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. Author manuscript; available in PMC: 2023 Mar 1.
Published in final edited form as: Neurosci Res. 2021 Oct 29;176:85–89. doi: 10.1016/j.neures.2021.10.010

Analysis of differential gene expression and transcript usage in hippocampus of Apoe null mutant mice: implications for Alzheimer’s disease

Andrew E Weller 1,*, Glenn A Doyle 1, Benjamin C Reiner 1, Richard C Crist 1, Wade H Berrettini 1
PMCID: PMC8960320  NIHMSID: NIHMS1754768  PMID: 34757086

Abstract

A dataset of single-nucleus RNA sequencing data was analyzed using Seurat, Sierra, and Ingenuity Pathway Analysis (IPA) programs to assess differentially expressed genes (DEGs) and differential transcript usage (DTU) in mouse hippocampal cell types. Seurat identified DEGs between the wild type (WT) and Apoe knockout (EKO) mice. IPA identified 11 statistically significant canonical pathways in >1 cell type. Sierra identified Sipa1l1 with DTU between WT and EKO samples. Analysis of the Sipa1l1 peak region identified an alternative non-canonical polyadenylation signal and a putative cytoplasmic polyadenylation element. APOE regulation of gene transcription and co-transcriptional RNA processing may underlie Alzheimer’s disease.

Keywords: Alzheimer’s disease (AD), Apolipoprotein E (APOE), Knockout mouse, Single-nucleus RNA sequencing (snRNAseq), Differential gene expression, Differential transcript usage

Alzheimer’s disease (AD) is the most common form of dementia (World Health Organization, 2020), and is a source of significant morbidity and mortality for patients and their families. Mouse models offer an important avenue for studying AD. One such mouse model, the Apoe knockout mouse, was developed as a model for studying hyperlipidemia and atherosclerosis, but has also been used in the study of neurodegenerative diseases (de Bem et al., 2020). Apoe knockout mice exhibit cognitive deficits similar to patients with AD, such as difficulties in spatial learning, which occur as early as 3 months of age, as well as working memory, which occur at 6–8 months of age (de Bem et al., 2020). The increased Aβ deposition that is commonly found in AD patients with the APOE4 risk allele does not appear to be confirmed in current studies using the Apoe knockout mouse (Shnerb Ganor et al., 2018), and a 40 year old human male subject with APOE deficiency showed normal cognition, normal findings on brain magnetic resonance imaging (MRI), and normal cerebrospinal fluid (CSF) levels of β-amyloid and tau proteins (Mak et al., 2014). However, one study showed impaired clearance of synthetic Aβ that was injected directly into the brains of Apoe knockout mice (de Bem et al., 2020; Ji et al., 2001) and studies have shown that APOE4 is less effective at promoting receptor-mediated Aβ clearance than APOE3 (Theendakara et al., 2018), suggesting that while there may not be any increase in Aβ deposition among Apoe knockout mice, Aβ clearance may still be impacted. Additional studies are needed to better characterize how APOE deficiency affects Aβ deposition, elimination, and accumulation in the brain.

Although the role of cholesterol in AD has not been established, some studies have implicated hypercholesterolemia as a risk factor in the development of AD (Xu et al., 2020; Xue-Shan et al., 2016; Xue et al., 2017). Furthermore, a study of Apoe knockout mice fed a high fat diet developed hyperlipidemia, and increased expression levels of PCSK9 and BACE1 in the hippocampus along with neuronal loss, suggesting that hyperlipidemia could increase neuronal apoptosis through this mechanism (Zhao et al., 2017). Taken together, in spite of some of the limitations posed by this mouse model, Apoe knockout mice are a suitable animal model for the study of AD phenotypes.

Single-cell (or single-nucleus) RNA sequencing (scRNAseq or snRNAseq) allows the analysis of the transcriptomes of single cells and can be used to identify functions of various cell types, as well as differences in the transcriptomes of those cell types between normal and disease states. A recent snRNAseq study that analyzed transcriptomes from the prefrontal cortices of 48 individuals diagnosed with AD identified cellular processes involved in myelination and inflammation, stressing the importance of complex interactions between neurons and glia in AD pathology (Mathys et al., 2019a, b). SnRNAseq studies conducted in AD mouse models can aid in identifying the cells and genes with altered transcription that result in development of AD-like phenotypes.

We hypothesized that constitutive lack of Apoe expression in Apoe knockout mice would cause transcriptomic and polyadenylation site usage changes in cell types expressing receptors for Apoe and other factors regulated by Apoe. Therefore, we analyzed publicly available snRNAseq data from pooled hippocampi of 9-month-old wild type and Apoe knockout mice to identify gene expression and transcript usage differences in specific cell types.

All analyses were performed on GEO dataset GSE166261 [dataset] (Shi et al., 2021; Shi Y. et al., 2021) comprised of snRNAseq data generated by droplet-based Chromium Single Cell 3’ Reagent Kit (10x Genomics) on 7 pooled hippocampi samples from 9-month-old C57BL/6J wild type (WT) mice (Sample GSM5067107) and 7 pooled hippocampi samples from 9-month-old Apoe knockout (EKO) mice (Sample GSM5067109). The initial EKO mouse was generated using the 129P2/OlaHsd-derived E14Tg2a ES cell line (Piedrahita et al., 1992). The mice used to generate the samples for the snRNAseq data for this study were produced by backcrossing at least 10 generations to C57BL/6J inbred mice.

The filtered matrix of gene by cell expression, barcodes file, and genes file for the two samples were loaded into Seurat and used to generate two Seurat (version 3.1.4 (Stuart et al., 2019)) objects, which were then merged for analysis. Quality control (QC) metrics included filtering out nuclei with over 2500 or less than 200 genes, and/or greater than 5% of total transcripts from mitochondrial genes. The data were normalized (using the default “LogNormalize”) and the 2000 most highly variable genes were identified via the FindVariableFeatures command with the “vst” selection method. After scaling by linear transformation, linear dimension reduction was done using principal component analysis (PCA) on the scaled data. The cells were clustered using the first 50 PCs at resolution = 0.6, yielding 17 distinct clusters. The FindMarkers function compared clusters to identify gene markers for each cluster, using default statistical settings of Wilcoxon Rank Sum test and Bonferroni correction, but logfc.threshold = 0. These data were used in combination with markers for specific hippocampus cell types to determine the cell types of each cluster.

Regtools (Cotto et al., 2021) from the Github repository were used to extract the transcript junction information (required for Sierra) in BED (browser extensible data) format from the WT and EKO BAM alignment files. The reference file was generated from the GRCm38/mm10 genome build. The whitelist.bc files were from the WT and EKO barcodes.tsv files obtained from GSE166261. Peaks were called in Sierra (version 0.99.24 (Patrick et al., 2020)) separately for the WT and EKO bamfiles and merged. Peaks for WT and EKO samples were counted separately and aggregated into one file. Peaks were annotated with BSgenome.Mmusculus.UCSC.mm10 genome file and transferred to a Seurat object. Differential transcript usage (DTU) was examined for the 17 clusters using the DUTest command with the parameter “exp.thres = 0.1”. DUTest results were filtered using a false discovery rate (FDR) adjusted p-value < 0.05 and log2 fold-change threshold of 0.25. The DUTest command was then used to generate a list of gene peaks (generated by DUTest command) differentiating the WT and EKO samples. This was done with the thresholds noted above. Plots of relative peak expression by cluster for the WT versus EKO samples were generated for those peaks with a fold change ≥ 2.0 in at least one cluster. Coverage plots for those gene peaks with DTU were visualized (PlotCoverage function).

After removing mitochondrial DNA genes, and genes without a known Ensembl ID, Differentially expressed genes (DEGs) in each cluster with an FDR-adjusted p-value < 0.05 were used in Ingenuity Pathway Analysis (IPA) (https://www.qiagenbioinformatics.com/products/ingenuity-pathway-analysis) (Krämer et al., 2014). Canonical pathway analysis was performed in IPA to generate a list of terms that had a q-value < 0.05 after FDR correction for multiple testing.

Analysis of the combined WT and EKO data in Seurat, identified 17 separate clusters corresponding to different cell type identities (Figure 1; separate uniform manifold and projection (UMAP) plots of WT and EKO samples are shown in Supplemental Figure 1). The FindMarkers function of Seurat generated lists of genes having differential expression between WT and EKO mice with a Bonferroni corrected p-value < 0.05 (Supplemental File 1). Input of these cluster-specific DEGs into IPA resulted in the identification of 11 canonical pathway terms, appearing in multiple clusters, with FDR-adjusted p-values < 0.05 (Figure 2; Supplemental File 2) and 6 pathway terms appearing in only one cluster (Supplemental Figure 2; Supplemental File 2).

Figure 1: Uniform manifold and projection (UMAP) plot and Dot plot of wild type (WT) and Apoe knockout (EKO) hippocampus samples combined.

Figure 1:

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(A) The transcriptome profiles of 10,808 single nuclei were utilized for unbiased clustering in Seurat and are presented as a UMAP dimension reduction plot of all single nuclei. Nuclei are color-coded by cluster, which corresponds to cell type. (B) A dot plot was generated in Seurat and clusters were annotated using genes known to be markers of different cell types. The size and color of the dots is proportional to the percentage of nuclei expressing the gene of interest (Pct. Exp.) and the average expression level of the gene (Avg. Exp.), respectively. Cluster numbers and colors match those of the UMAP in Figure 1A. Cluster 0 = Wfs1+,Dcn+,Rxfp1− CA1 pyramidal neurons (Excitatory neuron 1); Cluster 1 = C1ql2+ Granule cell of the dentate gyrus (Excitatory neuron 2); Cluster 2 = Wfs1+,Dcn−,Rxfp1+ CA1 pyramidal neurons (Excitatory neuron 3); Cluster 3 = Bok+ CA3 pyramidal neurons (Excitatory neuron 4); Cluster 4 = Wfs1−,Dcn−, pyramidal neurons (Excitatory neuron 5); Cluster 5 = Sst+,Erbb4+ CA1 basket cells (Inhibitory neuron 1); Cluster 6 = Slc1a3+ Astrocytes; Cluster 7 = Trf+ Myelin-forming mature oligodendrocytes (MFOLs); Cluster 8 = Vip+,Reln+,Htr3a+,Erbb4+ CA1 basket cells (Inhibitory neuron 2); Cluster 9 = Ndst4+,Rxfp1+,Dcn− CA1 pyramidal neurons (Excitatory neuron 6); Cluster 10 = C1qa+,Trf+ Microglia; Cluster 11 = Cldn5+ Endothelial cells; Cluster 12 = Htr2c+,Glra1− CA1 pyramidal cells (Excitatory neuron 7); Cluster 13 = Htr2c+,Glra1+ CA1 pyramidal cells (Inhibitory neuron 3); Cluster 14 = Amigo2+ CA2 pyramidal neurons (Excitatory neuron 8); Cluster 15 = Pdgfra+ Oligodendrocyte precursor cells (OPCs); Cluster 16 = Reln+,Dcn+ CA1 pyramidal cells (Excitatory neuron 9).

Figure 2: Heat map of significant canonical pathways identified by Ingenuity Pathway Analysis (IPA).

Figure 2:

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The canonical pathways shown are those with an FDR-adjusted p-value<0.05 in more than one cell cluster. ‘EIF2 Signaling’, ‘Regulation of eIF4 and p70S6K Signaling’, ‘mTOR Signaling’, and ‘Oxidative Phosphorylation’ were identified in CA1 pyramidal neurons (cluster 0), and granule cells of the dentate gyrus (cluster 1). ‘Mitochondrial Dysfunction’ was identified in CA1 pyramidal neurons (cluster 0), granule cells of the dentate gyrus (cluster 1), and microglia (cluster 10). ‘Glycolysis I’ was identified in myelin-forming mature oligodendrocytes (MFOLs) (cluster 7) and microglia (cluster 10). ‘Sirtuin Signaling Pathway’ was identified in granule cells of the dentate gyrus (cluster 1) and microglia (cluster 10). ‘Reelin Signaling in Neurons’ was identified in CA1 basket cells (cluster 8) and microglia (cluster 10). ‘Synaptogenesis Signaling Pathway’ was identified in CA1 pyramidal neurons (cluster 0), and astrocytes (cluster 6). ‘LXR/RXR Activation’ and ‘FXR/RXR Activation’ were identified in unspecified pyramidal neurons (cluster 4), CA1 basket cells (cluster 8), CA1 pyramidal neurons (cluster 9), and microglia (cluster 10).

DTU for WT and EKO mice revealed peaks with cluster-specific log2FC≥0.25 (Supplemental File 3). One peak in the signal induced proliferation associated 1 like 1 (Sipa1l1) gene, Sipa1l1: chr12:82235539–82235845:1, was found to be highly differentially expressed (log2FC≥2.30) in CA1, CA2, and CA3 pyramidal cells (Supplemental File 3). This Sipa1l1 peak corresponded to a transcript variant (Sipa1l1-209) that was expressed in WT mice but had extremely low levels of expression in the EKO mice by comparison (Figure 3).

Figure 3: Feature and coverage plots comparing wild type (WT) and Apoe knockout (EKO) samples for transcripts of Sipa1l1.

Figure 3:

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Two transcript peaks in Sipa1l1 called by Sierra are compared for differential transcript usage between WT and EKO mice. (A and B) Sipa1l1:chr12:82312754–82313088:1 shows similar levels of expression between EKO (A) and WT (B) mice. (C and D) Sipa1l1:chr12:82235539–82235845:1 is expressed at low levels in the EKO (C) mice, but expressed at higher levels in WT (D) mice. (E) The map and transcript structure of Sipa1l1 is shown across the top of the panel. Two transcript peaks called by Sierra are indicated under the transcript map: Peak1: Sipa1l1:chr12:82235539–82235845:1 and Peak 2: Sipa1l1:chr12:82312754–82313088:1. Sipa1l1:chr12:82312754–82313088:1 (peak 2) does not show differential levels of expression between WT and EKO samples. In contrast, Sipa1l1:chr12:82235539–82235845:1 (peak 1) is expressed in the WT sample but has very low levels of expression in the EKO sample.

Pearson’s chi-square contingency table analysis was run comparing the number of cells analyzed in the WT and EKO snRNAseq samples for each cluster (Supplemental File 5). Each chi-square analysis was run comparing the number of cells analyzed in the indicated cluster to the rest of the cells in that snRNAseq sample. After multiple testing correction, there were statistically significant differences for some of the cluster comparisons between the WT and EKO genotypes, namely clusters 0, 1, 2, 6, 7, 13, and 15. It is possible that the differences seen here were the result of the study being underpowered due to the small sample sizes used in this analysis. However, there could also be true differences in cluster sizes between these two genotypes. Determining this could be the focus of future studies.

Although some studies suggest that the Apoe knockout mice exhibit phenotypes that are protective against brain atrophy, neuroinflammation, and neurodegeneration (Shi et al., 2017), several of the canonical pathways identified in our DEG analysis have been implicated in AD, including those in microglial mitochondrial function. The canonical pathways ‘Mitochondrial Dysfunction’ and ‘Oxidative Phosphorylation’ both implicate nuclear encoded genes that function in mitochondria to produce energy for cellular functions. These data are consistent with previous studies indicating that mitochondrial pathophysiology contributes to AD risk (Swerdlow, 2018). The pathways ‘LXR/RXR Activation’ and ‘FXR/RXR Activation’ relate to retinoid signaling, and were both implicated in microglia, whose production of inflammatory cytokines is influenced by retinoids, which are thought to play a role in AD pathophysiology (Sodhi and Singh, 2014). The ‘Sirtuin Signaling Pathway’ was also implicated in microglia. Sirtuins are enzymes that regulate many cellular mechanisms involved in aging (Rizzi and Roriz-Cruz, 2018). One sirtuin protein, Sirtuin 1 (SIRT1) may be involved in cognitive function and synaptic plasticity (Zhao et al., 2019). SIRT1 is linked to many processes involved in AD such as APP processing, neuronal inflammation, neurodegeneration, and mitochondrial pathology (Rizzi and Roriz-Cruz, 2018), and SIRT1 expression has been linked to specific APOE4 isoform-specific signaling events (Theendakara et al., 2013; Theendakara et al., 2016). These microglial pathways implicate energy imbalance as a risk factor in AD through their effect on immune function and inflammation (Baik et al., 2019; Fairley et al., 2021; Shippy and Ulland, 2020).

A recent study uses snRNAseq to compare Apoe4 knock-in (Apoe4-KI) and Apoe3 knock-in (Apoe3-KI) mice. It found that the 10 KEGG pathways most correlated with Apoe expression in each neuronal subtype indicated pathways associated with “cellular metabolism, cell death, neurodegeneration, unfolded protein response, DNA damage and repair, and immune response” (Zalocusky et al., 2021). Similarly, another study (Lin et al., 2018) used CRISPR/Cas9 and induced pluripotent stem cells (iPSCs) to examine the effect of APOE4 on human brain cell types. These derived cell types were then used to perform RNAseq. The gene ontology (GO) analysis they conducted implicated genes involved in lipid metabolism, immunity, and tricarboxylic acid (TCA) cycle/respiratory electron transport. The IPA data generated as part of this study of Apoe knockout snRNAseq data implicate canonical pathways related to similar mechanisms, namely lipid metabolism, carbohydrate metabolism, metabolic disease, inflammation, cellular stress and injury, cell death and survival, neurotransmitters and other nervous system signaling, and DNA replication, recombination, and repair. Although Apoe knockout mice were not originally designed as a mouse model for AD, the overlap of these findings with those using RNAseq and snRNAseq to compare Apoe4-KI and Apoe3-KI mice suggests that our findings could be generalizable to AD patients carrying APOE4.

Some of these canonical pathways have been implicated in metabolic disease, diabetes mellitus, cardiac ischemia, lipid metabolism, energy homeostasis, bile acid metabolism, and cholesterol. All of these play some role in hyperlipidemia and the development of atherosclerosis, which is to be expected as Apoe knockout mice were developed as a mouse model to study these phenotypes. This is not surprising, as many patients with dementia actually present with a mixed picture of AD and vascular dementia (which results from atherosclerosis and hyperlipidemia), since these two dementia subtypes have risk factors in common (Serrano-Pozo and Growdon, 2019).

The single-cell resolution of these data highlights the importance of constitutive Apoe for the normal functioning of several hippocampal cell types. Our Sierra data implicate a role for constitutive Apoe expression in DTU. The constitutive absence of Apoe led to the loss of a DTU peak in Sipa1l1 between WT and EKO mice (Figure 3). These data are derived from nuclear rather than cytoplasmic RNA sequencing. Therefore, they suggest that the intron retention transcript variant (Sipa1l1-209) of the Sipa1l1 gene is cleaved and polyadenylated in the nucleus at an alternative polyadenylation signal. There is an alternative non-canonical polyadenylation signal and a putative cytoplasmic polyadenylation element (CPE) within the region of Sipa1l1 identified by the peak called by Sierra (Supplemental File 4). Additional investigation is needed to characterize the role of this CPE in Sipa1l1.

SIPA1L1 (a.k.a. SPAR1) is a Rap GTPase-activating protein (RapGAP) that is involved in the regulation of dendritic spine morphogenesis (Ang et al., 2008; Dolnik et al., 2016). Our Sipa1l1 DTU data are relevant to CA1, CA2, and CA3 pyramidal cells (known for having prominent dendrites), which degenerate in AD (Bekkers, 2011).

This study has several limitations. First, although it has been suggested that hyperlipidemia in humans is a risk factor in the development of AD (Xu et al., 2020; Xue-Shan et al., 2016; Xue et al., 2017), the Apoe knockout mouse might not be reflective of the risk conferred by APOE4 in humans. Additional limitations include slightly different genetic backgrounds of the WT and EKO mice (Piedrahita et al., 1992), which could influence effects on DEGs or DTU. However, although there may be 129 alleles in this mouse, after 10 generations of backcrossing to C57BL6/J, less than 0.05% of the genome is 129. Additionally, our data are correlative, as we have not done experiments to show causative effects. Finally, we have not confirmed the DEGs or DTU data, as this is beyond the scope of this exploratory study.

Our IPA findings from DEG lists highlight mitochondrial function and energy production as key pathways in AD. Malfunction of those pathways may increase AD risk. Our DTU data suggest that Apoe is involved in the generation of specific polyadenylated transcripts, as exemplified by the DTU in Sipa1l1. These findings have the potential to generate new avenues for AD research. Additional studies examining DTU at the single-cell level should be done in postmortem AD brain and in animal models.

Supplementary Material

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Supplemental Figure 1: Uniform manifold and projection (UMAP) plot of wild type (WT) and Apoe knockout (EKO) hippocampus samples separated. The transcriptome profiles of 10808 (5363 WT, 5445 EKO) single nuclei were utilized for unbiased clustering in Seurat with the WT and EKO nuclei separated and with clusters generated independently of one another. These data are presented as two separate UMAP dimension reduction plots of all single nuclei. Nuclei are color-coded by cluster, which corresponds to cell type. Cluster 0 = Wfs1+,Dcn+,Rxfp1− CA1 pyramidal neurons (Excitatory neuron 1); Cluster 1 = C1ql2+ Granule cell of the dentate gyrus (Excitatory neuron 2); Cluster 2 = Wfs1+,Dcn−,Rxfp1+ CA1 pyramidal neurons (Excitatory neuron 3); Cluster 3 = Bok+ CA3 pyramidal neurons (Excitatory neuron 4); Cluster 4 = Wfs1−,Dcn−, pyramidal neurons (Excitatory neuron 5); Cluster 5 = Sst+,Erbb4+ CA1 basket cells (Inhibitory neuron 1); Cluster 6 = Slc1a3+ Astrocytes; Cluster 7 = Trf+ Myelin-forming mature oligodendrocytes (MFOLs); Cluster 8 = Vip+,Reln+,Htr3a+,Erbb4+ CA1 basket cells (Inhibitory neuron 2); Cluster 9 = Ndst4+,Rxfp1+,Dcn− CA1 pyramidal neurons (Excitatory neuron 6); Cluster 10 = C1qa+,Trf+ Microglia; Cluster 11 = Cldn5+ Endothelial cells; Cluster 12 = Htr2c+,Glra1− CA1 pyramidal cells (Excitatory neuron 7); Cluster 13 = Htr2c+,Glra1+ CA1 pyramidal cells (Inhibitory neuron 3); Cluster 14 = Amigo2+ CA2 pyramidal neurons (Excitatory neuron 8); Cluster 15 = Pdgfra+ Oligodendrocyte precursor cells (OPCs); Cluster 16 = Reln+,Dcn+ CA1 pyramidal cells (Excitatory neuron 9).

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Supplemental Figure 2: Generation of a heat map from Ingenuity Pathway Analysis (IPA) canonical pathway data. The canonical pathways listed here are those with an FDR-adjusted p-value of <0.05 in one cell cluster. ‘HER-2 Signaling in Breast Cancer’ and ‘Coronavirus Pathogenesis Pathway’ were identified in granule cells of the dentate gyrus (cluster 1). ‘Maturity Onset Diabetes of Young (MODY) Signaling’ and ‘NADH Repair’ were identified in microglia (cluster 10). ‘Gluconeogenesis I’ was identified in myelin-forming mature oligodendrocytes MFOLS (cluster 7). ‘Lactose Degradation III’ was identified in CA1 basket cells (cluster 8).

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Highlights.

  • Single cell based analyses promote greater understanding of Alzheimer’s disease.

  • Mitochondrial function and energy production are implicated in a mouse model.

  • Mouse Apoe is involved in the generation of specific polyadenylated transcripts.

  • Single cell studies could inform treatments that target specific brain cell types.

Acknowledgements:

The preparation of this manuscript was supported by a National Institutes of Health grant R01 MH109260 (WHB is PI). AEW was supported by T32 MH014654 (WHB is PI) and by a Tobacco Settlement Act grant from the Pennsylvania Department of Health (WHB is PI). The authors wish to thank those providing support for this work. BCR was supported by a 2017 NARSAD Young Investigator Grant (#26634) from the Brain and Behavior Research Foundation as the Patrick A. Coffer Investigator, funding for which was generously provided by Ronald and Kathy Chandonais. The authors thank the researchers Yang Shi, PhD, David M. Holtzman MD, and their colleagues at Washington University in St. Louis for generating and graciously making publicly available the dataset GSE166261 which was used in our analysis.

Footnotes

Disclosure statement:

The authors declare no competing interest.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

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Supplemental Figure 1: Uniform manifold and projection (UMAP) plot of wild type (WT) and Apoe knockout (EKO) hippocampus samples separated. The transcriptome profiles of 10808 (5363 WT, 5445 EKO) single nuclei were utilized for unbiased clustering in Seurat with the WT and EKO nuclei separated and with clusters generated independently of one another. These data are presented as two separate UMAP dimension reduction plots of all single nuclei. Nuclei are color-coded by cluster, which corresponds to cell type. Cluster 0 = Wfs1+,Dcn+,Rxfp1− CA1 pyramidal neurons (Excitatory neuron 1); Cluster 1 = C1ql2+ Granule cell of the dentate gyrus (Excitatory neuron 2); Cluster 2 = Wfs1+,Dcn−,Rxfp1+ CA1 pyramidal neurons (Excitatory neuron 3); Cluster 3 = Bok+ CA3 pyramidal neurons (Excitatory neuron 4); Cluster 4 = Wfs1−,Dcn−, pyramidal neurons (Excitatory neuron 5); Cluster 5 = Sst+,Erbb4+ CA1 basket cells (Inhibitory neuron 1); Cluster 6 = Slc1a3+ Astrocytes; Cluster 7 = Trf+ Myelin-forming mature oligodendrocytes (MFOLs); Cluster 8 = Vip+,Reln+,Htr3a+,Erbb4+ CA1 basket cells (Inhibitory neuron 2); Cluster 9 = Ndst4+,Rxfp1+,Dcn− CA1 pyramidal neurons (Excitatory neuron 6); Cluster 10 = C1qa+,Trf+ Microglia; Cluster 11 = Cldn5+ Endothelial cells; Cluster 12 = Htr2c+,Glra1− CA1 pyramidal cells (Excitatory neuron 7); Cluster 13 = Htr2c+,Glra1+ CA1 pyramidal cells (Inhibitory neuron 3); Cluster 14 = Amigo2+ CA2 pyramidal neurons (Excitatory neuron 8); Cluster 15 = Pdgfra+ Oligodendrocyte precursor cells (OPCs); Cluster 16 = Reln+,Dcn+ CA1 pyramidal cells (Excitatory neuron 9).

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Supplemental Figure 2: Generation of a heat map from Ingenuity Pathway Analysis (IPA) canonical pathway data. The canonical pathways listed here are those with an FDR-adjusted p-value of <0.05 in one cell cluster. ‘HER-2 Signaling in Breast Cancer’ and ‘Coronavirus Pathogenesis Pathway’ were identified in granule cells of the dentate gyrus (cluster 1). ‘Maturity Onset Diabetes of Young (MODY) Signaling’ and ‘NADH Repair’ were identified in microglia (cluster 10). ‘Gluconeogenesis I’ was identified in myelin-forming mature oligodendrocytes MFOLS (cluster 7). ‘Lactose Degradation III’ was identified in CA1 basket cells (cluster 8).

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