Amyloid-beta and tau pathologies act synergistically to induce novel disease stage-specific microglia subtypes

Amyloid-beta and tau pathologies act synergistically to induce novel disease stage-specific microglia subtypes | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Amyloid-beta and tau pathologies act synergistically to induce novel disease stage-specific microglia subtypes Dong Won Kim, Kevin Tu, Alice Wei, Ashley Lau, Anabel Gonzalez-Gil, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1598611/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Background Amongst risk alleles associated with late-onset Alzheimer’s disease (AD), those that converged on the regulation of microglia activity have emerged as central to disease progression. Yet, how canonical amyloid-β (Aβ) and tau pathologies regulate microglia subtypes during the progression of AD remains poorly understood. Methods We use single-cell RNA-sequencing to profile microglia subtypes from mice exhibiting both Aβ and tau pathologies across disease progression. We identify novel microglia subtypes that are induced in response to both Aβ and tau pathologies in a disease stage-specific manner. To validate the observation in AD mouse models, we also generated snRNA-Seq dataset from the human superior frontal gyrus (SFG) and entorhinal cortex (ERC) at different Braak stages. Results We show that during early-stage disease, interferon signalling induces a subtype of microglia termed E arly-stage AD - A ssociated M icroglia (EADAM) in response to both Aβ and tau pathologies. During late-stage disease, a second microglia subtype termed L ate-stage AD - A ssociated M icroglia (LADAM) is detected. While similar microglia subtypes are observed in other models of neurodegenerative disease, the magnitude and composition of gene signatures found in EADAM and LADAM are distinct, suggesting the necessity of both Aβ and tau pathologies to elicit their emergence. Importantly, the pattern of EADAM- and LADAM-associated gene expression is observed in microglia from AD brains, during the early (Braak II)- or late (Braak VI/V)- stage of the disease, respectively. Furthermore, we show that several Siglec genes are selectively expressed in either EADAM or LADAM. Siglecg is expressed in white-matter-associated LADAM, and expression of Siglec - 10 , the human orthologue of Siglecg , is progressively elevated in an AD-stage-dependent manner but not shown in non-AD tauopathy. Conclusions Using scRNA-Seq in mouse models bearing amyloid-β and/or tau pathologies, we identify novel microglia subtypes induced by the combination of Aβ and tau pathologies in a disease stage-specific manner. Our findings suggest that both Aβ and tau pathologies are required for the disease stage-specific induction of EADAM and LADAM. In addition, we revealed Siglecs as biomarkers of AD progression, and potential therapeutic targets. Amyloid-β (Aβ) Tau Microglia Single-cell RNA-sequencing (scRNA-Seq) Sialic acid-binding immunoglobulin-type lectin (Siglec) Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Background Neuroinflammation is increasingly recognized as a key regulator of disease progression in neurodegenerative disorders [ 1 , 2 ], including Alzheimer’s disease (AD), which is the most common cause of dementia[ 3 ]. Recent studies suggest that neuroinflammation serves as a mechanistic link between the development of amyloid-β (Aβ) plaques and tau neurofibrillary tangles, and the canonical pathologies of AD that are thought to drive synapse loss and neuronal death[ 3 ]. In addition to well-characterized AD susceptibility genes such as APP, PSEN1 and PSEN2 (early-onset familial AD [ 4 ]), and APOE (late-onset AD [ 5 , 6 ]), several additional risk alleles for late-onset AD were identified in genes regulating immunomodulation, including TREM2 [ 7 , 8 ], phosphoinositide phospholipase Cγ2 ( PLCG2 ) [ 9 ] and CD33 [ 10 , 11 ]. As the brain’s resident innate immune cells, microglia play multifunctional roles in brain health and the progression of neurodegenerative diseases such as AD [ 1 , 2 , 12 ]. Depending on the disease stage, microglia may protect against neurodegenerative proteinopathy and/or contribute to inflammatory damage [ 2 , 13 ]. Microglia maintain brain health by clearing cellular debris, including Aβ plaques and tau aggregates [ 13 ]. Microglial activation correlates positively with cognition and gray matter volume in humans, indicating that microglia can be protective, at least during early-stage AD [ 14 ]. However, microglia also secrete proinflammatory cytokines and can directly contribute to tau pathology [ 14 , 15 ] and its subsequent neurotoxicity [ 1 , 2 ]. This dichotomous role of microglia in maintaining this balance between phagocytosis/clearance and pro-inflammatory mediator release is thought to be an essential and potentially targetable determinant of AD progression [ 16 ]. Recent studies have identified subtypes of microglia that display a dynamic range of responses and functions [ 17 ], emphasizing the ability of microglia to serve a variety of important physiological roles [ 12 ]. Transcriptomic analyses of bulk tissues revealed disease-associated changes in microglia associated with AD [ 18 , 19 ]. Subsequent single-cell RNA-Sequencing (scRNA-Seq) approaches, however, were necessary to identify disease context-dependent microglial subtypes. scRNA-Seq has shed light on the spatial and developmental heterogeneity of microglia and provides a high-resolution view of the transcriptional landscape of microglia subtypes during development and disease progression [ 17 , 20 – 23 ]. Since AD is a chronic disease with decades-long prodromal stages, understanding the disease stage-specific impacts of microglia subtypes is necessary to clarify the dual nature of microglia activation. The identification of disease-associated microglia (DAM), a unique TREM2-dependent subtype that expresses CD11c and is localized near Aβ plaques in an amyloidosis mouse model [ 20 ], supports this notion. Despite these advances, a critical unresolved question is whether disease stage-specific microglia subtypes exist that are activated in response to both Aβ and tau pathologies. These microglia subtypes would represent novel therapeutic targets for modifying disease progression. Here, we used an AD mouse model ( Tau4RΔK-AP mice), in which wild-type tau is converted into tau aggregates to drive neuron loss in a neuritic plaque-dependent manner [ 24 ]. This animal model serves as an excellent mouse model to profile microglia subtypes across different stages of AD-like disease progression. To assess the requirement for both Aβ and tau pathologies to induce disease stage-specific microglia subtypes, control mice accumulating either Aβ plaques ( APP;PS1 mice) or tau tangles ( Tau4RΔK mice) were also profiled. Using scRNA-Seq approaches, we found that microglia respond to the development of Aβ and tau pathologies in a disease stage-specific manner. During early-stage disease in 6-month-old Tau4RΔK-AP , but not APP;PS1 or Tau4RΔK mice, the presence of both Aβ and tau pathologies induced a novel microglia subtype we termed E arly-stage AD - A ssociated M icroglia (EADAM), which is distinct from DAM and express multiple interferon-regulated genes. We found that the signature genes in EADAM were also associated with a subgroup of microglia in the brains of early (Braak II) stages of AD. In late-stage disease (12-month-old Tau4RΔK-AP mice), we found that another novel microglia subtype emerged in response to tau pathology that we termed L ate-stage AD - A ssociated M icroglia (LADAM), and which expresses MHC and S100 family genes. We further observed a unique subtype of LADAM located near white matter, which is molecularly similar to a previously identified white matter-associated microglial subtype (WAM) that is increased during aging [ 25 ] and undergoes additional molecular transitions with Aβ and tau pathologies. LADAM microglia, including WAM-like LADAM, were observed in late (Braak VI), but not early (Braak II) stages of AD. Corroborating these findings, we found that sialic acid-binding immunoglobulin-like lectin ( Siglec ) family members are associated with specific subsets of microglia that are activated in a disease stage-specific manner in both mouse models of AD and AD patients. For example, Siglec-F is upregulated in response to Aβ pathology and is selectively expressed in Aβ-associated DAMs, while Siglec-G is upregulated in white-matter-associated LADAM in late-stage AD. These findings are consistent with a model whereby Aβ or tau pathologies stimulate activation of microglia in different pattern, both Aβ and tau pathologies are necessary to induce the emergence of EADAM and LADAM, and have important implications for the identification of novel molecular targets and therapeutic strategies for the treatment of AD. Methods Mice Tau4RΔK-AP ( APP swe ;PS1 D E9;CamKII-tTA;TetO-TauRD D K ), Tau4RΔK ( CamKII-tTA;TetO-TauRDΔK ) and APP swe ;PS1ΔE9 transgenic mice were generated as described previously. Tau4R DK were generated by crossbreeding TetO-TauRDΔK transgenic mice carrying mutant Tau fragment with regulatory element moPrP-tetP promoter [26] with CamKII-tTA mice to bring the Tau transgene under the control of the tet-off CamKII promoter [27]. Tau4RΔK mice were crossbred with APP swe ;PS1ΔE9 mice [28] to generate Tau4RΔK-AP mice that develop both Tau pathology and Aβ amyloidosis. Because of sex differences observed in Tau4RΔK-AP mice, only female mice were used in this study. Brains were collected from the mice at 6-month-old and 12-month-old for scRNA-Seq, histological and immunohistological studies. Among the three replicates (both 6-month-old and 12-month-old), two sets of the APP swe ;PS1ΔE9 and control mice also contain CamKII-tTA driver. Compared with the one set APP swe ;PS1ΔE9 and wild-type mice without CamKII-tTA driver, we did not observe a significant difference in microglia in our scRNA-Seq data. To suppress the potential impacts of TTA expression during postnatal development [29], mice were fed with Teklad Global 18% Protein Rodent Diet containing 200 mg/Kg doxycycline hydrochloride (Envigo Teklad Diets, Madison WI) during gestation and before weaning. CaMKIIα CreERT2 ; Tdp - 43 F/F mice with loxP sites flanking Tdp - 43 exon 3 were generated as described previously [30]. Oral tamoxifen citrate was administered in the feed (Harlan Teklad) at an average 40 mg/kg/day for 4 weeks beginning at 6- or 9 months of age to induce recombination in excitatory forebrain neurons. Mice were singly housed during this period to monitor tamoxifen-feed intake. Afterward, mice were returned to their original cage grouping with their littermates. Brain tissues were collected two months after the tamoxifen treatment for scRNA-Seq analysis. All mice were housed in a climate-controlled facility (14-hour dark and 10-hour light cycle) with ad libitum access to food and water managed by Research Animal Resources (RAR) at Johns Hopkins University. All animal procedures were in accordance strictly with the National Institutes of Health Guide for the Care and Use of Laboratory Animals and were approved by the Johns Hopkins University Animal Care and Use Committee. scRNA-Seq cell preparation Mouse cortices were collected and dissociated using a previously published protocol [31,32]. Basically, cerebral cortices and hippocampi were dissected into Hibernate-A media with a 2% B-27 and GlutaMAX supplement (0.5 mM final). Tissues were dissociated in papain (Worthington) and debris was removed using OptiPrep density gradient media following cell dissociation. Cells were then processed immediately for scRNA-Seq. Study design and participant The postmortem tissues used in the present study were provided by the Johns Hopkins Brain Resource Center. For histological analysis, formalin-fixed paraffin-embedded (FFPE) tissue sections (10 μm) of the inferior parietal region were obtained from 36 pathologically confirmed AD cases with Braak neurofibrillary stages II-VI and controls. Details of human cases used for histology are available in Table S10. The cohort (n= 36) included 22 males and 14 females ages 56 to 96 years (x= 77.58), 32 Whites, and 4 African-Americans (Table S11). The average postmortem interval was 22 hours. We also examined three FFPE hippocampal samples from patients with non-AD tauopathies for histology (Table S10). In addition to the FFPE sections, we also examined frozen samples from the superior frontal gyrus and entorhinal cortex in AD cases for snRNA-Seq (n=8), as well as 2 samples from patients with non-AD tauopathy. Details of human cases used for snRNA-Seq are available in Table S11. All AD subjects had been prospectively recruited, clinically characterized by the Johns Hopkins Alzheimer’s Disease Research Center (ADRC), and underwent neuropathologic postmortem examination excluding Lewy body disease or non-AD tauopathies. The clinical and autopsy components of this study were approved by the Johns Hopkins Medicine IRB. Isolation of nuclei from the frozen human brain for snRNA-Seq. Flash-frozen brain tissue was processed for snRNA-Seq following a modified 10x Genomics protocol. Briefly, lysis buffer containing (10 mM Tris-HCl pH7.4, 10 mM NaCl, 3 mM MgCl2, 0.1% Tween-20, 0.1% Nonidet P40, 0.01% Digitonin, 1 U/ul RNase inhibitor, 1% BSA) was added into a tube containing brain micropunch (~100 μm) and incubated on ice for 15 minutes with gentle pestle grinding (5 strokes every 3 min). Wash buffer (10 mM Tris-HCl pH7.4, 10 mM NaCl, 3 mM MgCl2, 0.1% Tween-20, 0.2 U/ul RNase inhibitor, 1% BSA) was added and filtered through a 50μm filter. Debris was removed using OptiPrep density gradient media and nuclei morphology was accessed under the light microscope. Nuclei were then immediately processed for snRNA-Seq. scRNA-Seq/snRNA-Seq generation Cells or nuclei were loaded into the 10x Genomics Chromium Single Cell System (10x Genomics) and libraries were generated using v3.1 chemistry following the manufacturer’s instructions. Three biological replicates, where each replicates consisted of littermate mice, were used for both 6-month-old and 12-month-old scRNA-Seq runs. Two technical replicates were used for TDPKO scRNA-Seq. Two biological replicates, where each biological replicate had a technical replicate, were used for human snRNA-Seq. Libraries were sequenced on Illumina NovaSeq6000. scRNA-Seq data were first processed through the Cell Ranger (v.3.1.0, 10x Genomics) with default parameters, aligned to the mm10 genome (refdata-cellranger-mm10-3.0.0), and matrix files were used for subsequent bioinformatic analysis. snRNA-Seq data were first processed through the Cell Ranger (v.5.0.0, 10x Genomics) with ‘include-introns’, aligned to the GRCh38 genome (refdata-gex-GRCh38-2020-A), and matrix files were used for subsequent bioinformatic analysis. scRNA-Seq data analysis 1. Processing scRNA-Seq datasets Seurat v3.15 [33] was used to process matrix files, first by selecting cells with more than 500 genes, 1000 UMI, and less than 50% mitochondrial genes and 20% ribosomal genes. Datasets were then normalized using Seurat ‘ scTransform ’ function with regressing the number of genes and UMIs using ‘ vars.to.regress ’, and Harmony v1.0 [34] was used to adjust for batch variation by treating individual scRNA-Seq run as a variance group. The top 20 variables obtained from Harmony analysis were used for UMAP dimensional reduction. The Louvain clustering algorithm was used to first identify main cell types with default resolution and the top 20 reduction variables obtained from Harmony analysis. Individual cell types were first identified by cross-referencing to previous scRNA-Seq datasets [32,35], ASCOT database[36], as well as other datasets obtained from brain myeloid and microglia [21,37,38]. Doublet cells - cells that express both cell-type-specific genes (~5% of the overall datasets) and unhealthy cells (when mitochondrial/ribosomal genes were expressed at much higher levels compared to other clusters) were excluded from further analysis. This was done in individual ages, in all genotypes. Individual microglia were subsetted and processed as described above. Any doublet cells (~2%) that couldn’t be previously identified were removed for further analysis. WAM [25], EADAM, or LADAM genes (top 50 genes from differential gene expression lists) were superimposed using Seurat AddModuleScore function to calculate module score. Differential gene tests on the scRNA-Seq datasets were initially performed using Seurat v3.15 ’ FindAllMarkers’ function ( test.use = “wilcox”, logfc.threshold = 0.5, min.pct = 0.2) . A cluster of microglia that was distributed almost equally among all four genotypes, and showed higher expression of immediate-early genes such as Fos, Atf3 , and Junb [39] , and this cluster is derived from technical artifacts during the heat-activated enzymatic dissociation step [39] and these microglia were then removed for any downstream analysis. To identify the percentage or expression level of EADAM or LADAM genes across genotypes, as well as in other mouse models of neurodegenerative disorders and in human AD samples, EADAM (Table ST3) and/or LADAM (Table ST5)-enriched genes were first identified(higher than average logfc > 0.2). Cells that express higher than UMI > 3 were flagged as positive cells, and then the percentage of positive cells relative to the total number of cells in a cluster was identified. These parameters can clearly be defined over 98% of EADAM or LADAM cells in our AD scRNA-Seq dataset. 2. Regulons To identify regulons controlling gene expression in different microglial populations across ages and genotypes, SCENIC [40] using python implemented pySCENIC v.0.10.0 Gene regulatory networks (using --masks_dropouts), regulons and network activity of regulons were calculated using default parameters with mm10 feather files on the microglial dataset using raw count matrix. Regulon specificity scores were ranked following the SCENIC pipeline and top regulons with z-score higher than 2 were identified as microglia-specific. 3. GO pathway analysis Top differential genes (adjusted p-value 0.5) in each microglial cluster were used as an input for GO pathway analysis using ClusterProfiler (v3.12.0) [41]. 4. RNA velocity RNA velocity [42] was utilized to understand the dynamic state of microglia, and how each genotype-specific disease-associated microglia cluster was initiated across AD progression. Kallisto v0.46.2 and bustools v2.27.9 [43,44] python wrapper kb-python was used to obtain spliced and unspliced transcripts using --lamanno with GRCm38 mouse genome. Scanpy v1.5.1 [45] and scVelo v0.2.1 [46] were used to process the Kallisto output with default parameters, based on UMAP coordinates obtained from Seurat. 5. Pseudotime analysis Monocle v3.0.2 [47] was used to perform pseudotime analysis to identify differences in gene expression across microglial disease states, where the trajectory routes used for pseudotime analysis were identified based on trajectories from RNA velocity analysis, and high-variance genes (q < 0.001) were used for pseudotime plotting. Histology and Immunohistochemical Analysis For the histological and immunohistochemical analysis, mice were anesthetized and brains were removed and weighed. Hemibrains were fixed by submerging into 4% PFA in PBS, embedded into the paraffin, and sectioned in the sagittal plane. For histological analyses, 10 μm brain sections were stained with hematoxylin and eosin (H&E) or Cresyl violet. For immunohistochemical analysis, sections were treated with 10 mM citrate buffer (pH 6.0) by microwave in high power for 6 minutes for antigen retrieval; endogenous peroxidase was quenched by treating with 0.3% H 2 O 2 , and nonspecific binding of antibodies was eliminated using blocking buffer (10% normal goat serum in PBS with 0.3% Triton-X) for one hour at room temperature. The primary antibody was prepared in a blocking buffer and was applied overnight at 4 o C, followed by a secondary antibody for 30 min incubation at room temperature. For the secondary antibody and avidin-biotinylated peroxidase system, we used the Vectastain Universal Elite ABC kit (Vector Laboratories). Brain sections were stained with: antiserum against Aβ peptides 6E10 (1:1,000; SIG-39300, Covance); rabbit antiserum against phosphorylated S422 of tau (1:2,000; 44764G, Invitrogen, Carlsbad, CA); polyclonal antiserum against GFAP (1:1,000, Z0334, Dako Corporation, Carpinteria, CA); polyclonal antiserum against microglial (IBA1, 1:1,000, CP290, Biocare Medical, CA); monoclonal antibody against NeuN (MAB377, Millipore); polyclonal antiserum against Siglec-10 (1:50, HPA027093, Sigma); Monoclonal Antibody Siglec-F (CD170, 1:300, 14-1702-82, ThermoFisher Scientific). The quantitative score of Siglec10 positive cells in the inferior parietal region of human brains was the average counts of at least three brain sections in a power (2,000X) microscope field using the ImageJ program. Siglec-10 signals in gray and white matter in the same section were counted separately. The histological section of the human inferior parietal region includes the cerebral cortex and subjacent white matter. These two compartments can be easily separated by their cytological features. The cortex contains a large number of neurons characterized by their large size and prominent nucleus and nucleolus. By contrast, the white matter is rich in oligodendrocytes, which are small, have a compact nucleus and virtually no cytoplasm; and astrocytes. Neurons are virtually absent in the white matter. Neurons in mouse brains were identified by NeuN staining. The quantitative scores of NeuN+, IBA1+, SiglecG+ cells were the average counts in a power (2,000X) microscope field of at least three sagittal sections at 2 mm from the midline of the mouse brains using the ImageJ program. To quantify the Amyloid plaques, at least three sagittal sections at 2 mm from the midline of the mouse brains were selected. The quantitative analysis was based on the area fraction of 6E10 immunoreactivity using the ImageJ program. Statistical Analysis All data were analyzed statistically by unpaired Student’s two-tailed t-test or one-way ANOVA with Tukey correction for multiple comparisons for cell counting analysis, and two-way ANOVA with Tukey’s multiple comparison test for scRNA-Seq cell distribution analysis (distribution of microglia clusters within each genotype and distribution of cell types within each genotype), using GraphPad Prism (GraphPad Software, La Jolla CA the USA). In all tests, values of p < 0.05 were considered to indicate significance. Results ScRNA-Seq of microglia in mice harboring Aβ plaques and/or tau deposition To identify microglia subtypes through AD-like pathology progression, we performed scRNA-Seq in the cerebral cortex and hippocampus of transgenic mice displaying Aβ plaques and/or tau pathologies. We took advantage of our previously characterized mouse model of AD ( Tau4RΔK-AP mice), which exhibits AD-like pathologies including Aβ plaques and tau tangles and result in progressive neuronal loss and brain atrophy [24]. In a cross-breeding strategy using mutant APP swe ;PS1ΔE9 (AP) [28] and Tau4RΔK mice, a cohort of Tau4RΔK-AP mice were generated. Female mice were aged to either 6-month-old or 12-month old. Six-month-old Tau4RΔK-AP mice mimic an early AD stage, characterized by low levels of Aβ plaques with minimum tau deposition in the hippocampus but not in the cortex (Figure S1A,B,E,F), and no loss of neurons. Twelve-month-old Tau4RΔK-AP mice mimic a late disease stage, characterized by robust Aβ plaques (Figure S1C,D), tau deposition (Figure S1G,H), loss of neurons and brain atrophy (Figure S1P,Q). For these two time points, in addition to Tau4RΔK-AP mice, we also collected cerebral cortices and hippocampi from littermate controls ( WT) , APP;PS1 (Aβ plaques (S1R)), and Tau4RΔK (tau deposition, neuronal loss (Figure S1P), and brain atrophy (Figure S1Q)) mice, and subjected these tissues across four genotypes to scRNA-Seq analysis. Compared to Tau4RΔK mice at 12 month of age, Aβ plaques accelerated tau pathogenesis and tau aggregation-dependent (Figure S1C,D,G,H) neuronal loss and brain atrophy (Figure S1P) in 12-month-old Tau4RΔK-AP mice, as previously shown [24]. On the other hand, tau aggregation has little effect on the deposition of Aβ plaques in Tau4RΔK-AP mice as compared with that of APP swe ;PS1ΔE9 (AP) mice (Figure S1R). We first analyzed female 6-month-old cerebral cortices and hippocampus, across four genotypes in triplicates (Figure S2A). We observed all major cell types of CNS, including neurons, astrocytes, oligodendrocytes, and microglia (Figure S2B,C, Table S1). As previously shown [24], we first confirmed the initiation of tau deposition in the absence of neuronal loss or brain atrophy in these mice (Figure S2B, S4A), along with the presence of Aβ plaques (Figure S1A,B). As expected, we observed a few DAM microglia in Tau4RΔK-AP brains in early-stage disease (Figure S1I,J,M). Analyzing three biological replicate samples, we observed that microglia in APP swe ;PS1ΔE9 and Tau4RΔK-AP mice (Figure S1I,K, S2B,D, S4a), expressed a high level of disease-associated microglia 1 (DAM1) marker including Cst7 (Figure S2E). However, we failed to observe DAM2 markers, including Gpnmb ,even in APP swe ;PS1ΔE9 and Tau4RΔK-AP mice at this age (Figure S2F). Using scRNA-Seq, we also identified cell clusters corresponding to major subtypes of neurons, glia, and immune cells (Figure S3A-C, Table S2) in 12-month-old mice of all four genotypes, analyzing three biological replicates. Consistent with our previous observation, AD-like pathologies in Tau4RΔK-AP mice led to gliosis and neuronal loss. An increase in the microglia population along with a corresponding decrease in the number of neurons was observed in the Tau4RΔK-AP mice (Figure S1K,L,N,O, S3B,D, S4B). Compared to the other three control mice ( WT , APP swe ;PS1ΔE9, and Tau4RΔK mice), Tau4RΔK-AP mice also showed an increase in immune cells as well (Figure S3). APP swe ;PS1ΔE9 , Tau4RΔK and Tau4RΔK-AP mice showed a marked increase in the number and proportion of microglia as compared to WT mice, (Figure S1K,L,N, S4B). Known DAM1 marker genes such as Cst7 , and DAM2 genes such as Gpnmb , were strongly expressed in both APP swe ;PS1ΔE9 and Tau4RΔK-AP mice (Figure S3E,F). These results establish that these scRNA-Seq datasets have sufficient quantitative power to identify microglia subtypes that are influenced by Aβ and/or tau deposition in a disease stage-specific manner. Identification of a novel E arly-stage AD - A ssociated M icroglia (EADAM) induced by both Aβ plaques and tau deposition We then subsetted and profiled microglia from the cerebral cortex and hippocampus of 6-month-old Tau4RΔK-AP mice, along with the three control mouse lines. Homeostatic microglia expressing genes such as Tmem119 and P2ry12 , were predominantly detected across all genotypes (Figure 1A), but two additional microglia clusters were also observed (Figure 1A, Table S3). The first microglia cluster expressed classic DAM1-like markers, including Apoe and Cst7 (Figure 1B). The second microglia cluster expressed DAM1-like markers but also displayed a unique gene module consisting of interferon-related genes, such as Ifitm3, Ifit27l2a , and Ifit3 (Figure 1B). Since this cluster was predominantly composed of Tau4RΔK-AP microglia and detected during the early and presymptomatic stage of disease (Figure 1C), we thus identify this cluster as E arly-stage AD - A ssociated M icroglia (EADAM). Our regulon and pathway analysis confirmed the association of EADAM with interferon regulating transcription factor Irf2/7/9 (Figure 1D) and linkage to the GO term ‘Response to Interferon-beta’ (Figure 1E). A small EADAM-like cluster is also detected in APP swe ;PS1ΔE9 microglia (Figure 1A,C). This observation is consistent with the previous report that the accumulation of Aβ plaques leads to activation of the interferon pathway [48–51]. A small cluster of cells that resemble EADAM-like microglia, expressing Irf7 and other EADAM-enriched genes, such as Ifit3, Isg15, Ifi27l2a , were also detected in 7-month-old 5xFAD snRNA-Seq data [23] (Figure 1F). We next compared differential gene expressions between EADAM populations across four genotypes. Despite a significant contribution of APP;PS1 microglia to EADAM cluster, the overall level of EADAM-enriched genes were expressed at a much lower level in APP swe ;PS1ΔE9 microglia than that of Tau4RΔK-AP EADAM (Figure 1F, Table S4). On the other hand, the expression of interferon pathway-related genes was not observed in Tau4RΔK microglia, suggesting that while the accumulation of Aβ plaques leads to activation of the interferon pathway, both Aβ plaques and tau aggregation are necessary to fully induce EADAM gene modules in Tau4RΔK-AP microglia. In summary, our data identify a novel microglia subtype termed EADAM that is induced by a combination of Aβ plaques and tau deposition during early-stage AD. Identification of L ate-stage AD - A ssociated M icroglia (LADAM) To further clarify the influence of Aβ plaques and tau tangles in microglia pathophysiology during late-stage AD, we unbiasedly subdivided subsetted-microglia populations into five distinguishable clusters across all four genotypes ( WT , APP swe ;PS1ΔE9, Tau4RΔK , and Tau4RΔK-AP ) in 12-month-old mice. A few notable differences were observed in 12-month-old mice compared to 6-month-old mice. The DAM cluster was further divided into two clusters - DAM1 and DAM2. The DAM1 cluster expressed genes such as Tyrobp, Ctsd, C1qa (Figure 2A,B, Table S5) , whereas DAM2 expressed DAM1-enriched genes and additional disease-related genes, such as Gpnmb , Cst7 , and Spp1 (Figure 2B). The induction of DAM2 in response to Aβ plaques has been documented in a variety of APP-based mouse models of AD [20]. Indeed, both clusters were highly represented in animal models with Aβ pathologies ( APP swe ;PS1ΔE9, and Tau4RΔK-AP mice, Figure 2A,C), but the DAM2 abundance in mouse models with only tau pathologies ( Tau4RΔK mice) was low (Figure 2A,C, Table S6). These observations are consistent with the view that these DAM clusters arise mainly as a result of Aβ plaques. Similar to situations in the EADAM population of 6-month old mice, Tau4RΔK-AP mice generally have higher levels of DAM enriched genes than that of APP swe ;PS1ΔE9 mice (Figure 2F). Since Aβ plaques level in Tau4RΔK-AP mice is similar to that of APP swe ;PS1ΔE9 mice (Figure S1R), this observation indicates that while Aβ plaques can induce DAM2, tau deposition and/or neuronal loss could further enhance DAM-enriched genes in Tau4RΔK-AP mice. A diminished EADAM cluster was still detected in 12-month-old Tau4RΔK-AP mice. Interestingly, EADAM-like microglia were also detected in 12-month-old Tau4RΔK mice, although they were absent at 6 months of age (Figure 2C). In addition to the wide spread tau aggregation, Tau4RΔK mice already show neuronal loss at 12-month-old [24], suggesting that while the presence of tau aggregation alone may not induce EADAM formation, cell death-induced inflammation may also induce EADAM-like gene expression in microglia. We also observed a unique microglia cluster that was not detected in 6-month-old mice (Figure 2A). Cells in this cluster expressed MHC Class II genes, such as Cd74 , H2-A2 , H2-Eb1 , and H2-Ab1 (Figure 2B, Table S4). We termed these microglia L ate-stage AD - A ssociated M icroglia (LADAM), which were found in Tau4RΔK-AP , as well as Tau4RΔK , and APP swe ;PS1ΔE9 samples (Figure 2A,C). Regulon analysis identified key transcription factors associated with gene regulatory networks across all microglial clusters. Notably, while Hif1a and Eomes are selectively expressed in the DAM2, Runx3 and Irf4/7 are expressed in LADAM (Figure 2D) and GO analysis on LADAM shows enrichment for ‘MHC Class II’ (Figure 2E). In addition to MHC class II genes, we also identified enriched expressions of S100a family genes, including S100a4, S100a6 , and S100a10 (Figure 2G). Although LADAM were observed in Tau4RΔK-AP , Tau4RΔK , and APP swe ;PS1ΔE9 samples, we found that many LADAM-specific genes (e.g. MHC Class II genes) are more highly expressed in Tau4RΔK than in APP swe ;PS1ΔE9 microglia (Figure 2G), suggesting that this microglia subtype might be induced by the development of tauopathies, such as tau tangles or tau phosphorylation. Moreover, relative to Tau4RΔK microglia, we found that LADAM gene signatures were more pronounced in Tau4RΔK-AP microglia (Figure 2G, Table S5), which exhibited Aβ plaques, more robust tau pathologies and neuronal loss. These datasets show that tau pathologies are sufficient to induce the emergence of LADAM and that their amplification is Aβ plaque-dependent. However, since MHC class II genes that are expressed in LADAM, such as Cd74 , H2-A2 , H2-Eb1 , and H2-Ab1 , have also been previously observed in microglia in both the CK-p25 neurodegeneration mouse model [52,53] and some mouse models of late-stage amyloidosis [48–50], some of LADAM gene modules may be induced at least in part by neuronal death. Requirement of both Aβ plaques and tau deposition to elicit the emergence of disease stage-dependent microglia subtypes To clarify how the emergence of EADAM, DAM2, and LADAM is regulated by Aβ plaques and tau deposition, we merged datasets obtained from microglia of both 6-month-old and 12-month-old samples (Figure 3A). RNA velocity analysis shows that DAM1 is likely to give rise to, or to be closely related to, both DAM2 (Aβ plaques driven) and EADAM (Aβ plaque and tau deposition driven), whereas the convergence of DAM2 (Aβ plaque-driven and tau deposition-enhanced) and EADAM may ultimately lead to induction of LADAM, as the result of tau deposition/cell loss-driven and Aβ plaque-formation (Figure 3B). As shown above, EADAM is composed mostly of cells derived from 6-month-old Tau4RΔK-AP mice (Figure 3B). The DAM2 clusters were enriched in 12-month-old APP swe ;PS1ΔE , and Tau4RΔK-AP mice (Figure 3B), and LADAM clusters were enriched in 12-month-old APP swe ;PS1ΔE9 , Tau4RΔK , and Tau4RΔK-AP mice (Figure 3B). Furthermore, RNA velocity coupled with pseudotime analysis (Figure 3C,D) identified gene expression changes occurring during the temporal progression from DAM1 to DAM2 to LADAM, with changes in multiple inflammation-related genes, such as Ccl6, Csf1, Cd34 (Figure 3C); as well as during the temporal progression from DAM1 to EADAM to LADAM, with changes in interferon genes, such as Ifiti family members (Figure 3D). A microglia cluster(s) that express either interferon genes and/or MHC Class II genes have been identified in other neurodegenerative models, such as CK-p25 [53] and Trem2 deficient [54,55] FTDP-17-linked tau model (P301L) [56] crossed with PS2APP [57] mice. However, none of the disease models showed development of separated EADAM and LADAM clusters in a stage-dependent manner. This led to the hypothesis that, while potential conservation of EADAM- or LADAM-like subtypes might occur across both neurodegeneration and neuroinflammation, combination of both Aβ plaques and tau deposition are critical for the induction of disease-stage specific microglia subtypes in AD. To test this hypothesis, we then used another neurodegenerative mouse model CamKII CreERT2 ;Tardbp lox/lox (TDP43 KO ), where a tamoxifen induction leads to conditional deletion of TDP-43 in the forebrain of the mice, resulting in the selective vulnerability of hippocampal CA3 neurons [30]. We generated scRNA-Seq data from the cerebral cortex and hippocampus of 2 different TDP43 KO samples, one being induced with 4-OHT at 6 months and collected at 9 months ( TDP43 KO 6->9 ) and the other being induced with 4-OHT at 12 months and collected at 15 months ( TDP43 KO 12->15 ) (Figure 4A-C). While both TDP43 KO mice showed DAM1-like clusters, TDP43 KO 6->9 showed both EADAM- and LADAM-like clusters that respectively selectively express interferon genes or MHC Class II genes (Figure 4A-C, Table S7). TDP43 KO 12->15 also showed an EADAM-like cluster (Figure 4A-C, Table S8), supporting the view that EADAM- or LADAM-like subtypes might occur across neurodegeneratives. We then integrated datasets from AD mice with those from TDP43 KO mice (Figure 4D,E). As mentioned above, both 6-month-old APP swe ;PS1ΔE9 and Tau4RΔK-AP microglia showed a similar expression pattern of EADAM-enriched genes (Figure 4D), but Tau4RΔK-AP microglia displayed much higher expression levels of these genes than did APP swe ;PS1ΔE9 microglia (Figure 4D). 12-month-old Tau4RΔK and Tau4RΔK-AP microglia showed overall lower expression levels, but broadly similar EADAM expression patterns (Figure 4D). Both TDP43 KO microglia showed a much weaker level of EADAM-enriched genes, similar to 6-month-old APP;PS1 microglia (Figure 4D). LADAM-enriched genes are composed of DAM2-enriched genes, MHC Class II genes, and S100a family genes. While LADAM-like cluster in TDP43 KO 6->9 microglia expressed MHC Class II genes, the level of expression was much lower than any of APP swe ;PS1ΔE9, Tau4RΔK and Tau4RΔK-AP microglia (Figure 4E). We also failed to detect any DAM2 like clusters, or either S100a family genes and Siglecg in TDP43 KO 6->9 microglia. A similar observation was also made in CK-p25 microglia [53](Figure S5A). While the integration of this dataset was not possible due to the use of different single-cell formats (SMART-Seq was used to generate the CK-p25 dataset), we noted that both EADAM-like interferon genes and LADAM-like MHC Class II genes were more highly expressed in CK-p25 microglia than the control (Figure S5B). However, both EADAM-like and LADAM-like microglia were intermingled, rather than forming separate clusters. We also failed to detect any S100a family genes and Siglecg expression in the CK-p25 microglia dataset, similar to our observations in the TDP43 KO dataset (Figure S5B). This indicates that Aβ plaque or tau pathology independently induces specific gene signatures in microglia like EADAM and LADAM, and similar microglia clusters can also be observed in other neurodegeneration models. Neuroinflammation and/or cell loss that occurs in neurodegeneration diseases could be a potential trigger for microglia to develop EADAM- or LADAM-like gene expression profiles. However, the combination of both Aβ plaque and tau pathology results in separated clusters of EADAM and LADAM at different stages of disease, much higher expression levels of the signature genes, and induction of additional molecular markers in LADAM. Identification of stage-specific EADAM and LADAM signatures in AD samples We next generated snRNA-Seq dataset from the human superior frontal gyrus (SFG) at Braak stage 2, which is devoid of any AD pathology, and entorhinal cortex (ERC) at Braak stage 2, where AD pathology is first detected and shares a pathological resemblance to our 6-month-old Tau4RΔK-AP mice (Figure 5A-C). We also generated snRNA-Seq from the superior frontal gyrus (SFG) at Braak stage 4 and 6, which represent late stages of AD, and share a pathological resemblance to the 12-month-old Tau4RΔK-AP mice. As control, we generated a snRNA-Seq dataset from the ERC and SFG of a non-AD neurodegenerative disorder, primary-age-related-tauopathy (PART) (Figure 5A-C). We initially checked the expression pattern of EADAM- and LADAM-enriched gene homologs in the human snRNA-Seq dataset (Figure S5C,D), and observed moderate conservation in gene expression pattern. Most EADAM-enriched genes were seen at Braak stage 2 SFG and ERC (Figure S5C), while LADAM-enriched genes were seen at Braak stage 4 and 6 SFG (Figure S5D). Some inconsistency of expression patterns might be due to the absence of AD-specific microglia clusters in the human snRNA-Seq dataset, despite having a high resolution of 6,000 cells. This observation has been previously shown in other human snRNA-Seq datasets from brains affected by AD or other neurodegenerative diseases [58–60]. The difference between human and animal model might be due to the fact that AD and other neurodegenerative diseases typically progress slowly, over many years, and all microglia populations may eventually end up transitioning to disease-associated states. This timing is different in virtually all animal models that were designed to undergo neurodegeneration much more rapidly. While the presence of EADAM- and LADAM-like microglial subtypes in AD demonstrated important similarities with our mouse models, we also wanted to analyze the expression of MHC and interferon-induced genes. We observed consistent changes in the expression of interferon-regulated genes. These include the transcription factors and co-regulators IRF2BP2 , IRF2 , and IRF9 , which showed higher expression at Braak stage 2 ERC than SFG, and whose expression levels were decreased in Braak stage 4/6 SFG (Figure 5D). There also was a similar trend in the expression of other interferon-regulated genes, including IFI44 , IFI44L (Figure 5D). We also observed a similar trend in MHC genes, such as HLA-A , HLA-B , and HLA-DPA1 , which showed a higher expression level in PART ERC and SFG, Braak stage 2 ERC, and in Braak stage 4/6 SFG than Braak stage 2 SFG (Figure 5E). More importantly, S100 genes, such as S100A4 , S100A11 , were exclusively detected in Braak stage 4/6 SFG but were not detected in PART or other AD snRNA-Seq (Figure 5E). This observation was also conserved in other high-quality human AD snRNA-Seq from prefrontal cortex [58](Figure 5F,G), where interferon-regulated, MHC, and S100 family genes were more highly expressed in AD samples than in the control (Figure 5F,G). These findings thus establish the emergence of EADAM-related genes in early AD-stage and LADAM-related genes in late AD-stage, and suggest that these microglia subtypes could be induced by both Aβ plaques and tau deposition in a disease stage-specific manner. Siglecs may serve as specific biomarkers to track Alzheimer’s disease stage-specific microglia subtypes Our analysis identifies multiple microglial genes that are selectively expressed in late-stage AD, such as Braak-stage-dependent expression of SIGLEC10 , a human homolog of Siglecg (Figure 5E). Since several of the Siglec family genes are thought to be relevant for AD progression [61], we focused on the expression of Siglec gene family at different microglia clusters. Homeostatic microglia expressed Mag , Siglece , Cd33 , Siglech (Figure S6A,B), EADAM expressed similar Siglec genes as homeostatic microglia, but also expressed Siglec1 (Figure S6A,B). Both DAM2 and LADAM clusters expressed high levels of Siglecf (Figure S6A,B). Both Siglec1 and Siglecg were enriched in the LADAM. Siglecf , in particular, was enriched in both APP;PS1 and Tau4RΔK-AP mice (Figure S6C,E) and showed increased expression in late-stage disease (Figure S6D,F). These data suggested that Siglec genes expressed in a pathology and/or stage dependent manner in microglia. To further characterize late-stage AD-specific genes, We identified 4 subclusters within the LADAM cluster of AD mouse models (Figure 6A). Sub-clusters 2 and 3, in particular, were most robustly and selectively expressed in Tau4RΔK-AP mice (Figure 6B, Table S9). Cluster 2, a LADAM sub-cluster that is marked by Siglecg , also exhibited genes previously shown to be expressed in W hite matter- A ssociated M icroglia (WAM), such as Lgals3 , Fam20c , Vim (Figure 6A,C) [25]. In WAM-like LADAM, we observed that this cluster also expressed S100a6 , S100a4, S100a11, H2-DMb2, Runx2, and Clec10a (Figure 6D, Table S9). While a small cluster that resembles WAM was also observed in the 6-month-old mouse cortex (Figure S6J), this small population did not express any LADAM markers in the 12-month-old cortex. We did not detect Siglec-G protein in the 6-month-old corpus callosum, hippocampus, and cortex (Figure 6E, S6G,H,K). Siglec-G immunoreactivity was robust in the 12-month-old corpus callosum in Tau4RΔK-AP microglia (Figure 6), as well as in the cortex and hippocampus (Figure S6I,K). However, we failed to observe Siglec-G immunoreactivity in APP;PS1 or Tau4RΔK microglia at the same age, despite increased microglia populations near the corpus callosum (Figure.6D, S6I,K). To corroborate this finding in AD, we used antisera directed against Siglec-10 (the human homolog of Siglec-G) [62] to screen AD brains at multiple Braak stages (Table S10). We found Braak stage-dependent accumulation of Siglec-10 in AD brains (Figure 6F, S7), with Siglec-10 signal was significantly increased from Braak stage 4 (Figure 6F, S7) in both gray and white matters, but a slightly higher number of Siglec-10+ cells were detected in the white matter. However, Siglec-10 signal in non-AD tauopathy is similar to that of healthy controls, consistent with our snRNA-Seq data (Figure 5G, 6F, S7). To further validate these observations, we checked snRNA-Seq datasets of human postmortem Braak stage 4 and 6 SFG (Figure 6G, Table S11). We observed a small population of WAM-like LADAM, as seen in 12-month-old Tau4RΔK-AP mice (Figure 6H), expressing genes like SIGLEC10, S100A6, LGALS3, FAM20C (Figure 6H). Siglecs, in particular Siglec10, are AD-enriched, as the level of Siglec-10+ cells in non-AD tauopathy was similar to that of control and low Braak stage AD (Figure 6F, S7), indicating that Siglec-10 could potentially serve as an AD biomarker and a potential therapeutic target associated with late-stage AD. Some LADAM markers, particularly MHC II genes, were expressed in other neurodegenerative models (Figure 5, S5), but S100 family of genes and Siglecg was unique to late-stage AD microglia and are seen in both animal models and late-stage AD patient brains. This data further supports the view that induction of the AD specific LADAM subcluster is Aβ- and tau-dependent. Discussion Identification of novel disease stage-specific microglial subtypes induced by Aβ plaques and tau pathology The identification of multiple AD risk alleles implicated in the regulation of inflammation [13,63] and of DAM induction in response to Aβ plaques [20], strongly support the hypothesis that microglia-driven neuroinflammation is a key regulator of AD progression. However, critical information regarding microglia subtypes that respond to disease stage-specific canonical AD pathologies of Aβ and tau, particularly during early disease stages, remains elusive. To address this issue, we took advantage of our Tau4RΔK-AP mouse model, which exhibits both Aβ plaques and tau deposition, and profiled microglia subtypes during disease progression using scRNA-Seq. To tease apart the influence of Aβ plaques from that of tau deposition, we also profiled microglial subtypes in APP;PS1 (model of Aβ plaques) and Tau4RΔK (model of tau deposition) littermate mice. We identified several disease stage-specific subtypes of microglia that are selectively induced in response to Aβ and/or tau. We discovered the emergence of novel microglia subtype, E arly-stage AD - A ssociated M icroglia (EADAM), that is selectively induced by both Aβ and tau pathologies but not either Aβ or tau pathologies alone, during early-stage disease before AD-like pathologies are observed. EADAM are characterized by many interferon-pathway-related genes ( Ifit3, Ifit204, Ifit2 ) and interferon regulating transcription factors ( Irf2/7/9) as well as inflammatory chemokines ( Ccl12, Cxcl10, Ccl5, Ccl2 ). Some of the genes in EADAM have been observed in other mouse models of amyloidosis that show early AD-like pathology [48–50], including APP;PS1 and 5xFAD mouse models. However, given that EADAM-enriched genes are expressed in much higher levels in our Tau4RΔK-AP microglia, this strongly supports the view that both Aβ and tau pathologies are required to induce disease stage-specific microglia subtypes in AD. We also identified previously described DAM in both mouse lines in which Aβ plaques are induced ( APP;PS1 and Tau4RΔK-AP ) [20,64]. However, DAM induction in Tau4RΔK mice was blunted, supporting the hypothesis that DAM are induced by Aβ plaques and/or inflammation, but not by tau pathology. While Aβ plaques alone are sufficient to induce DAM2, this subtype can be greatly amplified by both Aβ and tau pathologies, giving rise to LADAM. LADAM continues to express classic DAM genes but also upregulates additional genes, including Cd74 and MHC Class II genes. These broad LADAM marker-expressing microglia ( Cd74 and MHC Class II genes) have been previously shown in some amyloidosis mouse models at late stages (known as activated response microglia) [48–50], but these genes are likely to be contributed by mild tau phosphorylation shown in these amyloidosis mouse models, based on our findings from Tau4RΔK (Figure 2). In addition, since LADAM-like clusters and markers were observed in other neurodegeneration models, such as TDP43 KO and CK-p25 [52,53], LADAM-enriched genes might be induced by neurodegeneration and/or inflammation in addition to tau pathologies. However, much like EADAM, LADAM-enriched genes were expressed in Tau4RΔK-AP microglia, indicating that the full set of LADAM-specific genes is induced by the combination of both Aβ and tau pathologies. Furthermore, the combination of Aβ and tau pathologies induced LADAM to express unique S100a genes and Siglecg . In particular, a sub-cluster of LADAM during late-stage disease shares a similar gene signature with previously identified w hite-matter- a ssociated m icroglia (WAM). WAM have been previously shown to be present in 24-month-old wild-type mice [25]. Indeed, microglia near white matter tracts were detected in all four genotypes in 12-month-old mice, but only mice with Aβ plaques and tau deposition showed expression of specialized LADAM gene signatures such as Siglecg. These data indicated that Aβ plaques and tau deposition induce new molecular signatures in previously identified WAM, leading to the formation of this LADAM subcluster. While our discovery of novel microglia subtypes has important clinical implications, the mechanisms by which EADAM and LADAM emerge during different stages of disease progression in response to Aβ plaques and tau deposition remain to be fully established. Our current study identifies potential regulators of these microglia populations in response to Aβ plaques and tau deposition. Neuronal loss or brain atrophy induced by aging and/or tau pathologies may also play an essential role in regulating this process. In summary, our data indicated that while EADAM-related or LADAM-related gene activation represents a general response of microglia to various pathological contexts, there exists a unique gene signature that would define specific microglia subtypes that respond to specific pathology temporally during disease progression. Thus, by elucidating such “microglia response code”, one is expected to discover specific microglia subtypes relevant to the pathological context in human disease. Supporting this view, while we observed both EADAM-like and/or LADAM-like clusters in TDP43-KO or CK-p25 mouse models, there are substantial differences in expression levels of EADAM and LADAM genes in Tau4R △K-AP as compared to those of TDP43-KO or CK-p25 as well as Tau4R △K and APP;PS1 mice (Figure1,2,4,S5). We also found unique LADAM markers in Tau4R △K-AP mice that are not expressed in any other neurodegenerative models (Figure 2,4,S5). These data strongly support a model in which there exists an EADAM-like and LADAM-like “microglia code” harbored by microglia subtypes that respond to either a general or specific pathological context in the brain. We demonstrate for both Aβ and tau pathologies that this canonical AD pathology context selectively induces novel microglia subtypes in a disease stage-specific manner. These disease stage-dependent “microglial code” could not only serve as biomarkers for precise diagnosis but also as the therapeutic target for stage-specific intervention of specific neurodegenerative diseases. Identifications of disease stage-specific microglia subtypes in AD The emergence of EADAM during the presymptomatic AD stage and detection of LADAM when AD-like pathologies are visible led to the hypothesis that AD brains may harbor similar unique microglia clusters. EADAM were observed in the entorhinal cortex during early (Braak II) but not late (Braak VI) stage disease, strongly supporting the view that EADAM first emerge in response to initial stages of Aβ plaque formation and tau deposition, corresponding to a stage of negligible or mild cognitive impairment. The microglia cluster expressing IFN-related gene that is similar to EADAM, has been shown in the FTDP-17-linked tau model (P301L) [56] crossed with PS2APP [57], but only when Trem2 is deficient [54,55]. Since Trem2 deficiency can result in the spreading of tau aggregates [65], the FTDP-17-linked tau model (P301L) can result in mild tau aggregation, which resemble the early stages of our mouse model that is characterized with Aβ plaques stimulated pathological conversion of wild-type mouse tau to form tau tangle-like aggregates. These previous finding [55] aligns well with our finding that Aβ plaques and tau deposition are required to give rise to EADAM. In contrast, the emergence of LADAM in both the entorhinal cortex and superior frontal gyrus did not occur until Braak IV stage, implying that these subtypes are induced in response to more severe Aβ and tau pathologies accompanied by neuronal loss during late-stage disease. Some LADAM-enriched genes such as HLA-DRB1 and HLA-DRB5 positively correlate with AD pathology [66] , and share a few similar molecular signatures (e.g. MHC Class II genes) with lipid droplet-accumulating microglia [67].We also observed an upregulation of S100A genes in late-stage AD. Although interferon-signalling-related genes (EADAM-enriched) and MHC and S100 family genes (LADAM-enriched) are observed during human early- and late-AD stages, respectively, not all EADAM- or LADAM-enriched genes were detected in human samples. We were also unable to identify well-defined and distinct microglial clusters in human AD snRNA-Seq, in agreement with other studies. This might reflect the much longer time course of AD progression in humans compared to mouse models, potentially leading to less well-defined microglia subtypes. The cluster-basis analysis method which is typically used for the analysis of cell states in animal models might not be the best method to define microglia subtypes in human disease states. Nonetheless, gene profiling studies conducted in appropriate animal models of AD could yield insights to facilitate profiling of corresponding unique microglia subtypes or specific genes in AD brains, a task that can be technically challenging and difficult to interpret. The fact that EADAM are first detected in 6-month-old Tau4RΔK-AP mice and Braak stage II AD datasets, while LADAM are first detected in 12-month-old Tau4RΔK-AP mice and Braak stage IV AD datasets, demonstrates that disease stage-dependent changes in microglial subtypes in mice with Aβ and tau pathologies mimic those seen in AD. Tau4RΔK-AP mice faithfully model not only the development of canonical AD pathologies, including the pathological conversion of wild-type mouse tau [24], but also the disease stage-specific emergence of EADAM and LADAM in response to both Aβ plaques and tau deposition, emphasizing that our mouse model will prove useful in both analyzing AD disease mechanisms and testing therapeutic strategies for treating AD. Siglec signaling in EADAM and LADAM The identification of disease stage-enriched EADAM and LADAM in our Tau4RΔK-AP model and AD brains raise the possibility that specific signaling pathways may be important in the emergence of these microglial subtypes. CD33 (also known as Siglec-3) [68], is one of the several microglial-specific genes that have been linked to AD susceptibility via genome-wide association studies (GWAS) [69] and both mediates immune suppression by binding sialoglycan targets and attenuates phagocytosis by microglia during AD progression [68,70]. As a result, we first focused on characterizing the expression of sialic acid-binding immunoglobulin-like lectin (Siglec) family of proteins. Human Siglecs belong to a family of 14 distinct transmembrane proteins, many of which are expressed in overlapping subsets of immune cells and inhibit immune activation [62,71]. In addition to Siglec-3, other members of the Siglec family might regulate microglial function in AD. Our findings showing specific enrichment of Siglec-pathway genes in EADAM and LADAM strongly support this hypothesis. Siglec-G is enriched in microglia near myelinated regions, whereas Siglec-F+ microglia are more broadly expressed throughout the brain. Our data suggest that Siglec-F is dependent on Aβ deposition, that Siglec-G is dependent on tau deposition, and that Aβ and tau deposition synergistically enhance the expression of both Siglecs. The observation that Siglecg is highly expressed in LADAM while Siglec-10 (the human homolog of Siglec-G), is associated with late-stage AD pathologies exhibiting robust Aβ and Tau deposition, further supporting the view that Siglec10 signalling underlies the activation of LADAM in AD. Moreover, EADAM selectively expressed both Siglec1 and Siglecf , while DAM and MHC Class II clusters both expressed Siglecg , indicating that Siglec-1, Siglec-F, and Siglec-G, respectively, represent potential biomarkers for detection of early- and late-stage disease. Based on these findings, we hypothesize that Siglec-regulated signalling pathways may have an important function in regulating disease stage-specific microglia during AD progression. Future studies will directly address disease-associated functions of Siglec-regulated signalling pathways. However, it is often difficult to identify Siglec orthologues between mice and humans, as this gene family evolved rapidly [71,72]. Mice do not express Siglec-8, and mouse Siglec-3 (mSiglec‑3) has different effector domains, binding specificity, cellular distribution, and biological properties compared to those of human Siglec-3 (hSiglec‑3) [73,74]. However, current studies suggested these is functional conservation between mouse and human in Siglec gene family. Siglec In mouse microglia, immune inhibitory Siglec-F is among the most highly up-regulated genes (27-fold) in experimental neurodegenerative proteinopathy [75]. Siglec-F, a likely functional orthologue of Siglec-8 [76], is expressed in eosinophils as well as microglia [75,76], binds to the same sialoglycans and sialoglycan mimetics as Siglec-8 (unlike mSiglec-3) [77,78]. Both Siglec-F and Siglec-8 also regulate eosinophilic inflammation [79,80]. Siglec-F was upregulated on a subset of reactive microglia in models of neurodegeneration, indicating the important role for Siglec-F/Siglec-8 in regulating microglial activation during neurodegeneration [81]. In a recent study, RPTPζ S3L , a sialoglycoprotein was identified as a specific ligand in human cerebral cortex that binds CD33 as well as Siglec-8[82]. Interestingly, RPTPζ isoform in mice binds mouse Siglec-F and cross-reacts with human CD33 and Siglec-8[82], suggesting Siglec-F shares a similar ligand with CD33 and Siglec8 and is a potential functional counterpart of CD33 in humans. Importantly, we observed similar expression patterns of siglec signaling between mouse and human, such as Siglec-F/8 expression emerged at earlier disease stages than Siglec-G/10 in both our mouse models and in AD, indicating that our mouse model could serve as an important tool to study the functions of Siglec signalling in AD pathological stages and microglia subtypes Conclusions In this study, microglia subtypes in mouse model of AD that develop both Aβ and tau pathologies were profiled using scRNA-Seq. We found that during early-stage disease in 6-month-old Tau4RΔK-AP , the presence of both Aβ and tau pathologies induced a novel microglia subtype EADAM, which is distinct from DAM and express multiple interferon-regulated genes. In late-stage disease (12-month-old Tau4RΔK-AP mice), we found that another novel microglia subtype LADAM emerged in response to tau pathology, which expresses MHC and S100 family genes. We further observed a unique subtype of LADAM located near white matter, which is molecularly similar to a previously identified white matter-associated microglial subtype (WAM). We found that the signature genes in EADAM were also associated with a subgroup of microglia in the brains of early (Braak II) stages of AD. While LADAM microglia, including WAM-like LADAM, were observed in late (Braak VI), but not early (Braak II) stages of AD. Corroborating these findings, we found that Siglec family members are associated with specific subsets of microglia that are activated in a disease stage-specific manner in both mouse models of AD and AD patients. For example, Siglec-F is upregulated in response to Aβ pathology and is selectively expressed in Aβ-associated DAMs, while Siglec-G/10 is upregulated in white-matter-associated LADAM in late-stage AD. These findings are consistent with a model whereby Aβ or tau pathologies stimulate activation of microglia in different pattern, both Aβ and tau deposition are required to mediate the disease stage-specific induction of EADAM and LADAM, and can alter Siglec signalling across AD pathological stage and microglia subtypes. These findings may have important implications for the identification of novel molecular targets, offer new targets for the development of pre-symptomatic biomarkers and therapeutic strategies for AD. Our data indicated that while EADAM-related or LADAM-related gene activation represents a general response of microglia to various pathological contexts, there exists a unique gene signature that would define specific microglia subtypes that respond to specific pathology temporally during disease progression. Thus, by elucidating such “microglia response code”, one is expected to discover specific microglia subtypes relevant to the pathological context in human disease. Supporting this view, while we observed both EADAM-like and/or LADAM-like clusters in TDP43-KO or CK-p25 mouse models, there are substantial differences in expression levels of EADAM and LADAM genes in Tau4R△K-AP as compared to those of TDP43-KO or CK-p25 as well as Tau4R△K and APP;PS1 mice. We also found unique LADAM markers in Tau4R△K-AP mice that are not expressed in any other neurodegenerative models. These data strongly support a model in which there exists an EADAM-like and LADAM-like “microglia code” harbored by microglia subtypes that respond to either a general or specific pathological context in the brain. We demonstrate for both Aβ and tau pathologies that this canonical AD pathology context selectively induces novel microglia subtypes in a disease stage-specific manner. These disease stage-dependent “microglial code” could not only serve as biomarkers for precise diagnosis but also as the therapeutic target for stage-specific intervention of specific neurodegenerative diseases. Lastly, similar microglia cell types that share partial overlap in gene expression and low levels of expression of EADAM and LADAM markers can be detected in other mouse models of neurodegenerative disorders. While we have given unique names to AD-associated microglia in our AD models, a more standardized nomenclature for disease-associated microglial subtypes needs to be worked out by the research community in the near future. Abbreviations superior frontal gyrus (SFG), entorhinal cortex (ERC), E arly-stage AD - A ssociated M icroglia (EADAM), L ate-stage AD - A ssociated M icroglia (LADAM), Alzheimer’s disease (AD), amyloid-β (Aβ), Single-cell RNA-sequencing (scRNA-Seq), Sialic acid-binding immunoglobulin-type lectin (Siglec), formalin-fixed paraffin-embedded (FFPE), hematoxylin and eosin (H&E), APP swe ;PS1ΔE9 (AP), primary-age-related-tauopathy (PART) Declarations Ethics approval and consent to participate All animal procedures were in accordance strictly with the National Institutes of Health Guide for the Care and Use of Laboratory Animals and were approved by the Johns Hopkins University Animal Care and Use Committee Consent for publication Authors are consent for the publication. All authors read and approved the final manuscript. Availability of data and materials Further information and requests for resources and reagents should be directed to and will be fulfilled by the Lead Contact, Tong Li ( [email protected] ). All unique/stable reagents generated in this study are available from the Lead Contact without restriction. Single-cell RNA-seq data have been deposited at GEO (GSE175546) and are publicly available as of the date of publication. Accession numbers are listed in the key resources table. All other data reported in this paper will be shared by the lead contact upon request. Competing Interests: The authors declare no conflicts. Funds: This work was supported in part by the Maryland Stem Cell Research Fund (2019-MSCRFF-5124) to DWK, grants from the National Institute of Aging (R56AG068089) to RLS and TL, (R21AG073710) to TL, and the National Institute of Neurological Disorders and Stroke (R61NS115161 and R01NS095969) to PCW. Authors' contributions Study was designed by TL, DWK; mouse tissues were collected and characterized by TL, KJT, AW, AJL, TC, KB; JCT provided human specimen; AG, RS provided antibodies for siglecs, DWL, JPL, JCT, PCW, SB, RLS and TL prepared the manuscript. Acknowledgments: We thank the Transcriptomics and Deep Sequencing Core (Johns Hopkins) for the sequencing of scRNA-Seq libraries and the Johns Hopkins ADRC Neuropathology Core for brain tissues. References Bohlen CJ, Friedman BA, Dejanovic B, Sheng M. Microglia in Brain Development, Homeostasis, and Neurodegeneration. Annu Rev Genet. 2019;53:263–88. Hickman S, Izzy S, Sen P, Morsett L, El Khoury J. Microglia in neurodegeneration. Nat Neurosci. 2018;21:1359–69. 2021 Alzheimer’s disease facts and figures. Alzheimers Dement. 2021;17:327–406. Bekris LM, Yu C-E, Bird TD, Tsuang DW. Genetics of Alzheimer disease. J Geriatr Psychiatry Neurol. 2010;23:213–27. 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Scale bars = 100 μm. NS = not significant, * P < 0.05 , ** P <0.01 , *** P < 0.001 . FigureS2.png Figure S2. Distribution of cell types in 6-month-old cortex across genotypes. A, Schematic design of experiment: 4 genotypes ( WT, APP:PS1, Tau4RΔK, Tau4RΔK-AP ) in the cerebral cortex at 6-month-old and 12-month-old. B, Distribution of cell types across genotypes in the 6-month-old cortex. C, UMAP plot showing captured cell types in the 6-month-old cortex (all genotypes). D, UMAP plot showing the density of the captured cell types across genotypes in the 6-month-old cortex. E, UMAP plot showing DAM marker gene Cst7 . F, UMAP plot showing DAM marker gene Gpnmb . BAM = brain-associated macrophages, Ctx = Cortex, MSC = muscle stem cells, OPC = oligodendrocyte precursor cells, VSMC = vascular smooth muscle cells. FigureS3.png Figure S3. Distribution of cell types in 12-month-old cortex across genotypes. A, Schematic design of experiment: 4 genotypes ( WT, APP:PS1, Tau4RΔK, Tau4RΔK-AP ) in the cerebral cortex at 6 and 12-month-old. B, Distribution of cell types across genotypes in 12-month-old cortex. C, UMAP plot showing captured cell types in the 12-month-old cortex (all genotypes). D, UMAP plot showing the density of the captured cell types for each genotype in the 12-month-old cortex. E, UMAP plot showing DAM marker gene Cst7 . F, UMAP plot showing DAM marker gene Gpnmb . BAM = brain-associated macrophages, Ctx = cortex, MSC = muscle stem cells, OPC = oligodendrocyte precursor cells, VSMC = vascular smooth muscle cells. FigureS4.png Figure S4. Distribution of clusters across genotypes. A, Distribution of cell types across 6-month-old scRNA-Seq triplicates. BAM = brain-associated macrophages, MSC = muscle stem cells, OPC = oligodendrocyte precursor cells, VSMC = vascular smooth muscle cells. B, Distribution of cell types across 12-month-old scRNA-Seq triplicates. BAM = brain-associated macrophages, MSC = muscle stem cells, OPC = oligodendrocyte precursor cells, VSMC = vascular smooth muscle cells. R1 = Biological replicate 1, R2 = Biological replicate 2, R3 = Biological replicate 3. FigureS5.png Figure S5. Analysis of EADAM and LADAM markers in other mouse models of neurodegenerative disease and AD samples. A, UMAP plot showing CK-p25 neurodegenerative scRNA-Seq microglia dataset from Mathys et al., 2017. B, UMAP plot showing EADAM and LADAM-enriched genes. Note that while IFN and MHC genes are present, S100 genes and Siglecg are absent in the dataset. C,D Heatmap plot showing homologs of EADAM (c), and LADAM (D) genes in the human snRNA-Seq dataset. FigureS6.png Figure S6. Siglec-genes show cluster- and genotype-specific expression patterns, and histological validation of Siglec-F and Siglec-G across genotypes in 6 and 12-month-old mice. A, UMAP plot showing microglial clusters in the 6 and 12-month-old cortex (all genotypes). B, Heatmap plot showing Siglec genes expressions across microglial clusters. C, D, UMAP plot showing Siglecf expression in WT , APP;PS1 , Tau4RΔK , Tau4RΔK-AP at 6-month-old (c) and 12-month-old (d) (top). Note a higher expression of Siglecf in DAM2 in APP;PS1 and Tau4RΔK-AP mice at 12-month-old. Siglec-F immunostaining was performed in the cortex in WT , Tau4RΔK , APP;PS1 , Tau4RΔK-AP at 6-month-old (c) and 12-month-old (d) (bottom). E-I, Immunostaining of Siglec-F (E,F), and Siglec-G (G-I), in WT , APP;PS1 , Tau4RΔK , Tau4RΔK-AP ; at 6-month-old (E,G) and 12-month-old (F,H,I); hippocampus (E,F,H), and the cortex (F,G,I). J, UMAP plot of 6-month-old microglia, showing a small cluster (highlighted in red) that resembles molecular genes of WAM href="https://paperpile.com/c/5KiZhq/lJrOX" rel="noopener noreferrer" target="_blank">[25]. K, Quantification of Siglec-G staining in the 6-month-old and 12-month-old cortex and hippocampus. Ctx = Cortex, Hippo = Hippocampus. Red boxes show high magnification views. Scale bars = 100 μm. * P < 0.05 , ** P <0.01 , *** P < 0.001 . FigureS7.png Figure S7. Histological validation of tau and Aβ pathologies and Siglec-10 during AD progression and in nAD Tauopathy. Immunostaining of Tau (pS422) (A), Aβ (6E10) (B), and Siglec-10 (C) in Control, Braak stage 2, Braak stage 3, Braak stage 4, Braak stage 5, Braak stage 6, and in nAD Tauopathy in the cortex. Numbers indicate BRC# in Table S11. Red boxes show high magnification views. Scale bars = 100 μm. STables.xlsx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 01 Jun, 2022 Reviewers invited by journal 26 May, 2022 Editor assigned by journal 11 May, 2022 First submitted to journal 26 Apr, 2022 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-1598611","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":108939982,"identity":"4e1fd735-b096-45e4-b5f4-d6dd32a03daa","order_by":0,"name":"Dong Won Kim","email":"","orcid":"","institution":"Johns Hopkins University School of Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Dong","middleName":"Won","lastName":"Kim","suffix":""},{"id":108939983,"identity":"c610bc29-205b-4842-bed1-11289c709730","order_by":1,"name":"Kevin Tu","email":"","orcid":"","institution":"Johns Hopkins University School of Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Kevin","middleName":"","lastName":"Tu","suffix":""},{"id":108939984,"identity":"f950f8a8-9497-41ff-9e4e-d1f28fc0b64b","order_by":2,"name":"Alice Wei","email":"","orcid":"","institution":"Johns Hopkins University School of Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Alice","middleName":"","lastName":"Wei","suffix":""},{"id":108939985,"identity":"bb67db3b-3fa5-4a2a-810a-267f1277dcc4","order_by":3,"name":"Ashley Lau","email":"","orcid":"","institution":"Johns Hopkins University School of Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ashley","middleName":"","lastName":"Lau","suffix":""},{"id":108939986,"identity":"e3c8cd84-ca3d-4228-b9ac-6695afe13d86","order_by":4,"name":"Anabel Gonzalez-Gil","email":"","orcid":"","institution":"Johns Hopkins University School of Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Anabel","middleName":"","lastName":"Gonzalez-Gil","suffix":""},{"id":108939987,"identity":"13c8e41d-c1d5-4583-82f9-83fdbb967662","order_by":5,"name":"Tiaoyu Cao","email":"","orcid":"","institution":"Johns Hopkins University School of Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Tiaoyu","middleName":"","lastName":"Cao","suffix":""},{"id":108939988,"identity":"811046f2-382f-4a0e-8cfb-dd64c2efd7b4","order_by":6,"name":"Kerstin Braunstein","email":"","orcid":"","institution":"Johns Hopkins University School of Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Kerstin","middleName":"","lastName":"Braunstein","suffix":""},{"id":108939989,"identity":"bac207ce-0437-43f6-8c26-b3927970f38b","order_by":7,"name":"Jonathan Ling","email":"","orcid":"","institution":"Johns Hopkins University School of Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jonathan","middleName":"","lastName":"Ling","suffix":""},{"id":108939990,"identity":"36b97ebc-482b-4c94-9f75-286ab7d5a994","order_by":8,"name":"Juan Troncoso","email":"","orcid":"","institution":"Johns Hopkins University School of Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Juan","middleName":"","lastName":"Troncoso","suffix":""},{"id":108939991,"identity":"7fa0f37b-d7f8-41e7-8464-2e256d8757b2","order_by":9,"name":"Philip Wong","email":"","orcid":"","institution":"Johns Hopkins University School of Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Philip","middleName":"","lastName":"Wong","suffix":""},{"id":108939992,"identity":"430cfb52-eccb-49ed-a7b2-09c4e5ef00fc","order_by":10,"name":"Seth Blackshaw","email":"","orcid":"","institution":"Johns Hopkins University School of Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Seth","middleName":"","lastName":"Blackshaw","suffix":""},{"id":108939993,"identity":"500fb8f2-caa3-4185-91c9-bd72f9613005","order_by":11,"name":"Ronald Schnaar","email":"","orcid":"","institution":"Johns Hopkins University School of Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ronald","middleName":"","lastName":"Schnaar","suffix":""},{"id":108939994,"identity":"73d78fd9-8f6b-44c3-9992-6d991f333bc6","order_by":12,"name":"Tong Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAnklEQVRIiWNgGAWjYDACdsaGAx94DjCwAdkSROngYWZsPDiDRC0MzId5GA6AOcRpsWdmbjhsI3Mnmo+B+eBtHiId1nA4h+dZbhsDW7I1KVoOA7XwmEkTr8UCrIX/GwlaGCC2sBGp5TBjw8EekBZmNmPLOcRoYW9vf/zhZ8/h3PntzQ9vvCFGCxgw9gAJZqKVg8EP0pSPglEwCkbBCAMAFsgtaO8CDAMAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0001-6240-4287","institution":"Johns Hopkins University School of Medicine","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Tong","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2022-04-26 20:49:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1598611/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1598611/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":22128745,"identity":"a21f4ea2-afbe-4143-8bb1-1ee82eec000b","added_by":"auto","created_at":"2022-06-01 14:20:14","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":671665,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe microglia subtype EADAM is observed in the \u003cem\u003eTau4RΔK-AP\u003c/em\u003e mice in 6-month-old cortex.\u003c/strong\u003e \u003cstrong\u003eA,\u003c/strong\u003e UMAP plot showing microglia clusters - Homeostatic, DAM, and EADAM, across 4 genotypes in 6-month-old cortex (n = 3/genotype), and pie graphs showing the distribution of 3 microglia clusters across genotypes (n = 3/genotype). \u003cstrong\u003eB,\u003c/strong\u003e Violin plots showing top cluster genes of 3 microglia clusters. \u003cstrong\u003eC,\u003c/strong\u003e Bar graphs showing the distribution of 3 microglia clusters within each genotype (n = 3/genotype).\u0026nbsp;Note the significant increase in the EADAM cluster in \u003cem\u003eTau4RΔK-AP\u003c/em\u003e mice. \u003cstrong\u003eD,\u003c/strong\u003e Regulon analysis with SCENIC\u003ca href=\"https://paperpile.com/c/5KiZhq/0waxO\" rel=\"noopener noreferrer\" target=\"_blank\"\u003e[40]\u003c/a\u003e, showing key regulons in each microglia cluster. \u003cstrong\u003eE,\u003c/strong\u003e GO analysis of top differential genes reveals a biological function in each microglial cluster. \u003cstrong\u003eF, \u003c/strong\u003eUMAP plots of 7 month-old 5XFAD mice in Zhou et al 2020 \u003ca href=\"https://paperpile.com/c/5KiZhq/V0Dls\" rel=\"noopener noreferrer\" target=\"_blank\"\u003e[23]\u003c/a\u003e (top) and a small cluster of cells (red circle) expressing EADAM-enriched markers (bottom).\u0026nbsp;\u003cstrong\u003eG,\u003c/strong\u003e Heatmap showing expression of EADAM-enriched genes across genotypes. * \u003cem\u003eP \u0026lt; 0.05\u003c/em\u003e, ** \u003cem\u003eP \u0026lt;0.005\u003c/em\u003e, *** \u003cem\u003eP \u0026lt; 0.0001\u003c/em\u003e.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-1598611/v1/578d2e4d6e56001e8049833c.png"},{"id":22128746,"identity":"f02d2f15-c9e7-4922-bf33-68d22600d610","added_by":"auto","created_at":"2022-06-01 14:20:15","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":732280,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe microglia subtype LADAM is observed in the \u003cem\u003eTau4RΔK-AP\u003c/em\u003e mice in 12-month-old cortex.\u003c/strong\u003e \u003cstrong\u003eA,\u003c/strong\u003e UMAP plot showing microglia clusters - Homeostatic, DAM1, DAM2, EADAM, and LADAM, across 4 genotypes in 12-month-old cortex (n = 3/genotype), and pie graphs showing the distribution of 4 microglia clusters across genotypes (n = 3/genotype). \u003cstrong\u003eB,\u003c/strong\u003e Violin plots showing top cluster genes of 3 microglia clusters. \u003cstrong\u003eC,\u003c/strong\u003e\u0026nbsp;Bar graphs showing the distribution of 5 microglia clusters within each genotype (n = 3/genotype).\u0026nbsp;Note the significant increase in LADAM cluster in \u003cem\u003eTau4RΔK-AP\u003c/em\u003e mice. \u003cstrong\u003eD,\u003c/strong\u003e Regulon analysis with SCENIC \u003ca href=\"https://paperpile.com/c/5KiZhq/0waxO\" rel=\"noopener noreferrer\" target=\"_blank\"\u003e[40]\u003c/a\u003e, showing key regulons in each microglia cluster. \u003cstrong\u003eE,\u003c/strong\u003e GO analysis of top differential genes reveals a biological function in each microglia cluster. \u003cstrong\u003eF,\u003c/strong\u003e Heatmap showing expression of DAM-enriched genes across genotypes. \u003cstrong\u003eG,\u003c/strong\u003e Heatmap showing expression of LADAM-enriched genes across genotypes.\u0026nbsp;* \u003cem\u003eP \u0026lt; 0.05\u003c/em\u003e, ** \u003cem\u003eP \u0026lt;0.005\u003c/em\u003e, *** \u003cem\u003eP \u0026lt; 0.0001\u003c/em\u003e.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-1598611/v1/759a6551c1156971354fc4cc.png"},{"id":22130156,"identity":"1f2f6fc0-49bd-408e-9b0c-79c34a99fc8f","added_by":"auto","created_at":"2022-06-01 14:30:15","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1169466,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe emergence of disease stage-dependent microglia subtypes coincides with Aβ and tau deposition. A,\u003c/strong\u003e UMAP plot showing microglia clusters in the 6- and 12-month-old cortex (all genotypes) (left) and UMAP plot with RNA velocity (right) showing 2 potential transitions between DAM1 and LADAM, with transitions into EADAM and/or DAM2 in between. \u003cstrong\u003eB,\u003c/strong\u003e UMAP plot showing the density of the captured microglia clusters across genotypes in the 12-month-old cortex. \u003cstrong\u003eC,\u003c/strong\u003e Pseudotime analysis showing gene expression changes between DAM1, DAM2, and LADAM. \u003cstrong\u003eD,\u003c/strong\u003e Pseudotime analysis showing gene expression changes between DAM1, EADAM and LADAM.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-1598611/v1/3cda568307bc7a023f2a415b.png"},{"id":22129584,"identity":"526151fc-b019-4ce2-af61-30b2c89b7d48","added_by":"auto","created_at":"2022-06-01 14:25:15","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":716887,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAnalysis of EADAM and LADAM markers in other mouse models of neurodegenerative disease. A,\u003c/strong\u003e UMAP plot showing microglia clusters in the 6 and 12-month-old cortex (all genotypes). \u003cstrong\u003eB,\u003c/strong\u003e UMAP plot showing microglia clusters in \u003cem\u003eCamKII\u003c/em\u003e\u003csup\u003e\u003cem\u003eCreERT2\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e;Tardbp\u003c/em\u003e\u003csup\u003e\u003cem\u003elox/lox\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e \u003c/em\u003e(\u003cem\u003eTDP43 KO\u003c/em\u003e),\u0026nbsp;4-OHT induction at 6 months and collected at 9 months. \u003cstrong\u003eC,\u003c/strong\u003e UMAP plot showing microglia clusters in \u003cem\u003eCamKII\u003c/em\u003e\u003csup\u003e\u003cem\u003eCreERT2\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e;Tardbp\u003c/em\u003e\u003csup\u003e\u003cem\u003elox/lox\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e \u003c/em\u003e(\u003cem\u003eTDP43 KO\u003c/em\u003e), 4-OHT induction at 12 months and collected at 15 months. \u003cstrong\u003eD,\u003c/strong\u003e Heatmap plot showing expression of EADAM-enriched genes across AD genotypes and in \u003cem\u003eTDP43 KO\u003c/em\u003e. \u003cstrong\u003eE,\u003c/strong\u003e Heatmap plot showing expression of LADAM-enriched genes across AD genotypes and in \u003cem\u003eTDP43 KO\u003c/em\u003e.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-1598611/v1/9edb4338e432c39098f71387.png"},{"id":22128747,"identity":"b97aeb2a-40b8-4b7a-ab5a-5b228ac26019","added_by":"auto","created_at":"2022-06-01 14:20:15","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":748011,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e6-month-old EADAM at and 12-month-old LADAM clusters in \u003cem\u003eTau4RΔK-AP\u003c/em\u003e mice resemble microglial subtypes seen in snRNA-Seq from AD samples.\u003c/strong\u003e \u003cstrong\u003eA,\u003c/strong\u003e UMAP plot showing microglia clusters in the 6 and 12-month-old cortex (all genotypes) from Fig 4A. EADAM and LADAM-enriched gene homologs were studied in human snRNA-Seq. \u003cstrong\u003eB,\u003c/strong\u003e Schematic showing snRNA-Seq from human postmortem samples, primary age-related tauopathy (PART) entorhinal cortex (ERC), superior frontal gyrus (SFG); Stage 2 ERC, SFG; Stage 4 SFG; Stage 6 SFG. \u003cstrong\u003eC,\u003c/strong\u003e UMAP plot showing distribution of subsetted microglia from human snRNA-Seq. \u003cstrong\u003eD,\u003c/strong\u003e Violin plots showing expression of EADAM-like interferon family members. \u003cstrong\u003eE,\u003c/strong\u003e Violin plots showing expressions of LADAM-like MHC and S100 family members. \u003cstrong\u003eF\u003c/strong\u003e UMAP plot showing microglia from human snRNA-Seq dataset from Morabito et al., 2021. \u003cstrong\u003eG,\u003c/strong\u003e Violin plots showing IFN, MHC and S100\u0026nbsp;family members in the dataset from Morabito et al., 2021.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-1598611/v1/bd38a1ec5e5a0f777411d438.png"},{"id":22128749,"identity":"27b4ea46-2fee-4a05-b2cd-55f105cad2a9","added_by":"auto","created_at":"2022-06-01 14:20:15","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":4228388,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA LADAM subcluster is observed in white-matter-associated microglia. A,\u003c/strong\u003e UMAP plot of 12-month-old ‘LADAM’. \u003cstrong\u003eB, \u003c/strong\u003eBar plot showing the distribution of 4 genotypes across 4 LADAM clusters. \u003cstrong\u003eC,\u003c/strong\u003e UMAP plot of LADAM, showing cells/clusters (purple colored cells) that resemble WAM \u003ca href=\"https://paperpile.com/c/5KiZhq/lJrOX\" rel=\"noopener noreferrer\" target=\"_blank\"\u003e[25]\u003c/a\u003e. Note that C2 has the higher enrichment with WAM gene modules. \u003cstrong\u003eD,\u003c/strong\u003e UMAP plot showing gene expressions that are highly enriched genes, including WAM genes (\u003cem\u003eVim\u003c/em\u003e and \u003cem\u003eLgals3\u003c/em\u003e) in the LADAM C2 cluster (left), and heatmap showing expression of WAM gene modules in C2 across genotypes (right). \u003cstrong\u003eE,\u003c/strong\u003e Iba1 and Siglec-G immunostaining (left) and quantification (right) in 6-month-old (top) and 12-month-old (bottom) corpus callosum (CC) in \u003cem\u003eWT\u003c/em\u003e, \u003cem\u003eAPP;PS1\u003c/em\u003e, \u003cem\u003eTau4RΔK\u003c/em\u003e, and \u003cem\u003eTau4RΔK-AP\u003c/em\u003e. \u003cstrong\u003eF,\u003c/strong\u003e Siglec-10 immunostaining in human cortex samples in control, Braak stage 6, and nAD tauopathy (numbers on figure panels indicate BRC# In Table S11, immunostaining of other Braak stages are shown in Fig S6), with bar graphs showing quantification of Siglec-10 cells in gray (top) and white (bottom) matter. \u003cstrong\u003eG,\u003c/strong\u003e Schematic showing human snRNA-Seq on Stage 4 and 6 SFG (left) and UMAP plot showing LADAM C2 enriched gene module.\u003cstrong\u003e H, \u003c/strong\u003eUMAP plot showing expression of LADAM C2 cluster markers in human snRNA-Seq. Red boxes show high magnification views. Scale bars = 100 μm. * \u003cem\u003eP \u0026lt; 0.05\u003c/em\u003e, ** \u003cem\u003eP \u0026lt;0.01\u003c/em\u003e, *** \u003cem\u003eP \u0026lt; 0.001\u003c/em\u003e.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-1598611/v1/7c51d44dd02fb45b3f740735.png"},{"id":22130157,"identity":"3fdb010e-8a63-4f09-86ee-8877b09516fb","added_by":"auto","created_at":"2022-06-01 14:30:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":745899,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1598611/v1/46adc513-395d-4e6d-8556-f603e9976d6b.pdf"},{"id":22128755,"identity":"7380e994-9307-4d14-9312-009e65ec237b","added_by":"auto","created_at":"2022-06-01 14:20:15","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":9890729,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S1. Histological validation of tau and Aβ pathologies in\u003c/strong\u003e \u003cstrong\u003e6-month-old and 12-month-old \u003cem\u003eTau4RΔK-AP \u003c/em\u003emice at. A-L\u003c/strong\u003e, Immunostaining of Aβ (6E10) (a-d), tau (pS422) (e-h), and IBA1 (i-l) at 6-month-old (a, b, e, f, i, j) and 12-month-old (c, d, g, h, k, l); in the Ctx (a, c, e, g, i, k), and Hippo (b, d, f, h, j, l), in \u003cem\u003eWT\u003c/em\u003e, \u003cem\u003eAPP;PS1\u003c/em\u003e, \u003cem\u003eTau4RΔK\u003c/em\u003e,\u0026nbsp;\u003cem\u003eTau4RΔK-AP\u003c/em\u003e mice. \u003cstrong\u003eM-P\u003c/strong\u003e, Quantification of IBA1 (m-o) and NeuN (p) staining in the cortex (m-o), hippocampus (m-p), and corpus callosum (m-o) at 6-month-old (m, o) and 12-month-old (n-p). \u003cstrong\u003eQ\u003c/strong\u003e, Quantification of brain weight at 12-month-old. \u003cstrong\u003eR, \u003c/strong\u003eQuantification of Aβ\u0026nbsp;deposition covered areas in the hippocampus of 12-month-old mice. Ctx = Cortex, CC = Corpus Callosum, Hippo = Hippocampus. Scale bars = 100 μm. NS = not significant, * \u003cem\u003eP \u0026lt; 0.05\u003c/em\u003e, ** \u003cem\u003eP \u0026lt;0.01\u003c/em\u003e, *** \u003cem\u003eP \u0026lt; 0.001\u003c/em\u003e.\u003c/p\u003e","description":"","filename":"FigureS1.png","url":"https://assets-eu.researchsquare.com/files/rs-1598611/v1/23f5a3584f5e6e2e34c21e21.png"},{"id":22128752,"identity":"0f45f0c4-2ba9-4449-b606-b454b21f3a97","added_by":"auto","created_at":"2022-06-01 14:20:15","extension":"png","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":865691,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S2. Distribution of cell types in 6-month-old cortex across genotypes. A,\u003c/strong\u003e Schematic design of experiment: 4 genotypes (\u003cem\u003eWT, APP:PS1, Tau4RΔK,\u0026nbsp;Tau4RΔK-AP\u003c/em\u003e) in the cerebral cortex at 6-month-old and 12-month-old. \u003cstrong\u003eB,\u003c/strong\u003e Distribution of cell types across genotypes in the 6-month-old cortex. \u003cstrong\u003eC,\u003c/strong\u003e UMAP plot showing captured cell types in the 6-month-old cortex (all genotypes). \u003cstrong\u003eD, \u003c/strong\u003eUMAP plot showing the density of the captured cell types across genotypes in the 6-month-old cortex. \u003cstrong\u003eE,\u003c/strong\u003e UMAP plot showing DAM marker gene \u003cem\u003eCst7\u003c/em\u003e. \u003cstrong\u003eF,\u003c/strong\u003e UMAP plot showing DAM marker gene \u003cem\u003eGpnmb\u003c/em\u003e.\u003cem\u003e \u003c/em\u003eBAM = brain-associated macrophages, Ctx = Cortex, MSC = muscle stem cells, OPC = oligodendrocyte precursor cells, VSMC = vascular smooth muscle cells.\u003c/p\u003e","description":"","filename":"FigureS2.png","url":"https://assets-eu.researchsquare.com/files/rs-1598611/v1/a7605071548604a04b6c89fd.png"},{"id":22128748,"identity":"c1743fd2-14fb-4608-bed5-2e51a9bc6087","added_by":"auto","created_at":"2022-06-01 14:20:15","extension":"png","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":807589,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S3. Distribution of cell types in 12-month-old cortex across genotypes. A,\u003c/strong\u003e Schematic design of experiment: 4 genotypes (\u003cem\u003eWT, APP:PS1, Tau4RΔK, Tau4RΔK-AP\u003c/em\u003e) in the cerebral cortex at 6 and 12-month-old. \u003cstrong\u003eB,\u003c/strong\u003e Distribution of cell types across genotypes in 12-month-old cortex. \u003cstrong\u003eC,\u003c/strong\u003e UMAP plot showing captured cell types in the 12-month-old cortex (all genotypes). \u003cstrong\u003eD,\u003c/strong\u003e UMAP plot showing the density of the captured cell types for each genotype in the 12-month-old cortex. \u003cstrong\u003eE,\u003c/strong\u003e UMAP plot showing DAM marker gene \u003cem\u003eCst7\u003c/em\u003e. \u003cstrong\u003eF,\u003c/strong\u003e UMAP plot showing DAM marker gene \u003cem\u003eGpnmb\u003c/em\u003e.\u003cem\u003e \u003c/em\u003eBAM = brain-associated macrophages, Ctx = cortex, MSC = muscle stem cells, OPC = oligodendrocyte precursor cells, VSMC = vascular smooth muscle cells.\u003c/p\u003e","description":"","filename":"FigureS3.png","url":"https://assets-eu.researchsquare.com/files/rs-1598611/v1/d619fdf7a9e07a37607fbb29.png"},{"id":22128756,"identity":"8d634e18-780e-4d60-8399-494b7b11496c","added_by":"auto","created_at":"2022-06-01 14:20:15","extension":"png","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":133006,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S4. Distribution of clusters across genotypes. A,\u003c/strong\u003e Distribution of cell types across 6-month-old scRNA-Seq triplicates. BAM = brain-associated macrophages, MSC = muscle stem cells, OPC = oligodendrocyte precursor cells, VSMC = vascular smooth muscle cells. \u003cstrong\u003eB,\u003c/strong\u003e Distribution of cell types across 12-month-old scRNA-Seq triplicates. BAM = brain-associated macrophages, MSC = muscle stem cells, OPC = oligodendrocyte precursor cells, VSMC = vascular smooth muscle cells. R1 = Biological replicate 1, R2 = Biological replicate 2, R3 = Biological replicate 3.\u003c/p\u003e","description":"","filename":"FigureS4.png","url":"https://assets-eu.researchsquare.com/files/rs-1598611/v1/4139d9eef1082459dac64060.png"},{"id":22129585,"identity":"935bbfd3-0923-4860-8deb-59f51e17b18a","added_by":"auto","created_at":"2022-06-01 14:25:15","extension":"png","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":406537,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S5. Analysis of EADAM and LADAM markers in other mouse models of neurodegenerative disease and AD samples. A,\u003c/strong\u003e UMAP plot showing \u003cem\u003eCK-p25\u003c/em\u003e neurodegenerative scRNA-Seq microglia dataset from Mathys et al., 2017. \u003cstrong\u003eB, \u003c/strong\u003eUMAP plot showing EADAM and LADAM-enriched genes. Note that while IFN and MHC genes are present, S100 genes and \u003cem\u003eSiglecg\u003c/em\u003e are absent in the dataset. \u003cstrong\u003eC,D\u003c/strong\u003e Heatmap plot showing homologs of EADAM (c), and LADAM (D) genes in the human snRNA-Seq dataset.\u003c/p\u003e","description":"","filename":"FigureS5.png","url":"https://assets-eu.researchsquare.com/files/rs-1598611/v1/3cf6924bba0e17721197af00.png"},{"id":22128757,"identity":"b0e14f9d-2138-4799-8600-14e1ad1c9e04","added_by":"auto","created_at":"2022-06-01 14:20:15","extension":"png","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":6631003,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S6. Siglec-genes show cluster- and genotype-specific expression patterns, and histological validation of Siglec-F and Siglec-G across genotypes in 6 and 12-month-old mice. A,\u003c/strong\u003e UMAP plot showing microglial clusters in the 6 and 12-month-old cortex (all genotypes).\u003cstrong\u003e B,\u003c/strong\u003e Heatmap plot showing Siglec genes expressions across microglial clusters. \u003cstrong\u003eC, D,\u003c/strong\u003e UMAP plot showing \u003cem\u003eSiglecf\u003c/em\u003e expression in \u003cem\u003eWT\u003c/em\u003e, \u003cem\u003eAPP;PS1\u003c/em\u003e, \u003cem\u003eTau4RΔK\u003c/em\u003e, \u003cem\u003eTau4RΔK-AP\u003c/em\u003e at 6-month-old (c) and 12-month-old (d) (top). Note a higher expression of \u003cem\u003eSiglecf \u003c/em\u003ein DAM2 in \u003cem\u003eAPP;PS1\u003c/em\u003e and \u003cem\u003eTau4RΔK-AP\u003c/em\u003e mice at 12-month-old. Siglec-F immunostaining was performed in the cortex in \u003cem\u003eWT\u003c/em\u003e, \u003cem\u003eTau4RΔK\u003c/em\u003e, \u003cem\u003eAPP;PS1\u003c/em\u003e, \u003cem\u003eTau4RΔK-AP\u003c/em\u003e at 6-month-old (c) and 12-month-old (d) (bottom). \u003cstrong\u003eE-I, \u003c/strong\u003eImmunostaining of Siglec-F (E,F), and Siglec-G (G-I), in \u003cem\u003eWT\u003c/em\u003e, \u003cem\u003eAPP;PS1\u003c/em\u003e, \u003cem\u003eTau4RΔK\u003c/em\u003e, \u003cem\u003eTau4RΔK-AP\u003c/em\u003e; at 6-month-old (E,G) and 12-month-old (F,H,I); hippocampus (E,F,H), and the cortex (F,G,I). \u003cstrong\u003eJ\u003c/strong\u003e, UMAP plot of 6-month-old microglia, showing a small cluster (highlighted in red) that resembles molecular genes of WAM\u003ca href=\"https://paperpile.com/c/5KiZhq/lJrOX\" rel=\"noopener noreferrer\" target=\"_blank\"\u003e[25]\u003c/a\u003e. \u003cstrong\u003eK\u003c/strong\u003e, Quantification of Siglec-G staining in the 6-month-old and 12-month-old cortex and hippocampus. Ctx = Cortex, Hippo = Hippocampus.\u0026nbsp;Red boxes show high magnification views.\u0026nbsp;Scale bars = 100 μm. * \u003cem\u003eP \u0026lt; 0.05\u003c/em\u003e, ** \u003cem\u003eP \u0026lt;0.01\u003c/em\u003e, *** \u003cem\u003eP \u0026lt; 0.001\u003c/em\u003e.\u003c/p\u003e","description":"","filename":"FigureS6.png","url":"https://assets-eu.researchsquare.com/files/rs-1598611/v1/a52e5358966c71e32b6c8539.png"},{"id":22128758,"identity":"41791db6-046e-4b59-8ce5-4339b7279811","added_by":"auto","created_at":"2022-06-01 14:20:15","extension":"png","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":6693144,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S7.\u003c/strong\u003e \u003cstrong\u003eHistological validation of tau and Aβ pathologies and Siglec-10 during AD progression and in nAD Tauopathy. \u003c/strong\u003eImmunostaining of Tau (pS422) (A), Aβ (6E10) (B), and Siglec-10 (C) in Control, Braak stage 2, Braak stage 3, Braak stage 4,\u0026nbsp;Braak stage 5, Braak stage 6, and in nAD Tauopathy in the cortex. Numbers indicate BRC# in Table S11.\u0026nbsp;Red boxes show high magnification views. Scale bars = 100 μm.\u003c/p\u003e","description":"","filename":"FigureS7.png","url":"https://assets-eu.researchsquare.com/files/rs-1598611/v1/8cb64724d88ddcaab90f637d.png"},{"id":22129587,"identity":"2f02a23a-6549-44ec-a24e-803756004ff4","added_by":"auto","created_at":"2022-06-01 14:25:15","extension":"xlsx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":808696,"visible":true,"origin":"","legend":"","description":"","filename":"STables.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-1598611/v1/08b741409f2e9404d7443df2.xlsx"}],"financialInterests":"","formattedTitle":"Amyloid-beta and tau pathologies act synergistically to induce novel disease stage-specific microglia subtypes","fulltext":[{"header":"Background","content":"\u003cp\u003eNeuroinflammation is increasingly recognized as a key regulator of disease progression in neurodegenerative disorders [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], including Alzheimer\u0026rsquo;s disease (AD), which is the most common cause of dementia[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Recent studies suggest that neuroinflammation serves as a mechanistic link between the development of amyloid-β (Aβ) plaques and tau neurofibrillary tangles, and the canonical pathologies of AD that are thought to drive synapse loss and neuronal death[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In addition to well-characterized AD susceptibility genes such as \u003cem\u003eAPP, PSEN1 and PSEN2\u003c/em\u003e (early-onset familial AD [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]), and \u003cem\u003eAPOE\u003c/em\u003e (late-onset AD [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]), several additional risk alleles for late-onset AD were identified in genes regulating immunomodulation, including \u003cem\u003eTREM2\u003c/em\u003e [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], phosphoinositide phospholipase Cγ2 (\u003cem\u003ePLCG2\u003c/em\u003e) [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] and \u003cem\u003eCD33\u003c/em\u003e [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAs the brain\u0026rsquo;s resident innate immune cells, microglia play multifunctional roles in brain health and the progression of neurodegenerative diseases such as AD [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Depending on the disease stage, microglia may protect against neurodegenerative proteinopathy and/or contribute to inflammatory damage [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Microglia maintain brain health by clearing cellular debris, including Aβ plaques and tau aggregates [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Microglial activation correlates positively with cognition and gray matter volume in humans, indicating that microglia can be protective, at least during early-stage AD [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. However, microglia also secrete proinflammatory cytokines and can directly contribute to tau pathology [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] and its subsequent neurotoxicity [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. This dichotomous role of microglia in maintaining this balance between phagocytosis/clearance and pro-inflammatory mediator release is thought to be an essential and potentially targetable determinant of AD progression [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRecent studies have identified subtypes of microglia that display a dynamic range of responses and functions [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], emphasizing the ability of microglia to serve a variety of important physiological roles [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Transcriptomic analyses of bulk tissues revealed disease-associated changes in microglia associated with AD [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Subsequent single-cell RNA-Sequencing (scRNA-Seq) approaches, however, were necessary to identify disease context-dependent microglial subtypes. scRNA-Seq has shed light on the spatial and developmental heterogeneity of microglia and provides a high-resolution view of the transcriptional landscape of microglia subtypes during development and disease progression [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan additionalcitationids=\"CR21 CR22\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Since AD is a chronic disease with decades-long prodromal stages, understanding the disease stage-specific impacts of microglia subtypes is necessary to clarify the dual nature of microglia activation. The identification of disease-associated microglia (DAM), a unique TREM2-dependent subtype that expresses CD11c and is localized near Aβ plaques in an amyloidosis mouse model [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], supports this notion. Despite these advances, a critical unresolved question is whether disease stage-specific microglia subtypes exist that are activated in response to both Aβ and tau pathologies. These microglia subtypes would represent novel therapeutic targets for modifying disease progression.\u003c/p\u003e \u003cp\u003eHere, we used an AD mouse model (\u003cem\u003eTau4RΔK-AP\u003c/em\u003e mice), in which wild-type tau is converted into tau aggregates to drive neuron loss in a neuritic plaque-dependent manner [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. This animal model serves as an excellent mouse model to profile microglia subtypes across different stages of AD-like disease progression. To assess the requirement for both Aβ and tau pathologies to induce disease stage-specific microglia subtypes, control mice accumulating either Aβ plaques (\u003cem\u003eAPP;PS1\u003c/em\u003e mice) or tau tangles (\u003cem\u003eTau4RΔK\u003c/em\u003e mice) were also profiled. Using scRNA-Seq approaches, we found that microglia respond to the development of Aβ and tau pathologies in a disease stage-specific manner. During early-stage disease in 6-month-old \u003cem\u003eTau4RΔK-AP\u003c/em\u003e, but not \u003cem\u003eAPP;PS1\u003c/em\u003e or \u003cem\u003eTau4RΔK\u003c/em\u003e mice, the presence of both Aβ and tau pathologies induced a novel microglia subtype we termed \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eE\u003c/span\u003early-stage \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eAD\u003c/span\u003e-\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eA\u003c/span\u003essociated \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eM\u003c/span\u003eicroglia (EADAM), which is distinct from DAM and express multiple interferon-regulated genes. We found that the signature genes in EADAM were also associated with a subgroup of microglia in the brains of early (Braak II) stages of AD. In late-stage disease (12-month-old \u003cem\u003eTau4RΔK-AP\u003c/em\u003e mice), we found that another novel microglia subtype emerged in response to tau pathology that we termed \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eL\u003c/span\u003eate-stage \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eAD\u003c/span\u003e-\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eA\u003c/span\u003essociated \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eM\u003c/span\u003eicroglia (LADAM), and which expresses MHC and S100 family genes. We further observed a unique subtype of LADAM located near white matter, which is molecularly similar to a previously identified white matter-associated microglial subtype (WAM) that is increased during aging [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] and undergoes additional molecular transitions with Aβ and tau pathologies.\u003c/p\u003e \u003cp\u003eLADAM microglia, including WAM-like LADAM, were observed in late (Braak VI), but not early (Braak II) stages of AD. Corroborating these findings, we found that \u003cem\u003esialic acid-binding immunoglobulin-like lectin\u003c/em\u003e (\u003cem\u003eSiglec\u003c/em\u003e) family members are associated with specific subsets of microglia that are activated in a disease stage-specific manner in both mouse models of AD and AD patients. For example, Siglec-F is upregulated in response to Aβ pathology and is selectively expressed in Aβ-associated DAMs, while Siglec-G is upregulated in white-matter-associated LADAM in late-stage AD. These findings are consistent with a model whereby Aβ or tau pathologies stimulate activation of microglia in different pattern, both Aβ and tau pathologies are necessary to induce the emergence of EADAM and LADAM, and have important implications for the identification of novel molecular targets and therapeutic strategies for the treatment of AD.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eMice\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTau4R\u0026Delta;K-AP\u003c/em\u003e (\u003cem\u003eAPP\u003csup\u003eswe\u003c/sup\u003e;PS1\u003c/em\u003eD\u003cem\u003eE9;CamKII-tTA;TetO-TauRD\u003c/em\u003eD\u003cem\u003eK\u003c/em\u003e),\u003cem\u003eTau4R\u0026Delta;K\u003c/em\u003e (\u003cem\u003eCamKII-tTA;TetO-TauRD\u0026Delta;K\u003c/em\u003e) and \u003cem\u003eAPP\u003csup\u003eswe\u003c/sup\u003e;PS1\u0026Delta;E9 \u003c/em\u003etransgenic mice were generated as described previously. \u003cem\u003eTau4R\u003c/em\u003e\u003cem\u003eDK\u003c/em\u003e were generated by crossbreeding \u003cem\u003eTetO-TauRD\u0026Delta;K\u003c/em\u003e transgenic mice carrying mutant \u003cem\u003eTau\u003c/em\u003e fragment with regulatory element \u003cem\u003emoPrP-tetP\u003c/em\u003e promoter [26] with \u003cem\u003eCamKII-tTA\u003c/em\u003e mice to bring the Tau transgene under the control of the tet-off \u003cem\u003eCamKII\u003c/em\u003e promoter [27]. \u003cem\u003eTau4R\u0026Delta;K\u003c/em\u003e mice were crossbred with \u003cem\u003eAPP\u003csup\u003eswe\u003c/sup\u003e;PS1\u0026Delta;E9\u003c/em\u003e mice [28] to generate\u003cem\u003e Tau4R\u0026Delta;K-AP\u003c/em\u003e mice that develop both Tau pathology and A\u0026beta; amyloidosis. Because of sex differences observed in \u003cem\u003eTau4R\u0026Delta;K-AP\u003c/em\u003e mice, only female mice were used in this study. Brains were collected from the mice at 6-month-old and 12-month-old for scRNA-Seq, histological and immunohistological studies. Among the three replicates (both 6-month-old and 12-month-old), two sets of the \u003cem\u003eAPP\u003csup\u003eswe\u003c/sup\u003e;PS1\u0026Delta;E9\u003c/em\u003e and control mice also contain \u003cem\u003eCamKII-tTA\u003c/em\u003e driver. Compared with the one set \u003cem\u003eAPP\u003csup\u003eswe\u003c/sup\u003e;PS1\u0026Delta;E9\u003c/em\u003e and wild-type mice without \u003cem\u003eCamKII-tTA\u003c/em\u003e driver, we did not observe a significant difference in microglia in our scRNA-Seq data. To suppress the potential impacts of TTA expression during postnatal development [29], mice were fed with Teklad Global 18% Protein Rodent Diet containing 200 mg/Kg doxycycline hydrochloride (Envigo Teklad Diets, Madison WI) during gestation and before weaning. \u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCaMKII\u0026alpha;\u003csup\u003eCreERT2\u003c/sup\u003e\u003c/em\u003e;\u003cem\u003eTdp\u003c/em\u003e-\u003cem\u003e43\u003csup\u003eF/F\u003c/sup\u003e\u003c/em\u003e mice with \u003cem\u003eloxP\u003c/em\u003e sites flanking \u003cem\u003eTdp\u003c/em\u003e-\u003cem\u003e43\u003c/em\u003e exon 3 were generated as described previously [30]. Oral tamoxifen citrate was administered in the feed (Harlan Teklad) at an average 40 mg/kg/day for 4 weeks beginning at 6- or 9 months of age to induce recombination in excitatory forebrain neurons. Mice were singly housed during this period to monitor tamoxifen-feed intake. Afterward, mice were returned to their original cage grouping with their littermates. Brain tissues were collected two months after the tamoxifen treatment for scRNA-Seq analysis.\u003c/p\u003e\n\u003cp\u003eAll mice were housed in a climate-controlled facility (14-hour dark and 10-hour light cycle) with \u003cem\u003ead libitum\u003c/em\u003e access to food and water managed by Research Animal Resources (RAR) at Johns Hopkins University. All animal procedures were in accordance strictly with the National Institutes of Health Guide for the Care and Use of Laboratory Animals and were approved by the Johns Hopkins University Animal Care and Use Committee.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003escRNA-Seq cell preparation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMouse cortices were collected and dissociated using a previously published protocol [31,32]. Basically, cerebral cortices and hippocampi were dissected into Hibernate-A media with a 2% B-27 and GlutaMAX supplement (0.5 mM final). Tissues were dissociated in papain (Worthington) and debris was removed using OptiPrep density gradient media following cell dissociation. Cells were then processed immediately for scRNA-Seq. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy design and participant\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe postmortem tissues used in the present study were provided by the Johns Hopkins Brain Resource Center. For histological analysis, formalin-fixed paraffin-embedded (FFPE) tissue sections (10 \u0026mu;m) of the inferior parietal region were obtained from 36 pathologically confirmed AD cases with Braak neurofibrillary stages II-VI and controls. Details of human cases used for histology are available in Table S10. The cohort (n= 36) included 22 males and 14 females ages 56 to 96 years (x= 77.58), 32 Whites, and 4 African-Americans (Table S11). The average postmortem interval was 22 hours. We also examined three FFPE hippocampal samples from patients with non-AD tauopathies for histology (Table S10).\u003c/p\u003e\n\u003cp\u003eIn addition to the FFPE sections, we also examined frozen samples from the superior frontal gyrus and entorhinal cortex in AD cases for snRNA-Seq (n=8), as well as 2 samples from patients with non-AD tauopathy. Details of human cases used for snRNA-Seq are available in Table S11. All AD subjects had been prospectively recruited, clinically characterized by the Johns Hopkins Alzheimer\u0026rsquo;s Disease Research Center (ADRC), and underwent neuropathologic postmortem examination excluding Lewy body disease or non-AD tauopathies. The clinical and autopsy components of this study were approved by the Johns Hopkins Medicine IRB. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIsolation of nuclei from the frozen human brain for snRNA-Seq.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFlash-frozen brain tissue was processed for snRNA-Seq following a modified 10x Genomics protocol. Briefly, lysis buffer containing (10 mM Tris-HCl pH7.4, 10 mM NaCl, 3 mM MgCl2, 0.1% Tween-20, 0.1% Nonidet P40, 0.01% Digitonin, 1 U/ul RNase inhibitor, 1% BSA) was added into a tube containing brain micropunch (~100 \u0026mu;m) and incubated on ice for 15 minutes with gentle pestle grinding (5 strokes every 3 min). Wash buffer (10 mM Tris-HCl pH7.4, 10 mM NaCl, 3 mM MgCl2, 0.1% Tween-20, 0.2 U/ul RNase inhibitor, 1% BSA) was added and filtered through a 50\u0026mu;m filter. Debris was removed using OptiPrep density gradient media and nuclei morphology was accessed under the light microscope. Nuclei were then immediately processed for snRNA-Seq.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003escRNA-Seq/snRNA-Seq generation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCells or nuclei were loaded into the 10x Genomics Chromium Single Cell System (10x Genomics) and libraries were generated using v3.1 chemistry following the manufacturer\u0026rsquo;s instructions. Three biological replicates, where each replicates consisted of littermate mice, were used for both 6-month-old and 12-month-old scRNA-Seq runs. Two technical replicates were used for TDPKO scRNA-Seq. Two biological replicates, where each biological replicate had a technical replicate, were used for human snRNA-Seq. Libraries were sequenced on Illumina NovaSeq6000. scRNA-Seq data were first processed through the Cell Ranger (v.3.1.0, 10x Genomics) with default parameters, aligned to the mm10 genome (refdata-cellranger-mm10-3.0.0), and matrix files were used for subsequent bioinformatic analysis. snRNA-Seq data were first processed through the Cell Ranger (v.5.0.0, 10x Genomics) with \u0026lsquo;include-introns\u0026rsquo;, aligned to the GRCh38 genome (refdata-gex-GRCh38-2020-A), and matrix files were used for subsequent bioinformatic analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003escRNA-Seq data analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1. Processing scRNA-Seq datasets\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSeurat v3.15 [33] was used to process matrix files, first by selecting cells with more than 500 genes, 1000 UMI, and less than 50% mitochondrial genes and 20% ribosomal genes. Datasets were then normalized using Seurat \u0026lsquo;\u003cem\u003escTransform\u003c/em\u003e\u0026rsquo; function with regressing the number of genes and UMIs using \u0026lsquo;\u003cem\u003evars.to.regress\u003c/em\u003e\u0026rsquo;, and Harmony v1.0 [34] was used to adjust for batch variation by treating individual scRNA-Seq run as a variance group.\u003c/p\u003e\n\u003cp\u003eThe top 20 variables obtained from Harmony analysis were used for UMAP dimensional reduction. The Louvain clustering algorithm was used to first identify main cell types with default resolution and the top 20 reduction variables obtained from Harmony analysis. Individual cell types were first identified by cross-referencing to previous scRNA-Seq datasets [32,35], ASCOT database[36], as well as other datasets obtained from brain myeloid and microglia [21,37,38]. Doublet cells - cells that express both cell-type-specific genes (~5% of the overall datasets) and unhealthy cells (when mitochondrial/ribosomal genes were expressed at much higher levels compared to other clusters) were excluded from further analysis. This was done in individual ages, in all genotypes. Individual microglia were subsetted and processed as described above. Any doublet cells (~2%) that couldn\u0026rsquo;t be previously identified were removed for further analysis. WAM [25], EADAM, or LADAM genes (top 50 genes from differential gene expression lists) were superimposed using Seurat \u003cem\u003eAddModuleScore\u003c/em\u003e function to calculate module score.\u003c/p\u003e\n\u003cp\u003eDifferential gene tests on the scRNA-Seq datasets were initially performed using Seurat v3.15 \u0026rsquo;\u003cem\u003eFindAllMarkers\u0026rsquo; \u003c/em\u003efunction (\u003cem\u003etest.use = \u0026ldquo;wilcox\u0026rdquo;, logfc.threshold = 0.5, min.pct = 0.2)\u003c/em\u003e. A cluster of microglia that was distributed almost equally among all four genotypes, and showed higher expression of immediate-early genes such as \u003cem\u003eFos, Atf3\u003c/em\u003e, and \u003cem\u003eJunb \u003c/em\u003e\u003cem\u003e[39]\u003c/em\u003e, and this cluster is derived from technical artifacts during the heat-activated enzymatic dissociation step [39] and these microglia were then removed for any downstream analysis.\u003c/p\u003e\n\u003cp\u003eTo identify the percentage or expression level of EADAM or LADAM genes across genotypes, as well as in other mouse models of neurodegenerative disorders and in human AD samples, EADAM (Table ST3) and/or LADAM (Table ST5)-enriched genes were first identified(higher than average logfc \u0026gt; 0.2). Cells that express higher than UMI \u0026gt; 3 were flagged as positive cells, and then the percentage of positive cells relative to the total number of cells in a cluster was identified. These parameters can clearly be defined over 98% of EADAM or LADAM cells in our AD scRNA-Seq dataset.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2. Regulons\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo identify regulons controlling gene expression in different microglial populations across ages and genotypes, SCENIC [40] using python implemented pySCENIC v.0.10.0 Gene regulatory networks (using --masks_dropouts), regulons and network activity of regulons were calculated using default parameters with mm10 feather files on the microglial dataset using raw count matrix. Regulon specificity scores were ranked following the SCENIC pipeline and top regulons with z-score higher than 2 were identified as microglia-specific.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3. GO pathway analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTop differential genes (adjusted p-value \u0026lt; 0.05, fold change \u0026gt; 0.5) in each microglial cluster were used as an input for GO pathway analysis using ClusterProfiler (v3.12.0) [41].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4. RNA velocity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRNA velocity [42] was utilized to understand the dynamic state of microglia, and how each genotype-specific disease-associated microglia cluster was initiated across AD progression. Kallisto v0.46.2 and bustools v2.27.9 [43,44] python wrapper kb-python was used to obtain spliced and unspliced transcripts using --lamanno with GRCm38 mouse genome. Scanpy v1.5.1 [45] and scVelo v0.2.1 [46] were used to process the Kallisto output with default parameters, based on UMAP coordinates obtained from Seurat. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5. Pseudotime analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMonocle v3.0.2 [47] was used to perform pseudotime analysis to identify differences in gene expression across microglial disease states, where the trajectory routes used for pseudotime analysis were identified based on trajectories from RNA velocity analysis, and high-variance genes (q \u0026lt; 0.001) were used for pseudotime plotting.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHistology and Immunohistochemical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor the histological and immunohistochemical analysis, mice were anesthetized and brains were removed and weighed. Hemibrains were fixed by submerging into 4% PFA in PBS, embedded into the paraffin, and sectioned in the sagittal plane. For histological analyses, 10 \u0026mu;m brain sections were stained with hematoxylin and eosin (H\u0026amp;E) or Cresyl violet. For immunohistochemical analysis, sections were treated with 10 mM citrate buffer (pH 6.0) by microwave in high power for 6 minutes for antigen retrieval; endogenous peroxidase was quenched by treating with 0.3% H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e, and nonspecific binding of antibodies was eliminated using blocking buffer (10% normal goat serum in PBS with 0.3% Triton-X) for one hour at room temperature. The primary antibody was prepared in a blocking buffer and was applied overnight at 4\u003csup\u003eo\u003c/sup\u003eC, followed by a secondary antibody for 30 min incubation at room temperature. For the secondary antibody and avidin-biotinylated peroxidase system, we used the Vectastain Universal Elite ABC kit (Vector Laboratories). Brain sections were stained with: antiserum against A\u0026beta; peptides 6E10 (1:1,000; SIG-39300, Covance); rabbit antiserum against phosphorylated S422 of tau (1:2,000; 44764G, Invitrogen, Carlsbad, CA); polyclonal antiserum against GFAP (1:1,000, Z0334, Dako Corporation, Carpinteria, CA); polyclonal antiserum against microglial (IBA1, 1:1,000, CP290, Biocare Medical, CA); monoclonal antibody against NeuN (MAB377, Millipore); polyclonal antiserum against Siglec-10 (1:50, HPA027093, Sigma); Monoclonal Antibody Siglec-F (CD170, 1:300, 14-1702-82, ThermoFisher Scientific). \u003c/p\u003e\n\u003cp\u003eThe quantitative score of Siglec10 positive cells in the inferior parietal region of human brains was the average counts of at least three brain sections in a power (2,000X) microscope field using the ImageJ program. Siglec-10 signals in gray and white matter in the same section were counted separately. The histological section of the human inferior parietal region includes the cerebral cortex and subjacent white matter. These two compartments can be easily separated by their cytological features. The cortex contains a large number of neurons characterized by their large size and prominent nucleus and nucleolus. By contrast, the white matter is rich in oligodendrocytes, which are small, have a compact nucleus and virtually no cytoplasm; and astrocytes. Neurons are virtually absent in the white matter. \u003c/p\u003e\n\u003cp\u003eNeurons in mouse brains were identified by NeuN staining. The quantitative scores of NeuN+, IBA1+, SiglecG+ cells were the average counts in a power (2,000X) microscope field of at least three sagittal sections at 2 mm from the midline of the mouse brains using the ImageJ program.\u003c/p\u003e\n\u003cp\u003eTo quantify the Amyloid plaques, at least three sagittal sections at 2 mm from the midline of the mouse brains were selected. The quantitative analysis was based on the area fraction of 6E10 immunoreactivity using the ImageJ program.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data were analyzed statistically by unpaired Student\u0026rsquo;s two-tailed t-test or one-way ANOVA with Tukey correction for multiple comparisons for cell counting analysis, and two-way ANOVA with Tukey\u0026rsquo;s multiple comparison test for scRNA-Seq cell distribution analysis (distribution of microglia clusters within each genotype and distribution of cell types within each genotype), using GraphPad Prism (GraphPad Software, La Jolla CA the USA). In all tests, values of \u003cem\u003ep \u0026lt; 0.05\u003c/em\u003e were considered to indicate significance.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eScRNA-Seq of microglia in mice harboring Aβ plaques and/or tau deposition\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo identify microglia subtypes through AD-like pathology progression, we performed scRNA-Seq in the cerebral cortex and hippocampus of transgenic mice displaying Aβ plaques and/or tau pathologies. We took advantage of our previously characterized mouse model of AD (\u003cem\u003eTau4RΔK-AP \u003c/em\u003emice), which exhibits AD-like pathologies including Aβ plaques and tau tangles and result in progressive neuronal loss and brain atrophy [24]. In a cross-breeding strategy using mutant \u003cem\u003eAPP\u003csup\u003eswe\u003c/sup\u003e;PS1ΔE9 \u003c/em\u003e(AP) [28] and \u003cem\u003eTau4RΔK\u003c/em\u003e mice, a cohort of \u003cem\u003eTau4RΔK-AP\u003c/em\u003e mice were generated. Female mice were aged to either 6-month-old or 12-month old. Six-month-old \u003cem\u003eTau4RΔK-AP\u003c/em\u003e mice mimic an early AD stage, characterized by low levels of Aβ plaques with minimum tau deposition in the hippocampus but not in the cortex (Figure S1A,B,E,F), and no loss of neurons. Twelve-month-old \u003cem\u003eTau4RΔK-AP\u003c/em\u003e mice mimic a late disease stage, characterized by robust Aβ plaques (Figure S1C,D), tau deposition (Figure S1G,H), loss of neurons and brain atrophy (Figure S1P,Q). For these two time points, in addition to \u003cem\u003eTau4RΔK-AP \u003c/em\u003emice, we also collected cerebral cortices and hippocampi from littermate controls (\u003cem\u003eWT)\u003c/em\u003e, \u003cem\u003eAPP;PS1 \u003c/em\u003e(Aβ plaques (S1R)), and \u003cem\u003eTau4RΔK \u003c/em\u003e(tau deposition, neuronal loss (Figure S1P), and brain atrophy (Figure S1Q)) mice, and subjected these tissues across four genotypes to scRNA-Seq analysis. Compared to \u003cem\u003eTau4RΔK\u003c/em\u003e mice at 12 month of age, Aβ plaques accelerated tau pathogenesis and tau aggregation-dependent (Figure S1C,D,G,H) neuronal loss and brain atrophy (Figure S1P) in 12-month-old \u003cem\u003eTau4RΔK-AP\u003c/em\u003e mice, as previously shown [24]. On the other hand, tau aggregation has little effect on the deposition of Aβ plaques in \u003cem\u003eTau4RΔK-AP\u003c/em\u003e mice as compared with that of \u003cem\u003eAPP\u003csup\u003eswe\u003c/sup\u003e;PS1ΔE9 \u003c/em\u003e(AP) mice (Figure S1R).\u003c/p\u003e\n\u003cp\u003eWe first analyzed female 6-month-old cerebral cortices and hippocampus, across four genotypes in triplicates (Figure S2A). We observed all major cell types of CNS, including neurons, astrocytes, oligodendrocytes, and microglia (Figure S2B,C, Table S1). As previously shown [24], we first confirmed the initiation of tau deposition in the absence of neuronal loss or brain atrophy in these mice (Figure S2B, S4A), along with the presence of Aβ plaques (Figure S1A,B). As expected, we observed a few DAM microglia in \u003cem\u003eTau4RΔK-AP\u003c/em\u003e brains in early-stage disease (Figure S1I,J,M). Analyzing three biological replicate samples, we observed that microglia in \u003cem\u003eAPP\u003csup\u003eswe\u003c/sup\u003e;PS1ΔE9 \u003c/em\u003eand \u003cem\u003eTau4RΔK-AP \u003c/em\u003emice (Figure S1I,K, S2B,D, S4a), expressed a high level of disease-associated microglia 1 (DAM1) marker including \u003cem\u003eCst7\u003c/em\u003e (Figure S2E). However, we failed to observe DAM2 markers, including \u003cem\u003eGpnmb\u003c/em\u003e,even in \u003cem\u003eAPP\u003csup\u003eswe\u003c/sup\u003e;PS1ΔE9 \u003c/em\u003eand \u003cem\u003eTau4RΔK-AP \u003c/em\u003emice at this age (Figure S2F).\u003c/p\u003e\n\u003cp\u003eUsing scRNA-Seq, we also identified cell clusters corresponding to major subtypes of neurons, glia, and immune cells (Figure S3A-C, Table S2) in 12-month-old mice of all four genotypes, analyzing three biological replicates. Consistent with our previous observation, AD-like pathologies in \u003cem\u003eTau4RΔK-AP\u003c/em\u003e mice led to gliosis and neuronal loss. An increase in the microglia population along with a corresponding decrease in the number of neurons was observed in the \u003cem\u003eTau4RΔK-AP\u003c/em\u003e mice (Figure S1K,L,N,O, S3B,D, S4B). Compared to the other three control mice (\u003cem\u003eWT\u003c/em\u003e, \u003cem\u003eAPP\u003csup\u003eswe\u003c/sup\u003e;PS1ΔE9, \u003c/em\u003eand \u003cem\u003eTau4RΔK\u003c/em\u003e mice), \u003cem\u003eTau4RΔK-AP\u003c/em\u003e mice also showed an increase in immune cells as well (Figure S3). \u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAPP\u003csup\u003eswe\u003c/sup\u003e;PS1ΔE9\u003c/em\u003e, \u003cem\u003eTau4RΔK \u003c/em\u003eand \u003cem\u003eTau4RΔK-AP\u003c/em\u003e mice showed a marked increase in the number and proportion of microglia as compared to \u003cem\u003eWT \u003c/em\u003emice, (Figure S1K,L,N, S4B). Known DAM1 marker genes such as \u003cem\u003eCst7\u003c/em\u003e, and DAM2 genes such as \u003cem\u003eGpnmb\u003c/em\u003e, were strongly expressed in both \u003cem\u003eAPP\u003csup\u003eswe\u003c/sup\u003e;PS1ΔE9 \u003c/em\u003eand \u003cem\u003eTau4RΔK-AP\u003c/em\u003e mice (Figure S3E,F). These results establish that these scRNA-Seq datasets have sufficient quantitative power to identify microglia subtypes that are influenced by Aβ and/or tau deposition in a disease stage-specific manner.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIdentification of a novel \u003cu\u003eE\u003c/u\u003early-stage \u003cu\u003eAD\u003c/u\u003e-\u003cu\u003eA\u003c/u\u003essociated \u003cu\u003eM\u003c/u\u003eicroglia (EADAM) induced by both Aβ plaques and tau deposition\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe then subsetted and profiled microglia from the cerebral cortex and hippocampus of 6-month-old \u003cem\u003eTau4RΔK-AP\u003c/em\u003e mice, along with the three control mouse lines. Homeostatic microglia expressing genes such as \u003cem\u003eTmem119\u003c/em\u003e and \u003cem\u003eP2ry12\u003c/em\u003e, were predominantly detected across all genotypes (Figure 1A), but two additional microglia clusters were also observed (Figure 1A, Table S3). The first microglia cluster expressed classic DAM1-like markers, including \u003cem\u003eApoe \u003c/em\u003eand \u003cem\u003eCst7\u003c/em\u003e (Figure 1B). The second microglia cluster expressed DAM1-like markers but also displayed a unique gene module consisting of interferon-related genes, such as \u003cem\u003eIfitm3, Ifit27l2a\u003c/em\u003e, and \u003cem\u003eIfit3\u003c/em\u003e (Figure 1B). Since this cluster was predominantly composed of \u003cem\u003eTau4RΔK-AP\u003c/em\u003e microglia and detected during the early and presymptomatic stage of disease (Figure 1C), we thus identify this cluster as \u003cu\u003eE\u003c/u\u003early-stage \u003cu\u003eAD\u003c/u\u003e-\u003cu\u003eA\u003c/u\u003essociated \u003cu\u003eM\u003c/u\u003eicroglia (EADAM). Our regulon and pathway analysis confirmed the association of EADAM with interferon regulating transcription factor \u003cem\u003eIrf2/7/9\u003c/em\u003e (Figure 1D) and linkage to the GO term ‘Response to Interferon-beta’ (Figure 1E). \u003c/p\u003e\n\u003cp\u003eA small EADAM-like cluster is also detected in \u003cem\u003eAPP\u003csup\u003eswe\u003c/sup\u003e;PS1ΔE9 \u003c/em\u003emicroglia (Figure 1A,C). This observation is consistent with the previous report that the accumulation of Aβ plaques leads to activation of the interferon pathway [48–51]. A small cluster of cells that resemble EADAM-like microglia, expressing \u003cem\u003eIrf7\u003c/em\u003e and other EADAM-enriched genes, such as \u003cem\u003eIfit3, Isg15, Ifi27l2a\u003c/em\u003e, were also detected in 7-month-old 5xFAD snRNA-Seq data [23] (Figure 1F). \u003c/p\u003e\n\u003cp\u003eWe next compared differential gene expressions between EADAM populations across four genotypes. Despite a significant contribution of \u003cem\u003eAPP;PS1\u003c/em\u003e microglia to EADAM cluster, the overall level of EADAM-enriched genes were expressed at a much lower level in \u003cem\u003eAPP\u003csup\u003eswe\u003c/sup\u003e;PS1ΔE9\u003c/em\u003e microglia than that of \u003cem\u003eTau4RΔK-AP\u003c/em\u003e EADAM (Figure 1F, Table S4). On the other hand, the expression of interferon pathway-related genes was not observed in \u003cem\u003eTau4RΔK \u003c/em\u003emicroglia, suggesting that while the accumulation of Aβ plaques leads to activation of the interferon pathway, both Aβ plaques and tau aggregation are necessary to fully induce EADAM gene modules in \u003cem\u003eTau4RΔK-AP \u003c/em\u003emicroglia. In summary, our data identify a novel microglia subtype termed EADAM that is induced by a combination of Aβ plaques and tau deposition during early-stage AD.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIdentification of \u003cu\u003eL\u003c/u\u003eate-stage \u003cu\u003eAD\u003c/u\u003e-\u003cu\u003eA\u003c/u\u003essociated \u003cu\u003eM\u003c/u\u003eicroglia (LADAM) \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo further clarify the influence of Aβ plaques and tau tangles in microglia pathophysiology during late-stage AD, we unbiasedly subdivided subsetted-microglia populations into five distinguishable clusters across all four genotypes (\u003cem\u003eWT\u003c/em\u003e, \u003cem\u003eAPP\u003csup\u003eswe\u003c/sup\u003e;PS1ΔE9, Tau4RΔK\u003c/em\u003e, and \u003cem\u003eTau4RΔK-AP\u003c/em\u003e) in 12-month-old mice. A few notable differences were observed in 12-month-old mice compared to 6-month-old mice. The DAM cluster was further divided into two clusters - DAM1 and DAM2. The DAM1 cluster expressed genes such as \u003cem\u003eTyrobp, Ctsd, C1qa\u003c/em\u003e (Figure 2A,B, Table S5)\u003cem\u003e, \u003c/em\u003ewhereas DAM2 expressed DAM1-enriched genes and additional disease-related genes, such as \u003cem\u003eGpnmb\u003c/em\u003e, \u003cem\u003eCst7\u003c/em\u003e, and \u003cem\u003eSpp1\u003c/em\u003e (Figure 2B). The induction of DAM2 in response to Aβ plaques has been documented in a variety of APP-based mouse models of AD [20]. Indeed, both clusters were highly represented in animal models with Aβ pathologies (\u003cem\u003eAPP\u003csup\u003eswe\u003c/sup\u003e;PS1ΔE9, \u003c/em\u003eand \u003cem\u003eTau4RΔK-AP \u003c/em\u003emice, Figure 2A,C), but the DAM2 abundance in mouse models with only tau pathologies (\u003cem\u003eTau4RΔK\u003c/em\u003e mice) was low (Figure 2A,C, Table S6). These observations are consistent with the view that these DAM clusters arise mainly as a result of Aβ plaques. Similar to situations in the EADAM population of 6-month old mice, \u003cem\u003eTau4RΔK-AP\u003c/em\u003e mice generally have higher levels of DAM enriched genes than that of \u003cem\u003eAPP\u003csup\u003eswe\u003c/sup\u003e;PS1ΔE9\u003c/em\u003e mice (Figure 2F). Since Aβ plaques level in \u003cem\u003eTau4RΔK-AP\u003c/em\u003e mice is similar to that of \u003cem\u003eAPP\u003csup\u003eswe\u003c/sup\u003e;PS1ΔE9 \u003c/em\u003emice (Figure S1R), this observation indicates that while Aβ plaques can induce DAM2, tau deposition and/or neuronal loss could further enhance DAM-enriched genes in \u003cem\u003eTau4RΔK-AP\u003c/em\u003e mice. \u003c/p\u003e\n\u003cp\u003eA diminished EADAM cluster was still detected in 12-month-old \u003cem\u003eTau4RΔK-AP \u003c/em\u003emice. Interestingly, EADAM-like microglia were also detected in 12-month-old \u003cem\u003eTau4RΔK\u003c/em\u003e mice, although they were absent at 6 months of age (Figure 2C). In addition to the wide spread tau aggregation, \u003cem\u003eTau4RΔK\u003c/em\u003e mice already show neuronal loss at 12-month-old [24], suggesting that while the presence of tau aggregation alone may not induce EADAM formation, cell death-induced inflammation may also induce EADAM-like gene expression in microglia. \u003c/p\u003e\n\u003cp\u003eWe also observed a unique microglia cluster that was not detected in 6-month-old mice (Figure 2A). Cells in this cluster expressed MHC Class II genes, such as \u003cem\u003eCd74\u003c/em\u003e, \u003cem\u003eH2-A2\u003c/em\u003e, \u003cem\u003eH2-Eb1\u003c/em\u003e, and \u003cem\u003eH2-Ab1\u003c/em\u003e (Figure 2B, Table S4). We termed these microglia \u003cu\u003eL\u003c/u\u003eate-stage \u003cu\u003eAD\u003c/u\u003e-\u003cu\u003eA\u003c/u\u003essociated \u003cu\u003eM\u003c/u\u003eicroglia (LADAM), which were found in \u003cem\u003eTau4RΔK-AP\u003c/em\u003e, as well as \u003cem\u003eTau4RΔK\u003c/em\u003e, and \u003cem\u003eAPP\u003csup\u003eswe\u003c/sup\u003e;PS1ΔE9 \u003c/em\u003esamples (Figure 2A,C). Regulon analysis identified key transcription factors associated with gene regulatory networks across all microglial clusters. Notably, while \u003cem\u003eHif1a\u003c/em\u003e and \u003cem\u003eEomes\u003c/em\u003e are selectively expressed in the DAM2, \u003cem\u003eRunx3\u003c/em\u003e and \u003cem\u003eIrf4/7\u003c/em\u003e are expressed in LADAM (Figure 2D) and GO analysis on LADAM shows enrichment for ‘MHC Class II’ (Figure 2E). In addition to MHC class II genes, we also identified enriched expressions of \u003cem\u003eS100a \u003c/em\u003efamily genes, including \u003cem\u003eS100a4, S100a6\u003c/em\u003e, and \u003cem\u003eS100a10\u003c/em\u003e (Figure 2G).\u003c/p\u003e\n\u003cp\u003eAlthough LADAM were observed in \u003cem\u003eTau4RΔK-AP\u003c/em\u003e,\u003cem\u003eTau4RΔK\u003c/em\u003e, and \u003cem\u003eAPP\u003csup\u003eswe\u003c/sup\u003e;PS1ΔE9 \u003c/em\u003esamples, we found that many LADAM-specific genes (e.g. MHC Class II genes) are more highly expressed in \u003cem\u003eTau4RΔK \u003c/em\u003ethan in \u003cem\u003eAPP\u003csup\u003eswe\u003c/sup\u003e;PS1ΔE9 \u003c/em\u003emicroglia (Figure 2G), suggesting that this microglia subtype might be induced by the development of tauopathies, such as tau tangles or tau phosphorylation. Moreover, relative to \u003cem\u003eTau4RΔK \u003c/em\u003emicroglia, we found that LADAM gene signatures were more pronounced in \u003cem\u003eTau4RΔK-AP\u003c/em\u003e microglia (Figure 2G, Table S5), which exhibited Aβ plaques, more robust tau pathologies and neuronal loss. These datasets show that tau pathologies are sufficient to induce the emergence of LADAM and that their amplification is Aβ plaque-dependent. However, since MHC class II genes that are expressed in LADAM, such as \u003cem\u003eCd74\u003c/em\u003e, \u003cem\u003eH2-A2\u003c/em\u003e, \u003cem\u003eH2-Eb1\u003c/em\u003e, and \u003cem\u003eH2-Ab1\u003c/em\u003e, have also been previously observed in microglia in both the \u003cem\u003eCK-p25\u003c/em\u003e neurodegeneration mouse model [52,53] and some mouse models of late-stage amyloidosis [48–50], some of LADAM gene modules may be induced at least in part by neuronal death.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRequirement of both Aβ plaques\u003c/strong\u003e \u003cstrong\u003eand tau deposition to elicit the emergence of disease stage-dependent microglia subtypes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo clarify how the emergence of EADAM, DAM2, and LADAM is regulated by Aβ plaques and tau deposition, we merged datasets obtained from microglia of both 6-month-old and 12-month-old samples (Figure 3A). RNA velocity analysis shows that DAM1 is likely to give rise to, or to be closely related to, both DAM2 (Aβ plaques driven) and EADAM (Aβ plaque and tau deposition driven), whereas the convergence of DAM2 (Aβ plaque-driven and tau deposition-enhanced) and EADAM may ultimately lead to induction of LADAM, as the result of tau deposition/cell loss-driven and Aβ plaque-formation (Figure 3B). As shown above, EADAM is composed mostly of cells derived from 6-month-old \u003cem\u003eTau4RΔK-AP\u003c/em\u003e mice (Figure 3B). The DAM2 clusters were enriched in 12-month-old \u003cem\u003eAPP\u003csup\u003eswe\u003c/sup\u003e;PS1ΔE\u003c/em\u003e, and \u003cem\u003eTau4RΔK-AP\u003c/em\u003e mice (Figure 3B), and LADAM clusters were enriched in 12-month-old \u003cem\u003eAPP\u003csup\u003eswe\u003c/sup\u003e;PS1ΔE9\u003c/em\u003e, \u003cem\u003eTau4RΔK\u003c/em\u003e, and \u003cem\u003eTau4RΔK-AP\u003c/em\u003e mice (Figure 3B).\u003c/p\u003e\n\u003cp\u003eFurthermore, RNA velocity coupled with pseudotime analysis (Figure 3C,D) identified gene expression changes occurring during the temporal progression from DAM1 to DAM2 to LADAM, with changes in multiple inflammation-related genes, such as \u003cem\u003eCcl6, Csf1, Cd34\u003c/em\u003e (Figure 3C); as well as during the temporal progression from DAM1 to EADAM to LADAM, with changes in interferon genes, such as \u003cem\u003eIfiti \u003c/em\u003efamily members (Figure 3D). \u003c/p\u003e\n\u003cp\u003eA microglia cluster(s) that express either interferon genes and/or MHC Class II genes have been identified in other neurodegenerative models, such as \u003cem\u003eCK-p25 \u003c/em\u003e\u003cem\u003e[53]\u003c/em\u003e and Trem2 deficient [54,55] FTDP-17-linked tau model (P301L) [56] crossed with \u003cem\u003ePS2APP\u003c/em\u003e\u003cem\u003e[57]\u003c/em\u003emice. However, none of the disease models showed development of separated EADAM and LADAM clusters in a stage-dependent manner. This led to the hypothesis that, while potential conservation of EADAM- or LADAM-like subtypes might occur across both neurodegeneration and neuroinflammation, combination of both Aβ plaques and tau deposition are critical for the induction of disease-stage specific microglia subtypes in AD.\u003c/p\u003e\n\u003cp\u003eTo test this hypothesis, we then used another neurodegenerative mouse model \u003cem\u003eCamKII\u003csup\u003eCreERT2\u003c/sup\u003e;Tardbp\u003csup\u003elox/lox\u003c/sup\u003e (TDP43 KO\u003c/em\u003e), where a tamoxifen induction leads to conditional deletion of TDP-43 in the forebrain of the mice, resulting in the selective vulnerability of hippocampal CA3 neurons [30]. We generated scRNA-Seq data from the cerebral cortex and hippocampus of 2 different \u003cem\u003eTDP43 KO\u003c/em\u003e samples, one being induced with 4-OHT at 6 months and collected at 9 months (\u003cem\u003eTDP43 KO\u003c/em\u003e\u003csup\u003e6-\u0026gt;9\u003c/sup\u003e) and the other being induced with 4-OHT at 12 months and collected at 15 months (\u003cem\u003eTDP43 KO\u003c/em\u003e\u003csup\u003e12-\u0026gt;15\u003c/sup\u003e) (Figure 4A-C). While both \u003cem\u003eTDP43 KO\u003c/em\u003e mice showed DAM1-like clusters, \u003cem\u003eTDP43 KO\u003c/em\u003e\u003csup\u003e6-\u0026gt;9\u003c/sup\u003e showed both EADAM- and LADAM-like clusters that respectively selectively express interferon genes or MHC Class II genes (Figure 4A-C, Table S7). \u003cem\u003eTDP43 KO\u003c/em\u003e\u003csup\u003e12-\u0026gt;15\u003c/sup\u003e also showed an EADAM-like cluster (Figure 4A-C, Table S8), supporting the view that EADAM- or LADAM-like subtypes might occur across neurodegeneratives.\u003c/p\u003e\n\u003cp\u003eWe then integrated datasets from AD mice with those from \u003cem\u003eTDP43 KO\u003c/em\u003e mice (Figure 4D,E). As mentioned above, both 6-month-old \u003cem\u003eAPP\u003csup\u003eswe\u003c/sup\u003e;PS1ΔE9 \u003c/em\u003eand \u003cem\u003eTau4RΔK-AP\u003c/em\u003e microglia showed a similar expression pattern of EADAM-enriched genes (Figure 4D), but \u003cem\u003eTau4RΔK-AP\u003c/em\u003e microglia displayed much higher expression levels of these genes than did \u003cem\u003eAPP\u003csup\u003eswe\u003c/sup\u003e;PS1ΔE9 \u003c/em\u003emicroglia (Figure 4D). 12-month-old \u003cem\u003eTau4RΔK\u003c/em\u003e and \u003cem\u003eTau4RΔK-AP\u003c/em\u003e microglia showed overall lower expression levels, but broadly similar EADAM expression patterns (Figure 4D). Both \u003cem\u003eTDP43 KO\u003c/em\u003e microglia showed a much weaker level of EADAM-enriched genes, similar to 6-month-old \u003cem\u003eAPP;PS1\u003c/em\u003e microglia (Figure 4D). \u003c/p\u003e\n\u003cp\u003eLADAM-enriched genes are composed of DAM2-enriched genes, MHC Class II genes, and \u003cem\u003eS100a \u003c/em\u003efamily genes. While LADAM-like cluster in \u003cem\u003eTDP43 KO\u003c/em\u003e\u003csup\u003e6-\u0026gt;9\u003c/sup\u003e microglia expressed MHC Class II genes, the level of expression was much lower than any of \u003cem\u003eAPP\u003csup\u003eswe\u003c/sup\u003e;PS1ΔE9, Tau4RΔK\u003c/em\u003e and \u003cem\u003eTau4RΔK-AP\u003c/em\u003e microglia (Figure 4E). We also failed to detect any DAM2 like clusters, or either \u003cem\u003eS100a \u003c/em\u003efamily genes and \u003cem\u003eSiglecg \u003c/em\u003ein \u003cem\u003eTDP43 KO\u003c/em\u003e\u003csup\u003e6-\u0026gt;9\u003c/sup\u003e microglia.\u003c/p\u003e\n\u003cp\u003eA similar observation was also made in \u003cem\u003eCK-p25\u003c/em\u003e microglia [53](Figure S5A). While the integration of this dataset was not possible due to the use of different single-cell formats (SMART-Seq was used to generate the \u003cem\u003eCK-p25\u003c/em\u003e dataset), we noted that both EADAM-like interferon genes and LADAM-like MHC Class II genes were more highly expressed in \u003cem\u003eCK-p25\u003c/em\u003e microglia than the control (Figure S5B). However, both EADAM-like and LADAM-like microglia were intermingled, rather than forming separate clusters. We also failed to detect any \u003cem\u003eS100a \u003c/em\u003efamily genes and \u003cem\u003eSiglecg\u003c/em\u003e expression in the \u003cem\u003eCK-p25\u003c/em\u003e microglia dataset, similar to our observations in the \u003cem\u003eTDP43 KO\u003c/em\u003e dataset (Figure S5B).\u003c/p\u003e\n\u003cp\u003eThis indicates that Aβ plaque or tau pathology independently induces specific gene signatures in microglia like EADAM and LADAM, and similar microglia clusters can also be observed in other neurodegeneration models. Neuroinflammation and/or cell loss that occurs in neurodegeneration diseases could be a potential trigger for microglia to develop EADAM- or LADAM-like gene expression profiles. However, the combination of both Aβ plaque and tau pathology results in separated clusters of EADAM and LADAM at different stages of disease, much higher expression levels of the signature genes, and induction of additional molecular markers in LADAM. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIdentification of stage-specific EADAM and LADAM signatures in AD samples\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe next generated snRNA-Seq dataset from the human superior frontal gyrus (SFG) at Braak stage 2, which is devoid of any AD pathology, and entorhinal cortex (ERC) at Braak stage 2, where AD pathology is first detected and shares a pathological resemblance to our 6-month-old \u003cem\u003eTau4RΔK-AP\u003c/em\u003e mice (Figure 5A-C). We also generated snRNA-Seq from the superior frontal gyrus (SFG) at Braak stage 4 and 6, which represent late stages of AD, and share a pathological resemblance to the 12-month-old \u003cem\u003eTau4RΔK-AP\u003c/em\u003e mice. As control, we generated a snRNA-Seq dataset from the ERC and SFG of a non-AD neurodegenerative disorder, primary-age-related-tauopathy (PART) (Figure 5A-C). \u003c/p\u003e\n\u003cp\u003eWe initially checked the expression pattern of EADAM- and LADAM-enriched gene homologs in the human snRNA-Seq dataset (Figure S5C,D), and observed moderate conservation in gene expression pattern. Most EADAM-enriched genes were seen at Braak stage 2 SFG and ERC (Figure S5C), while LADAM-enriched genes were seen at Braak stage 4 and 6 SFG (Figure S5D). Some inconsistency of expression patterns might be due to the absence of AD-specific microglia clusters in the human snRNA-Seq dataset, despite having a high resolution of 6,000 cells. This observation has been previously shown in other human snRNA-Seq datasets from brains affected by AD or other neurodegenerative diseases [58–60]. The difference between human and animal model might be due to the fact that AD and other neurodegenerative diseases typically progress slowly, over many years, and all microglia populations may eventually end up transitioning to disease-associated states. This timing is different in virtually all animal models that were designed to undergo neurodegeneration much more rapidly.\u003c/p\u003e\n\u003cp\u003eWhile the presence of EADAM- and LADAM-like microglial subtypes in AD demonstrated important similarities with our mouse models, we also wanted to analyze the expression of MHC and interferon-induced genes. We observed consistent changes in the expression of interferon-regulated genes. These include the transcription factors and co-regulators \u003cem\u003eIRF2BP2\u003c/em\u003e, \u003cem\u003eIRF2\u003c/em\u003e, and \u003cem\u003eIRF9\u003c/em\u003e, which showed higher expression at Braak stage 2 ERC than SFG, and whose expression levels were decreased in Braak stage 4/6 SFG (Figure 5D). There also was a similar trend in the expression of other interferon-regulated genes, including \u003cem\u003eIFI44\u003c/em\u003e, \u003cem\u003eIFI44L \u003c/em\u003e(Figure 5D). \u003c/p\u003e\n\u003cp\u003eWe also observed a similar trend in MHC genes, such as \u003cem\u003eHLA-A\u003c/em\u003e,\u003cem\u003e HLA-B\u003c/em\u003e, and \u003cem\u003eHLA-DPA1\u003c/em\u003e, which showed a higher expression level in PART ERC and SFG, Braak stage 2 ERC, and in Braak stage 4/6 SFG than Braak stage 2 SFG (Figure 5E). More importantly, \u003cem\u003eS100\u003c/em\u003e genes, such as \u003cem\u003eS100A4\u003c/em\u003e, \u003cem\u003eS100A11\u003c/em\u003e, were exclusively detected in Braak stage 4/6 SFG but were not detected in PART or other AD snRNA-Seq (Figure 5E). This observation was also conserved in other high-quality human AD snRNA-Seq from prefrontal cortex [58](Figure 5F,G), where interferon-regulated, MHC, and \u003cem\u003eS100\u003c/em\u003e family genes were more highly expressed in AD samples than in the control (Figure 5F,G). These findings thus establish the emergence of EADAM-related genes in early AD-stage and LADAM-related genes in late AD-stage, and suggest that these microglia subtypes could be induced by both Aβ plaques and tau deposition in a disease stage-specific manner. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSiglecs may serve as specific biomarkers to track Alzheimer’s disease stage-specific microglia subtypes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur analysis identifies multiple microglial genes that are selectively expressed in late-stage AD, such as Braak-stage-dependent expression of \u003cem\u003eSIGLEC10\u003c/em\u003e, a human homolog of \u003cem\u003eSiglecg\u003c/em\u003e (Figure 5E). Since several of the Siglec family genes are thought to be relevant for AD progression [61], we focused on the expression of Siglec gene family at different microglia clusters. Homeostatic microglia expressed \u003cem\u003eMag\u003c/em\u003e, \u003cem\u003eSiglece\u003c/em\u003e, \u003cem\u003eCd33\u003c/em\u003e, \u003cem\u003eSiglech\u003c/em\u003e (Figure S6A,B), EADAM expressed similar Siglec genes as homeostatic microglia, but also expressed \u003cem\u003eSiglec1\u003c/em\u003e (Figure S6A,B). Both DAM2 and LADAM clusters expressed high levels of \u003cem\u003eSiglecf\u003c/em\u003e (Figure S6A,B). Both \u003cem\u003eSiglec1\u003c/em\u003e and \u003cem\u003eSiglecg \u003c/em\u003ewere enriched in the LADAM. \u003cem\u003eSiglecf\u003c/em\u003e, in particular, was enriched in both \u003cem\u003eAPP;PS1\u003c/em\u003e and \u003cem\u003eTau4RΔK-AP \u003c/em\u003emice (Figure S6C,E) and showed increased expression in late-stage disease (Figure S6D,F). These data suggested that Siglec genes expressed in a pathology and/or stage dependent manner in microglia. \u003c/p\u003e\n\u003cp\u003eTo further characterize late-stage AD-specific genes, We identified 4 subclusters within the LADAM cluster of AD mouse models (Figure 6A). Sub-clusters 2 and 3, in particular, were most robustly and selectively expressed in \u003cem\u003eTau4RΔK-AP\u003c/em\u003e mice (Figure 6B, Table S9). Cluster 2, a LADAM sub-cluster that is marked by \u003cem\u003eSiglecg\u003c/em\u003e, also exhibited genes previously shown to be expressed in \u003cu\u003eW\u003c/u\u003ehite matter-\u003cu\u003eA\u003c/u\u003essociated \u003cu\u003eM\u003c/u\u003eicroglia (WAM), such as \u003cem\u003eLgals3\u003c/em\u003e, \u003cem\u003eFam20c\u003c/em\u003e, \u003cem\u003eVim\u003c/em\u003e (Figure 6A,C) [25]. In WAM-like LADAM, we observed that this cluster also expressed \u003cem\u003eS100a6\u003c/em\u003e, \u003cem\u003eS100a4, S100a11, H2-DMb2, Runx2, \u003c/em\u003eand \u003cem\u003eClec10a\u003c/em\u003e (Figure 6D, Table S9). While a small cluster that resembles WAM was also observed in the 6-month-old mouse cortex (Figure S6J), this small population did not express any LADAM markers in the 12-month-old cortex. We did not detect Siglec-G protein in the 6-month-old corpus callosum, hippocampus, and cortex (Figure 6E, S6G,H,K). Siglec-G immunoreactivity was robust in the 12-month-old corpus callosum in \u003cem\u003eTau4RΔK-AP \u003c/em\u003emicroglia (Figure 6), as well as in the cortex and hippocampus (Figure S6I,K). However, we failed to observe Siglec-G immunoreactivity in \u003cem\u003eAPP;PS1\u003c/em\u003e or \u003cem\u003eTau4RΔK\u003c/em\u003e microglia at the same age, despite increased microglia populations near the corpus callosum (Figure.6D, S6I,K). To corroborate this finding in AD, we used antisera directed against Siglec-10 (the human homolog of Siglec-G) [62] to screen AD brains at multiple Braak stages (Table S10). We found Braak stage-dependent accumulation of Siglec-10 in AD brains (Figure 6F, S7), with Siglec-10 signal was significantly increased from Braak stage 4 (Figure 6F, S7) in both gray and white matters, but a slightly higher number of Siglec-10+ cells were detected in the white matter. However, Siglec-10 signal in non-AD tauopathy is similar to that of healthy controls, consistent with our snRNA-Seq data (Figure 5G, 6F, S7). To further validate these observations, we checked snRNA-Seq datasets of human postmortem Braak stage 4 and 6 SFG (Figure 6G, Table S11). We observed a small population of WAM-like LADAM, as seen in 12-month-old \u003cem\u003eTau4RΔK-AP \u003c/em\u003emice (Figure 6H), expressing genes like \u003cem\u003eSIGLEC10, S100A6, LGALS3, FAM20C\u003c/em\u003e (Figure 6H). Siglecs, in particular Siglec10, are AD-enriched, as the level of Siglec-10+ cells in non-AD tauopathy was similar to that of control and low Braak stage AD (Figure 6F, S7), indicating that Siglec-10 could potentially serve as an AD biomarker and a potential therapeutic target associated with late-stage AD. Some LADAM markers, particularly MHC II genes, were expressed in other neurodegenerative models (Figure 5, S5), but \u003cem\u003eS100\u003c/em\u003e family of genes and \u003cem\u003eSiglecg\u003c/em\u003e was unique to late-stage AD microglia and are seen in both animal models and late-stage AD patient brains. This data further supports the view that induction of the AD specific LADAM subcluster is Aβ- and tau-dependent.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e\u003cstrong\u003eIdentification of novel disease stage-specific microglial subtypes induced by Aβ plaques and tau pathology\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe identification of multiple AD risk alleles implicated in the regulation of inflammation [13,63] and of DAM induction in response to Aβ plaques [20], strongly support the hypothesis that microglia-driven neuroinflammation is a key regulator of AD progression. However, critical information regarding microglia subtypes that respond to disease stage-specific canonical AD pathologies of Aβ and tau, particularly during early disease stages, remains elusive. To address this issue, we took advantage of our \u003cem\u003eTau4RΔK-AP\u003c/em\u003e mouse model, which exhibits both Aβ plaques and tau deposition, and profiled microglia subtypes during disease progression using scRNA-Seq. To tease apart the influence of Aβ plaques from that of tau deposition, we also profiled microglial subtypes in \u003cem\u003eAPP;PS1 \u003c/em\u003e(model of Aβ plaques) and \u003cem\u003eTau4RΔK \u003c/em\u003e(model of tau deposition) littermate mice. We identified several disease stage-specific subtypes of microglia that are selectively induced in response to Aβ and/or tau. \u003c/p\u003e\n\u003cp\u003eWe discovered the emergence of novel microglia subtype, \u003cu\u003eE\u003c/u\u003early-stage \u003cu\u003eAD\u003c/u\u003e-\u003cu\u003eA\u003c/u\u003essociated \u003cu\u003eM\u003c/u\u003eicroglia (EADAM), that is selectively induced by both Aβ and tau pathologies but not either Aβ or tau pathologies alone, during early-stage disease before AD-like pathologies are observed. EADAM are characterized by many interferon-pathway-related genes (\u003cem\u003eIfit3, Ifit204, Ifit2\u003c/em\u003e) and interferon regulating transcription factors (\u003cem\u003eIrf2/7/9)\u003c/em\u003e as well as inflammatory chemokines (\u003cem\u003eCcl12, Cxcl10, Ccl5, Ccl2\u003c/em\u003e). Some of the genes in EADAM have been observed in other mouse models of amyloidosis that show early AD-like pathology [48–50], including \u003cem\u003eAPP;PS1\u003c/em\u003e and 5xFAD mouse models. However, given that EADAM-enriched genes are expressed in much higher levels in our \u003cem\u003eTau4RΔK-AP\u003c/em\u003e microglia, this strongly supports the view that both Aβ and tau pathologies are required to induce disease stage-specific microglia subtypes in AD. \u003c/p\u003e\n\u003cp\u003eWe also identified previously described DAM in both mouse lines in which Aβ plaques are induced (\u003cem\u003eAPP;PS1\u003c/em\u003e and \u003cem\u003eTau4RΔK-AP\u003c/em\u003e) [20,64]. However, DAM induction in \u003cem\u003eTau4RΔK \u003c/em\u003emice was blunted, supporting the hypothesis that DAM are induced by Aβ plaques and/or inflammation, but not by tau pathology. While Aβ plaques alone are sufficient to induce DAM2, this subtype can be greatly amplified by both Aβ and tau pathologies, giving rise to LADAM. LADAM continues to express classic DAM genes but also upregulates additional genes, including \u003cem\u003eCd74\u003c/em\u003e and MHC Class II genes. These broad LADAM marker-expressing microglia (\u003cem\u003eCd74\u003c/em\u003e and MHC Class II genes) have been previously shown in some amyloidosis mouse models at late stages (known as activated response microglia) [48–50], but these genes are likely to be contributed by mild tau phosphorylation shown in these amyloidosis mouse models, based on our findings from \u003cem\u003eTau4RΔK\u003c/em\u003e (Figure 2). In addition, since LADAM-like clusters and markers were observed in other neurodegeneration models, such as \u003cem\u003eTDP43 KO\u003c/em\u003e and \u003cem\u003eCK-p25\u003c/em\u003e [52,53], LADAM-enriched genes might be induced by neurodegeneration and/or inflammation in addition to tau pathologies. However, much like EADAM, LADAM-enriched genes were expressed in \u003cem\u003eTau4RΔK-AP\u003c/em\u003e microglia, indicating that the full set of LADAM-specific genes is induced by the combination of both Aβ and tau pathologies. Furthermore, the combination of Aβ and tau pathologies induced LADAM to express unique \u003cem\u003eS100a \u003c/em\u003egenes and \u003cem\u003eSiglecg\u003c/em\u003e. \u003c/p\u003e\n\u003cp\u003eIn particular, a sub-cluster of LADAM during late-stage disease shares a similar gene signature with previously identified \u003cu\u003ew\u003c/u\u003ehite-matter-\u003cu\u003ea\u003c/u\u003essociated \u003cu\u003em\u003c/u\u003eicroglia (WAM). WAM have been previously shown to be present in 24-month-old wild-type mice [25]. Indeed, microglia near white matter tracts were detected in all four genotypes in 12-month-old mice, but only mice with Aβ plaques and tau deposition showed expression of specialized LADAM gene signatures such as \u003cem\u003eSiglecg.\u003c/em\u003e These data indicated that Aβ plaques and tau deposition induce new molecular signatures in previously identified WAM, leading to the formation of this LADAM subcluster. \u003c/p\u003e\n\u003cp\u003eWhile our discovery of novel microglia subtypes has important clinical implications, the mechanisms by which EADAM and LADAM emerge during different stages of disease progression in response to Aβ plaques and tau deposition remain to be fully established. Our current study identifies potential regulators of these microglia populations in response to Aβ plaques and tau deposition. Neuronal loss or brain atrophy induced by aging and/or tau pathologies may also play an essential role in regulating this process. \u003c/p\u003e\n\u003cp\u003eIn summary, our data indicated that while EADAM-related or LADAM-related gene activation represents a general response of microglia to various pathological contexts, there exists a unique gene signature that would define specific microglia subtypes that respond to specific pathology temporally during disease progression. Thus, by elucidating such “microglia response code”, one is expected to discover specific microglia subtypes relevant to the pathological context in human disease. Supporting this view, while we observed both EADAM-like and/or LADAM-like clusters in \u003cem\u003eTDP43-KO\u003c/em\u003e or \u003cem\u003eCK-p25\u003c/em\u003e mouse models, there are substantial differences in expression levels of EADAM and LADAM genes in \u003cem\u003eTau4R\u003c/em\u003e\u003cem\u003e△K-AP\u003c/em\u003e as compared to those of \u003cem\u003eTDP43-KO\u003c/em\u003e or \u003cem\u003eCK-p25\u003c/em\u003e as well as \u003cem\u003eTau4R\u003c/em\u003e\u003cem\u003e△K\u003c/em\u003e and \u003cem\u003eAPP;PS1\u003c/em\u003e mice (Figure1,2,4,S5). We also found unique LADAM markers in \u003cem\u003eTau4R\u003c/em\u003e\u003cem\u003e△K-AP\u003c/em\u003e mice that are not expressed in any other neurodegenerative models (Figure 2,4,S5). These data strongly support a model in which there exists an EADAM-like and LADAM-like “microglia code” harbored by microglia subtypes that respond to either a general or specific pathological context in the brain. We demonstrate for both Aβ and tau pathologies that this canonical AD pathology context selectively induces novel microglia subtypes in a disease stage-specific manner. These disease stage-dependent “microglial code” could not only serve as biomarkers for precise diagnosis but also as the therapeutic target for stage-specific intervention of specific neurodegenerative diseases.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIdentifications of disease stage-specific microglia subtypes in AD\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe emergence of EADAM during the presymptomatic AD stage and detection of LADAM when AD-like pathologies are visible led to the hypothesis that AD brains may harbor similar unique microglia clusters. EADAM were observed in the entorhinal cortex during early (Braak II) but not late (Braak VI) stage disease, strongly supporting the view that EADAM first emerge in response to initial stages of Aβ plaque formation and tau deposition, corresponding to a stage of negligible or mild cognitive impairment. The microglia cluster expressing IFN-related gene that is similar to EADAM, has been shown in the FTDP-17-linked tau model (P301L) [56] crossed with PS2APP [57], but only when \u003cem\u003eTrem2\u003c/em\u003e is deficient [54,55]. Since \u003cem\u003eTrem2\u003c/em\u003e deficiency can result in the spreading of tau aggregates [65], the FTDP-17-linked tau model (P301L) can result in mild tau aggregation, which resemble the early stages of our mouse model that is characterized with Aβ plaques stimulated pathological conversion of wild-type mouse tau to form tau tangle-like aggregates. These previous finding [55] aligns well with our finding that Aβ plaques and tau deposition are required to give rise to EADAM.\u003c/p\u003e\n\u003cp\u003eIn contrast, the emergence of LADAM in both the entorhinal cortex and superior frontal gyrus did not occur until Braak IV stage, implying that these subtypes are induced in response to more severe Aβ and tau pathologies accompanied by neuronal loss during late-stage disease. Some LADAM-enriched genes such as \u003cem\u003eHLA-DRB1 \u003c/em\u003eand \u003cem\u003eHLA-DRB5 \u003c/em\u003epositively correlate with AD pathology [66]\u003cem\u003e, \u003c/em\u003eand share a few similar molecular signatures (e.g. MHC Class II genes) with lipid droplet-accumulating microglia [67].We also observed an upregulation of \u003cem\u003eS100A \u003c/em\u003egenes in late-stage AD.\u003c/p\u003e\n\u003cp\u003eAlthough interferon-signalling-related genes (EADAM-enriched) and MHC and S100 family genes (LADAM-enriched) are observed during human early- and late-AD stages, respectively, not all EADAM- or LADAM-enriched genes were detected in human samples. We were also unable to identify well-defined and distinct microglial clusters in human AD snRNA-Seq, in agreement with other studies. This might reflect the much longer time course of AD progression in humans compared to mouse models, potentially leading to less well-defined microglia subtypes. The cluster-basis analysis method which is typically used for the analysis of cell states in animal models might not be the best method to define microglia subtypes in human disease states. Nonetheless, gene profiling studies conducted in appropriate animal models of AD could yield insights to facilitate profiling of corresponding unique microglia subtypes or specific genes in AD brains, a task that can be technically challenging and difficult to interpret.\u003c/p\u003e\n\u003cp\u003eThe fact that EADAM are first detected in 6-month-old \u003cem\u003eTau4RΔK-AP \u003c/em\u003emice and Braak stage II AD datasets, while LADAM are first detected in 12-month-old\u003cem\u003eTau4RΔK-AP \u003c/em\u003emice and Braak stage IV AD datasets, demonstrates that disease stage-dependent changes in microglial subtypes in mice with Aβ and tau pathologies mimic those seen in AD. \u003cem\u003eTau4RΔK-AP\u003c/em\u003e mice faithfully model not only the development of canonical AD pathologies, including the pathological conversion of wild-type mouse tau [24], but also the disease stage-specific emergence of EADAM and LADAM in response to both Aβ plaques and tau deposition, emphasizing that our mouse model will prove useful in both analyzing AD disease mechanisms and testing therapeutic strategies for treating AD. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSiglec signaling in EADAM and LADAM\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe identification of disease stage-enriched EADAM and LADAM in our \u003cem\u003eTau4RΔK-AP\u003c/em\u003e model and AD brains raise the possibility that specific signaling pathways may be important in the emergence of these microglial subtypes. CD33 (also known as Siglec-3) [68], is one of the several microglial-specific genes that have been linked to AD susceptibility via genome-wide association studies (GWAS) [69] and both mediates immune suppression by binding sialoglycan targets and attenuates phagocytosis by microglia during AD progression [68,70]. As a result, we first focused on characterizing the expression of sialic acid-binding immunoglobulin-like lectin (Siglec) family of proteins. Human Siglecs belong to a family of 14 distinct transmembrane proteins, many of which are expressed in overlapping subsets of immune cells and inhibit immune activation [62,71]. In addition to Siglec-3, other members of the Siglec family might regulate microglial function in AD. Our findings showing specific enrichment of Siglec-pathway genes in EADAM and LADAM strongly support this hypothesis. Siglec-G is enriched in microglia near myelinated regions, whereas Siglec-F+ microglia are more broadly expressed throughout the brain. Our data suggest that Siglec-F is dependent on Aβ deposition, that Siglec-G is dependent on tau deposition, and that Aβ and tau deposition synergistically enhance the expression of both Siglecs. The observation that \u003cem\u003eSiglecg \u003c/em\u003eis highly expressed in LADAM while Siglec-10 (the human homolog of Siglec-G), is associated with late-stage AD pathologies exhibiting robust Aβ and Tau deposition, further supporting the view that Siglec10 signalling underlies the activation of LADAM in AD. Moreover, EADAM selectively expressed both \u003cem\u003eSiglec1\u003c/em\u003e and \u003cem\u003eSiglecf\u003c/em\u003e, while DAM and MHC Class II clusters both expressed \u003cem\u003eSiglecg\u003c/em\u003e, indicating that Siglec-1, Siglec-F, and Siglec-G, respectively, represent potential biomarkers for detection of early- and late-stage disease. Based on these findings, we hypothesize that Siglec-regulated signalling pathways may have an important function in regulating disease stage-specific microglia during AD progression. Future studies will directly address disease-associated functions of Siglec-regulated signalling pathways.\u003c/p\u003e\n\u003cp\u003eHowever, it is often difficult to identify Siglec orthologues between mice and humans, as this gene family evolved rapidly [71,72]. Mice do not express Siglec-8, and mouse Siglec-3 (mSiglec‑3) has different effector domains, binding specificity, cellular distribution, and biological properties compared to those of human Siglec-3 (hSiglec‑3) [73,74]. However, current studies suggested these is functional conservation between mouse and human in Siglec gene family. Siglec In mouse microglia, immune inhibitory Siglec-F is among the most highly up-regulated genes (27-fold) in experimental neurodegenerative proteinopathy [75]. Siglec-F, a likely functional orthologue of Siglec-8 [76], is expressed in eosinophils as well as microglia [75,76], binds to the same sialoglycans and sialoglycan mimetics as Siglec-8 (unlike mSiglec-3) [77,78]. Both Siglec-F and Siglec-8 also regulate eosinophilic inflammation [79,80]. Siglec-F was upregulated on a subset of reactive microglia in models of neurodegeneration, indicating the important role for Siglec-F/Siglec-8 in regulating microglial activation during neurodegeneration [81]. In a recent study, RPTPζ\u003csup\u003eS3L\u003c/sup\u003e, a sialoglycoprotein was identified as a specific ligand in human cerebral cortex that binds CD33 as well as Siglec-8[82]. Interestingly, RPTPζ isoform in mice binds mouse Siglec-F and cross-reacts with human CD33 and Siglec-8[82], suggesting Siglec-F shares a similar ligand with CD33 and Siglec8 and is a potential functional counterpart of CD33 in humans. Importantly, we observed similar expression patterns of siglec signaling between mouse and human, such as Siglec-F/8 expression emerged at earlier disease stages than Siglec-G/10 in both our mouse models and in AD, indicating that our mouse model could serve as an important tool to study the functions of Siglec signalling in AD pathological stages and microglia subtypes\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn this study, microglia subtypes in mouse model of AD that develop both Aβ and tau pathologies were profiled using scRNA-Seq.\u0026nbsp;We found that during early-stage disease in 6-month-old \u003cem\u003eTau4RΔK-AP\u003c/em\u003e, the presence of both Aβ and tau pathologies induced a novel microglia subtype EADAM, which is distinct from DAM and express multiple interferon-regulated genes. In late-stage disease (12-month-old \u003cem\u003eTau4RΔK-AP\u003c/em\u003e mice), we found that another novel microglia subtype LADAM emerged in response to tau pathology, which expresses MHC and S100 family genes. We further observed a unique subtype of LADAM located near white matter, which is molecularly similar to a previously identified white matter-associated microglial subtype (WAM). We found that the signature genes in EADAM were also associated with a subgroup of microglia in the brains of early (Braak II) stages of AD. While LADAM microglia, including WAM-like LADAM, were observed in late (Braak VI), but not early (Braak II) stages of AD. Corroborating these findings, we found that \u003cem\u003eSiglec\u003c/em\u003e family members are associated with specific subsets of microglia that are activated in a disease stage-specific manner in both mouse models of AD and AD patients. For example, Siglec-F is upregulated in response to Aβ pathology and is selectively expressed in Aβ-associated DAMs, while Siglec-G/10 is upregulated in white-matter-associated LADAM in late-stage AD. These findings are consistent with a model whereby Aβ or tau pathologies stimulate activation of microglia in different pattern, both Aβ and tau deposition are required to mediate the disease stage-specific induction of EADAM and LADAM, and can alter Siglec signalling across AD pathological stage and microglia subtypes. These findings may have important implications for the identification of novel molecular targets, offer new targets for the development of pre-symptomatic biomarkers and therapeutic strategies for AD.\u003c/p\u003e \u003cp\u003eOur data indicated that while EADAM-related or LADAM-related gene activation represents a general response of microglia to various pathological contexts, there exists a unique gene signature that would define specific microglia subtypes that respond to specific pathology temporally during disease progression. Thus, by elucidating such \u0026ldquo;microglia response code\u0026rdquo;, one is expected to discover specific microglia subtypes relevant to the pathological context in human disease. Supporting this view, while we observed both EADAM-like and/or LADAM-like clusters in \u003cem\u003eTDP43-KO\u003c/em\u003e or \u003cem\u003eCK-p25\u003c/em\u003e mouse models, there are substantial differences in expression levels of EADAM and LADAM genes in \u003cem\u003eTau4R△K-AP\u003c/em\u003e as compared to those of \u003cem\u003eTDP43-KO\u003c/em\u003e or \u003cem\u003eCK-p25\u003c/em\u003e as well as \u003cem\u003eTau4R△K\u003c/em\u003e and \u003cem\u003eAPP;PS1\u003c/em\u003e mice. We also found unique LADAM markers in \u003cem\u003eTau4R△K-AP\u003c/em\u003e mice that are not expressed in any other neurodegenerative models. These data strongly support a model in which there exists an EADAM-like and LADAM-like \u0026ldquo;microglia code\u0026rdquo; harbored by microglia subtypes that respond to either a general or specific pathological context in the brain. We demonstrate for both Aβ and tau pathologies that this canonical AD pathology context selectively induces novel microglia subtypes in a disease stage-specific manner. These disease stage-dependent \u0026ldquo;microglial code\u0026rdquo; could not only serve as biomarkers for precise diagnosis but also as the therapeutic target for stage-specific intervention of specific neurodegenerative diseases.\u003c/p\u003e \u003cp\u003eLastly, similar microglia cell types that share partial overlap in gene expression and low levels of expression of EADAM and LADAM markers can be detected in other mouse models of neurodegenerative disorders. While we have given unique names to AD-associated microglia in our AD models, a more standardized nomenclature for disease-associated microglial subtypes needs to be worked out by the research community in the near future.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003esuperior frontal gyrus (SFG), entorhinal cortex (ERC),\u0026nbsp;\u003cu\u003eE\u003c/u\u003early-stage \u003cu\u003eAD\u003c/u\u003e-\u003cu\u003eA\u003c/u\u003essociated \u003cu\u003eM\u003c/u\u003eicroglia (EADAM), \u003cu\u003eL\u003c/u\u003eate-stage \u003cu\u003eAD\u003c/u\u003e-\u003cu\u003eA\u003c/u\u003essociated \u003cu\u003eM\u003c/u\u003eicroglia (LADAM), Alzheimer\u0026rsquo;s disease (AD), amyloid-\u0026beta; (A\u0026beta;), Single-cell RNA-sequencing (scRNA-Seq), Sialic acid-binding immunoglobulin-type lectin (Siglec),\u0026nbsp;formalin-fixed paraffin-embedded (FFPE), hematoxylin and eosin (H\u0026amp;E), \u003cem\u003eAPP\u003csup\u003eswe\u003c/sup\u003e;PS1\u0026Delta;E9\u0026nbsp;\u003c/em\u003e(AP), primary-age-related-tauopathy (PART)\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll animal procedures were in accordance strictly with the National Institutes of Health Guide for the Care and Use of Laboratory Animals and were approved by the Johns Hopkins University Animal Care and Use Committee\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors are consent for the publication. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFurther information and requests for resources and reagents should be directed to and will be fulfilled by the Lead Contact, Tong Li ([email protected]).\u003c/p\u003e\n\u003cp\u003eAll unique/stable reagents generated in this study are available from the Lead Contact without restriction.\u003c/p\u003e\n\u003cp\u003eSingle-cell RNA-seq data have been deposited at GEO (GSE175546) and are publicly available as of the date of publication. Accession numbers are listed in the key resources table. All other data reported in this paper will be shared by the lead contact upon request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests: \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunds:\u003c/strong\u003e \u003c/p\u003e\n\u003cp\u003eThis work was supported in part by the Maryland Stem Cell Research Fund (2019-MSCRFF-5124) to DWK, grants from the National Institute of Aging (R56AG068089) to RLS and TL, (R21AG073710) to TL, and the National Institute of Neurological Disorders and Stroke (R61NS115161 and R01NS095969) to PCW. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStudy was designed by TL, DWK; mouse tissues were collected and characterized by TL, KJT, AW, AJL, TC, KB; JCT provided human specimen; AG, RS provided antibodies for siglecs, DWL, JPL, JCT, PCW, SB, RLS and TL prepared the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments: \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the Transcriptomics and Deep Sequencing Core (Johns Hopkins) for the sequencing of scRNA-Seq libraries and the Johns Hopkins ADRC Neuropathology Core for brain tissues.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBohlen CJ, Friedman BA, Dejanovic B, Sheng M. 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Alzheimer\u0026rsquo;s disease risk gene CD33 inhibits microglial uptake of amyloid beta. Neuron. 2013;78:631\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVarki A, Schnaar RL, Crocker PR. I-Type Lectins. In: Varki A, Cummings RD, Esko JD, Stanley P, Hart GW, Aebi M et al, editors. Essentials of Glycobiology. Cold Spring Harbor (NY): Cold Spring Harbor Laboratory Press; 2017.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVarki A. Multiple changes in sialic acid biology during human evolution. Glycoconj J. 2009;26:231\u0026ndash;45.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBhattacherjee A, Rodrigues E, Jung J, Luzentales-Simpson M, Enterina JR, Galleguillos D, et al. Repression of phagocytosis by human CD33 is not conserved with mouse CD33. Commun Biol. 2019;2:450.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrinkman-Van der Linden ECM, Angata T, Reynolds SA, Powell LD, Hedrick SM, Varki A. 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Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.1016/j.jbc.2022.101960\u003c/span\u003e\u003cspan address=\"10.1016/j.jbc.2022.101960\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"molecular-neurodegeneration","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mond","sideBox":"Learn more about [Molecular Neurodegeneration](http://molecularneurodegeneration.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/mond/default.aspx","title":"Molecular Neurodegeneration","twitterHandle":"@MolNeuro","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Amyloid-β (Aβ), Tau, Microglia, Single-cell RNA-sequencing (scRNA-Seq), Sialic acid-binding immunoglobulin-type lectin (Siglec)","lastPublishedDoi":"10.21203/rs.3.rs-1598611/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1598611/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAmongst risk alleles associated with late-onset Alzheimer\u0026rsquo;s disease (AD), those that converged on the regulation of microglia activity have emerged as central to disease progression. Yet, how canonical amyloid-β (Aβ) and tau pathologies regulate microglia subtypes during the progression of AD remains poorly understood.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe use single-cell RNA-sequencing to profile microglia subtypes from mice exhibiting both Aβ and tau pathologies across disease progression. We identify novel microglia subtypes that are induced in response to both Aβ and tau pathologies in a disease stage-specific manner. To validate the observation in AD mouse models, we also generated snRNA-Seq dataset from the human superior frontal gyrus (SFG) and entorhinal cortex (ERC) at different Braak stages.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eWe show that during early-stage disease, interferon signalling induces a subtype of microglia termed \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eE\u003c/span\u003early-stage \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eAD\u003c/span\u003e-\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eA\u003c/span\u003essociated \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eM\u003c/span\u003eicroglia (EADAM) in response to both Aβ and tau pathologies. During late-stage disease, a second microglia subtype termed \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eL\u003c/span\u003eate-stage \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eAD\u003c/span\u003e-\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eA\u003c/span\u003essociated \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eM\u003c/span\u003eicroglia (LADAM) is detected. While similar microglia subtypes are observed in other models of neurodegenerative disease, the magnitude and composition of gene signatures found in EADAM and LADAM are distinct, suggesting the necessity of both Aβ and tau pathologies to elicit their emergence. Importantly, the pattern of EADAM- and LADAM-associated gene expression is observed in microglia from AD brains, during the early (Braak II)- or late (Braak VI/V)- stage of the disease, respectively. Furthermore, we show that several Siglec genes are selectively expressed in either EADAM or LADAM. \u003cem\u003eSiglecg\u003c/em\u003e is expressed in white-matter-associated LADAM, and expression of \u003cem\u003eSiglec\u003c/em\u003e-\u003cem\u003e10\u003c/em\u003e, the human orthologue of \u003cem\u003eSiglecg\u003c/em\u003e, is progressively elevated in an AD-stage-dependent manner but not shown in non-AD tauopathy.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eUsing scRNA-Seq in mouse models bearing amyloid-β and/or tau pathologies, we identify novel microglia subtypes induced by the combination of Aβ and tau pathologies in a disease stage-specific manner. Our findings suggest that both Aβ and tau pathologies are required for the disease stage-specific induction of EADAM and LADAM. In addition, we revealed Siglecs as biomarkers of AD progression, and potential therapeutic targets.\u003c/p\u003e","manuscriptTitle":"Amyloid-beta and tau pathologies act synergistically to induce novel disease stage-specific microglia subtypes","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-06-01 14:20:12","doi":"10.21203/rs.3.rs-1598611/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2022-06-01T14:17:22+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2022-05-26T09:36:41+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2022-05-11T19:14:43+00:00","index":"","fulltext":""},{"type":"submitted","content":"Molecular Neurodegeneration","date":"2022-04-26T16:48:22+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"molecular-neurodegeneration","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mond","sideBox":"Learn more about [Molecular Neurodegeneration](http://molecularneurodegeneration.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/mond/default.aspx","title":"Molecular Neurodegeneration","twitterHandle":"@MolNeuro","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1d4d5b81-64d5-45ce-a881-cdce4d4b64be","owner":[],"postedDate":"June 1st, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2022-12-06T15:23:29+00:00","versionOfRecord":[],"versionCreatedAt":"2022-06-01 14:20:12","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1598611","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1598611","identity":"rs-1598611","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}