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

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.

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 coexpression 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.

Fig. 1 |. Characterization of six major cell types and three distinct astrocyte subpopulations.

a, Unsupervised clustering of snRNA-seq data and UMAP (Uniform Manifold Approximation and Projection) plot of all cells from putamen (pu) colored by cluster identity. UMAP plots were generated using default parameters except reduction = ’pca’, dims = 1:20. b, UMAP plot of all cells colored by marker gene expression levels. c–e, UMAP visualization of astrocyte subpopulations colored by cluster identity for putamen (c; total nuclei: control 1,203, AD 1,642, PD 1,433), ec (d; control 1,660, AD 702) and pfc (e; control 6,109, AD 7,144) astrocytes. f–h, UMAP visualization of astrocyte subpopulations colored by conserved marker gene expression levels for putamen (f), ec (g) and pfc astrocytes (h). i,j, Dot plot of conserved marker genes (i) and CD44 and TNC expression levels (j) in Ast-0, Ast-1 and Ast-2 astrocytes from the three brain regions. k, Venn diagram demonstrating overlap of conserved marker genes among the three brain regions for each astrocyte subpopulation. l,m, Violin plot showing the expression of Ast-2 conserved marker genes CD44 (l) and TNC (m) measured by snRNA-seq. n,o, CD44 (n) and TNC (o) expression validated by RNAScope in situ hybridization together with AQP4 immunohistochemistry staining in the putamen of control, AD and PD samples. CD44 and TNC, red; AQP4, tan. For all data, the experiment was performed once. FindConservedMarkers using Wilcoxon rank sum test and metap R package with meta-analysis combined P value < 0.05. Scale bars, 100 μm. CTRL, control; immune, immune cell; Ast, astrocyte; EP, endothelial cell and pericyte; OLIGO, oligodendrocytes; OPC, oligodendrocyte precursor cell.

Fig. 2 |. Transcriptomic comparison of astrocyte subpopulations.

a,b, Violin plots showing genes with conserved expression patterns in the putamen and ec (a) or the putamen and pfc (b) in FindConservedMarkers using Wilcoxon rank sum test and metap R package with meta-analysis combined P value < 0.05). c,d, KEGG pathway terms (c) and disease-related GO terms (d) enriched in the subcluster conserved marker genes (false discovery rate (FDR)-adjusted P value < 0.05, hypergeometric test, ≥ 5 query genes). e–j, Volcano plots showing significant DEGs comparing cells from AD (e–g, Ast-0 = 834, Ast-1 = 553, Ast-2 = 255 cells) or PD (h–j, Ast-0 = 784, Ast-1 = 427, Ast-2 = 222 cells) with cells from the controls (CTRL, Ast-0 = 683, Ast-1 = 358, Ast-2 = 161 cells). The x-axis specifies the log fold changes (logFCs), and the y-axis specifies the negative logarithm to the base 10 of the adjusted P values (−log₁₀(P_adj)). Magenta and cyan dots represent genes upregulated and downregulated in disease brains, respectively (Wilcoxon rank sum test, FDR-adjusted P value < 0.05 and absolute logFC > 0.25 using natural logarithm (ln)). k–m, Violin plots showing the expression level distributions of example DEGs of Ast-0 (k), Ast-1 (l) and Ast-2 (m). n, Violin plots showing F3 gene expression in all major cell types in the putamen. o, Representative images of RNAScope in situ hybridization analysis of F3 transcript expression in the putamen. p, Single-cell F3 in situ hybridization signal from four images each for four subjects from each group were quantified, AD (n = 863 cells), PD (n = 387 cells) and control (CTRL, n = 1,120 cells) using one-way analysis of variance with Tukey’s multiple comparisons test, ***P < 0.001, *P < 0.05, AD versus CTRL P value < 0.001, PD versus CTRL P value < 0.001, PD versus AD P value = 0.016). Data are presented as mean values ± standard deviation (s.d.). Minima = 3.69, maxima = 5.41, mean CTRL = 4.63, AD = 4.38, PD = 4.27. The lower and upper hinges correspond to the 25th and 75th percentiles. The upper/lower whisker extends from the hinge to the largest/smallest value no further than 1.5× interquartile range from the hinge. Down: downregulated; Up: upregulated; NotSig: not statistically significant; log₁₀(IntDen + 1): logarithm to the base 10 of the integrated density.

Fig. 3 |. Regional differences in astrocytic transcriptomic changes in disease.

a, Bar plot showing the number of up- and downregulated differentially expressed genes (DEG) in the three astrocyte subpopulations from the putamen (pu), ec and pfc (Wilcoxon rank sum test, FDR-adjusted P value < 0.05 and logFC > 0.25 using natural logarithm (ln)). Number of subjects: AD (n = 4), PD (n = 4) controls (n = 4). b, Heatmap of Pearson’s correlation coefficient of genome-wide gene expression logFC among the three astrocyte subpopulations from the pu, ec and pfc. The color represents the correlation’s directionality, and the shade of color represents the significant levels. Only significant correlations were plotted (FDR-correlated P value < 0.05). c, Top two biological process pathways enriched in the DEGs. d, Neurodegenerative disease-related KEGG pathways enriched in the DEGs (hypergeometric test, FDR-adjusted P value < 0.05, ≥ 5 query gene). e, Heatmaps showing the logFC of significant DEGs for GWAS AD- and PD-risk genes; GWAS genes differentially expressed in at least two subpopulations were plotted for visualization. Upregulated: upregulated in disease samples. Downregulated: downregulated in disease samples.

Fig. 4 |. Four distinct immune cell populations.

a,b, UMAP visualization of subclusters of immune cells (total nuclei: control 558, AD 827, PD 619) colored by cell cluster (a) or disease diagnosis (b). c,d, Violin plots showing the expression level distributions of genes for T cell, microglia and PVM shared markers and PVM unique markers (c); microglia-specific markers and microglia subpopulation markers (d). The color code is the same as in panel a. e, Subcluster signaturegene enriched GO terms in the Biological Process category (hypergeometric test, FDR-adjusted P value < 0.05, ≥ 5 query genes). f,g, Immunohistochemistry staining (brown) of marker protein P2RY12 and RNAscope in situ hybridization analysis (red) of AIF1 (f) and APOC1 (g) transcript expression in the adjacent tissue sections from the putamen tissue of a control, AD or PD brain. Hematoxylin-positive cell nuclei are shown in blue. For all data, the experiment was performed once. UMAP were generated using default parameters except reduction = ’pca’, dims = 1:30. Cell cluster were defined using resolution = 0.2. Conserved marker genes were determined by FindConservedMarkers using Wilcoxon Rank Sum test and metap R package with meta-analysis combined P value < 0.05. Number of subjects: AD (n = 4), PD (n = 4) and the controls (n = 4). Scale bars, 100 μm.

Fig. 5 |. Characterization of the human activated microglia.

a, Venn diagram demonstrating overlap (hypergeometric test) between murine DAM marker genes and the conserved marker genes of human activated microglia (Micr-1) of AD, PD and controls (left, P value = 6.46 × 10⁻³³) or marker genes of AD-only human activated microglia (right, P value = 3.03 × 10⁻⁴⁰). b–d, Violin plots showing the expression level distributions of (b) TREM2, APOE, B2M and TYROBP; (c) M1- and M2- microglia markers; (d) M1- and M2- microglia regulatory transcription factors (TF). e, Immunohistochemistry staining (brown) of marker protein P2RY12 and RNAscope in situ hybridization analysis (red) of TREM2 transcript expression in the adjacent tissue sections from the putamen of an AD and a PD case. For all data, the experiment was performed once. Hematoxylin-positive cell nuclei are shown in blue. f, Heatmap of Pearson’s correlation coefficient of M1- and M2- microglia marker gene expression for Micr-0 and Micr-1 microglia respectively. The shade of the color represents the significance levels (FDR-correlated P value < 0.05). The color represents the directionality of correlation. APOE, B2M and TYROBP were determined to be conserved cluster marker for Micr-1 by FindConservedMarkers using Wilcoxon rank sum test and metap R package with meta-analysis combined P value < 0.05 comparing gene expression in Micr-1 cluster with the other cell clusters for AD (n = 4), PD (n = 4), and the controls (n = 4). Scale bars, 100 μm.

Fig. 6 |. Transcriptome transition during microglia activation.

a–c, UMAP visualization of pseudotemporal trajectory for microglia of putamen (a), ec (b) and pfc (c). UMAP were generated using default parameters except reduction = ’pca’, dims = 1:10. Cell cluster were defined using resolution = 0.15. d–f, Heatmap of the three pseudotime DEG co-expression modules in the microglia of putamen (d), ec (e) and pfc (f) for each condition. Pseudotime DEGs are genes whose expression significantly associated with pseudotime progression (generalized additive model, FDR-adjusted P value < 0.05). g–i, Gene expression changes along pseudotime trajectory for example genes in module 1, 2 or 3 in the microglia of putamen (g), ec (h) and pfc (i) for each condition. LOESS Regression were performed using loess() function in R with 95% confidence intervals plotted. Only statistically significant pseudotime DEGs (FDR-adjusted P value < 0.05) were shown. j, Expression dynamics along the pseudotime trajectory of pseudotime DEGs shared by the control, AD and PD samples in module 1, 2 or 3. k, Microglia-activation-related gene ontology terms enriched in the core gene-co-expression module genes in module 1 (Mod1) or module 2 and 3 combined (Mod2+3) (hypergeometric test, FDR-adjusted P value < 0.05, ≥ 5 query genes).

Fig. 7 |. Comparison of genes and pathways of microglia activation-associated transcriptome changes.

a, Pseudotime DEGs (generalized additive model, FDR-adjusted P value < 0.05) shared by human activated microglia isolated from the putamen of cognitively normal controls (CTRL), AD and PD samples, pfc of control and AD samples, ec of control and AD samples and temporal or frontal lobes (tc) of glioblastoma multiforme samples reported by Sankowski et al. b, Cell death-related GO terms enriched in the pseudotime DEGs. c, GO terms related to Aβ pathology, tauopathy, and autophagy that were enriched in the pseudotime DEGs. d, Top KEGG pathways enriched in the pseudotime DEGs. Pathways with FDR-adjusted P value < 0.05 (hypergeometric test) and at least five query genes were considered statistically significant. Up: upregulated; down: downregulated; NotDEG: not pseudotime DEG.

Fig. 8 |. Characterization of neuronal subpopulations and gene expression changes in disease conditions.

a, UMAP visualization of neuron subpopulations colored by cluster identity. b, Dot plot of neuronal subpopulation conserved marker gene expression (FindConservedMarkers using Wilcoxon rank sum test and metap R package with meta-analysis combined P value < 0.05). c, Violin plot showing the expression of matrix- and patch-compartment marker gene expression in mD1, pD1, mD2 and pD2 neurons (FindConservedMarkers using Wilcoxon rank sum test and metap R package with meta-analysis combined P value < 0.05). d–f, GO terms enriched in the conserved cluster marker genes of each neuronal subpopulation related to cell death (d), stress response (e), amyloid and tau metabolism and unfolded protein response pathways (f). g, Neurodegeneration and addiction-related KEGG pathway terms enriched in the conserved cluster marker genes of each neuronal subpopulation. h, UMAP visualization of MSN neuron subpopulations with sMSN colored by cells expressing only DRD1 (left), only DRD2 (middle) or both DRD1 and DRD2 (right). i, Top five GO terms in the Biological Process category enriched in the DEGs of each neuronal subpopulation in AD and PD. Only cell subpopulations with enriched GO terms are shown. Pathways with FDR-adjusted P value < 0.05 (hypergeometric test) and at least five query genes were considered statistically significant.