each THY-Tau22 mouse were mounted on superfrost plus glass slides and dried overnight before being stained with Thioflavin S. Two wild-type littermate mice of each sex were used as negative controls. Sections were treated with 10% formalin (VWR, 11699404) for 10 min and washed twice 5 min with PBS (Merck, 524650) before being incubated in 0.25% potassium metabisulfite (Sigma, 60458). After being washed in PBS for 5 min twice, sections were incubated in 2% potassium metabisulfite (Sigma, 60458) and 1% oxalate (Sigma, 75668) for 3 min (until white), washed twice in water and stained in 0.015% Thioflavin S (Sigma, T1892) in 50% ethanol (VWR 85933.290) for 10 min. Stainings were cleared in 50% Ethanol for 5 min and rinsed for 5 min in water. Sections were dried and coverslipped using Fluoromount G. 24 h later, they were observed with the Axiolmager1 (Zeiss) with settings for Alexa 488 detection at ×20 magnification, and Thioflavin S positive cells were counted manually with a cell counter in the overall cortex and hippocampal CA1. For each mouse and each brain region, the number of cells counted was averaged. Results were statistically analyzed using the Student's T-test, after a normality check, by applying the Graphpad Prism 9 software (RRID:SCR_002798).

## RNA extraction and sequencing

RNA was extracted from the cortex using the RNeasy Universal Kit (Qiagen) as previously described [97]. RNA samples were considered of acceptable quality when their RNA integrity value exceeded 8.5, and their absorbance ratios at 260/230 and 260/280 were equal to or greater than 1.8 and 2, respectively. These high-quality RNA samples were subsequently preserved at -80 °C until they were used for analysis. For single-cell gene expression profiling, freshly dissected mouse cortex was dissociated using Adult - Brain Dissociation (Miltenyi Biotec). The procedures to obtain material for single-cell RNA-seq and sequencing (microfluidics, single-cell droplet encapsulation, next-generation sequencing library preparation, bar-coded sequencing) were performed as previously described [17].

## Single-cell data quality filtering and pre-processing

Analyses of the single-cell transcriptomic data were performed using the Seurat software package (version 4.3.0, RRID:SCR_007322) in the R programming language (version 4.2.2, RRID:SCR_001905) [98]. The data underwent a dedicated processing pipeline, covering multiple quality control, cell selection, and filtration steps, as previously described [17]. Standard quality checks in the Seurat software did not highlight any problematic sample, and all input samples were consequently included in the subsequent analytical stages. To ensure data reliability, cells of substandard quality were excluded if unique feature counts were greater than 2500 or less than 200 to address potential issues related to cell duplication or sequencing of empty droplets. In addition, cells with mitochondrial gene counts greater than 5% were also filtered out, as elevated expression levels of mitochondrial genes may indicate apoptotic or deteriorating cells. The data were then normalized and scaled using a global scaling normalization implemented in the function *SCTransform* of the Seurat package [99]. Furthermore, to remove uninformative features, genes with low variation were filtered out using the *FindVariableFeatures* function and retaining only the top 2500 genes with the highest variance in the selection. Finally, we introduced a linear scaling transformation using the *ScaleData* function with default parameters. After these pre-processing and quality control analyses, the final data set covered a total of 44,910 single cells as input for the subsequent analyses.

## Single-cell clustering and cell type annotation

To identify, visualize, and annotate clusters of cells representing different cell types in the pre-processed data, we mapped the data to a lower dimension using a principal component analysis (PCA) and the Uniform Manifold Approximation and Projection (UMAP) method [25]. A suitable number of principal components (PCs) for the subsequent clustering analysis was determined using the Elbow method from the Seurat package and the Cumulative Sums (*cumsum*) function from the R package *base*. Briefly, cumulative percentages were calculated for each PC to identify the optimal threshold value where the percentage change in variance between successive PCs falls below 0.1%. In this case, the first 15 PCs were selected. Next, we applied a shared nearest neighbor (SNN) graph-based clustering to robustly assign individual cells to homogeneous groups with similar gene expression profiles [24] (see Fig. 1). The number of clusters was chosen to maximize the average *Silhouette Width* cluster validity index [100], as implemented in the R package *cluster* (version 2.1.4, RRID:SCR_013505), and the results were visualized using the R package *ggplot2* (version 3.4.2, RRID:SCR_014601). The final clustering was

determined using the *FindNeighbors* and *FindClusters* functions in Seurat. To annotate the resulting clusters with corresponding cell types, we applied the method *ScType* [26]. This approach uses combinations of known marker genes for each cell type, derived from prior benchmark datasets, to assign cell type identities to the clusters according to the gene expression profiles of these known markers. Here, we obtained the list of cell marker genes from the public *Cell Marker* database (RRID:SCR_018503) [27]. Finally, the assigned cell type identities were validated by comparing the top differentially expressed genes between each labeled cluster and all other clusters against the established marker genes reported in the *Panglao* database [101] (RRID:SCR_022580). Overall, this clustering and annotation workflow enabled the cell type annotation of clusters with high confidence, facilitating subsequent analyses and data interpretation.

## Sex-dependent differential gene expression analysis

Differentially expressed genes (DEGs) between the sample groups of interest were scored by fitting a generalized linear model (GLM) using the Poisson distribution and the *FindMarkers* function in Seurat [99]. We then determined three types of DEGs: Sex-neutral DEGs (genes displaying a significant change (FDR < 0.05) between transgenic and wild type with a similar effect size in both sexes), sex-specific DEGs (genes with a significant differential expression (FDR < 0.05) in only one sex, while not approaching significance in the other sex (nominal *P*-value > 0.5)), and sex-dimorphic genes (genes with significant changes in both sexes (FDR < 0.05), but with different signs of the log fold change and a minimum log fold change difference of 0.5). In addition, we also investigated associations between gene expression and the interaction between sex and genotype to identify genes with potential sex differences affecting only the magnitude of the expression change but not the direction of the change (i.e., with the same sign of the log fold change). While we consider patterns of sex-dimorphism for the latter group of genes as less reliable than those for genes which also display a different sign of the log fold change and a minimum effect size difference, we list them as additional candidate sex-related DEGs in the Suppl. Materials and focus on the discussion of the first three groups of genes in the main manuscript. To investigate both cell type-specific and cell type-agnostic changes, we performed these analyses of DEGs both for each cell type individually and for the combination of cell types, enabling a comprehensive assessment of DEGs associated with both sex and cell type. Finally, we complemented our analyses in the THY-Tau22 model by conducting the same investigations for a human *post mortem* brain scRNA-seq dataset comparing AD patients vs. matched controls [14] and the scRNA-seq data for the Tg2576 (RRID:MGI:3710766) mouse model we have studied previously [17]. To ensure accurate assignments of standardized gene symbols for all datasets, we used the R package *HGNChelper* (version 0.8.1) for the correction of HUGO Gene Nomenclature Committee (HGNC) human gene symbols and Mouse Genome Informatics (MGI) mouse gene symbols.

To investigate the potential influence of sex on differential expression, we applied a pseudo-bulk approach with a dedicated interaction term for sex and transgene status [102]. For generating sex-related molecular signatures in each cell type, we employed the *veom* [103] pipeline from the R package *Limma* [104] (version 3.56.1, RRID:SCR_010943). To enhance the mean-variance trend modeling accuracy and mitigate the multiple testing correction impact, we applied the function *FilterByExpr* from the R package *edgeR* [105] (version 3.4.2, RRID:SCR_012802) with default parameters to remove lowly expressed genes. Next, we applied the trimmed mean of M values with singleton pairing (TMMwsp) method for normalization. Following this, we employed *veom*'s model fitting, incorporating a contrast matrix for each cell type-specific mutant-control comparison, and used an empirical Bayes estimation of standard errors [106]. DEGs were assigned as significant using stringent threshold criteria for the absolute log2 fold change (LFC > 0.25) and the adjusted *p*-value significance score (FDR < 0.05).

## Pathway enrichment analysis

To assess coordinated sex-dependent changes in cellular processes from public pathway databases, we conducted gene set enrichment analyses using the R package *clusterProfiler* [107] (version 4.8.1, RRID:SCR_016884) for the databases KEGG (RRID:SCR_018145) and Gene Ontology (RRID:SCR_010326). Mouse gene annotations were obtained from the R package *org.Mm.eg.db* (version 3.17.0, RRID:SCR_002811). An adjusted *p*-value below 0.05 was used as a threshold to determine significance. Finally, the enrichment analysis results were visualized using the R packages *enrichplot* (version 1.20.0), *cowplot* (version 1.1.1, RRID:SCR_018081), and *ggVennDiagram* [108] (version 1.2.3).
## Gene regulatory network (GRN) analysis

To analyze coordinated gene activity changes in regulatory sub-networks and their potential sex-dependent patterns, a gene regulatory network (GRN) was built using a dedicated network reconstruction algorithm [45] and the GeneGo MetaCore™ knowledge database (RRID:SCR_008125). This database covers curated, experimentally derived interactions between genes from the published literature. We specifically focused on curated interactions known to have a regulatory effect and clear directionality, including interactions annotated as "Regulation", "Transcriptional Regulation", "Influence on Expression", "Binding", and "co-Regulation of Transcription". To reconstruct regulatory networks specific for each genotype, we applied a condition-specific gene regulatory network (GRN) reconstruction method [45]. This approach eliminates interactions from the initially constructed network, assembled from the knowledge base, which are inconsistent with the binary gene expression states of the transgenic and wild-type phenotypes within a genetic algorithm-based optimization procedure. Since some interactions in the MetaCore database lack information regarding their activating or inhibiting effect, the algorithm automatically infers this missing information from the available gene expression data and the known network topology.

## Analysis of network perturbations

While the GRN analysis already aids in pinpointing key regulatory genes that influence the identified DEGs, a more detailed ranking of these regulators by their relevance for controlling downstream network alterations can be obtained by computationally simulating the outcomes of altering their activity. For this purpose, we employed an algorithmic network perturbation analysis [45], which scores the potential of candidate regulator genes (called perturbagens) to reverse downstream (sex-dependent) gene expression changes. Higher scores in this analysis signify that the corresponding regulators can at least partly reverse or rescue downstream pathological expression alterations for a significant number of regulated target genes. Thus, the top-ranked genes/proteins identified by this method may serve as initial candidate drug targets for further in silico druggability analyses and subsequent experimental validation.

## DATA AVAILABILITY

The source code used to analyze the scRNA-seq data in the current study is available online at the GitLab repository (https://gitlab.lcsb.uni.lu/bds/thy_tau22_scrnaseq/). The entorhinal cortex dataset from THY-Tau22 mice has been deposited in NCBI's Gene Expression Omnibus (GEO) [109] and is accessible via the GEO series accession number GSE245035.

## CODE AVAILABILITY

The source code used to analyze the scRNA-seq data in the current study is available online at the GitLab repository (https://gitlab.lcsb.uni.lu/bds/thy_tau22_scrnaseq/). The input files used for the GRN analysis and resulting output files are available at https://webdav-r3lab.uni.lu/public/data/sbpj-x776/.

## REFERENCES

1. Medeiros A, de M, Silva RH. Sex differences in Alzheimer's disease: where do we stand? J Alzheimers Dis. 2019;67:35-60.

2. Dubal DB. Sex difference in Alzheimer's disease: an updated, balanced and emerging perspective on differing vulnerabilities. Handb Clin Neurol. 2020;175:261-73.

3. Mielke MM, Vemuri P, Rocca WA. Clinical epidemiology of Alzheimer's disease: assessing sex and gender differences. Clin Epidemiol. 2014;6:37-48.

4. Oveisgharan S, Arvanitakis Z, Yu L, Farfel J, Schneider JA, Bennett DA. Sex differences in Alzheimer's disease and common neuropathologies of aging. Acta Neuropathol. 2018;136:887-900.

5. Hohman TJ, Dumitrescu L, Barnes LL, Thambisetty M, Beecham G, Kunkle B, et al. Sex-specific association of apolipoprotein E with cerebrospinal fluid levels of tau. JAMA Neurol. 2018;75:989-98.

6. Guerreiro RJ, Gustafson DR, Hardy J. The genetic architecture of Alzheimer's disease: beyond APP, PSEns and APOE. Neurobiol Aging. 2012;33:437-56.

7. Altmann A, Tian L, Henderson VW, Greicius MD. Sex modifies the APOE-related risk of developing Alzheimer disease. Ann Neurol. 2014;75:563-73.

8. Barron AM, Pike CJ. Sex hormones, aging, and Alzheimer's disease. Front Biosci. 2012;4:976-97.

9. Ibanez C, Shields SA, El-Etr M, Baulieu E-E, Schumacher M, Franklin RJM. Systemic progesterone administration results in a partial reversal of the age-

associated decline in CNS remyelination following toxin-induced demyelination in male rats. Neuropathol Appl Neurobiol. 2004;30:80-89.

10. Ghoumari AM, Baulieu EE, Schumacher M. Progesterone increases oligodendroglial cell proliferation in rat cerebellar slice cultures. Neuroscience. 2005;135:47-58.

11. Nirzhor SSR, Khan RI, Neelotpol S. The biology of glial cells and their complex roles in Alzheimer's disease: new opportunities in therapy. Biomolecules 2018. https://doi.org/10.3390/biom8030093.

12. Luchena C, Zuazo-Ibarra J, Alberdi E, Matute C, Capetillo-Zarate E. Contribution of neurons and glial cells to complement-mediated synapse removal during development, aging and in Alzheimer's disease. Mediat Inflamm. 2018;2018:2530414.

13. Paranjpe MD, Belonwu S, Wang JK, Oskotsky T, Gupta A, Taubes A, et al. Sex-specific cross tissue meta-analysis identifies immune dysregulation in women with Alzheimer's disease. Front Aging Neurosci. 2021;13:735611.

14. Grubman A, Chew G, Ouyang JF, Sun G, Choo XY, McLean C, et al. A single-cell atlas of entorhinal cortex from individuals with Alzheimer's disease reveals cell-type-specific gene expression regulation. Nat Neurosci. 2019;22:2087-97.

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17. Ali M, Huarte OU, Heurtaux T, Garcia P, Rodriguez BP, Grzyb K, et al. Single-cell transcriptional profiling and gene regulatory network modeling in Tg2576 mice reveal gender-dependent molecular features preceding Alzheimer-like pathologies. Mol Neurobiol. 2022. https://doi.org/10.1007/s12035-022-02985-2.

18. Schindowski K, Bretteville A, Leroy K, Bégard S, Brion J-P, Hamdane M, et al. Alzheimer's disease-like tau neuropathology leads to memory deficits and loss of functional synapses in a novel mutated tau transgenic mouse without any motor deficits. Am J Pathol. 2006;169:599-616.

19. Chatterjee S, Cassel R, Schneider-Anthony A, Merienne K, Cosquer B, Tzeplaeff L. et al. Reinstating plasticity and memory in a tauopathy mouse model with an acetyltransferase activator. EMBO Mol Med. 2018. https://doi.org/10.15252/emmm.201708587.

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22. Sundermann EE, Panizzon MS, Chen X, Andrews M, Galasko D, Banks SJ. Sex differences in Alzheimer's-related Tau biomarkers and a mediating effect of testosterone. Biol Sex Differ. 2020;11:33.

23. Hu Y-T, Boonstra J, McGurran H, Stormmesand J, Sluiter A, Balesar R, et al. Sex differences in the neuropathological hallmarks of Alzheimer's disease: focus on cognitively intact elderly individuals. Neuropathol Appl Neurobiol. 2021;47:958-66.

24. Xu C, Su Z. Identification of cell types from single-cell transcriptomes using a novel clustering method. Bioinformatics. 2015;31:1974-80.

25. McInnes L, Healy J, Melville J. UMAP: uniform manifold approximation and projection for dimension reduction. J Open Source Softw. 2018;3:861.

26. lanevski A, Giri AK, Aittokallio T. Fully-automated and ultra-fast cell-type identification using specific marker combinations from single-cell transcriptomic data. Nat Commun. 2022;13:1246.

27. Hu C, Li T, Xu Y, Zhang X, Li F, Bai J, et al. CellMarker 2.0: an updated database of manually curated cell markers in human/mouse and web tools based on scRNA-seq data. Nucleic Acids Res. 2023;51:D870-D876.

28. Uriarte Huarte O, Kyriakis D, Heurtaux T, Pires-Afonso Y, Grzyb K, Halder R, et al. Single-cell transcriptomics and in situ morphological analyses reveal microglia heterogeneity across the nigrostriatal pathway. Front Immunol. 2021;12:639613.

29. Heppner FL, Ransohoff RM, Becher B. Immune attack: the role of inflammation in Alzheimer disease. Nat Rev Neurosci. 2015;16:358-72.

30. Liang X, Wu H, Colt M, Guo X, Pluimer B, Zeng J, et al. Microglia and its Genetics in Alzheimer's disease. Curr Alzheimer Res. 2021;18:676-88.

31. Olabarria M, Noristani HN, Verkrhatsky A, Rodriguez JJ. Concomitant astroglial atrophy and astrogliosis in a triple transgenic animal model of Alzheimer's disease. Glia. 2010;58:831-8.

32. Matos M, Augusto E, Oliveira CR, Agostinho P. Amyloid-beta peptide decreases glutamate uptake in cultured astrocytes: involvement of oxidative stress and mitogen-activated protein kinase cascades. Neuroscience. 2008;156:898-910.
## Gene regulatory network (GRN) analysis

To analyze coordinated gene activity changes in regulatory sub-networks and their potential sex-dependent patterns, a gene regulatory network (GRN) was built using a dedicated network reconstruction algorithm [45] and the GeneGo MetaCore™ knowledge database (RRID:SCR_008125). This database covers curated, experimentally derived interactions between genes from the published literature. We specifically focused on curated interactions known to have a regulatory effect and clear directionality, including interactions annotated as "Regulation", "Transcriptional Regulation", "Influence on Expression", "Binding", and "co-Regulation of Transcription". To reconstruct regulatory networks specific for each genotype, we applied a condition-specific gene regulatory network (GRN) reconstruction method [45]. This approach eliminates interactions from the initially constructed network, assembled from the knowledge base, which are inconsistent with the binary gene expression states of the transgenic and wild-type phenotypes within a genetic algorithm-based optimization procedure. Since some interactions in the MetaCore database lack information regarding their activating or inhibiting effect, the algorithm automatically infers this missing information from the available gene expression data and the known network topology.

## Analysis of network perturbations

While the GRN analysis already aids in pinpointing key regulatory genes that influence the identified DEGs, a more detailed ranking of these regulators by their relevance for controlling downstream network alterations can be obtained by computationally simulating the outcomes of altering their activity. For this purpose, we employed an algorithmic network perturbation analysis [45], which scores the potential of candidate regulator genes (called perturbagens) to reverse downstream (sex-dependent) gene expression changes. Higher scores in this analysis signify that the corresponding regulators can at least partly reverse or rescue downstream pathological expression alterations for a significant number of regulated target genes. Thus, the top-ranked genes/proteins identified by this method may serve as initial candidate drug targets for further in silico druggability analyses and subsequent experimental validation.

## DATA AVAILABILITY

The source code used to analyze the scRNA-seq data in the current study is available online at the GitLab repository (https://gitlab.lcsb.uni.lu/bds/thy_tau22_scrnaseq/). The entorhinal cortex dataset from THY-Tau22 mice has been deposited in NCBI's Gene Expression Omnibus (GEO) [109] and is accessible via the GEO series accession number GSE245035.

## CODE AVAILABILITY

The source code used to analyze the scRNA-seq data in the current study is available online at the GitLab repository (https://gitlab.lcsb.uni.lu/bds/thy_tau22_scrnaseq/). The input files used for the GRN analysis and resulting output files are available at https://webdav-r3lab.uni.lu/public/data/sbpj-x776/.

## REFERENCES

1. Medeiros A, de M, Silva RH. Sex differences in Alzheimer's disease: where do we stand? J Alzheimers Dis. 2019;67:35-60.

2. Dubal DB. Sex difference in Alzheimer's disease: an updated, balanced and emerging perspective on differing vulnerabilities. Handb Clin Neurol. 2020;175:261-73.

3. Mielke MM, Vemuri P, Rocca WA. Clinical epidemiology of Alzheimer's disease: assessing sex and gender differences. Clin Epidemiol. 2014;6:37-48.

4. Oveisgharan S, Arvanitakis Z, Yu L, Farfel J, Schneider JA, Bennett DA. Sex differences in Alzheimer's disease and common neuropathologies of aging. Acta Neuropathol. 2018;136:887-900.

5. Hohman TJ, Dumitrescu L, Barnes LL, Thambisetty M, Beecham G, Kunkle B, et al. Sex-specific association of apolipoprotein E with cerebrospinal fluid levels of tau. JAMA Neurol. 2018;75:989-98.

6. Guerreiro RJ, Gustafson DR, Hardy J. The genetic architecture of Alzheimer's disease: beyond APP, PSEns and APOE. Neurobiol Aging. 2012;33:437-56.

7. Altmann A, Tian L, Henderson VW, Greicius MD. Sex modifies the APOE-related risk of developing Alzheimer disease. Ann Neurol. 2014;75:563-73.

8. Barron AM, Pike CJ. Sex hormones, aging, and Alzheimer's disease. Front Biosci. 2012;4:976-97.

9. Ibanez C, Shields SA, El-Etr M, Baulieu E-E, Schumacher M, Franklin RJM. Systemic progesterone administration results in a partial reversal of the age-

associated decline in CNS remyelination following toxin-induced demyelination in male rats. Neuropathol Appl Neurobiol. 2004;30:80-89.

10. Ghoumari AM, Baulieu EE, Schumacher M. Progesterone increases oligodendroglial cell proliferation in rat cerebellar slice cultures. Neuroscience. 2005;135:47-58.

11. Nirzhor SSR, Khan RI, Neelotpol S. The biology of glial cells and their complex roles in Alzheimer's disease: new opportunities in therapy. Biomolecules 2018. https://doi.org/10.3390/biom8030093.

12. Luchena C, Zuazo-Ibarra J, Alberdi E, Matute C, Capetillo-Zarate E. Contribution of neurons and glial cells to complement-mediated synapse removal during development, aging and in Alzheimer's disease. Mediat Inflamm. 2018;2018:2530414.

13. Paranjpe MD, Belonwu S, Wang JK, Oskotsky T, Gupta A, Taubes A, et al. Sex-specific cross tissue meta-analysis identifies immune dysregulation in women with Alzheimer's disease. Front Aging Neurosci. 2021;13:735611.

14. Grubman A, Chew G, Ouyang JF, Sun G, Choo XY, McLean C, et al. A single-cell atlas of entorhinal cortex from individuals with Alzheimer's disease reveals cell-type-specific gene expression regulation. Nat Neurosci. 2019;22:2087-97.

15. Mathys H, Davila-Velderrain J, Peng Z, Gao F, Mohammadi S, Young JZ, et al. Single-cell transcriptomic analysis of Alzheimer's disease. Nature. 2019;570:332-7.

16. Sierksma A, Lu A, Mancuso R, Fattorelli N, Thrup N, Salta E, et al. Novel Alzheimer risk genes determine the microglia response to amyloid-β but not to TAU pathology. EMBO Mol Med. 2020;12:e10606.

17. Ali M, Huarte OU, Heurtaux T, Garcia P, Rodriguez BP, Grzyb K, et al. Single-cell transcriptional profiling and gene regulatory network modeling in Tg2576 mice reveal gender-dependent molecular features preceding Alzheimer-like pathologies. Mol Neurobiol. 2022. https://doi.org/10.1007/s12035-022-02985-2.

18. Schindowski K, Bretteville A, Leroy K, Bégard S, Brion J-P, Hamdane M, et al. Alzheimer's disease-like tau neuropathology leads to memory deficits and loss of functional synapses in a novel mutated tau transgenic mouse without any motor deficits. Am J Pathol. 2006;169:599-616.

19. Chatterjee S, Cassel R, Schneider-Anthony A, Merienne K, Cosquer B, Tzeplaeff L. et al. Reinstating plasticity and memory in a tauopathy mouse model with an acetyltransferase activator. EMBO Mol Med. 2018. https://doi.org/10.15252/emmm.201708587.

20. Laurent C, Dorothée G, Hunot S, Martin E, Monnet Y, Duchamp M, et al. Hippocampal T cell infiltration promotes neuroinflammation and cognitive decline in a mouse model of tauopathy. Brain. 2017;140:184-200.

21. Jeugd A, Van der, Ahmed T, Burnouf S, Belarbi K, Hamdame M, Grosjean M-E, et al. Hippocampal tauopathy in tau transgenic mice coincides with impaired hippocampus-dependent learning and memory, and attenuated late-phase long-term depression of synaptic transmission. Neurobiol Learn Mem. 2011;95:296-304.

22. Sundermann EE, Panizzon MS, Chen X, Andrews M, Galasko D, Banks SJ. Sex differences in Alzheimer's-related Tau biomarkers and a mediating effect of testosterone. Biol Sex Differ. 2020;11:33.

23. Hu Y-T, Boonstra J, McGurran H, Stormmesand J, Sluiter A, Balesar R, et al. Sex differences in the neuropathological hallmarks of Alzheimer's disease: focus on cognitively intact elderly individuals. Neuropathol Appl Neurobiol. 2021;47:958-66.

24. Xu C, Su Z. Identification of cell types from single-cell transcriptomes using a novel clustering method. Bioinformatics. 2015;31:1974-80.

25. McInnes L, Healy J, Melville J. UMAP: uniform manifold approximation and projection for dimension reduction. J Open Source Softw. 2018;3:861.

26. lanevski A, Giri AK, Aittokallio T. Fully-automated and ultra-fast cell-type identification using specific marker combinations from single-cell transcriptomic data. Nat Commun. 2022;13:1246.

27. Hu C, Li T, Xu Y, Zhang X, Li F, Bai J, et al. CellMarker 2.0: an updated database of manually curated cell markers in human/mouse and web tools based on scRNA-seq data. Nucleic Acids Res. 2023;51:D870-D876.

28. Uriarte Huarte O, Kyriakis D, Heurtaux T, Pires-Afonso Y, Grzyb K, Halder R, et al. Single-cell transcriptomics and in situ morphological analyses reveal microglia heterogeneity across the nigrostriatal pathway. Front Immunol. 2021;12:639613.

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## ACKNOWLEDGEMENTS

Bioinformatics analyses presented in this paper partially utilized the High-Performance Computing (HPC) resources provided by the University of Luxembourg (http://hpc.uni.lu). In addition, we extend our gratitude to Prof. Michel Mittelbronn for his support, and the staff of the animal facility of the University of Luxembourg.

## AUTHOR CONTRIBUTIONS

EG and MB conceptualized the study and prepared the research grant application. EG supervised the processing and statistical analyses of the data, which were implemented by MA together with EG. MB supervised the mouse experiments, which were performed by TH, PG, LPL, AS, MT, TH, and LB. DB provided the transgenic mouse models. AS supervised the single-cell sequencing experiments, which were performed by KG and RH. MA, EG, and PG collaborated to draft the initial version of the manuscript. EG and MB conducted thorough reviews and made substantial revisions to various parts of the manuscript. All authors have reviewed and contributed to manuscript revisions.

## FUNDING

We gratefully acknowledge the sponsorship of this work by the Luxembourg Fondation Wivine. EG also acknowledges the support of the Luxembourg National Research Fund (FNR) as part of the projects DIGIPD (ERAPERMED 2020-314) and RECAST (INTER/22/17104370). AS was funded by PRIDE (Project: 17/12244779/PARK-QC) from the Luxembourg National Research Fund. Prof. Michel Mittelbronn was also supported by the Luxembourg National Research Fund (FNR PEARL Project: P16/BM/11192868).

## COMPETING INTERESTS

The authors declare no competing interests.

## ETHICS APPROVAL

The animal experiments adhered to the European FELASA guidelines for animal research and were granted ethical approval by the local Institutional Animal Experimentation Ethics Committee. Furthermore, these experiments were endorsed by the relevant Luxembourg government bodies, including the Ministries of Agriculture and Health. Notably, this research did not entail any involvement with human subjects.

## ADDITIONAL INFORMATION

**Supplementary information** The online version contains supplementary material available at https://doi.org/10.1038/s41420-024-01885-9.

**Correspondence** and requests for materials should be addressed to Enrico Glaab.

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85. Hu Y-T, Chen X-L, Huang S-H, Zhu Q-B, Yu S-Y, Shen Y, et al. Early growth response-1 regulates acetylcholinesterase and its relation with the course of Alzheimer's disease. Brain Pathol. 2019;29:502-12.

86. Renbaum P, Beeri R, Gabai E, Amiel M, Gal M, Ehrengruber MU, et al. Egr-1 upregulates the Alzheimer's disease presenilin-2 gene in neuronal cells. Gene. 2003;318:113-24.

87. Yang H, Xi X, Zhao B, Su Z, Wang Z. KLF4 protects brain microvascular endothelial cells from ischemic stroke induced apoptosis by transcriptionally activating MALAT1. Biochem Biophys Res Commun. 2018;495:2376-82.

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## ACKNOWLEDGEMENTS

Bioinformatics analyses presented in this paper partially utilized the High-Performance Computing (HPC) resources provided by the University of Luxembourg (http://hpc.uni.lu). In addition, we extend our gratitude to Prof. Michel Mittelbronn for his support, and the staff of the animal facility of the University of Luxembourg.

## AUTHOR CONTRIBUTIONS

EG and MB conceptualized the study and prepared the research grant application. EG supervised the processing and statistical analyses of the data, which were implemented by MA together with EG. MB supervised the mouse experiments, which were performed by TH, PG, LPL, AS, MT, TH, and LB. DB provided the transgenic mouse models. AS supervised the single-cell sequencing experiments, which were performed by KG and RH. MA, EG, and PG collaborated to draft the initial version of the manuscript. EG and MB conducted thorough reviews and made substantial revisions to various parts of the manuscript. All authors have reviewed and contributed to manuscript revisions.

## FUNDING

We gratefully acknowledge the sponsorship of this work by the Luxembourg Fondation Wivine. EG also acknowledges the support of the Luxembourg National Research Fund (FNR) as part of the projects DIGIPD (ERAPERMED 2020-314) and RECAST (INTER/22/17104370). AS was funded by PRIDE (Project: 17/12244779/PARK-QC) from the Luxembourg National Research Fund. Prof. Michel Mittelbronn was also supported by the Luxembourg National Research Fund (FNR PEARL Project: P16/BM/11192868).

## COMPETING INTERESTS

The authors declare no competing interests.

## ETHICS APPROVAL

The animal experiments adhered to the European FELASA guidelines for animal research and were granted ethical approval by the local Institutional Animal Experimentation Ethics Committee. Furthermore, these experiments were endorsed by the relevant Luxembourg government bodies, including the Ministries of Agriculture and Health. Notably, this research did not entail any involvement with human subjects.

## ADDITIONAL INFORMATION

**Supplementary information** The online version contains supplementary material available at https://doi.org/10.1038/s41420-024-01885-9.

**Correspondence** and requests for materials should be addressed to Enrico Glaab.

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