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Spatially resolved transcriptomics reveals genes associated with the vulnerability of middle temporal gyrus in Alzheimer’s disease

Chen, Shuo, Chang, Yuzhou, Li, Liangping, Acosta, Diana, Li, Yang, Guo, Qi, Wang, Cankun, Turkes, Emir, Morrison, Cody, Julian, Dominic, Hester, Mark E., Scharre, Douglas W., Santiskulvong, Chintda, Song, Sarah XueYing, Plummer, Jasmine T., Serrano, Geidy E., Beach, Thomas G., Duff, Karen E., Ma, Qin, Fu, Hongjun·2022·Acta Neuropathologica Communications
DOI 10.1186/s40478-022-01494-6PMC PMC9773466

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Abstract

Human middle temporal gyrus (MTG) is a vulnerable brain region in early Alzheimer’s disease (AD), but little is known about the molecular mechanisms underlying this regional vulnerability. Here we utilize the 10 × Visium platform to define the spatial transcriptomic profile in both AD and control (CT) MTG. We identify unique marker genes for cortical layers and the white matter, and layer-specific differentially expressed genes (DEGs) in human AD compared to CT. Deconvolution of the Visium spots showcases the significant difference in particular cell types among cortical layers and the white matter. Gene co-expression analyses reveal eight gene modules, four of which have significantly altered co-expression patterns in the presence of AD pathology. The co-expression patterns of hub genes and enriched pathways in the presence of AD pathology indicate an important role of cell–cell-communications among microglia, oligodendrocytes, astrocytes, and neurons, which may contribute to the cellular and regional vulnerability in early AD. Using single-molecule fluorescent in situ hybridization, we validated the cell-type-specific expression of three novel DEGs (e.g., KIF5A , PAQR6 , and SLC1A3 ) and eleven previously reported DEGs associated with AD pathology (i.e., amyloid beta plaques and intraneuronal neurofibrillary tangles or neuropil threads) at the single cell level. Our results may contribute to the understanding of the complex architecture and neuronal and glial response to AD pathology of this vulnerable brain region.

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Figures

Fig. 1 Spatial transcriptomics (SRT) of the human middle temporal gyrus (MTG). (A) Sequential 10 µm sections of control (CT) and Alzheimer (AD) MTG brain regions were used for all experiments. The middle section (No. 3) was used for SRT on the Visium platform, and the four adjacent sections (No.1, 2, 4, 5) were used for immunofluorescence (IF) staining. (B) Aligned image of SRT with IF staining of nuclei (DAPI, blue) and cell-type specific markers (GFAP (green), P2RY12 (purple), Olig2 (yellow), WFS1 (teal)) and AD pathological hallmarks (Aβ plaques (white) and AT8 + pathological tau (red)). (C) Uniform manifold approximation and projection (UMAP) plots show eight clusters (0–7) were identified by the Seurat integration framework using Visium spots from both CT (CT-1, CT-2 and CT-3) and AD (AD-1, AD-2 and AD-3) human MTG. The right panel shows spatial maps of the eight clusters for each individual sample generated from Seurat clustering. (D) Manual annotation of six cortical layers and the adjacent white matter (WM). The left panel figure shows the manually labeled spots (six cortical layers and the WM) on UMAP space based on the Seurat integration framework. The right panel of the spatial maps show the localization of the manually labeled spots for each individual sample. Noise, spots with tissue loss or folded regions
Fig. 1 Spatial transcriptomics (SRT) of the human middle temporal gyrus (MTG). (A) Sequential 10 µm sections of control (CT) and Alzheimer (AD) MTG brain regions were used for all experiments. The middle section (No. 3) was used for SRT on the Visium platform, and the four adjacent sections (No.1, 2, 4, 5) were used for immunofluorescence (IF) staining. (B) Aligned image of SRT with IF staining of nuclei (DAPI, blue) and cell-type specific markers (GFAP (green), P2RY12 (purple), Olig2 (yellow), WFS1 (teal)) and AD pathological hallmarks (Aβ plaques (white) and AT8 + pathological tau (red)). (C) Uniform manifold approximation and projection (UMAP) plots show eight clusters (0–7) were identified by the Seurat integration framework using Visium spots from both CT (CT-1, CT-2 and CT-3) and AD (AD-1, AD-2 and AD-3) human MTG. The right panel shows spatial maps of the eight clusters for each individual sample generated from Seurat clustering. (D) Manual annotation of six cortical layers and the adjacent white matter (WM). The left panel figure shows the manually labeled spots (six cortical layers and the WM) on UMAP space based on the Seurat integration framework. The right panel of the spatial maps show the localization of the manually labeled spots for each individual sample. Noise, spots with tissue loss or folded regionsCC BY · PubMed Central
Fig. 2 Layer-specific genes define the anatomical architecture of the human MTG and the frontal cortex. (A) Heatmap of Z-scores for layer-specific marker genes identified from CT and AD human MTG. The heatmap shows the expression levels of layer-specific marker genes in each layer except layer III, which does not show any conserved marker genes in all 6 samples. The red color indicates a relevantly higher expression than other layers, while the blue color indicates a lower expression. Identified layer-specific marker genes, SPARC (L1: layer I), CALB2 (L2: layer II), RORB (L4: layer IV), PCP4 (L5: layer V), DIRAS2 and KRT17 (L6: layer VI), and MBP (the WM), are boxed in red and were used for validation of cortical laminae in spatial maps using Loupe Browser shown in (B). (B) Spatial maps of layer-specific marker genes in our samples (color images) were validated by in situ hybridization (ISH) data (gray images) from the Allen Brain Institute’s Human Brain Atlas (Sample ID: 79,205,802, 80,225,788). ISH data is not available for DIRAS2 and KRT17. (C) Public available 10 × Visium SRT data of the human frontal cortex (Sample ID: Human Brian 1) is annotated by our layer-specific marker gene modules
Fig. 2 Layer-specific genes define the anatomical architecture of the human MTG and the frontal cortex. (A) Heatmap of Z-scores for layer-specific marker genes identified from CT and AD human MTG. The heatmap shows the expression levels of layer-specific marker genes in each layer except layer III, which does not show any conserved marker genes in all 6 samples. The red color indicates a relevantly higher expression than other layers, while the blue color indicates a lower expression. Identified layer-specific marker genes, SPARC (L1: layer I), CALB2 (L2: layer II), RORB (L4: layer IV), PCP4 (L5: layer V), DIRAS2 and KRT17 (L6: layer VI), and MBP (the WM), are boxed in red and were used for validation of cortical laminae in spatial maps using Loupe Browser shown in (B). (B) Spatial maps of layer-specific marker genes in our samples (color images) were validated by in situ hybridization (ISH) data (gray images) from the Allen Brain Institute’s Human Brain Atlas (Sample ID: 79,205,802, 80,225,788). ISH data is not available for DIRAS2 and KRT17. (C) Public available 10 × Visium SRT data of the human frontal cortex (Sample ID: Human Brian 1) is annotated by our layer-specific marker gene modulesCC BY · PubMed Central
Fig. 3 Cell type deconvolution analysis of snRNA-seq data and Visium SRT data from human MTG. (A) Manual annotations of six cortical layers and the adjacent WM in CT (CT-1) and AD (AD-1) human MTG, and spatial maps of the cell-type-specific distributions of seven main cell types (Astro, Endo, Exc, Inh, Micro, OPC and Oligo) [37] among spots and layers corresponding to these two cases using Cell2location (see details in Methods). Spots enriched for each cell subpopulation are shown in brown, which is divided into high and low expression based on the proportion of each individual cell subpopulation. (B) Stack bar plots highlight the proportion of seven main cell types in layers I-VI (L1-L6) and the adjacent WM of CT-1 and AD-1. NS, noise spots; Astro, astrocytes; Endo, endothelial cells; Exc, excitatory neurons; Inh, inhibitory neurons; Micro, microglia; Oligo, oligodendrocytes; and OPC, oligodendrocyte precursor cells
Fig. 3 Cell type deconvolution analysis of snRNA-seq data and Visium SRT data from human MTG. (A) Manual annotations of six cortical layers and the adjacent WM in CT (CT-1) and AD (AD-1) human MTG, and spatial maps of the cell-type-specific distributions of seven main cell types (Astro, Endo, Exc, Inh, Micro, OPC and Oligo) [37] among spots and layers corresponding to these two cases using Cell2location (see details in Methods). Spots enriched for each cell subpopulation are shown in brown, which is divided into high and low expression based on the proportion of each individual cell subpopulation. (B) Stack bar plots highlight the proportion of seven main cell types in layers I-VI (L1-L6) and the adjacent WM of CT-1 and AD-1. NS, noise spots; Astro, astrocytes; Endo, endothelial cells; Exc, excitatory neurons; Inh, inhibitory neurons; Micro, microglia; Oligo, oligodendrocytes; and OPC, oligodendrocyte precursor cellsCC BY · PubMed Central
Fig. 4 Differentially expressed genes (DEGs), Top-20 enriched pathways and layer-specific DEGs in AD vs CT human MTG. (A) The Fisher’s exact test between DEGs (AD vs CT 1008 upregulated) identified in our study and 14 previously published AD-related transcriptomic and proteomics datasets (Additional file 7: Table S6). The x-axis and y-axis indicate objects of Fisher’s exact test. Heatmap block color indicates the log-transformed odds ratio. Each heatmap block is labeled by its adjusted p-value. If the heatmap block has a non-significant adjusted p-value, the block color was assigned as white. WAM, WM-associated microglia; PIG, plaque-induced gene; OLIG, oligodendrocyte gene; DAM, disease-associated microglia; ARM, amyloid-response microglia; A1, A1 astrocytes. (B) Gene Ontology (GO) pathway analysis of DEGs between AD and CT cases. DEGs were assessed with the Seurat FindMarkers function with a log-fold-change threshold of 0.25. Bonferroni-adjusted p-values were used to determine significance at an FDR < 0.05. Dot plot shows the top 20 enriched GO terms which represent biological process terms from the DEGs between AD and CT. Each dot is colored by the Benjamini–Hochberg adjusted p-value. The dot size is scaled by the number of overlapping genes with the related GO terms. The x-axis is scaled by the ratio between the overlapping count and the total number of genes of the term. Gene set enrichment analysis was performed using the Enrichr web server. (C) Heatmaps of Z-scores for layer-specific upregulated DEGs identified between AD and CT cases. The heatmaps show the expression levels of layer-specific DEGs in each layer for each AD and CT case. Layer I of AD-2 is shown in gray since this layer was not present in our sampled brain block. The red color indicates a relevantly higher expression than other cases, while the blue color indicates a lower expression
Fig. 4 Differentially expressed genes (DEGs), Top-20 enriched pathways and layer-specific DEGs in AD vs CT human MTG. (A) The Fisher’s exact test between DEGs (AD vs CT 1008 upregulated) identified in our study and 14 previously published AD-related transcriptomic and proteomics datasets (Additional file 7: Table S6). The x-axis and y-axis indicate objects of Fisher’s exact test. Heatmap block color indicates the log-transformed odds ratio. Each heatmap block is labeled by its adjusted p-value. If the heatmap block has a non-significant adjusted p-value, the block color was assigned as white. WAM, WM-associated microglia; PIG, plaque-induced gene; OLIG, oligodendrocyte gene; DAM, disease-associated microglia; ARM, amyloid-response microglia; A1, A1 astrocytes. (B) Gene Ontology (GO) pathway analysis of DEGs between AD and CT cases. DEGs were assessed with the Seurat FindMarkers function with a log-fold-change threshold of 0.25. Bonferroni-adjusted p-values were used to determine significance at an FDR < 0.05. Dot plot shows the top 20 enriched GO terms which represent biological process terms from the DEGs between AD and CT. Each dot is colored by the Benjamini–Hochberg adjusted p-value. The dot size is scaled by the number of overlapping genes with the related GO terms. The x-axis is scaled by the ratio between the overlapping count and the total number of genes of the term. Gene set enrichment analysis was performed using the Enrichr web server. (C) Heatmaps of Z-scores for layer-specific upregulated DEGs identified between AD and CT cases. The heatmaps show the expression levels of layer-specific DEGs in each layer for each AD and CT case. Layer I of AD-2 is shown in gray since this layer was not present in our sampled brain block. The red color indicates a relevantly higher expression than other cases, while the blue color indicates a lower expressionCC BY · PubMed Central
Fig. 5 AD-associated gene modules and their co-expression patterns within areas of AD pathology. (A) Gene-ontology (GO) pathway analysis of four modules (Yellow, Turquoise, Pink and Brown) identified by weighted gene co-expression network analysis (WGCNA) of 10,000 highly variable genes from six samples (Additional file 1: Fig. S5). Dot plot shows top 10 enriched GO terms: biological process terms from the DEGs within each module (Additional file 9: Table S8). Each dot is colored by the Benjamini–Hochberg adjusted p-value. The dot size is scaled by the number of overlapping genes with the related GO terms. The x-axis is scaled by the ratio between the overlapping count and the total number of genes of the term. Gene set enrichment analysis was performed using the Enrichr web server. (B) Circos plots of the connectivity strength within and across modules for Visium spots in CT human MTG and Visium spots in AD human MTG with specific AD pathology (Aβ+, AT8+, and Aβ/AT8+). Circos plots are composed of four different modules (Yellow, Turquoise, Pink and Brown; top 30 hub genes from each module based on their closeness centrality score); nodes represent highly deregulated genes from each module; and edges (green lines) indicate connectivity (high: dark green, low: light green)
Fig. 5 AD-associated gene modules and their co-expression patterns within areas of AD pathology. (A) Gene-ontology (GO) pathway analysis of four modules (Yellow, Turquoise, Pink and Brown) identified by weighted gene co-expression network analysis (WGCNA) of 10,000 highly variable genes from six samples (Additional file 1: Fig. S5). Dot plot shows top 10 enriched GO terms: biological process terms from the DEGs within each module (Additional file 9: Table S8). Each dot is colored by the Benjamini–Hochberg adjusted p-value. The dot size is scaled by the number of overlapping genes with the related GO terms. The x-axis is scaled by the ratio between the overlapping count and the total number of genes of the term. Gene set enrichment analysis was performed using the Enrichr web server. (B) Circos plots of the connectivity strength within and across modules for Visium spots in CT human MTG and Visium spots in AD human MTG with specific AD pathology (Aβ+, AT8+, and Aβ/AT8+). Circos plots are composed of four different modules (Yellow, Turquoise, Pink and Brown; top 30 hub genes from each module based on their closeness centrality score); nodes represent highly deregulated genes from each module; and edges (green lines) indicate connectivity (high: dark green, low: light green)CC BY · PubMed Central
Fig. 6 AD pathology-associated gene signatures and pathways. (A) Heatmap of Z-scores for upregulated and downregulated DEGs specific to Visium spots localized with different AD pathologies (Aβ+, AT8+, and Aβ/AT8+). DEGs were identified from pathological regions (either Aβ+, AT8+, and Aβ/AT8+) vs. surrounding level 3 spots of three AD samples at the pseudo-bulk level. The red color indicates a relevantly higher expression than other pathologies, while the blue color indicates a lower expression. (B) Gene ontology (GO) pathway analysis of identified DEGs (Additional file 10: Table S9) specific to Visium spots localized with Aβ plaques (top row) or pathological tau, AT8 (bottom row). The Dot plot shows top 10 enriched GO terms. Each dot is colored by the Benjamini–Hochberg adjusted p-value. The dot size is scaled by the number of overlapping genes with the related GO terms. The x-axis is the ratio and indicating the proportion of overlapping genes between the query gene list and all genes in the GO term
Fig. 6 AD pathology-associated gene signatures and pathways. (A) Heatmap of Z-scores for upregulated and downregulated DEGs specific to Visium spots localized with different AD pathologies (Aβ+, AT8+, and Aβ/AT8+). DEGs were identified from pathological regions (either Aβ+, AT8+, and Aβ/AT8+) vs. surrounding level 3 spots of three AD samples at the pseudo-bulk level. The red color indicates a relevantly higher expression than other pathologies, while the blue color indicates a lower expression. (B) Gene ontology (GO) pathway analysis of identified DEGs (Additional file 10: Table S9) specific to Visium spots localized with Aβ plaques (top row) or pathological tau, AT8 (bottom row). The Dot plot shows top 10 enriched GO terms. Each dot is colored by the Benjamini–Hochberg adjusted p-value. The dot size is scaled by the number of overlapping genes with the related GO terms. The x-axis is the ratio and indicating the proportion of overlapping genes between the query gene list and all genes in the GO termCC BY · PubMed Central
Fig. 7 Validation of identified representative DEGs in layers II/III/V from AD and CT at the single-cell level using RNAscope smFISH. (A) Representative four rounds of whole-tiles RNAscope images were registered with their corresponding post-Immunofluorescence (IF) staining of Aβ, AT8, and GFAP (left panel of each quadrant). The representative high-mag images of layers II/III and V (left panel of each quadrant) were selected from the regions in the whole-tiles images circled by red (layer II/II) and green (layer V) rectangles. Individual cell was labeled as microglia (MG), astrocyte (AG), neuron (Neu), or oligodendrocyte (Oligo) according to its P2RY12/C1QB, GFAP, RBFOX3, and MBP expression respectively. (B) Stacked bar plots show the quantification of RNAscope probes against human SLC1A3, KIF5A, SNCG, STMN2, CSRP1, PLP1, GLUL, PAQR6, CD9, C1QB, SPP1, CD63, CRYAB, and YWHAH in microglia (P2RY12+/C1QB+), astrocytes (GFAP+), neurons (RBFOX3+), and oligodendrocytes (MBP+). RNA counts per cell were compared between three CT and three AD cases. For quantification, we identified 20–50 microglia, astrocytes, and oligodendrocytes, and 60–90 neurons from each case. Detailed information regarding quantification can be found in the methods section. * P < 0.05, ** P < 0.01, *** P < 0.001, **** P < 0.0001 (Mann–Whitney test, CT vs AD). Scale bar, 60 µm for layers II/II and V high-mag images, 360 µm for whole-tiles. See high-resolution RNAscope images at: https://bmbls.bmi.osumc.edu/scread/stofad-2
Fig. 7 Validation of identified representative DEGs in layers II/III/V from AD and CT at the single-cell level using RNAscope smFISH. (A) Representative four rounds of whole-tiles RNAscope images were registered with their corresponding post-Immunofluorescence (IF) staining of Aβ, AT8, and GFAP (left panel of each quadrant). The representative high-mag images of layers II/III and V (left panel of each quadrant) were selected from the regions in the whole-tiles images circled by red (layer II/II) and green (layer V) rectangles. Individual cell was labeled as microglia (MG), astrocyte (AG), neuron (Neu), or oligodendrocyte (Oligo) according to its P2RY12/C1QB, GFAP, RBFOX3, and MBP expression respectively. (B) Stacked bar plots show the quantification of RNAscope probes against human SLC1A3, KIF5A, SNCG, STMN2, CSRP1, PLP1, GLUL, PAQR6, CD9, C1QB, SPP1, CD63, CRYAB, and YWHAH in microglia (P2RY12+/C1QB+), astrocytes (GFAP+), neurons (RBFOX3+), and oligodendrocytes (MBP+). RNA counts per cell were compared between three CT and three AD cases. For quantification, we identified 20–50 microglia, astrocytes, and oligodendrocytes, and 60–90 neurons from each case. Detailed information regarding quantification can be found in the methods section. * P < 0.05, ** P < 0.01, *** P < 0.001, **** P < 0.0001 (Mann–Whitney test, CT vs AD). Scale bar, 60 µm for layers II/II and V high-mag images, 360 µm for whole-tiles. See high-resolution RNAscope images at: https://bmbls.bmi.osumc.edu/scread/stofad-2CC BY · PubMed Central
Fig. 8 Validation of identified representative DEGs associated with AD pathology at the single-cell level using RNAscope smFISH. (A) Representative RNAscope images (left panel) and their corresponding post-IF staining of Aβ, AT8, and GFAP (right panels) in layer V for cells distal to and proximal to pathological spots. All images from the same cell were aligned and registered by imageJ (see details in Methods). (B) RNAscope probes against human SLC1A3, KIF5A, SNCG, STMN2, CSRP1, PLP1, GLUL, PAQR6, CD9, C1QB, SPP1, CD63, CRYAB, and YWHAH in microglia (P2RY12+/C1QB+), astrocytes (GFAP+), neurons (RBFOX3+), and oligodendrocytes (MBP+) were quantified and compared within three AD cases. For the quantification and definition of proximal to pathology/distal to pathology, please find the details in Methods. For quantification, we identified 20–50 microglia, astrocytes, and oligodendrocytes, and 60–90 neurons from each case. * P < 0.05, ** P < 0.01, *** P < 0.001, **** P < 0.0001 (Mann–Whitney test, in proximal to pathology vs distal to pathology). Scale bar, 50 µm. See high-resolution RNAscope images at: https://bmbls.bmi.osumc.edu/scread/stofad-2
Fig. 8 Validation of identified representative DEGs associated with AD pathology at the single-cell level using RNAscope smFISH. (A) Representative RNAscope images (left panel) and their corresponding post-IF staining of Aβ, AT8, and GFAP (right panels) in layer V for cells distal to and proximal to pathological spots. All images from the same cell were aligned and registered by imageJ (see details in Methods). (B) RNAscope probes against human SLC1A3, KIF5A, SNCG, STMN2, CSRP1, PLP1, GLUL, PAQR6, CD9, C1QB, SPP1, CD63, CRYAB, and YWHAH in microglia (P2RY12+/C1QB+), astrocytes (GFAP+), neurons (RBFOX3+), and oligodendrocytes (MBP+) were quantified and compared within three AD cases. For the quantification and definition of proximal to pathology/distal to pathology, please find the details in Methods. For quantification, we identified 20–50 microglia, astrocytes, and oligodendrocytes, and 60–90 neurons from each case. * P < 0.05, ** P < 0.01, *** P < 0.001, **** P < 0.0001 (Mann–Whitney test, in proximal to pathology vs distal to pathology). Scale bar, 50 µm. See high-resolution RNAscope images at: https://bmbls.bmi.osumc.edu/scread/stofad-2CC BY · PubMed Central

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Metadata

Authors
Chen, S., Chang, Y., Li, L., Acosta, D., Li, Y., Guo, Q., Wang, C., Turkes, E., Morrison, C., Julian, D., Hester, M. E., Scharre, D. W., Santiskulvong, C., Song, S. X., Plummer, J. T., Serrano, G. E., Beach, T. G., Duff, K. E., Ma, Q., & Fu, H.
ORCID
Fu, Hongjun
Venue
Acta Neuropathologica Communications
Publisher
Springer Science and Business Media LLC
Published
2022-12-21
Volume
10
Issue
1
ISSN
2051-5960
DOI
10.1186/s40478-022-01494-6
PMC
PMC9773466
Cited by
144
References
31
Language
en
Licence
Funders
  • National Institute on Aging (R01-AG075092-01, K01-AG056673, R56-AG066782-01)
  • National Institute of General Medical Sciences (R01-GM131399)
  • Alzheimer's Association (AARF-17-505009)
  • U.S. Department of Defense (W81XWH1910309)
  • Foundation for the National Institutes of Health (U24-NS072026, P30-AG19610)

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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

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