Spatial Transcriptomics of Meningeal Inflammation Reveals Inflammatory Gene Signatures in Adjacent Brain Parenchyma

  1. Sachin P Gadani
  2. Saumitra Singh
  3. Sophia Kim
  4. Jingwen Hu
  5. Matthew D Smith
  6. Peter A Calabresi
  7. Pavan Bhargava

  • Curated by eLife

    eLife logo

    eLife Assessment

    Brain inflammation is a hallmark of multiple sclerosis. Using novel spatial transcriptomics methods, the authors provide solid evidence for a gradient of immune genes and inflammatory markers from the meninges toward the adjacent brain parenchyma in a mouse model. This important study advances our understanding of the mechanisms of brain damage in this autoimmune disease.

Reviewed by

Read the full article

Listed in

Log in to save this article

Abstract

While modern high efficacy disease modifying therapies have revolutionized the treatment of relapsing-remitting multiple sclerosis, they are less effective at controlling progressive forms of the disease. Meningeal inflammation is a recognized risk factor for cortical grey matter pathology which can result in disabling symptoms such as cognitive impairment and depression, but the mechanisms linking meningeal inflammation and grey matter pathology remain unclear. Here, we performed MRI-guided spatial transcriptomics in a mouse model of autoimmune meningeal inflammation to characterize the transcriptional signature in areas of meningeal inflammation and the underlying brain parenchyma. We found broadly increased activity of inflammatory signaling pathways at sites of meningeal inflammation, but only a subset of these pathways active in the adjacent brain parenchyma. Sub-clustering of regions adjacent to meningeal inflammation revealed the subset of immune programs induced in brain parenchyma, notably complement signaling and antigen processing/presentation. Trajectory gene and gene set modeling analysis confirmed variable penetration of immune signatures originating from meningeal inflammation into the adjacent brain tissue. This work contributes a valuable data resource to the field, provides the first detailed spatial transcriptomic characterization in a model of meningeal inflammation, and highlights several candidate pathways in the pathogenesis of grey matter pathology.

Article activity feed

  1. Version published to 10.7554/elife.88414.3 on eLife
  2. Version published to 10.7554/elife.88414 on eLife
  3. eLife

    eLife Assessment

    Brain inflammation is a hallmark of multiple sclerosis. Using novel spatial transcriptomics methods, the authors provide solid evidence for a gradient of immune genes and inflammatory markers from the meninges toward the adjacent brain parenchyma in a mouse model. This important study advances our understanding of the mechanisms of brain damage in this autoimmune disease.

  4. eLife

    Reviewer 1 (Public Review):

    Multiple sclerosis (MS) is a debilitating autoimmune disease that causes loss of myelin in neurons of the central nervous system. MS is characterized by the presence of inflammatory immune cells in several brain regions as well as the brain barriers (meninges). This study aims to understand the local immune hallmarks in regions of the brain parenchyma that are adjacent to the leptomeninges in a mouse model of MS. The leptomeninges are known to be a foci of inflammation in MS and perhaps "bleed" inflammatory cells and molecules to adjacent brain parenchyma regions. To do so, they use novel technology called spatial transcriptomics so that the spatial relationships between the two regions remain intact. The study identifies canonical inflammatory genes and gene sets such as complement and B cells enriched in the parenchyma in close proximity to the leptomeninges in the mouse model of MS but not control. The manuscript is very well written and easy to follow. The results will become a useful resource to others working in the field and can be followed by time series experiments where the same technology can be applied to the different stages of the disease.

  5. eLife

    Reviewer 2 (Public Review):

    Accumulating data suggests that the presence of immune cell infiltrates in the meninges of the multiple sclerosis brain contributes to the tissue damage in the underlying cortical grey matter by the release of inflammatory and cytotoxic factors that diffuse into the brain parenchyma. However, little is known about the identity and direct and indirect effects of these mediators at a molecular level. This study addresses the vital link between an adaptive immune response in the CSF space and the molecular mechanisms of tissue damage that drive clinical progression. In this short report the authors use a spatial transcriptomics approach using Visium Gene Expression technology from 10x Genomics, to identify gene expression signatures in the meninges and the underlying brain parenchyma, and their interrelationship, in the PLP-induced EAE model of MS in the SJL mouse. MRI imaging using a high field strength (11.7T) scanner was used to identify areas of meningeal infiltration for further study. They report, as might be expected, the upregulation of genes associated with the complement cascade, immune cell infiltration, antigen presentation, and astrocyte activation. Pathway analysis revealed the presence of TNF, JAK-STAT and NFkB signaling, amongst others, close to sites of meningeal inflammation in the EAE animals, although the spatial resolution is insufficient to indicate whether this is in the meninges, grey matter, or both.

    UMAP clustering illuminated a major distinct cluster of upregulated genes in the meninges and smaller clusters associated with the grey matter parenchyma underlying the infiltrates. The meningeal cluster contained genes associated with immune cell functions and interactions, cytokine production, and action. The parenchymal clusters included genes and pathways related to glial activation, but also adaptive/B-cell mediated immunity and antigen presentation. This again suggests a technical inability to resolve fully between the compartments as immune cells do not penetrate the pial surface in this model or in MS. Finally, a trajectory analysis based on distance from the meningeal gene cluster successfully demonstrated descending and ascending gradients of gene expression, in particular a decline in pathway enrichment for immune processes with distance from the meninges.

  6. eLife

    Author response:

    The following is the authors’ response to the previous reviews

    Reviewer 1 (Public Review):

    Multiple sclerosis (MS) is a debilitating autoimmune disease that causes loss of myelin in neurons of the central nervous system. MS is characterized by the presence of inflammatory immune cells in several brain regions as well as the brain barriers (meninges). This study aims to understand the local immune hallmarks in regions of the brain parenchyma that are adjacent to the leptomeninges in a mouse model of MS. The leptomeninges are known to be a foci of inflammation in MS and perhaps "bleed" inflammatory cells and molecules to adjacent brain parenchyma regions. To do so, they use novel technology called spatial transcriptomics so that the spatial relationships between the two regions remain intact. The study identifies canonical inflammatory genes and gene sets such as complement and B cells enriched in the parenchyma in close proximity to the leptomeninges in the mouse model of MS but not control. The manuscript is very well written and easy to follow. The results will become a useful resource to others working in the field and can be followed by time series experiments where the same technology can be applied to the diAerent stages of the disease.

    Comments on revised version:

    I agree that the authors successfully addressed most of my comments/critiques. However, the fact that the control mice were not injected with CFA and pertussis toxin is somewhat concerning, because it will be hard to interpret the cause of the transcriptomic readouts described in this study. Some of the described eAects might be due to CFA or pertussis (which was used in the EAE but not the "naive" group), and not necessarily to the relapsing-remitting EAE immune features recapitulated in this mouse model. Moreover, this caveat associated with the "naive" control group is not being clearly stated throughout the manuscript and might go unnoticed to readers.

    The authors should clearly state, in the methods section (in the section "Induction of SJL EAE"), that the naive control group was not injected with CFA or pertussis toxin.

    Additionally, this potential confounder, of not using a control group injected with the same CFA and pertussis toxin regimen of the EAE group, should be mentioned in paragraph two of the discussion alongside the other limitations of the study already highlighted by the authors (or in another section of the discussion).

    We thank the reviewer for highlighting this point. Our choice of healthy/naïve, rather than CFA only, controls was intentional, given our desire to sensitively measure genes changing during neuroinflammation. Ultimately, however, we believe the choice of control group had little effect on our conclusions. We would like to note that SJL-EAE does not require pertussis toxin, so the only difference between naïve and CFA only groups is a single injection of CFA 11 weeks prior to experiment endpoint. We have performed additional IHC imaging of naïve and CFA only groups, finding no difference in glial reactivity by MFI measurement of GFAP, IBA1, or CD68 (updated Supplementary Figure 1C–E).

    We have also added sections to the Results and Discussion section to clearly address this point. In the Results: “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 (Supplemental Figure 1C–E).” In the Discussion: “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 MFI. There was also no evidence of neurologic symptoms or LMI development in CFA only controls.”

    Reviewer 2 (Public Review):

    Accumulating data suggests that the presence of immune cell infiltrates in the meninges of the multiple sclerosis brain contributes to the tissue damage in the underlying cortical grey matter by the release of inflammatory and cytotoxic factors that diAuse into the brain parenchyma. However, little is known about the identity and direct and indirect eAects of these mediators at a molecular level. This study addresses the vital link between an adaptive immune response in the CSF space and the molecular mechanisms of tissue damage that drive clinical progression. In this short report the authors use a spatial transcriptomics approach using Visium Gene Expression technology from 10x Genomics, to identify gene expression signatures in the meninges and the underlying brain parenchyma, and their interrelationship, in the PLP-induced EAE model of MS in the SJL mouse. MRI imaging using a high field strength (11.7T) scanner was used to identify areas of meningeal infiltration for further study. They report, as might be expected, the upregulation of genes associated with the complement cascade, immune cell infiltration, antigen presentation, and astrocyte activation. Pathway analysis revealed the presence of TNF, JAK-STAT and NFkB signaling, amongst others, close to sites of meningeal inflammation in the EAE animals, although the spatial resolution is insuAicient to indicate whether this is in the meninges, grey matter, or both.

    UMAP clustering illuminated a major distinct cluster of upregulated genes in the meninges and smaller clusters associated with the grey matter parenchyma underlying the infiltrates. The meningeal cluster contained genes associated with immune cell functions and interactions, cytokine production, and action. The parenchymal clusters included genes and pathways related to glial activation, but also adaptive/B-cell mediated immunity and antigen presentation. This again suggests a technical inability to resolve fully between the compartments as immune cells do not penetrate the pial surface in this model or in MS. Finally, a trajectory analysis based on distance from the meningeal gene cluster successfully demonstrated descending and ascending gradients of gene expression, in particular a decline in pathway enrichment for immune processes with distance from the meninges.

    Comments on revised version:

    The authors have addressed all of my comments regarding the lack of spatial resolution between the grey matter and the overlying meninges and also concerning the diAiculties in extrapolating from this mouse model to MS itself.

    I am however very concerned about the lack of the correct control group. Immunization of rodents with complete freunds adjuvant and pertussis alone gives rise to widespread microglial activation, some immune cell infiltration and also structural changes to axons, particularly at nodes of Ranvier (https://doi.org/10.1097/NEN.0b013e3181f3a5b1). This will inevitably make it diAicult to interpret the transcriptomics results, depending on whether these changes are reversible or not and the time frame of the reversal. In the C57Bl6 EAE models adjuvant induced microglial activation becomes chronic, whereas the axonal changes do reverse by 10 weeks. Whether this is the same in SJL EAE model is not clear.

    We thank the reviewer for bringing up this concern regarding control group, which we discussed above in point 1.1. To specifically address reviewer 2’s point regarding microglial activation, we performed IHC analysis comparing naïve and CFA only groups of SJL animals. We found no substantial diAerence in astrocyte or microglial activation in these animals after 11 weeks, as measured by GFAP, IBA1, and CD68. This new data appears in updated Supplementary Figure 1C–D.

    Recommendations for the authors:

    Both reviewers agree that the revised version has improved and some of their major concerns were adequately addressed. However, both reviewers also agree that critical experimental controls are missing, including the FCA and pertussis toxin injected mice which likely show some degree of inflammation in their brain and are needed to compare your experimental MS group and interpret the transcriptomics data.

    We appreciate both reviewers’ important comments on the control group used in this study. In this revised manuscript we have described our rationale for choosing naïve controls, rather than CFA only, and believe they are the most appropriate comparison group. Additionally, we believe that both CFA only and naïve will have similar degrees of baseline neuroinflammation at the 11- week time point. We apologize for not clarifying before, but pertussis toxin is not used in the SJL-EAE, and therefore the “CFA only” control is much milder in SJL-EAE compared to C57Bl6-EAE. Given that many signs of inflammation resolve by 10 weeks in CFA only with pertussis controls (https://academic.oup.com/jnen/article/69/10/1017/2917071; https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10902151/), CFA only without pertussis controls are unlikely to have any substantial remaining neuroinflammation at 11 weeks. To test this, we performed an additional experiment directly comparing naïve and CFA only without pertussis.

    These groups showed similar degrees of glial reactivity.

    Given the costs of repeating a spatial transcriptomic experiment and inevitable batch effects should we add a group at this point, we have chosen to not as a CFA only control condition to our transcriptomics analysis. However, we believe our added text clarifying the rationale behind control choice and added immunofluorescence data gives readers the appropriate context to accurately interpret our results.

  7. Version published to 10.1101/2023.06.02.543421v3 on bioRxiv
  8. Version published to 10.7554/elife.88414.2 on eLife
  9. eLife

    Author response:

    The following is the authors’ response to the original reviews.

    Reviewer #1 (Recommendations For The Authors):

    This study is very well framed and the writing is very clear. The manuscript is well organized and easy to follow and overall the previous state of the art of the field is taken into account. I only have a couple of minor comments

    (1) There is a preprint that uses single nuclei RNA-Seq and ST on human MS subcortical white matter lesions doi: https://doi.org/10.1101/2022.11.03.514906. This work needs to be included in the discussion of the results.

    (1.1) We appreciate the reviewer bringing up this important preprint, and we have referenced it in the Discussion section of our updated manuscript.

    (2) The discussion should include the overall limitations of the study and how much it can be translated to human MS. Specifically, the current work uses EAE and therefore different disease stages are not captured in this study. This point is also raised by other reviewers.

    (1.2) We thank the reviewer for raising this important point, and we have included additional discussion about the limitations of EAE and its disease relevance to MS.

    Reviewer #2 (Recommendations For The Authors):

    The authors state that this EAE model is better for studying cortical gradients because previous models "such as directly injecting inflammatory cytokines into the meninges/cortex" cause a traumatic injury. It needs to be discussed that these models have now been superseded by more refined models involving long-term overexpression of pro-inflammatory cytokines in the sub-arachnoid space, thereby avoiding traumatic injury. The current results should be discussed in light of these newer models (James et al, 2020; 2022), which are more similar to MS cortical pathology and do exhibit lymphoid-like structures.

    (2.1) We thank the reviewer for pointing out these relevant studies, and we agree they describe non-traumatic and more MS-relevant models of leptomeningeal inflammation. We have included discussion of these works in the updated manuscript.

    • The study will be substantially improved if some of the ST data is validated at least partially with some RNAscope or other in situ hybridization using a subset of probes that capture the take-home message of the paper.

    (2.2) We agree with the reviewer that validation of transcriptomics results is important to support our conclusions. In the updated manuscript Figure 5 and Supplemental Figure 6 we have added RNAscope results for relevant genes. In agreement with the trends noted in the manuscript, expression of genes related to antigen processing and presentation such as B2m decreases gradually with distance from LMI. We also have included a reference to a newly published manuscript from our group (Gupta et al., 2023, J. Neuroinflammation) that characterizes meningeal inflammation and sub-pial changes in the SJL EAE model. In that manuscript, IHC is used to show accumulation of B cells and T cells in the leptomeningeal space, increased microglial and astrocyte reactivity adjacent to leptomeningeal inflammation, and reduction of neuronal markers adjacent to leptomeningeal inflammation.

    • The lack of change in signaling pathways involved in B-cell/T-cell interaction and cytokine/chemokine signaling, which would be expected in areas of immune cell aggregation in the meninges, needs discussion.

    (2.3) While we detected significant upregulation in antigen presentation, complement activation, and humoral immune signaling, areas of meningeal inflammation identified as cluster 11 showed upregulation of numerous other GO gene sets associated with immune cell interaction and cytokine signaling, as described in supplementary table 3. These include T-cell receptor binding, CCR chemokine receptor binding, interleukin 8 production, response to interleukin 1, positive regulation of interleukin-6 production, tumor necrosis factor production, leukocyte cell-cell adhesion. Overall, we believe that the collection of enriched gene sets is consistent with peripheral myeloid and lymphoid infiltration and cytokine production, with the most prominent cytokine / pathways being interferon ɣ/antigen processing and presentation, complement, and humoral inflammation.

    • Fig 4 subclusters includes T-cell activation, pos regulation of neuronal death, cellular response to IFNg, neg regulation of neuronal projections, Ig mediated immune response, cell killing, pos regulation of programmed cell death, pos regulation of apoptotic process, but none of these are discussed despite their obvious importance.

    (2.4) We agree with the reviewer that these upregulated genesets warrant additional discussion and have added additional reference to these genesets in the results section. Also, the genesets ‘positive regulation of programmed cell death’, ‘positive regulation of apoptotic process’, and ‘positive regulation of cell death’ were erroneously included in Figure 4F in the initial manuscript, as they are actually downregulated in cluster 1_4. This has been clarified in the text.

    • Subcluster 11 appears spatially to represent the meninges, but what pathways are expressed there? 330 genes/pathways altered independent of other clusters - immune cell regulation?

    (2.5) We refer the reviewer to Supplementary Table 3, which contains a complete list of GO genesets enriched within cluster 11 spots.

    • The surprising lack of immunoglobulin genes upregulated in the meninges of the mice, considering these are the genes most upregulated in the MS meninges. Should be pointed out and discussed.

    (2.6) We appreciate the reviewer bringing up immunoglobulin genes, which previous publications have shown are elevated in MS meninges and cortical grey matter lesions. Consistent with this, several immunoglobulin genes are elevated in cluster 11, including genes encoding IgG2b, IgA, and IgM. While these results were available within the original submission in Supplementary Table 2, we have included the graph in the updated Supplementary Figure 3.

    • Meningeal signature may be poorly represented given the individual slices shown in suppl 3A, which suggests that only 3 of the EAE slices had significant meningeal infiltrates, indicated by cluster 11 genes.

    (2.7) There was heterogeneity in the location and extent of meningeal infiltrate / cluster 11 in the EAE slices, as the reviewer points out. 2 slices had severe inflammation, 2 had moderate inflammation, and 2 had relatively mild inflammation, but all EAE slices were enriched in inflammation relative to naïve as demonstrated not only through clustering, but also through enriched marker analysis between EAE and Naive and Progeny analysis.

    • The ST is not resolving the meningeal tissue and the immediate underlying grey matter, as demonstrated by a high signal for both CXCL13 and GFAP in cluster 11.

    (2.8) We agree that the spatial transcriptomics strategy applied here is inadequate to precisely delineate between meningeal inflammation and the underlying brain parenchyma, and that the elevation of markers such as GFAP in cluster 11 indicates some ‘contamination’ of parenchymal cells into cluster 11. We have clarified this in the text and discussed the limitation of the spatial transcriptomics method used.

    • More information is required concerning how many animals were used in this study, to meet the requirements for complying with the 3Rs.

    (2.9) A total of 4 mice were used per group. In the naïve group one mouse contributed two slices, for a total of 5 naïve slices. In the EAE group two mice contributed two slices, for a total of 6 EAE slices. We have clarified this in the methods section of the updated manuscript.

    Reviewer #3 (Recommendations For The Authors):

    The authors should provide a more thorough description of the methodology, and there are a few minor concerns about experimental details, data presentation, and description that need to be addressed. In the next few lines, I will highlight a few important aspects that need to be addressed, propose some changes to the main manuscript, and suggest some additional experiments that, if successful, could confirm/support/further strengthen the conclusions that are at this point purely based on transcriptomic data.

    Major comments/suggestions:

    • The main gene expression changes between the control and EAE groups obtained via spatial transcriptomics need to be validated with another technique, at least partially. I suggest performing RNAscope or immunofluorescence imaging using brain sections from a new and independent cohort of animals, where cell-specific markers can also be tested. This type of assessment would work as a validation method and could also inform about the cell-specific contribution to the observed transcriptomic changes.

    (3.1) Please refer to response 2.2

    • The representative qualitative spatial expression heatmaps for each gene in Fig. 1F should be accompanied by corresponding graphs with quantitative measurements. Similar to what is done regarding the data in Fig. 2B and D.

    (3.2) We agree with the reviewer that quantitative graphs were missing, and we have included them in the updated Supplementary Figure 1.

    • A supplementary table discriminating all the DEGs (132 up and 70 downregulated) between cluster 11 and the other clusters has to be provided. What is the contribution of recruited encephalitogenic adaptive immune cells to this cluster 11 gene signature?

    (3.3) These unfiltered results are provided in Supplementary Table 2, and to view the up and down regulated genes the reader can sort the table based on fold change and adjusted P value. We believe providing the complete table is more useful to the reader, since the fold change and

    P value thresholds used to determine “significance” are arbitrary. Since the spatial transcriptomics method used in this work does not have single cell resolution, we cannot accurately estimate the contribution of encephalitogenic adaptive immune cells in cluster 11. However, given previously published work of lymphocyte infiltration into the subarachnoid space in SJL EAE (Gupta et al., 2023, J. Neuroinflammation) and the enrichment of Cd3e in cluster 11 (Log2FC 0.31, adjusted P-val 0.005) we assume some contribution of peripheral lymphocytes.

    • The authors mention that there is grey matter pathology in this relapse model, and this has been shown in a previous publication (Bhargava et al., 2021). However, the regions analyzed in the present study are different from the ones shown in the referenced paper. Is there an overexpression of genes involved in, or gene modules indicative of, neuronal stress and/or death that spatially overlap with clusters 1 and 2? If so, it would be important to provide information about those gene modules in the main figures. It would also be quite relevant to show the levels of cell stress/death proteins and of axonal stress/damage, by APP and/or nonphosphorylated SMI-32 staining, in the deep brain regions (like the thalamus), to corroborate the link between these phenomena and the gene signatures of subclusters 1_3, 1_4, and 2_6.

    (3.4) We thank the review for this insightful comment. We have recently published a manuscript that histologically analyzes leptomeningeal inflammation in the SJL EAE model, specifically assessing the areas looked at in our submitted manuscript (Gupta et al., 2023, J. Neuroinflammation). In that manuscript, IHC is used to show accumulation of B cells and T cells in the leptomeningeal space, increased microglial and astrocyte reactivity adjacent to leptomeningeal inflammation, and reduction of neuronal markers adjacent to leptomeningeal inflammation. To further describe the gene modules in the inflammatory subclusters 1_3/1_4/2_6, we have now provided heatmaps of the selected genesets and their constituent genes (Supplementary Figure 5).

    • It would be important to provide heatmaps discriminating the DEGs that make the gene modules that are significantly altered in subclusters 1_3, 1_4, and 2_6. The gene ontology terms are sometimes ambiguous. For instance, it would be very informative to the reader (and to the field) to know which altered genes compose the "lysosome", "immune response", "response to stress", or "B cell meditated immunity" pathways that are altered in the EAE subcluster 1_3 (Fig. 4E). The same applies to the gene modules altered in the other subclusters of interest. Authors should also consider generating a Venn diagram with the DEGs from subclusters 1_3, 1_4, and 2_6, to complement the GO term Venn presented in Fig. 4H. Having these pieces of information readily available, either as main or supplementary figures, would be a great addition.

    (3.5) We agree with the reviewer on this point and have included these heatmaps in Supplementary Figure 5.

    • The role of IFN-gamma as well as B cells (and Igs) in myelination/remyelination is mentioned in the discussion. However, there is very little evidence that these cells or their cytokines/Igs are mediating the described transcriptomic signatures at the level of the brain parenchyma of EAE mice undergoing relapse. Do the "antigen processing and presentation, cell killing, interleukin 6 production, and interferon gamma response" go terms, which better fitted the trajectory analysis, in fact include genes expressed almost exclusively by T and/or B cells? Are there genes that are downstream of IFN type I or II signaling?

    (3.6) Pathways including antigen processing / presentation, humoral inflammation, complement, among others were enriched in areas of meningeal inflammation and adjacent areas of parenchyma. These signaling pathways are mediated by effector molecules, many of which are produced by lymphocytes, but that can act on cells within the CNS parenchyma. The heatmaps in Supplementary Figure 5 demonstrate the significant role of MHC and complement genes, which could be expressed by leukocytes as well as glia, on many of the pathways.

    • Is the transcriptomic overlap between meningeal and brain parenchymal regions, or the appearance of signatures similar to the parenchymal subclusters 1_3, 1_4, and 2_6, prevented if the mice are treated with the murine versions of natalizumab or rituximab prior relapse?

    (3.6) We appreciate the reviewers suggestion. Our future directions for this work includes testing the effects of disease modifying therapies on spatial and single-cell transcriptomic readouts of disease in SJL EAE.

    • Please clarify what control group was used in this study. Naïve mice are mentioned in the Results section, does this mean that control animals were not injected with CFA? Authors should also elaborate on the descriptive methodology employed for the analysis of the spatial

    transcriptomics data - especially regarding the trajectory analysis. As is, overall, the methodology description might not favor reproducibility.

    (3.7) We appreciate the need for clarification here. Our control group in this study was naïve, not having received any CFA or pertussis toxin. While often used as the control in EAE studies focused on mechanisms of autoimmunity, CFA and pertussis toxin independently induce systemic inflammation. Since in this study we were interested in neuroinflammation broadly, we chose to use a naïve comparison group to maximize our ability to find genes enriched in neuroinflammation. We have elaborated our methods section, including methods related to trajectory analysis.

    Minor comments/suggestions:

    In Fig. 1D the indication of the rostral to ventral axis needs to be inverted.

    Addressed.

    In Fig. 1E the authors should also include a representative H&E staining of the same region in a control animal.

    Addressed.

    There is inconsistency in the number of clusters obtained after UMAP unbiased clustering of the spatial transcriptomic data:

    • Fig. 3A-E - twelve clusters are shown (cluster 0 to 11).
    • In the Results section eleven clusters are mentioned - "we performed unbiased UMAP clustering on the spatial transcriptomic dataset and identified 11 distinct clusters".

    The text was incorrect, there were 12 distinct clusters. This has been corrected.

    Considering the mice strain used was SJL/J mice, the peptide used to induce EAE should be PLP139-151, as mentioned in the Methods section "Induction of SJL EAE". However, the legend of Fig. 1 mentions "post immunization with MOG 35-55". Please correct this.

    Corrected.

    In the Methods section it is mentioned "At 12 weeks post-immunization, animals were euthanized", however the Results section mentions that tissues were harvested at 11 weeks post-immunization - "Brain slices were collected from four naïve mice and four EAE mice 11 weeks postimmunization". Please correct this.

    The Methods were incorrect, this has now been fixed.

    Please clarify the number of animals used for spatial transcriptomic analysis:

    • Legend of Fig. 1 mentions "Red arrows indicate MRI time points, black arrow indicates time of tissue harvesting (N = 6)." Whilst in the Results section it states "Brain slices were collected from four naïve mice and four EAE mice".

    The figure one legend has now been corrected (N = 4). Additionally, we have added clarification about the number of animals / slices used in the Methods section (see response 2.9).

    Please be consistent in the way of representing DEGs in the MA plots:

    • Fig. 3F shows the upregulated genes (in red) on the right and the downregulated genes (in blue) on the left.

    • Supplemental Fig. 2K shows the upregulated genes (in red) on the left and the downregulated genes (in blue) on the right.

    • Supplemental Fig. 4 shows the upregulated genes on the right in blue, while the downregulated genes are in red.

    This has been fixed.

    The letters attributed to each subcluster in panels E-G of Fig. 4 are different from the respective figure legend.

    This has been fixed.

    Correct the legend of supplemental figure 2: o "(G-H) Representative spatial feature plots of read count (F) and UMI (G) demonstrate expected anatomic variability in transcript amount and diversity.".

    This has been fixed.

    In Supplemental Fig. 4G there is probably an error with the XX axis, since the significantly up and down-regulated genes are not visible.

    This has been fixed.

  10. eLife

    eLife assessment

    Brain inflammation is a hallmark of multiple sclerosis. Using novel spatial transcriptomics methods, the authors provide solid evidence for a gradient of immune genes and inflammatory markers from the meninges toward the adjacent brain parenchyma in a mouse model. This important study advances our understanding of the mechanisms of brain damage in this autoimmune disease. However, the control mouse groups are not well designed to rule out confounding effects, a limitation that needs to be acknowledged and addressed.

  11. eLife

    Reviewer 1 (Public Review):

    Multiple sclerosis (MS) is a debilitating autoimmune disease that causes loss of myelin in neurons of the central nervous system. MS is characterized by the presence of inflammatory immune cells in several brain regions as well as the brain barriers (meninges). This study aims to understand the local immune hallmarks in regions of the brain parenchyma that are adjacent to the leptomeninges in a mouse model of MS. The leptomeninges are known to be a foci of inflammation in MS and perhaps "bleed" inflammatory cells and molecules to adjacent brain parenchyma regions. To do so, they use novel technology called spatial transcriptomics so that the spatial relationships between the two regions remain intact. The study identifies canonical inflammatory genes and gene sets such as complement and B cells enriched in the parenchyma in close proximity to the leptomeninges in the mouse model of MS but not control. The manuscript is very well written and easy to follow. The results will become a useful resource to others working in the field and can be followed by time series experiments where the same technology can be applied to the different stages of the disease.

    Comments on revised version:

    I agree that the authors successfully addressed most of my comments/critiques.
    However, the fact that the control mice were not injected with CFA is somewhat concerning, because it will be hard to interpret the cause of the transcriptomic readouts described in this study. Some of the described effects might be due to CFA (which was used in the EAE but not the "naive" group), and not necessarily to the relapsing-remitting EAE immune features recapitulated in this mouse model. Moreover, this caveat associated with the "naive" control group is not being clearly stated throughout the manuscript and might go unnoticed to readers.
    The authors should clearly state, in the methods section (in the section "Induction of SJL EAE"), that the naive control group was not injected with CFA.
    Additionally, this potential confounder, of not using a control group injected with the same CFA regimen of the EAE group, should be mentioned in paragraph two of the discussion alongside the other limitations of the study already highlighted by the authors (or in another section of the discussion).

  12. eLife

    Reviewer 2 (Public Review):

    Accumulating data suggests that the presence of immune cell infiltrates in the meninges of the multiple sclerosis brain contributes to the tissue damage in the underlying cortical grey matter by the release of inflammatory and cytotoxic factors that diffuse into the brain parenchyma. However, little is known about the identity and direct and indirect effects of these mediators at a molecular level. This study addresses the vital link between an adaptive immune response in the CSF space and the molecular mechanisms of tissue damage that drive clinical progression. In this short report the authors use a spatial transcriptomics approach using Visium Gene Expression technology from 10x Genomics, to identify gene expression signatures in the meninges and the underlying brain parenchyma, and their interrelationship, in the PLP-induced EAE model of MS in the SJL mouse. MRI imaging using a high field strength (11.7T) scanner was used to identify areas of meningeal infiltration for further study. They report, as might be expected, the upregulation of genes associated with the complement cascade, immune cell infiltration, antigen presentation, and astrocyte activation. Pathway analysis revealed the presence of TNF, JAK-STAT and NFkB signaling, amongst others, close to sites of meningeal inflammation in the EAE animals, although the spatial resolution is insufficient to indicate whether this is in the meninges, grey matter, or both.

    UMAP clustering illuminated a major distinct cluster of upregulated genes in the meninges and smaller clusters associated with the grey matter parenchyma underlying the infiltrates. The meningeal cluster contained genes associated with immune cell functions and interactions, cytokine production, and action. The parenchymal clusters included genes and pathways related to glial activation, but also adaptive/B-cell mediated immunity and antigen presentation. This again suggests a technical inability to resolve fully between the compartments as immune cells do not penetrate the pial surface in this model or in MS. Finally, a trajectory analysis based on distance from the meningeal gene cluster successfully demonstrated descending and ascending gradients of gene expression, in particular a decline in pathway enrichment for immune processes with distance from the meninges.

    Comments on revised version:

    The authors have addressed all of my comments regarding the lack of spatial resolution between the grey matter and the overlying meninges and also concerning the difficulties in extrapolating from this mouse model to MS itself.
    I am however very concerned about the lack of the correct control group. Immunization of rodents with complete freunds adjuvant (albeit with pertussis toxin) gives rise to widespread microglial activation, some immune cell infiltration and also structural changes to axons, particularly at nodes of Ranvier (https://doi.org/10.1097/NEN.0b013e3181f3a5b1). This will inevitably make it difficult to interpret the transcriptomics results, depending on whether these changes are reversible or not and the time frame of the reversal. In the C57Bl6 EAE models adjuvant induced microglial activation becomes chronic, whereas the axonal changes do reverse by 10 weeks. Whether this is the same in SJL EAE model using CFA alone is not clear.

  13. Version published to 10.1101/2023.06.02.543421v2 on bioRxiv
  14. Version published to 10.7554/elife.88414.1 on eLife
  15. eLife

    eLife assessment

    Brain inflammation is a hallmark of multiple sclerosis. Using novel spatial transcriptomics methods, the authors provide convincing evidence for a gradient of immune genes and inflammatory markers from the meninges toward the adjacent brain parenchyma in a mouse model. This important study advances our understanding of the mechanisms of brain damage in this autoimmune disease.

  16. eLife

    Reviewer #1 (Public Review):

    Multiple sclerosis (MS) is a debilitating autoimmune disease that causes loss of myelin in neurons of the central nervous system. MS is characterized by the presence of inflammatory immune cells in several brain regions as well as the brain barriers (meninges). This study aims to understand the local immune hallmarks in regions of the brain parenchyma that are adjacent to the leptomeninges in a mouse model of MS. The leptomeninges are known to be a foci of inflammation in MS and perhaps "bleed" inflammatory cells and molecules to adjacent brain parenchyma regions. To do so, they use novel technology called spatial transcriptomics so that the spatial relationships between the two regions remain intact. The study identifies canonical inflammatory genes and gene sets such as complement and B cells enriched in the parenchyma in close proximity to the leptomeninges in the mouse model of MS but not control. The manuscript is very well written and easy to follow. The results will become a useful resource to others working in the field and can be followed by time series experiments where the same technology can be applied to the different stages of the disease.

  17. eLife

    Reviewer #2 (Public Review):

    Accumulating data suggests that the presence of immune cell infiltrates in the meninges of the multiple sclerosis brain contributes to the tissue damage in the underlying cortical grey matter by the release of inflammatory and cytotoxic factors that diffuse into the brain parenchyma. However, little is known about the identity and direct and indirect effects of these mediators at a molecular level. This study addresses the vital link between an adaptive immune response in the CSF space and the molecular mechanisms of tissue damage that drive clinical progression. In this short report the authors use a spatial transcriptomics approach using Visium Gene Expression technology from 10x Genomics, to identify gene expression signatures in the meninges and the underlying brain parenchyma, and their interrelationship, in the PLP-induced EAE model of MS in the SJL mouse. MRI imaging using a high field strength (11.7T) scanner was used to identify areas of meningeal infiltration for further study. They report, as might be expected, the upregulation of genes associated with the complement cascade, immune cell infiltration, antigen presentation, and astrocyte activation. Pathway analysis revealed the presence of TNF, JAK-STAT and NFkB signaling, amongst others, close to sites of meningeal inflammation in the EAE animals, although the spatial resolution is insufficient to indicate whether this is in the meninges, grey matter, or both.

    UMAP clustering illuminated a major distinct cluster of upregulated genes in the meninges and smaller clusters associated with the grey matter parenchyma underlying the infiltrates. The meningeal cluster contained genes associated with immune cell functions and interactions, cytokine production, and action. The parenchymal clusters included genes and pathways related to glial activation, but also adaptive/B-cell mediated immunity and antigen presentation. This again suggests a technical inability to resolve fully between the compartments as immune cells do not penetrate the pial surface in this model or in MS. Finally, a trajectory analysis based on distance from the meningeal gene cluster successfully demonstrated descending and ascending gradients of gene expression, in particular a decline in pathway enrichment for immune processes with distance from the meninges.

    Although these results confirm what we already know about processes involved in the meninges in MS and its models and gradients of pathology in sub-pial regions, this is the first to use spatial transcriptomics to demonstrate such gradients at a molecular level in an animal model that demonstrates lymphoid like tissue development in the meninges and associated grey matter pathology. The mouse EAE model being used here does reproduce many, although not all, of the pathological features of MS and the ability to look at longer time points has been exploited well. However, this particular spatial transcriptomics technique cannot resolve at a cellular level and therefore there is a lot of overlap between gene expression signatures in the meninges and the underlying grey matter parenchyma.

    The short nature of this report means that the results are presented and discussed in a vague way, without enough molecular detail to reveal much information about molecular pathogenetic mechanisms.

    The trajectory analysis is a good way to explore gradients within the tissues and the authors are to be applauded for using this approach. However, the trajectory analysis does not tell us much if you only choose 2 genes that you think might be involved in the pathogenetic processes going on in the grey matter. It might be more useful to choose some genes involved in pathogenetic processes that we already know are involved in the tissue damage in the underlying grey matter in MS, for which there is already a lot of literature, or genes that respond to molecules we know are increased in MS CSF, although the animal models may be very different. Why were C3 and B2m chosen here?

    Strengths:
    - The mouse model does exhibit many of the features of the compartmentalized immune response seen in MS, including the presence of meningeal immune cell infiltrates in the central sulcus and over the surface of the cortex, with the presence of FDC's HEVs PNAd+ vessels and CXCL13 expression, indicating the formation of lymphoid like cell aggregates. In addition, disruption of the glia limitans is seen, as in MS. Increased microglial reactivity is also present at the pial surface.
    - Spatial transcriptomics is the best approach to studying gradients in gene expression in both white matter and grey matter and their relationship between compartments.
    - It would be useful to have more discussion of how the upregulated pathways in the two compartments fit with what we know about the cellular changes occurring in both, for which presumably there is prior information from the group's previous publications.

    Limitations:
    - EAE in the mouse is not MS and may be far removed when one considers molecular mechanisms, especially as MS is not a simple anti-myelin protein autoimmune condition. Therefore, this study could be following gene trajectories that do not exist in MS. This needs a significant amount of discussion in the manuscript if the authors suggest that it is mimicking MS.
    - The model does not have the cortical subpial demyelination typical of MS and it is unknown whether neuronal loss occurs in this model, which is the main feature of cytokine-mediated neurodegeneration in MS. If it does not then a whole set of genes will be missing that are involved in the neuronal response to inflammatory stimuli that may be cytotoxic.
    - Visium technology does not get down to single cell level and does not appear to allow resolution of the border between the meninges and the underlying grey matter.
    - Neuronal loss in the MS cortex is independent of demyelination and therefore not related to remyelination failure. There does not appear to be any cortical grey matter demyelination in these animals, so it is difficult to relate any of the gene changes seen here to demyelination.
    - No mention of how the ascending and descending patterns of gene expression may be due to the gradient of microglial activation that underlies meningeal inflammation, which is a big omission.

  18. eLife

    Reviewer #3 (Public Review):

    In this study, Gadani et al. induced EAE in SJL/J mice and performed a comprehensive spatial transcriptomic analysis in areas of meningeal inflammation during the relapse phase of the disease. The authors found specific enrichment in spatial gene signatures (cluster 11) in the regions of increased contrast-enhancement by MRI (where meningeal extravasation of activated immune cells is observed) that overlap with signatures in the adjacent brain parenchyma, namely the thalamus. Several pathways were similarly upregulated in the meningeal-associated cluster 11 and adjacent parenchymal clusters (like adaptive mediated immunity, and antigen processing and presentation), suggestive of a "leakage" of inflammatory mediators from the meninges into the brain during the re-activation of disease. The tested hypothesis, as well as the data presented in this study, is quite interesting and novel.

  19. eLife

    Author Response:

    We thank Reviewer #1 for their positive assessment of our work.

    Reviewer #2 (Public Review):

    […] Although these results confirm what we already know about processes involved in the meninges in MS and its models and gradients of pathology in sub-pial regions, this is the first to use spatial transcriptomics to demonstrate such gradients at a molecular level in an animal model that demonstrates lymphoid like tissue development in the meninges and associated grey matter pathology. The mouse EAE model being used here does reproduce many, although not all, of the pathological features of MS and the ability to look at longer time points has been exploited well. However, this particular spatial transcriptomics technique cannot resolve at a cellular level and therefore there is a lot of overlap between gene expression signatures in the meninges and the underlying grey matter parenchyma.

    We appreciate the reviewer’s concise summary and comments on our manuscript. We agree that the Visium spatial sequencing technology we applied is limited in its resolution and cannot precisely distinguish individual cells or anatomic regions. For that reason, there is undoubtedly some overlap between gene expression signatures in the meninges and underlying parenchyma, particularly in spots on the borders of the meningeal inflammation clusters. However, we believe that the majority of meningeal inflammation (“cluster 11”) spots are indeed in the meninges and represent the spatial transcriptome of that niche. To support this, in the revised manuscript we will provide H&E images with the UMAP clusters overlayed to demonstrate the anatomic borders that correlate with the clusters.

    The short nature of this report means that the results are presented and discussed in a vague way, without enough molecular detail to reveal much information about molecular pathogenetic mechanisms.

    We thank the reviewer for this comment. The goal of this work is to transcriptomically characterize the spatial relationship between areas of meningeal inflammation and the underlying parenchyma. While we agree that mechanistic studies are needed to further evaluate the role of presented signaling pathways, those experiments are beyond the scope of this brief report.

    The trajectory analysis is a good way to explore gradients within the tissues and the authors are to be applauded for using this approach. However, the trajectory analysis does not tell us much if you only choose 2 genes that you think might be involved in the pathogenetic processes going on in the grey matter. It might be more useful to choose some genes involved in pathogenetic processes that we already know are involved in the tissue damage in the underlying grey matter in MS, for which there is already a lot of literature, or genes that respond to molecules we know are increased in MS CSF, although the animal models may be very different. Why were C3 and B2m chosen here?

    We appreciate the reviewer’s points here. C3 and B2m were chosen as examples of genes that have differential fit to the gradient descending pattern to assist the reader in interpreting subsequent gene set trajectory analysis. However, we agree that there are many other genes of interest and will expand the number of genes displayed in our revised manuscript.

    Strengths:
    - The mouse model does exhibit many of the features of the compartmentalized immune response seen in MS, including the presence of meningeal immune cell infiltrates in the central sulcus and over the surface of the cortex, with the presence of FDC's HEVs PNAd+ vessels and CXCL13 expression, indicating the formation of lymphoid like cell aggregates. In addition, disruption of the glia limitans is seen, as in MS. Increased microglial reactivity is also present at the pial surface.
    - Spatial transcriptomics is the best approach to studying gradients in gene expression in both white matter and grey matter and their relationship between compartments.
    - It would be useful to have more discussion of how the upregulated pathways in the two .compartments fit with what we know about the cellular changes occurring in both, for which presumably there is prior information from the group's previous publications.

    Limitations:
    - EAE in the mouse is not MS and may be far removed when one considers molecular mechanisms, especially as MS is not a simple anti-myelin protein autoimmune condition. Therefore, this study could be following gene trajectories that do not exist in MS. This needs a significant amount of discussion in the manuscript if the authors suggest that it is mimicking MS.
    - The model does not have the cortical subpial demyelination typical of MS and it is unknown whether neuronal loss occurs in this model, which is the main feature of cytokine-mediated neurodegeneration in MS. If it does not then a whole set of genes will be missing that are involved in the neuronal response to inflammatory stimuli that may be cytotoxic.
    - Visium technology does not get down to single cell level and does not appear to allow resolution of the border between the meninges and the underlying grey matter.
    - Neuronal loss in the MS cortex is independent of demyelination and therefore not related to remyelination failure. There does not appear to be any cortical grey matter demyelination in these animals, so it is difficult to relate any of the gene changes seen here to demyelination.
    - No mention of how the ascending and descending patterns of gene expression may be due to the gradient of microglial activation that underlies meningeal inflammation, which is a big omission.

    We thank the reviewer for their insightful comments on the strengths and limitations of our study. Regarding the SJL EAE model we use in this paper, it certainly is not a perfect model of meningeal inflammation in MS, indeed we believe that no such animal model exists, but it does recapitulate several key features of human disease as described by the reviewer. Spatial transcriptomics of cortical grey matter lesions and overlying meninges of samples derived from patients with MS would be ideal, though access to this tissue is highly limited. In the revised manuscript we will include more detailed discussion of the limitations in applying these findings to MS. However, in addition to potential implications for MS research, our data contribute more generally to understanding of meningeal inflammation and penetrance of inflammation into brain tissue.

    We acknowledge that sub-pial neuronal loss has not been assessed in SJL EAE, and if present it would increase the relevance of this model to neurodegeneration. We are currently working to assess this.

    We agree with the reviewer that Visium technology is limited in its ability to discriminate individual cells, as discussed above (2.2).

    We agree that gene expression by activated microglia is likely a major driver of the transcriptomic changes observed in the parenchyma, and thank the reviewer for highlighting this. We will add discussion of this to our revised manuscript, and intend to generate additional data regarding the contribution of subpial microglial activation to the measured transcriptomic changes.

    Finally, we thank Reviewer #3 for their assessment of our work.

  20. Version published to 10.1101/2023.06.02.543421v1 on bioRxiv