Microglia modulate Aβ-dependent astrocyte reactivity in Alzheimer’s disease

Microglia modulate Aβ-dependent astrocyte reactivity in Alzheimer’s disease | 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 Article Microglia modulate Aβ-dependent astrocyte reactivity in Alzheimer’s disease Eduardo Zimmer, João Pedro Ferrari-Souza, Guilherme Povala, Nesrine Rahmouni, and 27 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5184011/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 06 Nov, 2025 Read the published version in Nature Neuroscience → Version 1 posted You are reading this latest preprint version Abstract Experimental evidence suggests that activated microglia induce astrocyte reactivity in neurodegenerative disorders, such as Alzheimer’s disease (AD). Here, we investigated the association between microglial activation and amyloid-β (Aβ) with reactive astrogliosis in the living AD human brain. We studied 101 individuals across the AD spectrum with positron emission tomography (PET) for Aβ aggregation ([ 18 F]AZD4694) and translocator protein (TSPO) microglial activation ([ 11 C]PBR28), along with the plasma biomarker for astrocyte reactivity glial fibrillary acidic protein (GFAP). We further assessed tau phosphorylation by plasma phosphorylated tau at threonine 217 (p-tau217) and tau aggregation using [ 18 F]MK-6240 PET. We found that Aβ pathology was associated with increased astrocyte reactivity across cortical brain regions only in the presence of elevated levels of microglial activation. Importantly, the microglia-dependent effects of Aβ on astrocyte reactivity were further related to cognitive impairment through tau phosphorylation and aggregation. Postmortem data from the Allen Human Brain Atlas revealed that TSPO mRNA expression patterns reflected the in-vivo Aβ-glia relationships, indicating that the interplay between AD pathophysiology and glial reactivity might be regulated at the gene expression level. Altogether, our results provide biomarker-based clinical evidence that microglial activation plays a key role in Aβ-related astrocyte reactivity, which, in turn, contributes to downstream pathological features of AD. These findings shed light on the intricate crosstalk between microglia and astrocytes in the AD brain, offering insights for the development of glia-targeting therapies. Biological sciences/Neuroscience/Glial biology/Microglia Health sciences/Diseases/Neurological disorders/Dementia/Alzheimer's disease Biological sciences/Neuroscience/Diseases of the nervous system/Alzheimer's disease Health sciences/Medical research/Biomarkers Biological sciences/Neuroscience/Glial biology/Astrocyte Alzheimer’s disease amyloid-β microglia astrocytes neuroinflammation biomarkers Figures Figure 1 Figure 2 Introduction Alzheimer’s disease (AD) is defined by the brain accumulation of amyloid-β (Aβ) and tau proteins 1 . However, a growing body of evidence indicates that these proteinopathies alone do not fully explain the clinical progression of AD 2 , 3 . This suggests that additional pathological mechanisms are involved in the AD pathological cascade. In fact, it has been proposed that neuroinflammation plays a pivotal role in determining patients’ susceptibility to dementia, with microglia and astrocytes orchestrating the repertoire of immune responses 4 , 5 . In pathological conditions, astrocytes undergo molecular, functional, and morphological changes, a process referred to as reactive astrogliosis 6 . Despite the well-established involvement of astrocytes in AD progression 3 , 7 , the biological underpinnings of reactive astrogliosis in this neurodegenerative condition remain elusive. Although Aβ pathology is an important trigger of astrocyte reactivity 8 – 11 , other pathological components of AD have also been implicated in promoting reactive astrogliosis, notably microglial activation 3 . Instead of solely being a response to Aβ deposition 12 – 14 , compelling experimental data demonstrate that activated microglia can induce astrocyte reactivity through the secretion of inflammatory cytokines 15 , highlighting a close relationship between Aβ and glial cells. This raises the possibility that microglial activation may account for the fact that Aβ drives astrocyte reactivity in some individuals but not in others 16 . However, no previous clinical study has addressed whether microglial activation can modulate astrocyte reactivity in response to Aβ pathology in the living human brain. Better understanding the microglia-astrocyte crosstalk in AD may provide valuable insights for developing therapies targeting glial cells 17 . Using imaging and fluid biomarkers for the quantification of glial reactivity and AD hallmark proteinopathies, we tested the hypothesis that microglia impact the effects of Aβ pathology on reactive astrogliosis in individuals across the aging and AD clinical spectrum. In addition, we assessed whether gene expression patterns reflect the in-vivo relationship between Aβ pathology and glial reactivity. Lastly, we investigated whether glial reactivity could aid in explaining the relationships between Aβ, tau, and cognitive impairment. Results The selection of study participants is displayed in Supplementary Fig. 1 . We genotyped 606 individuals for the rs6971 single nucleotide polymorphism (SNP) in the translocator protein ( TSPO ) gene, with 314 identified as TSPO high-affinity binders. From those, we studied 101 individuals aged 50 years or older who had complete data for positron emission tomography (PET) for Aβ ([ 18 F]AZD4694) and TSPO microglial activation ([ 11 C]PBR28), as well as clinical assessments and plasma glial fibrillary acidic protein (GFAP) measurements. Participants were also assessed for plasma plasma phosphorylated tau at threonine 217 (p-tau217) and [ 18 F]MK-6240 tau PET imaging. Among our study population, a total of 62 were cognitively unimpaired (CU), and 39 were cognitively impaired (26 with mild cognitive impairment [MCI] and 13 with AD dementia). Table 1 displays demographic and clinical information of our population. Table 1 Study participant characteristics. Overall Cognitively unimpaired Cognitively impaired a No. 101 62 39 Age, years 71.9 (6.5) 72.3 (5.8) 71.2 (7.5) Male, No. (%) 33 (32.7) 14 (22.6) 19 (48.7) Education, years 15.3 (3.6) 15.6 (3.8) 14.8 (3.3) APOE ε4 carrier, No. (%) 35 (34.7) 15 (24.2) 20 (51.3) Plasma GFAP, pg/mL 242.3 (119.8) 222.8 (115.8) 273.2 (120.8) Plasma p-tau217, pg/mL b 0.09 (0.08) 0.06 (0.06) 0.14 (0.09) Aβ PET SUVR 1.74 (0.60) 1.48 (0.39) 2.16 (0.65) Tau PET SUVR 1.13 (0.71) 0.86 (0.10) 1.57 (0.99) TSPO PET SUVR 1.31 (0.14) 1.28 (0.13) 1.34 (0.15) Microglial activation positivity, No. (%) 51 (50.5) 29 (46.8) 22 (56.4) MMSE score 28.2 (2.8) 29.3 (0.9) 26.4 (3.7) Continuous variables are presented as mean (SD). Aβ PET was measured with global [ 18 F]AZD4694 SUVR, microglial activation TSPO PET with posterior cingulate [ 11 C]PBR28 SUVR, and tau PET with temporal meta-ROI [ 18 F]MK6240 SUVR. a Composed of 26 individuals with MCI and 13 individuals with AD dementia. b Assessed in a subset of 93 individuals. Aβ pathology associates with astrocyte reactivity only in the presence of elevated levels of microglial activation We first investigated the association of TSPO PET and Aβ PET with plasma GFAP. Regression analyses revealed a significant positive association between global Aβ PET SUVR and plasma GFAP levels in TSPO microglial activation-positive (MA + TSPO ) individuals (β = 0.618, 95% confidence interval [CI] 0.310 to 0.926, P < 0.001) but not in TSPO microglial activation-negative (MA- TSPO ) individuals (β = 0.184, 95% CI -0.133 to 0.502, P = 0.247), as well as a significant interaction of Aβ PET SUVR and MA TSPO status with plasma GFAP levels (β = 0.528, 95% CI 0.172 to 0.883, P = 0.004; Fig. 1 a). Additionally, a significant interaction between continuous values of Aβ PET burden and TSPO PET uptake on plasma GFAP levels (β = 0.302, 95% CI 0.116 to 0.489, P = 0.002; Fig. 1 b ) further supported that microglial activation affects the association of Aβ pathology with reactive astrogliosis, independently of the threshold used to define MA + TSPO . Analysis of variance corroborated the adequacy of the interaction model compared to the reduced models (including only Aβ PET, only TSPO PET, or their additive effects; all P < 0.05). Similar findings were observed in sensitivity analyses including outliers ( Supplementary Table 1 ). Region-wise linear regressions showed that higher plasma GFAP levels were associated with higher Aβ PET burden only in the presence of MA + TSPO across cortical areas, including the frontal, parietal, temporal, and cingulate cortices (Fig. 1 c and Supplementary Fig. 2 ). In a subsequent analysis assessing continuous values for the topographical distribution of microglial activation, we found that the relationship between Aβ PET burden and plasma GFAP levels was influenced by TSPO PET uptake in the cingulate and frontal brain regions (Fig. 1 d and Supplementary Fig. 3 ). Using microarray-based postmortem data from the Allen Human Brain Atlas (AHBA), we found that the cerebral distribution of TSPO mRNA expression predicted the topography of microglia-related effects of Aβ on astrocyte reactivity which was observed in our study population ( P = 0.002; Fig. 1 e). Microglial and astrocyte reactivity jointly relate to tau phosphorylation and aggregation Next, we assessed the association of TSPO PET and plasma GFAP with tau biomarkers. Regression analysis showed that higher plasma GFAP levels were significantly associated with higher plasma p-tau217 levels in MA + TSPO individuals (β = 0.658, 95% CI 0.323 to 0.993, P < 0.001) but not in MA- TSPO individuals (β = 0.110, 95% CI -0.091 to 0.312, P = 0.276), and that the interaction between plasma GFAP and MA TSPO status was significantly associated with higher plasma p-tau217 levels (β = 0.470, 95% CI 0.125 to 0.816, P = 0.008; Fig. 2 a). Moreover, a significant interaction was also observed between continuous levels of plasma GFAP and TSPO PET uptake on plasma p-tau217 levels (β = 0.201, 95% CI 0.024 to 0.377, P = 0.026; Fig. 2 b), reinforcing that the relationship between reactive astrogliosis and tau phosphorylation depends on microglial activation levels, irrespective of the threshold used to determine MA + TSPO . The adequacy of the interaction model compared to the reduced models (including only plasma GFAP, only TSPO PET, or their additive effects) was supported by analysis of variance (all P < 0.05). Similar results were observed in sensitivity analyses including outliers ( Supplementary Table 2 ). In region-wise linear regressions, we observed that higher plasma GFAP levels were associated with tau PET accumulation only in the presence of MA + TSPO mainly in neocortical brain regions, comprising temporal structures, as well as association and sensorimotor cortices (Fig. 2 c and Supplementary Fig. 4 ). Glial reactivity contributes to AD-related cognitive impairment Lastly, we applied structural equation modeling to test the associations between Aβ, astrocyte reactivity, tau phosphorylation, tau tangles, and cognition according to MA TSPO status. In MA- TSPO individuals, we only observed direct effects of Aβ PET load on higher plasma p-tau217 levels and tau PET burden, with no significant associations with cognitive impairment (Fig. 2 d). This model explained 32% of the variance in cognitive impairment and fit the data poorly (root mean squared error of approximation [RMSEA] = 0.257, standardized root mean square residual [SRMR] = 0.069, comparative fit index [CFI] = 0.888). Conversely, in MA + TSPO individuals, we found that plasma GFAP levels partially mediated the effects of Aβ PET load on higher plasma p-tau217, which was further associated with cognitive impairment through increased tau PET accumulation. The model also demonstrated pathways by which Aβ PET was related to cognitive deterioration via direct associations with tau pathology biomarkers (plasma p-tau217 and tau PET; Fig. 2 e). This construct explained 76% of the variance in cognitive impairment and fit the data well (RMSEA = 0.000, SRMR = 0.018, CFI = 1.000). Supplementary Tables 3 and 4 report detailed coefficients and associated statistics for structural equation models. Discussion In the present study, we observed that microglial activation determines Aβ effects on astrocyte reactivity in the living human brain. We also found that the physiological distribution of TSPO gene expression in the postmortem brain resembles this in-vivo relationship between Aβ pathology and glial reactivity. Lastly, we showed that the microglia-dependent impact of Aβ on astrocyte reactivity was further associated with cognitive impairment through tau phosphorylation and aggregation. We found that Aβ pathology was associated with GFAP levels across cortical brain regions only in the presence of increased TSPO PET signal. It is well-established that Aβ pathology is closely related to astrocyte reactivity, with experimental studies revealing that Aβ aggregates increase markers of astrocyte reactivity 18 – 20 . Accordingly, neuropathological studies showed that GFAP-positive reactive astrocytes surround Aβ plaques and mirror the topographical distribution of Aβ deposition in the AD brain 11 , 21 , 22 . This is further supported by an increasing number of clinical studies showing that cerebrospinal fluid (CSF) and, more prominently, plasma levels of GFAP are closely associated with Aβ biomarkers 23 – 30 . Beyond Aβ accumulation, an animal study demonstrated that microglial activation also triggers astrocyte reactivity by secreting interleukin-1α (Il-α), tumor necrosis factor (TNF), and complement component 1, subcomponent q (C1q) cytokines 15 . Microglial reactivity is an important pathological feature of AD and has an intimate communication with reactive astrocytes in coordinating the innate immune response in the brain 4 , 5 , 31 . Here, we build on previous experimental data by showing the first clinical evidence that microglial activation modulates the Aβ-induced astrocyte reactivity in the living AD human brain. Analyzing postmortem data from the AHBA, we also demonstrated that the physiological brain distribution of TSPO mRNA expression resembled the topographical patterns of microglia-driven effects of Aβ on astrocyte reactivity observed in our study population. Altogether, these findings support the notion that a dysregulated cellular crosstalk between microglia and astrocytes may be a major neuroinflammatory phenomenon involved in AD pathogenesis, which might be at least partially explained at the level of gene expression. We observed that TSPO PET and plasma GFAP levels were jointly associated with tau biomarkers. Rather than merely being a response to the accumulation of AD hallmark proteins, a growing body of work supports the notion that both microglia and astrocytes are involved in tau pathogenesis through the exacerbation of tau spread and hyperphosphorylation, as well as tau-driven neurodegeneration 32 – 38 . Accordingly, recent biomarker-based clinical studies have suggested that glial reactivity might help to explain the link between Aβ and tau pathology. More specifically, it has been consistently demonstrated that astrocyte reactivity, indexed by plasma GFAP, determines Aβ downstream effects on tau pathology in individuals across the AD continuum 16 , 39 , 40 . Similarly, neuroimaging data demonstrated that the interaction between Aβ and activated microglia is crucial for tau pathology progression 41 . In this study, we propose a model in which the interplay between Aβ and reactive astrogliosis contributes to tau pathology in the presence of activated microglia. Noteworthy, tau biomarkers are closely related to AD-specific cognitive deterioration, but they assess distinct aspects of tau pathology: fluid p-tau detects soluble hyperphosphorylated tau, serving as an early measure of tau pathophysiology; while tau PET targets insoluble tau tangles, offering a later measure of tau aggregation 42 – 44 . Our results align with these concepts by revealing glial-mediated effects of Aβ on pathological tau phosphorylation, which were further associated with subsequent tau aggregation, ultimately leading to the manifestation of cognitive symptoms. Altogether, these observations reinforce the importance of microglial activation and astrocyte reactivity in AD progression, suggesting glial cells as potential therapeutic targets for future disease-modifying clinical trials. Methodological strengths of our study include the evaluation of a large cohort genetically enriched to increase the reliability of the microglial activation imaging agent. Moreover, data for multiple imaging and fluid biomarkers using the most advanced methodologies for the brain quantification of Aβ, tau, and glial reactivity were available. This study also has methodological limitations. TRIAD participants are volunteers who were motivated to participate in a study about dementia, which might limit the generalizability of our findings due to self-selection bias. Here, we used [ 11 C]PBR28 TSPO PET as an index of microglial activation 45 , 46 and plasma GFAP as an index of astrocyte reactivity 47 . Neuroinflammation involves various glial phenotypes during AD progression 4 , 6 , 48 ; however, this heterogeneity could not be captured in our biomarker-based study. Although TSPO is predominantly expressed in the AD brain by microglia 49 – 52 , it has also been detected in other cell types ( e.g. , astrocytes and endothelial cells) 51 – 54 . Thus, further investigation is needed to clarify the extent to which each cell type contributes to the TSPO PET signal in AD. In addition, there is an ongoing discussion about whether the TSPO PET signal more accurately reflects microglial activation or density/recruitment 55 . To elucidate the biological mechanisms underlying our findings, future research focusing on phenotypically characterizing the glial cells involved in AD neuroinflammatory response is needed. Lastly, given that this study has a cross-sectional design, our results should be expanded with multimodal longitudinal data. In conclusion, our results support the construct that activated microglia is a key player in Aβ-dependent reactive astrogliosis, which further contributes to cognitive impairment via the aggregation of phosphorylated tau in the living AD human brain. Methods Participants The present work included individuals enrolled in the Translational Biomarkers in Aging and Dementia (TRIAD) cohort ( https://triad.tnl-mcgill.com ), a biomarker-based study launched in 2017 which comprises participants from the community or outpatients at the McGill University Research Centre for Studies in Aging, Canada. Exclusion criteria encompassed inability to speak English or French, inadequate auditory and visual capacities for neuropsychologic testing, contraindications for PET or magnetic resonance imaging (MRI), recent head trauma or major surgery, inadequately treated conditions, current enrollment in other studies or active substance abuse. Written informed consent was obtained from all study participants. The study was approved by the Montreal Neurological Institute PET Working Committee and the Douglas Mental Health University Institute Research Ethics Board. TRIAD participants underwent genotyping for the Ala147Thr SNP of the TSPO gene (rs6971, www.ncbi.nlm.nih.gov/snp/rs6971 ), which affects the binding affinity of the [ 11 C]PBR28 radiotracer. Depending on this genotype, individuals can be low-, mixed- or high-affinity binders. Since the rs6971 SNP is a methodological caveat that does not influence TSPO levels, glial reactivity, or AD pathophysiology 54 , our study only included high-affinity binders to mitigate noise related to artificial uptake variations, as previously done 41 , 56 . Study participants had available Aβ PET, TSPO microglial activation PET, magnetic resonance imaging (MRI), plasma GFAP, and neuropsychological testing. We also assessed plasma p-tau217 and tau tangle PET data. Two individuals that had plasma GFAP levels three standard deviations (SD) above the mean of the population were considered outliers and excluded from the analyses, as previously done 57 – 59 . CU individuals had no objective cognitive impairment and a global Clinical Dementia Rating (CDR) of 0. Participants with MCI had preserved activities of daily living, subjective and/or objective cognitive impairment, and a global CDR of 0.5 60 . Patients with mild-to-moderate AD dementia met the National Institute on Aging and the Alzheimer’s Association (NIA-AA) criteria for probable AD 61 and had a global CDR score between 0.5 and 2. Plasma biomarkers Blood collection followed previously described procedures 62 . Plasma biomarkers were quantified using Single molecule array (Simoa) methods on the HD-X platform (Quanterix). Plasma GFAP concentration was measured with a commercial single-plex assay at the Clinical Neurochemistry Laboratory, University of Gothenburg 63 . Plasma p-tau217 concentration was measured at Johnson and Johnson Innovative Medicine with a previously described assay 64 . Brain gene expression We obtained TSPO gene expression data in the entire brain from the open-source AHBA ( http://www.brain-map.org ) 65 . Briefly, mRNA expression intensity values were derived from microarray data of 3702 samples from 6 healthy postmortem human brains ( Supplementary Table 5 ). Microarray-based TSPO mRNA brain expression map, generated using Gaussian process regression 66 , was retrieved from www.meduniwien.ac.at/neuroimaging/mRNA.html . Neuroimaging All participants had a T1-weighted MRI that was used for coregistration. Detailed information concerning MRI acquisition and processing has already been described 57 . Of note, structural MRI data were acquired at the Montreal Neurological Institute (MNI) on a 3T Siemens Magnetom scanner using a standard head coil. High-resolution structural images of the whole brain were obtained with the magnetization prepared rapid acquisition gradient echo MRI sequence. All PET scans were acquired at the MNI on the same brain-dedicated Siemens High-Resolution Research Tomograph. Aβ PET with [ 18 F]AZD4694 (acquired 40–70 min post-injection), tau tangle PET with [ 18 F]MK-6240 (acquired 90–110 min post-injection), and TSPO microglial activation PET with [ 11 C]PBR28 (acquired 60–90 min post-injection) were reconstructed with the ordered subset expectation maximization (OSEM) algorithm on a four-dimensional (4D) volume with three frames (3 × 600 s) 67 , four frames (4 × 300 s) 67 , and six frames (6 × 300 s) 41 , respectively. After acquisition, PET images were corrected for attenuation, motion, dead time, decay, and random and scattered coincidences. T1-weighted MRI images were corrected for non-uniformity and field distortions in accordance with an in-house pipeline. Then, imaging co-registration and spatial normalization to the Alzheimer’s Disease Neuroimaging Initiative (ADNI) template space was performed. To this end, PET images were automatically registered to the native T1-weighted MRI with linear transformations, and T1-weighted MRI images were linearly and nonlinearly registered to the ADNI template space. Subsequently, PET images were registered to the ADNI template space by applying the linear and nonlinear transformation parameters from PET to native MRI and native MRI to the ADNI template space. Aβ PET SUVR and TSPO PET SUVR were calculated using the whole cerebellar gray matter as reference region 41 , 68 . Tau PET SUVR was calculated using the inferior cerebellar gray matter as reference region 69 . PET images were spatially smoothed to achieve a final resolution of 8 mm full width at half-maximum. The Desikan-Killiany-Tourville atlas was used to determine the anatomical regions of interest (ROIs) 70 . The global Aβ PET SUVR composite was estimated from the following brain regions: precuneus, prefrontal, orbitofrontal, parietal, temporal, and cingulate 71 . The temporal meta-ROI tau PET SUVR composite was estimated from the following brain regions: entorhinal, hippocampus, fusiform, parahippocampal, inferior temporal, and middle temporal 71 . We identified higher [ 11 C]PBR28 uptake in the posterior cingulate as a cortical neuroinflammatory signature of AD ( Supplementary Fig. 5 ), similar to previous work 41 . Thus, we used posterior cingulate [ 11 C]PBR28 SUVR as a summary measure of TSPO PET. Microglial activation positivity (MA + TSPO ) was defined as SUVR values 2 SD above the mean from a separate population of 17 CU Aβ- young adults ( Supplementary Table 6 ), as previously reported 72 . Statistical analysis Statistical analyses were carried out using the R software (version 4.0.2, http://www.r-project.org/ ). The associations between biomarkers were tested using regression models accounting for age, sex, and cognitive status. Regression models including interaction terms also accounted for the main effects of the variables involved in the interaction. Continuous variables were standardized to facilitate comparison across estimates. Multiple comparisons correction at P < 0.05 was performed using the false discovery rate (FDR) method for region-wise analysis. Analysis of variance was used to test the adequacy of models with the interaction term compared to reduced models. Regression analysis also tested whether TSPO mRNA expression intensity resembles Aβ-glia relationships across cortical brain regions in our population. Structural equation modeling was applied to test the associations between Aβ, tau, glial reactivity, and cognition. Associations were adjusted for age, sex, and (if involving cognition) years of education. Noteworthy, structural equation models were constructed to assess specific hypotheses represented in the figure’s meta-models, and the following thresholds were used for considering a good fit: CFI > 0.97; RMSEA < 0.05; SRMR < 0.05 73,74 . For all analyses, we considered a two-tailed P -value < 0.05 as statistically significant. Declarations Acknowledgments: We acknowledge all study participants and the McGill Center for Studies in Aging staff. We thank Cerveau Technologies for the use of [ 18 F]MK-6240. We also thank Dean Jolly, Alexey Kostikov, Monica Samoila-Lactatus, Karen Ross, Mehdi Boudjemeline, and Sandy Li for assisting in the radiochemistry production, as well as Richard Strauss, Edith Strauss, Guylaine Gagne, Carley Mayhew, Tasha Vinet-Celluci, Karen Wan, Sarah Sbeiti, Meong Jin Joung, Miloudza Olmand, Rim Nazar, Hung-Hsin Hsiao, Reda Bouhachi, and Arturo Aliaga for helping with data acquisition. Funding: This research was supported by the Alzheimer’s Association (NIRG-12-92090 and NIRP-12-259245; PR-N), Brain Canada Foundation (CFI Project 34874 and 33397 to PR-N), CIHR-CCNA Canadian Consortium of Neurodegeneration in Aging (MOP-11-51-31; RFN 152985, 159815, and 162303 to PR-N), Fonds de Recherche du Québec – Santé (Chercheur Boursier, 2020-VICO-279314; PR-N), Weston Brain Institute (8400707, 8401154 and 8401103 to PR-N), and Colin Adair Charitable Foundation (PR-N). ERZ is funded by the Alzheimer’s Association (AARGD-21-850670), Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq; 312306/2021-0), National Academy of Neuropsychology (ALZ-NAN-22-928381), Fundação de Amparo à Pesquisa do Rio Grande do Sul (FAPERGS; 21/2551-0000673-0), and Instituto Serrapilheira (Serra-1912-31365). BB receives financial support from the Alzheimer’s Association (AARFD-22-974627). PCLF receives financial support from the Alzheimer’s Association (AARFD-22-923814). CA is supported by Global Brain Health Institute, Alzheimer’s Association, and Alzheimer’s Society (GBHI ALZ UK-23-971089). MADB is supported by Fundação de Amparo à pesquisa do Estado do RS (FAPERGS) and Alzheimer’s Association (AARFD-23-1148735). WVB is funded by the Alzheimer’s Association (AACSF-D 22-928689). HZ is a Wallenberg Scholar and a Distinguished Professor at the Swedish Research Council supported by grants from the Swedish Research Council (2023-00356; 2022-01018 and 2019-02397), the European Union’s Horizon Europe research and Innovation Programme under grant agreement No 101053962, Swedish State Support for Clinical Research (ALFGBG-71320), the Alzheimer Drug Discovery Foundation (ADDF), USA (201809-2016862), the AD Strategic Fund and the Alzheimer's Association (ADSF-21-831376-C, ADSF-21-831381-C, ADSF-21-831377-C, and ADSF-24-1284328-C), the European Partnership on Metrology, co-financed from the European Union’s Horizon Europe Research and Innovation Programme and by the Participating States (NEuroBioStand; 22HLT07), the Bluefield Project, Cure Alzheimer’s Fund, the Olav Thon Foundation, the Erling-Persson Family Foundation, Familjen Rönströms Stiftelse, Stiftelsen för Gamla Tjänarinnor, Hjärnfonden, Sweden (FO2022-0270), the European Union’s Horizon 2020 Research and Innovation Programme under the Marie Skłodowska-Curie grant agreement No 860197 (MIRIADE), the European Union Joint Programme – Neurodegenerative Disease Research (JPND2021-00694), the National Institute for Health and Care Research University College London Hospitals Biomedical Research Centre, and the UK Dementia Research Institute at UCL (UKDRI-1003). KB is supported by the Swedish Research Council (2017-00915 and 2022-00732), the Alzheimer Drug Discovery Foundation (ADDF; RDAPB-201809-2016615), Swedish Alzheimer Foundation (AF-930351, AF-939721 and AF-968270), Hjärnfonden (FO2017-0243 and ALZ2022-0006), the Swedish state under the agreement between the Swedish government and the County Councils, the ALF-agreement (ALFGBG-715986 and ALFGBG-965240), the European Union Joint Program - Neurodegenerative Disease Research (JPND2019-466-236) the National Institute of Health (NIH; R01AG068398), Alzheimer’s Association 2021 Zenith Award (ZEN-21-848495), and Alzheimer’s Association 2022-2025 Grant (SG-23-1038904 QC). TAP is supported by the National Institute of Health (R01AG075336 and R01AG073267) and the Alzheimer’s Association (AACSF-20-648075). Competing Interests WVB served as a speaker for Novo Nordisk. SG has served as a scientific advisor to Cerveau Technologies. GT-B and HCK are employees of Johnson and Johnson Innovative Medicine and receive salary and stock from its parent company, Johnson & Johnson. NJA has given lectures in symposia sponsored by Lilly and Quanterix. HZ has served on the scientific advisory boards and/or as a consultant for Abbvie, Acumen, Alector, Alzinova, ALZPath, Amylyx, Annexon, Apellis, Artery Therapeutics, AZTherapies, Cognito Therapeutics, CogRx, Denali, Eisai, LabCorp, Merry Life, Nervgen, Novo Nordisk, Optoceutics, Passage Bio, Pinteon Therapeutics, Prothena, Red Abbey Labs, reMYND, Roche, Samumed, Siemens Healthineers, Triplet Therapeutics, and Wave, has given lectures in symposia sponsored by Alzecure, Biogen, Cellectricon, Fujirebio, Lilly, Novo Nordisk, and Roche, and is a co-founder of Brain Biomarker Solutions in Gothenburg AB (BBS), which is a part of the GU Ventures Incubator Program (outside submitted work). KB has served as a consultant and at advisory boards for Abbvie, AC Immune, ALZPath, AriBio, BioArctic, Biogen, Eisai, Lilly, Moleac Pte. Ltd, Neurimmune, Novartis, Ono Pharma, Prothena, Roche Diagnostics, and Siemens Healthineers; has served at data monitoring committees for Julius Clinical and Novartis; has given lectures, produced educational materials and participated in educational programs for AC Immune, Biogen, Celdara Medical, Eisai and Roche Diagnostics; and is a co-founder of Brain Biomarker Solutions in Gothenburg AB (BBS), which is a part of the GU Ventures Incubator Program, outside the work presented in this paper. ERZ serves on the scientific advisory board of Next Innovative Therapeutics. All other authors declare that they have no competing interests. References Knopman, D.S., et al. Alzheimer disease. Nat Rev Dis Primers 7, 33 (2021). Herrup, K. The case for rejecting the amyloid cascade hypothesis. Nat Neurosci 18, 794–799 (2015). Carter, S.F., et al. 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Evaluating the Fit of Structural Equation Models: Tests of Significance and Descriptive Goodness-of-Fit Measures. Methods of Psychological Research 8, 23–74 (2003). Additional Declarations Yes there is potential Competing Interest. WVB served as a speaker for Novo Nordisk. SG has served as a scientific advisor to Cerveau Technologies. GT-B and HCK are employees of Johnson and Johnson Innovative Medicine and receive salary and stock from its parent company, Johnson & Johnson. NJA has given lectures in symposia sponsored by Lilly and Quanterix. HZ has served on the scientific advisory boards and/or as a consultant for Abbvie, Acumen, Alector, Alzinova, ALZPath, Amylyx, Annexon, Apellis, Artery Therapeutics, AZTherapies, Cognito Therapeutics, CogRx, Denali, Eisai, LabCorp, Merry Life, Nervgen, Novo Nordisk, Optoceutics, Passage Bio, Pinteon Therapeutics, Prothena, Red Abbey Labs, reMYND, Roche, Samumed, Siemens Healthineers, Triplet Therapeutics, and Wave, has given lectures in symposia sponsored by Alzecure, Biogen, Cellectricon, Fujirebio, Lilly, Novo Nordisk, and Roche, and is a co-founder of Brain Biomarker Solutions in Gothenburg AB (BBS), which is a part of the GU Ventures Incubator Program (outside submitted work). KB has served as a consultant and at advisory boards for Abbvie, AC Immune, ALZPath, AriBio, BioArctic, Biogen, Eisai, Lilly, Moleac Pte. Ltd, Neurimmune, Novartis, Ono Pharma, Prothena, Roche Diagnostics, and Siemens Healthineers; has served at data monitoring committees for Julius Clinical and Novartis; has given lectures, produced educational materials and participated in educational programs for AC Immune, Biogen, Celdara Medical, Eisai and Roche Diagnostics; and is a co-founder of Brain Biomarker Solutions in Gothenburg AB (BBS), which is a part of the GU Ventures Incubator Program, outside the work presented in this paper. ERZ serves on the scientific advisory board of Next Innovative Therapeutics. All other authors declare that they have no competing interests. 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(MA+\u003csub\u003eTSPO\u003c/sub\u003e; blue) individuals. The error bands indicate the 95% CI. β-estimates and \u003cem\u003eP\u003c/em\u003e-values were computed from linear regressions with standardized variables adjusted for age, sex, and cognitive status. The interaction model also accounted for Aβ PET and MA\u003csub\u003eTSPO\u003c/sub\u003e main effects. (\u003cstrong\u003eb\u003c/strong\u003e) Continuous association between global Aβ PET, posterior cingulate TSPO PET, and plasma GFAP. β-estimate and \u003cem\u003eP\u003c/em\u003e-value were computed from a linear regression with standardized variables adjusted for age, sex, and cognitive status, as well as Aβ PET and TSPO PET main effects. (\u003cstrong\u003ec\u003c/strong\u003e) Region-wise linear regression \u003cem\u003eT\u003c/em\u003e-map showing the association between Aβ PET SUVR and plasma GFAP levels across cortical areas in microglial activation-negative (MA-\u003csub\u003eTSPO\u003c/sub\u003e; left) and microglial activation-positive (MA+\u003csub\u003eTSPO\u003c/sub\u003e; right) individuals. Regression models with standardized variables were adjusted for age, sex, and cognitive status. (\u003cstrong\u003ed\u003c/strong\u003e) Region-wise linear regression \u003cem\u003eT\u003c/em\u003e-map showing the interaction of global Aβ PET and TSPO PET on plasma GFAP levels across cortical areas. Regression model with standardized variables was adjusted for age, sex, and cognitive status, as well as Aβ PET and TSPO PET main effects. (\u003cstrong\u003ee\u003c/strong\u003e) Brain map (top) depicting \u003cem\u003eTSPO\u003c/em\u003e mRNA expression levels across cortical areas in the postmortem brain of six healthy individuals from the AHBA. Color scales represent log2 mRNA expression intensity. The scatter plot (bottom) shows the results of regression analysis testing whether AHBA\u003cem\u003e TSPO\u003c/em\u003e mRNA expression patterns predict the topography of microglia-related effects (β-estimates) of Aβ on astrocyte reactivity across cortical brain regions in our population.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5184011/v1/d20f65bb2dc0695a72581653.png"},{"id":68205152,"identity":"74e1f883-dac4-450f-988a-fefa509b1910","added_by":"auto","created_at":"2024-11-04 16:10:02","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":682349,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMicroglia-dependent effects of Aβ on astrocyte reactivity contribute to cognitive impairment via tau pathology.\u003c/strong\u003e (\u003cstrong\u003ea\u003c/strong\u003e) Scatter plot displaying the association between plasma GFAP and plasma p-tau217 levels in microglial activation-negative (MA-\u003csub\u003e TSPO\u003c/sub\u003e; yellow) and microglial activation-positive (MA+\u003csub\u003e TSPO\u003c/sub\u003e; blue) individuals. The error bands indicate the 95% CI. (\u003cstrong\u003eb\u003c/strong\u003e) Continuous association between plasma GFAP, posterior cingulate TSPO PET, and plasma p-tau217. (\u003cstrong\u003ec\u003c/strong\u003e) Region-wise linear regression \u003cem\u003eT\u003c/em\u003e-map showing the association between plasma GFAP levels and tau PET SUVR across cortical areas in microglial activation-negative (MA-\u003csub\u003e TSPO\u003c/sub\u003e; left) and microglial activation-positive (MA+\u003csub\u003e TSPO\u003c/sub\u003e; right) individuals. In regression models, β-estimates and \u003cem\u003eP\u003c/em\u003e-values were computed from linear regressions with standardized variables adjusted for age, sex, and cognitive status. The interaction model also accounted for plasma GFAP and posterior cingulate TSPO PET main effects. Structural equation modeling testing the associations between Aβ (global Aβ PET), reactive astrogliosis (plasma GFAP), tau phosphorylation (plasma p-tau217), tau tangles (temporal meta-ROI tau PET), and cognition (MMSE) in (\u003cstrong\u003ed\u003c/strong\u003e) microglial activation-negative (MA-\u003csub\u003e TSPO\u003c/sub\u003e) and (\u003cstrong\u003ee\u003c/strong\u003e) microglial activation-positive (MA+\u003csub\u003e TSPO\u003c/sub\u003e) individuals. Associations were adjusted for age, sex, and (if involving cognition) years of education. Solid lines with standardized β-estimates and 95% CI represent significant associations, while dashed lines represent non-significant effects. Analyses involving plasma p-tau217 were performed in a subset of 93 individuals. From the total study population of 101 individuals, five participants did not have available plasma p-tau217 measurements, and three were excluded as they were considered outliers (plasma p-tau217 levels three SD above the mean of the population).\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5184011/v1/df3fface9642f7300ea81588.png"},{"id":95362791,"identity":"99ea61e1-ce2f-4491-bee1-f4833dc22e46","added_by":"auto","created_at":"2025-11-07 08:06:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3124290,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5184011/v1/cc8998ec-c931-47cc-b012-1c5e8a602449.pdf"},{"id":68205153,"identity":"2d3bfc4b-63e1-48b4-826c-22939f0fa243","added_by":"auto","created_at":"2024-11-04 16:10:03","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":3183189,"visible":true,"origin":"","legend":"","description":"","filename":"SuppInfoJPFS.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5184011/v1/e0babf700a4eab63ad22bd06.pdf"}],"financialInterests":"\u003cb\u003eYes\u003c/b\u003e there is potential Competing Interest.\nWVB served as a speaker for Novo Nordisk. SG has served as a scientific advisor to Cerveau Technologies. GT-B and HCK are employees of Johnson and Johnson Innovative Medicine and receive salary and stock from its parent company, Johnson \u0026 Johnson. NJA has given lectures in symposia sponsored by Lilly and Quanterix. HZ has served on the scientific advisory boards and/or as a consultant for Abbvie, Acumen, Alector, Alzinova, ALZPath, Amylyx, Annexon, Apellis, Artery Therapeutics, AZTherapies, Cognito Therapeutics, CogRx, Denali, Eisai, LabCorp, Merry Life, Nervgen, Novo Nordisk, Optoceutics, Passage Bio, Pinteon Therapeutics, Prothena, Red Abbey Labs, reMYND, Roche, Samumed, Siemens Healthineers, Triplet Therapeutics, and Wave, has given lectures in symposia sponsored by Alzecure, Biogen, Cellectricon, Fujirebio, Lilly, Novo Nordisk, and Roche, and is a co-founder of Brain Biomarker Solutions in Gothenburg AB (BBS), which is a part of the GU Ventures Incubator Program (outside submitted work). KB has served as a consultant and at advisory boards for Abbvie, AC Immune, ALZPath, AriBio, BioArctic, Biogen, Eisai, Lilly, Moleac Pte. Ltd, Neurimmune, Novartis, Ono Pharma, Prothena, Roche Diagnostics, and Siemens Healthineers; has served at data monitoring committees for Julius Clinical and Novartis; has given lectures, produced educational materials and participated in educational programs for AC Immune, Biogen, Celdara Medical, Eisai and Roche Diagnostics; and is a co-founder of Brain Biomarker Solutions in Gothenburg AB (BBS), which is a part of the GU Ventures Incubator Program, outside the work presented in this paper. ERZ serves on the scientific advisory board of Next Innovative Therapeutics. All other authors declare that they have no competing interests.","formattedTitle":"Microglia modulate Aβ-dependent astrocyte reactivity in Alzheimer’s disease","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAlzheimer\u0026rsquo;s disease (AD) is defined by the brain accumulation of amyloid-β (Aβ) and tau proteins\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. However, a growing body of evidence indicates that these proteinopathies alone do not fully explain the clinical progression of AD\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. This suggests that additional pathological mechanisms are involved in the AD pathological cascade. In fact, it has been proposed that neuroinflammation plays a pivotal role in determining patients\u0026rsquo; susceptibility to dementia, with microglia and astrocytes orchestrating the repertoire of immune responses\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn pathological conditions, astrocytes undergo molecular, functional, and morphological changes, a process referred to as reactive astrogliosis\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Despite the well-established involvement of astrocytes in AD progression\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e, the biological underpinnings of reactive astrogliosis in this neurodegenerative condition remain elusive. Although Aβ pathology is an important trigger of astrocyte reactivity\u003csup\u003e\u003cspan additionalcitationids=\"CR9 CR10\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, other pathological components of AD have also been implicated in promoting reactive astrogliosis, notably microglial activation\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Instead of solely being a response to Aβ deposition\u003csup\u003e\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e, compelling experimental data demonstrate that activated microglia can induce astrocyte reactivity through the secretion of inflammatory cytokines\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e, highlighting a close relationship between Aβ and glial cells. This raises the possibility that microglial activation may account for the fact that Aβ drives astrocyte reactivity in some individuals but not in others\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. However, no previous clinical study has addressed whether microglial activation can modulate astrocyte reactivity in response to Aβ pathology in the living human brain. Better understanding the microglia-astrocyte crosstalk in AD may provide valuable insights for developing therapies targeting glial cells\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eUsing imaging and fluid biomarkers for the quantification of glial reactivity and AD hallmark proteinopathies, we tested the hypothesis that microglia impact the effects of Aβ pathology on reactive astrogliosis in individuals across the aging and AD clinical spectrum. In addition, we assessed whether gene expression patterns reflect the \u003cem\u003ein-vivo\u003c/em\u003e relationship between Aβ pathology and glial reactivity. Lastly, we investigated whether glial reactivity could aid in explaining the relationships between Aβ, tau, and cognitive impairment.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe selection of study participants is displayed in \u003cb\u003eSupplementary Fig.\u0026nbsp;1\u003c/b\u003e. We genotyped 606 individuals for the rs6971 single nucleotide polymorphism (SNP) in the translocator protein (\u003cem\u003eTSPO\u003c/em\u003e) gene, with 314 identified as TSPO high-affinity binders. From those, we studied 101 individuals aged 50 years or older who had complete data for positron emission tomography (PET) for Aβ ([\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003eF]AZD4694) and TSPO microglial activation ([\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003eC]PBR28), as well as clinical assessments and plasma glial fibrillary acidic protein (GFAP) measurements. Participants were also assessed for plasma plasma phosphorylated tau at threonine 217 (p-tau217) and [\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003eF]MK-6240 tau PET imaging. Among our study population, a total of 62 were cognitively unimpaired (CU), and 39 were cognitively impaired (26 with mild cognitive impairment [MCI] and 13 with AD dementia). Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e displays demographic and clinical information of our population.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStudy participant characteristics.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCognitively unimpaired\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCognitively impaired\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e71.9 (6.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72.3 (5.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e71.2 (7.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale, No. (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33 (32.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (22.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19 (48.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.3 (3.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.6 (3.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.8 (3.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAPOE\u003c/em\u003eε4 carrier, No. (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35 (34.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (24.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20 (51.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlasma GFAP, pg/mL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e242.3 (119.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e222.8 (115.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e273.2 (120.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlasma p-tau217, pg/mL\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.09 (0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.06 (0.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.14 (0.09)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAβ PET SUVR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.74 (0.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.48 (0.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.16 (0.65)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTau PET SUVR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.13 (0.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.86 (0.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.57 (0.99)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTSPO PET SUVR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.31 (0.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.28 (0.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.34 (0.15)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMicroglial activation positivity, No. (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51 (50.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29 (46.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22 (56.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMMSE score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28.2 (2.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.3 (0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.4 (3.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eContinuous variables are presented as mean (SD). Aβ PET was measured with global [\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003eF]AZD4694 SUVR, microglial activation TSPO PET with posterior cingulate [\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003eC]PBR28 SUVR, and tau PET with temporal meta-ROI [\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003eF]MK6240 SUVR. \u003csup\u003ea\u003c/sup\u003eComposed of 26 individuals with MCI and 13 individuals with AD dementia. \u003csup\u003eb\u003c/sup\u003eAssessed in a subset of 93 individuals.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eAβ pathology associates with astrocyte reactivity only in the presence of elevated levels of microglial activation\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWe first investigated the association of TSPO PET and Aβ PET with plasma GFAP. Regression analyses revealed a significant positive association between global Aβ PET SUVR and plasma GFAP levels in TSPO microglial activation-positive (MA\u0026thinsp;+\u0026thinsp;\u003csub\u003eTSPO\u003c/sub\u003e) individuals (β\u0026thinsp;=\u0026thinsp;0.618, 95% confidence interval [CI] 0.310 to 0.926, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) but not in TSPO microglial activation-negative (MA-\u003csub\u003eTSPO\u003c/sub\u003e) individuals (β\u0026thinsp;=\u0026thinsp;0.184, 95% CI -0.133 to 0.502, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.247), as well as a significant interaction of Aβ PET SUVR and MA\u003csub\u003eTSPO\u003c/sub\u003e status with plasma GFAP levels (β\u0026thinsp;=\u0026thinsp;0.528, 95% CI 0.172 to 0.883, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). Additionally, a significant interaction between continuous values of Aβ PET burden and TSPO PET uptake on plasma GFAP levels (β\u0026thinsp;=\u0026thinsp;0.302, 95% CI 0.116 to 0.489, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb\u003cb\u003e)\u003c/b\u003e further supported that microglial activation affects the association of Aβ pathology with reactive astrogliosis, independently of the threshold used to define MA\u0026thinsp;+\u0026thinsp;\u003csub\u003eTSPO\u003c/sub\u003e. Analysis of variance corroborated the adequacy of the interaction model compared to the reduced models (including only Aβ PET, only TSPO PET, or their additive effects; all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Similar findings were observed in sensitivity analyses including outliers (\u003cb\u003eSupplementary Table\u0026nbsp;1\u003c/b\u003e). Region-wise linear regressions showed that higher plasma GFAP levels were associated with higher Aβ PET burden only in the presence of MA\u0026thinsp;+\u0026thinsp;\u003csub\u003eTSPO\u003c/sub\u003e across cortical areas, including the frontal, parietal, temporal, and cingulate cortices (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec and \u003cb\u003eSupplementary Fig.\u0026nbsp;2\u003c/b\u003e). In a subsequent analysis assessing continuous values for the topographical distribution of microglial activation, we found that the relationship between Aβ PET burden and plasma GFAP levels was influenced by TSPO PET uptake in the cingulate and frontal brain regions (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed and \u003cb\u003eSupplementary Fig.\u0026nbsp;3\u003c/b\u003e). Using microarray-based \u003cem\u003epostmortem\u003c/em\u003e data from the Allen Human Brain Atlas (AHBA), we found that the cerebral distribution of \u003cem\u003eTSPO\u003c/em\u003e mRNA expression predicted the topography of microglia-related effects of Aβ on astrocyte reactivity which was observed in our study population (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ee).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eMicroglial and astrocyte reactivity jointly relate to tau phosphorylation and aggregation\u003c/h2\u003e \u003cp\u003eNext, we assessed the association of TSPO PET and plasma GFAP with tau biomarkers. Regression analysis showed that higher plasma GFAP levels were significantly associated with higher plasma p-tau217 levels in MA\u0026thinsp;+\u0026thinsp;\u003csub\u003eTSPO\u003c/sub\u003e individuals (β\u0026thinsp;=\u0026thinsp;0.658, 95% CI 0.323 to 0.993, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) but not in MA-\u003csub\u003eTSPO\u003c/sub\u003e individuals (β\u0026thinsp;=\u0026thinsp;0.110, 95% CI -0.091 to 0.312, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.276), and that the interaction between plasma GFAP and MA\u003csub\u003eTSPO\u003c/sub\u003e status was significantly associated with higher plasma p-tau217 levels (β\u0026thinsp;=\u0026thinsp;0.470, 95% CI 0.125 to 0.816, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.008; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). Moreover, a significant interaction was also observed between continuous levels of plasma GFAP and TSPO PET uptake on plasma p-tau217 levels (β\u0026thinsp;=\u0026thinsp;0.201, 95% CI 0.024 to 0.377, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.026; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb), reinforcing that the relationship between reactive astrogliosis and tau phosphorylation depends on microglial activation levels, irrespective of the threshold used to determine MA\u0026thinsp;+\u0026thinsp;\u003csub\u003eTSPO\u003c/sub\u003e. The adequacy of the interaction model compared to the reduced models (including only plasma GFAP, only TSPO PET, or their additive effects) was supported by analysis of variance (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Similar results were observed in sensitivity analyses including outliers (\u003cb\u003eSupplementary Table\u0026nbsp;2\u003c/b\u003e). In region-wise linear regressions, we observed that higher plasma GFAP levels were associated with tau PET accumulation only in the presence of MA\u0026thinsp;+\u0026thinsp;\u003csub\u003eTSPO\u003c/sub\u003e mainly in neocortical brain regions, comprising temporal structures, as well as association and sensorimotor cortices (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec and \u003cb\u003eSupplementary Fig.\u0026nbsp;4\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eGlial reactivity contributes to AD-related cognitive impairment\u003c/h3\u003e\n\u003cp\u003eLastly, we applied structural equation modeling to test the associations between Aβ, astrocyte reactivity, tau phosphorylation, tau tangles, and cognition according to MA\u003csub\u003eTSPO\u003c/sub\u003e status. In MA-\u003csub\u003eTSPO\u003c/sub\u003e individuals, we only observed direct effects of Aβ PET load on higher plasma p-tau217 levels and tau PET burden, with no significant associations with cognitive impairment (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed). This model explained 32% of the variance in cognitive impairment and fit the data poorly (root mean squared error of approximation [RMSEA]\u0026thinsp;=\u0026thinsp;0.257, standardized root mean square residual [SRMR]\u0026thinsp;=\u0026thinsp;0.069, comparative fit index [CFI]\u0026thinsp;=\u0026thinsp;0.888). Conversely, in MA\u0026thinsp;+\u0026thinsp;\u003csub\u003eTSPO\u003c/sub\u003e individuals, we found that plasma GFAP levels partially mediated the effects of Aβ PET load on higher plasma p-tau217, which was further associated with cognitive impairment through increased tau PET accumulation. The model also demonstrated pathways by which Aβ PET was related to cognitive deterioration via direct associations with tau pathology biomarkers (plasma p-tau217 and tau PET; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ee). This construct explained 76% of the variance in cognitive impairment and fit the data well (RMSEA\u0026thinsp;=\u0026thinsp;0.000, SRMR\u0026thinsp;=\u0026thinsp;0.018, CFI\u0026thinsp;=\u0026thinsp;1.000). \u003cb\u003eSupplementary Tables\u0026nbsp;3 and 4\u003c/b\u003e report detailed coefficients and associated statistics for structural equation models.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn the present study, we observed that microglial activation determines Aβ effects on astrocyte reactivity in the living human brain. We also found that the physiological distribution of \u003cem\u003eTSPO\u003c/em\u003e gene expression in the \u003cem\u003epostmortem\u003c/em\u003e brain resembles this \u003cem\u003ein-vivo\u003c/em\u003e relationship between Aβ pathology and glial reactivity. Lastly, we showed that the microglia-dependent impact of Aβ on astrocyte reactivity was further associated with cognitive impairment through tau phosphorylation and aggregation.\u003c/p\u003e \u003cp\u003eWe found that Aβ pathology was associated with GFAP levels across cortical brain regions only in the presence of increased TSPO PET signal. It is well-established that Aβ pathology is closely related to astrocyte reactivity, with experimental studies revealing that Aβ aggregates increase markers of astrocyte reactivity\u003csup\u003e\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Accordingly, neuropathological studies showed that GFAP-positive reactive astrocytes surround Aβ plaques and mirror the topographical distribution of Aβ deposition in the AD brain \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. This is further supported by an increasing number of clinical studies showing that cerebrospinal fluid (CSF) and, more prominently, plasma levels of GFAP are closely associated with Aβ biomarkers\u003csup\u003e\u003cspan additionalcitationids=\"CR24 CR25 CR26 CR27 CR28 CR29\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Beyond Aβ accumulation, an animal study demonstrated that microglial activation also triggers astrocyte reactivity by secreting interleukin-1α (Il-α), tumor necrosis factor (TNF), and complement component 1, subcomponent q (C1q) cytokines\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Microglial reactivity is an important pathological feature of AD and has an intimate communication with reactive astrocytes in coordinating the innate immune response in the brain\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Here, we build on previous experimental data by showing the first clinical evidence that microglial activation modulates the Aβ-induced astrocyte reactivity in the living AD human brain. Analyzing postmortem data from the AHBA, we also demonstrated that the physiological brain distribution of \u003cem\u003eTSPO\u003c/em\u003e mRNA expression resembled the topographical patterns of microglia-driven effects of Aβ on astrocyte reactivity observed in our study population. Altogether, these findings support the notion that a dysregulated cellular crosstalk between microglia and astrocytes may be a major neuroinflammatory phenomenon involved in AD pathogenesis, which might be at least partially explained at the level of gene expression.\u003c/p\u003e \u003cp\u003eWe observed that TSPO PET and plasma GFAP levels were jointly associated with tau biomarkers. Rather than merely being a response to the accumulation of AD hallmark proteins, a growing body of work supports the notion that both microglia and astrocytes are involved in tau pathogenesis through the exacerbation of tau spread and hyperphosphorylation, as well as tau-driven neurodegeneration\u003csup\u003e\u003cspan additionalcitationids=\"CR33 CR34 CR35 CR36 CR37\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. Accordingly, recent biomarker-based clinical studies have suggested that glial reactivity might help to explain the link between Aβ and tau pathology. More specifically, it has been consistently demonstrated that astrocyte reactivity, indexed by plasma GFAP, determines Aβ downstream effects on tau pathology in individuals across the AD \u003cem\u003econtinuum\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e,\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. Similarly, neuroimaging data demonstrated that the interaction between Aβ and activated microglia is crucial for tau pathology progression\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. In this study, we propose a model in which the interplay between Aβ and reactive astrogliosis contributes to tau pathology in the presence of activated microglia. Noteworthy, tau biomarkers are closely related to AD-specific cognitive deterioration, but they assess distinct aspects of tau pathology: fluid p-tau detects soluble hyperphosphorylated tau, serving as an early measure of tau pathophysiology; while tau PET targets insoluble tau tangles, offering a later measure of tau aggregation\u003csup\u003e\u003cspan additionalcitationids=\"CR43\" citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. Our results align with these concepts by revealing glial-mediated effects of Aβ on pathological tau phosphorylation, which were further associated with subsequent tau aggregation, ultimately leading to the manifestation of cognitive symptoms. Altogether, these observations reinforce the importance of microglial activation and astrocyte reactivity in AD progression, suggesting glial cells as potential therapeutic targets for future disease-modifying clinical trials.\u003c/p\u003e \u003cp\u003eMethodological strengths of our study include the evaluation of a large cohort genetically enriched to increase the reliability of the microglial activation imaging agent. Moreover, data for multiple imaging and fluid biomarkers using the most advanced methodologies for the brain quantification of Aβ, tau, and glial reactivity were available. This study also has methodological limitations. TRIAD participants are volunteers who were motivated to participate in a study about dementia, which might limit the generalizability of our findings due to self-selection bias. Here, we used [\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003eC]PBR28 TSPO PET as an index of microglial activation\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e,\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e and plasma GFAP as an index of astrocyte reactivity\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. Neuroinflammation involves various glial phenotypes during AD progression\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e; however, this heterogeneity could not be captured in our biomarker-based study. Although TSPO is predominantly expressed in the AD brain by microglia\u003csup\u003e\u003cspan additionalcitationids=\"CR50 CR51\" citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e, it has also been detected in other cell types (\u003cem\u003ee.g.\u003c/em\u003e, astrocytes and endothelial cells)\u003csup\u003e\u003cspan additionalcitationids=\"CR52 CR53\" citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. Thus, further investigation is needed to clarify the extent to which each cell type contributes to the TSPO PET signal in AD. In addition, there is an ongoing discussion about whether the TSPO PET signal more accurately reflects microglial activation or density/recruitment\u003csup\u003e55\u003c/sup\u003e. To elucidate the biological mechanisms underlying our findings, future research focusing on phenotypically characterizing the glial cells involved in AD neuroinflammatory response is needed. Lastly, given that this study has a cross-sectional design, our results should be expanded with multimodal longitudinal data.\u003c/p\u003e \u003cp\u003eIn conclusion, our results support the construct that activated microglia is a key player in Aβ-dependent reactive astrogliosis, which further contributes to cognitive impairment via the aggregation of phosphorylated tau in the living AD human brain.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eThe present work included individuals enrolled in the Translational Biomarkers in Aging and Dementia (TRIAD) cohort (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://triad.tnl-mcgill.com\u003c/span\u003e\u003cspan address=\"https://triad.tnl-mcgill.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), a biomarker-based study launched in 2017 which comprises participants from the community or outpatients at the McGill University Research Centre for Studies in Aging, Canada. Exclusion criteria encompassed inability to speak English or French, inadequate auditory and visual capacities for neuropsychologic testing, contraindications for PET or magnetic resonance imaging (MRI), recent head trauma or major surgery, inadequately treated conditions, current enrollment in other studies or active substance abuse. Written informed consent was obtained from all study participants. The study was approved by the Montreal Neurological Institute PET Working Committee and the Douglas Mental Health University Institute Research Ethics Board.\u003c/p\u003e \u003cp\u003eTRIAD participants underwent genotyping for the Ala147Thr SNP of the \u003cem\u003eTSPO\u003c/em\u003e gene (rs6971, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"https://triad.tnl-mcgill.com\" target=\"_blank\"\u003ewww.ncbi.nlm.nih.gov/snp/rs6971\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.ncbi.nlm.nih.gov/snp/rs6971\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), which affects the binding affinity of the [\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003eC]PBR28 radiotracer. Depending on this genotype, individuals can be low-, mixed- or high-affinity binders. Since the rs6971 SNP is a methodological caveat that does not influence TSPO levels, glial reactivity, or AD pathophysiology\u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e, our study only included high-affinity binders to mitigate noise related to artificial uptake variations, as previously done\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e,\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e. Study participants had available Aβ PET, TSPO microglial activation PET, magnetic resonance imaging (MRI), plasma GFAP, and neuropsychological testing. We also assessed plasma p-tau217 and tau tangle PET data. Two individuals that had plasma GFAP levels three standard deviations (SD) above the mean of the population were considered outliers and excluded from the analyses, as previously done\u003csup\u003e\u003cspan additionalcitationids=\"CR58\" citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e. CU individuals had no objective cognitive impairment and a global Clinical Dementia Rating (CDR) of 0. Participants with MCI had preserved activities of daily living, subjective and/or objective cognitive impairment, and a global CDR of 0.5\u003csup\u003e60\u003c/sup\u003e. Patients with mild-to-moderate AD dementia met the National Institute on Aging and the Alzheimer\u0026rsquo;s Association (NIA-AA) criteria for probable AD\u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e and had a global CDR score between 0.5 and 2.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePlasma biomarkers\u003c/h2\u003e \u003cp\u003eBlood collection followed previously described procedures\u003csup\u003e\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e. Plasma biomarkers were quantified using Single molecule array (Simoa) methods on the HD-X platform (Quanterix). Plasma GFAP concentration was measured with a commercial single-plex assay at the Clinical Neurochemistry Laboratory, University of Gothenburg\u003csup\u003e\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e. Plasma p-tau217 concentration was measured at Johnson and Johnson Innovative Medicine with a previously described assay\u003csup\u003e\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eBrain gene expression\u003c/h3\u003e\n\u003cp\u003eWe obtained \u003cem\u003eTSPO\u003c/em\u003e gene expression data in the entire brain from the open-source AHBA (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.brain-map.org\u003c/span\u003e\u003cspan address=\"http://www.brain-map.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003csup\u003e65\u003c/sup\u003e. Briefly, mRNA expression intensity values were derived from microarray data of 3702 samples from 6 healthy postmortem human brains (\u003cb\u003eSupplementary Table\u0026nbsp;5\u003c/b\u003e). Microarray-based \u003cem\u003eTSPO\u003c/em\u003e mRNA brain expression map, generated using Gaussian process regression\u003csup\u003e\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e, was retrieved from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"https://triad.tnl-mcgill.com\" target=\"_blank\"\u003ewww.meduniwien.ac.at/neuroimaging/mRNA.html\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.meduniwien.ac.at/neuroimaging/mRNA.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e\n\u003ch3\u003eNeuroimaging\u003c/h3\u003e\n\u003cp\u003eAll participants had a T1-weighted MRI that was used for coregistration. Detailed information concerning MRI acquisition and processing has already been described\u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e. Of note, structural MRI data were acquired at the Montreal Neurological Institute (MNI) on a 3T Siemens Magnetom scanner using a standard head coil. High-resolution structural images of the whole brain were obtained with the magnetization prepared rapid acquisition gradient echo MRI sequence. All PET scans were acquired at the MNI on the same brain-dedicated Siemens High-Resolution Research Tomograph. Aβ PET with [\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003eF]AZD4694 (acquired 40\u0026ndash;70 min post-injection), tau tangle PET with [\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003eF]MK-6240 (acquired 90\u0026ndash;110 min post-injection), and TSPO microglial activation PET with [\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003eC]PBR28 (acquired 60\u0026ndash;90 min post-injection) were reconstructed with the ordered subset expectation maximization (OSEM) algorithm on a four-dimensional (4D) volume with three frames (3 \u0026times; 600 s)\u003csup\u003e\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e, four frames (4 \u0026times; 300 s)\u003csup\u003e\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e, and six frames (6 \u0026times; 300 s)\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e, respectively. After acquisition, PET images were corrected for attenuation, motion, dead time, decay, and random and scattered coincidences. T1-weighted MRI images were corrected for non-uniformity and field distortions in accordance with an in-house pipeline. Then, imaging co-registration and spatial normalization to the Alzheimer\u0026rsquo;s Disease Neuroimaging Initiative (ADNI) template space was performed. To this end, PET images were automatically registered to the native T1-weighted MRI with linear transformations, and T1-weighted MRI images were linearly and nonlinearly registered to the ADNI template space. Subsequently, PET images were registered to the ADNI template space by applying the linear and nonlinear transformation parameters from PET to native MRI and native MRI to the ADNI template space. Aβ PET SUVR and TSPO PET SUVR were calculated using the whole cerebellar gray matter as reference region\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e,\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u003c/sup\u003e. Tau PET SUVR was calculated using the inferior cerebellar gray matter as reference region\u003csup\u003e\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e. PET images were spatially smoothed to achieve a final resolution of 8 mm full width at half-maximum. The Desikan-Killiany-Tourville atlas was used to determine the anatomical regions of interest (ROIs)\u003csup\u003e\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/sup\u003e. The global Aβ PET SUVR composite was estimated from the following brain regions: precuneus, prefrontal, orbitofrontal, parietal, temporal, and cingulate\u003csup\u003e\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u003c/sup\u003e. The temporal meta-ROI tau PET SUVR composite was estimated from the following brain regions: entorhinal, hippocampus, fusiform, parahippocampal, inferior temporal, and middle temporal\u003csup\u003e\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u003c/sup\u003e. We identified higher [\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003eC]PBR28 uptake in the posterior cingulate as a cortical neuroinflammatory signature of AD (\u003cb\u003eSupplementary Fig.\u0026nbsp;5\u003c/b\u003e), similar to previous work\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Thus, we used posterior cingulate [\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003eC]PBR28 SUVR as a summary measure of TSPO PET. Microglial activation positivity (MA\u0026thinsp;+\u0026thinsp;\u003csub\u003eTSPO\u003c/sub\u003e) was defined as SUVR values 2 SD above the mean from a separate population of 17 CU Aβ- young adults (\u003cb\u003eSupplementary Table\u0026nbsp;6\u003c/b\u003e), as previously reported\u003csup\u003e\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses were carried out using the R software (version 4.0.2, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.r-project.org/\u003c/span\u003e\u003cspan address=\"http://www.r-project.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The associations between biomarkers were tested using regression models accounting for age, sex, and cognitive status. Regression models including interaction terms also accounted for the main effects of the variables involved in the interaction. Continuous variables were standardized to facilitate comparison across estimates. Multiple comparisons correction at \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was performed using the false discovery rate (FDR) method for region-wise analysis. Analysis of variance was used to test the adequacy of models with the interaction term compared to reduced models. Regression analysis also tested whether \u003cem\u003eTSPO\u003c/em\u003e mRNA expression intensity resembles Aβ-glia relationships across cortical brain regions in our population. Structural equation modeling was applied to test the associations between Aβ, tau, glial reactivity, and cognition. Associations were adjusted for age, sex, and (if involving cognition) years of education. Noteworthy, structural equation models were constructed to assess specific hypotheses represented in the figure\u0026rsquo;s meta-models, and the following thresholds were used for considering a good fit: CFI\u0026thinsp;\u0026gt;\u0026thinsp;0.97; RMSEA\u0026thinsp;\u0026lt;\u0026thinsp;0.05; SRMR\u0026thinsp;\u0026lt;\u0026thinsp;0.05 \u003csup\u003e73,74\u003c/sup\u003e. For all analyses, we considered a two-tailed \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 as statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe acknowledge all study participants and the McGill Center for Studies in Aging staff. We thank Cerveau Technologies for the use of [\u003csup\u003e18\u003c/sup\u003eF]MK-6240. We also thank Dean Jolly, Alexey Kostikov, Monica Samoila-Lactatus, Karen Ross, Mehdi Boudjemeline, and Sandy Li for assisting in the radiochemistry production, as well as Richard Strauss, Edith Strauss, Guylaine Gagne, Carley Mayhew, Tasha Vinet-Celluci, Karen Wan, Sarah Sbeiti, Meong Jin Joung, Miloudza Olmand, Rim Nazar, Hung-Hsin Hsiao, Reda Bouhachi, and Arturo Aliaga for helping with data acquisition.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis research was supported by the\u0026nbsp;Alzheimer\u0026rsquo;s Association (NIRG-12-92090 and NIRP-12-259245; PR-N), Brain Canada Foundation (CFI Project 34874 and 33397 to PR-N),\u0026nbsp;CIHR-CCNA Canadian Consortium of Neurodegeneration in Aging (MOP-11-51-31; RFN 152985, 159815, and 162303 to PR-N), Fonds de Recherche du Qu\u0026eacute;bec \u0026ndash; Sant\u0026eacute; (Chercheur Boursier, 2020-VICO-279314; PR-N),\u0026nbsp;Weston Brain Institute (8400707, 8401154 and 8401103 to PR-N), and\u0026nbsp;Colin Adair Charitable Foundation (PR-N).\u0026nbsp;ERZ is funded by\u0026nbsp;the Alzheimer\u0026rsquo;s Association (AARGD-21-850670),\u0026nbsp;Conselho Nacional de Desenvolvimento Cient\u0026iacute;fico e Tecnol\u0026oacute;gico (CNPq; 312306/2021-0),\u0026nbsp;National Academy of Neuropsychology (ALZ-NAN-22-928381),\u0026nbsp;Funda\u0026ccedil;\u0026atilde;o de Amparo \u0026agrave; Pesquisa do Rio Grande do Sul (FAPERGS; 21/2551-0000673-0), and Instituto Serrapilheira (Serra-1912-31365).\u0026nbsp;BB receives financial support from the Alzheimer\u0026rsquo;s Association (AARFD-22-974627).\u0026nbsp;PCLF receives financial support from the\u0026nbsp;Alzheimer\u0026rsquo;s Association (AARFD-22-923814). CA is supported by Global Brain Health Institute, Alzheimer\u0026rsquo;s Association, and Alzheimer\u0026rsquo;s Society (GBHI ALZ UK-23-971089). MADB is supported by Funda\u0026ccedil;\u0026atilde;o de Amparo \u0026agrave; pesquisa do Estado do RS (FAPERGS) and Alzheimer\u0026rsquo;s Association (AARFD-23-1148735). WVB is funded by the Alzheimer\u0026rsquo;s Association (AACSF-D 22-928689). HZ is a Wallenberg Scholar and a Distinguished Professor at the Swedish Research Council supported by grants from the Swedish Research Council (2023-00356; 2022-01018 and 2019-02397), the European Union\u0026rsquo;s Horizon Europe research and Innovation Programme under grant agreement No 101053962, Swedish State Support for Clinical Research (ALFGBG-71320), the Alzheimer Drug Discovery Foundation (ADDF), USA (201809-2016862), the AD Strategic Fund and the Alzheimer\u0026apos;s Association (ADSF-21-831376-C, ADSF-21-831381-C, ADSF-21-831377-C, and ADSF-24-1284328-C), the European Partnership on Metrology, co-financed from the European Union\u0026rsquo;s Horizon Europe Research and Innovation Programme and by the Participating States (NEuroBioStand; 22HLT07), the Bluefield Project, Cure Alzheimer\u0026rsquo;s Fund, the Olav Thon Foundation, the Erling-Persson Family Foundation, Familjen R\u0026ouml;nstr\u0026ouml;ms Stiftelse, Stiftelsen f\u0026ouml;r Gamla Tj\u0026auml;narinnor, Hj\u0026auml;rnfonden, Sweden (FO2022-0270), the European Union\u0026rsquo;s Horizon 2020 Research and Innovation Programme under the Marie Skłodowska-Curie grant agreement No 860197 (MIRIADE), the European Union Joint Programme \u0026ndash; Neurodegenerative Disease Research (JPND2021-00694), the National Institute for Health and Care Research University College London Hospitals Biomedical Research Centre, and the UK Dementia Research Institute at UCL (UKDRI-1003). KB is supported by the Swedish Research Council (2017-00915 and 2022-00732), the Alzheimer Drug Discovery Foundation (ADDF;\u0026nbsp;RDAPB-201809-2016615), Swedish Alzheimer Foundation (AF-930351, AF-939721 and AF-968270), Hj\u0026auml;rnfonden (FO2017-0243 and ALZ2022-0006), the Swedish state under the agreement between the Swedish government and the County Councils, the ALF-agreement (ALFGBG-715986 and ALFGBG-965240),\u0026nbsp;the European Union Joint Program - Neurodegenerative Disease Research (JPND2019-466-236)\u0026nbsp;the National Institute of Health (NIH;\u0026nbsp;R01AG068398), Alzheimer\u0026rsquo;s Association 2021 Zenith Award (ZEN-21-848495), and Alzheimer\u0026rsquo;s Association 2022-2025 Grant (SG-23-1038904 QC).\u0026nbsp;TAP is supported by the National Institute of Health (R01AG075336 and R01AG073267) and the Alzheimer\u0026rsquo;s Association (AACSF-20-648075).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWVB served as a speaker for Novo Nordisk. SG has served as a scientific advisor to Cerveau Technologies. GT-B and HCK are employees of Johnson and Johnson Innovative Medicine and receive salary and stock from its parent company, Johnson \u0026amp; Johnson. NJA has given lectures in symposia sponsored by Lilly and Quanterix. HZ has served on the scientific advisory boards and/or as a consultant for Abbvie, Acumen, Alector, Alzinova, ALZPath, Amylyx, Annexon, Apellis, Artery Therapeutics, AZTherapies, Cognito Therapeutics, CogRx, Denali, Eisai, LabCorp, Merry Life, Nervgen, Novo Nordisk, Optoceutics, Passage Bio, Pinteon Therapeutics, Prothena, Red Abbey Labs, reMYND, Roche, Samumed, Siemens Healthineers, Triplet Therapeutics, and Wave, has given lectures in symposia sponsored by Alzecure, Biogen, Cellectricon, Fujirebio, Lilly, Novo Nordisk, and Roche, and is a co-founder of Brain Biomarker Solutions in Gothenburg AB (BBS), which is a part of the GU Ventures Incubator Program (outside submitted work). KB has served as a consultant and at advisory boards for Abbvie, AC Immune, ALZPath, AriBio, BioArctic, Biogen, Eisai, Lilly, Moleac Pte. Ltd, Neurimmune, Novartis, Ono Pharma, Prothena, Roche Diagnostics, and Siemens Healthineers; has served at data monitoring committees for Julius Clinical and Novartis; has given lectures, produced educational materials and participated in educational programs for AC Immune, Biogen, Celdara Medical, Eisai and Roche Diagnostics; and is a co-founder of Brain Biomarker Solutions in Gothenburg AB (BBS), which is a part of the GU Ventures Incubator Program, outside the work presented in this paper. ERZ serves on the scientific advisory board of Next Innovative Therapeutics. All other authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKnopman, D.S., \u003cem\u003eet al.\u003c/em\u003e Alzheimer disease. 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Methods of Psychological Research 8, 23\u0026ndash;74 (2003).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Alzheimer’s disease, amyloid-β, microglia, astrocytes, neuroinflammation, biomarkers","lastPublishedDoi":"10.21203/rs.3.rs-5184011/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5184011/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eExperimental evidence suggests that activated microglia induce astrocyte reactivity in neurodegenerative disorders, such as Alzheimer\u0026rsquo;s disease (AD). Here, we investigated the association between microglial activation and amyloid-β (Aβ) with reactive astrogliosis in the living AD human brain. We studied 101 individuals across the AD spectrum with positron emission tomography (PET) for Aβ aggregation ([\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003eF]AZD4694) and translocator protein (TSPO) microglial activation ([\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003eC]PBR28), along with the plasma biomarker for astrocyte reactivity glial fibrillary acidic protein (GFAP). We further assessed tau phosphorylation by plasma phosphorylated tau at threonine 217 (p-tau217) and tau aggregation using [\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003eF]MK-6240 PET. We found that Aβ pathology was associated with increased astrocyte reactivity across cortical brain regions only in the presence of elevated levels of microglial activation. Importantly, the microglia-dependent effects of Aβ on astrocyte reactivity were further related to cognitive impairment through tau phosphorylation and aggregation. \u003cem\u003ePostmortem\u003c/em\u003e data from the Allen Human Brain Atlas revealed that \u003cem\u003eTSPO\u003c/em\u003e mRNA expression patterns reflected the \u003cem\u003ein-vivo\u003c/em\u003e Aβ-glia relationships, indicating that the interplay between AD pathophysiology and glial reactivity might be regulated at the gene expression level. Altogether, our results provide biomarker-based clinical evidence that microglial activation plays a key role in Aβ-related astrocyte reactivity, which, in turn, contributes to downstream pathological features of AD. These findings shed light on the intricate crosstalk between microglia and astrocytes in the AD brain, offering insights for the development of glia-targeting therapies.\u003c/p\u003e","manuscriptTitle":"Microglia modulate Aβ-dependent astrocyte reactivity in Alzheimer’s disease","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-04 16:09:57","doi":"10.21203/rs.3.rs-5184011/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-neuroscience","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"neuro","sideBox":"Learn more about [Nature Neuroscience](http://www.nature.com/neuro/)","snPcode":"","submissionUrl":"","title":"Nature Neuroscience","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Research","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"bae501de-7afd-4a4a-820e-746ce79a9e5b","owner":[],"postedDate":"November 4th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":39615579,"name":"Biological sciences/Neuroscience/Glial biology/Microglia"},{"id":39615580,"name":"Health sciences/Diseases/Neurological disorders/Dementia/Alzheimer's disease"},{"id":39615581,"name":"Biological sciences/Neuroscience/Diseases of the nervous system/Alzheimer's disease"},{"id":39615582,"name":"Health sciences/Medical research/Biomarkers"},{"id":39615583,"name":"Biological sciences/Neuroscience/Glial biology/Astrocyte"}],"tags":[],"updatedAt":"2025-11-07T08:06:19+00:00","versionOfRecord":{"articleIdentity":"rs-5184011","link":"https://doi.org/10.1038/s41593-025-02103-0","journal":{"identity":"nature-neuroscience","isVorOnly":false,"title":"Nature Neuroscience"},"publishedOn":"2025-11-06 05:00:00","publishedOnDateReadable":"November 6th, 2025"},"versionCreatedAt":"2024-11-04 16:09:57","video":"","vorDoi":"10.1038/s41593-025-02103-0","vorDoiUrl":"https://doi.org/10.1038/s41593-025-02103-0","workflowStages":[]},"version":"v1","identity":"rs-5184011","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5184011","identity":"rs-5184011","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}