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Human striatal glia differentially contribute to AD- and PD-specific neurodegeneration

Nature Aging volume 3, pages 346–365 (2023)Cite this article

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Abstract

The commonalities and differences in cell-type-specific pathways that lead to Alzheimer disease (AD) and Parkinson disease (PD) remain unknown. Here, we performed a single-nucleus transcriptome comparison of control, AD and PD striata. We describe three astrocyte subpopulations shared across different brain regions and evolutionarily conserved between humans and mice. We reveal common features between AD and PD astrocytes and regional differences that contribute toward amyloid pathology and neurodegeneration. In contrast, we found that transcriptomic changes in microglia are largely unique to each disorder. Our analysis identified a population of activated microglia that shared molecular signatures with murine disease-associated microglia (DAM) as well as disease-associated and regional differences in microglia transcriptomic changes linking microglia to disease-specific amyloid pathology, tauopathy and neuronal death. Finally, we delineate undescribed subpopulations of medium spiny neurons (MSNs) in the striatum and provide neuronal transcriptomic profiles suggesting disease-specific changes and selective neuronal vulnerability.

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Fig. 1: Characterization of six major cell types and three distinct astrocyte subpopulations.
Fig. 2: Transcriptomic comparison of astrocyte subpopulations.
Fig. 3: Regional differences in astrocytic transcriptomic changes in disease.
Fig. 4: Four distinct immune cell populations.
Fig. 5: Characterization of the human activated microglia.
Fig. 6: Transcriptome transition during microglia activation.
Fig. 7: Comparison of genes and pathways of microglia activation-associated transcriptome changes.
Fig. 8: Characterization of neuronal subpopulations and gene expression changes in disease conditions.

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Data availability

All raw and processed sequencing data generated in this study have been submitted to the NCBI Gene Expression Omnibus (GEO; http://www.ncbi.nlm.nih.gov/geo/) under accession number GSE161045. Interactive cell clustering and gene expression analyses based on the sequencing data processed in the current study are available through the following URL: http://epigenome.wustl.edu/snRNA_Put_ADPD. The gene expression data and metadata for Grubman et al.7 (accession number GSE138852), Lau et al.9 (accession number GSE157827) and Feleke et al.8 (accession number GSE178146) were downloaded from the Gene Expression Omnibus (GEO). The snRNA-seq data for Mathys et al.10 were downloaded at Synapse (https://www.synapse.org/#!Synapse:syn18485175) under the https://doi.org/10.7303/syn18485175. The ROSMAP metadata can be accessed at https://www.synapse.org/#!Synapse:syn3157322.

Code availability

Code used for whole population analysis, plotting, astrocyte and microglia subpopulation plotting is available at https://github.com/guoyanzhao/snRNA_Putamen_ADPD.

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Acknowledgements

We thank all participants and their families for their commitment and dedication to advancing research of diagnosis and treatment for AD and PD, and the Knight-ADRC and MDC research staff for their contributions. We thank N. Cairns, E.E. Franklin and M. Baxter of the Knight ADRC Neuropathology Core at Washington University School of Medicine (WUSM) for coordination of the tissue preparation and technical assistance. We thank the Genome Technology Access Center at the McDonnell Genome Institute at Washington University School of Medicine for help with genomic analysis. The Center is partially supported by NCI Cancer Center Support Grant P30 CA91842 to the Siteman Cancer Center from the National Center for Research Resources (NCRR), a component of the National Institutes of Health (NIH), and NIH Roadmap for Medical Research. We thank Brian Koebbe and Eric Martin from the High-Throughput Computing Facility at WUSM for providing high-throughput computational resources and support. The results published here are in whole or in part based on data obtained from the AD Knowledge Portal (https://adknowledgeportal.org). Samples for this study were provided by the Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago. Data collection was supported through funding by the NIA (grants P30AG10161, R01AG15819, R01AG17917, R01AG30146, R01AG36836, U01AG32984 and U01AG46152), the Illinois Department of Public Health and the Translational Genomics Research Institute. Finally, we thank the reviewers for their insightful suggestions that have enabled many of the discoveries reported in this manuscript. This work was supported by the GTAC@MGI Symposium Pilot Project Funding; the NIH/National Center for Advancing Translational Sciences (NCATS) grant UL1TR002345 to G.Z.; NIH grant 5T U24 HG012070 to T.W. and G.Z.; NIH grants R03AG070474, R21AG077643 and R01NS123571 in partial support of J.X. and G.Z.; NIH grants R01AG052550, R01AG054567 to T.L.S.B., research funded by NIH R01NS092865, R01AG054567, R01AG052550, U19AG032438 and P30AG06644 in support of H.L.F., W.C.K. and J.X.; by the Philip and Sima Needleman Student Fellowship in Regenerative Medicine in partial support of Y.H. and the Philippine Department of Science and Technology through the Philippine Council for Health Research and Development in partial support of B.A.L. This publication is solely the responsibility of the authors and does not necessarily represent the official view of the National Institute of Health. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript

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  1. Yiran Hou

    Present address: Department of Medical Microbiology and Immunology, University of Wisconsin-Madison, Madison, WI, USA

Authors and Affiliations

  1. The Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA

    Jinbin Xu, Huifangjie L. Farsad, William C. Knight, Tammie L. S. Benzinger & Joel S. Perlmutter

  2. Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA

    Yiran Hou, Qingyun Li & Ting Wang

  3. The Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO, USA

    Yiran Hou & Ting Wang

  4. Department of Neuroscience, Washington University School of Medicine, St. Louis, MO, USA

    Kia Barclay, Ben Anthony Lopez, Shinnosuke Yamada, Ibrahim Olabayode Saliu, Yiming Shi, Jason J. Yi, Qingyun Li, Joel S. Perlmutter & Guoyan Zhao

  5. MD-PhD in Molecular Medicine Program, College of Medicine, University of the Philippines Manila, Manila, Philippines

    Ben Anthony Lopez

  6. Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA

    Randall J. Bateman, Joel S. Perlmutter & John C. Morris

  7. Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO, USA

    Guoyan Zhao

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J.X. and G.Z. contributed to study concept, design and supervision. G.Z., Y.S., Y.H., B.A.L., S.Y., I.O.S. and T.W. contributed to bioinformatics analysis and database creation and maintenance. G.Z., H.L.F., K.B. and J.X. contributed to qualitative analysis of data. W.K., H.L.F. and J.X. prepared tissue. Q.L., J.J.Y., T.W., Y.H., T.L.S.B., R.J.B., J.C.M., J.S.P., K.B., B.A.L., S.Y., I.O.S. and G.Z. contributed to critical manuscript revision for important intellectual content. J.C.M. and J.S.P. provided human postmortem brain samples from AD, PD and control cohorts. G.Z., H.L.F., B.A.L. and J.X. wrote the manuscript, with feedback from all authors.

Corresponding authors

Correspondence to Jinbin Xu or Guoyan Zhao.

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Competing interests

The authors declare no competing interests. S.Y. is an employee of Daiichi Sankyo.

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Nature Aging thanks the anonymous reviewers for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 snRNA-seq profiling and characterization of major cell types.

a, Brain region analyzed with snRNA-seq. Created with BioRender.com. b, Comparison of age, postmortem interval (PMI), number of cells, the median number of transcripts and median number of genes per nucleus among control, AD and PD groups. c, Heatmap of the relative expression level of top 10 marker genes for each cell type. d, Violin plots of gene expression levels of known cell-type-specific marker genes. e, UMAP plot colored by experimental batch or individual label. UMAP were generated using the same parameters as described in Fig. 1. f,g, Percentage of cells from (f) each disease group or (g) individuals of each disease group in each of the major cell type. Ast: Astrocyte; EP: Endothelia cell and pericyte; Immune: Immune cell including microglia; OLIGO: Oligodendrocyte; OPC: Oligodendrocyte precursor cell. Conserved marker genes were determined by FindConservedMarkers using Wilcoxon Rank Sum test and metap R package with meta-analysis combined P value < 0.05 comparing gene expression in the given cluster with the other cell clusters for AD (n = 4), PD (n = 4) and the controls (n = 4).

Extended Data Fig. 2 Identification and validation of the three astrocytes subpopulations.

a, Heatmap plot of the adjusted rand index (ARI) of pair-wise clustering result comparison using all cells with a range of dimensionality (5–30) and resolution (0.05–0.35). The black star indicates the parameter selected for all downstream analyses including analyses of entorhinal and prefrontal cortex astrocytes (dimensionality = 15, resolution = 0.25). The black lines delineate the range of parameters that generated high ARIs. b,c, UMAP visualization of subclusters of astrocytes colored by (b) disease diagnosis or (c) individual identity. d, Distribution of cells from each diagnostic group in the astrocyte subpopulations. Each dot represents an individual except entorhinal cortex data where each dot represents samples from two subjects that were processed together. e, Distribution of cells from each astrocyte subpopulation in different diagnostic groups. f,g, RNAscope in situ hybridization (ISH) analysis of Ast-2 conserved marker genes CD44 (f) and TNC (g) transcript expression (red) and immunohistochemistry staining (brown) of AQP4 in the internal capsule tissue sections of the same subjects of the control (CTRL), AD and PD groups shown in Fig. 1. For all data, the experiment was performed once. Hematoxylin-positive cell nuclei are shown in blue. Scale bar = 100 µm.

Extended Data Fig. 3 Characterization of astrocyte subpopulations in the prefrontal cortex (pfc) of Mathys et al., 2019 and the anterior cingulate cortex (acc) of the Feleke et al. 2021 data.

a,b, UMAP visualization of astrocyte subpopulations colored by cluster identity for (a) prefrontal cortex and (b) anterior cingulate cortex astrocytes. c,d UMAP visualization of astrocyte subpopulations colored by conserved marker gene expression levels for (c) prefrontal cortex and (d) anterior cingulate cortex. e, Dot plot of conserved marker gene expression levels in Ast-0, Ast-1 and Ast-2 astrocytes from the two brain regions. f, Violin plot showing the expression of Ast-2 conserved marker genes shared with putamen Ast-2. g–j UMAP visualization of subclusters of astrocytes colored by (g,h) disease diagnosis or (i,j) individual identity. k, The distribution of cells from each astrocyte subpopulation in different diagnostic groups (left) and the distribution of cells from each diagnostic group in the astrocyte subpopulations (right) of the Mathys et al., 2019 data. Each dot represents an individual. Conserved marker genes were genes whose expression is significantly higher than its expression in other cell clusters in all diagnostic groups determined by FindConservedMarkers using Wilcoxon Rank Sum test and metap R package with meta-analysis combined P value < 0.05. Red asterisks (*) indicate statistical significant conserved marker genes.

Extended Data Fig. 4 Characterization and comparison of the three astrocytes subpopulations from the putamen (pu), entorhinal cortex (ec), and prefrontal cortex (pfc) of Lau et al. data.

a, Upset plot showing the overlap between putamen conserved marker genes of Ast-0, Ast-1 and Ast-2 astrocyte with marker genes of mouse DAA and Gfap-high astrocytes from Habib et al., 2020. b, Violin plots showing the expression level distributions of orthologous genes of murine DAA and Gfap-high astrocyte marker genes in the putamen astrocytes. c, PCA plot using murine DAA and Gfap-high astrocyte marker gene logFC of gene expression (comparing murine DAA and Gfap-high astrocyte with Gfap-low astrocytes, downloaded from Habib et al., 2020) and the logFC of the human orthologous genes (comparing putamen Ast-1 and Ast-2 with Ast-0 astrocytes). d,e, Violin plots showing the expression level distributions of reactive astrocyte marker genes in astrocytes from the (d) putamen and (e) prefrontal cortex. f, Violin plots showing the expression level distributions of A1-, A2-specific activated astrocyte markers and JAK-STAT3 pathway genes. g, Top 10 GO terms in the Biological Process category enriched in the astrocyte subpopulation signature genes (hypergeometric test, FDR-adjusted P value < 0.05, ≥ 5 query genes). Conserved marker genes plotted in panel (b), (d) and (e) were determined by FindConservedMarkers using Wilcoxon Rank Sum test and metap R package with meta-analysis combined P value < 0.05 comparing gene expression in the given cluster with the other cell clusters for AD (n = 4), PD (n = 4) and the controls (n = 4). Genes plotted in (f) were not statistically significantly higher in any of the astrocyte subpopulations.

Extended Data Fig. 5 Comparison of differentially expressed genes (DEGs) of the three astrocyte subpopulations from the putamen (pu), entorhinal cortex (ec), and prefrontal cortex (pfc) from the Lau et al., data.

a, UpSet plot showing the number of overlapping up- and downregulated DEGs among the three astrocyte subpopulations for AD (left) and PD (right) astrocytes. b, Venn diagram showing the overlap of up- and downregulated DEG between AD and PD in each putamen astrocyte subpopulation (hypergeometric test). c, UpSet plot showing the overlap of DEGs that were up- or downregulated in AD between putamen (pu), entorhinal cortex (ec) and prefrontal cortex (pfc) astrocyte subpopulations. d, Disease-related Gene Ontology (GO) terms enriched in the astrocyte DEGs (hypergeometric test, FDR-adjusted P value < 0.05, ≥ 5 query genes). UP: upregulated in disease samples. Down: downregulated in disease samples.

Extended Data Fig. 6 Identification of immune cells and validation of microglia subpopulations.

a,b, Violin plots showing the expression level distributions of marker genes for (a) PVM and (b) activated microglia. c, Distribution of percentage of cells from each subject in each immune cell cluster of the putamen (pu), entorhinal cortex (ec) from the Grubman et al. data and prefrontal cortex (pfc) from the Lau et al. data (one-way ANOVA or student’s t-test). Each dot represents a subject except the ec data. d,e, Immunohistochemistry staining (brown) of microglia marker protein P2RY12 and RNAscope in situ hybridization (ISH) analysis (red) of (d) AIF1 and (e) APOC1 transcript expression in the internal capsule tissue of the same subjects shown in Fig. 1. Hematoxylin-positive cell nuclei are shown in blue. For all data, the experiment was performed once. f–h, UMAP visualization of only microglia subpopulations from (f) pu, (g) ec and (h) pfc. UMAPs were generated using a dimensionality of 10 and resolution of 0.15. i–k, Violin plots showing the expression level distributions of conserved microglial subpopulation marker genes in putamen (i), entorhinal cortex (j) and preprontal cortex (k) microglia subpopulations. Conserved marker genes plotted in panel (a), and HLA-DRA, HLA-DPB1, FTL and CD14 plotted in panel (b) were determined by FindConservedMarkers using Wilcoxon Rank Sum test and metap R package with meta-analysis combined P value < 0.05 comparing gene expression in the given cluster with the other cell clusters for AD (n = 4), PD (n = 4) and the controls (n = 4).

Extended Data Fig. 7 Four distinct immune cell populations in (A-D) the prefrontal cortex (pfc) of the Mathys et al., and (E-H) the anterior cingulate cortex (acc) data of the Feleke et al. data.

a,e, UMAP visualization of subclusters of immune cells colored by cell cluster (left) or disease diagnosis (right). UMAPs were generated using parameters of dimensionality of 40 and resolution of 0.5 for the Mathys et al. data (AD n = 24, controls n = 24) and dimensionality of 20 and resolution of 0.15 for the Feleke et al. data (n = 7 each for the control, DLBD, PD and PDD samples). Violin plots showing the expression level distributions of genes for (b, f) T cell, microglia and PVM shared markers and PVM unique markers; (c, g) microglia-specific markers, and microglia subpopulation markers; (d, h) Micr-0 marker and activated microglia markers. The color code is the same as in (a) and (e), respectively. The conserved marker genes were determined by FindConservedMarkers using Wilcoxon Rank Sum test and metap R package with meta-analysis combined P value < 0.05 comparing gene expression in the cells of given cluster with that of the other cells. PVM: perivascular macrophage; CycM: cycling microglia.

Extended Data Fig. 8 Comparison of microglial pseudotime DEGs.

a, Venn diagram showing the overlap between pseudotime DEGs of control, AD and PD microglia with AD-risk genes (hypergeometric test). Pseudotime DEGs are genes whose expression significantly associated with pseudotime progression (generalized addictive model, FDR-adjusted P value < 0.05). b, UpSet plot showing the overlap between control, AD and PD microglial pseudotime gene co-expression modules from putamen microgla. c, Heatmap showing pseudotime DEGs shared by human activated microglia from the putamen (pu) of cognitively normal controls, AD and PD samples, from prefrontal cortex (pfc) of the control and AD samples and from the entorhinal cortex (ec) of the control and AD samples. d, GO terms related to immune functions enriched in the microglia pseudotime DEGs. e, Heatmap showing pseudotime DEGs shared by the mouse activated microglia DAM and ARM. f, Top 5 GO terms in the biological process category enriched in the microglia pseudotime DEGs. Pathways with FDR-adjusted P value < 0.05 (hypergeometric test) and at least five query genes were considered statistically significant. DAM: Disease-associated microglia; ARM: activated response microglia. UP: upregulated during pseudotime progress (module 2 and 3 genes). Down: downregulated during pseudotime progress (module 1 genes). Mod 1: module 1 genes; Mod 2 + 3: module 2 and 3 genes combined.

Extended Data Fig. 9 Microglia transcriptomic changes in disease contributed to Aβ pathology, tauopathy and neuronal death.

a–d, Volcano plots showing significant DEGs in Micr-0 and Micr-1 comparing cells from AD (left panels) or PD (right panels) with cells from the controls (CTRL). The x-axis specifies the logFC and the y-axis specifies the negative logarithm to the base 10 of the FDR-adjusted P values. Magenta and cyan dots represent genes expressed at significantly higher or lower levels respectively in disease samples (Wilcoxon Rank Sum test, FDR-adjusted P value < 0.05, absolute logFC > 0.25) comparing AD (Micr-0 = 440, Micr-1 = 299 cells) or PD (Micr-0 = 329, Micr-1 = 201 cells) microglia to the control (Micr-0 = 264, Micr-1 = 198 cells) microglia. Violin plots showing the expression level distributions of example DEGs that were (b) downregulated in both AD and PD microglia, (c) uniquely downregulated in AD or (d) uniquely upregulated in PD. e, GO terms related to neuron death, Aβ pathology and tauopathy enriched in microglial DEGs (hypergeometric test, FDR-adjusted P value < 0.05, ≥ 5 query genes). f, Heatmaps showing the logFC of expression level of significant DEGs for GWAS AD- and PD-risk genes; GWAS genes differentially expressed in at least two subpopulations were plotted for visualization. UP: upregulated in disease samples. Down: downregulated in disease samples.

Extended Data Fig. 10 Comparison of microglia DEGs.

a, Venn diagram demonstrating overlap between AD and PD DEGs in the Micr-0 and Micr-1 cells for DEGs upregulated (left) or downregulated (right) in the disease samples. b,c, Scatter plots showing pair-wise correlations of genome-wide gene expression logFC (b) between Micr-0 and Micr-1 in AD (left) or PD (right) samples and (c) between AD and PD samples in Micr-0 (left) or Micr-1 (right) cells respectively. d, Top 5 GO terms in the biological process category enriched in the DEGs of the microglia subpopulations from the putamen (pu), entorhinal cortex (ec), prefrontal cortex (pfc) (hypergeometric test, FDR-adjusted P value < 0.05, ≥ 5 query genes). e, Bar plot showing the number of DEGs for each subpopulation of microglia from the three brain regions (Wilcoxon Rank Sum test, FDR-adjusted P value < 0.05 and absolute logFC >0.25). UP: upregulated in disease samples. Down: downregulated in disease samples.

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Supplementary Methods and Figure 1.

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Xu, J., Farsad, H.L., Hou, Y. et al. Human striatal glia differentially contribute to AD- and PD-specific neurodegeneration. Nat Aging 3, 346–365 (2023). https://doi.org/10.1038/s43587-023-00363-8

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