Impact of 5xFAD and Trem2R47H mutations across major brain cell types
In this study we investigate regional, plaque proximal, and genotype specific gene expression changes induced by the Trem2R47H mutation. As this mutation is not sufficient to induce amyloid plaque pathology in mice, we utilize a hemizygous 5xFAD/homozygous Trem2R47H mouse model which induces Aβ pathology in concert with the Trem2R47H mutation, compared with matched 5xFAD (Aβ pathology only), Trem2R47H, and WT controls (Fig. 1A). By comparing these four genotypes, we uncover the transcriptomic alterations induced specifically by 5xFAD transgenes (independent of Trem2R47H mutation), specifically by Trem2R47H (independent of 5xFAD), and those induced by a combination of 5xFAD and Trem2R47H mutations.
We performed spatial transcriptomic analysis using MERFISH on 19 coronal half sections from 15 total animals with WT, 5xFAD, Trem2R47H and Trem2R47H; 5xFAD genotypes at 12 months of age. After quality control, this dataset results in 432,794 cells. Using a 300 gene panel, we identified 37 major cell types, and transcriptomically and spatially mapped 5xFAD and Trem2R47H transcriptomic alterations at the single-cell level. We also identified Aβ plaque locations in the same samples and assessed their relationship to spatial gene expression (Fig. 1B).
After spatial transcript decoding (Fig. 1C), cells were processed using our single-cell pipeline (Supp Fig. 1A-B), and clusters were identified based on reference to known cell type markers (Supp Fig. 1C), in conjunction with spatial location (Fig. 1D). Color coding genotype information on the UMAP shows strong 5xFAD induced cell type composition changes, particularly in astrocytes and microglia (Fig. 1E). Hierarchical clustering identified initial splits between non-neuronal and neuronal cells, followed by excitatory vs inhibitory, and spatial (subcortical, hippocampal, cortical) based splits in excitatory cell types (Fig. 1F).
Visualization of neuronal cell types (Fig. 2A, Supp Fig. 2) shows strong spatial localization, commensurate with previous region-based studies and atlases. Hippocampal excitatory cells define the primary structures of the hippocampal formation (DG, CA1, CA3), while cortical excitatory neurons divide into distinct layers across most of the cortex. We identify and visualize cell type specific markers for these distinct neuron types (Fig. 2B) to verify spatial fidelity with raw decoded transcripts.
Next, we segmented major brain regions, subdividing the cortex into three subregions: the neocortex (somatosensory, visual, parietal, retrosplenial, and auditory cortices), the limbic cortex (perirhinal, ectorhinal, entorhinal, and piriform cortices), and the cortical amygdala, and identify major structures in hippocampal and subcortical regions. This resulted in 10 identified major brain regions (Fig. 2C).
Finally, we visualized raw transcript counts of Tmem119 and Itgax to confirm microglia activation in the 5xFAD and Trem2R47H; 5xFAD mice. Tmem119 is a homeostatic microglia marker, while Itgax is a marker for disease associated microglia (DAM), a distinctive microglia subset whose activation is associated with neuroinflammatory responses, including response to Aβ plaque pathology. As expected, 5xFAD and Trem2R47H; 5xFAD mice show Itgax expression upregulation, indicating increased microglial activation (Fig. 2D-E, Itgax: p < 0.02, Tmem119: p < 10− 10, linear mixed effects model). Microglia transition to a fully activated state via a two-stage Trem2 dependent pathway, highlighting the importance of this gene in AD progression[47]. We note that Trem2 expression is significantly increased in the microglia of both 5xFAD and Trem2R47H; 5xFAD mice (Fig. 2D, 5xFAD: adjusted p = 2.6 x 10− 3, fold change = 1.88, Trem2R47H; 5xFAD: adjusted p = 7.1 x 10− 6, fold change = 1.89. Linear mixed effects model).
Overall, MERFISH spatial transcriptomics enables detection of high-level cell type clusters, visually identifiable and quantifiable transcriptomic differences in microglia and regional annotation and assignment of individual cells to specific coarse-grained spatial regions.
Glial and neuronal transcriptomes are affected by nearby plaques
Spatial transcriptomics can reveal local effects of pathology, such as Aβ plaques, on the regulation of gene expression in nearby cells. By co-staining coronal brain slices with both DAPI and thioflavin S (a canonical stain for Aβ plaques), we observed that DAPI brightly labels Aβ plaques in addition to nuclei [48] (Fig. 3A). We therefore applied DAPI staining to MERFISH prepared coronal slices and a machine learning approach to automatically detect and segment plaques in each of the MERFISH samples.
DAPI stained plaques are visually distinguishable from nuclei by their large size, greater brightness, and fibrous morphology and lack of circular cell soma shape (Fig. 3B-C). These features enable manual annotation of plaques in individual fields of view. We trained a modified cellpose model[34] to detect plaques, but not cells (Fig. 3B). In testing the model on holding out annotated plaque data, we identified only 1 false positive across 25 FOVs, and 28 total plaques, with an F1 score of 0.89.
We analyzed each 5xFAD and Trem2R47H; 5xFAD section using this model (Supp Fig. 3A-B) and verified that 1) the model does not detect cells (Fig. 3B), 2) the predicted plaques are morphologically distinct from cells (Fig. 3C), and 3) the predicted plaques have significantly lower transcript density when compared to cells (Fig. 3D, Wilcoxon rank-sum test, p < .01). In total we identified 5,616 plaques distributed across multiple brain regions.
Across brain regions, we found the closest cell to each identified plaque was often a microglial cell (62.6% of plaques in 5xFAD and 60.0% in Trem2R47H; 5xFAD)(Fig. 3E). Additionally, microglia density in the region within 100 µm of a plaque (proximal) was significantly higher than in the 100–500 µm region(distal) (5xFAD, proximal density = 17.6 +/- .935 x 10− 5, distal density = 7.81 +/- 0.665 x 10− 5, p = 7.55 x 10− 4; Trem2R47H; 5xFAD, proximal density = 17.6 +/- 0.790 x 10− 5, distal density = 6.87 +/- 2.15 x 10− 5, 1.31 x 10− 3, mean +/- s.e, plaques/µm2), while no genotype difference was detected for density either proximal or distal to plaques (p > 0.19). Astrocytes were the second most common cell type identified near plaques (7.9% of plaques in 5xFAD and 10.1% in Trem2R47H; 5xFAD) (Fig. 3E), however, overall astrocyte density showed no differences in density between proximal or distal areas in either genotype (p > 0.16). Thus, the typical microenvironment around plaques includes microglia, with astrocytes and other cell types at greater distances from the plaque (Fig. 3F)[24, 49].
Next, we assessed whether plaques appear proximal to neurons. The distance from a plaque to the closest neuron was significantly larger than the distance from a neuron to its closest neuronal neighbor (5xFAD: minimal plaque to neuron distance 56.4 +/- 10.7 µm, minimal neuron to neuron distance 21.6 +/- 1.24 µm, p = 0.012; Trem2R47H; 5xFAD: minimal plaque to neuron distance 48.7 +/- 2.53 µm, minimal neuron to neuron distance 21.0 +/- 0.733 µm, p = 1.47 x 10− 4, mean +/- s.e., plaques in corpus callosum excluded from analysis due to lack of nearby neurons, t-test). We examined the typical distance of each neuronal cell type to the nearest plaque. This analysis showed that subiculum excitatory neurons, layer 5 and layer 6 excitatory neurons have the lowest median distance to plaques among identified cell types (Supp Fig. 3C). However, none of the top 5 neuron types (ranked by median distance to plaque, excluding subiculum excitatory and SST-Chodl cells due to low cell numbers), exhibited significant density variation between plaque proximal (< 100 µm) and distal (100–500 µm) regions. This implies that neuronal plaque proximity is driven primarily by plaque density in the associated regions. Additionally, plaques on average form in regions nearly twice as far from the nearest neuron as the typical distance between neurons, but the average neuronal density does not appear to be decreased in plaque proximal vs plaque distal regions, implying a variation in plaque to neuron distance at the microscale (< 100 µm), but not at larger scales (< 500 µm).
The highest plaque density occurred in the corpus callosum (CC) (5xFAD average 5.61 x 10− 5, Trem2R47H; 5xFAD average 6.23 x 10− 5 plaques/µm2) and hippocampal areas (5xFAD average 2.89 x 10− 5, Trem2R47H; 5xFAD average 2.04 x 10− 5 plaques/µm2, averaged across CA1, CA3, and DG), followed by cortex (5xFAD average 3.92 x 10− 5, Trem2R47H; 5xFAD average 2.08 x 10− 5 plaques/µm2, averaged across neocortex, limbic cortex, and cortical amygdala), with the lowest densities in the subcortical regions (5xFAD average 2.71 x 10− 5, Trem2R47H; 5xFAD average 0.323 x 10− 5 plaques/µm2, averaged across midbrain, thalamus and hypothalamus) (Fig. 3G). Mice with the 5xFAD genotype had higher plaque density compared with Trem2R47H; 5xFAD mice in the midbrain, thalamus, and neocortex (p < 0.05, linear mixed effects model), but not the CC. This distribution is consistent with the pattern of median minimum distance to plaques (Supp Fig. 3C), with roughly all cell types showing larger distance to plaques in Trem2R47H; 5xFAD samples. High plaque density regions such as the subiculum and lower cortical layers contained neurons with the lowest median distance to the nearest plaque. Trem2R47H; 5xFAD animals exhibited larger plaque sizes than 5xFAD animals (1108 µm3 vs 984 µm3, p = .025, Wilcoxon rank sum test), though this appears to be gender and pathology dependent, as male animals showed the reverse effect (741.96 µm3 vs 799.60 µm3, p = .0046) as well as lower pathology levels (Supp Fig. 3A-B).
We next tested whether cells within 100 µm of the nearest Aβ plaque have altered patterns of gene expression (Fig. 3H). Due to the relatively low cell abundance proximal to plaques, we aggregated 5xFAD and Trem2R47H; 5xFAD samples, and separated individual cells by cluster. We analyzed plaque proximity based differential expression with two techniques. First, we identified cells within 100 µm of a plaque center. Using DESeq2 and treating cells as independent samples, we identified genes whose expression correlated with proximity to the nearest plaque (Fig. 3H, Supp Table 3). Continuous effects were identified primarily in microglia and astrocytes, with microglia showing an upregulation of typical DAM associated genes (e.g. Csf1, Apoe, Cst7), and a downregulation of P2ry12, a homeostatic microglia associated gene[50]. Similarly, C4b, Clu, and Gfap, markers of a previously known disease associated astrocyte (DAA) phenotype were also upregulated near plaques[51].
To validate these findings and to account for variability across biological replicates, we additionally performed a pseudobulk analysis of differential expression between plaque-proximal (within 100 µm of the closest plaque) and plaque-distal (100–500 µm to closest plaque). We applied a linear mixed effects model to pseudobulk expression for each cell type in each sample, accounting for batch as a random effect (Fig. 3I, Supp Table 4). Additionally, we filtered genes based on their known expression in each cell type from previous single-cell atlases[37, 38], to avoid spurious identification of differentially expressed genes due to technical (errors in segmentation) or biological (phagocytosis, overlapping cellular processes) effects[52].
Pseudobulk analysis was generally consistent with the DESeq2 results and identified both glial and neuronal changes (Fig. 3I, top row). Microglia and astrocytes exhibited typical disease associated profiles in cells located proximal (< 100 µm) to plaque centers. However, Nnat expression in astrocytes and oligodendrocytes, and Mmp14 expression in astrocytes decreased near plaques. This result contrasts with previous findings in humans and other mouse models showing upregulation of Mmp14 in reactive astrocytes in Alzheimer’s disease (AD) [53].
The pseudobulk analysis also revealed notable changes in gene expression affecting neurons proximal to plaques (Fig. 3I, bottom row). L6b neurons showed lower Ngf expression near plaques, a gene therapy target in AD [54]. Nr2f2, upregulated near plaques, is known to be dysregulated by AD associated single nucleotide polymorphisms in the APOE enhancer[55]. L2 intratelencephalic (IT) neurons near plaques showed downregulation of Dkk3 (a WNT signaling modulator whose presence reduces Aβ pathology in mouse models[56]) and of the potassium ion channel subunit Kcnd2 [57] near plaques. L5 NP cells show Grm1 upregulation and Chrna7 downregulation near plaques. Parvalbumin-expressing inhibitory cells shows Grin2a, Zbtb20, and Plagl1 downregulation near plaques. Excitatory neurons in the cortical amygdala exhibited downregulation of Ntf3 (associated with nervous system maintenance[58]), Nptx1 (associated with synapse remodeling, but typically downregulated in previous studies of cortical neurons near plaques[59]), and Camk2g (implicated in synaptic plasticity[60]). Because there were few plaques in subcortical regions, we did not test plaque-associated differential expression for neuronal cell types in this region.
Microglia and astrocytes exhibit distinct cell-type-specific spatial patterns of activation associated with 5xFAD mutation
We next directly analyzed spatial and transcriptomic variation of glia between 4 combinations of genotypes. We made four pairwise comparisons (5xFAD vs WT, Trem2R47H; 5xFAD vs Trem2R47H, Trem2R47H vs WT, and Trem2R47H; 5xFAD vs 5xFAD), to identify 5xFAD and Trem2R47H dependent variations (Supp Table 5).
We identified 19 differentially expressed genes in microglia and 8 in astrocytes across all 4 pairwise comparisons. By contrast, we found 1–2 differentially expressed genes in oligodendrocyte (OGC) and oligodendrocyte precursor cells (OPC) cell populations (Fig. 4A), and none in the other non-neuronal cell types. Microglia and astrocytes primarily exhibited 5xFAD dependent changes (similar differential expression results for both 5xFAD vs WT, and Trem2R47H; 5xFAD vs Trem2R47H), replicating the DAM/DAA gene upregulation and homeostatic gene downregulation identified in the plaque proximity analysis. Interestingly, neither Itgax nor Cd74 were identified as differentially expressed in plaque proximity analysis of microglia, whereas they are upregulated 9.58 and 15.7-fold in 5xFAD compared with WT animals, and 19.7 and 26.0-fold in Trem2R47H; 5xFAD compared with Trem2R47H animals. The Trem2 gene itself showed a small reduction in expression dependent on the Trem2R47H mutation, contrary to previous studies[22]. This may be due to reduced binding of gene probes overlapping the mutated region. Differential expression also shows a small but significant Trem2R47H specific upregulation in homeostatic microglia genes, including Tmem119 (fold change = 1.13, adjusted p = .019, Trem2R47H; 5xFAD vs 5xFAD), and P2ry12 (fold change = 1.27, adjusted p = 4.60 x 10− 4, Trem2R47H; 5xFAD vs 5xFAD). The consistent variation in both Trem2R47H vs WT and Trem2R47H; 5xFAD vs 5xFAD comparisons indicates this may be a plaque independent effect and corroborates the overall lower plaque burden in Trem2R47H; 5xFAD samples.
To explore the effects of AD risk genes in specific glial subtypes, we subclustered the microglia and astrocyte subpopulations. We identified several small clusters of microglia that appear to express neuronal or other glial markers, and we confirmed that these cells are located near cells expressing these markers. We removed these cells from this portion of the analysis. After removal, subclustering identifies 7 microglia clusters (Fig. 4B1). Pseudotime analysis identified a single linear trajectory across all microglial cell types (Fig. 4B2). We next examined the genotype proportions of these clusters. After normalizing by the number of cells per sample, we averaged across samples of the same genotype, and computed cluster proportions. This identifies a clear 5xFAD dependent bias, with two clusters (labeled homeostatic) exhibiting > 80% proportion coming from non 5xFAD (i.e. WT and Trem2R47H) samples. The remaining five clusters corresponded to disease associated microglia (DAM) enriched in 5xFAD and Trem2R47H; 5xFAD mice (Fig. 4B3).
We next aggregated homeostatic and DAM subgroupings and identified regional spatial biases (Fig. 4B4). DAMs were enriched in hippocampal area CA1. They were also enriched in thalamus, and midbrain, despite the relative lack of plaque density in these regions compared to the CA1 and CC (Fig. 3E). Finally, we identified markers for the individual microglia subpopulations, and plot normalized expression (Fig. 4B5).
We focused on the analysis of the genes differentially associated with late-stage DAMs as several genes exclusive to late-stage DAM (DAM2) were included (Itgax, Cst7, Csf1, Ccl6), as well as genes present across both stages (Apoe). Except for Ccl6, all of these genes are differentially expressed in 5xFAD and Trem2R47H; 5xFAD, with primary expression of DAM2 genes in C3-5 (later pseudotime). Apoe is evenly distributed across C2-5, reflecting its overexpression across the DAM developmental timeline (Fig. 4B5)[61].
Subclustering the astrocyte subpopulations, we aggregated clusters not exhibiting genotype specific bias (see methods for thresholds) into a single cluster (C1), retaining the genotype biased clusters (Fig. 4C1). Pseudotime trajectory analysis (Fig. 4C2) did not yield a distinctive pattern, however after analysis of genotype bias (identifying C1 as unbiased, C2/C3 as DAA, and C4/C5 as upregulated in WT/Trem2R47H samples, Fig. 4C3), we note that C5 and C4 exhibited distinct spatial distributions, with C4 appearing exclusively in cortex and hippocampus, and C5 appearing in subcortical regions (Fig. 4C4). The DAA exhibited a similar regional specificity, with C2 restricted to cortex and hippocampus. Cluster markers are identified and plotted (Fig. 4C5).
We next examined the spatial distribution of DAM and DAA cells by region. Disease associated microglia were enriched in the CC, subiculum and subcortical regions (Fig. 4D1). Computing the proportion of microglia identified as DAM by region (Fig. 4D2) showed similar proportions of DAMs between 5xFAD and Trem2R47H; 5xFAD samples by region, except in the DG, thalamus, midbrain, and hypothalamus. This corresponds with the plaque density bias in 5xFAD samples (Fig. 3G). In the cortex, we saw a significant increase in DAMs in the lower cortical layers (L5/L6) compared with the upper cortical layers (L2/L3) (p < .0001, linear mixed effects model, Fig. 4D3).
Disease associated astrocytes were concentrated in the CC and surrounding areas (Fig. 4E1). The only regions exceeding 40% DAA proportion are the CC and CA1 (Fig. 4E2). Virtually no disease associated astrocytes are present in upper cortical layers, but this population was significantly upregulated in deeper cortical layers (Fig. 4E3). We did not find significant genotype specific effects in other glial cells.
To further analyze the spatial variation of gene expression, we performed direct pseudobulk differential expression analysis of microglia, astrocytes, oligodendrocytes and oligo-precursors across the 10 identified major brain regions (Supp Fig. 4, Supp Table 6). We compared each region with the average across the remaining 9 regions. We also computed regional cell density and cell proportion for each of these cell types.
Analysis of microglia (Supp Fig. 4A) shows that ~ 60% of spatially variable genes are also differentially expressed across genotypes. For example, the canonical late-stage DAM markers Cst7 and Itgax were significantly upregulated in the corpus callosum. A small number of other genes (Ctss, C1qa, Zbtb20, Ly9, Tmem119) had spatially variable patterns of expression that were consistent across WT and Trem2R47H mice and dysregulated in 5xFAD and Trem2R47H; 5xFAD mice. Microglia cell populations also show drastic increases in both cell proportion and density across all brain regions.
Astrocytes (Supp Fig. 4B) exhibited large numbers of spatially variable genes with consistent patterns of expression across all genotypes (e.g. Erbb4, Nnat, Grin3a, Mmp14, Id4, Pax6, etc). We also found spatial variation in several disease associated genes (Aqp4, Gfap). These spatial variations were primarily observed between cortical and subcortical (thalamus, midbrain, hypothalamus) regions. However, astrocytes exhibited little genotype specific cell proportion or density variations between regions.
Oligodendrocytes exhibited two separate gene groupings (Supp Fig. 4C). One set (Snca, Dlg4, Nnat, Robo1, S100b, Ptgds) showed spatially variable expression across multiple regions, particularly between cortical/hippocampal and subcortical regions. The other set of genes (Adam10, Psen1, Olig1, etc) is primarily upregulated in CC and downregulated in amygdala, with very little variation in other regions. This pattern is not 5xFAD or Trem2R47H dependent and was observed even in WT oligodendrocytes cells. This second pattern is not replicated in oligodendrocyte precursor cells, though a significant cortex vs subcortical divide is present in OPCs (Supp Fig. 4D). Neither cell type exhibits significant genotype dependent cell proportion changes within regions.
Overall, our data show that spatial variation in microglia and astrocyte gene expression is more affected by 5xFAD than by Trem2R47H. Both disease-associated microglia and astrocytes exhibit specific spatial distributions. DAMs were distributed across the coronal section, but concentrated in the CC and subcortical regions, and DAA were biased almost exclusively to the CC and surrounding regions. Regional transcriptional variations were primarily impacted in 5xFAD for microglia and astrocytes, and both 5xFAD and Trem2R47H mutations were independent of regional variations in oligodendrocytes and oligodendrocyte precursors.
Neurons exhibit complex transcriptomic impacts of 5xFAD and Trem2R47H mutations
We next performed differential expression analysis for each of the four comparisons (5xFAD vs WT, Trem2R47H; 5xFAD vs Trem2R47H, Trem2R47H vs WT, and Trem2R47H; 5xFAD vs 5xFAD), as well as subclustering analysis, for each of the neuronal cell types (Supp Table 5). Analysis of cortical neurons identifies differentially expressed genes for all these comparisons in each cell type (Fig. 5A-C), as well as genotype biased subclusters for most neuron cell types (Fig. 5D-E, Supp Fig. 5). We first considered genes consistently identified as differentially expressed across multiple cortical neuronal cell types.
Cdh12, associated with calcium ion binding [62], was differentially expressed in 5 out of 9 cell types. Both L3 IT and L5 IT neurons exhibit upregulation of Cdh12 in Trem2R47H; 5xFAD over 5xFAD genotypes. Ntrk2, which encodes TrkB, a high affinity receptor for BDNF[63], is upregulated in Trem2R47H; 5xFAD vs 5xFAD and Trem2R47H vs WT comparisons in L2 IT, L3 IT, L6 IT and L6 CT excitatory cell types. On the other hand, Bdnf itself, expected to decrease in the 5xFAD context, was identified as significantly decreased only in L2 IT and L6b neurons. Fos, a molecular marker of neuron activity [64], was consistently identified as differentially expressed across 6 of the 9 cell types, for at least one comparison. In each case this gene was downregulated, implying downregulation of Fos induced by both 5xFAD and Trem2R47H mutations.
All other genes were differentially expressed in at most 3 cortical excitatory cell types. Of these, the most interesting are Wfs1, recently implicated in Tau clearance in AD[65], which is downregulated in Trem2R47H; 5xFAD compared with 5xFAD, and Grm2, a glutamate receptor downregulated in Trem2R47H; 5xFAD compared with 5xFAD in L3 IT and L6 IT neurons.
We next examined log fold changes without applying statistical thresholds, to identify possible patterns obfuscated by our choice of threshold (Fig. 5C). Cdh12 showed no consistent patterns in the 5xFAD comparisons but was consistently upregulated by the Trem2R47H mutation (Trem2R47H vs WT: 0.591 +/- 0.305, p = 3.79 x 10− 3; Trem2R47H; 5xFAD vs 5xFAD: 0.305 +/- 0.197, 8.49 x 10− 4; mean +/- sd, computed average across cell types, t-test). Ntrk2 exhibited a similar pattern (Trem2R47H vs WT: 0.706 +/- 0.366; Trem2R47H; 5xFAD vs 5xFAD: 0.554 +/- 0.330) and also showed a significant downregulation in the Trem2R47H; 5xFAD vs Trem2R47H comparison (-0.207 +/- 0.133, p = 8.25 x 10− 4). Bdnf did show a small decrease induced by 5xFAD (5xFAD vs WT: -0.179 +/- 0.132, p = 2.04 x 10− 3; Trem2R47H; 5xFAD vs Trem2R47H: -0.207+/- 0.133, p = 4.56 x 10− 4), and interestingly, a consistent upregulation induced by Trem2R47H (Trem2R47H vs WT: 0.581 +/- 0.360, p = 6.50 x 10− 4; Trem2R47H; 5xFAD vs 5xFAD: 0.273 +/- 0.374, p = 4.65 x 10− 2), except in L5 PT and L6b neurons. Both Wfs1 (Trem2R47H vs WT: -0.199 +/- 0.209, p = 1.47 x 10− 2; Trem2R47H; 5xFAD vs 5xFAD: -0.448 +/- 0.140, p = 3.18 x 10− 6) and Grm2 (Trem2R47H vs WT: -0.580 +/- 0.481, p = 6.76 x 10− 3; Trem2R47H; 5xFAD vs 5xFAD: -0.234 +/- 0.194, p = 6.91 x 10− 3) were consistently downregulated by the Trem2R47H mutation. Fos exhibits negative log fold changes in every cell type and comparison (5xFAD vs WT: -0.778 +/- 0.282, 3.47 x 10− 5; Trem2R47H; 5xFAD vs Trem2R47H: -0.522+/- 0.227, p = 7.96 x 10− 5; Trem2R47H vs WT: -0.850 +/- 0.370, p = 1.25 x 10− 4; Trem2R47H; 5xFAD vs 5xFAD: -0.552 +/- 0.228, p = 8.57 x 10− 5), indicating highly consistent activity downregulation induced by both 5xFAD and Trem2R47H mutations.
Subclustering cortical neurons identified genotype specific subpopulations in 7 cortical excitatory cell types (Fig. 5C,D; Supp Fig. 5). In all but one cell type (L3 IT), this represents a WT (or WT/Trem2R47H) enriched, and thus 5xFAD/Trem2R47H; 5xFAD reduced subpopulation. For IT cell populations, these genotype specific subtypes did not exhibit significant spatial localization. However, 5xFAD/Trem2R47H; 5xFAD reduced subtypes in L2 IT are spatially localized to the retrosplenial and visual cortices, and 5xFAD/Trem2R47H; 5xFAD reduced subtypes in L5 NP are spatially localized in the retrosplenial cortex near the subiculum, a plaque dense environment (Fig. 5E, Supp Fig. 5). This latter subtype upregulated Sulf2 (antibody staining has shown this is reduced in AD [66]) and Cplx1 (regulates synaptic transmission by preventing neurotransmitter release prior to action potential [67]) as top differentially expressed genes.
Among subcortical neurons, thalamic excitatory neurons exhibited the largest number of differentially expressed genes (Fig. 6A). Thalamic excitatory neurons exhibit 5xFAD induced upregulation of Grin2c, Epha10, Ptpru, and Crtac1, with downregulation of Syp, Bdnf, Negr1, and Gsto1, each of which has been linked to AD[68–74]. On the other hand, Ntsr1, Kcnh7, Map4k3, and Col11a1 are upregulated in Trem2R47H; 5xFAD over 5xFAD. No consistent effects can be attributed to either the 5xFAD or Trem2R47H in subcortical non-thalamic inhibitory and excitatory neurons.
Hippocampal CA1 excitatory neurons (Fig. 6B) exhibited upregulation of the expression of Ntrk2 and Mapk1, associated with the MAPK signaling pathway, in Trem2R47H; 5xFAD compared with 5xFAD animals. CA3 excitatory neurons showed several differentially expressed genes (Rph3a, Itga7, Hs3st1), but almost exclusively in the 5xFAD vs WT comparison, though Ntsr1 was upregulated in both Trem2R47H; 5xFAD vs 5xFAD, and Trem2R47H vs WT comparisons. The dentate gyrus showed upregulation of Dkk3 and downregulation of Adgra1 in both 5xFAD dependent comparisons.
Inhibitory cell types (Fig. 6C) consistently exhibited upregulation of Epha10 in 5xFAD compared with WT, and downregulation in Trem2R47H; 5xFAD compared with 5xFAD. Few other genes (Fos, Hrh3) were consistently differentially expressed between genotypes.
Subclustering subcortical and hippocampal neurons (Supp Fig. 6) reveals greater genotype proportion heterogeneity than in cortical excitatory neurons. In contrast with cortical excitatory neurons, hippocampal and thalamic excitatory neurons clustered into large numbers of variable genotype proportion subclusters. Thalamic excitatory neurons subcluster into three genotype enriched sets, including a 5xFAD/Trem2R47H; 5xFAD enriched subtype a Trem2R47H/Trem2R47H; 5xFAD enriched subtype and a WT/Trem2R47H enriched subtype. CA1 excitatory neurons identified 6 genotype enriched subclusters, though two of them are spatially localized in the ventral CA1, which is not included in some samples. These include two 5xFAD/Trem2R47H; 5xFAD enriched subtypes and two WT/Trem2R47H enriched subtypes. CA3 excitatory neurons subclustered into 5 genotype biased subclusters, including several localized to the ventral hippocampus (Fig. 6D, ventral hippocampus clusters not shown). One subcluster, labeled C2, present primarily in 5xFAD and Trem2R47H; 5xFAD samples, is spatially positioned in the intersection of the CA3 and dentate gyrus. This cluster upregulated Rph3a and Dkk3 (Supp Fig. 6C).
Overall, neuronal populations exhibit transcriptional alterations associated with both 5xFAD and Trem2R47H mutations. In cortical excitatory neurons, these changes are frequently replicated across cell types. Thalamic excitatory neurons, uniquely among subcortical populations, exhibit significant 5xFAD and Trem2R47H induced transcriptomic alterations. In the hippocampus, the CA1 shows the most transcriptional alteration among genes measured in this study. Neuronal subclusters show both genotype enrichment, and spatial localization, implying regional population variations induced by 5xFAD and Trem2R47H mutations.