal Inflammation Reveals Variable Penetrance of Inflammatory Gene Signatures into Adjacent Brain Parenchyma.

   https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE236963

1. 1. Absinta M
   2. Vuolo L
   3. Rao A
   4. Nair G
   5. Sati P
   6. Cortese ICM
   7. Ohayon J
   8. Fenton K
   9. Reyes-Mantilla MI
   10. Maric D
   11. Calabresi PA
   12. Butman JA
   13. Pardo CA
   14. Reich DS

   (2015) Gadolinium-based MRI characterization of leptomeningeal inflammation in multiple sclerosis

   Neurology 85:18–28.

   https://doi.org/10.1212/WNL.0000000000001587

   • PubMed
   • Google Scholar
2. 1. Arellano G
   2. Ottum PA
   3. Reyes LI
   4. Burgos PI
   5. Naves R

   (2015) Stage-specific role of interferon-gamma in experimental autoimmune encephalomyelitis and multiple sclerosis

   Frontiers in Immunology 6:492.

   https://doi.org/10.3389/fimmu.2015.00492

   • PubMed
   • Google Scholar
3. 1. Ashburner M
   2. Ball CA
   3. Blake JA
   4. Botstein D
   5. Butler H
   6. Cherry JM
   7. Davis AP
   8. Dolinski K
   9. Dwight SS
   10. Eppig JT
   11. Harris MA
   12. Hill DP
   13. Issel-Tarver L
   14. Kasarskis A
   15. Lewis S
   16. Matese JC
   17. Richardson JE
   18. Ringwald M
   19. Rubin GM
   20. Sherlock G

   (2000) Gene Ontology: tool for the unification of biology

   Nature Genetics 25:25–29.

   https://doi.org/10.1038/75556

   • Google Scholar
4. 1. Azevedo CJ
   2. Cen SY
   3. Khadka S
   4. Liu S
   5. Kornak J
   6. Shi Y
   7. Zheng L
   8. Hauser SL
   9. Pelletier D

   (2018) Thalamic atrophy in multiple sclerosis: a magnetic resonance imaging marker of neurodegeneration throughout disease

   Annals of Neurology 83:223–234.

   https://doi.org/10.1002/ana.25150

   • PubMed
   • Google Scholar
5. 1. Bedussi B
   2. van der Wel NN
   3. de Vos J
   4. van Veen H
   5. Siebes M
   6. VanBavel E
   7. Bakker EN

   (2017) Paravascular channels, cisterns, and the subarachnoid space in the rat brain: a single compartment with preferential pathways

   Journal of Cerebral Blood Flow and Metabolism 37:1374–1385.

   https://doi.org/10.1177/0271678X16655550

   • PubMed
   • Google Scholar
6. 1. Benjamini Y
   2. Hochberg Y

   (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing

   Journal of the Royal Statistical Society Series B 57:289–300.

   https://doi.org/10.1111/j.2517-6161.1995.tb02031.x

   • Google Scholar
7. 1. Benjamins JA
   2. Nedelkoska L
   3. Touil H
   4. Stemmer PM
   5. Carruthers NJ
   6. Jena BP
   7. Naik AR
   8. Bar-Or A
   9. Lisak RP

   (2019) Exosome-enriched fractions from MS B cells induce oligodendrocyte death

   Neurology - Neuroimmunology Neuroinflammation 6:e550.

   https://doi.org/10.1212/NXI.0000000000000550

   • PubMed
   • Google Scholar
8. 1. Bhargava P
   2. Kim S
   3. Reyes AA
   4. Grenningloh R
   5. Boschert U
   6. Absinta M
   7. Pardo C
   8. Van Zijl P
   9. Zhang J
   10. Calabresi PA

   (2021) Imaging meningeal inflammation in CNS autoimmunity identifies a therapeutic role for BTK inhibition

   Brain 144:1396–1408.

   https://doi.org/10.1093/brain/awab045

   • PubMed
   • Google Scholar
9. 1. Bhargava P
   2. Hartung HP
   3. Calabresi PA

   (2022) Contribution of B cells to cortical damage in multiple sclerosis

   Brain 145:3363–3373.

   https://doi.org/10.1093/brain/awac233

   • PubMed
   • Google Scholar
10. 1. Bø L
    2. Vedeler CA
    3. Nyland HI
    4. Trapp BD
    5. Mørk SJ

    (2003a) Subpial demyelination in the cerebral cortex of multiple sclerosis patients

    Journal of Neuropathology and Experimental Neurology 62:723–732.

    https://doi.org/10.1093/jnen/62.7.723

    • PubMed
    • Google Scholar
11. 1. Bø L
    2. Vedeler CA
    3. Nyland H
    4. Trapp BD
    5. Mørk SJ

    (2003b) Intracortical multiple sclerosis lesions are not associated with increased lymphocyte infiltration

    Multiple Sclerosis 9:323–331.

    https://doi.org/10.1191/1352458503ms917oa

    • PubMed
    • Google Scholar
12. 1. Brink BP
    2. Veerhuis R
    3. Breij ECW
    4. van der Valk P
    5. Dijkstra CD
    6. Bö L

    (2005) The pathology of multiple sclerosis is location-dependent: no significant complement activation is detected in purely cortical lesions

    Journal of Neuropathology and Experimental Neurology 64:147–155.

    https://doi.org/10.1093/jnen/64.2.147

    • PubMed
    • Google Scholar
13. 1. Carbon S
    2. Douglass E
    3. Good BM
    4. Unni DR
    5. Harris NL
    6. Mungall CJ
    7. Basu S
    8. Chisholm RL
    9. Dodson RJ
    10. Hartline E
    11. Fey P
    12. Thomas PD
    13. Albou LP
    14. Ebert D
    15. Kesling MJ
    16. Mi H
    17. Muruganujan A
    18. Huang X
    19. Mushayahama T
    20. LaBonte SA
    21. Siegele DA
    22. Antonazzo G
    23. Attrill H
    24. Brown NH
    25. Garapati P
    26. Marygold SJ
    27. Trovisco V
    28. dos Santos G
    29. Falls K
    30. Tabone C
    31. Zhou P
    32. Goodman JL
    33. Strelets VB
    34. Thurmond J
    35. Garmiri P
    36. Ishtiaq R
    37. Rodríguez-López M
    38. Acencio ML
    39. Kuiper M
    40. Lægreid A
    41. Logie C
    42. Lovering RC
    43. Kramarz B
    44. Saverimuttu SCC
    45. Pinheiro SM
    46. Gunn H
    47. Su R
    48. Thurlow KE
    49. Chibucos M
    50. Giglio M
    51. Nadendla S
    52. Munro J
    53. Jackson R
    54. Duesbury MJ
    55. Del-Toro N
    56. Meldal BHM
    57. Paneerselvam K
    58. Perfetto L
    59. Porras P
    60. Orchard S
    61. Shrivastava A
    62. Chang HY
    63. Finn RD
    64. Mitchell AL
    65. Rawlings ND
    66. Richardson L
    67. Sangrador-Vegas A
    68. Blake JA
    69. Christie KR
    70. Dolan ME
    71. Drabkin HJ
    72. Hill DP
    73. Ni L
    74. Sitnikov DM
    75. Harris MA
    76. Oliver SG
    77. Rutherford K
    78. Wood V
    79. Hayles J
    80. Bähler J
    81. Bolton ER
    82. De Pons JL
    83. Dwinell MR
    84. Hayman GT
    85. Kaldunski ML
    86. Kwitek AE
    87. Laulederkind SJF
    88. Plasterer C
    89. Tutaj MA
    90. Vedi M
    91. Wang SJ
    92. D’Eustachio P
    93. Matthews L
    94. Balhoff JP
    95. Aleksander SA
    96. Alexander MJ
    97. Cherry JM
    98. Engel SR
    99. Gondwe F
    100. Karra K
    101. Miyasato SR
    102. Nash RS
    103. Simison M
    104. Skrzypek MS
    105. Weng S
    106. Wong ED
    107. Feuermann M
    108. Gaudet P
    109. Morgat A
    110. Bakker E
    111. Berardini TZ
    112. Reiser L
    113. Subramaniam S
    114. Huala E
    115. Arighi CN
    116. Auchincloss A
    117. Axelsen K
    118. Argoud-Puy G
    119. Bateman A
    120. Blatter MC
    121. Boutet E
    122. Bowler E
    123. Breuza L
    124. Bridge A
    125. Britto R
    126. Bye-A-Jee H
    127. Casas CC
    128. Coudert E
    129. Denny P
    130. Estreicher A
    131. Famiglietti ML
    132. Georghiou G
    133. Gos A
    134. Gruaz-Gumowski N
    135. Hatton-Ellis E
    136. Hulo C
    137. Ignatchenko A
    138. Jungo F
    139. Laiho K
    140. Le Mercier P
    141. Lieberherr D
    142. Lock A
    143. Lussi Y
    144. MacDougall A
    145. Magrane M
    146. Martin MJ
    147. Masson P
    148. Natale DA
    149. Hyka-Nouspikel N
    150. Orchard S
    151. Pedruzzi I
    152. Pourcel L
    153. Poux S
    154. Pundir S
    155. Rivoire C
    156. Speretta E
    157. Sundaram S
    158. Tyagi N
    159. Warner K
    160. Zaru R
    161. Wu CH
    162. Diehl AD
    163. Chan JN
    164. Grove C
    165. Lee RYN
    166. Muller HM
    167. Raciti D
    168. Van Auken K
    169. Sternberg PW
    170. Berriman M
    171. Paulini M
    172. Howe K
    173. Gao S
    174. Wright A
    175. Stein L
    176. Howe DG
    177. Toro S
    178. Westerfield M
    179. Jaiswal P
    180. Cooper L
    181. Elser J

    (2021) The gene ontology resource: enriching a gold mine

    Nucleic Acids Research 49:D325–D334.

    https://doi.org/10.1093/nar/gkaa1113

    • PubMed
    • Google Scholar
14. 1. Faissner S
    2. Plemel JR
    3. Gold R
    4. Yong VW

    (2019) Progressive multiple sclerosis: from pathophysiology to therapeutic strategies

    Nature Reviews. Drug Discovery 18:905–922.

    https://doi.org/10.1038/s41573-019-0035-2

    • PubMed
    • Google Scholar
15. 1. Falcão AM
    2. van Bruggen D
    3. Marques S
    4. Meijer M
    5. Jäkel S
    6. Agirre E
    7. Floriddia EM
    8. Vanichkina DP
    9. Ffrench-Constant C
    10. Williams A
    11. Guerreiro-Cacais AO
    12. Castelo-Branco G

    (2018) Disease-specific oligodendrocyte lineage cells arise in multiple sclerosis

    Nature Medicine 24:1837–1844.

    https://doi.org/10.1038/s41591-018-0236-y

    • PubMed
    • Google Scholar
16. 1. Geurts JJG
    2. Barkhof F

    (2008) Grey matter pathology in multiple sclerosis

    The Lancet. Neurology 7:841–851.

    https://doi.org/10.1016/S1474-4422(08)70191-1

    • PubMed
    • Google Scholar
17. 1. Gracia Villacampa E
    2. Larsson L
    3. Mirzazadeh R
    4. Kvastad L
    5. Andersson A
    6. Mollbrink A
    7. Kokaraki G
    8. Monteil V
    9. Schultz N
    10. Appelberg KS
    11. Montserrat N
    12. Zhang H
    13. Penninger JM
    14. Miesbach W
    15. Mirazimi A
    16. Carlson J
    17. Lundeberg J

    (2021) Genome-wide spatial expression profiling in formalin-fixed tissues

    Cell Genomics 1:100065.

    https://doi.org/10.1016/j.xgen.2021.100065

    • PubMed
    • Google Scholar
18. Software

    1. Guangchuang Y

    (2023) enrichplot: visualization of functional enrichment result, version 1.24.4

    Enrichplot.

    https://yulab-smu.top/biomedical-knowledge-mining-book/
19. 1. Gupta K
    2. Kesharwani A
    3. Rua S
    4. Singh SS
    5. Siu C
    6. Jank L
    7. Smith MD
    8. Calabresi PA
    9. Bhargava P

    (2023) BAFF blockade in experimental autoimmune encephalomyelitis reduces inflammation in the meninges and synaptic and neuronal loss in adjacent brain regions

    Journal of Neuroinflammation 20:229.

    https://doi.org/10.1186/s12974-023-02922-7

    • PubMed
    • Google Scholar
20. 1. Hafemeister C
    2. Satija R

    (2019) Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression

    Genome Biology 20:296.

    https://doi.org/10.1186/s13059-019-1874-1

    • PubMed
    • Google Scholar
21. 1. Howell OW
    2. Reeves CA
    3. Nicholas R
    4. Carassiti D
    5. Radotra B
    6. Gentleman SM
    7. Serafini B
    8. Aloisi F
    9. Roncaroli F
    10. Magliozzi R
    11. Reynolds R

    (2011) Meningeal inflammation is widespread and linked to cortical pathology in multiple sclerosis

    Brain 134:2755–2771.

    https://doi.org/10.1093/brain/awr182

    • PubMed
    • Google Scholar
22. 1. Jäkel S
    2. Agirre E
    3. Mendanha Falcão A
    4. van Bruggen D
    5. Lee KW
    6. Knuesel I
    7. Malhotra D
    8. Ffrench-Constant C
    9. Williams A
    10. Castelo-Branco G

    (2019) Altered human oligodendrocyte heterogeneity in multiple sclerosis

    Nature 566:543–547.

    https://doi.org/10.1038/s41586-019-0903-2

    • PubMed
    • Google Scholar
23. 1. James RE
    2. Schalks R
    3. Browne E
    4. Eleftheriadou I
    5. Munoz CP
    6. Mazarakis ND
    7. Reynolds R

    (2020) Persistent elevation of intrathecal pro-inflammatory cytokines leads to multiple sclerosis-like cortical demyelination and neurodegeneration

    Acta Neuropathologica Communications 8:66.

    https://doi.org/10.1186/s40478-020-00938-1

    • PubMed
    • Google Scholar
24. 1. James Bates RE
    2. Browne E
    3. Schalks R
    4. Jacobs H
    5. Tan L
    6. Parekh P
    7. Magliozzi R
    8. Calabrese M
    9. Mazarakis ND
    10. Reynolds R

    (2022) Lymphotoxin-alpha expression in the meninges causes lymphoid tissue formation and neurodegeneration

    Brain 145:4287–4307.

    https://doi.org/10.1093/brain/awac232

    • PubMed
    • Google Scholar
25. 1. Kirby L
    2. Jin J
    3. Cardona JG
    4. Smith MD
    5. Martin KA
    6. Wang J
    7. Strasburger H
    8. Herbst L
    9. Alexis M
    10. Karnell J
    11. Davidson T
    12. Dutta R
    13. Goverman J
    14. Bergles D
    15. Calabresi PA

    (2019) Oligodendrocyte precursor cells present antigen and are cytotoxic targets in inflammatory demyelination

    Nature Communications 10:3887.

    https://doi.org/10.1038/s41467-019-11638-3

    • PubMed
    • Google Scholar
26. 1. Kueckelhaus J
    2. von Ehr J
    3. Ravi VM
    4. Will P
    5. Joseph K
    6. Beck J
    7. Hofmann UG
    8. Delev D
    9. Schnell O
    10. Heiland DH

    (2020) Inferring spatially transient gene expression pattern from spatial transcriptomic studies

    Bioinformatics 1:e6544.

    https://doi.org/10.1101/2020.10.20.346544

    • Google Scholar
27. 1. Kukanja P
    2. Langseth CM
    3. Rubio Rodríguez-Kirby LA
    4. Agirre E
    5. Zheng C
    6. Raman A
    7. Yokota C
    8. Avenel C
    9. Tiklová K
    10. Guerreiro-Cacais AO
    11. Olsson T
    12. Hilscher MM
    13. Nilsson M
    14. Castelo-Branco G

    (2024) Cellular architecture of evolving neuroinflammatory lesions and multiple sclerosis pathology

    Cell 187:1990–2009.

    https://doi.org/10.1016/j.cell.2024.02.030

    • PubMed
    • Google Scholar
28. 1. Langseth CM
    2. Kukanja P
    3. Rubio Rodríguez-Kirby LA
    4. Agirre E
    5. Raman A
    6. Yokota C
    7. Avenel C
    8. Tiklová K
    9. Guerreiro-Cacais AO
    10. Olsson T
    11. Hilscher MM
    12. Nilsson M
    13. Castelo-Branco G

    (2023) Single cell-resolution in situ sequencing elucidates spatial dynamics of multiple sclerosis lesion and disease evolution

    Cell Biology 1:e7074.

    https://doi.org/10.1101/2023.06.29.547074

    • Google Scholar
29. 1. Lerma-Martin C
    2. Badia-i-Mompel P
    3. Ramirez Flores RO
    4. Sekol P
    5. Hofmann A
    6. Thäwel T
    7. Riedl CJ
    8. Wünnemann F
    9. Ibarra-Arellano MA
    10. Trobisch T
    11. Eisele P
    12. Schapiro D
    13. Haeussler M
    14. Hametner S
    15. Saez-Rodriguez J
    16. Schirmer L

    (2022) Spatial cell type mapping of multiple sclerosis lesions

    Neuroscience 1:e4906.

    https://doi.org/10.1101/2022.11.03.514906

    • Google Scholar
30. 1. Lisak RP
    2. Nedelkoska L
    3. Benjamins JA
    4. Schalk D
    5. Bealmear B
    6. Touil H
    7. Li R
    8. Muirhead G
    9. Bar-Or A

    (2017) B cells from patients with multiple sclerosis induce cell death via apoptosis in neurons in vitro

    Journal of Neuroimmunology 309:88–99.

    https://doi.org/10.1016/j.jneuroim.2017.05.004

    • PubMed
    • Google Scholar
31. 1. Magliozzi R
    2. Columba-Cabezas S
    3. Serafini B
    4. Aloisi F

    (2004) Intracerebral expression of CXCL13 and BAFF is accompanied by formation of lymphoid follicle-like structures in the meninges of mice with relapsing experimental autoimmune encephalomyelitis

    Journal of Neuroimmunology 148:11–23.

    https://doi.org/10.1016/j.jneuroim.2003.10.056

    • PubMed
    • Google Scholar
32. 1. Magliozzi R
    2. Howell OW
    3. Reeves C
    4. Roncaroli F
    5. Nicholas R
    6. Serafini B
    7. Aloisi F
    8. Reynolds R

    (2010) A gradient of neuronal loss and meningeal inflammation in multiple sclerosis

    Annals of Neurology 68:477–493.

    https://doi.org/10.1002/ana.22230

    • PubMed
    • Google Scholar
33. 1. Magliozzi R
    2. Howell OW
    3. Nicholas R
    4. Cruciani C
    5. Castellaro M
    6. Romualdi C
    7. Rossi S
    8. Pitteri M
    9. Benedetti MD
    10. Gajofatto A
    11. Pizzini FB
    12. Montemezzi S
    13. Rasia S
    14. Capra R
    15. Bertoldo A
    16. Facchiano F
    17. Monaco S
    18. Reynolds R
    19. Calabrese M

    (2018) Inflammatory intrathecal profiles and cortical damage in multiple sclerosis

    Annals of Neurology 83:739–755.

    https://doi.org/10.1002/ana.25197

    • PubMed
    • Google Scholar
34. 1. Magliozzi R
    2. Howell OW
    3. Durrenberger P
    4. Aricò E
    5. James R
    6. Cruciani C
    7. Reeves C
    8. Roncaroli F
    9. Nicholas R
    10. Reynolds R

    (2019) Meningeal inflammation changes the balance of TNF signalling in cortical grey matter in multiple sclerosis

    Journal of Neuroinflammation 16:259.

    https://doi.org/10.1186/s12974-019-1650-x

    • PubMed
    • Google Scholar
35. 1. Magliozzi R
    2. Fadda G
    3. Brown RA
    4. Bar-Or A
    5. Howell OW
    6. Hametner S
    7. Marastoni D
    8. Poli A
    9. Nicholas R
    10. Calabrese M
    11. Monaco S
    12. Reynolds R

    (2022) “Ependymal-in” gradient of thalamic damage in progressive multiple sclerosis

    Annals of Neurology 92:670–685.

    https://doi.org/10.1002/ana.26448

    • PubMed
    • Google Scholar
36. 1. Mainero C
    2. Louapre C
    3. Govindarajan ST
    4. Giannì C
    5. Nielsen AS
    6. Cohen-Adad J
    7. Sloane J
    8. Kinkel RP

    (2015) A gradient in cortical pathology in multiple sclerosis by in vivo quantitative 7 T imaging

    Brain 138:932–945.

    https://doi.org/10.1093/brain/awv011

    • PubMed
    • Google Scholar
37. 1. Merkler D
    2. Schmelting B
    3. Czéh B
    4. Fuchs E
    5. Stadelmann C
    6. Brück W

    (2006) Myelin oligodendrocyte glycoprotein-induced experimental autoimmune encephalomyelitis in the common marmoset reflects the immunopathology of pattern II multiple sclerosis lesions

    Multiple Sclerosis 12:369–374.

    https://doi.org/10.1191/1352458506ms1290oa

    • PubMed
    • Google Scholar
38. 1. Newton Y
    2. Sedgewick AJ
    3. Cisneros L
    4. Golovato J
    5. Johnson M
    6. Szeto CW
    7. Rabizadeh S
    8. Sanborn JZ
    9. Benz SC
    10. Vaske C

    (2020) Large scale, robust, and accurate whole transcriptome profiling from clinical formalin-fixed paraffin-embedded samples

    Scientific Reports 10:17597.

    https://doi.org/10.1038/s41598-020-74483-1

    • PubMed
    • Google Scholar
39. 1. Pikor NB
    2. Prat A
    3. Bar-Or A
    4. Gommerman JL

    (2015) Meningeal tertiary lymphoid tissues and multiple sclerosis: a gathering place for diverse types of immune cells during CNS autoimmunity

    Frontiers in Immunology 6:657.

    https://doi.org/10.3389/fimmu.2015.00657

    • PubMed
    • Google Scholar
40. 1. Reich DS
    2. Lucchinetti CF
    3. Calabresi PA

    (2018) Multiple Sclerosis

    The New England Journal of Medicine 378:169–180.

    https://doi.org/10.1056/NEJMra1401483

    • PubMed
    • Google Scholar
41. 1. Schirmer L
    2. Velmeshev D
    3. Holmqvist S
    4. Kaufmann M
    5. Werneburg S
    6. Jung D
    7. Vistnes S
    8. Stockley JH
    9. Young A
    10. Steindel M
    11. Tung B
    12. Goyal N
    13. Bhaduri A
    14. Mayer S
    15. Engler JB
    16. Bayraktar OA
    17. Franklin RJM
    18. Haeussler M
    19. Reynolds R
    20. Schafer DP
    21. Friese MA
    22. Shiow LR
    23. Kriegstein AR
    24. Rowitch DH

    (2019) Neuronal vulnerability and multilineage diversity in multiple sclerosis

    Nature 573:75–82.

    https://doi.org/10.1038/s41586-019-1404-z

    • PubMed
    • Google Scholar
42. 1. Schubert M
    2. Klinger B
    3. Klünemann M
    4. Sieber A
    5. Uhlitz F
    6. Sauer S
    7. Garnett MJ
    8. Blüthgen N
    9. Saez-Rodriguez J

    (2018) Perturbation-response genes reveal signaling footprints in cancer gene expression

    Nature Communications 9:20.

    https://doi.org/10.1038/s41467-017-02391-6

    • PubMed
    • Google Scholar
43. 1. Silva BA
    2. Miglietta E
    3. Ferrari CC

    (2021) Neuroinflammation in cortical and meningeal pathology in multiple sclerosis: understanding from animal models

    Neuroimmunology and Neuroinflammation 2020:174–184.

    https://doi.org/10.20517/2347-8659.2020.47

    • Google Scholar
44. 1. Storch MK
    2. Bauer J
    3. Linington C
    4. Olsson T
    5. Weissert R
    6. Lassmann H

    (2006) Cortical demyelination can be modeled in specific rat models of autoimmune encephalomyelitis and is major histocompatibility complex (MHC) haplotype-related

    Journal of Neuropathology and Experimental Neurology 65:1137–1142.

    https://doi.org/10.1097/01.jnen.0000248547.13176.9d

    • PubMed
    • Google Scholar
45. 1. van Horssen J
    2. Brink BP
    3. de Vries HE
    4. van der Valk P
    5. Bø L

    (2007) The blood-brain barrier in cortical multiple sclerosis lesions

    Journal of Neuropathology and Experimental Neurology 66:321–328.

    https://doi.org/10.1097/nen.0b013e318040b2de

    • PubMed
    • Google Scholar
46. 1. Wicken C
    2. Nguyen J
    3. Karna R
    4. Bhargava P

    (2018) Leptomeningeal inflammation in multiple sclerosis: Insights from animal and human studies

    Multiple Sclerosis and Related Disorders 26:173–182.

    https://doi.org/10.1016/j.msard.2018.09.025

    • PubMed
    • Google Scholar
47. 1. Wu T
    2. Hu E
    3. Xu S
    4. Chen M
    5. Guo P
    6. Dai Z
    7. Feng T
    8. Zhou L
    9. Tang W
    10. Zhan L
    11. Fu X
    12. Liu S
    13. Bo X
    14. Yu G

    (2021) clusterProfiler 4.0: a universal enrichment tool for interpreting omics data

    Innovation 2:100141.

    https://doi.org/10.1016/j.xinn.2021.100141

    • PubMed
    • Google Scholar

Author details

1. Sachin P Gadani

   1. Division of Neuroimmunology, Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, United States
   2. Department of Neurology, University of Pittsburgh, Pittsburgh, United States

   Contribution

   Data curation, Formal analysis, Investigation, Visualization, Writing – original draft, Writing – review and editing

   Contributed equally with

   Saumitra Singh

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-9555-4553

2. Saumitra Singh

   Division of Neuroimmunology, Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, United States

   Contribution

   Data curation, Formal analysis, Validation, Investigation, Methodology, Writing – original draft, Writing – review and editing

   Contributed equally with

   Sachin P Gadani

   Competing interests

   No competing interests declared

3. Sophia Kim

   Division of Neuroimmunology, Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, United States

   Contribution

   Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

4. Jingwen Hu

   Division of Neuroimmunology, Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, United States

   Contribution

   Validation, Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

5. Matthew D Smith

   Division of Neuroimmunology, Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, United States

   Contribution

   Data curation, Formal analysis, Validation, Visualization, Writing – review and editing

   Competing interests

   No competing interests declared

6. Peter A Calabresi

   1. Division of Neuroimmunology, Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, United States
   2. Solomon Snyder, Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, United States

   Contribution

   Supervision, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-7776-6472

7. Pavan Bhargava

   Division of Neuroimmunology, Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, United States

   Contribution

   Conceptualization, Data curation, Supervision, Funding acquisition, Investigation, Writing – original draft, Project administration, Writing – review and editing

   For correspondence

   pbharga2@jhmi.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-7947-9418

• 1,590

  views

• 87

  downloads

• 0

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.