Cited by 540Alzheimer's disease research and treatmentsNeuroinflammation and Neurodegeneration MechanismsLipid metabolism and biosynthesis

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APOE4/4 is linked to damaging lipid droplets in Alzheimer’s disease microglia

Open access

Abstract

Several genetic risk factors for Alzheimer’s disease implicate genes involved in lipid metabolism and many of these lipid genes are highly expressed in glial cells 1 . However, the relationship between lipid metabolism in glia and Alzheimer’s disease pathology remains poorly understood. Through single-nucleus RNA sequencing of brain tissue in Alzheimer’s disease, we have identified a microglial state defined by the expression of the lipid droplet-associated enzyme ACSL1 with ACSL1-positive microglia being most abundant in patients with Alzheimer’s disease having the APOE4/4 genotype. In human induced pluripotent stem cell-derived microglia, fibrillar Aβ induces ACSL1 expression, triglyceride synthesis and lipid droplet accumulation in an APOE-dependent manner. Additionally, conditioned media from lipid droplet-containing microglia lead to Tau phosphorylation and neurotoxicity in an APOE-dependent manner. Our findings suggest a link between genetic risk factors for Alzheimer’s disease with microglial lipid droplet accumulation and neurotoxic microglia-derived factors, potentially providing therapeutic strategies for Alzheimer’s disease.

Overview

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TL;DR

Single-nucleus RNA sequencing of human Alzheimer’s disease brain identifies an ACSL1+ lipid-droplet-accumulating microglial state (LDAM) that is most abundant in APOE4/4 AD. In iPS-derived microglia, fibrillar Aβ induces ACSL1, triglyceride synthesis, and lipid droplets in an APOE-dependent manner, and conditioned media from lipid-droplet-high microglia induce Tau phosphorylation and neurotoxicity.

Key findings

  • snRNA-seq of frontal cortex yielded ~100,000 single-nucleus transcriptomes (n = 100,317 cells); ACSL1 was the most significantly differentially expressed gene in AD-APOE4/4 versus control microglia. ACSL1+ LDAM were a distinct state from homeostatic and DAM microglia, with the greatest percentage in AD-APOE4/4, followed by AD-APOE3/3, and least in age-matched controls (chi-square P < 0.0001); immunofluorescence confirmed ACSL1 abundance differences (n = 5 per group).
  • Oil Red O+ neutral lipid bodies were abundant in AD-APOE4/4 brain tissue, often near or at Aβ plaques, with a slight but not significant increase in AD-APOE4/4 versus AD-APOE3/3 (control n = 12; AD33 n = 7; AD44 n = 9). Lipid body counts negatively correlated with MMSE and positively correlated with Aβ plaque amounts and Tau pathology; microglial ACSL1 expression positively correlated with lipid body numbers. J20/APOE3 and J20/APOE4 mice had more LD+ microglia than age-matched wild-type mice (n = 3 mice per group).
  • In isogenic APOE4/4 versus APOE3/3 iMG, fibrillar Aβ strongly increased lipid droplet accumulation, exacerbated by APOE4 and absent in APOE-KO; fAβ upregulated PLIN2 and ACSL1. CARS spectra overlapped unsaturated/triglyceride spectra, and d-glucose 13C incorporation into triglycerides increased time-dependently after fAβ. CRISPR screens identified triglyceride metabolism regulators and ACSL1 as required for LD formation; the top druggable-genome hit in APOE4/4 iMG was PIK3CA, and ACSL1 inhibitor Triacin C or PI3K inhibitor GNE-317 reduced lipid droplets.
  • Conditioned media from LD-high APOE4/4 iMG induced high pTau (AT8) and caspase activation in human iPS cell-derived neurons; effects were similar with APOE3/3 and APOE4/4 iMG-conditioned media but absent with APOE-KO iMG-conditioned media. Neurons exposed to LD-high conditioned media showed increased LipidSpot staining and higher concentrations of triglyceride lipid species.

Methods

  • Human postmortem frontal cortex from AD-APOE4/4, AD-APOE3/3, and age/sex-matched control APOE3/3 individuals was processed for snRNA-seq (~100,000 nuclei), with CellRanger alignment, Scanpy/BBKNN integration, Leiden clustering, pseudobulk DESeq2, and MAST single-cell differential expression; microglia were subclustered and annotated using HOMEOSTATIC, DAM, and LDAM marker signatures.
  • Histology and imaging included Oil Red O, immunofluorescence for IBA1/ACSL1/Aβ in human tissue, and IBA1/LipidSpot imaging in J20/APOE3-KI and J20/APOE4-KI mice. Cell models included isogenic APOE3/3, APOE4/4, and APOE-KO iPS cell-derived microglia, iPS cell-derived human neurons, primary rat microglia, BV2 cells, and human macrophages; assays included fAβ treatment, live-cell microscopy, CARS, electron microscopy, lipidomics, cytokine/chemokine assays, ATAC-seq, and RNA-seq.
  • CRISPR–Cas9 screens were performed genome-wide in U937 cells and with a druggable-genome library in APOE4/4 iMG, using FACS for LD-high versus LD-low populations and casTLE analysis. Conditioned media from FACS-sorted LD-high and LD-low iMG were applied to iPS cell-derived neurons, followed by AT8 and cleaved caspase-3 quantification.

Limitations

  • The paper states that identifying the cell type(s) accumulating lipids requires simultaneous staining for lipids and several protein markers, which is challenging in aged human histological brain sections; that GNE-317 experiments do not conclusively show increased autophagic flux; and that 13C-labelled TAG lipids in mouse neurons treated with conditioned media were not robustly detected across several replicates, perhaps due to low isotopic enrichment.
  • Evident from the text: human postmortem associations are cross-sectional and correlational, and several experiments rely on small samples (for example, n = 3 mice per group and n = 3 replicate wells in multiple assays). The mechanistic work largely uses in vitro iPS cell-derived microglia/neurons and mouse models, which may not fully capture human AD.

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Registry

Crossref · 10.1038/s41586-024-07185-7

Cites

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Statistics

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Figures

Fig. 1 AD microglia have lipid transcriptional state defined by ACSL1. a, Schematic of snRNA-seq cohort and workflow (Methods). b, UMAP representation of all cells (n = 100,317) from snRNA-seq, coloured by annotated cell type. Data are shown after quality control and batch correction. c,d, Volcano plot representing MAST-based single-cell differential gene expression results (see section on ‘Single-cell differential gene expression’) of microglia from control individuals compared to microglia from individuals with AD and the APOE3/3 genotype (c) and from individuals with AD and the APOE4/4 genotype (d). Selected lipid- and metabolism-associated genes highlighted in red. e, Pathway diagram showing placement of differentially expressed gene ACSL1 in pathway starting from free fatty acid to LD formation. f, Violin plots showing ACSL1 expression across the cell types in the snRNA-seq dataset. Significance results indicate MAST-based adjusted P values (see section on ‘Single-cell differential gene expression’). g, Normalized and z-scored gene expression amounts of HOMEOSTATIC, DAM, LDAM and MACRO (macrophage) marker genes across the 11 subclusters identified in the microglia. HOMEOSTATIC, DAM and LDAM signature scores are shown across the 11 identified subclusters at the bottom. h, UMAP representation of microglia cells indicating the marker gene-based cell state annotation (bottom right) and the signature scores per cell for HOMEOSTATIC (top left), DAM (top right) and LDAM (bottom left) states. Contour lines indicate kernel density estimates of the signatures across the UMAP space. i, Bar plots indicating the percentage of cells from the three different cellular states (HOMEOSTATIC, DAM and LDAM) across microglia from control, AD-APOE3/3 and AD-APOE4/4 groups. Chi-square test results indicate the significance of the percentage differences between the groups (***P < 0.0001). j, Representative immunofluorescence images of human frontal cortex adjacent to the tissue used in snRNA-seq experiments stained for microglia marker IBA1 (green), ACSL1 (red) and DAPI (blue) in an aged-matched healthy control subject (left), an AD-APOE3/3 subject (middle) and an AD-APOE4/4 subject. Scale bars, 20 μm. k, Quantification of percentage of IBA1+ microglia positive for ACSL1. n = 5 per group; each dot represents individual subject; one-way analysis of variance (ANOVA); mean ± s.e.m. Schematics in a created with BioRender.com.
Fig. 1 AD microglia have lipid transcriptional state defined by ACSL1. a, Schematic of snRNA-seq cohort and workflow (Methods). b, UMAP representation of all cells (n = 100,317) from snRNA-seq, coloured by annotated cell type. Data are shown after quality control and batch correction. c,d, Volcano plot representing MAST-based single-cell differential gene expression results (see section on ‘Single-cell differential gene expression’) of microglia from control individuals compared to microglia from individuals with AD and the APOE3/3 genotype (c) and from individuals with AD and the APOE4/4 genotype (d). Selected lipid- and metabolism-associated genes highlighted in red. e, Pathway diagram showing placement of differentially expressed gene ACSL1 in pathway starting from free fatty acid to LD formation. f, Violin plots showing ACSL1 expression across the cell types in the snRNA-seq dataset. Significance results indicate MAST-based adjusted P values (see section on ‘Single-cell differential gene expression’). g, Normalized and z-scored gene expression amounts of HOMEOSTATIC, DAM, LDAM and MACRO (macrophage) marker genes across the 11 subclusters identified in the microglia. HOMEOSTATIC, DAM and LDAM signature scores are shown across the 11 identified subclusters at the bottom. h, UMAP representation of microglia cells indicating the marker gene-based cell state annotation (bottom right) and the signature scores per cell for HOMEOSTATIC (top left), DAM (top right) and LDAM (bottom left) states. Contour lines indicate kernel density estimates of the signatures across the UMAP space. i, Bar plots indicating the percentage of cells from the three different cellular states (HOMEOSTATIC, DAM and LDAM) across microglia from control, AD-APOE3/3 and AD-APOE4/4 groups. Chi-square test results indicate the significance of the percentage differences between the groups (***P < 0.0001). j, Representative immunofluorescence images of human frontal cortex adjacent to the tissue used in snRNA-seq experiments stained for microglia marker IBA1 (green), ACSL1 (red) and DAPI (blue) in an aged-matched healthy control subject (left), an AD-APOE3/3 subject (middle) and an AD-APOE4/4 subject. Scale bars, 20 μm. k, Quantification of percentage of IBA1+ microglia positive for ACSL1. n = 5 per group; each dot represents individual subject; one-way analysis of variance (ANOVA); mean ± s.e.m. Schematics in a created with BioRender.com.CC BY · PubMed Central
Fig. 2 Lipid accumulation is linked to AD pathology. a, Representative Oil Red O staining image for control, AD-APOE3/3 and AD-APOE4/4 human frontal cortex. Neutral lipids stained with Oil Red O (red) and nuclei stained with haematoxylin (blue). Scale bars, 50 μm. b, Quantification of Oil Red O staining. Bar plots represent average Oil Red O counts per image for each individual category (control, n = 12; AD33 n = 7; AD44 n = 9 individuals). Each dot represents average Oil Red O counts for an individual averaged over five ×20 image fields per individual; one-way ANOVA; mean ± s.e.m. c, Left, Oil Red O staining of individuals with AD-APOE4/4 and with IHC for Aβ. White arrowheads represent Oil Red O+ cells in or around Aβ plaques. Scale bar, 50 μm. Right, high magnification of representative Oil Red O stain with IHC for Aβ in an individual with AD-APOE4/4. Black arrowheads represent Oil Red O+ cells in or around Aβ plaques. Scale bar, 20 μm. d, Quantification of the frequency of Oil Red O+ cells in various distances from Aβ plaques (n = 4 per group; one-way ANOVA; mean ± s.e.m.). e, Scatter plot of average Oil Red O counts per individual averaged over five ×20 image fields per individual with individual’s metadata. Individual category coloured blue for control, orange for individuals with AD-APOE3/3 and red for those with AD-APOE4/4. P values determined by Spearman correlation. f, Scatter plot of average Oil Red O counts per individual averaged over five ×20 image fields per individual with individual’s snRNA-seq data. Individual category coloured blue for control, orange for individuals with AD-APOE3/3 and red for those with AD-APOE4/4. P values determined by Spearman correlation. g, Representative immunofluorescence images of mouse hippocampus tissue stained for microglia marker IBA1 (red), neutral lipids (LipidSpot, green) and DAPI (blue) in control age-matched non-transgenic mice (left), AD mouse model (J20) with human APOE3 knockin (middle) and AD mouse model (J20) with human APOE4 knockin (right). Scale bars, 20 μm. h, Quantification of average percentage of IBA1+ microglia with neutral lipid dye (LipidSpot) (n = 3 individual mice per group; one-way ANOVA; mean ± s.e.m).
Fig. 2 Lipid accumulation is linked to AD pathology. a, Representative Oil Red O staining image for control, AD-APOE3/3 and AD-APOE4/4 human frontal cortex. Neutral lipids stained with Oil Red O (red) and nuclei stained with haematoxylin (blue). Scale bars, 50 μm. b, Quantification of Oil Red O staining. Bar plots represent average Oil Red O counts per image for each individual category (control, n = 12; AD33 n = 7; AD44 n = 9 individuals). Each dot represents average Oil Red O counts for an individual averaged over five ×20 image fields per individual; one-way ANOVA; mean ± s.e.m. c, Left, Oil Red O staining of individuals with AD-APOE4/4 and with IHC for Aβ. White arrowheads represent Oil Red O+ cells in or around Aβ plaques. Scale bar, 50 μm. Right, high magnification of representative Oil Red O stain with IHC for Aβ in an individual with AD-APOE4/4. Black arrowheads represent Oil Red O+ cells in or around Aβ plaques. Scale bar, 20 μm. d, Quantification of the frequency of Oil Red O+ cells in various distances from Aβ plaques (n = 4 per group; one-way ANOVA; mean ± s.e.m.). e, Scatter plot of average Oil Red O counts per individual averaged over five ×20 image fields per individual with individual’s metadata. Individual category coloured blue for control, orange for individuals with AD-APOE3/3 and red for those with AD-APOE4/4. P values determined by Spearman correlation. f, Scatter plot of average Oil Red O counts per individual averaged over five ×20 image fields per individual with individual’s snRNA-seq data. Individual category coloured blue for control, orange for individuals with AD-APOE3/3 and red for those with AD-APOE4/4. P values determined by Spearman correlation. g, Representative immunofluorescence images of mouse hippocampus tissue stained for microglia marker IBA1 (red), neutral lipids (LipidSpot, green) and DAPI (blue) in control age-matched non-transgenic mice (left), AD mouse model (J20) with human APOE3 knockin (middle) and AD mouse model (J20) with human APOE4 knockin (right). Scale bars, 20 μm. h, Quantification of average percentage of IBA1+ microglia with neutral lipid dye (LipidSpot) (n = 3 individual mice per group; one-way ANOVA; mean ± s.e.m).CC BY · PubMed Central
Fig. 3 iMG increase ACSL1 and triglyceride lipid synthesis after fAβ challenge. a, Schematic of APOE3/3 and APOE4/4 iMGs. b, Quantification of lipid fluorescent dye (LipidSpot) in APOE3/3 and APOE4/4 iMG ± fAβ (n = 3 replicate wells per condition; mean ± s.e.m.). c, Average LipidSpot fluorescence per cell normalized to the not treated (NT) condition at final time point in b. Individual dots represent replicate wells (n = 3 replicate wells per condition; unpaired two-sided t-test; per condition, mean ± s.e.m). d, Normalized gene expression counts for significant differentially expressed genes in APOE4/4 iMG ± fAβ (n = 3 replicate wells per condition, P values determined by DEseq2; mean ± s.e.m). e, Primary rat microglia untreated (left) or with fAβ (right) with LipidSpot. Scale bar, 200 μm. f, Average LipidSpot fluorescence per cell normalized to untreated images in e (n = 3 replicate wells per condition; unpaired two-sided t-test; mean ± s.e.m). g, CARS images of APOE4/4 and APOE3/3 iMG ± fAβ. Scale bars, 20 μm. Data replicated in at least two independent experiments. h, Quantification of CARS microscopy. Each dot represents lipid measurements from individual cells (APOE33 n = 47; APOE44 n = 38; unpaired two-sided t-test). i, CARS spectra from fAβ-treated iMG (red) and reference spectra for common lipid species (black). j, Schematic of lipidomics measurement of d-glucose13C incorporation in BV2 cells + fAβ. k, Incorporation of d-glucose13C into triglycerides in microglia ± fAβ (n = 3 replicate wells per condition; one-way ANOVA; mean ± s.e.m). l, Incorporation of d-glucose13C into triglycerides in Aβ-treated microglia over time (n = 3 replicate wells per condition; one-way ANOVA; mean ± s.e.m). m, Volcano plot of genome-wide CRISPR-KO LD screen in U937 cell line. Genes passing a 10% FDR cutoff are highlighted in red and blue. n, Average LipidSpot fluorescence ± ACSL1 inhibitor (Triacin C) (n = 4 replicate wells per condition; unpaired two-sided t-test; mean ± s.e.m.). o, Schematic of ATAC-seq and RNA-seq in LD-high and LD-low iMGs. p, ATAC-seq peaks in LD-high versus LD-low iMGs. q, Motif analysis of differential peaks. Motifs enriched in lipid-associated macrophages are highlighted in red. r, Average percentage pHrodo zymosan+ iMGs ± LD (n = 4 replicate wells per condition; unpaired, two-sided t-test; mean ± s.e.m.). s, Average percentage lysotracker+ iMGs ± LD (n = 3 replicate wells per condition; unpaired, two-sided t-test; mean ± s.e.m.). KO, knockout; NS, not significant. Elements in j created with BioRender.com.
Fig. 3 iMG increase ACSL1 and triglyceride lipid synthesis after fAβ challenge. a, Schematic of APOE3/3 and APOE4/4 iMGs. b, Quantification of lipid fluorescent dye (LipidSpot) in APOE3/3 and APOE4/4 iMG ± fAβ (n = 3 replicate wells per condition; mean ± s.e.m.). c, Average LipidSpot fluorescence per cell normalized to the not treated (NT) condition at final time point in b. Individual dots represent replicate wells (n = 3 replicate wells per condition; unpaired two-sided t-test; per condition, mean ± s.e.m). d, Normalized gene expression counts for significant differentially expressed genes in APOE4/4 iMG ± fAβ (n = 3 replicate wells per condition, P values determined by DEseq2; mean ± s.e.m). e, Primary rat microglia untreated (left) or with fAβ (right) with LipidSpot. Scale bar, 200 μm. f, Average LipidSpot fluorescence per cell normalized to untreated images in e (n = 3 replicate wells per condition; unpaired two-sided t-test; mean ± s.e.m). g, CARS images of APOE4/4 and APOE3/3 iMG ± fAβ. Scale bars, 20 μm. Data replicated in at least two independent experiments. h, Quantification of CARS microscopy. Each dot represents lipid measurements from individual cells (APOE33 n = 47; APOE44 n = 38; unpaired two-sided t-test). i, CARS spectra from fAβ-treated iMG (red) and reference spectra for common lipid species (black). j, Schematic of lipidomics measurement of d-glucose13C incorporation in BV2 cells + fAβ. k, Incorporation of d-glucose13C into triglycerides in microglia ± fAβ (n = 3 replicate wells per condition; one-way ANOVA; mean ± s.e.m). l, Incorporation of d-glucose13C into triglycerides in Aβ-treated microglia over time (n = 3 replicate wells per condition; one-way ANOVA; mean ± s.e.m). m, Volcano plot of genome-wide CRISPR-KO LD screen in U937 cell line. Genes passing a 10% FDR cutoff are highlighted in red and blue. n, Average LipidSpot fluorescence ± ACSL1 inhibitor (Triacin C) (n = 4 replicate wells per condition; unpaired two-sided t-test; mean ± s.e.m.). o, Schematic of ATAC-seq and RNA-seq in LD-high and LD-low iMGs. p, ATAC-seq peaks in LD-high versus LD-low iMGs. q, Motif analysis of differential peaks. Motifs enriched in lipid-associated macrophages are highlighted in red. r, Average percentage pHrodo zymosan+ iMGs ± LD (n = 4 replicate wells per condition; unpaired, two-sided t-test; mean ± s.e.m.). s, Average percentage lysotracker+ iMGs ± LD (n = 3 replicate wells per condition; unpaired, two-sided t-test; mean ± s.e.m.). KO, knockout; NS, not significant. Elements in j created with BioRender.com.CC BY · PubMed Central
Fig. 4 LD+ microglia induce Tau phosphorylation and apoptosis in neurons. a, Schematic of LDAM-specific conditioned media (CM) exposure to neurons. b, Immunofluorescence images of iPS cell-derived neurons exposed to no CM (left), LD+ APOE4/4 iMG-CM (middle) and LD− APOE4/4 iMG-CM (left). Cells were stained for DAPI (blue), MAP2 (grey) and pTau (AT8, green). Scale bars, 20 μm. c, Immunofluorescence images of iPS cell-derived neurons exposed to LD+ APOE4/4 iMG-CM (left), LD+ APOE3/3 iMG-CM (middle), LD+ APOE-KO iMG-CM (right). Cells were stained for DAPI (blue), MAP2 (grey) and pTau (AT8, green). Scale bars, 20 μm. Data replicated in at least two independent experiments. d, Quantification of images as presented in c. Each dot represents a random filed image (n = 18) across three replicate wells per condition; one-way ANOVA; mean ± s.e.m. e, Immunofluorescence images of iPS cell-derived neurons under conditions in c stained for DAPI (blue), MAP2 (grey) and cleaved caspase-3 (red). Scale bars, 20 μm. f, Quantification of images as presented in e. Each dot represents a random filed image (n = 18) across three replicate wells per condition; one-way ANOVA; mean ± s.e.m. g, Images of neurons exposed to LD− APOE4/4 iMG-CM (left) and LD+ APOE4/4 iMG-CM (right). Cells were stained with LipidSpot (green) and activated caspase-3 dye (red). Scale bars, 200 μm. h, Quantification LipidSpot fluorescence (n = 4 replicate wells per condition; two-sided t-test; mean ± s.e.m). i, Schematic of lipidomics experimental design of neurons treated with CM. j, Volcano plot representing lipids detected in neurons after treatment of LD+ iMG-CMa versus LD− iMG-CM. Triglyceride species are highlighted in red. k, Lipidomic measurements of one lipid species detected in lipidomic analysis. Individual dots represent replicate wells (n = 3 replicate wells per condition; one-way ANOVA; mean ± s.e.m). l, Schematic of the proposed role of LD+ microglia in neurodegeneration. *P < 0.01, **P < 0.001, ****P < 0.0001.
Fig. 4 LD+ microglia induce Tau phosphorylation and apoptosis in neurons. a, Schematic of LDAM-specific conditioned media (CM) exposure to neurons. b, Immunofluorescence images of iPS cell-derived neurons exposed to no CM (left), LD+ APOE4/4 iMG-CM (middle) and LD− APOE4/4 iMG-CM (left). Cells were stained for DAPI (blue), MAP2 (grey) and pTau (AT8, green). Scale bars, 20 μm. c, Immunofluorescence images of iPS cell-derived neurons exposed to LD+ APOE4/4 iMG-CM (left), LD+ APOE3/3 iMG-CM (middle), LD+ APOE-KO iMG-CM (right). Cells were stained for DAPI (blue), MAP2 (grey) and pTau (AT8, green). Scale bars, 20 μm. Data replicated in at least two independent experiments. d, Quantification of images as presented in c. Each dot represents a random filed image (n = 18) across three replicate wells per condition; one-way ANOVA; mean ± s.e.m. e, Immunofluorescence images of iPS cell-derived neurons under conditions in c stained for DAPI (blue), MAP2 (grey) and cleaved caspase-3 (red). Scale bars, 20 μm. f, Quantification of images as presented in e. Each dot represents a random filed image (n = 18) across three replicate wells per condition; one-way ANOVA; mean ± s.e.m. g, Images of neurons exposed to LD− APOE4/4 iMG-CM (left) and LD+ APOE4/4 iMG-CM (right). Cells were stained with LipidSpot (green) and activated caspase-3 dye (red). Scale bars, 200 μm. h, Quantification LipidSpot fluorescence (n = 4 replicate wells per condition; two-sided t-test; mean ± s.e.m). i, Schematic of lipidomics experimental design of neurons treated with CM. j, Volcano plot representing lipids detected in neurons after treatment of LD+ iMG-CMa versus LD− iMG-CM. Triglyceride species are highlighted in red. k, Lipidomic measurements of one lipid species detected in lipidomic analysis. Individual dots represent replicate wells (n = 3 replicate wells per condition; one-way ANOVA; mean ± s.e.m). l, Schematic of the proposed role of LD+ microglia in neurodegeneration. *P < 0.01, **P < 0.001, ****P < 0.0001.CC BY · PubMed Central
Extended Data Fig. 1 Quality Control for single-nucleus RNA-sequencing data. a, Doublet score distributions obtained with the Scrublet (0.2.3) Python package. Vertical lines indicate the doublet score thresholds identified per sample based on the distributions shown. b-e, Distribution of total read counts (b), number of genes expressed per cell (c), percent of reads mapped to ribosomal genes (d) and percent of reads mapped to mitochondrial genes (e) within the processed data after quality control. f, Percent of explained variance across the first 48 principal components.
Extended Data Fig. 1 Quality Control for single-nucleus RNA-sequencing data. a, Doublet score distributions obtained with the Scrublet (0.2.3) Python package. Vertical lines indicate the doublet score thresholds identified per sample based on the distributions shown. b-e, Distribution of total read counts (b), number of genes expressed per cell (c), percent of reads mapped to ribosomal genes (d) and percent of reads mapped to mitochondrial genes (e) within the processed data after quality control. f, Percent of explained variance across the first 48 principal components.CC BY · PubMed Central
Extended Data Fig. 2 Annotations of CNS cell types from single-nucleus RNA-sequencing data. a-c, UMAP visualization of the whole snRNA-seq dataset after quality control and batch correction (n = 100,317). Cells are coloured by cell type annotation (a), subclusters drawn for cell type annotation (b), subject groups (control, AD-APOE3/3, AD-APOE4/4) (c). d, Bar chart indicating the total number of cells per cell type. e-j, Violin plots indicating gene expression levels of marker genes used for cell type annotations across the 6 identified cell types (n = 100,317). Violin plots are centred around the median, with their shape representing cell distribution. k-p, UMAP visualization of the whole snRNA-seq dataset coloured by cell type marker gene expression levels per cell.
Extended Data Fig. 2 Annotations of CNS cell types from single-nucleus RNA-sequencing data. a-c, UMAP visualization of the whole snRNA-seq dataset after quality control and batch correction (n = 100,317). Cells are coloured by cell type annotation (a), subclusters drawn for cell type annotation (b), subject groups (control, AD-APOE3/3, AD-APOE4/4) (c). d, Bar chart indicating the total number of cells per cell type. e-j, Violin plots indicating gene expression levels of marker genes used for cell type annotations across the 6 identified cell types (n = 100,317). Violin plots are centred around the median, with their shape representing cell distribution. k-p, UMAP visualization of the whole snRNA-seq dataset coloured by cell type marker gene expression levels per cell.CC BY · PubMed Central
Extended Data Fig. 3 Differential expression analysis and subclustering of microglia from single-nucleus RNA-sequencing data. a, Volcano plot representing pseudobulk differential gene expression results (see Methods, Microglia pseudobulk differential gene expression) of microglia from control individuals compared to microglia (left) from subjects with AD and the APOE3/3 genotype, (right) from subjects with AD and the APOE4/4 genotype. Select lipid and metabolism-associated genes highlighted in red. b, Volcano plot representing single-cell differential gene expression results of microglia from subjects with AD and the APOE3/3 genotype compared to microglia from subjects with AD and the APOE4/4 genotype. Select lipid and metabolism-associated genes highlighted in red. c, Selected KEGG61 pathway analysis terms and enrichment score for top 200 differential expressed genes in between control and AD-APOE4/4 microglia (top) and control and AD-APOE3/3 microglia (bottom). d-g, UMAP visualization of the microglia coloured by identified subclusters (macrophage cluster 10 is not shown) (d), identified microglial states (e), subject IDs (f) and subject groups (control, AD-APOE3/3, AD-APOE4/4). h, Top marker genes identified for the 3 microglial states (HOMEOSTATIC, DAM, LDAM) with ‘one versus rest’ marker identification. Single-cell differential gene expression was performed with MAST (see Methods, Single-cell differential expression). Heatmap indicates significant (adj. p-value < 0.05) log2-fold changes. i, Normalized and z-scored gene expression levels of HOMEOSTATIC, DAM, LDAM marker genes as well as marker genes identified by Olah et al.52 across the 11 subclusters identified within the microglia. j, UMAP representation of microglia cells indicating signature scores per cell for HOMEOSTATIC, DAM, LDAM marker genes as well as marker genes identified by Olah et al.52 Contour lines indicate kernel density estimates of the signatures across the UMAP space.
Extended Data Fig. 3 Differential expression analysis and subclustering of microglia from single-nucleus RNA-sequencing data. a, Volcano plot representing pseudobulk differential gene expression results (see Methods, Microglia pseudobulk differential gene expression) of microglia from control individuals compared to microglia (left) from subjects with AD and the APOE3/3 genotype, (right) from subjects with AD and the APOE4/4 genotype. Select lipid and metabolism-associated genes highlighted in red. b, Volcano plot representing single-cell differential gene expression results of microglia from subjects with AD and the APOE3/3 genotype compared to microglia from subjects with AD and the APOE4/4 genotype. Select lipid and metabolism-associated genes highlighted in red. c, Selected KEGG61 pathway analysis terms and enrichment score for top 200 differential expressed genes in between control and AD-APOE4/4 microglia (top) and control and AD-APOE3/3 microglia (bottom). d-g, UMAP visualization of the microglia coloured by identified subclusters (macrophage cluster 10 is not shown) (d), identified microglial states (e), subject IDs (f) and subject groups (control, AD-APOE3/3, AD-APOE4/4). h, Top marker genes identified for the 3 microglial states (HOMEOSTATIC, DAM, LDAM) with ‘one versus rest’ marker identification. Single-cell differential gene expression was performed with MAST (see Methods, Single-cell differential expression). Heatmap indicates significant (adj. p-value < 0.05) log2-fold changes. i, Normalized and z-scored gene expression levels of HOMEOSTATIC, DAM, LDAM marker genes as well as marker genes identified by Olah et al.52 across the 11 subclusters identified within the microglia. j, UMAP representation of microglia cells indicating signature scores per cell for HOMEOSTATIC, DAM, LDAM marker genes as well as marker genes identified by Olah et al.52 Contour lines indicate kernel density estimates of the signatures across the UMAP space.CC BY · PubMed Central
Extended Data Fig. 4 Additional microscopy and quantification of human AD brain tissue. a, Quantification of Oil Red O counts for each subject (n = 5 20x images per subject; mean ± s.e.m.). b-c, Representative Oil Red O staining of APOE4/4 AD subjects with IHC for Aβ. White arrowheads represent Oil Red O positive cells in or around Aβ plaques. Scale bars (black, bottom right) 50 μm. d, Representative immunofluorescence images of human frontal cortex adjacent to tissue used in snRNA-seq experiments stained for microglia marker IBA1 (green), ACSL1 (red) and DAPI (blue) and amyloid-beta (magenta) in an AD APOE4/4 subject. Scale bars (white, bottom right) 20 μm. Data in b-d replicated in at least two independent experiments.
Extended Data Fig. 4 Additional microscopy and quantification of human AD brain tissue. a, Quantification of Oil Red O counts for each subject (n = 5 20x images per subject; mean ± s.e.m.). b-c, Representative Oil Red O staining of APOE4/4 AD subjects with IHC for Aβ. White arrowheads represent Oil Red O positive cells in or around Aβ plaques. Scale bars (black, bottom right) 50 μm. d, Representative immunofluorescence images of human frontal cortex adjacent to tissue used in snRNA-seq experiments stained for microglia marker IBA1 (green), ACSL1 (red) and DAPI (blue) and amyloid-beta (magenta) in an AD APOE4/4 subject. Scale bars (white, bottom right) 20 μm. Data in b-d replicated in at least two independent experiments.CC BY · PubMed Central
Extended Data Fig. 5 Additional in vitro experiments on lipid droplet production and ACSL1 expression after fAβ treatment. a, Expression of microglia marker genes in iMG (n = 3 replicate wells; mean ± s.e.m). b, Toxicity of fAβ at different concentrations in APOE4/4 and APOE3/3 iMG after 24 hours incubation followed by staining with CytoxRed. (n = 3 replicate wells per condition; one-way ANOVA; mean ± s.e.m.). c, Dose-dependent effect of fAβ on lipid accumulation (n = 3 replicate wells per condition; one-way ANOVA; mean ± s.e.m.). d-e, iMG treated with 5 μM Aβ for 24 hours (left) with PLIN2 and ACSL1 Immunofluorescence quantification with (d) with representative images (e) (n = 4 replicate wells per condition; one-way ANOVA; mean ± s.e.m.; scale bars (white, bottom left) 200 μm). f, ACSL1 gene expression measured in human iMG after LPS treatment as described in Hasselmann et al. 201922 (https://rnaseq.mind.uci.edu/blurton-jones/bulkSeq/); box plot centred at median with boarders representing quartiles. mean ± s.e.m. g, Representative image of human macrophages (top) untreated (left) or treated (right) with 5 μM Aβ for 24 hours (left) with PLIN2 (green) and Aβ (red) staining. Scale bars (white, bottom right) 50 um. Representative image of mouse BV2 cells (bottom) untreated (left) or treated (right) with 5uM Aβ for 24 hours (left) with LipidSpot (green) and Aβ (red) staining. Scale bars (white, bottom right) 75 μm. Data in replicated in at least two independent experiments. h-i, Transmission electron microscopy of APOE3/3 iMG and APOE 4/4 iMG treated with 5 μM Aβ for 24 hours and untreated iMG (Scale bars (white, bottom right) 2 μm; n = 4 replicate wells per condition; one-way ANOVA; mean ± s.e.m.).
Extended Data Fig. 5 Additional in vitro experiments on lipid droplet production and ACSL1 expression after fAβ treatment. a, Expression of microglia marker genes in iMG (n = 3 replicate wells; mean ± s.e.m). b, Toxicity of fAβ at different concentrations in APOE4/4 and APOE3/3 iMG after 24 hours incubation followed by staining with CytoxRed. (n = 3 replicate wells per condition; one-way ANOVA; mean ± s.e.m.). c, Dose-dependent effect of fAβ on lipid accumulation (n = 3 replicate wells per condition; one-way ANOVA; mean ± s.e.m.). d-e, iMG treated with 5 μM Aβ for 24 hours (left) with PLIN2 and ACSL1 Immunofluorescence quantification with (d) with representative images (e) (n = 4 replicate wells per condition; one-way ANOVA; mean ± s.e.m.; scale bars (white, bottom left) 200 μm). f, ACSL1 gene expression measured in human iMG after LPS treatment as described in Hasselmann et al. 201922 (https://rnaseq.mind.uci.edu/blurton-jones/bulkSeq/); box plot centred at median with boarders representing quartiles. mean ± s.e.m. g, Representative image of human macrophages (top) untreated (left) or treated (right) with 5 μM Aβ for 24 hours (left) with PLIN2 (green) and Aβ (red) staining. Scale bars (white, bottom right) 50 um. Representative image of mouse BV2 cells (bottom) untreated (left) or treated (right) with 5uM Aβ for 24 hours (left) with LipidSpot (green) and Aβ (red) staining. Scale bars (white, bottom right) 75 μm. Data in replicated in at least two independent experiments. h-i, Transmission electron microscopy of APOE3/3 iMG and APOE 4/4 iMG treated with 5 μM Aβ for 24 hours and untreated iMG (Scale bars (white, bottom right) 2 μm; n = 4 replicate wells per condition; one-way ANOVA; mean ± s.e.m.).CC BY · PubMed Central
Extended Data Fig. 6 Bulk RNA-saq of iMG and phenotypic assays. a, Volcano plot of differential gene expression analysis of untreated and Aβ treated APOE4/4 iMG. b, Volcano plot of differential gene expression analysis of lipid droplet high and lipid droplet low APOE4/4 iMG. c, Volcano plot of differential gene expression analysis of lipid droplet high and lipid droplet low APOE3/3 iMG. d, Volcano plot of differential gene expression analysis of lipid droplet high APOE3/3 iMG and lipid droplet high APOE4/4 iMG. e, Example gating scheme for separating cells based on lipid droplet content. f, LysoTracker area per cell after incubation with fAβ in APOE3/3 and APOE4/4 iMG (n = 3 replicate wells per condition; one-way ANOVA; mean ± s.e.m.). g, Phrodo zymosan phagocytosis area per cell after incubation with fAβ in APOE3/3 and APOE4/4 iMG (n = 3 replicate wells per condition; one-way ANOVA; mean ± s.e.m.). h, Normalized gene expression counts for significant DEGs in LD-high versus LD-low iMGs (n = 2 replicate wells per condition). i, Secreted chemokines in LD-high versus LD-low iMGs (n = 2 replicate wells per condition).
Extended Data Fig. 6 Bulk RNA-saq of iMG and phenotypic assays. a, Volcano plot of differential gene expression analysis of untreated and Aβ treated APOE4/4 iMG. b, Volcano plot of differential gene expression analysis of lipid droplet high and lipid droplet low APOE4/4 iMG. c, Volcano plot of differential gene expression analysis of lipid droplet high and lipid droplet low APOE3/3 iMG. d, Volcano plot of differential gene expression analysis of lipid droplet high APOE3/3 iMG and lipid droplet high APOE4/4 iMG. e, Example gating scheme for separating cells based on lipid droplet content. f, LysoTracker area per cell after incubation with fAβ in APOE3/3 and APOE4/4 iMG (n = 3 replicate wells per condition; one-way ANOVA; mean ± s.e.m.). g, Phrodo zymosan phagocytosis area per cell after incubation with fAβ in APOE3/3 and APOE4/4 iMG (n = 3 replicate wells per condition; one-way ANOVA; mean ± s.e.m.). h, Normalized gene expression counts for significant DEGs in LD-high versus LD-low iMGs (n = 2 replicate wells per condition). i, Secreted chemokines in LD-high versus LD-low iMGs (n = 2 replicate wells per condition).CC BY · PubMed Central
Extended Data Fig. 7 CRISPR-KO screen in iMG for LD levels and effect of PIK3CA inhibition on LD levels. a, Schematic of CRISPR-KO screen in APOE4/4 iMG for lipid droplet formation following Aβ treatment. b, Volcano plot representing CRISPR screen results. Effect score represents log2 fold change in sgRNA counts in lipid droplet negative versus lipid droplet-positive cell fraction. Screen hits with P-value < 0.005 coloured blue. c, Live cell imaging of untreated APOE4/4 iMG, 5 μM Aβ treated iMG and 5 μM Aβ treated iMG with 10 μM GNE-317. The y axis represents average green fluorescence per cell normalized to untreated APOE4/4 iMG at the first time point and the x-axis represents imaging time points in hours (n = 3 replicate wells per condition; mean ± s.e.m.) (left). Representative images at the final time point (right) with LipidSpot signal represented in green. d, Quantification of PLIN2 Immunofluorescence in untreated, Aβ treated and Aβ treated with 10 uM GNE-317 conditions iMG for 24 hours. (n = 3 replicate wells per condition; one-way ANOVA; mean ± s.e.m.). e, Quantification of lysotracker staining in untreated, Aβ treated and Aβ treated with 10 μM GNE-317 conditions iMG for 24 hours (n = 3 replicate wells per condition; one-way ANOVA; mean ± s.e.m.). f, Measurement of secreted chemokines in cell culture media in untreated, Aβ treated and Aβ treated with 10 μM GNE-317 conditions iMG for 24 hours. Individual dots represent replicate wells (n = 2, one-way ANOVA; mean ± s.e.m.). g, Selected KEGG pathway enrichment terms for the top 200 significant downregulated genes ranked by p-value upon GNE-317 treatment in APOE4/4 iMG when challenged with Aβ. h, Normalized gene expression counts for significantly downregulated genes with GNE-317 treatment in APOE4/4 iMG when challenged with Aβ APOE4/4 iMG (n = 3 replicate wells per condition, mean ± s.e.m., *** P < 0.0001). P values determined by DEseq2. i, Selected KEGG pathway enrichment terms for top 200 significant upregulated genes ranked by p-value upon GNE-317 treatment in APOE4/4 iMG when challenged with Aβ. j, Normalized gene expression counts for significantly upregulated genes with GNE-317 treatment in APOE4/4 iMG when challenged with Aβ APOE4/4 iMG (n = 3 replicate wells per condition; mean ± s.e.m.; *** P < 0.0001). P values determined by DEseq2. k, Differential gene expression for genes in mTOR and autophagy pathways upon GNE-317 treatment with fAβ challenge in iMG. (n = 3 replicate wells per condition; mean ± s.e.m.;*** P < 0.0001). P values determined by DEseq2. l, Representative images of LC3B Immunofluorescence (yellow) upon fAβ and GNE-317 treatment. The scale bar (white, bottom left) represents 20μm. m, Quantification of LC3B immunofluorescence by integrated fluorescence intensity per DAPI signal. Individual dots represent replicate wells (n = 4 per condition; two-sided, t-test; mean ± s.e.m.). n, Toxicity measurements of GNE-317 as determined by cytox-red. (n = 4 per condition; mean ± s.e.m; one-way ANOVA).
Extended Data Fig. 7 CRISPR-KO screen in iMG for LD levels and effect of PIK3CA inhibition on LD levels. a, Schematic of CRISPR-KO screen in APOE4/4 iMG for lipid droplet formation following Aβ treatment. b, Volcano plot representing CRISPR screen results. Effect score represents log2 fold change in sgRNA counts in lipid droplet negative versus lipid droplet-positive cell fraction. Screen hits with P-value < 0.005 coloured blue. c, Live cell imaging of untreated APOE4/4 iMG, 5 μM Aβ treated iMG and 5 μM Aβ treated iMG with 10 μM GNE-317. The y axis represents average green fluorescence per cell normalized to untreated APOE4/4 iMG at the first time point and the x-axis represents imaging time points in hours (n = 3 replicate wells per condition; mean ± s.e.m.) (left). Representative images at the final time point (right) with LipidSpot signal represented in green. d, Quantification of PLIN2 Immunofluorescence in untreated, Aβ treated and Aβ treated with 10 uM GNE-317 conditions iMG for 24 hours. (n = 3 replicate wells per condition; one-way ANOVA; mean ± s.e.m.). e, Quantification of lysotracker staining in untreated, Aβ treated and Aβ treated with 10 μM GNE-317 conditions iMG for 24 hours (n = 3 replicate wells per condition; one-way ANOVA; mean ± s.e.m.). f, Measurement of secreted chemokines in cell culture media in untreated, Aβ treated and Aβ treated with 10 μM GNE-317 conditions iMG for 24 hours. Individual dots represent replicate wells (n = 2, one-way ANOVA; mean ± s.e.m.). g, Selected KEGG pathway enrichment terms for the top 200 significant downregulated genes ranked by p-value upon GNE-317 treatment in APOE4/4 iMG when challenged with Aβ. h, Normalized gene expression counts for significantly downregulated genes with GNE-317 treatment in APOE4/4 iMG when challenged with Aβ APOE4/4 iMG (n = 3 replicate wells per condition, mean ± s.e.m., *** P < 0.0001). P values determined by DEseq2. i, Selected KEGG pathway enrichment terms for top 200 significant upregulated genes ranked by p-value upon GNE-317 treatment in APOE4/4 iMG when challenged with Aβ. j, Normalized gene expression counts for significantly upregulated genes with GNE-317 treatment in APOE4/4 iMG when challenged with Aβ APOE4/4 iMG (n = 3 replicate wells per condition; mean ± s.e.m.; *** P < 0.0001). P values determined by DEseq2. k, Differential gene expression for genes in mTOR and autophagy pathways upon GNE-317 treatment with fAβ challenge in iMG. (n = 3 replicate wells per condition; mean ± s.e.m.;*** P < 0.0001). P values determined by DEseq2. l, Representative images of LC3B Immunofluorescence (yellow) upon fAβ and GNE-317 treatment. The scale bar (white, bottom left) represents 20μm. m, Quantification of LC3B immunofluorescence by integrated fluorescence intensity per DAPI signal. Individual dots represent replicate wells (n = 4 per condition; two-sided, t-test; mean ± s.e.m.). n, Toxicity measurements of GNE-317 as determined by cytox-red. (n = 4 per condition; mean ± s.e.m; one-way ANOVA).CC BY · PubMed Central
Extended Data Fig. 8 Detection of triglyceride synthesized in microglia taken up by neurons through labelled 13C-glucose tracing. a, Measurement of 13C-labelled triglycerides synthesized in microglia and profiled in neurons by lipidomics after exposure to microglia conditioned media. Microglia grown in uniformly labelled 13C-glucose (U-13C6-glucose) were challenged with fAβ or untreated. Each dot represents an individual replicate. n = 3 wells per condition; unpaired t-test; mean ± s.e.m.).
Extended Data Fig. 8 Detection of triglyceride synthesized in microglia taken up by neurons through labelled 13C-glucose tracing. a, Measurement of 13C-labelled triglycerides synthesized in microglia and profiled in neurons by lipidomics after exposure to microglia conditioned media. Microglia grown in uniformly labelled 13C-glucose (U-13C6-glucose) were challenged with fAβ or untreated. Each dot represents an individual replicate. n = 3 wells per condition; unpaired t-test; mean ± s.e.m.).CC BY · PubMed Central

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45 Own result · 15 Supported · 14 Own method · 7 No full text of the sources · 6 Partly supported · 6 Own interpretation · 2 Couldn’t confirm · 2 No basis given · 1 Supported so far · 1 Sources not found · accuracy not yet measured

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  • It may state two separate claims as one. 27
  • Probably not a substantial claim. 40
57 of 57
  • The paper relies on this without support.

    The relationship between lipid metabolism in glia and Alzheimer’s disease pathology remains poorly understood.

    “However, the relationship between lipid metabolism in glia and Alzheimer’s disease pathology remains poorly understood.”
  • The paper relies on this without support.

    The role lipids and innate immunity play in Alzheimer’s disease risk remains poorly understood.

    “However, the role lipids and innate immunity play in AD risk remains poorly understood.”
  • The paper relies on this without support.

    Human induced pluripotent stem cell-derived microglia and neurons are valid models of human Alzheimer’s disease microglia and neurons.

    “APOE4/4 and isogenic APOE3/3 iPS cells16 were differentiated into microglia (iMG) as previously described17–19”
  • The paper relies on this without support.

    The LDAM state is maladaptive and damaging.

    “maladaptive and damaging LDAM state”
  • The paper relies on this without support.

    The iPS cell-derived microglia model recapitulates key aspects of human microglial biology relevant to LDAM.

    “Differentiation into microglia was performed as previously described17–19.”
5 of 5

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Citations

Cites

Research works printed but not linked: 1

  1. Palovics, R., Wyss-Coray, T. & Mirzasoleiman, B. Purification of single-cell transcriptomics data with coreset selection. In The 2022 ICML Workshop on Computational Biology Paper 67 (ICML, 2022).

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Metadata

Authors
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., Tsai, A., Guldner, I. H., Lamichhane, B. S., Smith, A., Schaum, N., Calcuttawala, K., Shin, A., Wang, Y.-H., Wang, C., Koutsodendris, N., Serrano, G. E., Beach, T. G., Reiman, E. M., Glass, C. K., Abu-Remaileh, M., Enejder, A., Huang, Y., & Wyss-Coray, T.
Affiliations
  • Stanford Medicine (US)
  • Gladstone Institutes (US)
  • Stanford University (US)
  • University of California San Diego (US)
  • Banner Sun Health Research Institute (US)
  • Banner Alzheimer’s Institute
  • University of California, San Francisco (US)
Venue
Nature
Published
2024-03-13
Volume
628
Issue
8006
Pages
154-161
ISSN
  • 0028-0836 (ISSN-L)
  • 1476-4687
OpenAlex
W4392762752
Cited by
536
References
61
Citations by year
2026: 204 · 2025: 236 · 2024: 94
FWCI
125.62
Type
journal article
Language
en
Open access
Locations
  • Nature (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • PubMed Central (submittedVersion, cc-by)
  • RePEc: Research Papers in Economics (submittedVersion)
  • eScholarship (California Digital Library) (submittedVersion, cc-by)
Funders
  • Pew Charitable Trusts
  • Alzheimer's Association
  • Alexander and Margaret Stewart Trust
  • Arizona Department of Health Services (P30 AG19610, U24 NS072026, 05-901, 211002)
  • Arizona Biomedical Research Commission (P30 AG19610, 05–901, U24 NS072026, 211002)
  • National Institute on Aging (RF1AG064928, P30AG019610, R03AG071791, P30AG062429, P30AG059307, P30AG072980, P30AG066515, R01AG069453, P01AG073082, T32AG000266)
  • National Institute of Neurological Disorders and Stroke (U24NS072026)
Topics
  • Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Alzheimer's disease research and treatments
  • Life Sciences › Neuroscience › Neurology › Neuroinflammation and Neurodegeneration Mechanisms
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Biochemistry › Lipid metabolism and biosynthesis
Keywords
Alzheimer's disease, APOE ε4, microglia, lipid droplets, ACSL1, lipid metabolism, amyloid-beta, triglyceride synthesis, tau phosphorylation, neurotoxicity, single-nucleus RNA sequencing, induced pluripotent stem cell-derived microglia, genetic risk factors
MeSH terms
Alzheimer Disease, Animals, Coenzyme A Ligases, Female, Humans, Male, Phosphorylation, Triglycerides, Amyloid beta-Peptides, tau Proteins, Culture Media, Conditioned, Microglia, Genetic Predisposition to Disease, Mice, Apolipoprotein E4, Induced Pluripotent Stem Cells, Lipid Droplets

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