Multi-omics reveals changes in astrocyte fatty acid metabolism during early stages of Alzheimer's disease

doi:10.21203/rs.3.rs-4976800/v1

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Multi-omics reveals changes in astrocyte fatty acid metabolism during early stages of Alzheimer's disease

https://doi.org/10.21203/rs.3.rs-4976800/v1

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Background

Astrocytes undergo extensive changes during Alzheimer's disease (AD), including reactive transformations induced by Aβ deposition and adjustments in lipid metabolism, ion balance, neuronal support, and inflammatory responses. Although dysfunctional astrocytes are known to contribute to AD progression, a detailed characterization of the dynamic alterations in astrocytes at the levels of transcriptome, proteome and metabolome during the progression of AD, especially in its early stages, is lacking.

Methods

We conducted an integrated multi-omics profiling of astrocytes obtained from APPswe/PSEN1ΔE9 transgenic AD and WT mice, including transcriptomics, proteomics, spatial metabolomics, to characterize the dynamic changes in astrocyte profiles over the course of AD progression. To investigate whether similar changes are present in early human AD and related to disease outcomes, we also analyzed single-nucleus RNA sequencing data of human brain samples, and dietary profiles and cognitive function data in human subjects to establish the link between astrocyte phenotypes and AD progression.

Results

Multi-omics profiling revealed significant changes in fatty acid metabolism of astrocytes in 6-month-old AD mice, especially deficiency in synthesis of unsaturated fatty acids. Such dysregulation in fatty acid metabolism was also observed in astrocytes from human brain samples with low AD pathology. Analysis of human dietary profiles demonstrated significant associations between dietary composition of polyunsaturated and saturated fatty acids and cognitive function.

Conclusion

Our study identified abnormal fatty acid metabolism as a hallmark of astrocytes at early stages of AD before the onset of apparent symptoms, revealing a close link between dysregulated fatty acid metabolism and disease progression.

Multi-omics

Alzheimer's Disease

astrocytes

fatty acid metabolism

Alzheimer’s disease (AD) is a progressively insidious neurodegenerative disorder characterized by relentless deterioration in memory and cognitive functions¹. Currently, therapeutic options for AD are limited, with most medications providing only temporary symptomatic relief and failing to prevent neuronal degeneration and death². Only a small fraction are considered disease-modifying^3,4. To date, no therapy has been successful in restoring or reversing the memory loss or cognitive decline associated with AD. In the pathogenesis of AD, the initial deposition of Aβ plaques is followed by the formation of neurofibrillary tangles, which progressively cause structural changes in brain tissue and neuronal loss, ultimately resulting in impairments in memory and cognitive function^5,6. Notably, the formation of Aβ plaques begins approximately 10 to 15 years before the onset of clinical symptoms⁷. Therefore, investigating the stage of AD characterized by Aβ deposition prior to clinical manifestation is crucial for understanding the disease's mechanisms and for developing interventions to inhibit disease progression before symptoms arise.

Previous research on AD primarily focused on neuronal alterations and their functional implications. However, recent studies have elucidated that the pathogenesis of AD extends beyond neuronal loss, implicating astrocytes and other glial cells as critical contributors to disease progression⁸. Astrocytes, the most abundant and largest cell type within the central nervous system (CNS), play a crucial role in maintaining brain homeostasis by regulating molecular, cellular, structural, organ, and systemic stability through various mechanisms^9,10. Throughout the progression of AD, these cells undergo significant morphological and functional transformations. At the very early stages of AD, astrocyte atrophy is observable in postmortem brain tissues from AD patients and the brain tissues of AD mouse models^11,12. However, Near Aβ plaques, these cells become hypertrophic and exhibit extensive branching, known as reactive astrocytes^13,14. Notably, the typical senile plaques of AD are composed of aggregated microglia, reactive astrocytes, Aβ deposition and deformed neurites. Astrocytes exhibit dual roles in managing Aβ, inflammation, and oxidative stress, playing complex roles in the pathology of AD^15–18. However, our understanding of astrocyte functions in AD remains fragmented. Given their multifaceted roles, single-method analyses fail to comprehensively capture the diverse changes in gene expression, metabolism, and signaling pathways of astrocytes. Multi-omics technologies offer the potential to track the dynamic progression of AD from a healthy to a diseased state, providing crucial insights into disease mechanisms and facilitating the identification of effective therapeutic targets. Despite advances in multi-omics studies highlighting astrocyte changes related to lipid metabolism regulation, ion balance, neuronal support, and inflammatory responses in AD^19–21, a comprehensive understanding of the dynamic changes of astrocytes across different stages of AD remains unknown. In particular, the roles and metabolic shifts of astrocytes in the early stages of AD are still poorly characterized.

This study employs an integrated approach combining transcriptomics, proteomics, spatial metabolomics, single-cell omics, and nutritional epidemiology to comprehensively identify crucial molecular processes during the early progression of AD. Leveraging data from both AD animal models and human clinical studies, we systematically reveal significant disruptions in fatty acid metabolism within astrocytes during the early stages of AD and link between dietary fatty acids and AD progression. These findings not only corroborate the critical link between astrocytic activities and the progression of AD but also provide new perspectives for the development of targeted therapeutic strategies and dietary interventions.

Mice

APPswe/PS1ΔE9 double-transgenic mice were sourced from the Model Animal Research Center of Nanjing University (Nanjing, China). These mice are derived from the B6.Cg-Tg (APPswe/PS1ΔE9) 85Dbo/Mmjax line, originally developed at The Jackson Laboratory (JAX#034832). Age-matched C57BL/6J wild-type (WT) littermates served as controls. Mice were housed under specific pathogen-free (SPF) conditions in individually ventilated cages (IVC) at 23°C with 50–60% relative humidity and maintained under a controlled light-dark cycle. Mice aged 21–28 days were separated from their parents for genotyping. Genomic DNA extracted from ear biopsy samples was subjected to PCR amplification using primers specific for APP and PS1 genes. All experimental protocols were conducted in accordance with guidelines approved by the Animal Use and Care Committee of the Shenzhen Peking University-The Hong Kong University of Science and Technology Medical Center (SPHMC), under protocol number 2022-014. Only male mice were included in the study. All efforts were made to minimize animal suffering and reduce the number of animals used.

Astrocytes isolation

Astrocytes were isolated from WT and AD model mice from 6-month-old. Tissue from each brain was pooled to constitute a single experimental unit. Before sample collection, mice were deeply anesthetized. The entire brain was decapitated and immediately placed in cold HBSS (Thermo Fisher, 14170138) devoid of calcium and magnesium ions, and kept on ice for subsequent processing. Enzymatic cell dissociation was performed using an Adult Brain Dissociation Kit (Miltenyi Biotec, 130-107-677) on program 37C_ABDK_01 of the gentleMACS™ Octo Dissociator with Heaters. Afterward, the cell suspension was washed with HBSS and the astrocytes were magnetically labeled with anti-ACSA-2 microbeads (Miltenyi Biotec,130-097-678), according to the manufacturer’s protocol. The resulting cell were passed through LS columns (Miltenyi Biotec, 130-042-401) placed on an OctoMACS™ manual separator and collect the ACSA-2-positive cell fraction.

Immunostaining and imaging

For fresh specimens, mice were anesthetized with pentobarbital and then perfused with ice-cold PBS until the irrigation fluid was completely clear. This was followed by a 10-minute perfusion with ice-cold 4% paraformaldehyde (PFA). The mouse brain tissues were subsequently fixed in 4% PFA for 12 hours, dehydrated in sucrose, and embedded in optimal cutting temperature compound for coronal sectioning. Sections were cut to a thickness of 20 µm.

For slicing after MALDI-MSI data acquisition, excess matrix was removed by washing in 100% ethanol (2×5 min), 75% ethanol (1×5 min) and 50% ethanol (1×5 min), after which this MSI-analyzed-tissue-section was fixed using 4% PFA for 10 min²² .

The sections were blocked and permeabilized for 1 hour in PBS containing 4% bovine serum albumin (BSA) and 0.4% Triton X-100. Anti-GFAP antibody (1:400, BOSTER, BA0056), Purified anti-β-Amyloid (1:200, 6E10, Biolegend, 803001) were incubated overnight at 4℃, followed by corresponding fluorescent-labelled secondary antibodies (Invitrogen) for 1h at room temperature and 1×DAPI for 5 min. Images were captured with Zeiss Celldiscoverer 7, OLYMPUS VS200 or 3DHISTECH Pannoramic MIDI.

RNA sequencing (RNA-seq) analysis

The RNA-seq dataset analyzed for this work was generated as part of another study²³, where it is described in detail and are accessible via the GEO series accession number GSE137028. Briefly, the dataset includes samples from both WT and AD model mice at ages of 2, 4, 6, 9, and 12 months. Triplicate samples (n = 3) were used for all genotypes and time points. Astrocytes were isolated from fresh cerebral tissues of these mice and subjected to RNA sequencing. Sequencing was performed on an Illumina NovaSeq platform and 150-bp paired-end reads were generated. A reference genome index was constructed and the paired-end reads were aligned using Hisat2 v2.0.5²⁴. Feature Counts v1.5.0-p3²⁵ was employed to enumerate the reads aligned to each gene. Subsequently, the expression level of each gene was quantified as fragments per kilobase of transcript per million mapped reads (FPKM).

For the singular value decomposition (SVD) analysis, gene expression levels were quantified using FPKM values and ranked based on the median absolute deviation (MAD) to identify the top 10,000 genes. After selection, the data underwent a logarithmic transformation (log10) followed by Z-score normalization across columns. This processing resulted in a matrix, designated as Matrix A, encompassing expression data for 9,528 genes across 30 samples. These samples represented AD and WT groups across five time points (2, 4, 6, 9, and 12 months), with three replicates each. The SVD theorem²⁶ states that the matrix A can be written as

$$\:A=U\varSigma\:{V}^{T}$$

where U (a 9528×9528 matrix) and V (a 30×30 matrix) are orthogonal and Σ (a 9528×30 matrix) is a diagonal matrix containing the singular values. These r singular values are the square roots of the non-zero positive eigenvalues of both AA^T and A^TA, sorted in descending order of magnitude. The different columns in the ΣV^T matrix correspond to the times at which the corresponding expression data are measured, each row in ΣV^T matrix represents an adjusted feature mode, capturing distinct patterns of gene expression influenced by the magnitude of the corresponding singular values. We termed the first row of ΣV^T as mode 1. The contribution of each gene to mode 1 is calculated by the squared values of the first column of the left singular vector $\:{c}_{j}={\left({U}_{j,1}\right)}^{2}$. Based on this gene ranking, we conducted a Gene Set Enrichment Analysis (GSEA)²⁷ within Gene Ontology Biological Processes (GO-BP)²⁸.

For the Short Time-series Expression Miner (STEM)²⁹ analysis, gene expression levels were quantified using FPKM values and ranked based on the MAD to identify the top 10,000 genes. After selection, calculating the ratios of the average expression levels at each time point (2, 4, 6, 9, and 12 months) for the AD group relative to the corresponding WT group averages. These ratios were then transformed using a base-2 logarithm. The processed data were imported into the STEM software for analysis. The parameters set for the analysis included a maximum number of model profiles at 20 and the clustering method as the STEM clustering method, with all other settings remaining at their default values.

Differentially expressed genes (DEGs) between specified subgroups were identified utilizing the DESeq2³⁰ package, with a significance threshold set at an adjusted p-value of less than 0.05 and an absolute log₂ fold change (log2FC) greater than 0.5. Enrichment analysis for GO-BP ²⁸ and Kyoto Encyclopedia of Genes and Genomes (KEGG)³¹ pathways was conducted using the clusterProfiler³² package. Pathway enrichment results were visualized using the R package ggplot2³³ or Cytoscape³⁴. In Cytoscape, the enrichmentMap plugin was employed to construct a similarity-based network of enriched biological processes. GO terms were represented as nodes, with edges drawn based on a combined similarity coefficient (Jaccard + Overlap score > 0.375). The Markov Cluster Algorithm (MCL) was used to annotate clusters of enriched GO terms. The genes in profile 5 were intersected with the downregulated DEGs at 6 months using the VennDiagram³⁵ package.

Quantitative RT-PCR validation of selected genes

Astrocytes isolated from the brain tissues of 6-month-old AD and WT mice were used for RNA extraction. RNA was extracted following the instructions of the RNA-easy Kit (Vazyme, R701-01). The obtained RNA was reverse transcribed into cDNA using a reverse transcription kit (Promega, 1725124). Quantitative PCR (qPCR) was performed in triplicate in 96-well plates using a qPCR machine (LC480, Roche) and the SYBR Green I Master mixture (Roche, 4887352001) to detect amplification products. The following thermocycling protocol was used: initial denaturation at 95°C for 10 minutes, followed by 40 cycles of 95°C for 15 seconds and 60°C for 1 minute, with a final cycle at 25°C for 15 seconds. Relative quantification of mRNA expression was performed using the comparative cycle threshold (Ct) method, calculated as the ratio of the gene of interest to Gapdh. Gene expression levels were quantified using the 2^−ΔΔCt method. All primers were designed using Primer Bank³⁶, and the primer sequences are listed in Additional file: Table S3.

Protein extraction and mass-spectrometry

Protein from astrocytes of 6-month-old AD and WT mice (n = 3) was extracted and digested using the iST kit (PreOmics) following the manufacturer's protocol. Briefly, the process involved lysing the cells, digesting the proteins enzymatically, and preparing the peptides for subsequent analysis. Briefly, add LYSE reagent with each astrocyte sample and incubate for 10 mins at 95°C. Centrifuge to collect supernatant. Following lysis, proceed to enzymatic digestion by adding DIGEST and RESUSPEND agents, incubate for 60 mins at 37°C, then halt the reaction with STOP solution. Purification involves twice centrifugation steps with wash solutions, followed by twice elution steps into a fresh tube. Finally, dry the purified peptide for 4D-label free liquid chromatography-mass spectrometry (LC-MS) analysis.

For label-free LC-MS analysis, samples were loaded onto a 25 cm C18 analytical column (Ionopticks) at a flow rate of 300 nL/min and subjected to gradient elution using solvents A (H₂O-FA, 99.9:0.1 v/v) and B (ACN-FA, 99.9:0.1 v/v). The gradient conditions were set as follows: from 2–22% B over 75 min, 22–37% B from 75 to 80 min, 37–80% B from 80 to 85 min, and held at 80% B for the final 5 min. The QE mass spectrometer (Thermo, USA) operated with a capillary voltage of 1.5 kV, drying gas at 180°C and a flow rate of 3.0 L/min. The mass spectrometer scanned from 100 to 1700 m/z, with collision energy settings adjustable between 20 and 59 eV to optimize ion fragmentation.

For the database search conducted using MaxQuant, utilizing the Andromeda search engine for label-free quantification (LFQ) analysis. The search was controlled with a false discovery rate (FDR) of 0.01 to limit the identification of false positives. Enzymatic digestion was modeled with trypsin allowing for up to two missed cleavages. A tolerance of 20 parts per million (ppm) for both MS and MS/MS. Fixed and variable modifications were defined as carbamidomethyl (C) and oxidation (M) respectively. The database utilized was the 'uniprot-reviewed_yes + taxonomy_10090.fasta', incorporating sequences specifically reviewed for mouse. A decoy database with a reverse pattern was used to assess the rate of false identifications.

Proteomics data analysis

For data preprocess, proteins absent in more than three samples were excluded using the 50% rule. The limma³⁷ package was used for normalization and differential expression analysis between the WT and AD groups, setting a threshold of p < 0.05 and an absolute log2FC greater than 0.3 to identify differentially expressed proteins (DEPs). Gene regulation at the RNA and protein levels was compared using the VennDiagram³⁵ package. Pathway analysis was performed using KEGG³¹ pathways as described in the RNA-seq analysis section.

Sample preparation for matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI)

For 6-month-old AD and WT mice (n = 3) anesthetized and killed by cervical dislocation. 10% carboxymethyl cellulose (Sigma) was used to embed brain tissue in the cassettes. The cassettes were then immediately submerged in an isopentane-dry ice bath until the medium solidified and turned white.

Tissues were stored at -80°C prior to analysis. One hour before use, they were transferred to -20°C, and sections were then prepared using a Leica CM1950 cryostat (Leica Microsystems GmbH). Tissue sections, 20 µm thick, were thaw-mounted onto ITO-coated microscopic slides (Bruker) for MALDI-MSI analysis. Adjacent sections were collected for immunofluorescence staining. The mounted tissue sections were dried for 30 minutes in a desiccator before matrix application, and subsequently vacuum-sealed and stored at -80°C.

The matrix consisted of 9-AA dissolved in 70% ethanol for MALDI-2 was prepared and applied to the conductive slides bearing tissue sections using a TM-Sprayer (HTX Technologies). The parameters of the matrix application set in the TM-sprayer were as follows: spray nozzle velocity (1200 mm/min), track spacing (3 mm), flow rate (0.12 mL/min), spray nozzle temperature (90°C), and nitrogen gas pressure (10 psi).

Mass spectrometry imaging

Metabolites in the samples were imaged using a timsTOF fleX MALDI 2 (Bruker) equipped with a 10 kHz smartbeam 3D laser. MALDI2 MSI was operated in positive ion mode in full scan mode for m/z 100–1200. MALDI2 MSI was performed at 20 µm spatial resolution. The frequency of the laser was set to 10,000 Hz, with 60% the laser energy and 400 laser shots per pixel.

MALDI-MSI Data Analysis

Raw imaging data were processed using SCiLS™ Lab 2021 (Bruker Daltonics), yield two datasets: the first detailing mass spectrometry data for each spot, including m/z values and signal intensities, and the second listing the X and Y coordinates of each spot. Metabolite identification was achieved by matching the accuracy of m/z values (< 10 ppm) against an in-house database and the Bruker Library MS-Metabobase 3.0 database.

A SeuratObject was subsequently constructed utilizing the Seurat³⁸ package. Data normalization employed a dual strategy involving both 9-AA reference compound normalization and total ion count (TIC) normalization. To correct for batch effects in the analysis, we applied the ComBat method from the sva³⁹ package.

This dataset was further used for uniform manifold approximation and projection (UMAP) analyses⁴⁰. UMAP reduction results (cell.embeddings) were then used as input data for k-means clustering, specifying the number of clusters (centers) as five and visualized using ggplot2³³ package. Adjusted to four clusters to better reflect underlying patterns. The differential abundance of metabolites between clusters were analyzed using FindAllMarkers function in Seurat³⁸, with criteria of average log2FC greater than 0.5 and pct.1 greater than 0.7.

Regions of interest (ROIs) were designated by identifying areas with accumulations of reactive astrocytes in the immunofluorescence-stained sections from MALDI-MSI analysis and their adjacent sections. Using a QuPath to SCiLS™ Lab software plugin in QuPath⁴¹, the delineated ROI files were then transferred into SCiLS™ Lab, where the position, contours, and names of the regions were displayed. Subsequently, this information was exported to previously constructed SeuratObject. Spots that were both within interested k-means cluster and belonged to the ROIs were identified. Differential expression analysis between spots within the ROIs and those outside it was performed using the FindMarkers function. Metabolic markers were identified based on criteria of an absolute log2FC greater than 0.5 and a p-value less than 0.05.

For integrated analysis, genes that are downregulated in astrocytes at the RNA level at 6 months of age, along with their fold changes, and the fold changes of all metabolites when comparing the ROIs to the unselected regions of cluster 1 were used as input for the Joint Pathway Analysis function in the MetaboAnalyst⁴² tool. This analysis was conducted to elucidate the interactions at the pathway level between transcriptomics and metabolomics, results were displayed as enriched KEGG pathways.

Western blot

Three 6-month-old mice were anesthetized and euthanized using the same method as previously described. The entire right hemisphere of the mouse brain and the left cortex and hippocampus were dissected. Tissues were lysed by adding a protein lysis buffer containing RIPA (Beyotime), 10% protease-inhibitor cocktail (Roche), and 1% phosphatase inhibitor (Thermo Fisher), followed by homogenization using a tissue lyser (IKA) in an ice-water mixture. Lysates containing 1 × SDS-PAGE Sample Loading Buffer (Beyotime) were denatured for 10 min at 95°C. Samples were separated on 12% SDS-PAGE and then transferred to PVDF membranes. Membranes were blocked with 5% skimmed milk (BBI life science) in tris-buffered saline tween (TBST) and incubated with primary antibodies at 4°C overnight and then washed and incubated with horseradish peroxidase (HRP)-conjugated secondary antibodies for 1 h at room temperature. Finally, membranes were visualized with ECL substrate (Millipore). The primary and secondary antibodies used in this study were as follows: Perilipin-2/ADFP (1:500, Novus Biologicals, NB110-40877), GAPDH (1:1000, CST, 2118S), and HRP-AffiniPure Goat Anti-Rabbit IgG (H + L) (1:10000, Jackson ImmunoResearch, 111–035 − 003).

Single-nucleus RNA sequencing (snRNA-seq) analysis

The snRNA-seq data utilized in this study were obtained from the Allen Institute for Brain Science and are available through the Seattle Alzheimer’s Disease Brain Cell Atlas (SEA-AD)⁴³ database (https://registry.opendata.aws/allen-sea-ad-atlas). The database contains sequencing samples from the temporal neocortex of 84 donors, who were elderly with ages at death ranging from a minimum of 65 to an average of 88 years. Based on quantitative neuropathological assessments, these individuals were classified into different stages of Alzheimer's disease neuropathologic change (ADNC): 9 donors as Not AD, 12 as Low, 21 as Intermediate, and 42 as High ADNC. For the purposes of this research, data about astrocytes of 9 donors without Alzheimer's neuropathologic changes and 12 with low levels of neuropathologic changes were downloaded for analysis.

For data preprocessing, the downloaded counts table and metadata were used to construct a SeuratObject utilizing the Seurat³⁸ package. Quality control was then applied with criteria specifying that each cell must have more than 2,000 but fewer than 7,500 RNA sequencing features, and mitochondrial gene content less than 4%. Data normalization was carried out using the LogNormalize method with a scaling factor set at 10,000. Principal component analysis (PCA) was performed based on the top 2,000 most variable genes, followed by UMAP analysis using the top 30 principal components. Clustering was executed at a resolution of 0.5.

For the single-cell metabolic pathway analysis, the SCPA⁴⁴ R package was utilized, offering a novel graph-based test to define pathway activity. Normalized data were derived from astrocytes with low ADNC and no ADNC. A comprehensive set of 243 metabolic pathways was compiled from the Hallmark⁴⁵, KEGG³¹, and Reactome⁴⁶ databases. Pathway comparisons were executed using the compare_pathways() function in SCPA, setting downsample = 5000 to manage data volume. The fatty acid metabolism score for each cell was calculated based on the expression levels of 157 genes from the "HALLMARK_FATTY_ACID_METABOLISM" pathway.

$$\:G=\left\{{g}_{1},{g}_{2},\dots\:,{g}_{n}\right\},\:\text{w}\text{i}\text{t}\text{h}\:n=157$$

$$\:S\left({c}_{i}\right)=\sum\:_{j=1}^{n}E({g}_{j},{c}_{i})$$

where $\:G$ represents the genes in the pathway, $\:{g}_{j}$ represents a specific gene within the pathway, and $\:S\left({c}_{i}\right)$ denotes the fatty acid metabolism score of cell $\:{c}_{i}$.

Analysis of dietary fatty acids and cognitive function

The National Health and Nutrition Examination Survey (NHANES) is a cross-sectional survey, designed to provide a representative sample of the US non-institutionalized civilian population⁴⁷. For these analyses, the two cycles (2011/2012, 2013/2014) with information on saturated fatty acids (SFA), monounsaturated fatty acids (MUFA), and polyunsaturated fatty acids (PUFA) intake as well as cognitive performance measures, were used to conduct a cross-sectional analysis in the combined set of participants.

Total estimated dietary SFA, MUFA, and PUFA intake (grams, gm), was averaged over the two recall periods (if only the first day was available, that value was used instead of an average). Additionally, participants' intake from dietary supplements during the same periods was considered, with average intakes over the two days calculated when data was available. Total intake of SFA, MUFA, and PUFA was then derived by summing dietary and supplemental sources. Finally, the proportion of each type of fatty acid relative to the total fatty acids was calculated and labeled accordingly as SFAT, MFAT, and PFAT.

For 2011–2014, cognitive testing was performed for participants aged 60 years and older. There were three tests administered: the CERAD Word Learning sub-test (CERAD W-L) to assess immediate and delayed recall of new verbal information (memory sub-domain); the Animal Fluency test to assess categorical verbal fluency (component of executive function); and the Digit Symbol Substitution Test (DSST) to assess processing speed, sustained attention, and working memory.

The analysis encompassed a pooled dataset of 2,502 individuals aged 60 years and older from two survey cycles. Each participant had completed assessments for cognitive performance and provided detailed data on SFA, MUFA, and PUFA, as well as information on critical confounding factors including age, sex, body mass index (BMI), smoking status, alcohol consumption, family income (poverty income ratio, PIR), and educational attainment. Confounding factors are controlled by incorporating them as covariates within the regression model.

Data analysis was carried out using the survey⁴⁸ R package. The Kruskal-Wallis test was first used to explore the relationship between quartile-based categories of fatty acid intake and cognitive function scores across four tests. Subsequently, linear regression models were employed to examine the relationships between fatty acid intake proportions and cognitive measures. Fatty acid intake was considered both as quartiles and as a continuous variable in the analysis.

Statistical analysis

For each computational analysis undertaken using previously published codes or resources, the statistical methodologies were adopted as specified in the respective publications and detailed in the corresponding sections of the methods. For bulk-RNA sequencing data, differential expression analysis was conducted using the `DESeq()` function with default settings from the DESeq2³⁰ package. In proteomics analysis, the `eBayes()` function from the limma³⁷ package was applied, also under default settings. Spatial metabolomics data were analyzed using the `FindMarkers` or `FindAllMarkers` functions with the Wilcoxon test (`test.use = 'wilcox'`) from the Seurat³⁸ package. snRNA-seq data were analyzed using the `compare_seurat()` function within the SCPA⁴⁴ package. In vitro experiments involved statistical comparisons across multiple experimental groups using one-way analysis of variance (ANOVA) complemented by Dunnett's multiple comparison test, while the two groups’ comparisons were handled through unpaired t-tests.

Data and code availability

The proteomics data generated in this study have been deposited in iProX (IPX0009463000) and are publicly available. The spot and spectra data related to MALDI-MSI have been deposited in Figshare (https://doi.org/10.6084/m9.figshare.26778319.v1) and are publicly available. All original code has been deposited in GitHub (https://github.com/Jie-Z-159/AD-astrocytes-multiomics.git) and is publicly available.

Astrocytes undergo significant changes in fatty acid metabolism prior to the onset of AD symptoms

To study the dynamic changes in astrocytes during AD, we conducted RNA-seq on astrocytes from AD and WT mice at five time points (2, 4, 6, 9, and 12 months of age) (Fig. 1A). Previous studies on the APPswe/PSEN1ΔE9 transgenic AD mouse model^49,50, corroborated by our immunofluorescence validation results (Additional file: Fig. S1), indicate that 2 months of age corresponds to the maturation to early adulthood in mice, during which no significant differences are observed between AD and WT mice. By 6 months, noticeable Aβ deposition can be observed in the hippocampus and cortical regions of AD mouse brains, and no significant behavioral abnormalities are yet apparent compared to WT mice. This stage represents the early progression of AD. At 12 months, substantial Aβ plaque formation is accompanied by evident memory and cognitive impairments in AD mice, indicating the stage where clinical symptoms become apparent^51–53. These five selected time points encompass the entire disease progression in the AD mouse model, providing a foundation for dynamic observational analysis.

In the analysis of DEGs across various time points, we found that no single gene was consistently upregulated or downregulated throughout the entire series (Additional file: Fig. S2A-B), suggesting complex dynamic changes that affect different sets of genes at different stages of AD progression. We then employed SVD to identify representative dynamic patterns in gene expression during the time course. Our analysis revealed that the first singular value (519.44) is significantly higher than the second (75.47) and subsequent singular values (Fig. 1B), suggesting a consensus dynamic pattern that dominates the expressional dynamics of most genes. This consensus dynamic pattern is reflected by the first row of the ΣV^T matrix associated with the first singular value, which we termed as mode 1. The profile of mode 1 showed significant difference between AD and WT astrocytes at 6 months of age (t-test, p-value = 0.002, Fig. 1C). After ranking genes based on their contribution to mode 1, we employed GSEA to assess pathway enrichment. The analysis revealed that the top 15 enriched pathways are primarily associated with organic acid metabolism, metal ion homeostasis, neuroprotection, and oxidative stress (Fig. 1D).

To further investigate the trends in gene expression during AD progression and identify important functions dysregulated at 6 months of age, we computed log₂(AD/WT) values of gene expression levels to quantify the transcriptomic alterations at each time point, and performed STEM to identify representative dynamics in those transcriptomic changes. The results revealed that profiles 5, 10, and 16 were statistically significant, with the top two profiles, profile 16 and profile 5, showing notable differential expression in the AD group compared to the WT group at 6 months (Fig. 1E). GO-BP analysis revealed that genes within profile 16 are predominantly associated with signal transduction and gene expression regulation (Additional file: Fig. S2F). while profile 5 is strongly associated with organic acid metabolic processes, which is consistent with pathways enriched in mode 1 in our previous GSEA results (Fig. 1F). Given this, we focused on profile 5, encompassing 416 genes that show significant downregulation in astrocytes of AD mice compared to WT mice at 6 months, with less pronounced changes at other time points. Further analysis intersected the genes in profile 5 with the downregulated DEGs at 6 months, resulting in 244 overlapping genes (Fig. 1G-H). Additionally, we selected the top 6 genes from these 244 for validation using qPCR (Additional file: Fig. S2C-D). KEGG pathway analysis of these 244 genes revealed that 4 out of the 5 enriched pathways were directly related to fatty acid metabolism, including the PPAR signaling pathway, biosynthesis of unsaturated fatty acids, fatty acid elongation, and fatty acid metabolism (Fig. 1I). These findings suggest that 6 months is a critical time point for changes in astrocytes in AD, characterized by significant disruptions in fatty acid metabolism.

Downregulation of fatty acid metabolism-related proteins in astrocytes at early stages of AD

To investigate the protein-level changes in astrocytes at 6 months, we collected astrocytes from 6-month-old AD and WT mice for proteomic analysis. Normalized data were subjected to PCA analysis, revealing distinct separation between AD and WT samples in three-dimensional space (Fig. 2A). We then compared the most significantly altered proteins between AD and WT mice, identifying a total of 584 DEPs, with 395 upregulated and 189 downregulated (Fig. 2B). The upregulated proteins in astrocytes from 6-month-old AD mice were involved in pathways such as synaptic vesicle cycling, endocytosis, and endocrine-regulated calcium reabsorption (Fig. 2C). These pathways indicate an increased activity of biological processes closely related to neurotransmission and intracellular signal transduction. The pathways associated with the downregulated proteins include butanoate metabolism, valine, leucine and isoleucine degradation, the spliceosome, PPAR signaling pathway (Fig. 2D). Notably, butanoate metabolism and the PPAR signaling pathway are related to fatty acid metabolism. Butyrate, a short-chain fatty acid, can be transported to the brain through the bloodstream and is believed to benefit cellular energy metabolism, neuroprotection, and anti-inflammatory processes⁵⁴. Downregulation of the PPAR signaling pathway was also observed at the RNA level. PPARs are nuclear receptors playing a central role in fatty acid metabolism, including facilitating fatty acid uptake, transport, and breakdown⁵⁵. We also performed an integrative analysis of transcriptomic and proteomic data for astrocytes at 6 months. Although the correlation of fold change between the transcriptomics and proteomics is weak (Pearson correlation = 0.11, Fig. S3A), genes differentially regulated at the transcriptomic and proteomic levels show significant overlap (hypergeometric test p-value = 8.32×10^− 9 for up-regulated genes and 2.06×10^− 8 for down-regulated genes, Fig. S3B-C). Among the 23 overlapping downregulated genes (Additional file: Fig. S3C), GO-MF pathway analysis showed significant associations with fatty acid ligase activity and other related pathways (Additional file: Fig. S3F), indicating changes in the activity of fatty acid metabolism at both transcriptomic and proteomic levels.

Alterations of fatty acid-related metabolites in regions of reactive astrocyte aggregation

As previously mentioned, astrocytes in AD mice exhibit signs of disrupted fatty acid metabolism at both RNA and protein levels. This finding prompted us to further investigate the region-specific changes in metabolite levels in the brain at this stage. We conducted MALDI-MSI on fresh frozen brain tissue from 6-month-old AD and WT mice with a resolution of 20 µm (Fig. 3A). After data preprocessing, peak annotation, and batch effect removal (Methods, Fig. S4A-B), we identified 188 compounds covering various categories such as lipids, carbohydrates, amino acids, and others (Fig. 3B). We then performed k-means clustering to characterize the spatial heterogeneity of metabolite abundances, categorizing all spots on the brain sections into four clusters with distinct metabolic profiles (Fig. 3C). Comparison with the mouse brain atlas (Fig. S4C) shows that the four clusters closely match the anatomical structure of the brain tissue. Cluster 1 predominantly covered the hippocampus and cortex; cluster 2 was mainly located in the lateral ventricle or non-tissue areas caused by sectioning; cluster 3 concentrated in the fiber tracts, thalamus, and hypothalamic lateral zone; and cluster 4 involved the periventricular region, hypothalamic medial zone and part of the midbrain. For each cluster, we identified metabolic markers as metabolites significantly enriched in that cluster compared to other clusters (average log2FC > 0.5 and adjusted p-value < 0.05, Fig. 3D). The spatial distribution of metabolites is consistent with structure and function of brain regions. For instance, markers of cluster 3 included many sulfatides (ST), which are primarily found in the myelin sheath of the CNS produced by oligodendrocytes. This cluster encompassed regions with dense nerve fiber bundles (axons) tightly wrapped in myelin, demonstrating that the abundance of ST in this cluster is consistent with regions of intense myelination.

Among the four clusters, cluster 1, which colocalized with critical AD-related regions, hippocampus and cortex, was significantly enriched with various fatty acids such as palmitelaidic acid, docosahexaenoic acid (DHA), arachidonic acid, dinoelaidic acid, dihomo-gamma-linolenic acid, and palmitic acid. Fluorescence staining revealed that Aβ plaques predominantly form in these regions by 6 months (Fig. S1). Damage to the cortex and hippocampus is closely linked to the core symptoms of AD, including memory loss and cognitive decline. This finding further highlights the crucial role of fatty acid metabolism in the hippocampus and cortex, and its potential association with Alzheimer's disease. Furthermore, the lipid droplet-associated protein Perilipin 2 (Plin2) was also highly expressed in the hippocampus and cortex compared to the whole brain (Fig. 3E), indicating higher fatty acid content in these regions, consistent with the spatial metabolomics.

To further investigate astrocyte-specific metabolic activities, the MALDI-MSI analyzed brain slices and the adjacent slices were subsequently stained with the reactive astrocyte marker GFAP⁵⁶ and imaged using immunofluorescence microscopy (Additional file: Fig. S4D) to identify areas with aggregation of reactive astrocytes, our ROIs (Fig. 3F). Senile plaques are complex structures formed by Aβ deposition, deformed neurites, reactive astrocytes, and activated microglia⁵⁷. Therefore, our defined ROIs is closely associated with the core pathological regions of AD. Since reactive astrocytes predominantly appeared in the hippocampus and cortex of AD brain slices, which corresponded largely with cluster 1, we selected the ROIs within cluster 1 of the AD brain slices and compared them to the unselected regions of cluster 1 to characterize the features of the reactive astrocytes more accurately. We identified six metabolites that exhibited significant differences in the ROIs compared to other unselected areas within cluster 1 (Fig. 3G-H). Among these, two upregulated metabolites were identified as phosphatidylinositol (PI) compounds, namely PI(40:6) and PI(38:6). PI compounds are known to play critical roles in signal transduction and the maintenance of cellular membrane integrity. The four downregulated metabolites included adenosine-5'-triphosphate (ATP), phosphoenolpyruvic acid, 2-aceto-2-hydroxy-butanoate, and phosphatidylserine (40:4) (PS (40:4)). Previous studies have reported that glucose metabolism and glycolytic pathways are generally impaired in the brains of AD patients compared to normal controls, with a marked reduction in ketone body metabolites such as β-hydroxybutyrate⁵⁸. The observed downregulation of ATP and phosphoenolpyruvic acid in our results further underscores the energy deficits in the pathological core regions. PS compound, is associated with the activation of signals such as Akt, regulation of synaptic receptors, and storage of DHA⁵⁹. Regarding 2-aceto-2-hydroxy-butanoate, it is known as an α-hydroxy acid, though reports on this metabolite are currently limited.

To elucidate the relationship between gene expression and metabolite alterations, we performed an integrative analysis combining transcriptomic and spatial metabolomic data. Our dataset comprised genes downregulated at the RNA level in astrocytes at 6 months of age, along with their respective fold changes, and the fold changes of all metabolites when comparing ROIs to the unselected regions of cluster 1. These altered genes and metabolites were subsequently mapped onto known KEGG biological pathways to identify pathways significantly altered at both transcriptomic and metabolomic levels. The results revealed enrichment in several KEGG pathways, with synthesis of unsaturated fatty acids ranking first (Fig. 3I), suggesting a connection between the downregulated genes and changes in metabolite abundances in astrocytes that lead to a deficiency in unsaturated fatty acid synthesis within the pathological core regions of 6-month-old AD mouse models.

To summarize, by an integrated analysis of transcriptomics, proteomics, and spatial metabolomics data in a mouse model of AD, we have identified dysregulation of fatty acid metabolism, especially that related to unsaturated fatty acids, as a critical phenotype of astrocytes in early progression of AD. Next, we will extend our analysis to determine whether the relationship between fatty acid metabolism and AD is conserved in human.

Human astrocytes have deficiency in fatty acid metabolism in early AD

To extend our findings from animal models to human pathology, we analyzed single-nucleus RNA sequencing data from the Seattle Alzheimer’s Disease Brain Cell Atlas (SEA-AD)⁴³ (Fig. 4A). This dataset includes samples from longitudinally characterized brain donors, classified into no AD, low, intermediate, or high pathology states according to the ADNC grades developed by the National Institute on Aging and Alzheimer's Association (NIA-AA). We selected transcriptomic profiles of astrocytes from individuals with low ADNC (n = 12) to represent the state of early AD and compared them to control individuals with no AD neuropathological change (n = 9) from the SEA-AD dataset (Fig. 4B-C).

To investigate the differentially regulated metabolic pathways between astrocytes in the low ADNC group and those in the control group with no AD, we performed single-cell pathway analysis using the R package SCPA⁴⁴. SCPA uses a graph-based non-parametric statistical model to assign each pathway a score, which reflects the extent of differential distribution of genes in that pathway between low ADNC group and no AD group. We analyzed a total of 243 metabolic pathways obtained from the Hallmark⁴⁵, KEGG³¹, and Reactome⁴⁶ databases using SCPA. These pathways were ranked based on their SCPA score (Fig. 4D), among which "Hallmark fatty acid metabolism" ranked second, highlighting significant differences in astrocytic fatty acid metabolism between the early pathological stage and no pathology in human AD data (Fig. 4E). The top ten differentially regulated metabolic pathways included Reactome carbohydrate metabolism (first), Reactome amino acid and derivative metabolism (third), Hallmark glycolysis (fourth), and KEGG oxidative phosphorylation (fifth). Notably, all these pathways exhibited negative fold changes in the low ADNC group compared to the no ADNC group (log2FC<-1 for all pathways, Fig. 4D), indicating downregulation in gene expression consistent with previous findings based on transcriptomics, proteomics, and spatial metabolomics in mouse model, further suggesting deficiency of energy metabolism in astrocytes during early AD. To further confirm the downregulation of fatty acid metabolism in astrocytes during early AD, we assessed the activity of fatty acid metabolism in astrocytes at the single-cell level using the expression values of 157 genes within the Hallmark fatty acid metabolism pathway to compute a fatty acid metabolism score for each cell (Methods). We found that astrocytes from low ADNC group have significantly lower activity of fatty acid compared to the no AD group (t test, p-value = 2.22×10^− 16, Fig. 4F). The top ten genes with the most significant changes within the fatty acid metabolism pathway included PCBD1 (pterin-4 alpha-carbinolamine dehydratase 1), MIF (macrophage migration inhibitory factor), and ADIPOR2 (adiponectin receptor 2) (Fig. 4G). These results suggest that abnormal fatty acid metabolism is also a metabolic marker of astrocytes during early progression of AD in human.

Dietary composition of fatty acids is associated with cognitive function

Our previous analyses have revealed that during early AD progression, dysregulation of fatty acid metabolism, especially those related to unsaturated fatty acids, occurs in astrocytes of both mice and humans. This finding prompts us to further investigate the potential links between dietary intake of different types of fatty acids and human cognitive function. Utilizing dietary intake profiles and cognitive function data of 2,502 human participants from the NHANES database⁴⁷, we evaluated the associations between dietary intake proportions of three types of fatty acids (SFA, MUFA and PUFA) and cognitive outcomes (Fig. 5A). The majority of these participants (52%) were aged between 60 and 69 years, with a relatively even gender distribution. The median intake of fatty acids among the selected participants was 21 gm/day for SFA, 24 gm/day for MUFA, and 15 gm/day for PUFA. In terms of cognitive function scores, the median CERAD total score (immediate word recall scores from three trials, excluding delayed recall) was 20, the median CERAD delayed recall score was 7, the median animal fluency test score was 18, and the median digit symbol substitution test score was 54 (Additional file: Table S1).

To investigate the relationship between the proportions of different dietary fatty acids and cognitive function, we categorized the SFAT, MFAT, and PFAT into quartiles (Q1 to Q4, with Q1 being the lowest intake and Q4 the highest) as categorical variables. Using the R package survey⁴⁸, we constructed a complex sampling design and applied the Kruskal-Wallis test to analyze the associations between the intake proportions of these three fatty acids and four cognitive function scores (Fig. 5B and Additional file: Table S2). The Kruskal-Wallis test results indicated SFAT and PFAT were both significantly associated with the four cognitive function scores, whereas MFAT showed no significant associations with these scores. This suggests that different types of dietary fatty acids may have varying impacts on cognitive health.

To further explore the impact of different dietary fatty acid proportions on cognitive functions, we constructed multivariate linear regression models for weighted complex survey data (Fig. 5C). The analysis included both full models (incorporating fatty acid intake proportions) and quartile regression models (comparing Q4, Q3, Q2 with Q1 for fatty acid intake proportions). The covariates considered were gender, age, race/ethnicity, BMI, household income, education level, alcohol consumption, and smoking status. Our analysis revealed that PFAT had positive β values for all four cognitive function scores. Notably, the β values comparing Q3 to Q1 for PFAT were significant (p-value < 0.05) for both the CERAD total score and delayed recall score. Conversely, SFAT exhibited predominantly negative β values across the four cognitive function scores, with a β value of -4.3 for SFAT in the animal fluency test score, which was statistically significant (p-value < 0.05). The β values for MFAT were generally close to zero, with no significant p-values observed for any of the cognitive function scores. These linear regression results align well with the findings from the previous Kruskal-Wallis tests. Collectively, the results suggest that a higher proportion of SFA intake has a negative association with cognitive function, while a higher proportion of PUFA intake has a positive association. In contrast, the proportion of MUFA intake appears to have a negligible association on cognitive function.

AD is a neurodegenerative disorder characterized by profound complexity⁶⁰. Astrocytes remain insufficiently explored regarding their dynamic roles. Integrating data from AD animal models and human clinical studies, we systematically confirm the disruption of fatty acid metabolism in astrocytes during early AD at the RNA, protein, and metabolite levels. This cellular-level focus differentiates our work from broader studies, providing detailed insights into the role of astrocytes in AD progression. Furthermore, nutritional epidemiology highlights the complex interplay between fatty acids and AD pathology.

Our analysis emphasizes the dysfunction of fatty acid metabolism in astrocytes. This finding not only provides an entry point for exploring the mechanisms of astrocyte function, but also offers a scientific basis for the early diagnosis and treatment of AD. Specifically, we found that in early AD, the genes Fabp7, Slc27a1, and Hmgcs1, which are related to fatty acid metabolism, are downregulated at both RNA and protein levels in astrocytes (Additional file: Fig. S3D). These genes are involved in the PPAR signaling pathway and PPARs have been proposed as a potential therapeutic target for AD^61,62. Our research suggests that Fabp7, Slc27a1, and Hmgcs1 may also serve as candidate targets, underscoring the significance of the PPAR signaling pathway in early AD astrocytes.

Additionally, previous research has reported that lower levels of DHA in the blood may be associated with mild cognitive impairment (MCI) or AD⁶³. Supplementation with omega-3 fatty acids has shown potential in improving cognitive functions in mild AD patients⁶⁴. However, definitive evidence confirming fatty acids as biomarkers for AD is still lacking. Combined with our findings, leveraging fatty acids, particularly polyunsaturated fatty acids, for the prediction and dietary intervention of AD holds great promise.

Our study has observed early disturbances in fatty acid metabolism of astrocytes, yet the mechanisms behind such alterations and how they connect to subsequent cognitive impairment remain unclear. Existing research already suggested that astrocytes are the primary cell type responsible for fatty acid metabolism in brain tissue⁶⁵. Furthermore, fatty acids and their metabolites play a significant role in various neurological functions, including cell survival⁶⁶ and synapse formation⁶⁷. Additionally, the presence of the APOE4 gene may impair the ability of astrocytes to metabolize fatty acids, leading to abnormal lipid accumulation within these cells. Such lipid accumulation could potentially play a role in the progression of neurodegenerative diseases⁶⁸. However, understanding the mechanisms requires further investigation. For instance, an initial step could involve establishing an in vitro co-culture system with Aβ₄₂ oligomers to simulate the AD microenvironment. This methodology permits the observation of changes in fatty acid metabolism and facilitates the investigation of interactions between astrocytes and other cells such as neurons and microglia under conditions of induced fatty acid dysregulation. Subsequent investigations may employ in vivo models combined with behavioral experiments to assess changes in cognitive function.

To achieve a comprehensive understanding of the alterations in astrocytic metabolism during AD, there is still room for further exploration. For instance, in vivo 13C-Metabolic Flux Analysis (13C-MFA) that directly measures metabolic fluxes related to fatty acids can be conducted to validate our findings and precisely analyze the metabolic changes within astrocytes in AD. However, 13C-MFA is specifically designed to quantify fluxes within small metabolic networks (typically central carbon metabolism), and its implementation can be both experimentally and computationally costly, especially for in vivo systems⁶⁹. Furthermore, the current limitations of spatial metabolomics in metabolite identification and resolution, combined with a lack of information on astrocytic metabolic biomarkers, hindered this study’s ability to identify the specific metabolites associated with fatty acid metabolism in early AD astrocytes. Moreover, the animal model used in this experiment, the APP/PS1 mouse, does not simulate tau protein-related neurofibrillary tangles, which is also a limitation of this study. Nevertheless, despite these limitations, our work provides clues for advancing the understanding of astrocytic fatty acid metabolism during early AD, providing insights that could inform the development of future diagnostic and therapeutic strategies.

Declaration of interests

The authors declare no conflict of interest.

Author Contribution

J.Z. designed the study, performed the experiments, analyzed the data, and wrote the manuscript. M.H.L. performed the experiments. Z.W.D. conceived and supervised the study, analyzed the data, wrote the manuscript, and provided funding. J.W. conceived and supervised the study, and provided funding.

Acknowledgement

This work was supported by National Key Research and Development Program of China (2021YFF1201000 to Z.D.), Guangxi Natural Science Foundation (2020GXNSFDA238009 to J.W.), National Natural Scientific Foundation of China (82071586 to J.W. and 12371489 to Z.D.), Guangdong Innovative and Entrepreneurial Research Team Program (2021ZT09Y104 to J.W.), Guangdong Program (2021QN02Y856 to Z.D.) and Shenzhen Science and Technology Basic Research Program (JCYJ20220818100215033 to J.W.). We are grateful for the support of the Shenzhen - Peking University - the Hong Kong University of Science and Technology Medical Center and Southern University of Science and Technology Platform.

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No competing interests reported.

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Multi-omics reveals changes in astrocyte fatty acid metabolism during early stages of Alzheimer's disease

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Abstract

Background

Methods

Results

Conclusion

Figures

Background

Methods

Mice

Astrocytes isolation

Immunostaining and imaging

RNA sequencing (RNA-seq) analysis

Quantitative RT-PCR validation of selected genes

Protein extraction and mass-spectrometry

Proteomics data analysis

Sample preparation for matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI)

Mass spectrometry imaging

MALDI-MSI Data Analysis

Western blot

Single-nucleus RNA sequencing (snRNA-seq) analysis

Analysis of dietary fatty acids and cognitive function

Statistical analysis

Data and code availability

Results

Astrocytes undergo significant changes in fatty acid metabolism prior to the onset of AD symptoms

Downregulation of fatty acid metabolism-related proteins in astrocytes at early stages of AD

Alterations of fatty acid-related metabolites in regions of reactive astrocyte aggregation

Human astrocytes have deficiency in fatty acid metabolism in early AD

Dietary composition of fatty acids is associated with cognitive function

Discussion

Declarations

Declaration of interests

Author Contribution

Acknowledgement

References

Additional Declarations

Supplementary Files

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