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Spatially resolved gene signatures of white matter lesion progression in multiple sclerosis

Nature Neuroscience volume 27, pages 2341–2353 (2024)Cite this article

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Abstract

Multiple sclerosis (MS) is an inflammatory disease of the central nervous system characterized by myelin loss and progressive neurodegeneration. To understand MS lesion initiation and progression, we generate spatial gene expression maps of white matter (WM) and grey matter (GM) MS lesions. In different MS lesion types, we detect domains characterized by a distinct gene signature, including an identifiable rim around active WM lesions. Expression changes in astrocyte-specific, oligodendrocyte-specific and microglia-specific gene sets characterize the active lesion rims. Furthermore, we identify three WM lesion progression trajectories, predicting how normal-appearing WM can develop into WM active or mixed active–inactive lesions. Our data shed light on the dynamic progression of MS lesions.

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Fig. 1: ST detects distinct WM MS lesion profiles.
Fig. 2: ST detects a rim around active lesions.
Fig. 3: Gene co-expression network analysis of lesion rims.
Fig. 4: MS-associated changes in microglia delineate lesion rims.
Fig. 5: PLWM is transcriptionally distinct from NAWM.
Fig. 6: Trajectory analysis predicts multiple MS lesion endpoints.

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Data availability

The raw data generated with the 10× Genomics Visium and Nanostring GeoMx Digital Spatial Profiler platforms are available through Gene Expression Omnibus at https://www.ncbi.nlm.nih.gov/geo with accession number GSE208747. Visium spatial gene expression data can be visualized with an interactive web application accessible at http://msspatialexpression.com. Other raw and processed data, including overview imaging scans for RNAscope, immunofluorescence and IHC, are available upon reasonable request to the corresponding author. ST data were aligned to the human GRCh38 reference package from 10× Genomics (https://www.10xgenomics.com/support/software/space-ranger/latest/advanced/custom-references). snRNA-seq data from MS lesions from a previous publication14 were downloaded from the Sequence Read Archive with accession number PRJNA544731.

Code availability

The code used for the Visium GeoMx data is on https://github.com/astrid12345/Visium_ST. All other codes used to support the findings of this study are available upon reasonable request.

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Acknowledgements

We thank L. de Vries for developing the interactive web application that visualizes gene expression patterns. We thank CARTANA Sweden (part of 10× Genomics) for performing ISS experiments. We thank N. Dijksman, M. Meijer and K. Sjollema for their support in obtaining the microscopy data. We thank J. Konings from Amsterdam UMC for providing an active lesion sample, E. Gerrits for bioinformatic input, E. Boddeke for scientific discussions, D. Spierings of the ERIBA Research Sequencing Facility for her expert assistance with next-generation sequencing and the Netherlands Brain Bank for brain tissues. A.A. was supported by the De Cock-Hadders Foundation (no. 2019-04) and a grant from the Dutch MS Research Foundation (no. 19-1058). M.H.C.W. was supported by a grant from the De Cock-Hadders Foundation (no. 2020-14). M.K. is supported by the Graduate School of Medical Sciences of the University of Groningen and a grant from the Dutch MS Research Foundation and Fund Girn managed by the King Baudouin Foundation (no. 23-1195 MS). S.M.K. was supported by the Dutch MS Research Foundation (no. 16-947, no. 22-1177 MS). S.M.K., J.M.K. and B.J.L.E. were supported by a grant from the Dutch MS Research Foundation (no. 20-1104). W.B. and B.J.L.E. were supported by grants from the Dutch MS Research Foundation (no. 18-733c, no. 18-1001), the latter made possible by MoveS. Part of the work has been performed at the UMCG Microscopy and Imaging Center (UMIC), which is sponsored by NWO-grants 40-00506-98-9021 (TissueFAXS) and 175-010-2009-023 (Zeiss 2p).

Author information

Author notes

  1. These authors contributed equally: Astrid M. Alsema, Marion H. C. Wijering, Anneke Miedema, Janssen M. Kotah.

  2. These authors jointly supervised this work: Wia Baron, Susanne M. Kooistra, Bart J. L. Eggen.

Authors and Affiliations

  1. Department of Biomedical Sciences, Section Molecular Neurobiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands

    Astrid M. Alsema, Marion H. C. Wijering, Anneke Miedema, Janssen M. Kotah, Mirjam Koster, Hilmar R. J. van Weering, Wia Baron, Susanne M. Kooistra & Bart J. L. Eggen

  2. MS Centrum Noord Nederland, Groningen, The Netherlands

    Astrid M. Alsema, Marion H. C. Wijering, Anneke Miedema, Janssen M. Kotah, Mirjam Koster, Hilmar R. J. van Weering, Wia Baron, Susanne M. Kooistra & Bart J. L. Eggen

  3. Department of Molecular Cell Biology and Immunology, Amsterdam UMC, Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands

    Merel Rijnsburger & Helga E. de Vries

  4. MS Center Amsterdam, Amsterdam UMC, Location VU Medical Center, Amsterdam, The Netherlands

    Merel Rijnsburger & Helga E. de Vries

  5. Amsterdam Neuroscience, Amsterdam UMC, Amsterdam, The Netherlands

    Merel Rijnsburger & Helga E. de Vries

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Contributions

A.M.A., A.M., M.H.C.W. and J.M.K. contributed equally to this work. S.M.K., B.J.L.E. and W.B. jointly supervised this project. B.J.L.E. and S.M.K. conceptualized the design of the study. A.M., M.H.C.W., J.M.K. and M.K. collected data, including ST, GeoMx and IHC/IF validation experiments. A.M.A., M.H.C.W., J.M.K. and M.K. performed bioinformatic analyses and visualized the data. W.B. guided the tissue selection of MS samples. H.R.J.W. collected and analyzed microscopy data and assisted in artwork. M.R. performed the RNAscope experiments. A.M.A., A.M., M.H.C.W., J.M.K., M.K., M.R., H.E.V., W.B., S.M.K. and B.J.L.E. interpreted the data. A.M.A., A.M., M.H.C.W., J.M.K. and M.K. wrote the manuscript with the supervision of W.B., S.M.K. and B.J.L.E. All authors read and approved the manuscript.

Corresponding author

Correspondence to Bart J. L. Eggen.

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

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Extended data

Extended Data Fig. 1 ST recapitulates known features of WM and GM tissues.

a) Overview experimental workflow and tissue selection criteria for Visium ST. Lesions were included based on IHC characterisation of inflammation (HLA-DR) and demyelination (PLP), and RNA integrity (RIN > 5.7). Consecutive tissue sections flanking the sections used for Visium were stained for HLA-DR and PLP to confirm lesion classification. b) Representative PLP and HLA-DR staining images of white and grey matter tissue used for lesion classification. Scale bars: 1 mm. c) HE staining (left) of representative samples of the tissue sections analysed on Visium ST slides, dotted tissue capture area is 6.5×6.5 mm. Representative spatial gene expression plots of SNAP25 and PLP1 per WM and GM tissue group to differentiate WM from GM (right). Reduced PLP1 expression corresponds to lesions; increased IGKC and HLA-DRA expression indicate the inflammatory state of the lesion and GFAP indicates the presence of astrocytes. Grey scale indicates log-normalised gene expression. act/inact, mixed active/inactive lesion; CGM, control grey matter; CWM, control white matter; GM, grey matter; HE, hematoxylin-eosin; IHC, immunohistochemistry; MS, multiple sclerosis; NAGM, normal appearing grey matter; NAWM, normal-appearing white matter; RIN, RNA integrity number; ST, 10x Visium spatial transcriptomics; WM, white matter.

Extended Data Fig. 2 ST of GM lesions is driven by cortical layers.

a) Overview of GM tissues analysed with ST. b) BayesSpace clustering of spatial plots in representative GM samples. Cluster annotation indicates WM clusters in grey shades, and GM clusters corresponding to cortical layers 1-6 in colour. c) PCA plot of pseudobulked neuronal layers per sample (WM and cortical layer 1 to 6); squares depict female (f), circles male (m) donors. WM n = 10, layer 1 n = 6, layer 2 n = 5, layer 3 n = 6, layer 4 n = 6, layer 5 n = 7 and layer 6 n = 8 samples. d) HE staining and spatial plots depicting the computationally deconvoluted proportions per cell type (i), or the cell-type composition per spot (ii) for representative GM samples. In i, colour scales indicate predicted cell type proportion (between 0-1) per spatial spot. In ii, the proportions of cell types in each spot is represented by overlaid pie charts where colours depict cell types. e) i) BayesSpace clustering in a subpial lesion (SG2) containing meninges (cluster 7 and 9). Colours: cortical layers, grey shades: WM, red shades: meninges. ii) Heatmap of the top 5 marker genes per cluster (grouped by cluster, sorted by fold change) in subpial lesion SG2. Colour scale indicates scaled expression. The annotation bar on top of the heatmap indicates spatial clusters as shown in e (i). Each column represents one spatial spot. Marker genes for meningeal clusters (cluster 7 and 9) are outlined by red boxes. iii) Spatial gene expression plot of IGKC. Grey scale indicates log-normalised gene expression. iv) Spatial plot mapping the expression of MS susceptibility genes12 to spatial clusters. Colour scale depicts the number of MS GWAS genes per cluster. ex-neurons, excitatory neurons; in-neurons, inhibitory neurons; MG, microglia; MΦ, macrophages; OPCs, oligodendrocyte progenitor cells.

Extended Data Fig. 3 Distribution of clusters and deconvolution of spatial spots using single nucleus RNA-seq data.

a) Spatial plots depicting Seurat clustering of spatial spots from all WM samples combined. Colours correspond to clusters in Fig. 1d. b) Representative spatial plots of NAWM, active and act/inact lesion samples with the predicted cell type proportions per spatial spot over HE images. Colour scales refer to the proportions of WM astrocytes, GM astrocytes, microglia (MG)/macrophages (MΦ), oligodendrocytes, oligodendrocyte progenitor cells (OPCs), excitatory neurons (ex-neurons), inhibitory neurons (in-neurons), and cell types related to the brain vasculature. Deconvolution was performed using a re-analysed single nucleus RNA-seq reference dataset14.

Extended Data Fig. 4 BayesSpace clustering and reassignment to subgroups per WM sample.

Spatial plots depicting within-sample BayesSpace clustering in WM samples. Sample IDs are indicated on the upper left corner of each box, border colours correspond to the WM tissue group. Based on the BayesSpace clusters and cluster marker genes, spatial spots were reassigned into spatially distinct subgroups: lesion core (core, depicted in corresponding lesion colour), lesion rim (rim, depicted in black), perilesional white matter (PLWM, depicted in light grey) and grey matter (GM, depicted in dark grey). Clusters with low number of UMI counts and clusters representing GM areas in the WM tissues were excluded from downstream analyses.

Extended Data Fig. 5 Expression of known MS genesets and deconvoluted cellular composition in WM subgroups.

a) Violin plots of module scores per WM tissue subgroup for genes associated with functional processes altered in MS or subtypes of astrocytic, microglial/macrophage and oligodendrocytic lineage from single cell or single nucleus sequencing studies. Horizontal line depicts median module score. Statistics were performed for subgroup averages, that is, at the sample level. Module scores within active lesion subgroups (n = 4 samples) and act/inact lesion subgroups (n = 6 samples) were compared using repeated measures ANOVA followed by dependent-samples t-test. Module score averages for AIMS, WM-astrocytes and WM inflammatory were not normally distributed and tested using the non-parametric Friedman test followed by a post-hoc Wilcoxon signed-rank test. b) Heatmap depicting the co-occurrence of two different cell types in the same spatial spot. Scale bar indicates the Jaccard index; 1 indicating two cell types always co-occurred, 0 indicating two cell types never co-occurred in spatial spots. Estimated cell type proportions <1% of the total content of the spot were excluded. c) Average estimated percentage of indicated cell types per WM tissue group, CWM and NAWM data presented as samples (circles) and mean (circles), while active and act/inact data presented as mean (circles) ± SD (error bars). Colours refer to deconvoluted cell types. Statistical significance was determined within active lesions (n = 4 samples) and act/inact lesions (n = 6 samples) by repeated measures ANOVA followed by dependent-samples t-test. All statistical tests were two-tailed, and all post-hoc tests were adjusted with Bonferroni’s method. *P < 0.05, **P < 0.01, ***P < 0.001, for exact p-values see Supplementary Table 6. AIMS, astrocytes inflamed in MS; AST, astrocytes; DAM, disease-associated microglia; ex-neuron, excitatory neurons; in-neuron, inhibitory neurons; MG, microglia; MIMS, microglia inflamed in MS; MΦ, macrophages; OLs, oligodendrocytes; OPCs, oligodendrocyte progenitor cells.

Extended Data Fig. 6 Mapping of GWAS susceptibility genes and identification of MS-blood vessels in WM samples.

a) Spatial plots mapping MS susceptibility genes onto spatial clusters. Colour scale depicts bins of the number of MS GWAS genes12 expressed per cluster; border colours depict WM tissue subgroup. MS GWAS genes could not be mapped on sample C2, as only one cluster was identified (that is, the relative expression between clusters could not be computed). b) Overview of blood vessel (BV) analysis in WM lesion samples. (i) Representative spatial plot of a WM lesion sample with BV cluster 3 in red. (ii) Identification of 12 MS-BV signature genes by overlapping BV cluster marker genes from 4 WM lesion samples. c) Representative spatial plots of the BV gene module in active and act/inact lesion samples. Red spots indicate positive MS-BV spots. d) Scatter plot depicting the percentage of positive MS-BV spots per subgroup for active and act/inact lesions. Horizontal lines depict mean expression per subgroup. Within samples, statistical significance between subgroups was determined for active lesions (n = 4 samples) and for act/inact lesions (n = 6 samples) by repeated measures ANOVA followed by two-tailed dependent-samples t-test with Bonferroni correction, *P < 0.05. Between samples, differences between subgroups were tested using a linear mixed model with two-tailed t-tests based on Satterthwaite’s method with Bonferroni correction, using the act/inact lesion core as reference. #P < 0.05, ###P < 0.001, for exact p-values see Supplementary Table 10.

Extended Data Fig. 7 Identification of spatial gene co-expression networks in active and act/inact lesion samples using SpaceX.

a) Bar plot depicting the connectivity of genes in a representative active (left) and act/inact (right) lesion rim sample, where connectivity is the percentage of other genes in the network a gene is connected to. Genes are ordered by connectivity. Horizontal dashed lines illustrate the 10% connectivity cut-off of genes in the network that defines a hub gene. Colour bars indicate genes that are considered to be active lesion rim hubs (left; red) and act/inact lesion rim hubs (right; orange). b) Co-expression network plots for all active and act/inact lesion rim samples not included in Fig. 3b. Annotation bars above the plots denote lesion type; red denotes active lesion rim and orange denotes act/inact lesion rim. Coloured dots represent hubs in the network, where colour corresponds to association with a particular cell type (that is hub annotation). Dot size indicates the degree of connectivity of the gene to other genes in the network. c) Circular connectivity plots depicting the co-expression between consistent active lesion rim hubs in all active lesion rim samples, and between act/inact lesion rim hubs in all act/inact lesion rim samples not included in Fig. 3d. Annotation bars above the plots denote lesion subgroup; red is active and orange act/inact. Colours refer to the hub annotation from panel b. OL, oligodendrocytic.

Extended Data Fig. 8 Additional GeoMx data and distance-related gene expression gradients in active and act/inact lesion samples.

a/b/c) GeoMx spatial plots depicting clustering of regions of interest (ROI) of samples with (a) an active lesion and (b/c) act/inact lesions, coloured by clustering result (left). Scale bars: 500 µm. Heatmap of the top 15 marker genes (sorted by fold change) of each cluster (right). Heatmap colour scale indicates scaled expression. Annotation bars on top of the heatmap refer to the cluster annotated to each ROI. Rim clusters are labelled by closeness to lesion core, with rim1 being closest. d) GeoMx spatial plots of representative active and act/inact lesions depicting the expression of GPNMB. Coloured scale bar represents GPNMB expression in counts. e) Schematic representation of the distance-related gene expression gradients plotted in panels g and h. f) A centre point (cyan) was manually selected in the lesion core of each sample. g/h) Distance related gene expression gradients in representative samples per WM tissue group for PLP1 (panel g), and GPNMB (panel h). Log-normalised expression was plotted over Euclidean distance from the centre point. Trend line represents the LOESS smooth mean line with the 95% confidence interval.

Extended Data Fig. 9 Additional information on pseudotime analysis and validation of S100A11.

a) Distribution of spots from each WM sample in the UMAP along the pseudotime trajectory. b) UMAP of the pseudotime trajectory depicting marker gene expression for myelin (CNP) and lesions (GPNMB, FTL, S100A11, APOC1). c) UMAPs depicting enrichment scores of cell (sub)type or state marker genes. Colour scale indicates module score. d,e) Two endpoints (‘a’, and ‘b’) observed in the pseudotime trajectory that were not further analysed. Spots in these endpoints were marked by a lower number of genes detected compared to the starting point (1), likely reflecting technical factors. f) IHC staining of PLP in CWM, NAWM, active lesion rim, act/inact lesion rim, active lesion core, act/inact lesion core. Dashed lines outline lesion cores. Scale bars: 1 mm (red) and 70 µm (black). g) Quantification of IHC for S100A11 in active (n = 8 samples/donors) and act/inact (n = 12 samples/donors) lesions at distinct distances from lesion core. Data are presented as individual samples (circles), boxes depicting the median, first and third quartile, with whiskers extending to the highest and lowest values within the 1.5x interquartile range. Friedman test was performed followed by Wilcoxon signed-rank test with Bonferroni correction, *P < 0.05, two-tailed. For exact p-values see Supplementary Table 8. AIMS, astrocytes inflamed in MS; MIMS, microglia inflamed in MS; OPCs, oligodendrocyte progenitor cells.

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Alsema, A.M., Wijering, M.H.C., Miedema, A. et al. Spatially resolved gene signatures of white matter lesion progression in multiple sclerosis. Nat Neurosci 27, 2341–2353 (2024). https://doi.org/10.1038/s41593-024-01765-6

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