Cited by 439Neuroinflammation and Neurodegeneration MechanismsAlzheimer's disease research and treatmentsSingle-cell and spatial transcriptomics

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Alzheimer’s Patient Microglia Exhibit Enhanced Aging and Unique Transcriptional Activation

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Srinivasan, Karpagam, Friedman, Brad A., Etxeberria, Ainhoa, Huntley, Melanie A., van der Brug, Marcel P., Foreman, Oded, Paw, Jonathan S., Modrusan, Zora, Beach, Thomas G., Serrano, Geidy E., Hansen, David V.·2020·Cell Reports

Published version of a preprint

Abstract

Damage-associated microglia (DAM) profiles observed in Alzheimer's disease (AD)-related mouse models reflect an activation state that could modulate AD risk or progression. To learn whether human AD microglia (HAM) display a similar profile, we develop a method for purifying cell types from frozen cerebrocortical tissues for RNA-seq analysis, allowing better transcriptome coverage than typical single-nucleus RNA-seq approaches. The HAM profile we observe bears little resemblance to the DAM profile. Instead, HAM display an enhanced human aging profile, in addition to other disease-related changes such as APOE upregulation. Analyses of whole-tissue RNA-seq and single-cell/nucleus RNA-seq datasets corroborate our findings and suggest that the lack of DAM response in human microglia occurs specifically in AD tissues, not other neurodegenerative settings. These results, which can be browsed at http://research-pub.gene.com/BrainMyeloidLandscape, provide a genome-wide picture of microglial activation in human AD and highlight considerable differences between mouse models and human disease.

Overview

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TL;DR

Human Alzheimer’s disease microglia (HAM) purified from frozen cortex show a transcriptional profile that is largely distinct from the mouse damage-associated microglia (DAM) profile, with a prominent enhanced aging signature and APOE upregulation. The HAM signature is validated across cortical regions and whole-tissue datasets, while DAM-like induction is stronger in multiple sclerosis lesions and human microglia xenotransplanted into 5xFAD mice than in AD microglia.

Key findings

  • In sorted CD11b+ microglia from superior frontal gyrus, genome-wide differential expression identified 45 genes increased and 21 decreased in AD versus control; only APOE upregulation survived genome-wide correction (fold change = 4.1, adjusted p = 0.0004).
  • The HAM profile bore little resemblance to mouse DAM: the mouse neurodegeneration-related/DAM module was only slightly increased in AD microglia (p = 0.045), and among more than 100 module genes only APOE was significantly increased.
  • AD microglia displayed an enhanced human aging phenotype: age-related DE scores were significantly higher in AD microglia than controls, with older-subject genes such as IL15, MS4A6A, MS4A4A, NME8, and GPR141 trending upward and CECR2 trending downward; this was not explained by age differences between groups.
  • Validation in fusiform gyrus qRT-PCR replicated the direction of effect for nearly every DE gene tested (22 genes), and whole-tissue datasets (GSE95587, GSE125583, ROSMAP) showed significant elevation of the HAM-Up gene set, most apparent in later Braak stages. DAM induction was stronger in MS lesion microglia and human xenografted microglia in 5xFAD mouse brains than in AD microglia.

Methods

  • The authors adapted a method for isolating cell types from frozen post-mortem human brain: brief Accutase treatment at 4°C, mechanical dissociation, ethanol fixation, immunolabeling, and FACS for NeuN+ neurons, GFAP+ astrocytes, CD31+ endothelial cells, and CD11b+ microglia/myeloid cells, followed by RNA-seq or qPCR.
  • Frontal cortex (superior frontal gyrus) samples were processed from 22 AD and 21 control tissues; after QC, 113 cell-type-specific expression profiles were retained, including microglia profiles from 15 control and 10 AD subjects. Temporal cortex (fusiform gyrus) samples totaled 25 AD and 21 control tissues and were analyzed by qRT-PCR.
  • Analyses included DESeq2 differential expression with Cook’s distance filtering, gene set scoring, myeloid balancing for whole-tissue datasets, and pseudobulk analysis of published single-cell/single-nucleus RNA-seq datasets from AD, MS, and xenotransplanted microglia.

Limitations

  • The paper states that RNA quality after purification was poor, with Bioanalyzer RIN scores typically 1–3 due to freeze-thaw, fixation, and post-mortem tissue processing; alternative dissociation and fixation conditions were unsuccessful or counterproductive.
  • The authors state that the sorted-cell approach does not afford single-cell resolution, and the SFG RNA-seq cohort was small (10 AD and 15 control microglia profiles). They also note that snRNA-seq datasets suffer from extensive gene dropout, making downregulated gene sets difficult to detect.
  • The text makes evident additional limitations: APOE4 carrier status was imbalanced between AD and control groups (0 controls versus 9 AD subjects overall; 0 versus 6 among QC-passing myeloid profiles), and the new whole-tissue cohort had imperfect age matching, with AD cases on average about a half decade younger. The authors also present alternative interpretations for aging-related changes, including possible increased peripheral monocyte/macrophage infiltration versus microglial transcriptional modulation toward a monocyte-like state.

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Crossref · 10.1016/j.celrep.2020.107843

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Figures

Figure 1. Expression Profiling of Human Cell Populations Sorted from Frozen, Post-Mortem SFG (A) Experimental overview. See Figure S1 for the FACS gating scheme. (B) Expression of known cell-type markers, derived from previously published human cell data from fresh brains (Zhang et al., 2016), in QC-passing expression profiles indicates high cell-type purity. Each gene was Z score normalized across all profiles of all cell types. See Figure S2 for QC analyses. (C) Principal-component analysis using most variable genes reveals separation of four cell types. The juxtaposition of astrocyte and endothelial cell profiles, and the modest detection of astrocyte markers in endothelial cell samples (see D), may have resulted from astrocytic endfeet (which contain mRNAs; Boulay et al., 2017) remaining associated with endothelial cell bodies. (D) Expression levels ± SEM of selected cell-type markers. (E) Distributions of gene counts in various human microglial gene expression datasets. Each boxplot shows the indicated (10th,25th,50th,75th, and 90th) quantile across all genes, of raw gene counts for each sample of bulk-sorted microglia or, for syn18485175, for each sample’s pseudobulk microglia.
Figure 1. Expression Profiling of Human Cell Populations Sorted from Frozen, Post-Mortem SFG (A) Experimental overview. See Figure S1 for the FACS gating scheme. (B) Expression of known cell-type markers, derived from previously published human cell data from fresh brains (Zhang et al., 2016), in QC-passing expression profiles indicates high cell-type purity. Each gene was Z score normalized across all profiles of all cell types. See Figure S2 for QC analyses. (C) Principal-component analysis using most variable genes reveals separation of four cell types. The juxtaposition of astrocyte and endothelial cell profiles, and the modest detection of astrocyte markers in endothelial cell samples (see D), may have resulted from astrocytic endfeet (which contain mRNAs; Boulay et al., 2017) remaining associated with endothelial cell bodies. (D) Expression levels ± SEM of selected cell-type markers. (E) Distributions of gene counts in various human microglial gene expression datasets. Each boxplot shows the indicated (10th,25th,50th,75th, and 90th) quantile across all genes, of raw gene counts for each sample of bulk-sorted microglia or, for syn18485175, for each sample’s pseudobulk microglia.CC BY · PubMed Central
Figure 2. Sorted Cells from Frozen Human SFG Specimens Exhibit Preferential Expression of Many AD Risk Genes in Microglia (A) Heatmap of Z scores for each AD risk gene’s normalized reads per kilobase gene model per million total reads (nRPKM) expression value in each sample, with a sample’s Z score for a given gene representing its distance in standard deviations from the mean expression value across all samples for that gene. Gene selection was informed by genome-wide association study (GWAS) reports (Hollingworth et al., 2011; Lambert et al., 2013; Naj et al., 2011; Ramanan et al., 2015; Sims et al., 2017) and specific efforts to identify causal genes in GWAS-identified loci ( Huanget al., 2017; Novikova et al., 2019; Rathore et al., 2018). (B) Expression values are plotted for each AD risk gene in each cell type sorted from frozen SFG of controls (Ctl) or AD patients. Bars and lines represent mean expression ± SEM, with asterisks marking DE in AD versus control cells based on unadjusted DESeq2 p values (*p < 0.05, **p < 0.01, ***p < 0.001).
Figure 2. Sorted Cells from Frozen Human SFG Specimens Exhibit Preferential Expression of Many AD Risk Genes in Microglia (A) Heatmap of Z scores for each AD risk gene’s normalized reads per kilobase gene model per million total reads (nRPKM) expression value in each sample, with a sample’s Z score for a given gene representing its distance in standard deviations from the mean expression value across all samples for that gene. Gene selection was informed by genome-wide association study (GWAS) reports (Hollingworth et al., 2011; Lambert et al., 2013; Naj et al., 2011; Ramanan et al., 2015; Sims et al., 2017) and specific efforts to identify causal genes in GWAS-identified loci ( Huanget al., 2017; Novikova et al., 2019; Rathore et al., 2018). (B) Expression values are plotted for each AD risk gene in each cell type sorted from frozen SFG of controls (Ctl) or AD patients. Bars and lines represent mean expression ± SEM, with asterisks marking DE in AD versus control cells based on unadjusted DESeq2 p values (*p < 0.05, **p < 0.01, ***p < 0.001).CC BY · PubMed Central
Figure 3. Human Microglia Exhibit an AD-Associated DE Profile in Both Frontal and Temporal Cortices (A) Heatmap of AD DE genes (rows; DESeq2 adjusted p ≤ 0.05 and maximum Cook’s p ≥ 0.01) in control and AD SFG-derived microglia expression profiles (columns, sorted by AD-associated DE). “Panel B genes” indicates genes that were subsequently assayed by qPCR in microglia sorted from FuG tissues, with colors from (B). (B) 4-way comparison of AD-associated DE in SFG microglia measured by RNA-seq (x axis) with DE in FuG microglia measured by qPCR (y axis). Each point represents one gene colored by whether the adjusted p value was ≤ 0.05 in one or both DE analyses (red for SFG RNA-seq, green for FuG qPCR, or blue for both). Corresponding numbers of DE genes are shown near the borders of the plot. For example, the red 11 on the right reflects the number of genes that were significantly up in SFG and trended up but did not meet significance in FuG, whereas the blue 3 at the top right indicates the number of genes significantly upin both regions. Genes were selected manually for validation, consisting of about 1/3 of the DE genes from the RNA-seq study and several other cell-type markers and genes of interest. Diagonal line: y = x. (See Figure S3A for subject-wise SFG-FuG microglia DE correlations, Figure S3B for selected qPCR data plots, and Data S2 columns EK–GH for qPCR expression statistics for all 39 genes in the panel.) (C) SFG microglia DE is reproduced in FuG microglia. DE scores (see STAR Methods) are shown for each SFG and FuG microglia sample, using the 22 SFG DE genes that were included in the qPCR panel. For FuG microglia samples, open circles indicate that a QC-passing SFG RNA-seq microglia profile was not available from that subject. p value, t test. (D) Detection of upregulated HAM profile genes is recapitulated in myeloid-balanced whole AD tissues from frontal and temporal cortical regions and is more robust than DAM changes predicted by mouse microglia profiles. Each study was separately myeloid balanced to create a subset of whole-tissue samples with similar myeloid gene set scores, and neuronal genes were removed from each gene set. (See Figure S4C for division by Braak stage with all samples and all genes included.) Each panel shows gene set scores for the indicated gene sets for each of the myeloid-balanced AD or control samples. Δ, mean log2 fold change; p value, t test.
Figure 3. Human Microglia Exhibit an AD-Associated DE Profile in Both Frontal and Temporal Cortices (A) Heatmap of AD DE genes (rows; DESeq2 adjusted p ≤ 0.05 and maximum Cook’s p ≥ 0.01) in control and AD SFG-derived microglia expression profiles (columns, sorted by AD-associated DE). “Panel B genes” indicates genes that were subsequently assayed by qPCR in microglia sorted from FuG tissues, with colors from (B). (B) 4-way comparison of AD-associated DE in SFG microglia measured by RNA-seq (x axis) with DE in FuG microglia measured by qPCR (y axis). Each point represents one gene colored by whether the adjusted p value was ≤ 0.05 in one or both DE analyses (red for SFG RNA-seq, green for FuG qPCR, or blue for both). Corresponding numbers of DE genes are shown near the borders of the plot. For example, the red 11 on the right reflects the number of genes that were significantly up in SFG and trended up but did not meet significance in FuG, whereas the blue 3 at the top right indicates the number of genes significantly upin both regions. Genes were selected manually for validation, consisting of about 1/3 of the DE genes from the RNA-seq study and several other cell-type markers and genes of interest. Diagonal line: y = x. (See Figure S3A for subject-wise SFG-FuG microglia DE correlations, Figure S3B for selected qPCR data plots, and Data S2 columns EK–GH for qPCR expression statistics for all 39 genes in the panel.) (C) SFG microglia DE is reproduced in FuG microglia. DE scores (see STAR Methods) are shown for each SFG and FuG microglia sample, using the 22 SFG DE genes that were included in the qPCR panel. For FuG microglia samples, open circles indicate that a QC-passing SFG RNA-seq microglia profile was not available from that subject. p value, t test. (D) Detection of upregulated HAM profile genes is recapitulated in myeloid-balanced whole AD tissues from frontal and temporal cortical regions and is more robust than DAM changes predicted by mouse microglia profiles. Each study was separately myeloid balanced to create a subset of whole-tissue samples with similar myeloid gene set scores, and neuronal genes were removed from each gene set. (See Figure S4C for division by Braak stage with all samples and all genes included.) Each panel shows gene set scores for the indicated gene sets for each of the myeloid-balanced AD or control samples. Δ, mean log2 fold change; p value, t test.CC BY · PubMed Central
Figure 4. DE Genes in Mouse Microglia Studies Are Mostly Unchanged in HAM (A) Distribution of scores for mouse- and human-derived gene sets in SFG microglia profiles indicates that mouse-derived microglia gene modules undergo little or no change in AD microglia. The DAM gene set was called "neurodegeneration-related" in the previous manuscript. p, (unadjusted) t test; Δ, log2 fold changes in score; *p ≤ 0.05. (See Figures S5A–S5C for heatmaps of individual genes from DAM, microglia, and BrainMyeloid modules.) (B) DE gene set scores, similar to (A) but with DE genes from specific mouse datasets instead of from meta-analysis-derived gene modules. In this case, the scores are DE scores, meaning that they used signed means rather than means (with the sign indicating the direction of DE) so that up- and downregulated genes can be considered together. PVMs relative to parenchymal microglia; age, 22 months relative to young (≤12 month) microglia; cerebellum relative to cortical microglia; infiltrating macrophages (induced by irradiation) relative to tissue-resident microglia. p, (unadjusted) t test; Δ, log2 fold changes in score; *p ≤ 0.05. (For the three comparisons that reached significance, see Figure S5D for 4-way plots of individual gene fold changes in the respective mouse study compared to fold changes in AD versus control SFG microglia. See also Figure S6 for analysis of whether DE genes from the HAM profile are altered in mouse microglia in models of neurodegeneration or other activating conditions.)
Figure 4. DE Genes in Mouse Microglia Studies Are Mostly Unchanged in HAM (A) Distribution of scores for mouse- and human-derived gene sets in SFG microglia profiles indicates that mouse-derived microglia gene modules undergo little or no change in AD microglia. The DAM gene set was called "neurodegeneration-related" in the previous manuscript. p, (unadjusted) t test; Δ, log2 fold changes in score; *p ≤ 0.05. (See Figures S5A–S5C for heatmaps of individual genes from DAM, microglia, and BrainMyeloid modules.) (B) DE gene set scores, similar to (A) but with DE genes from specific mouse datasets instead of from meta-analysis-derived gene modules. In this case, the scores are DE scores, meaning that they used signed means rather than means (with the sign indicating the direction of DE) so that up- and downregulated genes can be considered together. PVMs relative to parenchymal microglia; age, 22 months relative to young (≤12 month) microglia; cerebellum relative to cortical microglia; infiltrating macrophages (induced by irradiation) relative to tissue-resident microglia. p, (unadjusted) t test; Δ, log2 fold changes in score; *p ≤ 0.05. (For the three comparisons that reached significance, see Figure S5D for 4-way plots of individual gene fold changes in the respective mouse study compared to fold changes in AD versus control SFG microglia. See also Figure S6 for analysis of whether DE genes from the HAM profile are altered in mouse microglia in models of neurodegeneration or other activating conditions.)CC BY · PubMed Central
Figure 5. AD-Associated HAM Profile Overlaps Substantially with Age-Related DE Patterns in Human Microglia (A) 4-way DE plot (analogous to Figure 3B) shows age-related DE from Galatro et al. (2017) on the x axis and AD-related DE on the y axis. Color indicates p ≤ 0.05 significance with aging only (red), with AD only (green), or with both (blue). Most red genes, DE with age, trended in a consistent direction with AD versus control microglia (bottom-left and top-right quadrants), indicating that AD microglia exhibit enhanced aging. The green genes, including APOE, indicate an AD-related signature that is distinct from DE of normal aging. (B) Distribution of subject ages in both studies. (C) Previously reported DE pattern in normal, aged human microglia is recapitulated in control subjects of this study. The 4-way plot shows age-related DE from Galatro et al. (2017)’s dataset on the x axis, as in (A), and age-related DE from this study’s control SFG microglia profiles on the y axis. Genes in red met an adjusted p ≤ 0.05 cutoff in Galatro et al. (2017); other genes are shown as a smoothed density in shades of gray. No DE genes from Galatro et al. (2017) met the p ≤ 0.05 cutoff for age-related DE in our dataset, but most trended in a consistent direction (bottom-left and top-right quadrants). The lack of statistical significance and muted fold changes in our study may resultfromfar fewersamples and our samples coming mainly from subjects. (D) Aging DE score was calculated for each SFG microglia sample in our study—a signed average of the age-related DE genes from Galatro et al. (2017). Regression lines show the increasing trend of this score in both diagnosis groups with age, as well as the elevated score in the AD group relative to controls of similar ages. (E) Aging DE score is elevated in AD microglia relative to controls. y coordinates as in (D); p value, t test.
Figure 5. AD-Associated HAM Profile Overlaps Substantially with Age-Related DE Patterns in Human Microglia (A) 4-way DE plot (analogous to Figure 3B) shows age-related DE from Galatro et al. (2017) on the x axis and AD-related DE on the y axis. Color indicates p ≤ 0.05 significance with aging only (red), with AD only (green), or with both (blue). Most red genes, DE with age, trended in a consistent direction with AD versus control microglia (bottom-left and top-right quadrants), indicating that AD microglia exhibit enhanced aging. The green genes, including APOE, indicate an AD-related signature that is distinct from DE of normal aging. (B) Distribution of subject ages in both studies. (C) Previously reported DE pattern in normal, aged human microglia is recapitulated in control subjects of this study. The 4-way plot shows age-related DE from Galatro et al. (2017)’s dataset on the x axis, as in (A), and age-related DE from this study’s control SFG microglia profiles on the y axis. Genes in red met an adjusted p ≤ 0.05 cutoff in Galatro et al. (2017); other genes are shown as a smoothed density in shades of gray. No DE genes from Galatro et al. (2017) met the p ≤ 0.05 cutoff for age-related DE in our dataset, but most trended in a consistent direction (bottom-left and top-right quadrants). The lack of statistical significance and muted fold changes in our study may resultfromfar fewersamples and our samples coming mainly from subjects. (D) Aging DE score was calculated for each SFG microglia sample in our study—a signed average of the age-related DE genes from Galatro et al. (2017). Regression lines show the increasing trend of this score in both diagnosis groups with age, as well as the elevated score in the AD group relative to controls of similar ages. (E) Aging DE score is elevated in AD microglia relative to controls. y coordinates as in (D); p value, t test.CC BY · PubMed Central
Figure 6. Monocyte-Enriched Genes May Contribute to Both Late Aging and AD Microglial Signatures (A) Example gene expression plots. Each point shows the expression of the indicated gene in a single sample in one of the three studies. In the middle column (Galatro et al., 2017), the dashed line indicates the best linear fit. (B) Monocyte DE profiles relative to microglia are similar in human and mouse studies. The 4-way plot is similar to Figure 3B but with DE genes between monocyte and microglia profiles shown with human and mouse studies on the x and y axes, respectively. (C) Many DE changes elevated or depleted in aged human microglia (x axis) are also elevated or depleted, respectively, in blood monocytes relative to microglia (y axis). The 4-way plot shows DE genes with p ≤ 0.05 in the aging study colored red, DE genes with p ≤ 0.05 and fold change ≥ 8 between monocytes and microglia colored green, and DE genes that meet both criteria colored blue. (D) Heatmap of DE genes from the HAM profile in three datasets. Gene ordering was based on the direction of change in this study and then by effect size (fold change per decade) in aging. The subset of HAM-Down genes that show reduced expression in aged microglia generally shows higher expression in microglia than in monocytes. The subset of HAM-Up genes that show increased expression in aged microglia generally shows higher expression in monocytes than in microglia.
Figure 6. Monocyte-Enriched Genes May Contribute to Both Late Aging and AD Microglial Signatures (A) Example gene expression plots. Each point shows the expression of the indicated gene in a single sample in one of the three studies. In the middle column (Galatro et al., 2017), the dashed line indicates the best linear fit. (B) Monocyte DE profiles relative to microglia are similar in human and mouse studies. The 4-way plot is similar to Figure 3B but with DE genes between monocyte and microglia profiles shown with human and mouse studies on the x and y axes, respectively. (C) Many DE changes elevated or depleted in aged human microglia (x axis) are also elevated or depleted, respectively, in blood monocytes relative to microglia (y axis). The 4-way plot shows DE genes with p ≤ 0.05 in the aging study colored red, DE genes with p ≤ 0.05 and fold change ≥ 8 between monocytes and microglia colored green, and DE genes that meet both criteria colored blue. (D) Heatmap of DE genes from the HAM profile in three datasets. Gene ordering was based on the direction of change in this study and then by effect size (fold change per decade) in aging. The subset of HAM-Down genes that show reduced expression in aged microglia generally shows higher expression in microglia than in monocytes. The subset of HAM-Up genes that show increased expression in aged microglia generally shows higher expression in monocytes than in microglia.CC BY · PubMed Central
Figure 7. HAM Signature Is Elevated in Multiple Neurodegenerative Settings, whereas DAM Response Is Weaker in AD Microglia Control-centered scores (log2 scale) for the indicated gene sets were calculated for each sample in the indicated datasets. For snRNA-seq datasets (Mathys et al., 2019, frozen AD tissues, syn18485175; Jä kel et al., 2019, frozen MS tissues, GEO: GSE118257) and scRNA-seq datasets (Masuda et al., 2019, freshly resected MS lesions, GEO: GSE124335; Hasselmann et al., 2019, human induced pluripotent stem cell [iPSC]-derived xMG into 5xFAD mouse brains, GEO: GSE133433), each datapoint represents a pseudobulk microglia profile from pooling individual nuclei/cells from a given subject. (See Data S4, panels 2 and 3, for definitions of microglia clusters used to generate pseudobulk profiles from sn/scRNA-seq datasets.) Other datasets are bulk-sorted brain myeloid cells from frozen AD tissues (this study, GEO: GSE125050) or fresh mouse model tissues (PS2APP b-amyloid and PS19 Tau-P301S models, GEO: GSE89482 and GSE93180). D, log2 fold change of group means; p values from t test. For syn18485175, t test and D were between low- and high-pathology groups. The p value was omitted for GEO: GSE118257, because only one control sample was available (see STAR Methods). See STAR Methods (Gene Set Analysis section) for gene lists and Figure S7 for depictions of individual DE genes across studies.
Figure 7. HAM Signature Is Elevated in Multiple Neurodegenerative Settings, whereas DAM Response Is Weaker in AD Microglia Control-centered scores (log2 scale) for the indicated gene sets were calculated for each sample in the indicated datasets. For snRNA-seq datasets (Mathys et al., 2019, frozen AD tissues, syn18485175; Jä kel et al., 2019, frozen MS tissues, GEO: GSE118257) and scRNA-seq datasets (Masuda et al., 2019, freshly resected MS lesions, GEO: GSE124335; Hasselmann et al., 2019, human induced pluripotent stem cell [iPSC]-derived xMG into 5xFAD mouse brains, GEO: GSE133433), each datapoint represents a pseudobulk microglia profile from pooling individual nuclei/cells from a given subject. (See Data S4, panels 2 and 3, for definitions of microglia clusters used to generate pseudobulk profiles from sn/scRNA-seq datasets.) Other datasets are bulk-sorted brain myeloid cells from frozen AD tissues (this study, GEO: GSE125050) or fresh mouse model tissues (PS2APP b-amyloid and PS19 Tau-P301S models, GEO: GSE89482 and GSE93180). D, log2 fold change of group means; p values from t test. For syn18485175, t test and D were between low- and high-pathology groups. The p value was omitted for GEO: GSE118257, because only one control sample was available (see STAR Methods). See STAR Methods (Gene Set Analysis section) for gene lists and Figure S7 for depictions of individual DE genes across studies.CC BY · PubMed Central

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  • The authors state this as an assumption.

    Damage from the freeze-thaw process led to cellular disintegration when cells remained unfixed for too long or were incubated at 37°C.

    “We presume that damage from the freeze-thaw process led to cellular disintegration when cells remained unfixed for too long or were incubated at 37°C.”
  • The paper relies on this without support.

    Gray matter atrophy was pronounced in the fusiform gyrus from AD subjects.

    “since gray matter atrophy was pronounced in FuG from AD subjects”
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    Activated microglia often display elevated CD45 reactivity.

    “since we did not want to exclude activated microglia which often display elevated CD45 reactivity”
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    There was a lack of other human AD datasets available at the time for cross-comparison.

    “the lack of other human AD datasets available at the time for cross-comparison”
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Metadata

Authors
Srinivasan, K., Friedman, B. A., Etxeberria, A., Huntley, M. A., van der Brug, M. P., Foreman, O., Paw, J. S., Modrusan, Z., Beach, T. G., Serrano, G. E., & Hansen, D. V.
Affiliations
  • Genentech (US)
  • Banner Sun Health Research Institute (US)
Corresponding author
  • Friedman, Brad A.
  • Hansen, David V.
Venue
Cell Reports
Publisher
Elsevier BV
Published
2020-06
Volume
31
Issue
13
Pages
107843
ISSN
2211-1247 (ISSN-L)
OpenAlex
W3039072393
Cited by
427
References
52
Citations by year
2026: 55 · 2025: 60 · 2024: 68 · 2023: 61 · 2022: 74 · 2021: 89 · 2020: 18
FWCI
24.01
Type
journal article
Language
en
Open access
Locations
  • Cell Reports (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nd)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • Michael J. Fox Foundation for Parkinson&apos;s Research (GSE125050, GSE125583)
  • Genentech
  • Arizona Department of Health Services (211002)
  • Arizona Biomedical Research Commission (0011, 05-901, 1001, 4001)
  • National Institute on Aging (P30 AG19610)
  • National Institute of Neurological Disorders and Stroke (U24 NS072026)
Topics
  • Life Sciences › Neuroscience › Neurology › Neuroinflammation and Neurodegeneration Mechanisms
  • Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Alzheimer's disease research and treatments
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
Keywords
Alzheimer's disease, human microglia, microglial activation, aging, apolipoprotein E, transcriptome, single-nucleus RNA sequencing, single-cell RNA sequencing, mouse model, neuroinflammation
MeSH terms
Aged, Aged, 80 and over, Alzheimer Disease, Animals, Female, Frontal Lobe, Frozen Sections, Humans, Male, Monocytes, Multiple Sclerosis, Phenotype, Risk Factors, Temporal Lobe, Reproducibility of Results, Transcriptional Activation, Cellular Senescence, Microglia, Genetic Predisposition to Disease, Gene Expression Profiling, Databases, Genetic, Mice, Heterografts

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