Single nuclei RNA-Seq (snRNA-seq) data and metadata were accessedfrom their respective repositories: the prefrontal cortex from the Accelerating Medicines Partnership Alzheimer’s Disease Project (AMP-AD) Knowledge Portal under the Religious Orders Study and Memory and Aging Project (ROSMAP)¹,², and the entorhinal cortex from a data repository provided³ by Grubman et al. (2019). Data from the entorhinal cortex may also be accessed from the Gene Expression Omnibus under the accession number GSE138852. Access to the prefrontal cortex dataset requires a formal request to ROSMAP. To enable other researchers to explore these datasets, all code necessary for recreating the reported analyses and figures within R, are available on Github at https://github.com/stebel5/AD_APOE_snRNAseq.

We acquired publicly available snRNA-seq datasets from repositories specified by the first two single-cell transcriptomic AD studies (Grubman et al., 2019; Mathys et al., 2019). The prefrontal cortex dataset comprised 17,296 genes and 70,634 cells while the entorhinal cortex comprised 10,850 genes and 13,214 cells. Across both datasets, there was an overlap of 9,868 genes. Samples were classified based on tau neurofibrillary tangles, and Aβ plaque burden, using Braak clinical staging and Consortium to Establish a Registry for Alzheimer’s Disease (CERAD) scores, respectively (Mirra et al., 1991). Cases were identified as individuals with severe tau deposition (Braak stage ≥ 4) and high Aβ load (CERAD score ≤ 2), while non-AD controls were identified as individuals with low tau deposition (Braak stage ≤ 3) and low Aβ load (CERAD score ≥ 3). We also restricted our analysis to include samples with APOE3/3 (homozygous for allele ε3) and APOE3/4 (heterozygous ε3/ε4) genotypes due to the limited number of samples for relatively rare APOE genotypes (Figure 1).

The prefrontal cortex dataset initially consisted of age and sex matched samples from 48 individuals with varying levels of AD pathology. For the prefrontal cortex APOE genotype-stratified analysis, samples consisted of 14 APOE3/3 controls, 1 APOE3/4 control, 9 APOE3/3 cases and 8 APOE3/4 cases (Table 1).

The entorhinal cortex dataset initially consisted of age and sex matched samples from 6 AD and 6 control subjects, as indicated by Grubman et al. (2019). Cases were noted to have a history of AD, while controls had no history of AD or cognitive impairment, as reported by treating general practitioners. Braak scores were provided only for cases, while clinical history and amyloid pathology records were provided for all subjects. Amyloid pathology information was provided using the categories: “Numerous diffuse and neuritic Aβ plaque,” “Occasional diffuse plaque in cortex,” and “None.” Using criteria from the Rush Alzheimer’s Disease Center clinical codebook provided with the prefrontal cortex dataset, we converted these measures of neuritic plaques into CERAD scores of 1 (Definite), 3 (Possible), and 4 (No AD), respectively. This allowed us to systematically identify cases and controls in both datasets using the same criteria. For the entorhinal cortex APOE genotype-stratified analysis, samples consisted of 4 cases, and 5 controls (Table 2). Three of the cases were from APOE3/4 individuals, while one was from an APOE3/3 individual, and of the controls, four were from APOE3/3 individuals and the one was from an APOE3/4 individual.

All data processing was conducted separately for each dataset with (R Core Team, 2020) version 4.0.0 (2020-04-24) using RStudio (R Studio Team, 2020), using Seurat (Stuart et al., 2019) (v3.1.5). We generated visualizations using BioRender⁴ (Figure 1), dittoSeq (Bunis et al., 2020) (v1.0.2), an R package for analysis and color blind friendly visualization of single-cell and bulk RNA-Seq data, ggplot2 (Wickham, 2009), and UpsetR (Conway et al., 2017).

To generate transcriptomic disease signatures relative to APOE genotype in each cell type, we used Limma-Voom (Law et al., 2014; Ritchie et al., 2015; Phipson et al., 2016). We included the risk factor sex as a covariate in our design formula for both datasets. In the entorhinal cortex dataset, sex, instead of batch, also accounted for the confounding relationships introduced by the original study design, allowed for an appropriate model fit, and avoided the collinearity limitation observed with including batch in the design. Additionally, as samples were age matched, we also did not include age in our design formula. A DGEList object was then created from a matrix of counts extracted from the corresponding Seurat objects. To improve the accuracy of mean-variance trend modeling and lower the severity of multiple testing correction, lowly expressed genes were filtered out using edgeR’s FilterByExpr with default parameters. Normalization was performed with Trimmed Mean of M-values with singleton pairing (TMMwsp), followed by voom, model fitting with a contrast matrix of each case-control comparison for each cell type-APOE group, and Empirical Bayes fitting of standard errors. We performed a cell type-specific AD versus control gene expression comparison in each APOE variant group separately in our defined prefrontal cortex cohort, entorhinal cohort, and male-only prefrontal cortex cohort, in which we excluded sex as a covariate. Differentially expressed genes (DEGs) were selected using a Benjamini-Hochberg (BH) corrected p-value less than 0.05, and an absolute log base twofold change (log2 FC) greater than 0.25, meaning greater than 20% change in expression. We visualized unique and shared disease related gene expression changes in cell types of each APOE genotype using pairwise and violin plots of gene expression, hierarchical clustering of samples using AD compared to control pseudobulk cell type gene expression, and Upset plots, where genes with more overlaps across the groups compared were prioritized for labeling.

We performed an overrepresentation analysis of DEGs from the cell type-specific APOE genotype-stratified analysis of cells from the prefrontal and entorhinal cortex using gprofiler (Raudvere et al., 2019), a web tool for functional enrichment using an input gene list. We queried DEGs comparison split by upregulated and downregulated expression to identify enriched pathways. In addition to Gene Ontology, we include pathways from KEGG Reactome and WikiPathways; miRNA targets from miRTarBase and regulatory motif matches from TRANSFAC; tissue specificity from Human Protein Atlas; protein complexes from CORUM, and human disease phenotypes from Human Phenotype Ontology. We followed a previously established protocol (Reimand et al., 2019) for network enrichment analysis on pathway results derived from our cell type-specific DEGs. Briefly, pathway results were imported into the Cytoscape visualization application, EnrichmentMap. We collapsed redundant and related pathways into single biological themes and further filtered significant pathways using a BH adjusted p-value < 0.01. Individual biological themes were defined and summarized using the AutoAnnotate Cytoscape application.

We classified samples into AD and control groups based on tau tangle and Aβ plaque burdens, using Braak clinical staging and CERAD scores (Mirra et al., 1991), respectively (AD: Braak stage ≥ 4, CERAD score ≤ 2; Control: Braak stage ≤ 3, CERAD score ≥ 3) (Figure 1). Next, from the prefrontal cortex cohort (Table 1), we analyzed snRNA-seq data containing 43,831 cells (Supplementary Table 1) and 17,593 genes, and from the entorhinal cortex cohort (Table 2), we analyzed snRNA-seq data containing 9,587 cells (Supplementary Table 2) and 10,850 genes. Both datasets were acquired from different sets of individuals.

Due to the limited number of samples for relatively rare APOE genotypes, we focused our analysis on comparisons between AD and non-AD groups with APOE3/3 (homozygous for allele ε3) and APOE3/4 (heterozygous ε3/ε4) genotypes. We performed an APOE genotype-stratified differential gene expression (DGE) analysis comparing age-matched AD cases to controls, with sex as a covariate, in excitatory (Ex) and inhibitory (In) neurons for the prefrontal cortex specifically, undistinguished neurons (Neu) for the entorhinal cortex, and astrocytes (Ast), microglia (Mic), oligodendrocytes (Oli), and OPCs for both cohorts (Supplementary Figures 1, 2). DEGs were selected using cutoffs of a BH adjusted p-value < 0.05 and >20% change in expression. DEGs were further passed as inputs to identify pathways for subsequent network analysis. We examined gene expression and network changes in AD compared to non-AD samples to identify cell type-specific and shared changes based on APOE genotype (Figure 1).

Leveraging data from Mathys et al. (2019), we identified DEGs in all cell type and APOE genotype pairings when comparing AD to control tissue from 43,831 cells covering 17,593 genes (Supplementary Figure 2). Interestingly, DEGs were primarily downregulated in APOE3/4 astrocytes, oligodendrocytes and OPCs, while they were primarily upregulated in both APOE3/3 and APOE3/4 neurons (Figure 2A). Altogether, across all cell types we identified 278 unique DEGs (Supplementary Table 3). Of the 278 DEGs, 8 were specific to APOE3/3 and 135 were specific to APOE3/4. We observed DEGs previously linked to AD [CLU (Kok et al., 2011; Karch and Goate, 2015), CCK (Mazurek and Beal, 1991; Chen et al., 2019; Plagman et al., 2019), NRGN (Thorsell et al., 2010; Jin et al., 2013), DHFR (Cario et al., 2011; Philip et al., 2015), ERBB4 (Mitchell et al., 2013; Mouton-Liger et al., 2020), NRXN1 (Mozhui et al., 2011)], which were shared by APOE3/3 and APOE3/4 cells. In most cases, expression differences in these genes were in the same direction across genotypes, but with greater fold changes in APOE3/4 as compared to APOE3/3 cells (Figure 2B). Across cell types, while the majority of DEGs were shared and in consistent direction across APOE3/3 and APOE3/4 cells (Figure 2C, yellow color and Supplementary Figure 2), there were a few shared DEGs with opposite directionality of expression changes, such as DOCK4 in microglia, SPARCL1 in neurons, and FRYL in oligodendrocytes (Figure 2C, pink colorand Supplementary Figure 2).

Notably, some DEGs in AD patients relative to controls were shared across multiple cell types (Figure 3A). Examples of some DEGs in AD patients relative to controls that overlap most across cell types within or across APOE genotypes include APP binding family B member 1 interacting protein (APBB1IP), and DOCK8, a protein highly involved in brain development and immune response (Nishikimi et al., 2013). Both were differentially expressed in most APOE3/4 cell types and in APOE3/3 neurons (Figure 3A). Interestingly, for both APBB1IP and DOCK8, we observed cell type-specific effects. Both genes were downregulated in astrocytes, oligodendrocytes and OPCs and upregulated in microglia and neurons from APOE3/4 AD patients versus APOE3/4 controls. In APOE3/3 individuals, both genes were only significantly upregulated in neurons in AD patients versus controls. APOE itself was also differentially expressed in AD patients versus controls, with an increase in both APOE3/4 and APOE3/3 neurons and in APOE3/3 microglia as well as a decrease in APOE3/4 astrocytes, oligodendrocytes, and OPCs. MTRNR2L12 expression, which encodes a humanin isoform necessary for neuroprotection and anti-apoptotic function suggested to have utility as a blood marker for cognitive disability and early dementia for adults with Down Syndrome (Bik-Multanowski et al., 2015; Mahajan et al., 2018), was very similar to APOE expression. Hierarchical clustering of samples using AD compared to control pseudobulk cell type gene expression (Figure 2D) showed samples to cluster by APOE genotype before cell type identity for all cell types except neurons. Generally, through our APOE genotype-stratified analysis we observed more similarities in AD versus control DEGs across APOE genotypes in neuronal populations (both excitatory and inhibitory neurons), and differences primarily in non-neuronal cells (astrocytes, oligodendrocytes, and OPCs) (Figure 3A). In addition to identifying shared DEGs across cell types and APOE genotypes, we also observed a larger range of expression change in the analysis of APOE3/4 AD versus control (−0.834, 1.032; median = −0.273) compared to the analysis of APOE3/3 AD versus control (−0.503,1.115; median = 0.342), which we visualized in a few shared DEGs such as LINGO1, NRXN1, RASGEF1B, and CLU (Figure 3B).

As the prefrontal cohort contained a sole non-AD sample with the APOE3/4 genotype from a male donor, we performed a sensitivity analysis in male samples to determine whether similar gene signatures remain. We identified 300 unique DEGs across all cell types (Supplementary Figure 3 and Supplementary Table 4). Of these DEGs, 18 were specific to APOE3/3 cells and 128 to APOE3/4 cells. Like the previous analysis, we observed more differences in perturbed gene profiles across APOE genotypes in astrocytes, oligodendrocytes, and OPCs, where DEGs were primarily downregulated in APOE3/4 cells. Additionally, clustering samples using AD compared to control pseudobulk cell type gene expression also showed a stronger clustering by APOE genotype than cell type identity (Supplementary Figure 3).

Leveraging data from Grubman et al. (2019), we identified DEGs in all cell type and APOE genotype pairings when comparing AD to control tissue from 9,587 cells and 10,850 genes. We found DEGs to be primarily downregulated in APOE3/3 AD versus control and upregulated in APOE3/4 AD versus control (Figure 4A). Altogether, across all cell types we identified 232 unique DEGs (Supplementary Table 5). Of the DEGs, 29 were specific to the APOE3/4 AD, and none were specific to the APOE3/3 AD. In each cell type, we observed more DEGs in the APOE3/4 comparison, some of which were shared with the APOE3/3 analysis, though often with consistent opposite directionality [Figure 4B; yellow (same) and pink (opposite) colors and Supplementary Figure 2]. For DEGs shared across APOE genotypes with consistent directionality, we observed differences in fold changes (Figure 4C). We also observed a higher proportion of common DEGs across APOE groups in microglia and oligodendrocytes than in other cell types, and in most cases, there was opposite directionality of gene expression changes between the APOE3/3 AD versus control comparison and APOE3/4 AD versus control comparison. Overall, clustering samples using AD compared to control pseudobulk cell type gene expression (Figure 4D) showed consistent clustering of samples by APOE genotype.

When surveying DEG overlaps across cell types in the entorhinal cortex, consistent with the prefrontal cortex analysis, we observed more DEGs in AD patients relative to controls shared across APOE3/4 cell types than across APOE3/3 cell types (Figure 5A). To highlight some of these DEGs that overlap most across cell types, in the APOE3/3 case-control comparisons, six DEGs –ATP1B1 (Wen et al., 2018), a sodium and potassium ATPase necessary for regulating ionic gradients; CST3 (Hua et al., 2012), an AD risk factor; GPC5 (Shin et al., 2013), a neurotrophic factor; MEG3 (Zhou et al., 2012), a long non-coding RNA and apoptosis regulator; NRXN1; and LINC00486, a relatively uncharacterized long non-coding transcript – were shared by all cell types. LINC00486 was upregulated in all APOE3/3 cell types in AD, ATP1B1, GPC5, MEG3, and NRXN1 were downregulated in all APOE3/3 cell types in AD, and CST3 was downregulated in all APOE3/3 cell types in AD, except OPCs where it was upregulated. These DEG’s were also reflected in APOE3/4 cells, with LINC00486 upregulated in all cell types, ATP1B1 and MEG3 upregulated in non-neuronal cell types, NRXN1 upregulated in oligodendrocytes and downregulated in all other cell types, GPC5 downregulated in astrocytes and upregulated in all other cell types, and CST3 downregulated in astrocytes and upregulated in neurons and oligodendrocytes in case-control comparisons.

Overall, in the APOE3/4 case-control comparisons, 87 DEGs were shared in all cell types, with 64 consistently upregulated in AD tissue and 23 with mixed directionality across cell types when comparing AD to control tissue (Figure 5A). Of these shared DEGs, a few with higher expression changes between AD and controls include MBP, a gene important for myelination (Koenning et al., 2012; Ferrer and Andrés-Benito, 2020) that was upregulated in all APOE3/4 cell types in AD except oligodendrocytes, and LINGO1, which was upregulated in all APOE3/4 cell types as well as APOE3/3 astrocytes and OPCs in AD. Interestingly the average expression change for LINGO1 in APOE3/4 AD samples (3.52) was much higher than that of the APOE3/3 AD samples (0.451). Additionally, protein folding HSPA1A, the neuroprotective chaperone and apoptosis regulator CRYAB (Ousman et al., 2007), and quinoid dihydropteridine reductase (QDPR) were upregulated in all APOE3/4 cell types in AD. However, HSPA1A was downregulated in APOE3/3 microglia, oligodendrocytes, and OPCs, CRYAB was downregulated in APOE3/3 oligodendrocytes, and QDPR was downregulated in APOE3/3 microglia in AD. The latter two genes have previously been observed to be upregulated in oligodendrocytes and OPCs of pathologically confirmed AD individuals (Mathys et al., 2019), most of them are usually APOE4 carriers. We also observed a larger range of case-control expression change in APOE3/4 cells (−2.918, 3.839; median = 0.688) compared to APOE3/3 cells (−2.385, 2.227; median = −0.436), which we visualized in a few shared DEGs such as LINGO1, NRXN1, FTL, and ADGRL3 (Figure 5B). Largely, when comparing AD to non-AD cells in the entorhinal cortex, while we observed changes relevant to AD pathophysiology across APOE3/3 and APOE3/4 genotypes, we also observed flipped DEG expression profiles across both APOE genotypes primarily in non-neuronal cells, and more universal transcriptional changes and changes of higher amplitude in the APOE3/4 AD versus control comparison as compared to APOE3/3 AD versus control comparison.

We observed a higher number of DEGs and larger expression change magnitudes across cell types in the entorhinal cortex than in the prefrontal cortex in AD. The number of shared DEGs within cell types across APOE genotype groups was highest in the entorhinal cortex in AD, while the number of shared DEGs within cell types across brain regions was highest in APOE3/4 cells in AD (Figure 6A). With hierarchical clustering of per-cell and genotype group pseudobulk expression, while we do not observe total clustering by any of the variables examined, we see some clustering by brain region, and within these brain regions, by APOE genotype (Figure 6B).

Pathway enrichment was performed using g:Profiler (Raudvere et al., 2019), a web tool that performs functional enrichment using an input of gene lists. Separate lists of upregulated and downregulated DEGs in AD relative to control, with a BH corrected adjusted p-value < 0.05 and a relaxed expression change cutoff of above 0.1, in each cell type and APOE genotype were used as inputs for g:Profiler (Supplementary Tables 6–9). A network analysis was performed to cluster the disease enriched pathways into biologically relevant groups using pathways with an adjusted p-value < 0.01 as inputs. Modules of biological themes were generated for each cell type based on the APOE genotype (Figure 7 and Supplementary Figures 4, 5).

Through GO enrichment analysis for the prefrontal cortex, we observed pathways related to cell junctions in excitatory neurons, postsynaptic density in inhibitory neurons, Glutamatergic synapse in astrocytes, and Scavenging by Class A Receptors in microglia to be most significantly perturbed in APOE3/3 diseased samples (Supplementary Table 6). In APOE3/4 diseased samples, the top significantly perturbed pathways were related to cell junctions and upregulated in excitatory neurons, inhibitory neurons, and astrocytes, while Apelin signaling pathway was upregulated and most significant in microglia (Supplementary Table 7). Also, we observed terms associated with inflammatory processes such as TNF-α and NF-kappa B signaling complex to be upregulated in APOE3/3 diseased astrocytes compared to controls, but not in APOE3/4. Functional pathways related to synapse organization, neuron differentiation, neurogenesis, and axon development were downregulated in APOE3/4 diseased individuals compared to controls only.

In astrocytes from the prefrontal cortex, we identified six enriched functional modules in both APOE3/3 and APOE3/4 AD relative to controls (Figure 7A). Five out of six were downregulated in AD, and one, the LINGO1-TROY-NgR complex, which was previously suggested to be important for modulating glial-neuronal interactions in demyelinating lesions, was upregulated in AD (Satoh et al., 2007). In APOE3/3 astrocytes, ion and acid transport, glutamate receptor activity (mGLUR2, mGLUR3, mGLUR4, mGLUR7, and mGLUR8), metabolic (aspartate uptake and astrocytic metabolism) as well as autolysosome activities (scavenging class receptors, secondary lysosome, and autolysosome) were downregulated in AD, and myelin maintenance (PRNP and ASAH1), cell adhesion (FLRT3, LPHN3, UNC5B, and UNC5D), and Vascular endothelial growth factor (VEGF) induced heat shock protein 90 (hsp90) complex were upregulated in AD, indicating perturbation in processes important for autophagy and stress response which are known to accompany disease progression (Karch and Goate, 2015; Long and Holtzman, 2019). APOE3/4 astrocytes uniquely showed upregulation in pathways related to post-synaptic scaffold proteins (e.g., DLGAP1, DLG4, DLC1, and SHANK3) and actin assembly at cell junctions, but downregulation of synaptic membrane and neurotransmitter pathways, neurogenesis, and nervous system development in AD.

In APOE3/3 astrocytes of the entorhinal cortex, we observed a downregulation of ion and neurotransmitter transport related pathways (intracellular ion and ferritin iron sequestering) in AD. APOE3/4 astrocytes in the entorhinal cortex had mostly upregulated pathway enrichment modules in AD, in contrast to what was observed in prefrontal cortex (Supplementary Figure 5). Many of these pathways governing cellular homeostasis, such as ATP synthesis, transmembrane cation transport, amyloid fibril formation and exosome regulation, and macromolecule and protein plasma membrane localization.

Microglia, the resident brain macrophage, contributes to neuroinflammation in AD and produces APOE upon activation in the brain (Lane et al., 2018; Long and Holtzman, 2019). Differentially enriched pathways were predominantly upregulated in APOE3/4 microglia in AD patients in both prefrontal (Supplementary Figure 4) and entorhinal cortices (Figure 7B), while downregulated in APOE3/3 microglia. Within the entorhinal cortex, changes in gliogenesis, myelination, cation transmembrane transport, cellular projection, synaptic spine development, and synaptic junction assembly pathway network modules were shared in APOE3/3 AD and APOE3/4 AD but perturbed in opposite directions, downregulated in APOE3/3, and upregulated in APOE3/4 microglia (Figure 7B). The ITGAV-ITGB-SPP1 complex, not previously linked to AD to our knowledge, was significantly upregulated in both brain regions in APOE3/3 microglia in AD, but only in the prefrontal cortex in APOE3/4 microglia in AD (Figure 7B and Supplementary Figure 4). The downregulation of iron homeostasis and ferritin complex, a protein that binds to iron and reflects the level of iron storage in the body, was observed in APOE3/3 microglia and astrocytes of both prefrontal and entorhinal cortex in AD (Figures 7A,B and Supplementary Figure 4).

The GO enrichment analysis of the entorhinal cortex revealed the most pronounce difference between APOE3/3 and APOE3/4 were pathways related to cellular projection development in microglia and astrocytes, such as morphogenesis of plasma membrane, cellular compartment, and plasma membrane bounded cell projection (Supplementary Tables 8, 9). Moreover, GO terms related to homeostatic process, neurogenesis, regulation of transport, and multicellular signaling process were also upregulated in APOE4 all cell types except neurons and downregulated in APOE3/3 microglia and oligodendrocytes.

Overall, network analysis comparing neurons from two brain regions yielded many similar perturbed biological processes within each APOE genotype in AD (Figure 7C). In APOE3/3 neurons, shared differentially perturbed processes between brain regions were mostly related to regulation of membrane homeostasis, neuron projection, and synaptic development. Pathway networks in APOE3/3 neurons specific to the prefrontal cortex pertain to cell structure development (actomyosin actin-based structure, extension growth development, anchoring junction, cell adherens), while the entorhinal cortex showed unique modules relevant to cellular energy production (oxidative respirasome synthesis and metabolic ATP nucleotide process). From APOE3/4 neurons, we observed a more diverse population of shared network modules between the two brain regions, including functional processes related to protein trafficking vesicles, myelination, membrane assembly, and voltage gated channel and neurotransmitter receptor regulation. Amyloid fibril formation was uniquely differentially regulated in APOE3/4 neurons and observed in both brain regions in AD, while an amyloid beta precursor formation module was only observed APOE3/4 neurons in prefrontal cortex in AD.

In oligodendrocytes, which provide myelination, APOE3/3 carriers in the prefrontal cortex showed an upregulation of the ITGAV-ITGB-SPP1 complex and downregulation of pathways related to myelin organization (e.g., juxtaparanode region of axon), ion transport activity, protein refolding, and regulation of MAP kinase signaling activity (e.g., positive regulation of Erk1 and Erk2 in AD). APOE3/4 oligodendrocytes, on the other hand, showed upregulation of stress responses through chaperone mediated protein folding, and downregulation of axon guidance and nervous system development processes in AD. In the entorhinal cortex, we observed modules of processes including neurogenesis, gliogenesis, amyloidosis, aerobic metabolic processes, and exocytosis to be upregulated in APOE3/4 cells and downregulated in APOE3/3 cells in AD (Supplementary Figure 5). Lastly, we observed postsynaptic structural specialization to be uniquely downregulated in APOE3/4 oligodendrocytes.

For OPCs in the prefrontal cortex, there were no common network modules across APOE genotypes. In APOE3/3 AD, we identified downregulation for brain cell development processes (AHI1-NPHP1-HAP1) (Supplementary Figure 4). In APOE3/4 OPCs, we observed upregulated modules for the ferritin, GAIT and LINGO1-TROY-NgR complexes, and downregulation for glutamatergic synaptic activity, plasma membrane and cell organization, and lipoprotein density in AD, which may have implications for neuronal integrity and lipid transport and metabolism. In the entorhinal cortex of AD, we also observed upregulation of the LINGO1-TROY-NgR, and downregulation of glutamatergic signaling in APOE3/3 OPCs in AD. Specific to APOE3/4 OPCs in AD, we identified upregulation of processes related to aerobic metabolic processes, stress response, autophagy, amyloid fibril regulation, demyelination, and immune response.