Single Nucleus RNA Sequencing Demonstrates an Autosomal Dominant Alzheimer’s Disease Profile and Possible Mechanisms of Disease Protection

Maria Camila Almeida; Sarah J Eger; Caroline He; Morgane Audouard; Arina Nikitina; Stella M K Glasauer; Dasol Han; Barbara Mejía-Cupajita; Juliana Acosta–Uribe; Nelson David Villalba-Moreno; Jessica Lisa Littau; Megan Elcheikhali; Erica Keane Rivera; Daniel Carneiro Carrettiero; Carlos Andrés Villegas–Lanau; Diego Sepulveda-Falla; Francisco Lopera; Kenneth S Kosik

doi:10.1016/j.neuron.2024.02.009

. Author manuscript; available in PMC: 2025 Jun 5.

Published in final edited form as: Neuron. 2024 Feb 27;112(11):1778–1794.e7. doi: 10.1016/j.neuron.2024.02.009

Single Nucleus RNA Sequencing Demonstrates an Autosomal Dominant Alzheimer’s Disease Profile and Possible Mechanisms of Disease Protection

Maria Camila Almeida ^1,^2,⁺, Sarah J Eger ^1,⁺, Caroline He ¹, Morgane Audouard ^1,⁶, Arina Nikitina ¹, Stella M K Glasauer ^1,⁶, Dasol Han ^1,⁶, Barbara Mejía-Cupajita ^1,³, Juliana Acosta–Uribe ^1,³, Nelson David Villalba-Moreno ⁴, Jessica Lisa Littau ⁴, Megan Elcheikhali ^5,⁷, Erica Keane Rivera ¹, Daniel Carneiro Carrettiero ^1,², Carlos Andrés Villegas–Lanau ³, Diego Sepulveda-Falla ⁴, Francisco Lopera ^3,^✉, Kenneth S Kosik ^1,^✉,^*

PMCID: PMC11156559 NIHMSID: NIHMS1968817 PMID: 38417436

SUMMARY

Highly penetrant autosomal dominant Alzheimer’s Disease (ADAD) comprises a distinct disease entity as compared to the far more prevalent form of AD in which common variants collectively contribute to risk. The downstream pathways that distinguish these AD forms in specific cell types have not been deeply explored. We compared single nucleus transcriptomes among a set of 27 cases divided among PSEN1-E280A ADAD carriers, sporadic AD and controls. Autophagy genes and chaperones clearly defined the PSEN1-E280A cases compared to sporadic AD. Spatial transcriptomics validated the activation of chaperone-mediated autophagy genes in PSEN1-E280A. The PSEN1-E280A case in which much of the brain was spared neurofibrillary pathology and harbored a homozygous APOE3 Christchurch variant revealed possible explanations for protection from AD pathology including over-expression of LRP1 in astrocytes, increased expression of FKBP1B, and decreased PSEN1 expression in neurons. The unique cellular responses in ADAD and sporadic AD require consideration when designing clinical trials.

Keywords: PSEN1-E280A, spatial transcriptomics, autophagy, chaperones, single nucleus sequencing, APOE3 Christchurch

Graphical Abstract

graphic file with name nihms-1968817-f0001.jpg

eTORC Blurb:

Almeida, Eger, and colleagues find specific autophagy and chaperone gene signatures that distinguish autosomal dominant Alzheimer’s Disease (ADAD) from sporadic cases. Protection from dementia due to the APOE3 Christchurch variant may arise from increased LRP1 expression in astrocytes and consequently, their increased TAU uptake.

INTRODUCTION

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that ultimately impairs the ability to carry out the simplest activities of daily living. Although more granular classifications of AD genotypes and phenotypes are described, the disease can be broadly discriminated as highly penetrant autosomal dominant AD (ADAD) or sporadic AD due to a complex interplay of genetic risks and environmental contributions. While sporadic AD is often sub-divided into late-onset AD (LOAD) and early-onset AD (EOAD), genetic evidence suggests that they represent a continuum rather than two distinct groups¹ and there can be overlap in age at onset with ADAD². PSEN1, PSEN2 and APP are the three genes that carry multiple different ADAD mutations. Variants in these genes are often highly penetrant with most carriers developing AD by midlife³. A glutamic acid to alanine mutation at codon 280 in PSEN1 (NM_000021:c.839 A > C, p.Glu280Ala, here referred as PSEN1-E280A) [rs63750231] discovered in Antioquia Colombia affects the largest kindred in the world with ADAD^4,5. PSEN1-E280A mutation carriers usually develop memory deficits, followed by gradual impairments in other cognitive skills, such as verbal fluency and executive function. The median lifespan after onset of dementia (~49 years old) is approximately 10 years⁴. Although ADAD cases represent fewer than 1% of all AD cases, patients with these variants have greatly informed mechanistic studies of the disease. Nevertheless, the dysregulated pathways that lead to sporadic AD versus ADAD are unknown; as are the molecular consequences of the somewhat distinct co-pathologies associated with these conditions ⁶. Greater degrees of neuritic plaques, neurofibrillary tangle (NFT) formation and cerebral amyloid angiopathy were found in ADAD⁷. Highly prevalent AD co-pathologies are not restricted to the oldest-old, but are common even in early-onset AD⁸. AD pathology unrelated to plaques and tangles is well-known to occur in ADAD, particularly, the coexistence of Lewy bodies composed primarily of alpha-synuclein filaments occurs frequently ^9–11.

Our hypothesis is that single-cell gene expression vulnerabilities in ADAD, as exemplified by PSEN1-E280A, differ from sporadic AD including LOAD. Because clinical and pathological variation do exist among cases with different PSEN1 variants, focusing on a single PSEN1 mutation will reduce that variation. The very large PSEN1-E280A kindred precisely provides the opportunity to do this. Understanding the cellular and molecular differences between these forms of AD is critical because ADAD cases are often utilized in “prevention” trials that take advantage of the strong genetic predisposition to identify participants before the onset of clinical symptoms; however, the results from such trials remain of questionable application to the larger LOAD population. Identification of differences between these conditions may spur separate treatment limbs in clinical trials and provide a better-informed route toward precision medicine.

In this study we performed single nucleus RNA sequencing (sn-RNA seq) from postmortem frontal cortex in non-AD controls, sporadic AD and ADAD patients to detect a PSEN1-E280A transcriptomic profile that can distinguish this disease from sporadic AD and controls. We also performed a spatial transcriptomics analysis in samples from frontal cortex and the CA1 hippocampal region in PSEN1-E280A cases compared to non-diseased control individuals to validate our findings. Finally, we compared single nucleus transcriptomes from the frontal cortex of the PSEN1-E280A case carrying the APOE3 homozygous Christchurch variant (PSEN1-E280A_APOE3-CC-hom) with the variety of PSEN1-E280A mutation settings in which prominent AD pathology was observed, including occipital cortex from PSEN1-E280A_APOE3-CC-hom, seven PSEN1-E280A carriers that do not have the Christchurch variant (PSEN1-E280A_APOE3-CC-negative), and three PSEN1-E280A APOE3-Christchurch heterozygotes (PSEN1-E280A_APOE3-CC-het).

RESULTS

Single Nucleus-RNA Sequencing of frontal cortex

The samples under study were from ten carriers of PSEN1-E280A, eight non-PSEN1-E280A carriers with sporadic AD and eight non-AD controls, both male and female subjects with a range of APOE genotypes (E2/E3, E3/3, E3/4, E4/4), plus one additional patient carrying two alleles of the APOE3 Christchurch variant (Supplementary Table 1). Although age at onset in sporadic AD cases skewed older there was significant overlap in the two groups (Figure 1A, B, Supplementary Table 1). All brains were neuropathologically examined by immunohistochemistry for β-amyloid (Aβ) and pathological TAU (AT8). Virtually no AD-related neuropathological markers were detected in controls, while both PSEN1-E280A carriers and sporadic AD cases were positive for both markers (Figure 1C, Supplementary Figure 1). Disease stage, as assessed by Thal phase, BRAAK stage and CERAD scores were similarly advanced in both PSEN1-E280A and sporadic AD individuals (Figure 1D and Supplementary Table 1). sn-RNA seq was performed on the frontal pole from all individuals (Supplementary Figure 2A–B). After quality control filtering, 54,960 nuclei with an average of 4,128 transcripts and 2,074 genes per nucleus were detected (Supplementary Figure 2C, Supplementary Table 1). After integrating data from the occipital cortex and frontal pole of the patient carrying two alleles of the APOE3 Christchurch variant (PSEN1-E280A_APOE3-CC-hom), the total count of nuclei analyzed summed to 66,250, with an average of 4,163 transcripts and 2,076 genes per nucleus (Supplementary Table 1).

The postmortem time to brain retrieval did not correlate with the number of transcripts or genes detected per cell (Supplementary Figure 2D). The cells clustered as inhibitory neurons, excitatory neurons, astrocytes, microglia, oligodendrocytes, oligodendrocyte precursor cells (OPCs), endothelial cells and pericytes (Supplementary Figure 3A, B, Figure 1E–F). The nuclei types, markers, and proportions of nuclei types matched previous sn-RNA seq data from adult human cortex^12–15.

All the cell types were detected in the three sample groups (Figure 1G). To assess whether the proportions of broad cell types were affected by diagnosis (control, PSEN1-E280A and sporadic AD), we computed the relative abundance of each cell type for each individual according to diagnosis. Statistical significance was determined using beta regression. The relative abundance of oligodendrocytes in PSEN1-E280A (p = 1.2 E-2) compared to controls was increased, whereas sporadic AD did not differ from controls in oligodendrocyte abundance (p = 6.5 E-2). However, a direct comparison between PSEN1-E280A cases compared to sporadic AD cases was not significant (p = 5.3 E-1) for oligodendrocyte abundance. Both PSEN1-E280A and sporadic AD cases showed a decrease in the excitatory neuron population compared to controls (Figure 1H). No sample variable, such as donor control, disease status, sex, age at death, postmortem interval time to brain retrieval, or neuropathological staging was exclusively associated with any cluster (Supplementary Figure 3C).

Cellular systems dysregulated in PSEN1-E280A Alzheimer’s disease astrocytes

Of 3958 nuclei from diseased patients annotated as astrocytes (2101 from PSEN1-E280A and 1857 from sporadic AD), we assessed differential gene expression (DGE) within the total astrocytic population in PSEN1-E280A vs sporadic AD by aggregating the counts for a pseudobulk DGE analysis¹⁶. We identified 186 genes upregulated and 53 genes downregulated in PSEN1-E280A when compared to the sporadic AD cases (Figure 2A, Supplementary Table 2).

Figure 2: — (A) Volcano plots showing up and downregulated genes in *PSEN1*-E280A astrocytes compared to sporadic AD.

(B) Gene set enrichment annotation of differentially overexpressed genes in Astrocytes. Bar graph representing annotation into two categories, Reactome (top), and GO Biological Processes (bottom).

(C) Scores for the chaperone-mediated autophagy (CMA) according to diagnosis. CMA score for both *PSEN1*-E280A and sporadic AD is normalized by CMA score for non-diseased individuals.

(D) Heatmap and hierarchical clustering of the genes (rows) associated with autophagy overexpressed in *PSEN1*-E280A. Collum’s show individual cases.

(E) Network plot showing the top 25 hub genes associated with module Ast-M2 identified by hd-WGCNA.

(F) Overlap of genes differentially expressed revealed by Pseudobulk analysis and genes in Ast-M2 module revealed by hd-WGCNA

(G) Enrichment of genes matching membership term: autophagy. The outer pie shows the number and the percentage of genes in the background that are associated with the membership (in black); the inner pie shows the number and the percentage of genes in the individual input gene list that are associated with the membership. The *p-value* indicates whether the membership is statistically significantly enriched in the list. See also Figure S2 and S4

Among the gene set enriched terms associated with the genes overexpressed in PSEN1-E280A, the top terms included autophagy (which was accompanied by other related terms such as “macroautophagy”, “mitophagy”, and “chaperone-mediated autophagy”), and respiratory electron transport-related terms (which included genes encoding for mitochondrial complexes I, III, IV and V) (Figure 2B). Computing a chaperone-mediated autophagy (CMA) score¹⁷ from the changes in the mRNA levels of components of the CMA network in astrocytes revealed a significantly higher score in PSEN1-E280A versus sporadic AD cases (Figure 2C–D). The overexpressed genes associated with autophagy in astrocytes from PSEN1-E280A included two heat shock genes (HSP90AB1, HSPA9), the heat shock inducer EEF1A1, cytoskeleton-associated genes (DYNLL1, TUBA1B, TUBA4A), the members of the ATG8 family (GABARAPL2, GABARAPL1) and the mitochondrial-associated genes (VDAC1, TOMM20, TOMM7, ATP5F1A, ATP5F1B, CHCHD2). Interestingly, several genes associated with “regulation of protein modification process” were also overexpressed in PSEN1-E280A, and include the molecular chaperone known to regulate protein isomerization at proline residues (PPIA) and ubiquitin B and C (UBB and UBC), suggesting that the transcriptional activation of protein degradation and autophagic responses in PSEN1-E280A cases qualitatively differs from that which occurs in sporadic AD (Figure 2E).

Next, we ran high dimensional weighted gene co-expression analysis (hd-WGCNA), which groups together co-regulated genes as modules, in astrocytes from controls (916 nuclei), PSEN1-E280A and sporadic AD. Among the ten modules identified, two modules (Ast-M1 – green module, and Ast-M2 – turquoise module) were significantly upregulated in PSEN1-E280A compared to sporadic AD cases, while Ast-M8 – purple module were significantly downregulated in PSEN1-E280A compared to sporadic AD cases by eigengene expression values (Wilcoxon Rank Sum p-value < 0.01, effect size > 0.3, Figure 2F, Supplementary Figure 4A–C, Supplementary Table 3). A significant overlap between Ast-M2 genes and overexpressed DGEs found by pseudobulk analysis was found (p = 4.8 E-98, odds ratio = 37.3, Supplementary Table 3), with 157 out of the 180 DGEs identified by pseudobulk also presented in Ast-M2 (Figure 2G). Ast-M2 contained 127 genes that matched the membership term “autophagy” (p = 2.5 E-08), which included 25 genes significantly overexpressed in PSEN1-E280A compared to sporadic AD (Figure 2H). These results were consistent with the DGE results described above and further support the observation that transcriptional activation of protein degradation and autophagy in PSEN1-E280A cases qualitatively differs from the sporadic AD profile.

Unique and shared transcriptional regulation in PSEN1-E280A and sporadic AD neurons

Nuclei assigned as neuronal (control: 7,233 control, PSEN1-E280A: 10,382 and sporadic AD: 5,447) clustered into 14 subpopulations (Neu-0 – Neu-13, Figure 3A, Supplementary Figure 5A), including ten clusters of excitatory neurons (78.2%), that shared the expression of the pan-excitatory marker SLC17A7, and four clusters of inhibitory neurons (21.8%), positive for the pan-inhibitory GAD1 marker (Figure 3B). Among excitatory subtypes, we identified layers 2–3 (Neu-0 and Neu-2, expressing CUX2/LAMP5 and CUX2/COL5A2, respectively), layers 4–5 RORB positive (RORB/PCP4, RORB/IL1RAPL2, RORB/PLCH1/MME, Neu-1, Neu-3 and Neu-9), layers 5–6 (NFIA/THEMIS, Neu-5), layer 6 (TRPM3/SEMA5A, THEMIS/NTNG2/NR4A2, Neu-6 and Neu-13), deeper layer 6b (FEZF2/CTGF/SEMA3D, Neu-10), and deep layer glutamatergic neuron (Neu-12). Inhibitory nuclei consisted of LHX6 expressing neurons (Neu-7 and Neu-8) and were comprised of PVALB and SST subtypes, while the other inhibitory clusters were positive for ADARB2 (Neu-4 and Neu-11) and included VIP/CALB2 and LAMP5/KIT subclusters (Supplementary Figure 5B–C, Supplementary Table 4).

A significant reduction in the relative abundance of nuclei in the inhibitory subpopulation expressing SST (Neu-8) was observed for both PSEN1-E280A (p = 4.0 E-2) and sporadic AD (p < 1.0 E-3) individuals when compared to controls (Figure 3C). Inhibitory neurons expressing VIP/CALB2/PROX1 (Neu-4) were significantly reduced only in sporadic AD (p = 6.0 E-3)¹⁸. Thus, the susceptibility of these interneuron subclass populations differs between PROX1 and SST clusters with PSEN1-E280A cases limited in their inhibitory neuronal loss to the SST subpopulation. Both PSEN1-E280A and sporadic AD individuals showed a significant reduction in layer 4–5 RORB positive excitatory neurons as shown for sporadic AD¹⁹.

In inhibitory neurons, DGE analysis revealed 494 genes over-expressed and 291 genes under-expressed in PSEN1-E280A compared to sporadic AD. In excitatory neurons 1452 genes were upregulated and 833 genes downregulated in PSEN1-E280A compared to sporadic AD (Figure 3D). To check whether neurons also exhibited an autophagy-related gene difference, as seen in astrocytes, we calculated the CMA score. The CMA score was significantly higher in PSEN1-E280A when compared to sporadic AD for both excitatory (p = 4.4 E-2) and inhibitory neurons (p = 2.9 E-2, Figure 3E–F). Gene set enrichment analysis on the genes over-expressed in both inhibitory and excitatory neurons from PSEN1-E280A revealed several molecular chaperones, potentially involved with protein folding and degradation, autophagy and cellular respiration (Figure 3G, Supplementary Table 2). Widespread changes of genes encoding the mitochondrial electron transport chain components were recently reported in brains from sporadic AD patients²⁰. The upregulation of MT3 is also of interest. MT3 can control lysosomal pH by moving zinc to lysosomes and can control the expression levels of the lysosomal membrane proteins LAMP1/2 by glycosylation, thus balancing the lysosomal biogenesis, making autophagy possible under various stress situations and inducing a smooth fusion of autophagosomes and lysosomes²¹.

Hd-WGCNA was performed in excitatory neurons. One module (Exc-M2 – turquoise) out of the 5 modules found, was significantly upregulated in PSEN1-E280A compared to sporadic AD cases by eigengene expression values (Supplementary Figure 4D–F). The genes in module Exc-M2 significantly overlapped with upregulated DGEs in PSEN1-E280A identified by pseudobulk (Figure 3H–J), further confirming the findings.

To further characterize transcriptional changes in NFT-bearing gray matter, spatial transcriptomics was performed on post-mortem samples from hippocampus and frontal pole of two PSEN1-E280A carriers and one control (Figure 4A). Immunostaining of adjacent slices showed positive labeling for AT8 (which may stain multiple forms of TAU pathology including neuritic plaque-associated TAU in dystrophic axons and neuropil threads) and beta-amyloid plaques (Supplementary Figure 6A). A total of 13,538 capture spots remained after quality control filtering, 7,540 from PSEN1-E280A and 5,998 from control. Spots from the same sample were clustered at a low-resolution (res=0.1) for all five samples individually, yielding two clusters that corresponded to white and gray matter in every sample (Figure 4B, C and Supplementary Figure 6B–C). All spots were integrated and jointly clustered (res = 0.1). Integrated cluster identities corresponded with the gray (n = 5,945) and white (n = 6,435) matter spot sample-level assignments (Figure 4D–G). 1,158 spots were not assigned to white or gray matter. Those spots contained low expressed neuronal and oligodendrocyte markers that could not be assigned to a distinct cluster (Figure 4F). Clustering did not separate spots by brain region or diagnosis (Figure 4E, Supplementary Figure 6D).

Figure 4. — (A) Spatial transcriptomics overview.

(B-C) Expression of (B) neuron (*SNAP25*) and (C) oligodendrocyte (*MBP*) markers delineates gray and white matter in frontal cortex.

(D-F) UMAP of spots from *PSEN1*-E280A and control individuals colored by (D) cluster identity (E) brain region, and (F) white/gray matter.

(G) Spots colored by white/gray matter on frontal cortex sample.

(H-I) Volcano plots for genes differentially expressed in E280A patients as compared to controls

(H) in gray matter and (I) white matter. See also Figure S6

Using a generalized linear mixed model (GLMM) to compare PSEN1-E280A vs control, we identified 2,083 gray matter and 1,071 white matter significant DEGs (Figure 5H–I, Supplementary Table 5). There was a significant overlap of 306 genes that were differentially expressed in both gray and white matter (Fisher’s exact test p = 4.7 E-2, Supplementary Table 5). The significant GO terms for genes upregulated in PSEN1-E280A white matter compared to control included ‘Signaling by Rho GTPases, Miro GTPases and RHOBTB3’ and ‘Pathways of neurodegeneration’. For genes downregulated in PSEN1-E280A white matter compared to control, ‘membrane lipid metabolic process’ and ‘amyloid precursor protein metabolic process’ were among the top significant GO terms. For gray matter, ‘Signaling by Rho GTPases, Miro GTPases and RHOBTB3’ and ‘regulation of cellular response to stress’ were also upregulated in PSEN1-E280A.

Figure 5. — (A) Schematics showing the results of the hypergeometric distribution test showing top Reactome pathways over-represented in the overexpressed DGEs in *PSEN1*-E280A versus sporadic AD astrocytes, excitatory neurons and inhibitory neurons.

(B) Table displaying results from gene set enrichment analysis, rows represent gene sets, and columns provide information about enrichment results. Gene sets represents amyloid beta production and secretion genes (Abetaset) and NFT-associated genes (NFTset). The statistics represents the results from the DGEs in *PSEN1*-E280A compared to sporadic AD by their z-value. ES: Enrichment Score, NES: Normalized Enrichment Score

Given that all PSEN1-E280A patients were AT8+ in our immunohistochemical analysis, NFTs are likely abundant throughout their gray matter. As such, of the 227 synaptic genes Otero-Garcia et al²² identified as differentially expressed in AT8+ excitatory neuron clusters, 58 were also dyregulated in PSEN1-E280A gray matter. We performed a gene set enrichment analysis (GSEA) by ranking genes significantly differentially expressed in PSEN1-E280A patients’ gray matter compared to controls by their z-value. This ranked list of 2083 genes was significantly enriched for the 227 synaptic genes (Normalized Enrichment Score = −1.96, p = 3.5 E-4). The negative value meaning that the NFT genes are located among the gray matter DEGs with low z-values (Supplementary Figure 6E).

Oligodendrocyte transcriptomic signatures do not differ PSEN1-E280A and sporadic AD

18,009 nuclei were annotated as oligodendrocytes. Pseudobulk analysis revealed only six genes significantly overexpressed and three genes under-expressed in the comparison between PSEN1-E280A and sporadic AD cases (Supplementary Table 2). Hd-WGCNA in oligodendrocytes revealed seven distinct modules of co-regulated genes (Supplementary Figure 7A) but no module presented eigengene expression values that were significantly different between PSEN1-E280A compared to sporadic AD cases (Wilcoxon Rank Sum p-value < 0.01, effect size > 0.3, Supplementary Figure 7B–C).

A more pronounced Human Alzheimer Microglia (HAM) profile in sporadic AD patients compared to PSEN1-E280A AD

We annotated 2,663 nuclei as microglia. A direct comparison of PSEN1-E280A with sporadic cases in microglia did not yield a significant number of DGEs to conclude that specific biological pathways distinguish these conditions (Supplementary Table 2). Nevertheless, using the gene set associated with a human AD microglia (HAM) profile previously published²³, we calculated a HAM score (see Methods) and found that, compared to controls, the set of upregulated HAM genes was only modestly increased in PSEN1-E280A (p = 4.5 E-2), but significantly enhanced in sporadic AD cases (p < 1 E-4). The HAM score was also significantly higher in sporadic AD cases when directly compared to PSEN1-E280A (p = 3.1 E-3) (Supplementary Figure 7 D–E), while differences among downregulated HAM genes were minimal. These results quantitatively distinguish PSEN1-E280A microglial transcriptomes from those of sporadic AD cases.

The PSEN1-E280A mutation affects Aβ-associated pathways but not TAU

To provide mechanistic insight into sets of differentially expressed genes in PSEN1-E280A compared to sporadic AD across cell types, we performed pathway enrichment analysis in Reactome²⁴ on overexpressed genes in astrocytes, excitatory and inhibitory neurons. The top significantly enriched pathways identified were related to protein folding, respiratory electron transport and autophagy (Supplementary Table 6, Figure 5A). In another analysis, we used a curated set of 45 genes associated with amyloid beta (Aβ) production¹⁸ known to regulate Aβ production and secretion across various cell types. Using the ranked DGEs (PSEN1-E280A versus sporadic AD) according to their z-values, we observed enrichment for these Aβ-associated genes in four of the five cell types analyzed—excitatory neurons, inhibitory neurons, oligodendrocytes, and microglia; in astrocytes the enrichment did not reach statistical significance (p = 9.1 E-2, Figure 5B). This distinctive pattern of Aβ-associated gene enrichment across multiple cell types relative to sporadic AD suggests a homeostatic response to APP degradation intended to maintain levels of APP and in contrast to sporadic AD in which Aβ accumulation is not driven by increased production but failure of resorption²⁵. Interestingly, a similar GSEA analysis of NFT-associated gene enrichment²² with a ranked list of genes in neurons did not result in significant enrichment when comparing genes differentially expressed in PSEN1-E280A compared to sporadic AD (Figure 5B). This result provides a sanity check for the well-known relationship between the PSEN-related pathway and amyloidogenesis.

Protein expression validation of single-nucleus RNA sequencing

We validated the results in postmortem formalin-fixed paraffinized brain tissue slices from PSEN1-E280A and sporadic AD cases. We selected HSP90 (Figure 6A–B) and PPIA (Figure 6C–D) due to their relative higher expression and specificity and performed a cell-wise co-localization analysis in neurons and astrocytes. Both HSP90 and PPIA expression were significantly increased in astrocytes (p < 1.0 E-4 for both) and neurons (p < 1.0 E-4 for HSP90 and p = 4.3 E-2 for PPIA) from PSEN1-E280A compared to sporadic AD.

Figure 6. — (A) Representative immunofluorescence micrographs of frontal cortex GFAP and HSP90 labeling from *PSEN1*-E280A and sporadic AD cases, as well as quantification of HSP90 expression in astrocytes.

(B) Representative immunofluorescence micrographs of frontal cortex MAP2 and HSP90 labeling from *PSEN1*-E280A and sporadic AD cases, as well as quantification of HSP90 expression in neurons.

(C) Representative immunofluorescence micrographs of frontal cortex GFAP and PPIA labeling from *PSEN1*-E280A and sporadic AD cases, as well as quantification of PPIA expression in astrocytes.

(D) Representative immunofluorescence micrographs of frontal cortex MAP2 and PPIA labeling from *PSEN1*-E280A and sporadic AD cases, as well as quantification of PPIA expression in astrocytes. Scale bar = 20 μm. n represents the number of cells analyzed in each group.

A protective profile of PSEN1-E280A APOE3 Christchurch

In the large PSEN1-E280A kindred a single individual, homozygous for the Christchurch variant on an APOE3 background (NM_000041 c.460C>A Arg154Ser, rs121918393) was found to be relatively spared of dementia well into her seventies²⁶. She showed minimal NFT pathology in the expected distribution²⁷; specifically, her NFT pathology was mostly and atypically restricted to the occipital cortex while the frontal cortex was spared. The protective effect of APOE3-R154S was recently confirmed in two independent animal studies^28,29. We sought to detect differential gene expression associated with APOE3-R154S homozygosity. Given the inherent limitations of data from a single individual, albeit the only known individual in the world with this genotypic/phenotypic presentation, we conducted multiple types of analyses: (a) A comparison of frontal cortex from PSEN1-E280A_APOE3-CC-hom to all seven PSEN1-E280A carriers that do not have the Christchurch variant (PSEN1-E280A_APOE3-CC-negative) using a GLMM to estimate the fixed effect of the homozygous genotype while accounting for pseudo-replication bias with a random effect for patient³⁰. (b) A comparison of the relatively spared frontal cortex to the heavily affected occipital cortex in PSEN1-E280A_APOE3-CC-hom. (c) A comparison of all seven PSEN1-E280A_APOE3-CC-negative to the three PSEN1-E280A_APOE3-Christchurch heterozygotes (PSEN1-E280A_APOE3-CC-het) using a GLMM. Although a larger sample of the heterozygotes has recently revealed a modest delay in age at onset (under review), this subset of patients was not delayed in their age at onset (Supplementary Table 1), and therefore we hypothesized they would not show the DGE profile observed in PSEN1-E280A_APOE3-CC-hom. (d) Immunohistochemical validation of the gene expression differences in PSEN1-E280A_APOE3-CC-hom.

To undertake these analyses, we integrated our dataset with sn-RNA seq from the frontal and occipital cortices of this patient who was homozygous for the APOE3 Christchurch variant (PSEN1-E280A_APOE3-CC-hom) (Figure 7A). A total of 11,190 nuclei with 8,247 nuclei from the frontal cortex and 3,043 nuclei from the occipital cortex were integrated into our dataset. All seven major cell types identified in our dataset, i.e. inhibitory neurons, excitatory neurons, astrocytes, microglia, oligodendrocytes, OPCs, endothelial cells and pericytes were also identified in both frontal and occipital cortices of the PSEN1-E280A_APOE3-CC-hom individual (Figure 7A).

A comparison of PSEN1-E280A_APOE3-CC-hom to all seven carriers of PSEN1-E280A without the Christchurch variant (PSEN1-E280A_APOE3-CC-negative) (Supplementary Table 7) revealed 232 genes in astrocytes that were significantly upregulated and 60 genes downregulated. Consistent with a role for APOE3 Christchurch in lipid metabolism³¹, astrocytic genes involved in cholesterol and lipid synthesis (CYP46A1, CYP2J2, CYP4V2, DEGS1, ACAA1, PLCG1) and metabolism (ACAA1, ACAA2, ECHS1, ELOVL2) (Figure 7B) increased their expression. For example, CYP46A1 regulates the conversion of cholesterol to 24S-hydrocholesterol, which controls cholesterol efflux from the brain and thereby plays a major role in regulating brain cholesterol homeostasis. In mouse, decreased expression of the Cyp46a1 gene increased the amounts of cholesterol in neurons leading to apoptotic death of neurons and thereby cognitive impairments³². Oxidoreductase activity-related genes (ERO1A, CYP2J2, DEGS1, CYP46A1, CYP4V2) were also overexpressed in astrocytes from PSEN1-E280A_APOE3-CC-hom and may serve as a possible protective mechanism to cope with the increased oxidative stress in neurons resulting from the PSEN1-E280A variant³³. Notably, in astrocytes from PSEN1-E280A_APOE3-CC-hom, Lipoprotein Receptor-Related Protein 1 (LRP1) was also upregulated (estimate = 0.5, p_adjusted = 4.1 E-2, Figure 7B). To validate this result, immunofluorescence labeling of LRP1 revealed significantly higher expression of LRP1 in astrocytes from PSEN1-E280A_APOE3-CC-hom in comparison to PSEN1-E280A_APOE3-CC-negative (Figure 7C).

The transcriptional profiles of neurons provided additional possible mechanisms of PSEN1-E280A_APOE3-CC-hom protection (Figure 7D). VPS35, Vacuolar sorting protein 35, a key component of the retromer that when dysfunctional is a risk factor for AD³⁴, was upregulated in PSEN1-E280A_APOE3-CC-hom compared to PSEN1-E280A_APOE3-CC-negative (estimate = 0.25 ± 0.03, p_adjusted = 1.2 E-11). Furthermore, excitatory neurons in PSEN1-E280A_APOE3-CC-hom showed significant downregulation of PSEN1 (estimate = −0.19 ± 0.04, p_adjusted = 1.9 E-4) and FKBP5 (estimate = −0.88 ± 0.22, p_adjusted = 3.9 E-3) and upregulation of FKBP1B (estimate = 0.60 ± 0.07, p_adjusted = 2.0 E-16) compared to PSEN1-E280A_APOE3-CC-negative excitatory neurons. Increased expression of FKBP1B, a peptidyl-prolyl cis-trans isomerase and member of the FK506-binding protein family, has an ameliorative effect on TAU inclusions ^35–37, while age-associated with FKBP51 increase (encoded by FKBP5) and its engagement with HSP90, has been described to promote accumulation of neurotoxic TAU³⁸. To confirm the transcriptional result described above, protein expression levels of FKBP1B was verified by immunofluorescence, with significantly higher levels in PSEN1-E280A_APOE3-CC-hom neurons than the PSEN1-E280A_APOE3-CC-negative neurons (Figure 7E), which may contribute to the PSEN1-E280A_APOE3-CC-hom individual’s TAU pathology resistance.

In a comparison of astrocytes from the spared frontal cortex to the intensely affected occipital cortex in PSEN1-E280A_ APOE3-CC-hom, we observed overlapping genes also expressed in the comparison with all seven PSEN1-E280A cases that lack any Christchurch allele. These genes included ones involved in fatty acid metabolism (ACAA2, ECHS1, ELOVL2, ACAA1) and in oxidoreductase activity (CYP2J2, CYP46A1) (Supplementary Table 7). In neurons, the upregulation of FKBP1B (log2FC = 0.4, p_adjusted= 2.6 E-24) and the downregulation of FKBP5 (log2FC = −1.4, p_adjusted = 5.9 E-11) was replicated in the frontal cortex TAU-sparing region. We again found LRP1 upregulated in frontal cortex, (log2FC = −0.55, p_adjusted = 3.1 E-15) (Figure 7F, Supplementary Table 7). Because LRP1 mediates TAU uptake and is involved in TAU spread in neurons³⁹, we hypothesized that neuronal TAU spread might be prevented by its uptake in astrocytes. Thus, we performed immunofluorescence staining of either TAU (Tau-5) or pathological TAU (AT-8) and quantified its levels in astrocytes in samples from both frontal cortex and occipital cortex from PSEN1-E280A_APOE3-CC-hom case (Figure 7G). While levels of co-localization of Tau-5 with GFAP are lower in samples from frontal than occipital cortex, pathological TAU, as stained by AT8, presents higher levels of co-localization in astrocytes from frontal than occipital cortex (Figure 7G), further suggesting that pathological TAU spread might be prevented by its uptake in astrocytes.

Finally, to check whether heterozygosity for APOE3-Christchurch variant is sufficient to activate a protective transcriptomic profile, as observed by homozygosity, we compared three PSEN1-E280A_ APOE3-CC-hets to all seven carriers of PSEN1-E280A without the Christchurch variant (PSEN1-E280A_APOE3-CC-negative) (Figure 7H, Supplementary Table 7). This subset of PSEN1-E280A_APOE3-CC-hets did not have delayed onset (Supplementary Table 1); therefore, we expected their profiles to resemble those cases without either Christchurch allele. In astrocytes, only 10 and 45 genes were significantly differentially overexpressed or underexpressed, respectively, in PSEN1-E280A_APOE3-CC-het, when compared to the PSEN1-E280A_APOE3-CC-negative patients (Figure 7H, Supplementary Table 7). None of these DEGs included any of the cholesterol and lipid synthesis genes, and the oxidoreductase activity-related genes, which suggests that this astrocyte profile seen in the PSEN1-E280A_APOE3-CC-hom was not shared by the three PSEN1-E280A_APOE3-CC-het cases. Furthermore, LRP1 was not significantly upregulated in the PSEN1-E280A_APOE3-CC-het astrocytes compared to PSEN1-E280A_APOE3-CC-negative. In neurons, the PSEN1-E280A_APOE3-CC-hom profile was also not observed in the PSEN1-E280A_APOE3-CC-het patients. In the excitatory neurons of the PSEN1-E280A_APOE3-CC-het patients, PSEN1 and FKBP5 were not downregulated, and the genes FKBP1B and VPS35 which are protective when upregulated, were not upregulated compared to PSEN1-E280A cases without a Christchurch allele. Overall, the absence of a shared transcriptomic profile in astrocytes for differentially expressed genes in PSEN1-E280A_APOE3-CC-hom, likely indicates that heterozygotes without delayed disease onset do not activate the protective network seen in PSEN1-E280A_APOE3-CC-hom astrocytes.

Discussion

Unraveling the distinct mechanisms by which ADAD and sporadic AD lead to convergent phenotypes is a broadly relevant question especially with regard to clinical trials that attempt to extrapolate treatment efficacy conclusions across distinct AD etiologies. In this study, we have studied multiple cases of the same ADAD mutation and therefore limited variation related to the mutation. PSEN1 mutations other than PSEN1-E280A may not show this same pattern. The exceptionally large PSEN1-E280A kindred and their generosity toward brain donation made it possible to obtain these data⁵. Some PSEN1 mutations, even those in the same protein domain such as PSEN1(NM_000021:c.851C>T p.Pro284Leu) (rs63750863), present with spastic paraparesis along with cognitive decline and with cotton wool amyloid-beta plaques^40–42 that may induce a distinct transcriptional profile. We recognize that variation remains among the sporadic AD cases.

What most distinguishes this cohort of identical PSEN1 mutations from sporadic AD is the increased expression of autophagy genes and chaperones. Considering the complexity of presenilin folding, the PSEN1-E280A mutation may trigger a host of cell type specific homeostatic corrective measures that includes multiple chaperones and related folding catalysts required to position and shape an active γ-secretase catalytic complex. Full-length presenilins undergo endoproteolysis, but remain physically associated as a high-molecular weight stable complex in which the two catalytically essential aspartates in separate fragments must come into proximity⁴³ to form a catalytically active γ-secretase complex with the additional proteins nicastrin (NCT), anterior-pharynx defective-1 (APH1), and presenilin-enhancer-2 (PEN2) ^44,45. The active site lies at the interface between the two presenilin subunits and the active site conformation is altered by AD-causing PSEN1 mutations⁴⁵ possibly necessitating additional chaperone mitigation.

From these data, we can infer that sporadic AD and ADAD caused by the PSEN1-E280A variant have distinct molecular signatures largely defined cell type specific increases in chaperone/autophagy gene expression. Spatial transcriptomics and immunofluorescence in tissue further validated the activation of chaperone-mediated autophagy genes in PSEN1-E280A compared to controls. While proteostatic networks have been noted to contribute to disease progression ^46,47, the PSEN1-E280A mutation further activates this pathway in a qualitatively and cell type specific manner.

The very large family also made it possible to spot the highly improbable individual who carried not only the rare PSEN1-E280A variant, but also two copies of a second rare variant, APOE3 Christchurch. The sn-RNA-seq results suggest a mechanism that might explain the most striking feature of this individual, a relative paucity of NFT pathology. The expected spread of TAU inclusions in a pattern described by Braak⁴⁸ did not occur in this patient. Therefore, it was remarkable that LRP1, which mediates TAU spread, was increased in astrocytes from the PSEN1-E280A_APOE3-CC-hom in the frontal cortex, but not in the occipital cortex which carried a heavy NFT burden. This observation raises the surprising possibility that TAU uptake in astrocytes can prevent TAU spread possibly in combination with protein degradation machinery that is made selectively available in the astrocytes. The known association of APOE with LRP1^49,50 might induce upregulation of LRP1 when APOE3 Christchurch is present in the homozygous condition. Other possible protective mechanisms that might explain the paucity of NFT in PSEN1-E280A_APOE3-CC-hom occur in excitatory neurons, among them upregulation of the TAU peptidyl-prolyl cis-trans isomerase, FKBP1B, and downregulation of FKBP5, which could reduce the pathogenicity of TAU^35–38.

E280A is one of 13 different PSEN1 mutations that have been reported in the Colombian population⁵¹. We have previously speculated that the apparent high prevalence of PSEN1 mutations became fixed in the population due to positive selection from infectious diseases during the conquest and colonial period⁵¹. The increased production of A-beta that occurs in carriers of PSEN1 mutations could function as an anti-microbial. Upregulation of SOD1 and PRDX3 in the PSEN1-E280A cases may also be related to their action as peptide anti-microbials, and similarly the upregulation of SERF2 along with ITM2B can enhance amyloid aggregation⁵². Astrocytes from PSEN1-E280A carriers overexpress genes implicated in the immune response to bacterial pathogens, such as CALM1, CALM3, CLTA, DNM1 and YWHAH, as well genes potentially involved with T-cell activation (PAK3, PRKCB). In the same vein, genes known to regulate inflammatory responses to viral infection, such as PARP1 and AKT1, and VPS4A, VPS28, CHMP5, CHMP3, CHMP4B, all of which part of the ESCRT pathway, which may contribute to cellular response to viral infection are upregulated in PSEN1-E280A excitatory neurons, suggesting a unique immunophenotype in these individuals that may influence their susceptibility or response to AD pathology (Supplementary Table 7).

Many studies have implicated TAU in the APOE network^53–55. Most compelling regarding this link is the case from Colombia with the Christchurch variant of APOE3 and two follow-up studies in animal models that support the protective effects of this variant ^29,56. However, the mechanism for the protection is unknown. We suggest a broad-based protection affecting several pathways and several cell types. For example, Heparan sulfate proteoglycan (HSPG) has been proposed to facilitate the aggregation of Aβ and the neuronal uptake of extracellular TAU, while the binding of APOE may be a requisite factor for some of these observed effects^50,51. When comparing the transcriptomic profiles of the frontal cortex from PSEN1-E280A_APOE3-CC-hom with PSEN1-E280A carriers negative for the Christchurch variant, B3GAT3, which is involved in heparan synthesis, was found to be upregulated in excitatory neurons, and EXTL2, another participant in heparan synthesis, was found to be upregulated in inhibitory neurons and astrocytes. These findings suggest that upregulation of B3GAT3 and EXTL2 in specific cell types could potentially indicate a broad regulatory response PSEN1-E280A_APOE3-CC-hom. Hopefully these studies will lead to a more precise understanding of AD genotypes and that can become incorporated into precision medicine clinical trials.

STAR Methods

RESOURCE AVAILABILITY

Lead contact

Further information and requests for resources and reagents should be directed to and will be fulfilled by the lead contact, Kenneth S. Kosik (kosik@ucsb.edu).

Materials availability

This study did not generate new unique reagents.

Data and code availability

Raw single nucleus RNA sequencing have been deposited at GEO under accession numbers GSE222494 and GSE222495 and are publicly available as of the date of publication. Raw spatial transcriptomics sequencing data have been deposited at GEO under accession number GSE221365 and are publicly available as of the date of publication. Accession numbers are listed in the key resources table. Microscopy data reported in this paper will be shared by the lead contact upon request.
All original code has been deposited at Zenodo and is publicly available as of the date of publication⁵⁷. DOIs are listed in the key resources table.
Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.

KEY RESOURCES TABLE.

REAGENT or RESOURCE	SOURCE	IDENTIFIER
Antibodies
mouse beta amyloid monoclonal antibody (BAM-10)	ThermoFisher Scientific	Cat# MA1-91209, RRID:AB_1954846
Phospho-Tau (Ser202, Thr205) Monoclonal Antibody (AT8)	ThermoFisher Scientific	Cat # MN1020, RRID:AB_223647
GFAP antibody	Synaptic Systems	Cat # 173 308, RRID:AB_2905596
HSP90 alpha-recombinant rabbit monoclonal antibody (4D1)	Thermo Fisher Scientific	Cat# MA5-33174, RRID:AB_2811990
Tau Monoclonal Antibody (TAU5)	Thermo Fisher Scientific	Cat# AHB0042 RRID:AB_2536235)
Phospho-Tau (Ser202, Thr205) Monoclonal Antibody (AT8)	ThermoFisher Scientific	Cat # MN1020, RRID:AB_223647
Rabbit Anti-MAP2 Polyclonal Antibody, Unconjugated	Abcam	Cat# ab32454, RRID:AB_776174
Anti-LRP1 (N-terminal) antibody produced in rabbit	Sigma Aldrich	Cat# L2295, RRID:AB_10610711
Cyclophilin A Polyclonal Antibody	Thermo Fisher Scientific	Cat# PA1-025, RRID:AB_2169124
FKBP1B antibody	Proteintech	Cat# 15114-1-AP, RRID:AB_11182817
Phospho-Tau (Ser202, Thr205) Monoclonal Antibody (AT8)	ThermoFisher Scientific	Cat # MN1020, RRID:AB_223647
GFAP antibody	Synaptic Systems	Cat # 173 308, RRID:AB_2905596
HSP90 alpha-recombinant rabbit monoclonal antibody (4D1)	Thermo Fisher Scientific	Cat# MA5-33174, RRID:AB_2811990
Tau Monoclonal Antibody (TAU5)	Thermo Fisher Scientific	Cat# AHB0042 RRID:AB_2536235)
Phospho-Tau (Ser202, Thr205) Monoclonal Antibody (AT8)	ThermoFisher Scientific	Cat # MN1020, RRID:AB_223647
Rabbit Anti-MAP2 Polyclonal Antibody, Unconjugated	Abcam	Cat# ab32454, RRID:AB_776174
Anti-LRP1 (N-terminal) antibody produced in rabbit	Sigma Aldrich	Cat# L2295, RRID:AB_10610711
Cyclophilin A Polyclonal Antibody	Thermo Fisher Scientific	Cat# PA1-025, RRID:AB_2169124
FKBP1B antibody	Proteintech	Cat# 15114-1-AP, RRID:AB_11182817
Biological Samples
Human, Post-Mortem Frontal Cortex Samples from Alzheimer’s Disease (familial and sporadic) and Non-Symptomatic Patients	Grupo de Neurociencias de Antioquia brain bank	https://www.gna.org.co/
Human, Post-Mortem Hippocampal Samples from Alzheimer’s Disease (familial) and Non-Symptomatic Patients	Grupo de Neurociencias de Antioquia brain bank	https://www.gna.org.co/
Human, Post-Mortem occipital cortex sample	Grupo de Neurociencias de Antioquia	https://www.gna.org.co/
Human, Post-Mortem Hippocampal Samples from Alzheimer’s Disease (familial) and Non-Symptomatic Patients	Grupo de Neurociencias de Antioquia brain bank	https://www.gna.org.co/
Human, Post-Mortem occipital cortex sample	Grupo de Neurociencias de Antioquia	https://www.gna.org.co/
Chemicals, Peptides, and Recombinant Proteins
Glycerin (Glycerol), 50% (v/v) Aqueous Solution	Thermo Fisher	cat# 329032
Low TE Buffer	Thermo Fisher	cat#12090-015
Sytox green nucleic acid stain	Life Tech	cat# S7020
Molecular biology grade ethanol	Fisher	cat# BP2818500
RNAse inhibitor	Lucigen	cat# 30281-1
Nuclei Isolation Kit: Nuclei EZ Prep	Millipore Sigma	Cat# NUC101-1KT
SPRI Select beads	Beckman Coulter	Cat# B23318
Xylene	Millipore-Sigma	Cat# 534056-4L; CAS: 1330-20-7
2-propanol	Millipore-Sigma	Cat# 05279801001
CC1	Ventana Medical Systems, Inc	Cat# 05279801001
CC2	Ventana Medical Systems, Inc	Cat# 05279798001
Shandon Instant Hematoxylin	Thermo-Fisher	Cat# 12687926
Eosin yellowish	PanReac	Cat# 348111; CAS: 17372-87-1
Consul-Mount^™ Histology Media, Medium Viscosity	Thermo-Fisher	Cat# 9990440
Reaction Buffer Concentrate (10X)	Ventana Medical Systems, Inc	Cat# 5353955001
LCS	Ventana Medical Systems, Inc	Cat# 5264839001
10X EZ PREP SOLUTION, 2L	Ventana Medical Systems, Inc	Cat# 5279771001
PROTEASE 1	Ventana Medical Systems, Inc	Cat# 5266688001
ANTIBODY DILUENT	Ventana Medical Systems, Inc	Cat# 5261899001
Methanol, for HPLC, ≥99.9%	Millipore Sigma	Cat# 34860
Eosin Y solution, aqueous	Millipore Sigma	Cat# HT110216-500ML
Hematoxylin Solution, Mayer’s	Millipore Sigma	Cat# MHS16-500ML
Bluing Buffer, Dako	Agilent	Cat# CS70230-2
Tris Base	Thermo Fisher Scientific	Cat# BP152-500
Potassium Hydroxide Solution, 8M	Millipore Sigma	Cat# P4494-50ML
SSC Buffer 20X Concentrate	Millipore Sigma	Cat# S66391L
Hydrochloric Acid Solution, 0.1N	Fisher Chemical	Cat# SA54-1
Qiagen Buffer EB	Qiagen	Cat# 19086
KAPA SYBR FASTqPCR Master Mix	Roche	KK4600
Low TE Buffer	Thermo Fisher	cat#12090-015
Sytox green nucleic acid stain	Life Tech	cat# S7020
Molecular biology grade ethanol	Fisher	cat# BP2818500
RNAse inhibitor	Lucigen	cat# 30281-1
Nuclei Isolation Kit: Nuclei EZ Prep	Millipore Sigma	Cat# NUC101-1KT
SPRI Select beads	Beckman Coulter	Cat# B23318
Xylene	Millipore-Sigma	Cat# 534056-4L; CAS: 1330-20-7
2-propanol	Millipore-Sigma	Cat# 05279801001
CC1	Ventana Medical Systems, Inc	Cat# 05279801001
CC2	Ventana Medical Systems, Inc	Cat# 05279798001
Shandon Instant Hematoxylin	Thermo-Fisher	Cat# 12687926
Eosin yellowish	PanReac	Cat# 348111; CAS: 17372-87-1
Consul-Mount^™ Histology Media, Medium Viscosity	Thermo-Fisher	Cat# 9990440
Reaction Buffer Concentrate (10X)	Ventana Medical Systems, Inc	Cat# 5353955001
LCS	Ventana Medical Systems, Inc	Cat# 5264839001
10X EZ PREP SOLUTION, 2L	Ventana Medical Systems, Inc	Cat# 5279771001
PROTEASE 1	Ventana Medical Systems, Inc	Cat# 5266688001
ANTIBODY DILUENT	Ventana Medical Systems, Inc	Cat# 5261899001
Methanol, for HPLC, ≥99.9%	Millipore Sigma	Cat# 34860
Eosin Y solution, aqueous	Millipore Sigma	Cat# HT110216-500ML
Hematoxylin Solution, Mayer’s	Millipore Sigma	Cat# MHS16-500ML
Bluing Buffer, Dako	Agilent	Cat# CS70230-2
Tris Base	Thermo Fisher Scientific	Cat# BP152-500
Potassium Hydroxide Solution, 8M	Millipore Sigma	Cat# P4494-50ML
SSC Buffer 20X Concentrate	Millipore Sigma	Cat# S66391L
Hydrochloric Acid Solution, 0.1N	Fisher Chemical	Cat# SA54-1
Qiagen Buffer EB	Qiagen	Cat# 19086
KAPA SYBR FASTqPCR Master Mix	Roche	KK4600
Critical Commercial Assays
Visium Spatial Gene Expression Slide & Reagent Kit	10x Genomics	Cat # 1000187
Chromium Next GEM Single Cell 3’ GEM, Library & Gel Bead Kit v3.1	10x Genomics	Cat # 1000121
UltraView Universal DAB Detection Kit	Ventana Medical Systems, Inc	Cat# 5269806001
Chromium Next GEM Single Cell 3’ GEM, Library & Gel Bead Kit v3.1	10x Genomics	Cat # 1000121
UltraView Universal DAB Detection Kit	Ventana Medical Systems, Inc	Cat# 5269806001
Deposited Data
Single Nucleus RNA Sequencing Data	This Study	https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE222494 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE222495
Spatial Transcriptomic Sequencing Data	This study	https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?&acc=GSE221365
Single Nucleus RNA Sequencing Data	Sepulveda-Falla et al, 2022	GEO: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE206744
Software and Algorithms
Prism (version 6.1.0)	GraphPad	https://www.graphpad.com/scientificsoftware/prism/; RRID:SCR_002798
Microsoft Excel	Microsoft 365	https://microsoft.com; RRID:SCR_016137
Cell Ranger (version 3.0)	10x Genomics	https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/latest/what-is-cell-ranger; RRID:SCR_017344
RStudio	Posit	https://RStudio.com; RRID:SCR_000432
Seurat (version 4.1.1)	Stuart et al. (2019)	https://satijalab.org/seurat/index.html; RRID:SCR_016341
Doubletfinder (version 2.0.3)	McGinnis et al. (2019)	https://github.com/chris-mcginnis-ucsf/DoubletFinder
QPath (v.0.1.2)	Bankhead et al. (2017)	https://qupath.github.io/; RRID:SCR_018257
Inkscape (v1.2.1)	The Inkscape Project	https://inkscape.org/; RRID:SCR_014479
Metascape	Zhou et al. (2019)	https://metascape.org; RRID:SCR_016620
Space Ranger (version 1.3.1)	10x Genomics	https://support.10xgenomics.com/spatial-gene-expression/software/pipelines/latest/what-is-space-ranger
Libra	Squair et al. (2021)	https://github.com/neurorestore/Libra
lme4 package	Bates et al. (2015)	http://www.jstatsoft.org/v67/i01/
fgsea	Korotkevich et al. (2021)	https://github.com/ctlab/fgsea
Microsoft Excel	Microsoft 365	https://microsoft.com; RRID:SCR_016137
Cell Ranger (version 3.0)	10x Genomics	https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/latest/what-is-cell-ranger; RRID:SCR_017344
RStudio	Posit	https://RStudio.com; RRID:SCR_000432
Seurat (version 4.1.1)	Stuart et al. (2019)	https://satijalab.org/seurat/index.html; RRID:SCR_016341
Doubletfinder (version 2.0.3)	McGinnis et al. (2019)	https://github.com/chris-mcginnis-ucsf/DoubletFinder
QPath (v.0.1.2)	Bankhead et al. (2017)	https://qupath.github.io/; RRID:SCR_018257
Inkscape (v1.2.1)	The Inkscape Project	https://inkscape.org/; RRID:SCR_014479
Metascape	Zhou et al. (2019)	https://metascape.org; RRID:SCR_016620
Space Ranger (version 1.3.1)	10x Genomics	https://support.10xgenomics.com/spatial-gene-expression/software/pipelines/latest/what-is-space-ranger
Libra	Squair et al. (2021)	https://github.com/neurorestore/Libra
lme4 package	Bates et al. (2015)	http://www.jstatsoft.org/v67/i01/
fgsea	Korotkevich et al. (2021)	https://github.com/ctlab/fgsea
Custom computer code used in this manuscript	Almeida et al (2024)	https://doi.org/10.5281/zenodo.10460116

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EXPERIMENTAL MODEL AND STUDY PARTICIPANT DETAILS

Post-mortem human cohort

This study utilized post-mortem human brain samples from 27 donors in the Grupo de Neurociencias de Antioquia brain bank. Brain donation followed informed consent and ethical approval from the Institutional Review Board (IRB) of the Medical Research institute, School of Medicine, Universidad de Antioquia (IORG0010323, FWA00028864). Written informed consent following the guidelines of the Code of Ethics of the World Medical Association, Helsinki declaration and Belmont Report were obtained from the donors legally authorized proxies.

We grouped the samples into three categories: individuals without neurodegenerative diseases or systemic diseases compromising the central nervous system, here referred as controls (n=8); patients with autosomal dominant AD who were carriers of the presenilin-1 (PSEN1) NM_000021:c.839 A > C, p.Glu280Ala mutation, here referred as PSEN1-E280A (n=10); one patient with autosomal dominant AD who was a carrier of the presenilin-1 (PSEN1) and also homozygote for APOE3-Christchurch variant (see below), and patients with AD who didn’t have first or second degree relatives affected by neurodegenerative illnesses, here referred as sporadic AD (n=8). Five samples carried the minor APOEε4 allele within the cohort and three of the PSEN1-E280A samples were heterozygotes for the APOE3-Christchurch variant. Specific characteristics of the individuals are displayed in Supplementary Table 1.

METHOD DETAILS

Genetic sequencing

DNA from all donors was tested for APOE alleles rs429358 and rs7412 using next-generation sequencing and/or real time PCR.

Neuropathological and immunohistochemical analysis

Brain donation was performed after informed consent signature and ethical approval as described above. We determined the presence of AD pathological hallmarks by microscopic examination of 17 brain areas including medial frontal gyrus, superior temporal gyrus, medial temporal gyrus, inferior temporal gyrus, hippocampus, amygdala, insula, gyrus cinguli, lenticular nucleus, caudate nucleus, thalamus, inferior parietal lobule, occipital lobule, cerebellum, mesencephalon, pons, and medulla oblongata. Tissue was cut in 5μm thick sections and stained with hematoxylin and eosin (H&E). Immunohistochemistry (IHC) for amyloid beta (Aβ, 1:200; mouse monoclonal BAM-10, Catalog # MA1–91209, ThermoFisher Scientific, USA), and hyperphosphorylated TAU Ser 202 and Thr 205 (TAU, 1:1200; mouse monoclonal AT8, MN1020; ThermoFisher Scientific, Dreieich, Germany) was performed with a Ventana Benchmark GX system (Roche AG, Basel, Switzerland) according to manufacturer instructions. Briefly, after dewaxing and inactivation of endogenous peroxidases (PBS/3% hydrogen peroxide), antibody specific antigen retrieval was performed, sections were blocked and afterwards incubated with the primary antibody. For detection of specific binding, the Ultra View Universal 3,3´-Diaminobenzidine (DAB) Detection Kit (Ventana, Roche) was used which contains secondary antibodies, DAB stain and counter staining reagent. Sections were scanned using a Ventana DP200 (Roche, USA) to obtain images of whole stained sections at a resolution of at least 1 pixel per μm.

Single Nucleus RNA-Sequencing

A schematic of the nuclei isolation and sequencing workflow is shown in Supplementary Figure 2A. In sn-RNA seq, for each individual, we evaluated tissue from frontal cortex (FC). Nuclei isolation. FC samples were dissociated, and nuclei isolation was performed separately for each sample using the Nuclei Isolation Kit: Nuclei EZ Prep (Sigma, #NUC101) as described in ⁵⁸. Briefly, tissue samples were dounce homogenized in 2 ml of ice-cold EZ PREP and incubated on ice for 5 min. Following dounce homogenization, an additional 2ml of EZ PREP was added and the samples were incubated for 5 min. Nuclei suspension was centrifuged (500 × g, 5 min and 4 °C) washed 1x in ice-cold EZ PREP buffer, and 1x in Nuclei Suspension Buffer (NSB; consisting of 1× PBS, 1% (w/v) BSA and 0.2 U/μl RNase inhibitor (Clontech, #2313A), resuspended in 1 ml of NSB and filtered through a 40 μm cell strainer. Nuclei were stained with SYTOX green (1:1000) and counted twice. A final concentration of 1000 nuclei per μl was used for loading onto the 10X Chromium (10X Genomics). Library construction was performed using the Chromium Single Cell 3′ Library & Gel Bead Kit v3.1 (10X Genomics) and sequencing on one high-output lane of the NextSeq 4000 (Illumina).

Mapping single nuclei reads to the genome.

Using the GRCh38 (1.2.0) reference from 10x Genomics, we made a pre-mRNA reference according to the steps detailed by 10X Genomics (https://support.10xgenomics.com/single-cell-gene512expression/software/pipelines/latest/advanced/references). Sequencing reads were aligned to the human pre-mRNA reference transcriptome using the 10x Genomics CellRanger pipeline (version 3.0.0; RRID: SCR_017344) with default parameters.

Quality Control for expression matrix.

Downstream analysis was performed using Seurat 4.0 in RStudio Version 4.1.0. An individual Seurat object was generated for each sample. Cells with fewer than 200 detected genes and with more than 5% of reads mapped to mitochondrial genes were filtered out. Doublets were identified using the DoubletFinder package⁵⁹ and removed assuming a doublet rate formation of 3%. In downstream analysis, the clusters that were highly scored with multiple cell type gene sets were further filtered from the dataset.

Data Processing, Analyses, Visualization, and Differential Expression Testing.

All samples were merged into a single Seurat object. Data were then normalized and scaled using the SCTransform function in Seurat using the default parameters. Anchor-based sample Integration was performed on the normalized counts, with the number of features in the anchor finding process set to 3000. Non-linear dimensionality reduction was performed by running UMAP on the first 30 PCs. Clustering was performed on the top 30 PCs as input in the FindNeighbors function, and a high resolution (res=.6) was set in the FindClusters function to obtain small clusters. By doing so, nuclei were separated into 89 pre-clusters. By choosing this high resolution, clusters with no clear identity can be further subsetted. Those clusters identified as doublets/multiplets scored high for multiple cell types and with the high resolution for clustering, these small clusters were separated from singlets. Next, we defined cell type specific gene sets for Neurons, Astrocytes, Oligodendrocytes, Oligodendrocyte precursor cells, Microglia, Endothelial cells and Pericytes (Supplementary Table 1) using the top 100 cell type enriched genes from the literature^60–62 and from PanglaoDB Augmented database⁶³. For cell type annotation, DefaultAssay was set to “RNA” and data were normalized using NormalizeData function. Using AddModuleScore function, a cell type ModuleScore was set for each cluster, and the highest ModuleScore was used to annotate cell types. Clusters that scored for more than one cell type were further removed. By so doing, we obtained a dataset with 54,960 nuclei. The expression of known canonical marker genes for cell types found in human brain was further verified for each subcluster.

Identification of differentially expressed genes in cell-type subpopulations.

After cell type annotation, differential gene expression (DGE) was performed for each cell typed cluster. To identify genes differentially expressed by a cell-type subpopulation across PSEN1-E280A and sporadic AD, we used a pseudo-bulk approach using the LIBRA package¹⁶ using the following arguments: de_method = edgeR and de_type = LRT. The pseudobulk analyses were confirmed with a generalized linear mixed model (GLMM). The glmer.nb function from the lme4 package⁶⁴ was used to run the negative binomial generalized linear mixed-effect models, which included a fixed effect for genotype and a random effect for patient to account for pseudo-replication bias and overdispersion in the data^30,65. An FDR-corrected p-value of ≤ 0.05 was considered for both analysis (Supplementary Table 2). The application of a GLMM produced a very high overlap (Supplementary Table 2) in significant genes with the pseudobulk DGE method. Gene enrichment analysis for terms among DGEs was performed using EnrichR, Metascape⁶⁶, and DAVID⁶⁷. The outcome found using all three tools were similar, and the reported results are from EnrichR. For heatmaps of relative gene expression across cell-type subpopulations, RNA normalized counts of each gene were z-score transformed across all cells and then averaged across cells in each cluster to enhance visualization of differences among clusters. Thus, genes with high relative expression had above-average expression (positive z-scores), and genes with low relative expression had below-average expression (negative z-scores).

hd-WGCNA

High-dimensional weighted gene co-expression network analysis (hd-WGCNA) was performed using the package developed by Morabito et al⁶⁸. Of glial cells, only oligodendrocytes (18,009 nuclei) and astrocytes (4,874 nuclei) had a large enough nuclei population to run hd-WGCNA. Of neuronal cells, only excitatory neurons (16,954 nuclei) had a large enough nuclei population to run hd-WGCNA. No modules were found in oligodendrocytes that distinguished the conditions (PSEN1-E280A vs sporadic AD); thus the analysis was focused on astrocytes and excitatory neurons as follows:

Metacell formation. Nuclei were subset from the Seurat object and data were pre-processed by running the NormalizeData, VariableFeatures, ScaleData, RunHarmony, RunPCA, RunUMAP, and FindNeighbors functions. Similar cells were grouped by same cell type and biological sample into metacells using MetacellsByGroups with k=15. Metacell expression matrix was normalized using NormalizeMetacells function.

Formation and identification of Modules of interest

To perform a co-expression network analysis and construct a co-expression network we used ConstructNetwork function with a soft power threshold of eight as determined by TestSoftPowers. This step identified 10 modules in astrocytes, 7 modules in oligodendrocytes and 6 modules in excitatory neurons using TOM, as visualized in the dendrograms (Supplementary Figures 4 and 6). The ModuleEigengenes function was used to calculate the module eigengene (hME) value for each module within each single nuclei and stored that value in the metadata of the Seurat object. Significant differences in the hME values between diagnosis were assessed by the Kruskal-Wallis test (one-way nonparametric ANOVA), and a p-value threshold < 0.01 was considered. Wilcoxon Rank Sum tests were performed to identify specific diagnosis comparisons in modules of genes that are significantly. Only comparisons that meet both a p-value threshold < 0.01 and Wilcoxon effect size > 0.3 (moderate effect) were considered significantly different. The p-value was adjusted due to the multiple comparisons using the Holm-Bonferroni method. To infer biological significance to the genes within each module, we ran GO/pathway analysis using Metascape⁶⁶. To identify GO terms of interest, we looked at the particular genes from the module associated with a particular GO term and averaged their kME values. Terms with associated kME values that were higher than the average kME value of the overall module were considered, as it indicated that the genes that were associated with that term are highly connected and influential within the module network. To further identify significant GO terms, we also focused on GO terms which met a LogQ threshold of < −10.

Spatial Transcriptomics

A schematic of the Visium spatial gene expression workflow is depicted in Figure 4A. Slide preparation. Hippocampal and frontal cortical samples were cryosectioned at −20°C. The 10 μm sections were placed on a Visium Tissue Optimization Slide (10x Genomics, #3000394) and Visium Spatial Gene Expression Slide (10x Genomics, #2000233). By following the Visium Spatial Tissue Optimization User Guide (10x Genomics, #CG000238 Rev E), the optimal permeabilization time was determined to be six minutes. The gene expression slides underwent methanol fixation, H&E staining, and brightfield imaging using a 10x objective on a Nikon Eclipse Ti2-E (10x Genomics, #CG000160 Rev C). Immediately after imaging, the tissues were permeabilized and the remaining steps for library construction were completed according to the user guide (10x Genomics, #CG000239 Rev F).

Mapping spatial gene expression reads to the genome and microscope images. Using Loupe Brower (version 6.2.0) the fiducial frame was manually identified and only spots under tissue were selected for the 10x Genomics Space Ranger pipeline (version 1.3.1).

Spot-level and gene-level filtering. Spots with more than 30% of reads mapped to mitochondrial genes were removed from the dataset. Genes expressed in less than 3 spots per sample and mitochondrial genes were excluded.

Clustering, visualization, and integration. Using Seurat 4.1, data from each sample was individually normalized via SCTransform, followed by dimensionality reduction via RunPCA. The top 10 PCs were used for the FindNeighbors function, then FindClusters with resolution set to 0.1 and RunUMAP were run to produce a UMAP with 2–3 clusters per sample. Clusters were annotated based on the highest expression of known gray matter/neurons and white matter/oligodendrocytes marker genes, such as those visualized in Figure 4B–C ⁶⁹. The normalized data from all samples was merged and then prepared for integration via PrepSCTIntegration, which used 5,000 features from the SelectIntegrationFeatures function. Anchors were identified by the FindIntegrationAnchors function and used as an input for the IntegrateData function. The same reduction and clustering steps as before were then performed on the integrated Seurat object. This produced a UMAP in which the gray/white matter assignments from the single-sample clustering corresponded with the two major integrated clusters (Figure 4F). Additionally, the identity of the two integrated clusters was further confirmed with additional neuron and oligodendrocyte markers (Supplementary Figure 6). Differential gene expression analysis. The data was stratified by white/gray matter assignments to identify genes differentially expressed between PSEN1-E280A and controls in the two strata. Differential gene expression was performed using a negative binominal GLMM with CDR as a fixed effect and individual as a random effect on the spatial data (PSEN1-E280A vs Control).

Immunofluorescence

Immunofluorescence was performed on formalin-fixed paraffin-embedded (FFPE) brain tissue from the frontal cortex (superior frontal gyrus) of sporadic AD and PSEN1-E280A patients, at least 2 cases per group, 2 slices per case, and on FFPE brain tissue from the frontal cortex and occipital cortex of one PSEN1-E280A case that harbored the APOE3 Christchurch variant. 4μm thick sections were mounted on Superfrost plus slides and further processed for immunofluorescence staining for glial fibrillary acidic protein acidic protein (GFAP, 1:200; 173308, Synaptic-Systems), MAP2 (1:500; ab32454, Abcam), heat shock protein 90 (HSP90, 1:200; MA5– 33174, Invitrogen), cyclophilin A (CyPA, 1:100; PA1–025, ThermoFisher Scientific), total TAU protein (Tau-5, 1:200; AHB0042, ThermoFisher Scientific), Phospho-Tau Ser202, Thr205 (AT8, 1:500; MN1020, ThermoFisher Scientific), Low density lipoprotein receptor-related protein 1 (LRP1 1:100; L2295, Sigma-Aldrich) and FKBP1B (1:200; 15114–1AP, ThermoFisher Scientific). After deparaffinization, heat-induced epitope retrieval was performed using R-Universal buffer (AP0530–500; Aptum Biologics, Southampton, UK) in a pressure cooker for 20 minutes, sections were then blocked for 1 hour with blocking medium (MAXblock^™, 15252; Active Motif GmbH) followed by incubation with primary antibodies at 4°C overnight. For detection of specific binding, secondary antibodies were incubated at room temperature for 1 hour. After washing mounting was performed with 4’,6-Diamidino-2-phenylindole (DAPI) Fluroromount-G for nuclear counterstaining. High-resolution images were obtained with a Leica TCS SP8 confocal laser scanning microscope (Leica Microsystems, Mannheim, Germany) using a 63X immersion oil lens objective.

Multi dataset analyses

For the analysis of the PSEN1-E280A_APOE3-CC-hom, we used the data from sn-RNA seq in the frontal cortex and occipital cortex previously published by our group (available at GEO under accession number GSE206744). We also integrated to this data, additional sequencing runs for the same brain regions (frontal cortex and occipital cortex) more recently performed at our laboratory.

QUANTIFICATION AND STATISTICAL ANALYSIS

Statistics

Throughout this manuscript, quantified nuclei outputs are displayed as the arithmetic mean (± s.d., if applicable), and plots were generated using the ggplot2 and ggpubr packages in R and/or using GraphPad Prism (v 6.01, Graphpad Software, Boston, MA, USA) unless otherwise noted.

Cell-specific co-localization analysis

For quantification of levels of HSP90 and PPIA expression in cells expressing GFAP or MAP2, image processing was done using Python 3.10.12. All image morphology and filtering operations were done using scikit-image 0.19.3 package. Immunohistochemistry images were uploaded using readlif 0.6.5 package. Maximum intensity projections of DAPI, MAP2 and/or GFAP z-stacks were summed and used for cell body segmentation. The summed image was filtered with gaussian (s=1) and cells were identified using triangle thresholding. Individual cells were identified using Euclidean distance transform followed by a local maxima search and a watershed segmentation. Objects with an area smaller than 500 pixels were removed. For cell-wise estimation of a protein of interest expression level a sum of the z-stack was used, normalized by its maximum.

Immunofluorescence

Immunofluorescence colocalization analysis for TAU5 and AT8 were performed with FIJI ImageJ 1.53q Software⁷⁰, following image automatic thresholding by the Costes method. Manders overlap coefficients and colocalization volumes for each channel were assessed. Results were analyzed with a two-way Student’s T test, using Graphpad Prism.

Calculation of CMA activation score

CMA score was calculated according to Bourdenx et al¹⁷. Briefly, for each cell type, a CMA activation score was calculated for each individual. To do so, LAMP2 was attributed a weight of 2 (as it is the rate limiting component of CMA) and every other element was attributed a weight of 1. Every element received a direction score (+1 or −1) based on the known effect (activator or inhibitor) of a given element on CMA activity. The score was then calculated as the weighted/directed average of expression counts of every element of the CMA network for samples from PSEN1-E280A, sporadic AD and controls. The CMA score results for PSEN1-E280A and sporadic AD are normalized by control CMA score values.

Beta regression

The relative abundance of a given cell cluster or cell type (ranging from 0 to 1), was calculated for each sample. Statistical significance of changes in the relative abundance of a given cluster or cell type across diagnosis (control, PSEN1-E280A and sporadic AD), were determined using beta regression (betareg package, version 3.1–4), using the formula relative.abundance ~ Diagnosis for the precision model, and the bias-corrected maximum likelihood estimator. P values obtained from beta regression were corrected for multiple hypotheses using Bonferroni correction.

Gene set HAM (Human AD microglia) score

The HAM gene set scores were calculated using the function “AddModuleScore” from Seurat. The list of genes overexpressed and underpressed associated with the HAM profile used as the “feature” argument in the function were obtained from Olah and colleagues⁷¹.

Overlap analysis

To evaluate the overlap between particular DGEs sets and modules found in the hd-WGCNA analysis, overlap analysis on the gene lists associated with positive markers for astrocytes and excitatory neurons and hd-WGCNA modules was performed. Essentially, the list of genes associated with each module, and the list of positive cluster markers for each of the population (p_adjusted < 0.05, Log2FC > 0.25) were retrieved. The R package GeneOverlap was used to perform a Fisher’s Exact Test and to evaluate the overlap between the markers and module lists.

Negative binomial generalized linear mixed-effect model

The R package lme4 was used to measure the fixed effect of the APOE3ch genotypes. Every gene expressed in more than 10% of nuclei for a given cell type was modeled. Microglia, OPCs, endothelial cells and pericytes could not be modeled due to the relatively small numbers of nuclei when only a subset of patients were included in these analyses. The glmer.nb function was used to run the negative binomial generalized linear mixed-effect models, all of which included a fixed effect for genotype and a random effect for patient to account for pseudo-replication bias and overdispersion in the data^30,65.

Randomization

No randomization was used in the analysis of snRNA-seq.

Sample size estimation

No methods were used to predetermine the sample size used in this study. However, our donor cohort is similar in size to previously published works.

Supplementary Material

NIHMS1968817-supplement-1.pdf^{(2MB, pdf)}

Supplementary Table 1. Neuropathological information and data metrics. Related to Figures 1–7.

NIHMS1968817-supplement-2.xlsx^{(34.1KB, xlsx)}

Supplementary Table 2: Differential gene expression analysis summary results for the comparison between PSEN1-E280A versus Sporadic AD cases. Related to Figures 2–3.

NIHMS1968817-supplement-3.xlsx^{(4.1MB, xlsx)}

Supplementary Table 3: High dimensional weighted gene co-expression analysis (hd-WGCNA) summary results. Related to Figure 2–3.

NIHMS1968817-supplement-4.xlsx^{(1.2MB, xlsx)}

Supplementary Table 4: Neuronal subcluster markers. Related to Figure 3.

NIHMS1968817-supplement-5.xlsx^{(1.1MB, xlsx)}

Supplementary Table 5: Spatial transcriptomics summary results. Related to Figure 4.

NIHMS1968817-supplement-6.xlsx^{(2.2MB, xlsx)}

Supplementary Table 6: Pathway analysis (Reactome) summary results. Related to Figure 5.

NIHMS1968817-supplement-7.xlsx^{(323.8KB, xlsx)}

Supplementary Table 7: Differential gene expression analysis summary results for the comparison between PSEN1-E280A_APOE3-CC-hom versus PSEN1-E280A_APOE3-CC-het or PSEN1-E280A_APOE3-CC-negative. Related to Figure 7.

NIHMS1968817-supplement-8.xlsx^{(6.6MB, xlsx)}

Highlights:

An autophagy gene profile distinguishes PSEN1-E280A ADAD cases from sporadic AD

A possible mechanism for the PSEN1-E280A Christchurch variant effect observed

Unique cellular responses in ADAD and sporadic AD may impact clinical trial design

ACKNOWLEDGMENTS

We thank the individuals and the families who participated in this study. We thank the Grupo Neurociencias de Antioquia (GNA) staff who helped with the participant recruitment, evaluation and sample processing and especially Duvan Cardona and Brian Vicaño from the BrainBank. We thank Alexander Franks for expert statistical advice and Ana Maria Cuervo for gene lists involved in chaperone mediated autophagy, and Davis Westover for assisting with code annotation. The authors acknowledge the use of the DNA Technologies and Expression Analysis Core at the UC Davis Genome Center, supported by NIH Shared Instrumentation Grant 1S10OD010786–01 and the use of the Neuroscience Research Institute, Molecular, Cell and Developmental Biology Microscopy Facility. This study was funded by NIH 1RF1AG062479–01 grant to KSK and FL, FAPESP grant 2019/22819–8 to MCA and FAPESP grant 2019/22708–1 to DCC.

Footnotes

Declaration of Interest

K.S.K. consult for ADRx and Expansion Therapeutics and is a member of the Tau Consortium BOD. FL: Consult for Biogen, Viewmind. FL Has grants supported by NIH, Red-Lat, Alzheimeŕs Association, Biogen, DIAN-TU, DIAN-Obs, Large PD and Enroll-HD. JAU is a consultant for a pharmaceutical company Tecnoquimicas (Colombia).

Declaration of Generative AI and AI-assisted technologies in the writing process

During the preparation of this work the authors used ChatGPT to condense text and enhance clarity. After using this tool, the authors reviewed and edited the content as needed and take full responsibility for the content of the publication.

Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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Associated Data

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

Supplementary Materials

NIHMS1968817-supplement-1.pdf^{(2MB, pdf)}

Supplementary Table 1. Neuropathological information and data metrics. Related to Figures 1–7.

NIHMS1968817-supplement-2.xlsx^{(34.1KB, xlsx)}

Supplementary Table 2: Differential gene expression analysis summary results for the comparison between PSEN1-E280A versus Sporadic AD cases. Related to Figures 2–3.

NIHMS1968817-supplement-3.xlsx^{(4.1MB, xlsx)}

Supplementary Table 3: High dimensional weighted gene co-expression analysis (hd-WGCNA) summary results. Related to Figure 2–3.

NIHMS1968817-supplement-4.xlsx^{(1.2MB, xlsx)}

Supplementary Table 4: Neuronal subcluster markers. Related to Figure 3.

NIHMS1968817-supplement-5.xlsx^{(1.1MB, xlsx)}

Supplementary Table 5: Spatial transcriptomics summary results. Related to Figure 4.

NIHMS1968817-supplement-6.xlsx^{(2.2MB, xlsx)}

Supplementary Table 6: Pathway analysis (Reactome) summary results. Related to Figure 5.

NIHMS1968817-supplement-7.xlsx^{(323.8KB, xlsx)}

NIHMS1968817-supplement-8.xlsx^{(6.6MB, xlsx)}

Data Availability Statement

Raw single nucleus RNA sequencing have been deposited at GEO under accession numbers GSE222494 and GSE222495 and are publicly available as of the date of publication. Raw spatial transcriptomics sequencing data have been deposited at GEO under accession number GSE221365 and are publicly available as of the date of publication. Accession numbers are listed in the key resources table. Microscopy data reported in this paper will be shared by the lead contact upon request.
All original code has been deposited at Zenodo and is publicly available as of the date of publication⁵⁷. DOIs are listed in the key resources table.
Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.

KEY RESOURCES TABLE.

REAGENT or RESOURCE	SOURCE	IDENTIFIER
Antibodies
mouse beta amyloid monoclonal antibody (BAM-10)	ThermoFisher Scientific	Cat# MA1-91209, RRID:AB_1954846
Phospho-Tau (Ser202, Thr205) Monoclonal Antibody (AT8)	ThermoFisher Scientific	Cat # MN1020, RRID:AB_223647
GFAP antibody	Synaptic Systems	Cat # 173 308, RRID:AB_2905596
HSP90 alpha-recombinant rabbit monoclonal antibody (4D1)	Thermo Fisher Scientific	Cat# MA5-33174, RRID:AB_2811990
Tau Monoclonal Antibody (TAU5)	Thermo Fisher Scientific	Cat# AHB0042 RRID:AB_2536235)
Phospho-Tau (Ser202, Thr205) Monoclonal Antibody (AT8)	ThermoFisher Scientific	Cat # MN1020, RRID:AB_223647
Rabbit Anti-MAP2 Polyclonal Antibody, Unconjugated	Abcam	Cat# ab32454, RRID:AB_776174
Anti-LRP1 (N-terminal) antibody produced in rabbit	Sigma Aldrich	Cat# L2295, RRID:AB_10610711
Cyclophilin A Polyclonal Antibody	Thermo Fisher Scientific	Cat# PA1-025, RRID:AB_2169124
FKBP1B antibody	Proteintech	Cat# 15114-1-AP, RRID:AB_11182817
Phospho-Tau (Ser202, Thr205) Monoclonal Antibody (AT8)	ThermoFisher Scientific	Cat # MN1020, RRID:AB_223647
GFAP antibody	Synaptic Systems	Cat # 173 308, RRID:AB_2905596
HSP90 alpha-recombinant rabbit monoclonal antibody (4D1)	Thermo Fisher Scientific	Cat# MA5-33174, RRID:AB_2811990
Tau Monoclonal Antibody (TAU5)	Thermo Fisher Scientific	Cat# AHB0042 RRID:AB_2536235)
Phospho-Tau (Ser202, Thr205) Monoclonal Antibody (AT8)	ThermoFisher Scientific	Cat # MN1020, RRID:AB_223647
Rabbit Anti-MAP2 Polyclonal Antibody, Unconjugated	Abcam	Cat# ab32454, RRID:AB_776174
Anti-LRP1 (N-terminal) antibody produced in rabbit	Sigma Aldrich	Cat# L2295, RRID:AB_10610711
Cyclophilin A Polyclonal Antibody	Thermo Fisher Scientific	Cat# PA1-025, RRID:AB_2169124
FKBP1B antibody	Proteintech	Cat# 15114-1-AP, RRID:AB_11182817
Biological Samples
Human, Post-Mortem Frontal Cortex Samples from Alzheimer’s Disease (familial and sporadic) and Non-Symptomatic Patients	Grupo de Neurociencias de Antioquia brain bank	https://www.gna.org.co/
Human, Post-Mortem Hippocampal Samples from Alzheimer’s Disease (familial) and Non-Symptomatic Patients	Grupo de Neurociencias de Antioquia brain bank	https://www.gna.org.co/
Human, Post-Mortem occipital cortex sample	Grupo de Neurociencias de Antioquia	https://www.gna.org.co/
Human, Post-Mortem Hippocampal Samples from Alzheimer’s Disease (familial) and Non-Symptomatic Patients	Grupo de Neurociencias de Antioquia brain bank	https://www.gna.org.co/
Human, Post-Mortem occipital cortex sample	Grupo de Neurociencias de Antioquia	https://www.gna.org.co/
Chemicals, Peptides, and Recombinant Proteins
Glycerin (Glycerol), 50% (v/v) Aqueous Solution	Thermo Fisher	cat# 329032
Low TE Buffer	Thermo Fisher	cat#12090-015
Sytox green nucleic acid stain	Life Tech	cat# S7020
Molecular biology grade ethanol	Fisher	cat# BP2818500
RNAse inhibitor	Lucigen	cat# 30281-1
Nuclei Isolation Kit: Nuclei EZ Prep	Millipore Sigma	Cat# NUC101-1KT
SPRI Select beads	Beckman Coulter	Cat# B23318
Xylene	Millipore-Sigma	Cat# 534056-4L; CAS: 1330-20-7
2-propanol	Millipore-Sigma	Cat# 05279801001
CC1	Ventana Medical Systems, Inc	Cat# 05279801001
CC2	Ventana Medical Systems, Inc	Cat# 05279798001
Shandon Instant Hematoxylin	Thermo-Fisher	Cat# 12687926
Eosin yellowish	PanReac	Cat# 348111; CAS: 17372-87-1
Consul-Mount^™ Histology Media, Medium Viscosity	Thermo-Fisher	Cat# 9990440
Reaction Buffer Concentrate (10X)	Ventana Medical Systems, Inc	Cat# 5353955001
LCS	Ventana Medical Systems, Inc	Cat# 5264839001
10X EZ PREP SOLUTION, 2L	Ventana Medical Systems, Inc	Cat# 5279771001
PROTEASE 1	Ventana Medical Systems, Inc	Cat# 5266688001
ANTIBODY DILUENT	Ventana Medical Systems, Inc	Cat# 5261899001
Methanol, for HPLC, ≥99.9%	Millipore Sigma	Cat# 34860
Eosin Y solution, aqueous	Millipore Sigma	Cat# HT110216-500ML
Hematoxylin Solution, Mayer’s	Millipore Sigma	Cat# MHS16-500ML
Bluing Buffer, Dako	Agilent	Cat# CS70230-2
Tris Base	Thermo Fisher Scientific	Cat# BP152-500
Potassium Hydroxide Solution, 8M	Millipore Sigma	Cat# P4494-50ML
SSC Buffer 20X Concentrate	Millipore Sigma	Cat# S66391L
Hydrochloric Acid Solution, 0.1N	Fisher Chemical	Cat# SA54-1
Qiagen Buffer EB	Qiagen	Cat# 19086
KAPA SYBR FASTqPCR Master Mix	Roche	KK4600
Low TE Buffer	Thermo Fisher	cat#12090-015
Sytox green nucleic acid stain	Life Tech	cat# S7020
Molecular biology grade ethanol	Fisher	cat# BP2818500
RNAse inhibitor	Lucigen	cat# 30281-1
Nuclei Isolation Kit: Nuclei EZ Prep	Millipore Sigma	Cat# NUC101-1KT
SPRI Select beads	Beckman Coulter	Cat# B23318
Xylene	Millipore-Sigma	Cat# 534056-4L; CAS: 1330-20-7
2-propanol	Millipore-Sigma	Cat# 05279801001
CC1	Ventana Medical Systems, Inc	Cat# 05279801001
CC2	Ventana Medical Systems, Inc	Cat# 05279798001
Shandon Instant Hematoxylin	Thermo-Fisher	Cat# 12687926
Eosin yellowish	PanReac	Cat# 348111; CAS: 17372-87-1
Consul-Mount^™ Histology Media, Medium Viscosity	Thermo-Fisher	Cat# 9990440
Reaction Buffer Concentrate (10X)	Ventana Medical Systems, Inc	Cat# 5353955001
LCS	Ventana Medical Systems, Inc	Cat# 5264839001
10X EZ PREP SOLUTION, 2L	Ventana Medical Systems, Inc	Cat# 5279771001
PROTEASE 1	Ventana Medical Systems, Inc	Cat# 5266688001
ANTIBODY DILUENT	Ventana Medical Systems, Inc	Cat# 5261899001
Methanol, for HPLC, ≥99.9%	Millipore Sigma	Cat# 34860
Eosin Y solution, aqueous	Millipore Sigma	Cat# HT110216-500ML
Hematoxylin Solution, Mayer’s	Millipore Sigma	Cat# MHS16-500ML
Bluing Buffer, Dako	Agilent	Cat# CS70230-2
Tris Base	Thermo Fisher Scientific	Cat# BP152-500
Potassium Hydroxide Solution, 8M	Millipore Sigma	Cat# P4494-50ML
SSC Buffer 20X Concentrate	Millipore Sigma	Cat# S66391L
Hydrochloric Acid Solution, 0.1N	Fisher Chemical	Cat# SA54-1
Qiagen Buffer EB	Qiagen	Cat# 19086
KAPA SYBR FASTqPCR Master Mix	Roche	KK4600
Critical Commercial Assays
Visium Spatial Gene Expression Slide & Reagent Kit	10x Genomics	Cat # 1000187
Chromium Next GEM Single Cell 3’ GEM, Library & Gel Bead Kit v3.1	10x Genomics	Cat # 1000121
UltraView Universal DAB Detection Kit	Ventana Medical Systems, Inc	Cat# 5269806001
Chromium Next GEM Single Cell 3’ GEM, Library & Gel Bead Kit v3.1	10x Genomics	Cat # 1000121
UltraView Universal DAB Detection Kit	Ventana Medical Systems, Inc	Cat# 5269806001
Deposited Data
Single Nucleus RNA Sequencing Data	This Study	https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE222494 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE222495
Spatial Transcriptomic Sequencing Data	This study	https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?&acc=GSE221365
Single Nucleus RNA Sequencing Data	Sepulveda-Falla et al, 2022	GEO: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE206744
Software and Algorithms
Prism (version 6.1.0)	GraphPad	https://www.graphpad.com/scientificsoftware/prism/; RRID:SCR_002798
Microsoft Excel	Microsoft 365	https://microsoft.com; RRID:SCR_016137
Cell Ranger (version 3.0)	10x Genomics	https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/latest/what-is-cell-ranger; RRID:SCR_017344
RStudio	Posit	https://RStudio.com; RRID:SCR_000432
Seurat (version 4.1.1)	Stuart et al. (2019)	https://satijalab.org/seurat/index.html; RRID:SCR_016341
Doubletfinder (version 2.0.3)	McGinnis et al. (2019)	https://github.com/chris-mcginnis-ucsf/DoubletFinder
QPath (v.0.1.2)	Bankhead et al. (2017)	https://qupath.github.io/; RRID:SCR_018257
Inkscape (v1.2.1)	The Inkscape Project	https://inkscape.org/; RRID:SCR_014479
Metascape	Zhou et al. (2019)	https://metascape.org; RRID:SCR_016620
Space Ranger (version 1.3.1)	10x Genomics	https://support.10xgenomics.com/spatial-gene-expression/software/pipelines/latest/what-is-space-ranger
Libra	Squair et al. (2021)	https://github.com/neurorestore/Libra
lme4 package	Bates et al. (2015)	http://www.jstatsoft.org/v67/i01/
fgsea	Korotkevich et al. (2021)	https://github.com/ctlab/fgsea
Microsoft Excel	Microsoft 365	https://microsoft.com; RRID:SCR_016137
Cell Ranger (version 3.0)	10x Genomics	https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/latest/what-is-cell-ranger; RRID:SCR_017344
RStudio	Posit	https://RStudio.com; RRID:SCR_000432
Seurat (version 4.1.1)	Stuart et al. (2019)	https://satijalab.org/seurat/index.html; RRID:SCR_016341
Doubletfinder (version 2.0.3)	McGinnis et al. (2019)	https://github.com/chris-mcginnis-ucsf/DoubletFinder
QPath (v.0.1.2)	Bankhead et al. (2017)	https://qupath.github.io/; RRID:SCR_018257
Inkscape (v1.2.1)	The Inkscape Project	https://inkscape.org/; RRID:SCR_014479
Metascape	Zhou et al. (2019)	https://metascape.org; RRID:SCR_016620
Space Ranger (version 1.3.1)	10x Genomics	https://support.10xgenomics.com/spatial-gene-expression/software/pipelines/latest/what-is-space-ranger
Libra	Squair et al. (2021)	https://github.com/neurorestore/Libra
lme4 package	Bates et al. (2015)	http://www.jstatsoft.org/v67/i01/
fgsea	Korotkevich et al. (2021)	https://github.com/ctlab/fgsea
Custom computer code used in this manuscript	Almeida et al (2024)	https://doi.org/10.5281/zenodo.10460116

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PERMALINK

Single Nucleus RNA Sequencing Demonstrates an Autosomal Dominant Alzheimer’s Disease Profile and Possible Mechanisms of Disease Protection

Maria Camila Almeida

Sarah J Eger

Caroline He

Morgane Audouard

Arina Nikitina

Stella M K Glasauer

Dasol Han

Barbara Mejía-Cupajita

Juliana Acosta–Uribe

Nelson David Villalba-Moreno

Jessica Lisa Littau

Megan Elcheikhali

Erica Keane Rivera

Daniel Carneiro Carrettiero

Carlos Andrés Villegas–Lanau

Diego Sepulveda-Falla

Francisco Lopera

Kenneth S Kosik

SUMMARY

Graphical Abstract

eTORC Blurb:

INTRODUCTION

RESULTS

Single Nucleus-RNA Sequencing of frontal cortex

Figure 1. Neuropathological evaluation for the individuals used in the study and expression profiling of human nuclei populations from frozen, post-mortem frontal cortex.

Cellular systems dysregulated in PSEN1-E280A Alzheimer’s disease astrocytes

Figure 2: Astrocytes from PSEN1-280A exhibit mitochondrial and autophagy-associated gene over-expression in comparison to Sporadic AD.

Unique and shared transcriptional regulation in PSEN1-E280A and sporadic AD neurons

Figure 3. Excitatory neuronal loss is detected in both PSEN1-E280A and sporadic AD.

Figure 4. Spatial transcriptomics in post-mortem brain tissue reveals differentially expressed genes in PSEN1-E280A patients that are specific to white and gray matter regions.

Figure 5. Mechanistic insight into sets of differentially expressed genes in PSEN1-E280A compared to sporadic AD.

Oligodendrocyte transcriptomic signatures do not differ PSEN1-E280A and sporadic AD

A more pronounced Human Alzheimer Microglia (HAM) profile in sporadic AD patients compared to PSEN1-E280A AD

The PSEN1-E280A mutation affects Aβ-associated pathways but not TAU

Protein expression validation of single-nucleus RNA sequencing

Figure 6. Protein expression profile validates the transcriptional alterations detected by Single-nuc RNA Sequencing.

A protective profile of PSEN1-E280A APOE3 Christchurch

Figure 7. A protective profile in NFT-free frontal cortex of a PSEN1-E280A APOE3 Christchurch homozygous carrier.

Discussion

STAR Methods

RESOURCE AVAILABILITY

Lead contact

Materials availability

Data and code availability

KEY RESOURCES TABLE.

EXPERIMENTAL MODEL AND STUDY PARTICIPANT DETAILS

Post-mortem human cohort

METHOD DETAILS

Genetic sequencing

Neuropathological and immunohistochemical analysis

Single Nucleus RNA-Sequencing

Mapping single nuclei reads to the genome.

Quality Control for expression matrix.

Data Processing, Analyses, Visualization, and Differential Expression Testing.

Identification of differentially expressed genes in cell-type subpopulations.

hd-WGCNA

Formation and identification of Modules of interest

Spatial Transcriptomics

Immunofluorescence

Multi dataset analyses

QUANTIFICATION AND STATISTICAL ANALYSIS

Statistics

Cell-specific co-localization analysis

Immunofluorescence

Calculation of CMA activation score

Beta regression

Gene set HAM (Human AD microglia) score

Overlap analysis

Negative binomial generalized linear mixed-effect model

Randomization

Sample size estimation

Supplementary Material

Highlights:

ACKNOWLEDGMENTS

Footnotes

REFERENCES

Associated Data

Supplementary Materials