AD Progression Research Articles

Microglia from individuals with mild cognitive impairment (MCI) and AD show differences in transcriptional burst kinetics

AbstractBackgroundMicroglia are the innate immune cells in the central nervous system that responds to brain injury and pathogens. The loss of homeostatic microglia is associated with neurodegenerative diseases. Multiple studies of AD mouse models have identified AD‐associated microglia subtypes, e.g., disease associated microglia (DAM). However, studies of human microglia have not reported similar microglia subtypes. Therefore, how human microglia change in the presence of amyloid‐ protein deposition and phosphorylated Tau remains an open question.MethodWe have developed an approach that estimates transcriptional kinetics from scRNA‐seq data. This approach models how “bursty” and variable a gene expression is. We hypothesize that transcriptional bursts of key genes are different within AD cases compared to MCI individuals. We used the ROSMAP scRNAseq microglia dataset of 8 AD cases and 4 MCI cases and applied our approach to estimate the transcriptional kinetics for each subject. We then compared the difference in transcriptional kinetics of genes that are estimated both in AD and MCI.ResultFirst, we found that 96% of microglia cells in AD and MCI shows a similar co‐occurrence pattern of expressed genes and, therefore, likely reflect the same cell type. Within the assumed homogeneous microglia cells, we identified two genes, C1QC and ACTB, showing significant differences in their transcriptional kinetics between AD and MCI cases. Both C1QC and ACTB show larger burst sizes in MCI than in AD cases (p‐value for C1QC:0.0038; p‐value for ACTB: 0.00918). Consequently, C1QC and ACTB show a higher expression level variability among microglia cells in MCI cases than AD.ConclusionOur result suggests C1QC and ACTB may be associated with AD risk via changes in variability of gene expression rather than total gene expression levels. The increased variability in transcription of C1QC and ACTB in MCI individuals relative to AD cases might indicate more mobile and active physiological states of microglia. These results may point to functional changes in these genes with AD progression and are prime targets for further experimental validation.

In vivo data‐driven patterns of Tau accumulation associated with AD progression using 18F‐MK‐6240 PET

AbstractBackgroundAlzheimer’s disease is a neurodegenerative disease associated with accumulation of amyloid beta and tau neurofibrillary tangles following a pattern known as Braak stages (Braak 1995,2011). Recent papers (Pascoal 2020) indicate possibility to recapitulate Braak histo‐pathological stages in vivo using tau tangles PET tracer 18F‐MK‐6240, using manually defined regions of interest. This study analyzes patterns of Tau accumulation associated with AD in a completely data‐driven fashion.MethodWe used T1w MRI and 18F‐MK‐6240 Pet scans from healthy controls in the community or outpatients at the McGill University Research Centre for Studies in Aging. The following number of datasets from each diagnostic group [n scans total (m unique participants)]: cognitively normal: 347(194), mild cognitive impairment: 163(99), Alzheimer’s disease dementia: 114(77).T1w MRIs were pre‐processed (non‐uniformity correction, intensity normalization, stereotaxic registration, brain masking, tissue classification); FALCON (Fonov 2020) was used to extract the mid‐cortical surface.PET scans were linearly registered to the T1w MRI and the standardized uptake value ratio (SUVR) was calculated using whole cerebellum grey matter as the reference. SUVR values were sampled along cortical mid‐surfaces and geodesically smoothed with a 10mm gaussian and resampled to the cortical surface of MNI2009c template (Fonov 2011).ResultFigure 1 shows Mean SUVR values on top of Figure 1, and latent factors at the bottom. Overall low values of tau in the temporal, partial and occipital region are associated with normal cognition (F1). Tau changes in the temporal lobe (F2, similar to stage 1&2) are associated with a slight cognitive change. Change in parietal and posterior temporal areas (F3, similar to stage 3&4) are associated with even more cognitive loss. Finally changes in the motor strip and ventral portion of the occipital lobe and remaining cortex (F4 and F5) are associated with the very advanced stages (5&6) of cognitive decline.ConclusionWe discovered patterns of tau accumulation associated with progression of cognitive decline in a completely data‐driven fashion .

Sequential grey matter atrophy related to Alzheimer’s disease in adults with Down’s syndrome

AbstractBackgroundDue to the triplication of the APP gene on chromosome 21, virtually all individuals with Down’s syndrome (DS) present AD neuropathological hallmarks by the age of 40, and have a lifetime risk of developing dementia >90%. However, whether DS follows a stereotypical pattern of AD atrophy has not been investigated. Here, we aimed at defining the sequential grey matter (GM) volume loss across the whole AD continuum in adults with DS.MethodCross‐sectional design. 248 adults with DS (n = 145 asymptomatic [preclinical], n = 93 prodromal/demented AD) and 181 euploid cognitively unimpaired (CU) individuals from the Down Alzheimer Barcelona Neuroimaging Initiative underwent a 3T‐MRI protocol (Table.1). T1‐weighted images were preprocessed using CAT12. GM volumes adjusted for demographics (age, sex) and nuisance variables (TIV, MRI scanner) were computed for each cortical region of the Hammer Atlas and for each DS participant, using the CU group as a reference. Adjusted volumes (W‐scores) were binarized using a threshold of ‐2.33 (corresponding to a regional volume <99th percentiles of the CU group) to examine the brain regions most frequently atrophied in DS compared to CU. Conditional probability analyses were additionally performed in DS to assess if a brain region was more likely to present with atrophy than another.ResultAdults with DS most frequently showed atrophy in the medial temporal lobe, anterior cingulate, and temporo‐parietal regions. This pattern remains essentially similar when using a dynamic range of thresholds (Fig1.A) and across clinical AD stages (Fig1.B), even though the proportion of individuals showing atrophy increased with disease progression (Fig1.C). Interestingly, the anterior cingulate was the most frequently atrophied region in asymptomatic but not prodromal/demented DS. Conditional probability analyses revealed that the hippocampus, amygdala, and anterior cingulate have a significantly higher probability of showing GM loss before any other regions. A second cluster of tempo‐parietal regions appears more likely to show atrophy than most other regions.ConclusionIn DS, GM atrophy appears to follow a stereotypical pattern that strongly resembles the one of sporadic AD. Brain developmental specificities (e.g., anterior cingulate) are coupled with AD‐like atrophy early in the disease and become less predominant with AD progression.

Mapping Medial Temporal Lobe Longitudinal Change in Preclinical Alzheimer’s Disease

AbstractBackgroundThe medial temporal lobe (MTL) is a site of early tau pathology and neurodegeneration in AD. In recent work [1], we reported that rates of change in MTL subregion volumes on longitudinal structural T1‐MRI differ significantly between cognitively unimpaired individuals with and without amyloid pathology (A+CU vs A‐CU), suggesting that these measures are effective biomarkers for tracking disease progression in preclinical AD. Here, we develop a specialized pipeline that quantifies pointwise longitudinal change across the MTL, revealing more informative spatial patterns of early neurodegeneration than summary volumetric longitudinal measures.MethodOur pipeline, SkelDBM, combines skeletonization and deformation‐based morphometry to generate maps of annualized rate of atrophy on the skeleton of an MTL template (Figure 1). SkelDBM samples subjects’ atrophy maps along the skeleton of an MTL template, increasing robustness to subject‐to‐template registration errors. We evaluate SkelDBM using a subset of ADNI data studied in [1] (Table 1). Two preclinical AD groups (A+CU and tau‐positive A+CU) are compared to a control group (A‐CU) using general linear models (GLM) with age as nuisance covariate; permutation testing is used to derive statistically significant clusters.ResultThe full A+CU vs A‐CU comparison shows significant differences in rates of change in right anterior hippocampus, right entorhinal cortex, right transentorhinal cortex, and left posterior hippocampus (Figure 2). Limiting the A+CU group to tau‐positive individuals yields more widespread differences, including most of the right anterior MTL cortex and both hippocampi. These patterns align with volumetric longitudinal data reported in [1] but provide a richer characterization of spatial atrophy patterns.ConclusionSkelDBM is sensitive to longitudinal atrophy occurring over relatively short periods (∼2 years) in preclinical AD and characterizes patterns of atrophy more comprehensively than summary volumetric measures. Summary measures are also subject to biases, since larger structures are typically associated with less measurement error and hence may show exaggerated statistical effects compared to smaller structures even when the underlying neurodegeneration rates are the same. Regional maps of longitudinal change may be more robust to this bias and may help discovery of “signature regions” associated with specific pathological drivers of neurodegeneration. [1] Xie et al., Human Brain Mapping, 2020.

Mapping Medial Temporal Lobe Longitudinal Change in Preclinical Alzheimer’s Disease

AbstractBackgroundThe medial temporal lobe (MTL) is a site of early tau pathology and neurodegeneration in AD. In recent work [1], we reported that rates of change in MTL subregion volumes on longitudinal structural T1‐MRI differ significantly between cognitively unimpaired individuals with and without amyloid pathology (A+CU vs A‐CU), suggesting that these measures are effective biomarkers for tracking disease progression in preclinical AD. Here, we develop a specialized pipeline that quantifies pointwise longitudinal change across the MTL, revealing more informative spatial patterns of early neurodegeneration than summary volumetric longitudinal measures.MethodsOur pipeline, SkelDBM, combines skeletonization and deformation‐based morphometry to generate maps of annualized rate of atrophy on the skeleton of an MTL template (Figure 1). SkelDBM samples subjects’ atrophy maps along the skeleton of an MTL template, increasing robustness to subject‐to‐template registration errors. We evaluate SkelDBM using a subset of ADNI data studied in [1] (Table 1). Two preclinical AD groups (A+CU and tau‐positive A+CU) are compared to a control group (A‐CU) using general linear models (GLM) with age as nuisance covariate; permutation testing is used to derive statistically significant clusters.ResultsThe full A+CU vs A‐CU comparison shows significant differences in rates of change in right anterior hippocampus, right entorhinal cortex, right transentorhinal cortex, and left posterior hippocampus (Figure 2). Limiting the A+CU group to tau‐positive individuals yields more widespread differences, including most of the right anterior MTL cortex and both hippocampi. These patterns align with volumetric longitudinal data reported in [1] but provide a richer characterization of spatial atrophy patterns.ConclusionSkelDBM is sensitive to longitudinal atrophy occurring over relatively short periods (∼2 years) in preclinical AD and characterizes patterns of atrophy more comprehensively than summary volumetric measures. Summary measures are also subject to biases, since larger structures are typically associated with less measurement error and hence may show exaggerated statistical effects compared to smaller structures even when the underlying neurodegeneration rates are the same. Regional maps of longitudinal change may be more robust to this bias and may help discovery of “signature regions” associated with specific pathological drivers of neurodegeneration.[1] Xie et al., Human Brain Mapping, 2020.

Microglia transcriptional heterogeneity across human and experimental models

AbstractBackgroundThe molecular data from multiple brain regions, cohorts, experimental models are seldom integrated comprehensively, although it provides a unique opportunity to study the cellular heterogeneity and molecular changes across AD etiology and progression. We have developed a new reference model to bridge across datasets.MethodWe leveraged single‐nuclei transcriptomic profiles (snRNA‐seq) of human microglia (parietal cortex from 67 donors from the Knight ADRC and DIAN brain banks; N = 15,726; Dataset1) [1], iPSC‐MLC CRISPRi/a‐based genetic screens in human iPSC‐derived microglia (N = 15,350; Dataset2) [2], and WT and R47H TREM2 xenografted microglia (xMGs), isolated from chimeric AD mice (N = 25,407; Dataset3) [3] to build a comprehensive atlas of microglia heterogeneity, that captures transcriptional states fairly well represented in each dataset. We employed Seurat v4 [4] to integrate the data, and scArches [5] to build classifiers.ResultWe identified homeostatic, activated, interferon (IFN), and IL1B clusters across datasets present in all data we had integrated. In addition, we identified MHCII. We observed that some of the clusters of original states were combined into single clusters. For example, CXCL10‐IFN and INF. We also observed that the integrated data shows a more distinctive expression profile of Type I+II IFN. Furthermore, we build a multiclassifier to label novel microglia generated from hundreds of samples, and data from various experimental models. The cross‐validation analyses indicate that the overall accuracy of the classifier (scArchers) is 0.85 (Fig. 1), although it changes for the distinct datasets [5]. For human microglia in Dataset1 the accuracy was 0.79, while for iPSC‐MLC was 0.73 and 0.96 for Dataset 3. Our results indicate that this multi‐model reference can capture subtle differences better that study‐centric analyses of the data.ConclusionWe are repurposing microglia transcriptomic single‐cell data to build a cross‐species and experimental iPSC‐MLC to bridge across cohorts, species, and experiments. Our results, indicate that this new resource allows to simultaneously interrogate gene expression across different experiments and transcriptional states, and providing the foundation to integrate additional single‐cell molecular data from model organism, ATAC‐seq, and spatially resolved transcriptomics.

Novel metabolic biomarkers for diagnosis of Alzheimer’s disease

AbstractBackgroundThe need for biomarkers for the etiological identification of dementia syndromes can not be overemphasized. Blood metabolites are attractive targets for identifying biomarkers with predictive values for AD diagnosis and progression. This study aims to identify novel AD blood metabolic biomarkers and develop a high‐sensitivity and specificity blood‐based test for AD.MethodIn the discovery phase, the serum metabolome of AD patients (n = 10) and age, sex‐matched healthy controls (n = 10) were analyzed with a mass spectrometry (MS)‐based approach. Supervised machine learning was used to identify the biomarker candidates and develop a computational model for AD diagnosis. Finally, the computational model and biomarker candidates were verified with a second AD cohort in the verification phase, where the serum metabolome of AD patients (n = 27) and age, sex‐matched healthy controls (n = 30) were analyzed.ResultIn this discovery study, 7075 metabolic features were quantified. Among them, the abundance of 750 unique features significantly differed between AD and Controls (t‐test p‐value <0.05). Support vector machine (SVM) with radial basis function (RBF) kernel was used for features optimization and identified a panel of 14 metabolic features which predicted AD with 0.0% classification error rate (4‐fold cross‐validation). In the verification study, 10595 metabolic features were quantified. Among them, 908 unique features significantly differed between AD and Controls (t‐test with Permutation q‐value <0.05). Then, the computational model and biomarker panel identified in the discovery phase were tested with the verification dataset and confirmed that they could predict AD with high accuracy (0.0% classification error rate, 4‐fold cross‐validation).ConclusionThis study comprehensively profiled the AD serum metabolome and identified a panel of metabolic biomarkers and a computational model for AD diagnosis, which will serve as a foundation for a high‐performance, blood‐based test for clinical AD screening and diagnosis.

P71: Predicting amyloid-ß deposition status in amnestic mild cognitive impairment using neuropsychological profiles.

Objective:Previous studies investigating neuropsychological profiles of cognitive impairment people have found a learning curve can be a useful indicator of AD diagnosis or progression. However, the data on the relationship between amyloid β (Aβ) deposition status and the learning curve in amnestic mild cognitive impairment (aMCI) are limited. In this study, we investigate the role of the learning curve in predicting Aβ deposition status in patients with aMCI.Methods:This is a cross-sectional study of 67 aMCI patients (N = 67; 33 aMCI with amyloid positive (Aβ-PET (+)), and 34 aMCI with amyloid negative (Aβ-PET (-))). All participants underwent Seoul Neuropsychological Screening Battery for a comprehensive neuropsychological test battery and brain MRI. To determine Aβ deposition status, each participant underwent amyloid PET scans using 18F-florbetaben. The learning curve was obtained using immediate recall of Seoul Verbal Learning Test-learning curve (SVLT- learning curve). The association of cognitive test scores and dichotomized Aβ deposition status was examined using logistic regression models in patients with aMCI. Receiver operating characteristic (ROC) curves were used to examine the predictive ability of cognitive test to detect Aβ deposition status in aMCI.Results:Logistic regression models showed that SVLT-learning curve and Rey Complex Figure Test- delayed recall (RCFT-delayed recall) scores were significantly associated with Aβ deposition status. In ROC analysis to assess the predictive power, SVLT-learning curve (area under the curve (AUC) = 0.734, P = 0.001) and RCFT-delayed recall (AUC = 0.739, P = 0.001) independently discriminated Aβ-PET (+) and Aβ-PET (-). The combination of these clinical markers (SVLT-learning curve and RCFT-delayed recall) improved the predictive accuracy of Aβ-PET (+) (AUC = 0.833, P < 0.001).Conclusions:Our findings of association of Aβ deposition status with SVLT-learning curve and RCFT- delayed recall suggest that these cognitive tests could be a useful screening tool for Aβ deposition status among aMCI patients in resource-limited clinics.

Cell‐Based Genetic Subtypes Predict Neuroimaging Biomarkers and Clinical Conversion to Alzheimer’s Disease

AbstractBackgroundThe complex etiology and slow progression of AD have complicated efforts to establish robust patient subtypes resulting in high attrition rates among late‐stage clinical trials. We developed a novel procedure to stratify at risk individuals to develop AD using network‐based polygenic risk scores from brain cell‐type specific transcriptome signatures.MethodWe previously computed cell‐based polygenic risk scores (cbPRSs) in the Alzheimer’s Disease Neuroimaging Initiative (Sahelijo et al. 2022). Recently, we computed cbPRSs in 8,481 Framingham Heart Study (FHS) participants using the same genetic markers used in ADNI. We defined low‐ and high‐risk genetic subtypes using the first and the fourth quartiles of the cbPRS distributions, respectively. We conducted association tests with domain specific cognitive tests and neuroimaging biomarkers using subtype risk status as a binary outcome in cognitively normal (CN), mild cognitive impairment (MCI), AD, and all subjects from ADNI. We compared conversion rates from CN and MCI to AD between subtypes using a Cox proportional hazard regression in ADNI and FHS. Using brain‐wide gray matter volume maps with voxel‐based morphometry we compared brain atrophy patterns between subtypes in ADNI corrected for multiple comparisons using Standard FDR at q = 0.05.ResultGenetic subtypes for astrocytes (Ast‐M2) and oligodendrocytes (Oli‐M45) showed significant association with memory and executive function impairment, decreased hippocampal volume, increased amyloid deposition, and decreased glucose metabolism using all subjects from ADNI (P<1.39E‐3). These associations for memory impairment, global amyloid deposition, and decreased glucose metabolism remained significant only in MCI subjects (best P<7.23E‐09). Conversion rates for Ast‐M2 and Oli‐M45 genetic subtypes were significantly different in both ADNI (P<8.67E‐5) and FHS (P<3.90E‐9). These differential conversion rates for Ast‐M2 and Oli‐M45 in FHS were not fully explained by presence of APOE ɛ4 allele (P<0.05 in ɛ4 non‐carriers). Voxel‐wise whole brain analysis identified significant atrophy in bilateral hippocampi, entorhinal cortex, and amygdala for Ast‐M2 (P = 0.0007) and Oli‐M45 (P = 0.001).ConclusionWe demonstrate that polygenic risk scores informed by cell type specific brain transcriptomic networks can stratify at risk subjects with AD related changes in imaging biomarkers and higher chance of progression to AD. These findings provide insight into early detection of cell‐type specific at‐risk individuals for AD.

Effects of hippocampal atrophy and white matter lesions on cognitive function in Alzheimer’s disease

AbstractBackgroundWe investigated the effects of white matter lesions and hippocampal atrophy on head MRI on cognitive function in AD patients.MethodWe examined 47 patients who were diagnosed with Alzheimer’s disease according to the ATN classification at the geriatric department of Tokyo Medical University Hospital. We evaluated the severity of white matter damage by the sum of PVH and DWMH in the Fazekas classification on head MRI at the first visit, and hippocampal atrophy by VSRAD.ResultThere was a significant correlation between white matter impairment and cognitive changes (r = 0.292, p<0.05). And the correlation test for hippocampus atrophy and MMSE change was also performed with correlation coefficients r = 0.00926 and p = 0.951 (>0.05).ConclusionThe sum of PVH and DWMH and the amount of change in MMSE were positively correlated in AD patients. Therefore, it was concluded that the more severe the white matter disorder, the slower the progression of cognitive decline. These results suggest that advanced white matter disorders may inhibit the progression of AD in AD patients.

Using subject‐specific disease progression as an estimation of the disease time to model longitudinal neurodegeneration in AD

AbstractBackgroundWhen patients with clinically probable‐AD get their diagnosis, it is difficult to determine exactly when the disease started. Moreover, not all subjects progress in the same manner and rate. However, clinical measurements such as cognitive scores can help give an estimation of where in the disease trajectory each patient stands. To address the issue of estimating the time of disease onset, we investigated a new technique that leverages cognitive test scores to estimate a latent timeline that models the subject‐specific disease progression.MethodWe used the work of Kühnel et al. (2021) to model the progression of AD using cognitive scores (ADAS13 and MMSE). This method aligns patients along a continuous latent timeline based on their predicted disease progression. Data included the MRI scans of 677 amyloid‐positive subjects from ADNI. Scan resolution was increased to 0.5mm isotropic voxel‐size by super‐sampling (Manjón et al. 2010) before non‐linear registration to an ADNI‐based unbiased template. The resulting deformation fields were used to compute the Jacobian determinant map for each subject and find mean atrophy for different ROIs. we were then able to plot the changes in different regions of the brain across the estimated disease progression timeline and compare slopes for the two methods.ResultAssuming a zero time‐shift for normal subjects, the average time‐shift for the eMCI, lMCI, and AD group was 36.8, 87.5, and 128.8 months respectively. Using the latent time‐shift variable to offset the individual baseline scans, we were able to plot the changes in different regions of the brain across the disease timeline. The results for Hippocampus are shown in figure 1, comparing the estimated timeline to the age as the time‐related axes. We saw much sharper slopes (‐0.01 vol/estimated_time vs ‐0.0025 vol/age) when using the individualized time offset.ConclusionCalendar time is perhaps not a good measure for disease progression. The new method offers an estimation of a latent timeline that models the subject‐specific disease progression and may better model the longitudinal changes in different brain regions, and in turn, would enable us to better compare the magnitude and speed of degeneration across brain regions.

ALZpath Dx: A novel accessible, validated, and scalable phosphorylated‐tau 217 (pTau217) assay in blood

AbstractBackgroundBlood‐based biomarkers for phosphorylated tau demonstrate strong diagnostic accuracy and compare favorably with current corresponding CSF and neuroimaging biomarkers. Of the four major pTau assays in use (pTau181, pTau217, and pTau231, Nfl), pTau217 appears to be the most robust marker, consistently outperforming plasma pTau 181, neurofilament light, and imaging markers. pTau217 has been shown to correlate with post‐mortem AD pathology, with the ability to differentiate AD from other types of dementia and predict future progression from both normal cognition and mild cognitive impairment to AD.(1,2) Additionally, pTau217 can be used to monitor AD progression because longitudinal increases in pTau217 is indicative of worsening of brain atrophy and cognition in AD.(3) pTau217, therefore, represents a cost‐effective and accessible blood‐based biomarker for tau pathophysiology, that can transform the AD diagnostic field.MethodALZpath has developed reagents for pTau217 and establish a robust and scalable fit for purpose plasma‐based ultra‐sensitive assay utilizing a proprietary monoclonal pTau217 antibody.ResultThe ultra‐sensitive blood‐based ELISA assay, using a peptide calibrator, has been developed on the semi‐automated single‐molecule array Simoa® platform. The assay has been launched for clinical use as laboratory‐developed test (LDT). Evaluation in independent clinical cohorts with multiple co‐morbidities is currently being established to advance to an in‐vitro diagnostic (IVD).ConclusionThe ALZpath pTau217 Simoa® assay is now a fit for purpose validated assay now available for commercial use. Clinical use of this assay is described in Snyder et al. Gouda et al. and Ashton et al. AAIC 2023.

Longitudinal Quantification of Sub‐Regional Hippocampal Amyloid‐Beta Burden in Ex‐Vivo slices from the AppNL‐G‐F Mouse Model of Alzheimer’s Disease

AbstractBackgroundAmyloid‐beta (Aβ) pathology is suggested to precede the onset of clinical Alzheimer’s disease symptoms by up to two decades. The humanised AppNL‐G‐F knock‐in model of amyloidopathy has accelerated pathogenesis and can be used to study AD progression. Here we used it to quantify the temporal and spatial characteristics of Aβ plaque accumulation as they first appear in the hippocampus, and as pathology continues to develop.MethodAppNL‐G‐F knock‐in mice and wild‐type mice at 1, 3, 6, and 9 months of age were transcardially perfused with PBS followed by 4% paraformaldehyde (PFA), followed by preparation of coronal slices for immunohistochemistry analysis. Samples were stained with anti‐Aβ antibody (6E10) together with a neuronal marker, NeuN, and DAPI. Images were taken of sub‐hippocampal regions (CA1, CA3, DG), in left/right hemispheres, and ventral/dorsal segments using a confocal microscope (Leica SP8). Summation of intensity across Z‐stacks in Fiji was used to quantify Aβ plaques in regions of interest.ResultWe found age to be a statistically significant factor on determining area density, with hippocampal deposition observed by 3 months. Bin analysis showed a near‐exponential reduction in plaque number with increasing plaque size, increasing with age. Regionally, there was an increase in plaque density in CA3, with non‐linear regression analysis indicating area density for CA3 is most strongly correlated with age. Within the CA1 region, the densest plaque formation was in the pyramidal cell layer. Further analysis at the aged 9‐month time point suggested a differential pathology across the axial plane and greater left‐hemispheric lateralisation.ConclusionThis data provides further evidence for the protracted timeline to which AppNL‐G‐F knock‐in mice recapitulate key AD neuropathologies including insoluble amyloidosis beginning before 3 months in the hippocampus. It also indicates potentially novel differential sub‐regional Aβ accumulation patterns, axial variations, and hemispheric asymmetries, offering mechanistic insights for plaque localisation and differential susceptibility to Aβ. This could aid advancement of Αβ‐targeting therapies.

Sleep restoration by optogenetic targeting of GABAergic neurons reprograms microglia and ameliorates pathological phenotypes in an Alzheimer’s disease model

BackgroundAlzheimer’s disease (AD) patients exhibit memory disruptions and profound sleep disturbances, including disruption of deep non-rapid eye movement (NREM) sleep. Slow-wave activity (SWA) is a major restorative feature of NREM sleep and is important for memory consolidation.MethodsWe generated a mouse model where GABAergic interneurons could be targeted in the presence of APPswe/PS1dE9 (APP) amyloidosis, APP-GAD-Cre mice. An electroencephalography (EEG) / electromyography (EMG) telemetry system was used to monitor sleep disruptions in these animals. Optogenetic stimulation of GABAergic interneurons in the anterior cortex targeted with channelrhodopsin-2 (ChR2) allowed us to examine the role GABAergic interneurons play in sleep deficits. We also examined the effect of optogenetic stimulation on amyloid plaques, neuronal calcium as well as sleep-dependent memory consolidation. In addition, microglial morphological features and functions were assessed using confocal microscopy and flow cytometry. Finally, we performed sleep deprivation during optogenetic stimulation to investigate whether sleep restoration was necessary to slow AD progression.ResultsAPP-GAD-Cre mice exhibited impairments in sleep architecture including decreased time spent in NREM sleep, decreased delta power, and increased sleep fragmentation compared to nontransgenic (NTG) NTG-GAD-Cre mice. Optogenetic stimulation of cortical GABAergic interneurons increased SWA and rescued sleep impairments in APP-GAD-Cre animals. Furthermore, it slowed AD progression by reducing amyloid deposition, normalizing neuronal calcium homeostasis, and improving memory function. These changes were accompanied by increased numbers and a morphological transformation of microglia, elevated phagocytic marker expression, and enhanced amyloid β (Aβ) phagocytic activity of microglia. Sleep was necessary for amelioration of pathophysiological phenotypes in APP-GAD-Cre mice.ConclusionsIn summary, our study shows that optogenetic targeting of GABAergic interneurons rescues sleep, which then ameliorates neuropathological as well as behavioral deficits by increasing clearance of Aβ by microglia in an AD mouse model.

Sleep fragmentation affects glymphatic system through the different expression of AQP4 in wild type and 5xFAD mouse models

AbstractBackgroundSleep is an important physiological process, during which extracellular metabolic wastes, such as amyloid and tau protein, are cleared via paravascular pathway. Changes in the timing and structure of sleep occur across the lifespan. Increased sleep fragmentation and reductions in slow wave sleep (SWS) represent the hallmark signs of age‐related changes in sleep. The origin of sleep disturbances in AD is thought to be multifactorial. In the aging brain, the impairment of glymphatic pathway function slows the clearance of interstitial Aβ, rendering the aging brain vulnerable to neurodegenerative disease. Several authors have independently shown that glymphatic flux depends upon the expression and perivascular localization of the astroglial water channel aquaporin‐4 (AQP4).MethodIn this study, we performed and validated a mouse model of AD and chronic sleep fragmentation, a sleep disorder that mimics more correctly a real condition of intermittent and continuous awakening. We analyzed the effect of sleep fragmentation in wild type and 5xFAD mice. All the animals underwent to behavioral studies to analyze anxiety and spatial and working memory. We had validated sleep fragmentation and its effect through EEG and biomolecular analysis, by observing all the macro‐areas implied in sleep regulation.ResultWe noticed that sleep fragmentation induces a general acceleration of AD progression in 5xFAD mice, while in wild type mice it affects cognitive behaviors in particular learning and memory. Both these events may be correlated to AQP4 modulation, a crucial player of the glymphatic system activity. In particular, sleep fragmentation differentially affects AQP4 expression according to the stage of the disease, with an up‐regulation in younger animals, while such change cannot be detected in older ones. Moreover, in wild type mice sleep fragmentation affects cognitive behaviors, in particular learning and memory, by compromising the glymphatic system through the decrease of AQP4.ConclusionNevertheless, an in‐depth study is needed to better understand the mechanism by which AQP4 is modulated and whether it could be considered a risk factor for the disease development in wild type mice. If our hypotheses are going to be confirmed, AQP4 modulation may represent the convergence point between AD and sleep disorder pathogenic mechanisms

A novel DYRK1a inhibitor, DYR533, reduces tau pathology and TNF alpha in the 3xTg‐AD and PS19 mouse models

AbstractBackgroundDual specificity tyrosine phosphorylation kinase 1a (DYRK1a) directly phosphorylates tau and amyloid precursor protein and is upregulated in postmortem brain tissue of patients with disorders including Alzheimer’s (AD) and Picks disease (a tauopathy). Our lab has shown that treatment with a DYRK1a inhibitor (DYR219) reduced Aβ plaque deposition and decreased phosphorylated tau at Serine 396 (pTauSer396) in the 3xTg‐AD mouse model. Here, we developed and tested a novel DYRK1a inhibitor (DYR533), which has an increased half‐life and bioavailability, in 3xTg‐AD mice that develop both Aβ and pathological tau, and PS19 mice that develop solely pathological tau.MethodWe dosed eight‐month‐old 3xTg‐AD and four‐month‐old PS19 mice daily with either 1.0‐, 2.5‐, or 5.0‐mg/kg DYR533 or a vehicle dose for two or four months respectively per model. Treatment start age occurred at the onset of neuropathology. Mice underwent rotarod testing to assess motor function and water maze testing to assess spatial learning and memory, with testing starting 2 weeks prior to the end of treatment. Blood and brain tissue were collected for neuropathology assessment. We assessed Aβ 40‐42 in the 3xTg‐AD mice, and pTauSer396 and the pro‐inflammatory cytokine tumor necrosis factor (Tnf)α in the hippocampus of both models. Blood plasma was extracted and analyzed for Tnfα levels.ResultIn both 3xTg‐AD and PS19 mice, DYR533 reduced soluble and insoluble fractions of pTau Ser396 in a dose dependent manner. Notably, DYR533 in 3xTg‐AD mice reduced pTau at threonine 217, which was recently identified as an early marker of AD progression. Soluble cortical Aβ42 levels were significantly reduced in the 1.0 mg/kg and 5.0 mg/kg 3xTg‐AD groups. DYR533 significantly reduced the levels of Tnfα in blood plasma and the hippocampus of both 3xTg‐AD and PS19 mice.ConclusionIn conclusion, the DYRK1a inhibitor, DYR533, reduces pTauSer396, as well as blood plasma and hippocampal levels of Tnf α in both 3xTg‐AD and PS19 models. Ongoing efforts include testing the effects of DYR533 in a rodent model of Down syndrome (Ts65Dn), which harbors three copies of DYRK1a, and completion of 3xTg‐AD and PS19 experiments. Thus far, these results support DYR533 as a potential therapeutic for AD and the tauopathies.

Validation of high resolution subcortical MRI signal using voxel to voxel histology to neuroimaging correlation in AD continuum: advancing neuroimaging biomarker tools for early AD staging

AbstractBackgroundStudies suggest a decrease in LC volume by 8% each Braak stage, starting at Braak 0‐1, while in normal aging the LC volume remains intact. Thus, LC volumetry is an attractive potential biomarker to monitor AD progression from precognitive stages. However, precise LC volumetry neuroimaging is challenging due to the LC’s small size and location in the brainstem that’s prone to artifacts. To validate the precision/utility of neuroimaging sequences designed for LC volumetry, we developed a pipeline allowing voxel‐to‐voxel histology to neuroimaging comparisons. We tested this pipeline to analyze the neuro‐histological counterpart of an LC volume obtained from manual segmentation from a MRI neuromelanin(NM)sensitive GRE image.MethodWhole brain high resolution(0.7mm)3D‐T1 images and 2D‐2mm thick NM‐sensitive axial GRE images of the brainstem were obtained postmortem in situ before brain removal from a single subject using a 7T MRI(Siemens Magnetom). We combined celloidin‐based histological processing and 3D reconstructions of the histology volume at microscopic resolution which was then aligned to T1. We used structures visible in structural MRI(T1)(brainstem surface, red nucleus, and 4th ventricle) to calculate Dice coefficient to measure registration accuracy. Next, we performed an overlap percentage analysis to investigate how well GRE sequence detected LC borders compared to histology.ResultWe ran Dice coefficient analysis for brainstem structures to measure histology to MRI registration accuracy(Table1). The LC GRE to histology masks overlap percentage was 35.29% and 10.77% compared to the MRI and histology mask total voxels(voxel size = 0.34mm3). The overlap percentage obtained for the rostral and middle LC thirds ranged from 9.43% to 75%when compared to total voxels in MRI/histology masks, while the caudal third had zero overlap(Table2,Fig.1).ConclusionOur pipeline enables voxel‐to‐voxel correlation analysis between histology and high resolution LC MRIs for neuroimaging validation. The caudal third of the LC in our case has shown a clear lack of overlap potentially due to lack of signal specificity in MRI when compared to the gold standard that is histology. Current analysis of another five other cases is ongoing. Validation of LC MRI signal will inform interpretations of signal results in living patients with the potential to act as a diagnostic tool for ADRD.

C/EBPβ/δ‐secretase Signaling Drives Neurodegenerative Diseases

AbstractBackgroundC/EBPb/AEP pathway are escalated in the brain in an age‐dependent manner and proteolytically cleaves APP and Tau and a‐Synuclein, facilitating AD and PD pathologies.MethodC/EBPb/AEP pathway couples different risk factors that elicit inflammation to Alzheimer’s disease. To explore why women are more vulnerable for AD, we investigated different hormones altered during menopause and found FSH (follicle‐stimulating hormone) strongly activated this signalling, leading to AD pathogenesis in different mouse models. Employing immuno‐EM and knockout tissues, we demonstrated that FSHR is expressed in mouse and human brain neurons. Notably, FSH administration drives AD onset in young male and female mice, whereas anti‐FSH robustly diminishes AD progression in overiactomized female 3xTg mice. Moreover, deletion of FSHR or C/EBPb from the neurons of 3xTg mice attenuates AD pathologies.ResultElevated FSH during menopause activates C/EBPb/AEP signalling, which subsequently cleaves both APP and Tau, triggering AD onset. Blockade of FSH abrogates overiactomy‐induced AD pathologies and restores the cognitive dysfunctions in AD mouse models.ConclusionFSH elevation after menopause elicits women susceptibility to AD via activating C/EBPb/AEP pathway. Inhibition of FSH/FSHR may provide an innovative therapeutic strategy for treating AD.

Synapse dysfunction and astrocyte reactivity are associated independently of amyloid‐β and tau pathologies

AbstractBackgroundBiomarkers of astrocyte reactivity have the potential to improve diagnostic precision, disease monitoring, and treatment efficacy. Glial fibrillary acidic protein (GFAP) protein is expressed in astrocytes, which is important to synaptic plasticity, cell communication, and reactive gliosis.MethodCerebrospinal fluid (CSF) biomarkers were measured in participants of the McGill TRIAD cohort. 75 individuals (>50 years old, 44 cognitively unimpaired (CU), and 31 with cognitive impairment (CI)), had available Aβ and tau‐PET. We measured CSF GFAP and synaptic markers (growth‐associated protein 43 (GAP‐43), neurogranin (Ng), synaptotagmin 1 (SYT1), presynaptic protein synaptosomal‐associated protein 25 (SNAP‐25)). Linear regressions adjusted for age, sex, clinical diagnosis, and Aβ/tau‐PET were used to test the associations between astrocyte reactivity and synaptic function.ResultDemographic information is shown in Table 1. We found an association between CSF GFAP and presynaptic markers (GAP‐43: p<0.0001, β = 0.1076; SYT1: p<0.0001, β = 0.0024; SNAP‐25 long: p<0.0001, β = 0.0008) as well as postsynaptic markers (Ng: p<0.05, β = 0.0066) independently of Aβ and tau burden (Figure 1).ConclusionOur biomarker results support experimental literature suggesting that astrocyte reactivity plays a role in downstream synaptic dysfunction independent of the brain levels of Aβ and tau tangles pathologies. This supports in vitro literature suggesting that therapeutic interventions targeting astrocyte reactivity can contribute to halting AD progression.

Basic Science and Pathogenesis - Part 2.

The Immunoproteasome is a dynamic system found in microglia that is induced under inflammatory conditions and previously hypothesized to be causative of neurodegenerative disorders like Alzheimer's Disease. Mice with immunoproteasome deficiency (LMPPS) and mice with intact immunoproteasome (PS19) were analyzed for AD progression through total tau aggregation and microglia activity. Primary microglia were extracted from normal and LMPKO mouse strains and their total engulfment of tau-aggregate containing cells were compared. LMPPS mice had significantly higher mean aggregation of tau compared to PS19 mice. LMPPS mice also showed a significantly increased activation of microglia and a slightly increased number of total microglia compared to PS19 mice. In-vitro results showed increased total engulfment of tau-aggregate containing cells for LMPKO microglia but statistically insignificant difference when comparing proportion of engulfing microglia. To our knowledge, our results are the first to show that the immunoproteasome is neuroprotective against tauopathies like Alzheimer's Disease in mice.