Switch of innate to adaptative immune responses in the brain of patients with Alzheimer’s disease correlates with tauopathy progression

Switch of innate to adaptative immune responses in the brain of patients with Alzheimer’s disease correlates with tauopathy progression | 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 Brief Communication Switch of innate to adaptative immune responses in the brain of patients with Alzheimer’s disease correlates with tauopathy progression Marcos Costa This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3404778/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 18 Mar, 2024 Read the published version in npj Aging → Version 1 posted You are reading this latest preprint version Abstract Neuroinflammation is a key feature of Alzheimer's disease (AD). In this work, I analyze single-cell RNA-sequencing (scRNA-seq) data obtained from the brain of patients with AD and show evidence supporting a switch from an innate to an adaptative immune response during tauopathy progression, with both disease-associated microglia (DAM) and CD8 + T cells becoming more frequent at advanced Braak stages. Biological sciences/Neuroscience/Diseases of the nervous system/Alzheimer's disease Biological sciences/Neuroscience/Diseases of the nervous system/Dementia/Alzheimer's disease Neuroinflammation Alzheimer's disease T cells Microglia CXCL16 Figures Figure 1 Figure 2 Figure 3 INTRODUCTION In the brain of patients with Alzheimer’s disease (AD), both extracellular deposition of amyloid-β as neuritic plaques and intracellular accumulation of hyperphosphorylated tau as neurofibrillary tangles are associated with innate and adaptative immune responses [ 1 – 4 ]. Similarly, inflammatory responses are observed in virtually every animal model of amyloidopathy and tauopathy [ 5 ], indicating an intimate interaction between these pathological processes. However, the influence of different immune cell types/subtypes to AD pathology onset and progression remains largely unknown. Animal models of tauopathy (Tau(P301S)) develop a unique innate and adaptive immune response in the brain, characterized by a marked increase in the numbers of T cells, especially cytotoxic T lymphocytes, and correlated with the extent of neuronal loss [ 6 ]. Depletion of T cells in Tau(P301S) mice via intraperitoneal administration of anti-CD4 and anti-CD8 antibodies is sufficient to ameliorate brain atrophy and improves behavior, suggesting that infiltration of T cells in the brain parenchyma is a key step in neurodegeneration. However, it remains to be demonstrated that a similar association between neurodegeneration and adaptative immune response occurs in the brain of patients with AD. In this work, I took advantage of the largest single-nucleus RNA-sequencing (snRNA-seq) dataset available from patients with AD and age-matched controls [ 7 ] to characterize the innate and adaptative immune responses in the brain of patients with AD at different pathological stages. I show that the number of T cells increase in the brain of patients with AD at late compared to early Braak stages. This phenomenon coincides with changes in microglial subpopulations and activation of signaling mechanisms between microglia and T cells, including the CXCL16 pathway. METHODS Analyses of single-cell RNA-sequencing data Data used in this study was obtained from the Seattle Alzheimer's Disease Brain Cell Atlas (SEA-AD) consortium, which includes the Allen Institute for Brain Science, the University of Washington, and Kaiser Permanente Washington Research Institute. SEA-AD is supported by the National Institutes on Aging (NIA) grant U19AG060909. Study data were generated from postmortem brain tissue obtained from the University of Washington BioRepository and Integrated Neuropathology (BRaIN) laboratory and Precision Neuropathology Core, which is supported by the NIH grants for the UW Alzheimer's Disease Research Center (P50AG005136 and P30AG066509) and the Adult Changes in Thought Study (U01AG006781 and U19AG066567). The ACT study is a longitudinal population-based prospective cohort study of brain aging and incident dementia in the Seattle metropolitan area. ACT is a repository at the Kaiser Permanente Washington Health Research Institute, which has established policies and procedures for sharing data with external investigators. Data available from this study web site do not require any additional Institutional Review Board (IRB) approval or permissions. Transcriptomic data from 40,000 cells originally annotated as microglia/peri-vascular macrophages (Micro/PVM) was downloaded from the Chan Zuckerberg CELL by GENE - https://cellxgene.cziscience.com/collections/1ca90a2d-2943-483d-b678-b809bf464c30 Using Seurat, I first removed cells from “Reference”, performed SCT normalization and clustered the remaining 38,905 cells into 16 clusters using unsupervised clustering. Cell type/subtype annotation was performed based on the expression of selected markers described throughout the results section. To quantitatively infer and analyze intercellular communication networks from scRNA-seq data, we used CellChatDB [ 8 ]. Classification of microglial subtypes/states was performed using CellID [ 9 ]. Comparison of cell type/subtype abundance was performed using generalized linear models (GLMs) to make predictions for cell type proportions that depend on predictors of interest (e.g., Braak stage), as well as accounting for batch effects and other variables, such as APOE status, sex and age at death. Odds ratios were calculated using emmeans and tables with summary statistics for the different comparisons performed in this work are provided as supplementary material. Code availability A basic R script to reproduce the analyses described in this work is provided as supplementary material. RESULTS Switch of innate to adaptative immune responses in the brain of patients with AD To investigate the cellular dynamics associated with innate and adaptative immune responses in the brain of patients with different degrees of tauopathy, I systematically compared the microglia and white blood cell populations in scRNA-seq data (n = 38,905 cells) obtained from the middle temporal gyrus (MTG) of 42 elderly individuals diagnosed with dementia and 42 age-matched healthy individuals [ 7 ]. Unsupervised clustering identified 16 immune cell clusters expressing PTPRC (CD45) that could be annotated using a combination of cell markers such as ITGAM (CD11B), TREM2 , P2RY12 (microglia), CD3D , CD3G (T cells), CD19 , JCHAIN (B cells), MRC1 (macrophages), S100A8 , FCGR3A , NR4A1 (monocytes/neutrophils) (Fig. 1) and other genes differentially expressed in each cluster compared to all others (Supplementary Table 1). Based on these profiles, I could identify clusters of microglial cells, T cells, B cells, macrophages, monocytes and neutrophils (Fig. 1A). Comparison of the proportion of immune cells in patients’ brains pathologically classified in different Braak stages (0 to II – “low”; III to IV – “mid”; V to VI - “high”) using a generalized linear model (GLM) and including as co-variables sex, age at death and APOE haplotype (see Methods) revealed that the proportion of microglial cells was significantly lower in mid vs low, as well as in high vs mid and high vs low Braak stage comparisons (Fig. 1B; Supplementary Table 2). Conversely, the proportions of all adaptive immune cells were significantly higher in mid vs low and high vs mid Braak stage comparisons (Fig. 1B), indicating a progressive shift from innate to adaptative immune responses during tauopathy progression in the brain of AD patients. Increased intercellular communication among immune cell populations in AD To identify possible alterations in signaling pathways that could help to explain the shift in the composition of immune cell populations in the brain of AD patients, I used CellChat to quantitatively infer and analyze intercellular communication networks from scRNA-seq data [ 10 ]. I found that the number of interactions among immune cells increased from 192 in brains with low tauopathy to 486 in those with high tauopathy (Fig. 1C). These interactions could be classified into 48 signaling patterns, of which 17 were up-regulated and 8 were down-regulated in high compared to low Braak stages (Fig. 1D). Noteworthy, the CXCL16 signaling between microglia and T cells was strongly up-regulated in brains at high compared to low Braak stages (Fig. 1D), suggesting a similar mechanism for T cells trafficking to the brain parenchyma as described in the cerebrospinal fluid (CSF) of cognitive impaired individuals [ 11 ]. Progressive increase in the proportion of T cell populations in the brain of patients with AD To further characterize the adaptative immune response in the human brain with different degrees of tauopathy, I separated cells of this response from the complete dataset and clustered them again using unsupervised clustering (Fig. 2A). I detected 11 clusters that could be annotated into 8 major cell types/subtypes based on the expression of top markers (Supplementary Table 3) and cell-type specific markers such as CD3D , CD3G , CD8A and CD8B (CD4 + and CD8 + T cells), NKG7 and GNLY (natural killer – NK cells), CD19 , JCHAIN (B cells), MRC1 , CD68 and CD14 (macrophages), S100A8 , CD68 (neutrophils), FCGR3A and NR4A1 (monocytes) (Fig. 2A-B). Using GLM, I found that cells from cluster 0 (CD8 + T cells) were 1.56 times more common at mid compared to low Braak stages. Cells from cluster 9 (CD4 + T cells) and 10 (B cells) became progressively more common in brains at mid and high compared to lower Braak stages (Fig. 2C, Supplementary Table 4). The cluster 5 (CD8 + T cells) was not found in brains at low Braak stages but could be detected in one brain at mid Braak stage and 5 brains at late Braak stages (Fig. 2C). Among the genes differentially expressed in CD8 + T cells from cluster 5 compared to cluster 0, I observed an up-regulation of activated ( CXCR6 , CCL5 , NKG7 , HLA-A and HLA-C ), and a downregulation of naïve CD8 + T cells markers ( IL7R , XIST and SYTL3 ) (Fig. 2D; Supplementary Table 5). Together, these findings suggest that T cells shift from naïve to activated states in the human brain with Tau pathology. Shift of T cell states correlates with the increase of DAM populations in the brain of patients with AD To characterize microglial states associated with Tau pathology in the brain of patients with AD, I performed the unsupervised clustering of microglial cells from the original dataset and identified 14 microglial clusters (Fig. 3A). The relative proportion of cells in all these clusters significantly changed according to Braak stages (Fig. 3B), indicating a high dynamic of microglial states during Tau pathology progression. The odds ratio of cells belonging to clusters 2, 4, 5, 6, 9, 10, 12 and 13 showed the higher increments (up to 2- to 38-folds) between Braak stages (Supplementary Table 6). Next, I took advantage of CellID [ 9 ] to extract microglia gene signatures at single-cell resolution using a large set of genes previously identified in well-defined conditions [ 1 , 3 , 12 ]. This strategy helps to circumvent potential limitations in microglial subset classification/annotation based exclusively on a small set of markers. I used CellID to calculate the gene signatures for each microglial cell and perform hypergeometric tests against a list of genes previously identified as homeostatic (HOM; n = 484 genes), disease-associated microglia (DAM; n = 306 genes [ 3 ]), plaque-associated microglia (PAM; n = 57 genes [ 1 ]) and activated-responsive microglia (ARM; n = 131 genes [ 12 ]) (Supplementary Table 7). I observed that one-third of cells within clusters 0, 4, 6 and 13 were significantly enriched for ARM and DAM signatures (Fig. 3C-D). Clusters 0 and 13 also showed more than 20% of cells enriched for PAM signatures at the same time, whereas cluster 12 showed about 30% of cells enriched for DAM signature, 15% of cells enriched for ARM and less than 1% of cells enriched for PAM (Fig. 3C-D). Thus, among the 8 microglial states showing the greatest increments in proportion according to Braak stages, five harbored ARM, DAM and PAM subtypes. Interestingly, all clusters harboring cells enriched for those signatures expressed high levels of CXCL16 (Fig. 3E), suggesting that activated microglia could mediate T cell trafficking into the brain via the CXCL16-CXCR6 signaling axis. DISCUSSION Neuroinflammation is a key pathological hallmark of AD [ 15 , 16 ]. Several lines of evidence suggest that inflammatory response in AD has a dual function, playing a neuroprotective role during early stages of disease, but becoming detrimental at later stages when a chronic response is mounted [ 17 ]. In this work, I show evidence that the inflammatory response in the brain of patients with AD switches from an innate to an adaptative immune response during disease progression as indicated by postmortem Braak staging. This switch is mainly characterized by an increase in DAM and CD8 + T cells, as previously described in AD mouse models [ 3 , 6 ], and could suggest that these cell populations contribute to the detrimental effects of neuroinflammation. I also show that CD8 + T cell trafficking to the brain parenchyma could be mediated by the CXCL16-CXCR6 pathway, thus highlighting a potential target for inflammatory modulation in AD. The CXCL16-CXCR6 pathway was previously shown to be upregulated in the CSF of cognitively impaired subjects mainly due to an increased expression of the CXCR6 in CD8 T cells and CXCL16 in monocytes [ 11 ]. My analysis indicate that microglial cells may also release CXCL16, thus contributing to CD8 + T cell trafficking to the brain parenchyma. In agreement with this hypothesis, transcriptomic profiles from brain tissues of AD patients and mouse models support an active role of dysregulated CXCL16 during AD pathology progression [ 18 ]. It would be interesting to investigate whether the pharmacological blockage of the CXCL16-CXCR6 pathway could reduce the microglia-mediated T cell trafficking in the brain and mitigate the neurodegeneration associated with tauopathy [ 6 ]. DECLARATIONS Acknowledgements: I would like to thank Dr. Bruno Colombo (Université d’Évry, France) for the discussions about the results and for suggestions in the text. Declaration of interest: None Funding sources: No particular funding was received for conducting this study. REFERENCES Chen WT, Lu A, Craessaerts K, Pavie B, Sala Frigerio C, Corthout N, et al. Spatial Transcriptomics and In Situ Sequencing to Study Alzheimer’s Disease. Cell 2020;182:976-991.e19. https://doi.org/10.1016/j.cell.2020.06.038. Sala Frigerio C, Wolfs L, Fattorelli N, Thrupp N, Voytyuk I, Schmidt I, et al. The Major Risk Factors for Alzheimer’s Disease: Age, Sex, and Genes Modulate the Microglia Response to Aβ Plaques. 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Additional Declarations (Not answered) Supplementary Files SupplementaryTable1ClusterMarkersAllimmunecells.csv SupplementaryTable2AllimmunecellsGLMBraak.csv SupplementaryTable3ClusterMarkersAdaptativeimmunecells.csv SupplementaryTable4AdaptativeGLMBraak.csv SupplementaryTable5DEGsclusters5vs0DESeq2.csv SupplementaryTable6MicroGLMBraak.csv SupplementaryTable7MicroglialsignaturesCellID.xlsx Cite Share Download PDF Status: Published Journal Publication published 18 Mar, 2024 Read the published version in npj Aging → Version 1 posted 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-3404778","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Brief Communication","associatedPublications":[],"authors":[{"id":238713375,"identity":"f5bfacda-31a6-43e4-80c0-0353169e75d9","order_by":0,"name":"Marcos Costa","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0002-4928-2163","institution":"Inserm","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Marcos","middleName":"","lastName":"Costa","suffix":""}],"badges":[],"createdAt":"2023-10-02 12:20:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3404778/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3404778/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41514-024-00145-5","type":"published","date":"2024-03-18T04:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":44524491,"identity":"c0826f08-7785-44ff-acb3-abe6824b3b0b","added_by":"auto","created_at":"2023-10-12 16:56:49","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":478551,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIncreased proportion of adaptative immune cells in the brain of patients with late-stage AD. \u003c/strong\u003e(A) UMAP representation of the different cell types in the brain of AD and age-matched control subjects identified using snRNA-seq. (B) Box plot showing the proportion of the different cell types in subject’s brains pathologically classified at low, mid and high Braak stages (**** p\u0026lt;0.0001; GLM). (C) Circle plots showing the number of intercellular interactions among immune cells at early- (Braak low) and late-stages (Braak high) of tauopathy. (D) Heatmap showing the overall signaling patterns identified. Pathways highlighted in bold are upregulated, whereas those highlighted in gray are downregulated at late-stages of tauopathy (Braak high).\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3404778/v1/14207e2136261086e2047512.jpg"},{"id":44524059,"identity":"3005e5eb-682d-4de1-b61c-bfaa65eb2af8","added_by":"auto","created_at":"2023-10-12 16:48:49","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":348445,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentification of activated CD8+ T cells in the brain of AD patients at late-stages of tauopathy. \u003c/strong\u003e(A) UMAP representations of the different adaptative immune cell types and subtypes in the brains of subjects at different Braak stages. (B) Dot plot showing the expression of cell-specific markers. (C) Box plot showing the proportion of the different cell types in subject’s brains pathologically classified at low, mid and high Braak stages (*p\u0026lt;0.05; **p\u0026lt;0.01; ***p\u0026lt;0.001; **** p\u0026lt;0.0001; GLM). (D) Violin plots showing the expression of genes differentially expressed in CD8+ T cell subtypes.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3404778/v1/0dd4d38adfb6da9b2a5fde04.jpg"},{"id":44522327,"identity":"dc2c650c-0f9e-484f-a6ac-897403992441","added_by":"auto","created_at":"2023-10-12 16:40:49","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":534019,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDynamics of microglial states in the brain of AD patients at different stages of tauopathy. \u003c/strong\u003e(A) UMAP representations of the different microglial subtypes/states in the brains of subjects at different Braak stages. (B) Box plot showing the proportion of the different cell types in subject’s brains pathologically classified at low, mid and high Braak stages (**** p\u0026lt;0.0001; GLM). (C) feature plots showing the enrichment for HOM, ARM, DAM and PAM gene signatures in microglial cells. (D) Proportion of cells significantly enriched for those signatures per cluster. (E) Violin plot showing the predominant expression of CXCL16 in DAM clusters.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3404778/v1/9e77fdd7bbe93e8a8487f88c.jpg"},{"id":52965160,"identity":"f4a8a702-136d-4a86-bd5b-7cabb1c729fa","added_by":"auto","created_at":"2024-03-19 07:11:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":732056,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3404778/v1/f9279755-cbbd-4c0b-a433-f267f381254f.pdf"},{"id":44522331,"identity":"22923b40-b509-4832-a5cf-85d5684b0f88","added_by":"auto","created_at":"2023-10-12 16:40:49","extension":"csv","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":976994,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable1ClusterMarkersAllimmunecells.csv","url":"https://assets-eu.researchsquare.com/files/rs-3404778/v1/8bc32c155d4574b8bd242a04.csv"},{"id":44522330,"identity":"81db9c25-a1dc-4a5d-95c4-89c22088b570","added_by":"auto","created_at":"2023-10-12 16:40:49","extension":"csv","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":2457,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable2AllimmunecellsGLMBraak.csv","url":"https://assets-eu.researchsquare.com/files/rs-3404778/v1/45333ecd3e4309c013e202df.csv"},{"id":44522335,"identity":"da4c9a3b-1959-494e-ab77-0dd43f9b8cfb","added_by":"auto","created_at":"2023-10-12 16:40:49","extension":"csv","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":677092,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable3ClusterMarkersAdaptativeimmunecells.csv","url":"https://assets-eu.researchsquare.com/files/rs-3404778/v1/b1217e2e67208f3abd70d5ec.csv"},{"id":44524061,"identity":"0db27704-4b73-4da4-acd4-38a2f03db7c4","added_by":"auto","created_at":"2023-10-12 16:48:49","extension":"csv","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":4367,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable4AdaptativeGLMBraak.csv","url":"https://assets-eu.researchsquare.com/files/rs-3404778/v1/adf8584cd44dbcae764ebb98.csv"},{"id":44522336,"identity":"b5b543fd-e309-4b8d-90e2-6501423230a9","added_by":"auto","created_at":"2023-10-12 16:40:49","extension":"csv","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":344680,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable5DEGsclusters5vs0DESeq2.csv","url":"https://assets-eu.researchsquare.com/files/rs-3404778/v1/72cca70275a0b1e23c7fb3e4.csv"},{"id":44522332,"identity":"350a7f8e-4270-4613-b356-f0a41f2a60e0","added_by":"auto","created_at":"2023-10-12 16:40:49","extension":"csv","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":5201,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable6MicroGLMBraak.csv","url":"https://assets-eu.researchsquare.com/files/rs-3404778/v1/ee69a639d88cbe1c4d8f83f5.csv"},{"id":44522334,"identity":"210218a0-22fd-41a4-91a6-7c8878f7a980","added_by":"auto","created_at":"2023-10-12 16:40:49","extension":"xlsx","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":29061,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable7MicroglialsignaturesCellID.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3404778/v1/978760a7e001f1aac6b21d44.xlsx"}],"financialInterests":"(Not answered)","formattedTitle":"Switch of innate to adaptative immune responses in the brain of patients with Alzheimer’s disease correlates with tauopathy progression","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eIn the brain of patients with Alzheimer\u0026rsquo;s disease (AD), both extracellular deposition of amyloid-\u0026beta; as neuritic plaques and intracellular accumulation of hyperphosphorylated tau as neurofibrillary tangles are associated with innate and adaptative immune responses [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e]. Similarly, inflammatory responses are observed in virtually every animal model of amyloidopathy and tauopathy [\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e], indicating an intimate interaction between these pathological processes. However, the influence of different immune cell types/subtypes to AD pathology onset and progression remains largely unknown.\u003c/p\u003e\n\u003cp\u003eAnimal models of tauopathy (Tau(P301S)) develop a unique innate and adaptive immune response in the brain, characterized by a marked increase in the numbers of T cells, especially cytotoxic T lymphocytes, and correlated with the extent of neuronal loss [\u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e]. Depletion of T cells in Tau(P301S) mice via intraperitoneal administration of anti-CD4 and anti-CD8 antibodies is sufficient to ameliorate brain atrophy and improves behavior, suggesting that infiltration of T cells in the brain parenchyma is a key step in neurodegeneration. However, it remains to be demonstrated that a similar association between neurodegeneration and adaptative immune response occurs in the brain of patients with AD.\u003c/p\u003e\n\u003cp\u003eIn this work, I took advantage of the largest single-nucleus RNA-sequencing (snRNA-seq) dataset available from patients with AD and age-matched controls [\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e] to characterize the innate and adaptative immune responses in the brain of patients with AD at different pathological stages. I show that the number of T cells increase in the brain of patients with AD at late compared to early Braak stages. This phenomenon coincides with changes in microglial subpopulations and activation of signaling mechanisms between microglia and T cells, including the CXCL16 pathway.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003e\u003cem\u003eAnalyses of single-cell RNA-sequencing data\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eData used in this study was obtained from the Seattle Alzheimer's Disease Brain Cell Atlas (SEA-AD) consortium, which includes the Allen Institute for Brain Science, the University of Washington, and Kaiser Permanente Washington Research Institute. SEA-AD is supported by the National Institutes on Aging (NIA) grant U19AG060909. Study data were generated from \u003cem\u003epostmortem\u003c/em\u003e brain tissue obtained from the University of Washington BioRepository and Integrated Neuropathology (BRaIN) laboratory and Precision Neuropathology Core, which is supported by the NIH grants for the UW Alzheimer's Disease Research Center (P50AG005136 and P30AG066509) and the Adult Changes in Thought Study (U01AG006781 and U19AG066567). The ACT study is a longitudinal population-based prospective cohort study of brain aging and incident dementia in the Seattle metropolitan area. ACT is a repository at the Kaiser Permanente Washington Health Research Institute, which has established policies and procedures for sharing data with external investigators. Data available from this study web site do not require any additional Institutional Review Board (IRB) approval or permissions.\u003c/p\u003e\n\u003cp\u003eTranscriptomic data from 40,000 cells originally annotated as microglia/peri-vascular macrophages (Micro/PVM) was downloaded from the Chan Zuckerberg CELL by GENE - \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cellxgene.cziscience.com/collections/1ca90a2d-2943-483d-b678-b809bf464c30\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eUsing Seurat, I first removed cells from \u0026ldquo;Reference\u0026rdquo;, performed SCT normalization and clustered the remaining 38,905 cells into 16 clusters using unsupervised clustering. Cell type/subtype annotation was performed based on the expression of selected markers described throughout the results section. To quantitatively infer and analyze intercellular communication networks from scRNA-seq data, we used CellChatDB [\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e]. Classification of microglial subtypes/states was performed using CellID [\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e]. Comparison of cell type/subtype abundance was performed using generalized linear models (GLMs) to make predictions for cell type proportions that depend on predictors of interest (e.g., Braak stage), as well as accounting for batch effects and other variables, such as APOE status, sex and age at death. Odds ratios were calculated using emmeans and tables with summary statistics for the different comparisons performed in this work are provided as supplementary material.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA basic R script to reproduce the analyses described in this work is provided as supplementary material.\u003c/p\u003e"},{"header":"RESULTS ","content":"\u003cp\u003e\u003cem\u003eSwitch of innate to adaptative immune responses in the brain of patients with AD\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTo investigate the cellular dynamics associated with innate and adaptative immune responses in the brain of patients with different degrees of tauopathy, I systematically compared the microglia and white blood cell populations in scRNA-seq data (n\u0026thinsp;=\u0026thinsp;38,905 cells) obtained from the middle temporal gyrus (MTG) of 42 elderly individuals diagnosed with dementia and 42 age-matched healthy individuals [\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e]. Unsupervised clustering identified 16 immune cell clusters expressing \u003cem\u003ePTPRC\u003c/em\u003e (CD45) that could be annotated using a combination of cell markers such as \u003cem\u003eITGAM\u003c/em\u003e (CD11B), \u003cem\u003eTREM2\u003c/em\u003e, \u003cem\u003eP2RY12\u003c/em\u003e (microglia), \u003cem\u003eCD3D\u003c/em\u003e, \u003cem\u003eCD3G\u003c/em\u003e (T cells), \u003cem\u003eCD19\u003c/em\u003e, \u003cem\u003eJCHAIN\u003c/em\u003e (B cells), \u003cem\u003eMRC1\u003c/em\u003e (macrophages), \u003cem\u003eS100A8\u003c/em\u003e, \u003cem\u003eFCGR3A\u003c/em\u003e, \u003cem\u003eNR4A1\u003c/em\u003e (monocytes/neutrophils) (Fig.\u0026nbsp;1) and other genes differentially expressed in each cluster compared to all others (Supplementary Table\u0026nbsp;1). Based on these profiles, I could identify clusters of microglial cells, T cells, B cells, macrophages, monocytes and neutrophils (Fig.\u0026nbsp;1A). Comparison of the proportion of immune cells in patients\u0026rsquo; brains pathologically classified in different Braak stages (0 to II \u0026ndash; \u0026ldquo;low\u0026rdquo;; III to IV \u0026ndash; \u0026ldquo;mid\u0026rdquo;; V to VI - \u0026ldquo;high\u0026rdquo;) using a generalized linear model (GLM) and including as co-variables sex, age at death and \u003cem\u003eAPOE\u003c/em\u003e haplotype (see Methods) revealed that the proportion of microglial cells was significantly lower in mid vs low, as well as in high vs mid and high vs low Braak stage comparisons (Fig.\u0026nbsp;1B; Supplementary Table\u0026nbsp;2). Conversely, the proportions of all adaptive immune cells were significantly higher in mid vs low and high vs mid Braak stage comparisons (Fig.\u0026nbsp;1B), indicating a progressive shift from innate to adaptative immune responses during tauopathy progression in the brain of AD patients.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eIncreased intercellular communication among immune cell populations in AD\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTo identify possible alterations in signaling pathways that could help to explain the shift in the composition of immune cell populations in the brain of AD patients, I used CellChat to quantitatively infer and analyze intercellular communication networks from scRNA-seq data [\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e]. I found that the number of interactions among immune cells increased from 192 in brains with low tauopathy to 486 in those with high tauopathy (Fig.\u0026nbsp;1C). These interactions could be classified into 48 signaling patterns, of which 17 were up-regulated and 8 were down-regulated in high compared to low Braak stages (Fig.\u0026nbsp;1D). Noteworthy, the CXCL16 signaling between microglia and T cells was strongly up-regulated in brains at high compared to low Braak stages (Fig.\u0026nbsp;1D), suggesting a similar mechanism for T cells trafficking to the brain parenchyma as described in the cerebrospinal fluid (CSF) of cognitive impaired individuals [\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eProgressive increase in the proportion of T cell populations in the brain of patients with AD\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTo further characterize the adaptative immune response in the human brain with different degrees of tauopathy, I separated cells of this response from the complete dataset and clustered them again using unsupervised clustering (Fig.\u0026nbsp;2A). I detected 11 clusters that could be annotated into 8 major cell types/subtypes based on the expression of top markers (Supplementary Table\u0026nbsp;3) and cell-type specific markers such as \u003cem\u003eCD3D\u003c/em\u003e, \u003cem\u003eCD3G\u003c/em\u003e, \u003cem\u003eCD8A\u003c/em\u003e and \u003cem\u003eCD8B\u003c/em\u003e (CD4\u0026thinsp;+\u0026thinsp;and CD8\u0026thinsp;+\u0026thinsp;T cells), \u003cem\u003eNKG7\u003c/em\u003e and \u003cem\u003eGNLY\u003c/em\u003e (natural killer \u0026ndash; NK cells), \u003cem\u003eCD19\u003c/em\u003e, \u003cem\u003eJCHAIN\u003c/em\u003e (B cells), \u003cem\u003eMRC1\u003c/em\u003e, \u003cem\u003eCD68\u003c/em\u003e and \u003cem\u003eCD14\u003c/em\u003e (macrophages), \u003cem\u003eS100A8\u003c/em\u003e, \u003cem\u003eCD68\u003c/em\u003e (neutrophils), \u003cem\u003eFCGR3A\u003c/em\u003e and \u003cem\u003eNR4A1\u003c/em\u003e (monocytes) (Fig.\u0026nbsp;2A-B). Using GLM, I found that cells from cluster 0 (CD8\u0026thinsp;+\u0026thinsp;T cells) were 1.56 times more common at mid compared to low Braak stages. Cells from cluster 9 (CD4\u0026thinsp;+\u0026thinsp;T cells) and 10 (B cells) became progressively more common in brains at mid and high compared to lower Braak stages (Fig.\u0026nbsp;2C, Supplementary Table\u0026nbsp;4). The cluster 5 (CD8\u0026thinsp;+\u0026thinsp;T cells) was not found in brains at low Braak stages but could be detected in one brain at mid Braak stage and 5 brains at late Braak stages (Fig.\u0026nbsp;2C). Among the genes differentially expressed in CD8\u0026thinsp;+\u0026thinsp;T cells from cluster 5 compared to cluster 0, I observed an up-regulation of activated (\u003cem\u003eCXCR6\u003c/em\u003e, \u003cem\u003eCCL5\u003c/em\u003e, \u003cem\u003eNKG7\u003c/em\u003e, \u003cem\u003eHLA-A\u003c/em\u003e and \u003cem\u003eHLA-C\u003c/em\u003e), and a downregulation of na\u0026iuml;ve CD8\u0026thinsp;+\u0026thinsp;T cells markers (\u003cem\u003eIL7R\u003c/em\u003e, \u003cem\u003eXIST\u003c/em\u003e and \u003cem\u003eSYTL3\u003c/em\u003e) (Fig.\u0026nbsp;2D; Supplementary Table\u0026nbsp;5). Together, these findings suggest that T cells shift from na\u0026iuml;ve to activated states in the human brain with Tau pathology.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eShift of T cell states correlates with the increase of DAM populations in the brain of patients with AD\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTo characterize microglial states associated with Tau pathology in the brain of patients with AD, I performed the unsupervised clustering of microglial cells from the original dataset and identified 14 microglial clusters (Fig.\u0026nbsp;3A). The relative proportion of cells in all these clusters significantly changed according to Braak stages (Fig.\u0026nbsp;3B), indicating a high dynamic of microglial states during Tau pathology progression. The odds ratio of cells belonging to clusters 2, 4, 5, 6, 9, 10, 12 and 13 showed the higher increments (up to 2- to 38-folds) between Braak stages (Supplementary Table\u0026nbsp;6).\u003c/p\u003e\n\u003cp\u003eNext, I took advantage of CellID [\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e] to extract microglia gene signatures at single-cell resolution using a large set of genes previously identified in well-defined conditions [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e]. This strategy helps to circumvent potential limitations in microglial subset classification/annotation based exclusively on a small set of markers. I used CellID to calculate the gene signatures for each microglial cell and perform hypergeometric tests against a list of genes previously identified as homeostatic (HOM; n\u0026thinsp;=\u0026thinsp;484 genes), disease-associated microglia (DAM; n\u0026thinsp;=\u0026thinsp;306 genes [\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e]), plaque-associated microglia (PAM; n\u0026thinsp;=\u0026thinsp;57 genes [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e]) and activated-responsive microglia (ARM; n\u0026thinsp;=\u0026thinsp;131 genes [\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e]) (Supplementary Table\u0026nbsp;7). I observed that one-third of cells within clusters 0, 4, 6 and 13 were significantly enriched for ARM and DAM signatures (Fig.\u0026nbsp;3C-D). Clusters 0 and 13 also showed more than 20% of cells enriched for PAM signatures at the same time, whereas cluster 12 showed about 30% of cells enriched for DAM signature, 15% of cells enriched for ARM and less than 1% of cells enriched for PAM (Fig.\u0026nbsp;3C-D). Thus, among the 8 microglial states showing the greatest increments in proportion according to Braak stages, five harbored ARM, DAM and PAM subtypes. Interestingly, all clusters harboring cells enriched for those signatures expressed high levels of \u003cem\u003eCXCL16\u003c/em\u003e (Fig.\u0026nbsp;3E), suggesting that activated microglia could mediate T cell trafficking into the brain via the CXCL16-CXCR6 signaling axis.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eNeuroinflammation is a key pathological hallmark of AD [\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e]. Several lines of evidence suggest that inflammatory response in AD has a dual function, playing a neuroprotective role during early stages of disease, but becoming detrimental at later stages when a chronic response is mounted [\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e]. In this work, I show evidence that the inflammatory response in the brain of patients with AD switches from an innate to an adaptative immune response during disease progression as indicated by \u003cem\u003epostmortem\u003c/em\u003e Braak staging. This switch is mainly characterized by an increase in DAM and CD8\u0026thinsp;+\u0026thinsp;T cells, as previously described in AD mouse models [\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e], and could suggest that these cell populations contribute to the detrimental effects of neuroinflammation. I also show that CD8\u0026thinsp;+\u0026thinsp;T cell trafficking to the brain parenchyma could be mediated by the CXCL16-CXCR6 pathway, thus highlighting a potential target for inflammatory modulation in AD. The CXCL16-CXCR6 pathway was previously shown to be upregulated in the CSF of cognitively impaired subjects mainly due to an increased expression of the CXCR6 in CD8 T cells and CXCL16 in monocytes [\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e]. My analysis indicate that microglial cells may also release CXCL16, thus contributing to CD8\u0026thinsp;+\u0026thinsp;T cell trafficking to the brain parenchyma. In agreement with this hypothesis, transcriptomic profiles from brain tissues of AD patients and mouse models support an active role of dysregulated CXCL16 during AD pathology progression [\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e]. It would be interesting to investigate whether the pharmacological blockage of the CXCL16-CXCR6 pathway could reduce the microglia-mediated T cell trafficking in the brain and mitigate the neurodegeneration associated with tauopathy [\u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e"},{"header":"DECLARATIONS","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eI would like to thank Dr. Bruno Colombo (Universit\u0026eacute; d\u0026rsquo;\u0026Eacute;vry, France) for the discussions about the results and for suggestions in the text.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of interest:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding sources:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo particular funding was received for conducting this study.\u003c/p\u003e"},{"header":"REFERENCES","content":"\u003col\u003e\n\u003cli\u003eChen WT, Lu A, Craessaerts K, Pavie B, Sala Frigerio C, Corthout N, et al. Spatial Transcriptomics and In Situ Sequencing to Study Alzheimer\u0026rsquo;s Disease. Cell 2020;182:976-991.e19. https://doi.org/10.1016/j.cell.2020.06.038.\u003c/li\u003e\n\u003cli\u003eSala Frigerio C, Wolfs L, Fattorelli N, Thrupp N, Voytyuk I, Schmidt I, et al. The Major Risk Factors for Alzheimer\u0026rsquo;s Disease: Age, Sex, and Genes Modulate the Microglia Response to A\u0026beta; Plaques. Cell Rep 2019;27:1293-1306.e6. https://doi.org/10.1016/j.celrep.2019.03.099.\u003c/li\u003e\n\u003cli\u003eKeren-Shaul H, Spinrad A, Weiner A, Matcovitch-Natan O, Dvir-Szternfeld R, Ulland TK, et al. A Unique Microglia Type Associated with Restricting Development of Alzheimer\u0026rsquo;s Disease. Cell 2017;169:1276-1290.e17. https://doi.org/10.1016/j.cell.2017.05.018.\u003c/li\u003e\n\u003cli\u003eFriedman BA, Srinivasan K, Ayalon G, Meilandt WJ, Lin H, Huntley MA, et al. Diverse Brain Myeloid Expression Profiles Reveal Distinct Microglial Activation States and Aspects of Alzheimer\u0026rsquo;s Disease Not Evident in Mouse Models. Cell Rep 2018;22:832\u0026ndash;47. https://doi.org/10.1016/j.celrep.2017.12.066.\u003c/li\u003e\n\u003cli\u003eSaito T, Saido TC. Neuroinflammation in mouse models of Alzheimer\u0026rsquo;s disease. Clin Exp Neuroimmunol 2018;9:211\u0026ndash;8. https://doi.org/10.1111/cen3.12475.\u003c/li\u003e\n\u003cli\u003eChen X, Firulyova M, Manis M, Herz J, Smirnov I, Aladyeva E, et al. Microglia-mediated T cell infiltration drives neurodegeneration in tauopathy. Nature 2023;615:668\u0026ndash;77. https://doi.org/10.1038/s41586-023-05788-0.\u003c/li\u003e\n\u003cli\u003eGabitto MI, Travaglini KJ, Rachleff VM, Kaplan ES, Long B, Ariza J, et al. Integrated multimodal cell atlas of Alzheimer\u0026amp;#039;s disease. BioRxiv 2023:2023.05.08.539485. https://doi.org/10.1101/2023.05.08.539485.\u003c/li\u003e\n\u003cli\u003eJin S, Guerrero-Juarez CF, Zhang L, Chang I, Ramos R, Kuan C-H, et al. Inference and analysis of cell-cell communication using CellChat. Nat Commun 2021;12:1088. https://doi.org/10.1038/s41467-021-21246-9.\u003c/li\u003e\n\u003cli\u003eCortal A, Martignetti L, Six E, Rausell A. Gene signature extraction and cell identity recognition at the single-cell level with Cell-ID. Nat Biotechnol 2021. https://doi.org/10.1038/s41587-021-00896-6.\u003c/li\u003e\n\u003cli\u003eJin S, Guerrero-Juarez CF, Zhang L, Chang I, Ramos R, Kuan CH, et al. Inference and analysis of cell-cell communication using CellChat. Nat Commun 2021;12:1\u0026ndash;20. https://doi.org/10.1038/s41467-021-21246-9.\u003c/li\u003e\n\u003cli\u003ePiehl N, van Olst L, Ramakrishnan A, Teregulova V, Simonton B, Zhang Z, et al. Cerebrospinal fluid immune dysregulation during healthy brain aging and cognitive impairment. Cell 2022;185:5028-5039.e13. https://doi.org/10.1016/j.cell.2022.11.019.\u003c/li\u003e\n\u003cli\u003eMancuso R, Van Den Daele J, Fattorelli N, Wolfs L, Balusu S, Burton O, et al. Stem-cell-derived human microglia transplanted in mouse brain to study human disease. Nat Neurosci 2019;22:2111\u0026ndash;6. https://doi.org/10.1038/s41593-019-0525-x.\u003c/li\u003e\n\u003cli\u003eKlein SL, Flanagan KL. Sex differences in immune responses. Nat Rev Immunol 2016;16:626\u0026ndash;38. https://doi.org/10.1038/nri.2016.90.\u003c/li\u003e\n\u003cli\u003eNebel RA, Aggarwal NT, Barnes LL, Gallagher A, Goldstein JM, Kantarci K, et al. Understanding the impact of sex and gender in Alzheimer\u0026rsquo;s disease: A call to action. Alzheimers Dement 2018;14:1171\u0026ndash;83. https://doi.org/10.1016/j.jalz.2018.04.008.\u003c/li\u003e\n\u003cli\u003eKinney JW, Bemiller SM, Murtishaw AS, Leisgang AM, Salazar AM, Lamb BT. Inflammation as a central mechanism in Alzheimer\u0026rsquo;s disease. Alzheimer\u0026rsquo;s Dement Transl Res Clin Interv 2018;4:575\u0026ndash;90. https://doi.org/10.1016/j.trci.2018.06.014.\u003c/li\u003e\n\u003cli\u003eGuerrero A, De Strooper B, Arancibia-C\u0026aacute;rcamo IL. Cellular senescence at the crossroads of inflammation and Alzheimer\u0026rsquo;s disease. Trends Neurosci 2021;44:714\u0026ndash;27. https://doi.org/10.1016/j.tins.2021.06.007.\u003c/li\u003e\n\u003cli\u003eWyss-Coray T, Mucke L. Review Inflammation in Neurodegenerative Disease\u0026mdash;A Double-Edged Sword emanating from injured neurons, or by imbalances be- tween pro-and antiinflammatory processes. Inflammatory responses also recruit immune mecha. Neuron 2002;35:419\u0026ndash;32.\u003c/li\u003e\n\u003cli\u003eLi X, Zhang DF, Bi R, Tan LW, Chen X, Xu M, et al. Convergent transcriptomic and genomic evidence supporting a dysregulation of CXCL16 and CCL5 in Alzheimer\u0026rsquo;s disease. Alzheimer\u0026rsquo;s Res Ther 2023;15:1\u0026ndash;14. https://doi.org/10.1186/s13195-022-01159-5.\u003c/li\u003e\n\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":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Neuroinflammation, Alzheimer's disease, T cells, Microglia, CXCL16","lastPublishedDoi":"10.21203/rs.3.rs-3404778/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3404778/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eNeuroinflammation is a key feature of Alzheimer's disease (AD). In this work, I analyze single-cell RNA-sequencing (scRNA-seq) data obtained from the brain of patients with AD and show evidence supporting a switch from an innate to an adaptative immune response during tauopathy progression, with both disease-associated microglia (DAM) and CD8\u0026thinsp;+\u0026thinsp;T cells becoming more frequent at advanced Braak stages.\u003c/p\u003e","manuscriptTitle":"Switch of innate to adaptative immune responses in the brain of patients with Alzheimer’s disease correlates with tauopathy progression","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-10-12 16:40:44","doi":"10.21203/rs.3.rs-3404778/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a17d61d0-5de5-4466-9f95-52df8bd7af80","owner":[],"postedDate":"October 12th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":25247123,"name":"Biological sciences/Neuroscience/Diseases of the nervous system/Alzheimer's disease"},{"id":25247124,"name":"Biological sciences/Neuroscience/Diseases of the nervous system/Dementia/Alzheimer's disease"}],"tags":[],"updatedAt":"2024-03-19T07:11:49+00:00","versionOfRecord":{"articleIdentity":"rs-3404778","link":"https://doi.org/10.1038/s41514-024-00145-5","journal":{"identity":"npj-aging","isVorOnly":false,"title":"npj Aging"},"publishedOn":"2024-03-18 04:00:00","publishedOnDateReadable":"March 18th, 2024"},"versionCreatedAt":"2023-10-12 16:40:44","video":"","vorDoi":"10.1038/s41514-024-00145-5","vorDoiUrl":"https://doi.org/10.1038/s41514-024-00145-5","workflowStages":[]},"version":"v1","identity":"rs-3404778","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3404778","identity":"rs-3404778","version":["v1"]},"buildId":"rHA-KDH7Qsr4HCuvH75dn","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}