Single-cell transcriptomic analysis of Alzheimer’s disease
Open accessAbstract
Alzheimer’s disease is a pervasive neurodegenerative disorder, the molecular complexity of which remains poorly understood. Here, we analysed 80,660 single-nucleus transcriptomes from the prefrontal cortex of 48 individuals with varying degrees of Alzheimer’s disease pathology. Across six major brain cell types, we identified transcriptionally distinct subpopulations, including those associated with pathology and characterized by regulators of myelination, inflammation, and neuron survival. The strongest disease-associated changes appeared early in pathological progression and were highly cell-type specific, whereas genes upregulated at late stages were common across cell types and primarily involved in the global stress response. Notably, we found that female cells were overrepresented in disease-associated subpopulations, and that transcriptional responses were substantially different between sexes in several cell types, including oligodendrocytes. Overall, myelination-related processes were recurrently perturbed in multiple cell types, suggesting that myelination has a key role in Alzheimer’s disease pathophysiology. Our single-cell transcriptomic resource provides a blueprint for interrogating the molecular and cellular basis of Alzheimer’s disease. Single-cell transcriptomics from 48 individuals with varying degrees of Alzheimer’s disease pathology demonstrates that gene-expression changes in Alzheimer’s disease are both cell-type specific and shared, and that transcriptional responses show sexual dimorphism.
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snRNA-seq analyses of human brain samples have failed to detect a consistent microglial activation signature in Alzheimer's disease.
“snRNA-seq analyses of human brain samples have failed to detect a consistent microglial activation signature in Alzheimer’s disease.”
From Single-Nucleus RNA-Seq Is Not Suitable for Detection of Microglial Activation Genes in Humans (2020) · cites this paper as Mathys et al., 2019
In “Identification of AD-associated cellular subpopulations” (page not specified), the paper reports an AD-associated human microglial subpopulation, Mic1, with immune-related marker genes that significantly overlap disease-associated microglial signatures. It also reports immunohistochemical confirmation of MHC class II–expressing microglia in AD-pathology samples. This is contrary to attributing a failure to detect a microglial activation signature to this study, though the paper does not establish consistency across all human snRNA-seq studies. (LLM, conf=0.87)
Checked by a reasoning model — may be wrong.
Experimental — accuracy not yet measuredThe opposing protective and harmful microglial states are accompanied by broad changes in gene expression, suggesting that defined transcriptional programs in microglia may govern their neuroprotective or neurotoxic functions.
“These opposing states are accompanied by broad changes in gene expression12–17, suggesting that defined transcriptional programs in microglia may govern their neuroprotective or neurotoxic functions.”
From Lymphoid gene expression supports neuroprotective microglia function (2025) · cites this paper as [14]
The section on AD-associated cellular subpopulations identifies a distinct microglial state with AD-associated marker genes, supporting a link between microglial states and changes in gene expression. It does not establish that the states are opposing protective and harmful states; the Discussion says distinguishing neuroprotection from pathogenicity remains future work. The suggestion that transcriptional programs govern those functions is the citing authors’ inference, not a finding established by this source. (LLM, conf=0.84)
Experimental — accuracy not yet measuredMicroarrays and bulk RNA-Seq are scalable and cost-effective but lack single-cell resolution.
“Microarrays and bulk RNA-Seq are scalable and cost-effective but lack single-cell resolution.”
From Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions (2025) · cites this paper as [118]
The discussion of differential expression reports that bulk RNA-seq misses changes in some cell types and cannot capture genes changing in opposite directions across cell types, supporting the resolution limitation. Our copy does not establish that microarrays and bulk RNA-seq are scalable or cost-effective, or specifically discuss microarrays in this comparison. No page numbers are available in our copy. (LLM, conf=0.85)
Experimental — accuracy not yet measuredSingle-cell and single-nucleus methods deliver high granularity but require significant technical and computational resources.
“Single-cell and single-nucleus methods deliver high granularity but require significant technical and computational resources.”
From Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions (2025) · cites this paper as [118]
The Abstract (page unavailable) supports high granularity: the study profiles individual nuclei and identifies cell-type-specific changes. The Methods describe specialized sequencing equipment and a multistep computational pipeline, but do not establish that these methods generally require significant resources. (LLM, conf=0.82)
Experimental — accuracy not yet measuredEpigenetic profiles in glial cells may differ substantially from those in neurons and may play critical roles in processes such as amyloid plaque deposition and neuroinflammation.
“This level of resolution is essential, as epigenetic profiles in glial cells may differ substantially from those in neurons and may play critical roles in processes such as amyloid plaque deposition and neuroinflammation118.”
From Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions (2025) · cites this paper as [118]
The differential-expression analysis (page not available) finds that 95% of altered genes were perturbed only in neurons or in one glial cell type, supporting the need for cell-level resolution. But this study measures transcriptional changes, not differences in epigenetic profiles, and the cited passages do not establish a role for those profiles in amyloid plaque deposition or neuroinflammation. (LLM, conf=0.82)
Experimental — accuracy not yet measured
Single-cell RNA sequencing has revealed distinct microglial and astrocytic subpopulations, such as disease-associated microglia and reactive astrocytes, which are involved in plaque clearance, neuroinflammation, and synaptic modulation.
“This technique has revealed distinct microglial and astrocytic subpopulations, such as disease-associated microglia (DAM) and reactive astrocytes, which are involved in plaque clearance, neuroinflammation, and synaptic modulation118,162, 163, 164.”
From Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions (2025) · cites this paper as [118]
The ‘Identification of AD-associated cellular subpopulations’ section (page not provided) identifies an AD-associated microglial state and an astrocyte subpopulation with a marker linked to reactive astrocytes. It does not establish that these subpopulations perform plaque clearance, neuroinflammation, or synaptic modulation; the Discussion (page not provided) says the mechanistic basis of the observed changes remains unknown. (LLM, conf=0.88)
Experimental — accuracy not yet measuredSingle-cell RNA sequencing and spatial transcriptomics have transformed understanding of cellular heterogeneity in AD by revealing disease-associated cell states and spatially localized inflammatory responses.
“Recent advances in single‐cell RNA sequencing and spatial transcriptomics (ST) have transformed our understanding of cellular heterogeneity in AD by revealing disease‐associated cell states and spatially localized inflammatory responses.”
From Diffusion MRI reveals amyloid plaque–associated cellular signatures through spatial transcriptomic integration (2026) · cites this paper as [6]
The Abstract and ‘Identification of AD-associated cellular subpopulations’ section (page numbers unavailable) report that single-nucleus RNA sequencing revealed AD-associated cell subpopulations, including a distinct microglial state. The paper does not report spatial transcriptomics or use it to reveal spatially localized inflammatory responses; it describes immunohistochemistry of selected cells in white matter instead. (LLM, conf=0.88)
Experimental — accuracy not yet measuredAβ plaque deposition is accompanied by localized cellular responses involving microglial activation, astrocytic reactivity, neuronal stress, and myelin disruption.
“is accompanied by localized cellular responses involving microglial activation, astrocytic reactivity, neuronal stress, and myelin disruption”
From Diffusion MRI reveals amyloid plaque–associated cellular signatures through spatial transcriptomic integration (2026) · cites this paper as [6]
The ‘Identification of AD-associated cellular subpopulations’ section reports AD-pathology-associated microglial and astrocyte states, neuronal changes, and stress-related signatures. It does not establish that these responses are localized around Aβ plaques; it describes demyelination only as a possible consequence of CRYAB dysfunction, not an observed myelin disruption. Page numbers are unavailable in our copy. (LLM, conf=0.87)
Experimental — accuracy not yet measuredIn AD, spatial transcriptomics and single-cell techniques have revealed plaque-associated transcriptional niches and cell-state changes in situ.
“In AD, spatial transcriptomics and single‐cell techniques have revealed plaque‐associated transcriptional niches and cell‐state changes in situ. 6 , 8”
From Diffusion MRI reveals amyloid plaque–associated cellular signatures through spatial transcriptomic integration (2026) · cites this paper as [6]
The Abstract and Discussion report single-nucleus transcriptomic analysis identifying AD-associated cell subpopulations and transcriptional changes. Extended Data describes targeted RNA in situ validation, but the paper does not report spatial transcriptomics or establish plaque-associated transcriptional niches in situ. (LLM, conf=0.88)
Experimental — accuracy not yet measuredST revealed increased microglial and astrocytic gene expression and decreased oligodendrocyte- and neuron-associated signatures.
“ST revealed increased microglial and astrocytic gene expression and decreased oligodendrocyte‐ and neuron‐associated signatures, consistent with previous immunohistochemical and transcriptomic studies. 6 , 8 , 77 , 78”
From Diffusion MRI reveals amyloid plaque–associated cellular signatures through spatial transcriptomic integration (2026) · cites this paper as [6]
In “Systematic differential expression analysis” (page not provided), the paper reports predominantly increased expression among differentially expressed genes in microglia and astrocytes, and decreased expression in neurons. It reports predominantly increased—not decreased—expression in oligodendrocytes. The cited study used single-cell transcriptomics, so it supports these findings as a previous transcriptomic study rather than as the source of the ST result. (LLM, conf=0.90)
Experimental — accuracy not yet measuredST identified upregulation of microglia-associated genes and downregulation of oligodendrocyte-associated genes in plaque regions.
“ST identified upregulation of microglia‐associated genes and downregulation of oligodendrocyte‐associated genes, corresponding to reported enrichment of immune‐response, oxidative‐stress, lysosomal‐function, and myelination pathways. 6 , 8”
From Diffusion MRI reveals amyloid plaque–associated cellular signatures through spatial transcriptomic integration (2026) · cites this paper as [6]
The Identification of AD-associated cellular subpopulations section reports AD-associated microglial immune markers, and the Discussion reports altered myelination-related genes in oligodendrocytes. But this is a single-cell study, not a spatial transcriptomics (ST) study; the cited passages do not establish gene-expression changes in plaque regions or a general downregulation of oligodendrocyte-associated genes. (LLM, conf=0.85)
Experimental — accuracy not yet measuredAPOE is a soluble factor expressed by many cell types.
“Insight into APOE4-mediated pathogenesis is complicated by the fact that APOE is a soluble factor expressed by many cell types12,13.”
From APOE4 impairs myelination via cholesterol dysregulation in oligodendrocytes (2022) · cites this paper as [12]
The section on cell-type-specific alterations reports APOE expression changes in both microglia and astrocytes, supporting expression in multiple cell types. Our copy does not say that APOE is a soluble factor, so that part of the claim is unsupported. (LLM, conf=0.82)
Experimental — accuracy not yet measuredMost DAM genes were not identified as activation genes using the Mathys et al. snRNA-seq protocol.
“The study identified 28 DAM genes as marker genes of the Mic1 response cluster (shown in orange in Figure 2F); however, most DAM genes were not identified as activation genes using their snRNA-seq protocol (purple in Figure 2F).”
From Single-Nucleus RNA-Seq Is Not Suitable for Detection of Microglial Activation Genes in Humans (2020) · cites this paper as Mathys et al., 2019
The Identification of AD-associated cellular subpopulations section reports a significant overlap between Mic1 markers and mouse disease-associated microglia genes, while describing the states as distinct. It does not say that most DAM genes were missed or quantify how many were not identified as activation genes by its snRNA-seq analysis. Page numbers were not available in our copy. (LLM, conf=0.78)
Experimental — accuracy not yet measuredHuman RNA-seq meta-analysis stratified by sex observed ~2.5 times more DEGs in women than in men.
“Strikingly, we observed ~2.5 times more DEGs in women than in men, consistent with other recently published work (Mathys et al., 2019).”
From Meta-Analysis of the Alzheimer’s Disease Human Brain Transcriptome and Functional Dissection in Mouse Models (2020) · cites this paper as Mathys et al., 2019
The ‘Sex-specific differential response to AD pathology’ section reports stronger pathology-associated expression changes in female neurons than in male neurons, supporting the citing sentence’s broad comparison. It also reports stronger oligodendrocyte responses in males and does not give an overall female-to-male DEG count or a ~2.5-fold ratio. The cited paper therefore supports sex-specific differences, but not the specific numerical comparison. (LLM, conf=0.83)
Experimental — accuracy not yet measuredOur sample sizes are similar to those reported in previous publications.
“but our sample sizes are similar to those reported in previous publications5–14”
From Integrated multimodal cell atlas of Alzheimer’s disease (2024) · cites this paper as [5]
The section ‘Single-nucleus RNA-seq profiling of prefrontal cortex in human subjects with AD pathology’ reports 48 participants, split into two groups of 24. This supports citing the paper as a previous publication with a reported sample size, but the source alone cannot establish that the citing study’s sample size is similar. (LLM, conf=0.82)
Experimental — accuracy not yet measuredThe snRNA-seq dataset contains over 100,000 astrocyte nuclei in each brain region with ≈6,000 average total reads and ≈2,500 average total genes detected, which is orders of magnitude higher than previous studies.
“resulting in over 100,000 astrocyte nuclei in each brain region with ≈6,000 average total number of reads and ≈2,500 average total number of genes detected, which is orders of magnitude higher than previous studies9–15”
From Astrocyte transcriptomic changes along the spatiotemporal progression of Alzheimer’s disease (2024) · cites this paper as [9]
The citing sentence uses Mathys et al. as a previous study for comparison, not as the source of its own dataset’s figures. In “Single-nucleus RNA-seq profiling of prefrontal cortex in human subjects with AD pathology” (page unavailable), Mathys et al. report 80,660 nuclei in total, so their study provides a smaller dataset for comparison. Our copy does not establish the claimed orders-of-magnitude comparison for astrocyte counts, reads, or genes detected. (LLM, conf=0.79)
Experimental — accuracy not yet measuredIn the central nervous system, APOE is produced predominantly by astrocytes, although it can also be produced by reactive microglia and, to a lesser extent, stressed neurons and other glial cells.
“In the CNS, APOE is produced predominantly by astrocytes, although it can also be produced by reactive microglia and, to a lesser extent, stressed neurons and other glial cells96,97.”
From Multifaceted roles of APOE in Alzheimer disease (2024) · cites this paper as [97]
In “Systematic differential expression analysis” (page unavailable), the paper reports APOE expression in both astrocytes and microglia, with APOE upregulated in microglia but downregulated in astrocytes in AD pathology. “Identification of AD-associated cellular subpopulations” also identifies APOE as a marker of an AD-associated microglial subpopulation. Neither passage establishes that astrocytes are the predominant CNS source or that stressed neurons and other glial cells produce smaller amounts. (LLM, conf=0.86)
Experimental — accuracy not yet measuredApolipoprotein E transcripts are significantly upregulated in AD-associated microglia.
“The research found apolipoprotein E transcripts to be significantly upregulated in AD-associated microglia [88,89].”
From The Role of Cortisol in Chronic Stress, Neurodegenerative Diseases, and Psychological Disorders (2023) · cites this paper as [89]
In brain tissue from human Alzheimer's disease patients, microglia display the morphology and gene expression profile characteristic of an activated cell state and cluster around β-amyloid plaques.
“In brain tissue from human AD patients, microglia display the morphology (Heppner et al., 2015) and gene expression profile (Gjoneska et al., 2015; Mathys et al., 2019) characteristic of an activated cell state and cluster around β-amyloid plaques.”
From A novel molecular class that recruits HDAC/MECP2 complexes to PU.1 motifs reduces neuroinflammation (2023) · cites this paper as Mathys et al., 2019
The mouse disease-associated microglia transcriptome profile may not be fully present in Alzheimer's disease brain, because human microglia show a distinct Alzheimer's-related gene signature.
“although the mouse DAM transcriptome profile may not be fully present in AD brain, as human microglia show a distinct AD-related gene signature (Jaitin et al., 2019; Mathys et al., 2019).”
From Alzheimer's-associated PU.1 expression levels regulate microglial inflammatory response (2021) · cites this paper as Mathys et al., 2019
The maturation of single-cell technologies enables large-scale comparisons of cell states within complex tissues, providing the appropriate resolution to dissect cell-type-specific responses to perturbation.
“The maturation of single-cell technologies now enables large-scale comparisons of cell states within complex tissues, thus providing the appropriate resolution to dissect cell-type-specific responses to perturbation5,6.”
From Confronting false discoveries in single-cell differential expression (2021) · cites this paper as [6]
Within the compendium, fourteen studies had control groups that included six or more samples.
“Within our compendium, we identified a total of fourteen studies with control groups that included six or more samples5,6,15,49–59.”
From Confronting false discoveries in single-cell differential expression (2021) · cites this paper as [6]
Expression of TREM2 is increased in AD pathology.
“Single-nucleus RNA sequencing (snRNA-seq) data on postmortem samples from AD patients and on samples from AD mice model also revealed that the expression of TREM2 was increased in AD pathology.164,213,214”
From Role of neuroinflammation in neurodegeneration development (2023) · cites this paper as [164]
RNA-seq studies using human AD autopsy samples have identified myelin-related transcripts among the top altered gene clusters in patients with AD.
“RNA-seq studies using human AD autopsy samples have identified myelin-related transcripts among the top altered gene clusters in patients with AD35,46.”
From Myelin dysfunction drives amyloid-β deposition in models of Alzheimer’s disease (2023) · cites this paper as [46]
Transcriptomic studies have shown changes in microglia, astrocytes, and oligodendrocytes in human Alzheimer's disease tissue.
“Recently, transcriptomic studies have shown changes not only in microglia and astrocytes but also in oligodendrocytes, the myelinating cells of the central nervous system, in both human AD tissue [5,6]”
From Selective suppression of oligodendrocyte-derived amyloid beta rescues neuronal dysfunction in Alzheimer’s disease (2024) · cites this paper as [5]
Human single nucleus RNA sequencing data from Mathys and colleagues and Lake and colleagues were obtained from The Myeloid Landscape 2 portal.
“Human single nucleus RNA sequencing data from Mathys and colleagues [5] and Lake and colleagues [12] were obtained from The Myeloid Landscape 2 portal (http://research-pub.gene.com/BrainMyeloidLandscape) [70].”
From Selective suppression of oligodendrocyte-derived amyloid beta rescues neuronal dysfunction in Alzheimer’s disease (2024) · cites this paper as [5]
Myelin alterations have been implicated in Alzheimer's disease.
“myelin alterations have been implicated in AD7–9”
From Oligodendrocytes produce amyloid-β and contribute to plaque formation alongside neurons in Alzheimer’s disease model mice (2024) · cites this paper as [8]
Previous single-cell analysis uncovered AD-associated oligodendrocyte subpopulations highlighting a role of myelin-related processes in AD pathogenesis.
“Our previous single-cell analysis uncovered AD-associated oligodendrocyte subpopulations highlighting a role of myelin-related processes in AD pathogenesis12, consistent with reports of hypomyelination and white matter changes in AD23,24.”
From APOE4 impairs myelination via cholesterol dysregulation in oligodendrocytes (2022) · cites this paper as [12]
Consensus co-expression network analysis of microglia from three Alzheimer's disease snRNA-seq studies yielded multiple gene modules corresponding to disease-associated microglia and polygenic risk of AD.
“We performed consensus co-expression network analysis of microglia from three Alzheimer’s disease (AD) snRNA-seq studies,10,11,12 yielding multiple gene modules corresponding to disease-associated microglia and polygenic risk of AD.”
From hdWGCNA identifies co-expression networks in high-dimensional transcriptomics data (2023) · cites this paper as [10]
Consensus co-expression network analysis using microglia integrated from three human AD snRNA-seq datasets identified four consensus modules.
“Next, we performed consensus co-expression network analysis using microglia integrated from three human AD snRNA-seq datasets,10,11,12 identifying four consensus modules (Figure 6C; Data S1).”
From hdWGCNA identifies co-expression networks in high-dimensional transcriptomics data (2023) · cites this paper as [10]
Markers of the human microglial response to AD (Mic1) are depleted in nuclei.
“Markers of this response (referred to by Mathys et al., 2019 as “Mic1”) had a NES score of −2.14 (padj = 3.6e-05), indicating that they were depleted in nuclei (Figure 2C).”
From Single-Nucleus RNA-Seq Is Not Suitable for Detection of Microglial Activation Genes in Humans (2020) · cites this paper as Mathys et al., 2019
Our results are consistent with prior observations of sex effects in both mice and humans.
“Our results are consistent with prior observations in both mice (Jiao et al., 2016) and humans (Altmann et al., 2014; Li and Singh, 2014; Mayeux and Stern, 2012; Mathys et al., 2019; Sala Frigerio et al., 2019).”
From Meta-Analysis of the Alzheimer’s Disease Human Brain Transcriptome and Functional Dissection in Mouse Models (2020) · cites this paper as Mathys et al., 2019
APOE is a lipid-related Alzheimer's disease risk gene and is highly upregulated in human microglia in Alzheimer's disease.
“APOE is one such lipid-related AD risk gene, which is highly upregulated in human microglia in AD3”
From APOE4/4 is linked to damaging lipid droplets in Alzheimer’s disease microglia (2024) · cites this paper as [3]
APOE expression in microglia is increased in the brains of AD patients and is a marker for disease-associated microglia (DAM).
“Indeed, APOE expression in microglia is increased in the brains of AD patients and is a marker for disease‐associated microglia (DAM). 52 , 53”
From Deletion of miR-33, a regulator of the ABCA1–APOE pathway, ameliorates neuropathological phenotypes in APP/PS1 mice (2024) · cites this paper as [52]
Previous studies described AD-associated molecular and cellular changes; however, cross-study comparisons are challenging without common cell annotations.
“Previous studies described AD-associated molecular and cellular changes5–15; however, cross-study comparisons are challenging without common cell annotations.”
From Integrated multimodal cell atlas of Alzheimer’s disease (2024) · cites this paper as [5]
The study harmonized snRNA-seq data and associated donor metadata from the PFC from ten additional AD studies spanning 707 donors.
“To corroborate the results, we harmonized snRNA-seq data and associated donor metadata from the PFC from ten additional AD studies spanning 707 donors5–14.”
From Integrated multimodal cell atlas of Alzheimer’s disease (2024) · cites this paper as [5]
ScRNAseq and snRNAseq are becoming established as valuable tools to gain insight into basal and reactive-astrocyte heterogeneity.
“ScRNAseq and snRNAseq are becoming established as valuable tools to gain insight into basal94 and reactive-astrocyte heterogeneity (Fig. 1e)40,78,95.”
From Reactive astrocyte nomenclature, definitions, and future directions (2021) · cites this paper as [95]
The study's sample sizes are similar to those reported in previous publications.
“our sample sizes are similar to those reported in previous publications9–17”
From Astrocyte transcriptomic changes along the spatiotemporal progression of Alzheimer’s disease (2024) · cites this paper as [9]
APOE is greatly upregulated in human AD microglia from human AD brains.
“APOE is greatly upregulated in human AD microglia (HAM) from human AD brains”
From ApoE4 reduction: An emerging and promising therapeutic strategy for Alzheimer's disease (2022) · cites this paper as Mathys et al 2019
Expression of ApoE is downregulated in astrocytes in AD.
“Interestingly, the expression of ApoE, which regulates lipid homeostasis, is downregulated in astrocytes in AD (9, 10)”
From Nuclear SphK2/S1P signaling is a key regulator of ApoE production and Aβ uptake in astrocytes (2024) · cites this paper as [9]
ApoE is downregulated in astrocytes in AD.
“apolipoprotein E (ApoE), a major mediator of lipid transport in the brain, is downregulated in astrocytes in AD (9, 10)”
From Nuclear SphK2/S1P signaling is a key regulator of ApoE production and Aβ uptake in astrocytes (2024) · cites this paper as [9]
Studies of single nucleus RNA sequencing from human brain reported either no detectable population structure in microglial cells or a maximum of four transcriptionally distinct microglial subsets.
“Recently, studies of single nucleus RNA sequencing from human brain reported either no detectable population structure in microglial cells7,8 or a maximum of four transcriptionally distinct microglial subsets9.”
From Single cell RNA sequencing of human microglia uncovers a subset associated with Alzheimer’s disease (2020) · cites this paper as [9]
Spatial transcriptomics adds a critical spatial dimension but may trade off resolution or transcriptomic depth.
“Spatial transcriptomics adds a critical spatial dimension but may trade off resolution or transcriptomic depth114,118,162,166.”
From Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions (2025) · cites this paper as [118]
The Abstract describes single-nucleus RNA sequencing and its cellular-level view of Alzheimer’s pathology. A search of our copy for spatial transcriptomics and trade-offs in spatial resolution or transcriptomic depth found no passage addressing either point. (LLM, conf=0.76)
Experimental — accuracy not yet measuredMachine learning applied to metabolomic data can identify how microbial metabolites affect neuronal health and inflammatory status.
“Machine learning applied to metabolomic data can also identify how microbial metabolites affect neuronal health and inflammatory status118.”
From Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions (2025) · cites this paper as [118]
The Discussion describes single-cell transcriptomes and neuronal and glial responses to Alzheimer’s pathology, not metabolomic analysis. Searches for microbial metabolites, metabolomics, and machine learning found no passage addressing whether such analysis can identify effects on neuronal health or inflammation. (LLM, conf=0.76)
Experimental — accuracy not yet measuredAβ plaque deposition alters tissue microstructure and metal homeostasis.
“altering tissue microstructure and metal homeostasis”
From Diffusion MRI reveals amyloid plaque–associated cellular signatures through spatial transcriptomic integration (2026) · cites this paper as [6]
The Discussion and the section on cell-type-specific changes report transcriptional and myelination-related responses to AD pathology, not changes in tissue microstructure or metal homeostasis caused by Aβ plaques. Searches for “metal homeostasis” and “tissue microstructure” found no passage addressing those effects; page numbers were unavailable in our copy. (LLM, conf=0.77)
Experimental — accuracy not yet measuredApart from PSEN2, oligodendrocytes had elevated expression of all genes involved in amyloid beta production in human single-nucleus RNA sequencing datasets.
“We found that, apart from PSEN2 (which is not essential for gamma secretase function when PSEN1 is present [13]), oligodendrocytes had elevated expression of all of these genes (Fig 1A–1D).”
From Selective suppression of oligodendrocyte-derived amyloid beta rescues neuronal dysfunction in Alzheimer’s disease (2024) · cites this paper as [5]
The citation is attached to the parenthetical claim that PSEN2 is not essential for gamma-secretase function when PSEN1 is present; the oligodendrocyte-expression finding is presented as the citing authors’ own result. The cited paper’s Discussion and cell-type differential-expression section discuss oligodendrocyte transcriptional changes, but the passages retrieved for PSEN1, PSEN2, and gamma-secretase do not address that functional claim. No page numbers were available. (LLM, conf=0.76)
Experimental — accuracy not yet measuredSGK1 expression is increased in P301S Tau transgenic mice and in postmortem brain tissue from AD patients.
“RNA sequencing (RNAseq) of the prefrontal cortex (PFC) of the P301S human Tau transgenic mouse model of dementia has identified Serum and Glucocorticoid-regulated Kinase-1 (Sgk1) as one of the top-ranking upregulated genes [10].”
From Elevated SGK1 increases Tau phosphorylation and microtubule instability in Alzheimer’s patient-derived cortical neurons (2026) · cites this paper as [11]
The ‘Single-nucleus RNA-seq profiling’ section describes postmortem human prefrontal-cortex samples, while the differential-expression section reports cell-type-specific changes without identifying SGK1. Searches for SGK1 and P301S found no passage establishing increased SGK1 expression in either AD brain tissue or P301S mice; the copy does not address the specific claim. (LLM, conf=0.72)
Experimental — accuracy not yet measuredPLXNB1 is specifically upregulated in astrocytes but not other cell types in human AD brains.
“We next analyzed recent snRNA-seq datasets of AD patients from two independent studies28, 33, both showing a specific upregulation of PLXNB1 in astrocytes but not other cell types in human AD brains (Fig. 2B).”
From Regulation of cell distancing in peri-plaque glial nets by Plexin-B1 affects glial activation and amyloid compaction in Alzheimer’s disease (2024) · cites this paper as [28]
The systematic differential-expression analysis and Discussion report cell-type-specific changes in human AD tissue, including astrocytes, but do not identify PLXNB1. Searches for “PLXNB1” and “plexin B1 astrocytes AD pathology upregulated” found no passage establishing its upregulation in astrocytes or its absence in other cell types. The indexed passages have section labels but no page numbers. (LLM, conf=0.72)
Experimental — accuracy not yet measuredPLXNB1 is one of the top induced genes in astrocytes in early stages of AD.
“single nucleus (sn) RNA-sequencing (-seq) identified PLXNB1 as one of the top induced genes in astrocytes in early stages of AD28.”
From Regulation of cell distancing in peri-plaque glial nets by Plexin-B1 affects glial activation and amyloid compaction in Alzheimer’s disease (2024) · cites this paper as [28]
The section on cell-type-specific changes during pathological progression reports early transcriptional changes, and the section on AD-associated cellular subpopulations identifies astrocyte-associated genes. Neither identifies PLXNB1 as an induced gene in early-stage AD astrocytes; a search for PLXNB1 in our copy found no matching passage. (LLM, conf=0.70)
Experimental — accuracy not yet measuredThe exhausted phase may have confounded previous studies that compared only control and severe AD donors or grouped late- and end-stage donors (Braak stage V/VI) together.
“may have confounded previous studies that compared only control and severe AD donors or grouped late- and end-stage donors (Braak stage V/VI) together9–15”
From Astrocyte transcriptomic changes along the spatiotemporal progression of Alzheimer’s disease (2024) · cites this paper as [9]
The ‘Cell-type-specific changes during pathological progression’ section distinguishes early from late AD pathology and reports different transcriptional changes, but does not identify an exhausted phase or say it confounded earlier studies. Searches for ‘exhausted phase,’ control-versus-severe comparisons, and grouping of Braak V/VI donors found no passage addressing that attribution. (LLM, conf=0.75)
Experimental — accuracy not yet measuredIn addition to downregulation of genes involved in synaptic transmission and synapse organization, there is prominent upregulation of genes involved in cell stress, immune response activation and cell death in PFC of AD patients.
“In addition to the downregulation of genes involved in synaptic transmission and synapse organization, there is also prominent upregulation of genes involved in cell stress, immune response activation and cell death in PFC of AD patients 257–259.”
From Mechanisms of synaptic transmission dysregulation in the prefrontal cortex: pathophysiological implications (2022) · cites this paper as [257]
Single-nucleus analysis of human Alzheimer's disease brain has shown a microglial response that partly matches mouse disease-associated microglia, plus oligodendrocyte and myelination changes that depend on sex.
“Single nuclei analysis of human AD brain specimens has revealed a microglial transcriptional response that partially recapitulates the mouse DAM signature along with perturbation of oligodendrocytes and myelination that is gender-dependent20.”
From Human and mouse single-nucleus transcriptomics reveal TREM2-dependent and TREM2-independent cellular responses in Alzheimer’s disease (2020) · cites this paper as [20]
Several cell-type-specific genes upregulated in the Mathys et al. study, such as MHC class II and APOE in microglia and QDPR, CA2, and SLC38A2 in oligodendrocytes, were also increased in our AD samples.
“Several cell type-specific upregulated genes identified by Mathys et al., including MHC class II and APOE in microglia and QDPR, CA2, and SLC38A2 in oligodendrocytes, were also upregulated in our AD samples.”
From Human and mouse single-nucleus transcriptomics reveal TREM2-dependent and TREM2-independent cellular responses in Alzheimer’s disease (2020) · cites this paper as [20]
Our human findings partly agree with a recent snRNA-seq study of human AD and control samples.
“Our human data are partially concordant with a recent study examining human AD and control samples by snRNA-seq20.”
From Human and mouse single-nucleus transcriptomics reveal TREM2-dependent and TREM2-independent cellular responses in Alzheimer’s disease (2020) · cites this paper as [20]
Single cell transcriptomic profiles in AD postmortem brains suggested altered molecular profiles in oligodendrocytes.
“In line with this, single cell transcriptomic profiles in AD postmortem brains suggested altered molecular profiles in oligodendrocytes24.”
From A computational tool (H-MAGMA) for improved prediction of brain-disorder risk genes by incorporating brain chromatin interaction profiles (2020) · cites this paper as [24]
Cell-type-specific differentially expressed genes in Alzheimer's disease postmortem brains were obtained from Mathys et al., 2019.
“Cell-type specific DEG in AD postmortem brains was obtained from Mathys et al., 201924.”
From A computational tool (H-MAGMA) for improved prediction of brain-disorder risk genes by incorporating brain chromatin interaction profiles (2020) · cites this paper as [24]
OLIG associates strongly (odds ratio: 4.06) with the human AD-associated oligodendrocytes Oli0.
“OLIG associates strongly (odds ratio: 4.06) with the human AD-associated oligodendrocytes Oli0 (Mathys et al. , 2019) (Figure S3E).”
From Spatial Transcriptomics and In Situ Sequencing to Study Alzheimer’s Disease (2020) · cites this paper as Mathys et al., 2019
Among the 41 cellular subpopulations in a human dataset, the highest association of PIGs is with an AD-associated microglial signature (Mic1, odds ratio: 4.28).
“We evaluated whether the PIGs could be identified in a human dataset (Mathys et al., 2019). Among the 41 cellular subpopulations (Mathys et al. , 2019), the highest association of PIGs is with an AD-associated microglial signature (Mic1, odds ratio: 4.28; Figure S3E).”
From Spatial Transcriptomics and In Situ Sequencing to Study Alzheimer’s Disease (2020) · cites this paper as Mathys et al., 2019
Single cell transcriptomic experiments have recently shown a positive correlation of AD pathology with increased expression of genes responsible for myelination in oligodendrocytes in AD patients.
“However, single cell transcriptomic experiments have recently shown a positive correlation of AD pathology with increased expression of genes responsible for myelination in oligodendrocytes in AD patients (Grubman et al. , 2019; Mathys et al. , 2019).”
From Spatial Transcriptomics and In Situ Sequencing to Study Alzheimer’s Disease (2020) · cites this paper as Mathys et al., 2019
The COVID-19-associated microglial subpopulation is marked by expression of activation genes such as C1QC, CD74, FTL and FTH1, and by downregulation of homeostatic markers including P2RY12.
“This subpopulation was marked by expression of microglial activation genes previously associated with human disease4,5, such as complement C1QC, CD74, FTL and FTH1, and downregulation of the homeostatic markers including P2RY12”
From Dysregulation of brain and choroid plexus cell types in severe COVID-19 (2021) · cites this paper as [5]
Microglial subclusters associated with disease have been identified for various neurodegenerative diseases.
“Microglial subclusters that are associated with disease have been identified for various neurodegenerative diseases4,5.”
From Dysregulation of brain and choroid plexus cell types in severe COVID-19 (2021) · cites this paper as [5]
The dysregulated pathways in neurons and oligodendrocytes are associated with synaptic signaling and myelination, respectively; these findings are similar to those in previous studies despite variations in study cohorts, brain regions, sample preparation, and sequencing protocols.
“For example, we showed that the dysregulated pathways in neurons and oligodendrocytes are associated with synaptic signaling and myelination, respectively; these findings are similar to those in previous studies despite variations in study cohorts, brain regions, sample preparation, and sequencing protocols (12, 13, 23).”
From Single-nucleus transcriptome analysis reveals dysregulation of angiogenic endothelial cells and neuroprotective glia in Alzheimer’s disease (2020) · cites this paper as [12]
273 cell type-specific DEGs were in common between our dataset and Mathys et al. snRNA-seq data: 53 in astrocytes, 97 in excitatory neurons, 24 in inhibitory neurons, 13 in microglia, and 86 in oligodendrocytes.
“We found 273 cell type-specific DEGs in common between the two datasets, including 53 in astrocytes, 97 in excitatory neurons, 24 in inhibitory neurons, 13 in microglia, and 86 in oligodendrocytes (SI Appendix, Fig. S4 A, B, D, E, G, H, J, K, M, and N).”
From Single-nucleus transcriptome analysis reveals dysregulation of angiogenic endothelial cells and neuroprotective glia in Alzheimer’s disease (2020) · cites this paper as [12]
Such changes were not observed in previous snRNA-seq studies, possibly due to a lower proportion of endothelial nuclei being sampled.
“However, such changes were not observed in previous snRNA-seq studies (12, 13), possibly due to a lower proportion of endothelial nuclei being sampled.”
From Single-nucleus transcriptome analysis reveals dysregulation of angiogenic endothelial cells and neuroprotective glia in Alzheimer’s disease (2020) · cites this paper as [12]
The previous snRNA-seq dataset (12) had a low proportion of endothelial cells, rendering it unsuitable for validating our findings in endothelial cells.
“However, the proportion of endothelial cells in the previous dataset was low, rendering it unsuitable for validating our findings in endothelial cells.”
From Single-nucleus transcriptome analysis reveals dysregulation of angiogenic endothelial cells and neuroprotective glia in Alzheimer’s disease (2020) · cites this paper as [12]
Nuclei with ≤200 genes, ≥20,000 unique molecular identifiers, or ≥20% mitochondrial genes were filtered out to exclude potential dead cells and cell debris.
“To exclude potential dead cells and cell debris from the dataset, we filtered out nuclei with ≤200 genes, ≥20,000 unique molecular identifiers, or ≥20% mitochondrial genes as described in several previous snRNA-seq studies (12, 13, 22, 53).”
From Single-nucleus transcriptome analysis reveals dysregulation of angiogenic endothelial cells and neuroprotective glia in Alzheimer’s disease (2020) · cites this paper as [12]
A previously proposed dynamic model guided removal of nuclei with high mitochondrial gene expression.
“We removed nuclei with high mitochondria gene expression following a previously proposed dynamic model61.”
From Meningeal lymphatics affect microglia responses and anti-Aβ immunotherapy (2021) · cites this paper as [61]
Human single-nuclear transcriptomics identified microglial subtypes with increased Apoe expression but did not identify DAM/MGnD subpopulations similar to mouse.
“Single nuclear transcriptomic techniques have recently been applied to human AD brain specimens and have identified microglial subtypes with increased Apoe expression but have failed to identify DAM or MGnD subpopulations similar to what has been seen in the mouse brain (Mathys et al., 2019).”
From Alzheimer Disease: An Update on Pathobiology and Treatment Strategies (2019) · cites this paper as Mathys et al., 2019
By performing snRNA-seq in >80,000 single nuclei from 48 individuals with Alzheimer's disease pathology, many more DE genes were detected in individual cell types than bulk tissue.
“By performing snRNA-seq in >80,000 single nuclei from 48 individuals with Alzheimer’s disease (AD) pathology (Mathys et al., 2019), many more differentially expressed (DE) genes were detected in individual cell types than bulk tissue.”
From Single-Cell Sequencing of Brain Cell Transcriptomes and Epigenomes (2021) · cites this paper as Mathys et al., 2019
Gene dysregulation in glial cells was detected in snRNA-seq, but not bulk RNA-seq, due to relatively low contribution of glia to total RNA in tissue.
“In particular, gene dysregulation in glial cells was detected in snRNA-seq, but not bulk RNA-seq, due to the relatively low contribution of glia to the total RNA in the tissue.”
From Single-Cell Sequencing of Brain Cell Transcriptomes and Epigenomes (2021) · cites this paper as Mathys et al., 2019
Transcriptomic signatures of disease can be studied by snRNA-seq applied to post-mortem brain tissue from patients.
“Transcriptomic signatures of disease can be studied by snRNA-seq applied to post-mortem brain tissue from patients.”
From Single-Cell Sequencing of Brain Cell Transcriptomes and Epigenomes (2021) · cites this paper as Mathys et al., 2019
AD risk genes are enriched for genes that are upregulated in microglia from postmortem AD brain.
“Notably, they were enriched for genes that are upregulated in microglia from postmortem AD brain50 (Fig. 4f).”
From Neuronal and glial 3D chromatin architecture informs the cellular etiology of brain disorders (2021) · cites this paper as [50]
Recent single-cell sequencing studies identify genetic risk enrichment and gene expression changes within microglia in AD, accompanied by significant gene expression changes in other cells, including astrocytes.
“This is consistent with recent single-cell sequencing studies identifying genetic risk enrichment and gene expression changes within microglia in AD, accompanied by significant gene expression changes in other cells, including astrocytes50–52, which is where we observe epigenetic signature enrichment.”
From Neuronal and glial 3D chromatin architecture informs the cellular etiology of brain disorders (2021) · cites this paper as [50]
The clusters were annotated to seven major brain cell types: excitatory neurons, astrocytes, oligodendrocytes, inhibitory/GABAergic neurons, oligodendrocyte progenitor cells, microglia, and endothelial cells.
“We then annotated the clusters using the following markers of major brain cell types that are consistently detected in single cell/nucleus transcriptomics studies (7,8,20,23,24) : excitatory neurons, astrocytes, oligodendrocytes, inhibitory/GABAergic neurons, oligodendrocyte progenitor cells (OPCs), microglia, and endothelial cells ( Fig 1C-D ).”
From Single cell transcriptome profiling of the human alcohol-dependent brain (2020) · cites this paper as [8]
Proportions of the different cell types were comparable to other snRNA-seq studies, with neurons (particularly excitatory neurons) over-represented relative to non-neuronal cell types.
“Proportions of the different cell types were comparable to other snRNA-seq studies (8,23) with neurons (particularly excitatory neurons) being over-represented relative to non-neuronal cell types ( Fig S2 ).”
From Single cell transcriptome profiling of the human alcohol-dependent brain (2020) · cites this paper as [8]
Bulk and scRNA-seq studies often exclude non-coding RNAs from their analyses.
“Bulk and scRNA-seq studies often exclude non-coding RNAs from their analyses (3,8,35) .”
From Single cell transcriptome profiling of the human alcohol-dependent brain (2020) · cites this paper as [8]
Other studies have also found that microglial expression, in general, is particularly underrepresented in bulk transcriptomes, relative to that of other brain cell types.
“Other studies have also found that microglial expression, in general, is particularly underrepresented in bulk transcriptomes, relative to that of other brain cell types (8,42) .”
From Single cell transcriptome profiling of the human alcohol-dependent brain (2020) · cites this paper as [8]
Both iNs and iPSC-iNs show the highest transcriptome similarity to cortical excitatory neurons from single-cell RNA-seq data.
“Similar to iNs, iPSC-iN cultures contained vGlut1+ and GABA+ neurons and expressed marker genes for both subtypes, and both showed the highest similarity to cortical excitatory neurons from single-cell RNA-seq (scRNA-seq) data (Figure 7D; Figure S5; Mathys et al., 2019).”
From Age-dependent instability of mature neuronal fate in induced neurons from Alzheimer’s patients (2021) · cites this paper as Mathys et al., 2019
The observation of reduced frequency of one microglial subpopulation in Alzheimer's disease is replicated by repurposing DLPFC single nucleus RNA sequencing data from an independent set of MAP subjects.
“We then replicate this observation by repurposing DLPFC single nucleus RNA sequencing data generated from an independent set of MAP subjects9.”
From Single cell RNA sequencing of human microglia uncovers a subset associated with Alzheimer’s disease (2020) · cites this paper as [9]
A recent publication generated single-nucleus RNA-seq data from dorsolateral prefrontal cortex in 48 donors from the ROSMAP cohort.
“A recent publication9 generated single-nucleus RNA-seq data from dorsolateral prefrontal cortex in 48 donors (comprising subsets of donors with and without diagnoses of Alzheimer’s disease) from the ROSMAP cohort”
From Single cell RNA sequencing of human microglia uncovers a subset associated with Alzheimer’s disease (2020) · cites this paper as [9]
A comprehensive molecular characterization of region-specific differences in Alzheimer's disease is currently lacking.
“Although some brain regions relevant to AD have been studied individually at scale or jointly in samples from a few individuals8–16, a comprehensive molecular characterization of region-specific differences in AD is currently lacking”
From Single-cell multiregion dissection of Alzheimer’s disease (2024) · cites this paper as [12]
AD DEGs were consistent with published results both for region-specific DEGs and for DEGs computed jointly over all regions for multiple AD variables, and were further corroborated by comparisons with various independent studies.
“AD DEGs were consistent with published results both for region-specific DEGs and for DEGs computed jointly over all regions for multiple AD variables, and were further corroborated by comparisons with various independent studies11,12,15,19,47–53”
From Single-cell multiregion dissection of Alzheimer’s disease (2024) · cites this paper as [12]
Microglial functions vary across different ages, disease pathologies, and between sexes.
“In addition, microglial functions vary across different ages, disease pathologies, and between sexes10–16.”
From Genetic analysis of the human microglial transcriptome across brain regions, aging and disease pathologies (2022) · cites this paper as [11]
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Erbin Is Required for Myelination in Regenerated Axons after Injury
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Bex1 is involved in the regeneration of axons after injury
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Protective and Therapeutic Role for α B-Crystallin in Autoimmune Demyelination
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Alzheimer Disease: An Update on Pathobiology and Treatment Strategies
Long, J. M., & Holtzman, D. M. (2019) · Cell · doi:10.1016/j.cell.2019.09.001 · Cited by 3,204 · see citing works
Reactive astrocyte nomenclature, definitions, and future directions
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Self-citationThe Amyloid-β Pathway in Alzheimer’s Disease
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Role of neuroinflammation in neurodegeneration development
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“In normal human brains, APOE is mainly derived from astrocytes,(35,163) while its expression by microglia is induced by ageing as well as amyloid and tau pathology.(35,164,165) There are three isoforms of APOE gene in humans, respectively, APOE2, APOE3, APOE4.”
BackgroundResultMicroglia Biology: One Century of Evolving Concepts
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Transcriptome-scale spatial gene expression in the human dorsolateral prefrontal cortex
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Self-citationConfronting false discoveries in single-cell differential expression
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Disease-associated astrocytes in Alzheimer’s disease and aging
Habib, N., McCabe, C., Medina, S., Varshavsky, M., Kitsberg, D., Dvir-Szternfeld, R., Green, G., Dionne, D., Nguyen, L., Marshall, J. L., et al. (2020) · Nature Neuroscience · doi:10.1038/s41593-020-0624-8 · Cited by 1,009 · see citing works
Grubman, A., Chew, G., Ouyang, J. F., Sun, G., Choo, X. Y., McLean, C., Simmons, R. K., Buckberry, S., Vargas-Landin, D. B., Poppe, D., et al. (2019) · Nature Neuroscience · doi:10.1038/s41593-019-0539-4 · Cited by 956 · see citing works
APOE and Alzheimer’s disease: Advances in genetics, pathophysiology, and therapeutic approaches
Serrano-Pozo, A., Das, S., & Hyman, B. T. (2021) · The Lancet Neurology · doi:10.1016/s1474-4422(20)30412-9 · Cited by 1,037 · see citing works
Spatial Transcriptomics and In Situ Sequencing to Study Alzheimer’s Disease
Chen, W.-T., Lu, A., Craessaerts, K., Pavie, B., Sala Frigerio, C., Corthout, N., Qian, X., Laláková, J., Kühnemund, M., Voytyuk, I., et al. (2020) · Cell · doi:10.1016/j.cell.2020.06.038 · Cited by 949 · see citing works
A human brain vascular atlas reveals diverse mediators of Alzheimer’s risk
Yang, A. C., Vest, R. T., Kern, F., Lee, D. P., Agam, M., Maat, C. A., Losada, P. M., Chen, M. B., Schaum, N., Khoury, N., et al. (2022) · Nature · doi:10.1038/s41586-021-04369-3 · Cited by 797 · see citing works
Brain cell type–specific enhancer–promoter interactome maps and disease <b>-</b> risk association
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The Semantics of Microglia Activation: Neuroinflammation, Homeostasis, and Stress
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hdWGCNA identifies co-expression networks in high-dimensional transcriptomics data
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ApoE in Alzheimer’s disease: pathophysiology and therapeutic strategies
Raulin, A.-C., Doss, S. V., Trottier, Z. A., Ikezu, T. C., Bu, G., & Liu, C.-C. (2022) · Molecular Neurodegeneration · doi:10.1186/s13024-022-00574-4 · Cited by 756 · see citing works
Single-nucleus chromatin accessibility and transcriptomic characterization of Alzheimer’s disease
Morabito, S., Miyoshi, E., Michael, N., Shahin, S., Martini, A. C., Head, E., Silva, J., Leavy, K., Perez-Rosendahl, M., & Swarup, V. (2021) · Nature Genetics · doi:10.1038/s41588-021-00894-z · Cited by 643 · see citing works
Single cell RNA sequencing of human microglia uncovers a subset associated with Alzheimer’s disease
Olah, M., Menon, V., Habib, N., Taga, M. F., Ma, Y., Yung, C. J., Cimpean, M., Khairallah, A., Coronas-Samano, G., Sankowski, R., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-19737-2 · Cited by 722 · see citing works
Self-citationFunction and therapeutic value of astrocytes in neurological diseases
Lee, H.-G., Wheeler, M. A., & Quintana, F. J. (2022) · Nature Reviews Drug Discovery · doi:10.1038/s41573-022-00390-x · Cited by 662 · see citing works
Dysregulation of brain and choroid plexus cell types in severe COVID-19
Yang, A. C., Kern, F., Losada, P. M., Agam, M. R., Maat, C. A., Schmartz, G. P., Fehlmann, T., Stein, J. A., Schaum, N., Lee, D. P., et al. (2021) · Nature · doi:10.1038/s41586-021-03710-0 · Cited by 620 · see citing works
Deep Multilayer Brain Proteomics Identifies Molecular Networks in Alzheimer’s Disease Progression
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“, 2013), and identified cell type specific alterations in AD (Mathys et al., 2019).”
BackgroundMicroglia and Central Nervous System–Associated Macrophages—From Origin to Disease Modulation
Prinz, M., Masuda, T., Wheeler, M. A., & Quintana, F. J. (2021) · Annual Review of Immunology · doi:10.1146/annurev-immunol-093019-110159 · Cited by 584 · see citing works
Brain-Derived Neurotrophic Factor in Alzheimer’s Disease and Its Pharmaceutical Potential
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Microglia in Neuroinflammation and Neurodegeneration: From Understanding to Therapy
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Molecular basis of astrocyte diversity and morphology across the CNS in health and disease
Endo, F., Kasai, A., Soto, J. S., Yu, X., Qu, Z., Hashimoto, H., Gradinaru, V., Kawaguchi, R., & Khakh, B. S. (2022) · Science · doi:10.1126/science.adc9020 · Cited by 560 · see citing works
The Role of Cortisol in Chronic Stress, Neurodegenerative Diseases, and Psychological Disorders
Knezevic, E., Nenic, K., Milanovic, V., & Knezevic, N. N. (2023) · Cells · doi:10.3390/cells12232726 · Cited by 564 · see citing works
“The research found apolipoprotein E transcripts to be significantly upregulated in AD-associated microglia [88,89].”
BackgroundAPOE4/4 is linked to damaging lipid droplets in Alzheimer’s disease microglia
Haney, M. S., Pálovics, R., Munson, C. N., Long, C., Johansson, P. K., Yip, O., Dong, W., Rawat, E., West, E., Schlachetzki, J. C. M., et al. (2024) · Nature · doi:10.1038/s41586-024-07185-7 · Cited by 536 · see citing works
“Subsampled microglia data were normalized as described previously 3,52 .”
Nagy, C., Maitra, M., Tanti, A., Suderman, M., Théroux, J.-F., Davoli, M. A., Perlman, K., Yerko, V., Wang, Y. C., Tripathy, S. J., et al. (2020) · Nature Neuroscience · doi:10.1038/s41593-020-0621-y · Cited by 532 · see citing works
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Molecular characterization of selectively vulnerable neurons in Alzheimer’s disease
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Self-citationAPOE4 impairs myelination via cholesterol dysregulation in oligodendrocytes
Blanchard, J. W., Akay, L. A., Davila-Velderrain, J., von Maydell, D., Mathys, H., Davidson, S. M., Effenberger, A., Chen, C.-Y., Maner-Smith, K., Hajjar, I., et al. (2022) · Nature · doi:10.1038/s41586-022-05439-w · Cited by 493 · see citing works
“The protocol for the isolation of nuclei of the 8 samples prepared using the Chromium Single Cell 3′ Reagent Kits v2 is described in a previous study 12 .”
InfluentialSelf-citationMyelin dysfunction drives amyloid-β deposition in models of Alzheimer’s disease
Depp, C., Sun, T., Sasmita, A. O., Spieth, L., Berghoff, S. A., Nazarenko, T., Overhoff, K., Steixner-Kumar, A. A., Subramanian, S., Arinrad, S., et al. (2023) · Nature · doi:10.1038/s41586-023-06120-6 · Cited by 476 · see citing works
“…Allen mouse brain atlas is publicaly available under https://mouse.brain-map.org/experiment/thumbnails/100042147?image_type=atlas nRNA-seq data from Mathys et al. 2019 (snRNAseqPFC_BA10) and Zhou et al. 2020 (snRNAseqAD_TREM2) were obtained from AD Knowledge Portal (https:// adknowledgeportal.org).”
MethodWhite matter aging drives microglial diversity
Safaiyan, S., Besson-Girard, S., Kaya, T., Cantuti-Castelvetri, L., Liu, L., Ji, H., Schifferer, M., Gouna, G., Usifo, F., Kannaiyan, N., et al. (2021) · Neuron · doi:10.1016/j.neuron.2021.01.027 · Cited by 477 · see citing works
Molecular and spatial signatures of mouse brain aging at single-cell resolution
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“6,11,13,15,16 Recent transcriptomic studies of normal brain aging 17–20 and neurodegenerative disease, 21–24 as well as studies focusing on specific non-neuronal cell types such as astrocytes, 25–27 microglia, 28,29 and endothelial cells, 30 have further highlighted a role for inflammatory activation…”
Mapping microglia states in the human brain through the integration of high-dimensional techniques
Sankowski, R., Böttcher, C., Masuda, T., Geirsdottir, L., Sagar, Sindram, E., Seredenina, T., Muhs, A., Scheiwe, C., Shah, M. J., et al. (2019) · Nature Neuroscience · doi:10.1038/s41593-019-0532-y · Cited by 449 · see citing works
Alzheimer’s Patient Microglia Exhibit Enhanced Aging and Unique Transcriptional Activation
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., et al. (2020) · Cell Reports · doi:10.1016/j.celrep.2020.107843 · Cited by 427 · see citing works
Meningeal lymphatics affect microglia responses and anti-Aβ immunotherapy
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Distinct amyloid-β and tau-associated microglia profiles in Alzheimer’s disease
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Astrocyte contribution to dysfunction, risk and progression in neurodegenerative disorders
Brandebura, A. N., Paumier, A., Onur, T. S., & Allen, N. J. (2023) · Nature Reviews Neuroscience · doi:10.1038/s41583-022-00641-1 · Cited by 427 · see citing works
Genome-wide CRISPRi/a screens in human neurons link lysosomal failure to ferroptosis
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Integrated multimodal cell atlas of Alzheimer’s disease
Gabitto, M. I., Travaglini, K. J., Rachleff, V. M., Kaplan, E. S., Long, B., Ariza, J., Ding, Y., Mahoney, J. T., Dee, N., Goldy, J., et al. (2024) · Nature Neuroscience · doi:10.1038/s41593-024-01774-5 · Cited by 397 · see citing works
“Previous studies described AD-associated molecular and cellular changes 5–15 ; however, cross-study comparisons are challenging without common cell annotations.”
InfluentialMicroglia: Agents of the CNS Pro-Inflammatory Response
Rodríguez-Gómez, J. A., Kavanagh, E., Engskog-Vlachos, P., Engskog, M. K. R., Herrera, A. J., Espinosa-Oliva, A. M., Joseph, B., Hajji, N., Venero, J. L., & Burguillos, M. A. (2020) · Cells · doi:10.3390/cells9071717 · Cited by 365 · see citing works
Transcriptional signature in microglia associated with Aβ plaque phagocytosis
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Enhancing myelin renewal reverses cognitive dysfunction in a murine model of Alzheimer’s disease
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Chaperone-mediated autophagy prevents collapse of the neuronal metastable proteome
Bourdenx, M., Martín-Segura, A., Scrivo, A., Rodriguez-Navarro, J. A., Kaushik, S., Tasset, I., Diaz, A., Storm, N. J., Xin, Q., Juste, Y. R., et al. (2021) · Cell · doi:10.1016/j.cell.2021.03.048 · Cited by 337 · see citing works
Single-cell multiregion dissection of Alzheimer’s disease
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Self-citationMicroglia Phenotypes in Aging and Neurodegenerative Diseases
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Microglia in Alzheimer's disease at single-cell level. Are there common patterns in humans and mice?
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Single-Nucleus RNA-Seq Is Not Suitable for Detection of Microglial Activation Genes in Humans
Thrupp, N., Sala Frigerio, C., Wolfs, L., Skene, N. G., Fattorelli, N., Poovathingal, S., Fourne, Y., Matthews, P. M., Theys, T., Mancuso, R., et al. (2020) · Cell Reports · doi:10.1016/j.celrep.2020.108189 · Cited by 329 · see citing works
“Many these genes have been implicated in AD, for example, through genetic risk studies (Efthymiou and Goate, 2017), but have not been consistently detected in snRNA-seq studies of AD (Del-Aguila et al., 2019; Grubman et al., 2019; Mathys et al., 2019).”
BackgroundMyelin plasticity: sculpting circuits in learning and memory
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Astrocytic interleukin-3 programs microglia and limits Alzheimer’s disease
McAlpine, C. S., Park, J., Griciuc, A., Kim, E., Choi, S. H., Iwamoto, Y., Kiss, M. G., Christie, K. A., Vinegoni, C., Poller, W. C., et al. (2021) · Nature · doi:10.1038/s41586-021-03734-6 · Cited by 322 · see citing works
Single-cell and spatial transcriptomics: deciphering brain complexity in health and disease
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Beyond the GFAP-Astrocyte Protein Markers in the Brain
Jurga, A. M., Paleczna, M., Kadluczka, J., & Kuter, K. Z. (2021) · Biomolecules · doi:10.3390/biom11091361 · Cited by 319 · see citing works
Lipid accumulation induced by APOE4 impairs microglial surveillance of neuronal-network activity
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Emerging roles of innate and adaptive immunity in Alzheimer’s disease
Chen, X., & Holtzman, D. M. (2022) · Immunity · doi:10.1016/j.immuni.2022.10.016 · Cited by 297 · see citing works
Wan, Y.-W., Al-Ouran, R., Mangleburg, C. G., Perumal, T. M., Lee, T. V., Allison, K., Swarup, V., Funk, C. C., Gaiteri, C., Allen, M., et al. (2020) · Cell Reports · doi:10.1016/j.celrep.2020.107908 · Cited by 295 · see citing works
“5 times more DEGs in women than in men, consistent with other recently published work (Mathys et al., 2019).”
ResultSelf-citationYang, C., Farias, F. H. G., Ibanez, L., Suhy, A., Sadler, B., Fernandez, M. V., Wang, F., Bradley, J. L., Eiffert, B., Bahena, J. A., et al. (2021) · Nature Neuroscience · doi:10.1038/s41593-021-00886-6 · Cited by 290 · see citing works
Tau Activation of Microglial cGAS–IFN Reduces MEF2C‐mediated Cognitive Resilience
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- Authors
- Mathys, H., Davila-Velderrain, J., Peng, Z., Gao, F., Mohammadi, S., Young, J. Z., Menon, M., He, L., Abdurrob, F., Jiang, X., Martorell, A. J., Ransohoff, R. M., Hafler, B. P., Bennett, D. A., Kellis, M., & Tsai, L.-H.
- Affiliations
- Massachusetts Institute of Technology (US)
- Broad Institute (US)
- Rush University Medical Center (US)
- Corresponding author
- Kellis, Manolis
- Tsai, Li-Huei
- ORCID
- Venue
- Nature
- Published
- 2019-05-01
- Volume
- 570
- Issue
- 7761
- Pages
- 332-337
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- PMC
- PMC6865822
- OpenAlex
- W2942678593
- Semantic Scholar
- 2a3f8208aadb36d1c90d869252fa5f052bace572
- Cited by
- 2,568
- References
- 61
- Citations by year
- 2026: 343 · 2025: 403 · 2024: 414 · 2023: 371 · 2022: 375 · 2021: 368 · 2020: 244 · 2019: 45 · 2018: 1 · 2014: 1
- FWCI
- 87.80
- Type
- journal article
- Language
- en
- Update notices
- correction (2019-06-17)
- Open access
- Licence
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- Research Explorer (The University of Manchester) (publishedVersion, other-oa)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (acceptedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (151885)
- National Institute on Aging (R01AG017917, R01AG058002, R01AG062335, RF1AG054012, U01AG046152, R01AG015819, P30AG010161, RF1AG062377, R01AG046174, RF1AG054321, RF1AG057473, P30AG062421)
- National Institute of Mental Health (U01MH119509, R01MH109978)
- National Human Genome Research Institute (R01HG008155)
- National Eye Institute (K08EY026652)
- National Institute of Neurological Disorders and Stroke (U01NS110453)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Keywords
- Alzheimer's disease, single-nucleus transcriptomics, prefrontal cortex, cell type-specific gene expression, myelination, oligodendrocytes, sex differences, inflammation, neuronal survival, stress response, disease progression, neurodegeneration
- MeSH terms
- Aging, Alzheimer Disease, Female, Humans, Male, Organ Specificity, RNA, Messenger, Sex Characteristics, Prefrontal Cortex, Sequence Analysis, RNA, Disease Progression, Gene Expression Profiling, Single-Cell Analysis, Transcriptome
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