Tool For Alzheimer Research Articles

Postbiotics as a Therapeutic Tool in Alzheimer's Disease: Insights into Molecular Pathways and Neuroprotective Effects.

Alzheimer's disease (AD) is a progressive neurodegenerative disease, characterized by oxidative stress, neuroinflammation, mitochondrial dysfunction, neurotransmitter imbalance, tau hyperphosphorylation, and amyloid beta (Aβ) accumulation in brain regions. The gut microbiota (GM) has a major impact on brain function due to its bidirectional interaction with the gut through the gut-brain axis. The gut dysbiosis has been associated with neurological disorders, emphasizing the importance of gut homeostasis in maintaining appropriate brain function. The changes in the composition of microbiomes influence neuroinflammation and Aβ accumulation by releasing pro-inflammatory cytokines, decreasing gut and blood-brain barrier (BBB) integrity, and microglial activation in the brain. Postbiotics, are bioactive compounds produced after fermentation, have been shown to provide several health benefits, particularly in terms of neuroinflammation and cognitive alterations associated with AD. Several research studies on animal models and human have successfully proven the effects of postbiotics on enhancing cognition and memory in experimental animals. This article explores the protective effects of postbiotics on cellular mechanisms responsible for AD pathogenesis and studies highlighting the influence of postbiotics as a total combination and specific compounds, including short-chain fatty acids (SCFAs). In addition, postbiotics act as a promising option for future research to deal with AD's progressive nature and improve an individual's life quality using microbiota modulation.

Machine Learning Approaches in Alzheimer’s Disease: Early Diagnosis, Prognosis, and Treatment Optimization

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that has emerged as a significant global health challenge, affecting millions worldwide. It is characterised by cognitive decline, memory loss, and behavioural changes, significantly burdening patients, caregivers, and healthcare systems. Early diagnosis and effective intervention remain critical yet challenging due to the complex nature of the disease and its heterogeneous progression. Traditional diagnostic methods rely on invasive, expensive, and often time-consuming procedures, underscoring the urgent need for innovative diagnostic and therapeutic strategies.Machine learning (ML), a subset of artificial intelligence, has revolutionised the field of medical research by enabling the analysis of complex and high-dimensional datasets. In Alzheimer’s research, ML has shown immense potential in enhancing.diagnostic accuracy, predicting disease progression, and personalising treatment plans. ML models leverage data from various sources, including neuroimaging, genomics, biomarkers, and clinical records, to identify subtle patterns and accurately predict outcomes. Applying supervised learning, deep learning techniques like convolutional neural networks (CNNs) for imaging data, and unsupervised clustering algorithms for patient stratification have significantly advanced the field.This paper focuses on three critical areas: (1) early diagnosis of AD through ML-based analysis of neuroimaging and biomarkers, (2) prediction of disease progression using longitudinal data and predictive models, and (3) optimisation of treatment plans and personalised care through reinforcement learning and predictive analytics. The study also explores the challenges associated with ML applications, such as data heterogeneity, interpretability, and ethical concerns, and discusses potential solutions to address these barriers.In conclusion, machine learning represents a transformative tool in Alzheimer’s research. It promises to improve early detection, enhance disease management, and pave the way for precision medicine. Its integration into clinical practice can potentially significantly mitigate the global burden of AD.

Isotope-Labeled Chemoselective Probes for Labeling, Separation, and Comprehensive Quantitative Analysis of Sub-Metabolome.

The significance of small molecule metabolites as biomarkers for disease diagnosis and prognosis is growing increasingly evident, necessitating the development of highly sensitive qualitative and quantitative methods. Herein, multi-chemoselective probes are synthesized and applied for profiling metabolites, including carboxyl, phosphate, hydroxyl, amino, thiol, and carbonyl compounds. This approach seamlessly integrates magnetic solid-phase materials, orthogonal cleavage sites, isotopic tags, and selective coupling sites, minimizes matrix interference, and enhances quantitative accuracy. Meanwhile, a homemade program, High-Resolution Isotope-Assisted Identification and Quantitative (HRIAIQuant) is developed to process the data, which adeptly filters through 33,874 ion pairs present in human serum, leading to the identification of 701 known metabolites and a remarkable 1,062 potential novel ones. This method is successfully applied to analyze metabolites in multiple brain regions of SAMP8 and SAMR1 models, offering a novel tool for Alzheimer's disease research.

Impact of Independent Review on Potential MMSE Score Inflation at Screening in AD trials

AbstractBackgroundThe Mini‐Mental State Examination (MMSE) is widely used as a screening tool in Alzheimer’s Disease (AD) clinical trials. Screening score inflation is documented in clinical trials of depression and anxiety. We investigated whether score distributions on several multi‐site multinational AD trials showed truncated scores around MMSE inclusion criteria cut‐off, and whether Independent Review (IRev), where site assessments are reviewed by independent clinicians, is an effective methodology to mitigate inclusion bias.MethodMMSE screening score distributions from several clinical trials of Early Symptomatic and Mild to Moderate AD were examined. To assess the impact of IRev on screening score distribution, we compared two studies with similar inclusion criteria (Early AD, MMSE ≥ 22; CDR GS = 0.5 or 1) one with no IRev and one with IRev via audio recording.ResultMMSE screening frequency distributions showed a statistically significant difference between IRev and No‐IREV groups (p < 0.001). The distributions for the two groups were also compared using several score ranges around the threshold. Significant group differences were observed across the score ranges; 20‐24 (D = 0.099, p = 0.01), 19‐25 (D = 0.094, p = 0.002), and 18‐26 (D = 0.0987, p < 0001), suggesting a smaller inclusion bias around the threshold in the IRev group.ConclusionWe observed that screening score inflation occurs in AD trials and Independent Review may be an effective strategy to reduce the number of subjects inappropriately enrolled in the study. IRev should be applied to assessments that fall around the required scored for study participation.

NcRNAs: A synergistically antiapoptosis therapeutic tool in Alzheimer's disease.

The aim of this review is to systematically summarize and analyze the noncoding RNAs (ncRNAs), especially microRNAs (miRNAs), long noncoding RNAs (lncRNAs), and circular RNAs (circRNAs), in the cell apoptosis among Alzheimer's disease (AD) in recent years to demonstrate their value in the diagnosis and treatment of AD. We systematically summarized in vitro and in vivo studies focusing on the ncRNAs in the regulation of cell apoptosis among AD in PubMed, ScienceDirect, and Google Scholar. We discover three patterns of ncRNAs (including 'miRNA-mRNA', 'lncRNA-miRNA-mRNA', and 'circRNA-miRNA-mRNA') form the ncRNA-based regulatory networks in regulating cell apoptosis in AD. This review provides a future diagnosis and treatment strategy for AD patients based on ncRNAs.

Chitosan-Based Nanoparticles for Targeted Nasal Galantamine Delivery as a Promising Tool in Alzheimer's Disease Therapy.

Natural alkaloid galantamine is widely used for the treatment of mild to moderate Alzheimer's dementia. Galantamine hydrobromide (GH) is available as fast-release tablets, extended-release capsules, and oral solutions. However, its oral delivery can cause some unwanted side effects, such as gastrointestinal disturbances, nausea, and vomiting. Intranasal administration is one possible way to avoid such unwanted effects. In this work, chitosan-based nanoparticles (NPs) were studied as potential GH delivery vehicles for nasal application. The NPs were synthesized via ionic gelation and studied using dynamic light scattering (DLS) as well as by spectroscopic and thermal methods. The GH-loaded chitosan-alginate complex particles were also prepared as a way to modify the release of GH. The high loading efficiency of the GH was confirmed for both types of particles, at 67% for the GH-loaded chitosan NPs and 70% for the complex chitosan/alginate GH-loaded particles. The mean particle size of the GH-loaded chitosan NPs was about 240 nm, while the sodium alginate coated chitosan particles loaded with GH were expectedly bigger, with a mean particle size of ~286 nm. GH release profiles in PBS at 37 °C were obtained for both types of NPs, and it was found that the GH-loaded chitosan NPs allowed the prolonged release of the incorporated drug for a period of 8 h, while the complex GH-loaded chitosan/alginate NPs released the incorporated GH faster. The stability of the prepared GH-loaded NPs was also demonstrated after 1 year of storage at 5 °C ± 3 °C.

DeepAD: A deep learning application for predicting amyloid standardized uptake value ratio through PET for Alzheimer's prognosis.

Amyloid deposition is a vital biomarker in the process of Alzheimer's diagnosis. 18F-florbetapir PET scans can provide valuable imaging data to determine cortical amyloid quantities. However, the process is labor and doctor intensive, requiring extremely specialized education and resources that may not be accessible to everyone, making the amyloid calculation process inefficient. Deep learning is a rising tool in Alzheimer's research which could be used to determine amyloid deposition. Using data from the Alzheimer's Disease Neuroimaging Initiative, we identified 2,980 patients with PET imaging, clinical, and genetic data. We tested various ResNet, EfficientNet, and RegNet convolutional neural networks and later combined the best performing model with Gradient Boosting Decision Tree algorithms to predict standardized uptake value ratio (SUVR) of amyloid in each patient session. We tried several configurations to find the best model tuning for regression-to-SUVR. We found that the RegNet X064 architecture combined with a grid search-tuned Gradient Boosting Decision Tree with 3 axial input slices and clinical and genetic data achieved the lowest loss. Using the mean-absolute-error metric, the loss converged to an MAE of 0.0441, equating to 96.4% accuracy across the 596-patient test set. We showed that this method is more consistent and accessible in comparison to human readers from previous studies, with lower margins of error and substantially faster calculation times. We implemented our deep learning model on to a web application named DeepAD which allows our diagnostic tool to be accessible. DeepAD could be used in hospitals and clinics with resource limitations for amyloid deposition and shows promise for more imaging tasks as well.

The spike-and-slab elastic net as a classification tool in Alzheimer's disease.

Alzheimer’s disease (AD) is the leading cause of dementia and has received considerable research attention, including using neuroimaging biomarkers to classify patients and/or predict disease progression. Generalized linear models, e.g., logistic regression, can be used as classifiers, but since the spatial measurements are correlated and often outnumber subjects, penalized and/or Bayesian models will be identifiable, while classical models often will not. Many useful models, e.g., the elastic net and spike-and-slab lasso, perform automatic variable selection, which removes extraneous predictors and reduces model variance, but neither model exploits spatial information in selecting variables. Spatial information can be incorporated into variable selection by placing intrinsic autoregressive priors on the logit probabilities of inclusion within a spike-and-slab elastic net framework. We demonstrate the ability of this framework to improve classification performance by using cortical thickness and tau-PET images from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) to classify subjects as cognitively normal or having dementia, and by using a simulation study to examine model performance using finer resolution images.

Analysis of plasma proteins using 2D gels and novel fluorescent probes: in search of blood based biomarkers for Alzheimer\u2019s disease

BackgroundThe Australian Imaging and Biomarker Lifestyle (AIBL) study of aging is designed to aid the discovery of biomarkers. The current study aimed to discover differentially expressed plasma proteins that could yield a blood-based screening tool for Alzheimer’s disease.MethodsThe concentration of proteins in plasma covers a vast range of 12 orders of magnitude. Therefore, to search for medium to low abundant biomarkers and elucidate mechanisms of AD, we immuno-depleted the most abundant plasma proteins and pre-fractionated the remaining proteins by HPLC, prior to two-dimensional gel electrophoresis. The relative levels of approximately 3400 protein species resolved on the 2D gels were compared using in-gel differential analysis with spectrally resolved fluorescent protein detection dyes (Zdyes™). Here we report on analysis of pooled plasma samples from an initial screen of a sex-matched cohort of 72 probable AD patients and 72 healthy controls from the baseline time point of AIBL.ResultsWe report significant changes in variants of apolipoprotein E, haptoglobin, α1 anti-trypsin, inter-α trypsin inhibitor, histidine-rich glycoprotein, and a protein of unknown identity. α1 anti-trypsin and α1 anti-chymotrypsin demonstrated plasma concentrations that were dependent on APOE ε4 allele dose. Our analysis also identified an association with the level of Vitamin D binding protein fragments and complement factor I with sex. We then conducted a preliminary validation study, on unique individual samples compared to the discovery cohort, using a targeted LC-MS/MS assay on a subset of discovered biomarkers. We found that targets that displayed a high degree of isoform specific changes in the 2D gels were not changed in the targeted MS assay which reports on the total level of the biomarker.ConclusionsThis demonstrates that further development of mass spectrometry assays is needed to capture the isoform complexity that exists in theses biological samples. However, this study indicates that a peripheral protein signature has potential to aid in the characterization of AD.

The crosstalk between beta‐amyloid, retina, and sleep for the early diagnosis of Alzheimer’s disease: A mini review

AbstractBackgroundRetinal imaging has the potential to be used as an early diagnostic tool in Alzheimer’s disease (AD). However, direct visualisation of AD related hallmark proteins in the live human retina is challenging and may require the inclusion of additional factors to be effective. This mini review gathers evidence to understand the associations and underlying mechanisms between beta‐amyloid (Aβ, a hallmark feature found in the AD brain), the retina and sleep as collectively they would enhance the diagnostic accuracy of the retinal markers in AD.MethodRetinal imaging has the potential to be used as an early diagnostic tool in Alzheimer’s disease (AD). However, direct visualisation of AD related hallmark proteins in the live human retina is challenging and may require the inclusion of additional factors to be effective. This mini review gathers evidence to understand the associations and underlying mechanisms between beta‐amyloid (Aβ, a hallmark feature found in the AD brain), the retina and sleep as collectively they would enhance the diagnostic accuracy of the retinal markers in AD.ResultRetinal imaging has the potential to be used as an early diagnostic tool in Alzheimer’s disease (AD). However, direct visualisation of AD related hallmark proteins in the live human retina is challenging and may require the inclusion of additional factors to be effective. This mini review gathers evidence to understand the associations and underlying mechanisms between beta‐amyloid (Aβ, a hallmark feature found in the AD brain), the retina and sleep as collectively they would enhance the diagnostic accuracy of the retinal markers in AD.ConclusionThere is mounting evidence that the coupling of chronobiological information such as circadian rhythm dysfunction and sleep deficiency to retinal biomarker changes will increase the potential utility of retinal imaging in early AD diagnosis.

Применение гистохимических красителей для флуоресцентного выявления амилоидных скоплений в тканях человека

Recently, fluorescence microscopy becomes more available, presenting new opportunities to face several challenges of experimental biology and medicine. The study was aimed to assess the effectiveness of fluorescence microscopy for the identification of amyloid deposits in human tissues. Post-mortem samples of the myocardium (n = 12) and cerebral cortex (n = 8) obtained from subjects of both sexes aged 60–98 with verified amyloidosis were used as a material for the study. The specimens were stained using 11 different histochemical dyes and subsequently analyzed by light and fluorescence microscopy. Qualitative and quantitative analysis has shown that Thioflavin T is the most effective stain for fluorescence detection of β- and transthyretin amyloid in human tissues. Congo red staining is highly effective for the detection of transthyretin amyloidosis, however, it is ill-suited for the identification of β-amyloid plaques. It has been found that the ability of Congo red to exhibit fluorescence when binding to amyloid fibrils can be used for verification of amyloid deposits instead of the traditional polarized light microscopy. As has been first noted, methyl violet can selectively bind to β-amyloid with fluorescent complex formation. In addition, methyl violet treatment effectively reduces the autofluorescent background in the nervous tissue. This makes methyl violet staining a promising diagnostic tool for Alzheimer's-type pathology.

Multifunctional Liposomes Modulate Purinergic Receptor-Induced Calcium Wave in Cerebral Microvascular Endothelial Cells and Astrocytes: New Insights for Alzheimer\u2019s disease

In light of previous results, we assessed whether liposomes functionalized with ApoE-derived peptide (mApoE) and phosphatidic acid (PA) (mApoE-PA-LIP) impacted on intracellular calcium (Ca2+) dynamics in cultured human cerebral microvascular endothelial cells (hCMEC/D3), as an in vitro human blood-brain barrier (BBB) model, and in cultured astrocytes. mApoE-PA-LIP pre-treatment actively increased both the duration and the area under the curve (A.U.C) of the ATP-evoked Ca2+ waves in cultured hCMEC/D3 cells as well as in cultured astrocytes. mApoE-PA-LIP increased the ATP-evoked intracellular Ca2+ waves even under 0 [Ca2+]e conditions, thus indicating that the increased intracellular Ca2+ response to ATP is mainly due to endogenous Ca2+ release. Indeed, when Sarco-Endoplasmic Reticulum Calcium ATPase (SERCA) activity was blocked by cyclopiazonic acid (CPA), the extracellular application of ATP failed to trigger any intracellular Ca2+ waves, indicating that metabotropic purinergic receptors (P2Y) are mainly involved in the mApoE-PA-LIP-induced increase of the Ca2+ wave triggered by ATP. In conclusion, mApoE-PA-LIP modulate intracellular Ca2+ dynamics evoked by ATP when SERCA is active through inositol-1,4,5-trisphosphate-dependent (InsP3) endoplasmic reticulum Ca2+ release. Considering that P2Y receptors represent important pharmacological targets to treat cognitive dysfunctions, and that P2Y receptors have neuroprotective effects in neuroinflammatory processes, the enhancement of purinergic signaling provided by mApoE-PA-LIP could counteract Aβ-induced vasoconstriction and reduction in cerebral blood flow (CBF). Our obtained results could give an additional support to promote mApoE-PA-LIP as effective therapeutic tool for Alzheimer’s disease (AD).

Indices From Visibility Graph Complexity of Spontaneous Speech Signal: An Efficient Nonlinear Tool for Alzheimer's Disease Diagnosis

Indices From Visibility Graph Complexity of Spontaneous Speech Signal: An Efficient Nonlinear Tool for Alzheimer's Disease Diagnosis

Associations of visual paired associative learning task with global cognition and its potential usefulness as a screening tool for Alzheimer's Dementia.

Appropriate screening is integral to the early diagnosis and management of Alzheimer's Dementia (AD). The Paired Associates Learning (PAL) task is a digital cognitive task that is free of cultural, language, and educational biases. This study examined the association between the PAL task performance and global cognition and the usefulness of the PAL task as a screening tool for AD. Cross-sectional. Academic hospital. Twenty-five participants with AD and 22 healthy comparators (HC) were included. The Cambridge Neuropsychological Test Automated Battery PAL task and the Montreal Cognitive Assessment (MoCA) were used to assess cognition. We assessed the relationship between the PAL task and MoCA performance using Pearson correlation and linear regression. We also examined the PAL task's ability to distinguish between AD and HC participants using Receiver Operating Characteristic curve (ROC) analysis. MoCA Total Score had a strong positive correlation with PAL Stages Completed score (r = 0.8, p < 0.001), and a strong negative correlation with PAL Total Errors (adjusted) score (r = -0.9, p < 0.001). Further, PAL Total Errors (adjusted) score predicted the MoCA Total Score (F (4, 46) = 37.2, p < 0.001). On ROC analysis, PAL Total Errors (adjusted) score cut-off of 54 errors had 92% sensitivity and 86% specificity to detect AD. Performance on the PAL task is highly associated with global cognition. Further, the PAL task can differentiate patients with AD from HCs with high sensitivity and specificity. Thus, the PAL task may hold potential usage as an easy-to-administer screening tool for AD.

CRANAD-28: A Robust Fluorescent Compound for Visualization of Amyloid Beta Plaques.

CRANAD-28, a difluoroboron curcumin analogue, has been demonstrated in earlier reports to successfully label amyloid beta (Aβ) plaques for imaging both ex vivo and in vivo. CRANAD-28’s imaging brightness, ability to penetrate the blood brain barrier, and low toxicity make the compound a potentially potent imaging tool in Alzheimer’s research. In this study, the Aβ-labeling ability of CRANAD-28 was investigated in further detail using histological staining to assess different criteria, including stained Aβ plaque brightness, Aβ plaque size, and Aβ plaque number count. The results of this study demonstrated CRANAD-28 to be superior across all criteria assessed. Furthermore, CRANAD-28 and IBA-1 antibody were used to label Aβ-plaques and microglia respectively. Statistical analysis with Spearman regression revealed a statistically significant negative correlation between the size of labeled Aβ plaques and surrounding microglia density. This finding provides interesting insight into Aβ plaque and microglia dynamism in AD pathology and corroborates the findings of previous studies. In addition, we found that CRANAD-28 provided distinct spectral signatures for Aβs in the core and periphery of the plaques. Based on the study’s results, CRANAD-28 could be considered as an alternative standard for imaging Aβ-plaques in future research studies.

Human induced pluripotent stem cells as a research tool in Alzheimer's disease.

Human-induced pluripotent stem cells (iPSCs) offer a novel, timely approach for investigating the aetiology of neuropsychiatric disorders. Although we are starting to gain more insight into the specific mechanisms that cause Alzheimer's disease and other forms of dementia, this has not resulted in therapies to slow the pathological processes. Animal models have been paramount in studying the neurobiological processes underlying psychiatric disorders. Nonetheless, these human conditions cannot be entirely recapitulated in rodents. Human cell models derived from patients' cells now offer new hope for improving our understanding of the early molecular stages of these diseases, through to validating therapeutics. The impact of dementia is increasing, and a new model to investigate the early stages of this disease is heralded as an essential, new platform for translational research. In this paper, we review current literature using iPSCs to study Alzheimer's disease, describe drug discovery efforts using this platform, and discuss the future potential for this technology in psychiatry research.

A fluorescence assay for detecting amyloid-β using the cytomegalovirus enhancer/promoter.

Robust assays for detecting the effects of elevated concentrations of amyloid-β (Aβ) may facilitate Alzheimer’s disease research. An appropriate assay would be high-throughput and enable identification of drugs and genetic mutations that block the effects of Aβ, potentially leading to treatments for Alzheimer’s disease. We discovered that the commonly used cytomegalovirus (CMV) enhancer/promoter is sensitive to the effects of Aβ. By combining the CMV enhancer/promoter with a fluorescent protein, we created a reporter system that produces changes in intracellular fluorescence in response to Aβ. Using hippocampal neurons, we quantified the ability of a CMV-fluorescent protein recombinant reporter to detect both exogenously applied and overexpressed Aβ. This is the first report of a high-throughput enhancer/promoter-based Aβ detection method. The reporter is able to detect the effects of elevated concentrations of Aβ in a high-throughput fashion, providing a new tool for Alzheimer’s disease research and important knowledge about the commonly used CMV enhancer/promoter.

Adrenergic Drugs Blockers or Enhancers for Cognitive Decline ? What to Choose for Alzheimer's Disease Patients?

The adrenergic system has an important role in normal central nervous system function as well as in brain disease. The locus coeruleus, the main source of norepinephrine in brain, is involved in the regulation of learning and memory, reinforcement of sleep-wake cycle and synaptic plasticity. In Alzheimer's disease, locus coeruleus degeneration is observed early in the course of the disease, years before the onset of clinical cognitive signs, with neurofibrillary detected at the stage of mild cognitive impairment, preceding amyloid deposition. Thus, in the last years, a great interest has grown in evaluating the possibility of central adrenergic system modulation as a therapeutic tool in Alzheimer's disease. However, evidences do not show univocal results, with some studies suggesting that adrenergic stimulation might be beneficial in Alzheimer's Disease and some others favoring adrenergic blockade. In this review, we summarize data from both hypothesis and describe the pathophysiological role of the adrenergic system in neurodegeneration.

A Spherical Brain Mapping of MR Images for the Detection of Alzheimer's Disease.

Magnetic Resonance Imaging (MRI) is of fundamental importance in neuroscience, providing good contrast and resolution, as well as not being considered invasive. Despite the development of newer techniques involving radiopharmaceuticals, it is still a recommended tool in Alzheimer's Disease (AD) neurological practice to assess neurodegeneration, and recent research suggests that it could reveal changes in the brain even before the symptomatology appears. In this paper we propose a method that performs a Spherical Brain Mapping, using different measures to project the three-dimensional MR brain images onto two-dimensional maps revealing statistical characteristics of the tissue. The resulting maps could be assessed visually, but also perform a significant feature reduction that will allow further supervised or unsupervised processing, reducing the computational load while maintaining a large amount of the original information. We have tested our methodology against a MRI database comprising 180 AD affected patients and 180 normal controls, where some of the mappings have revealed as an optimum strategy for the automatic processing and characterization of AD patterns, achieving up to a 90.9% of accuracy, as well as significantly reducing the computational load. Additionally, our maps allow the visual analysis and interpretation of the images, which can be of great help in the diagnosis of this and other types of dementia.

Super-Resolution Microscopy of Cerebrospinal Fluid Biomarkers as a Tool for Alzheimer's Disease Diagnostics.

Alzheimer's disease (AD) is neuropathologically characterized by aggregates of amyloid-β peptides (Aβ) and tau proteins. The consensus in the AD field is that Aβ and tau should serve as diagnostic biomarkers for AD. However, their aggregates have been difficult to investigate by conventional fluorescence microscopy, since their size is below the diffraction limit (∼200 nm). To solve this, we turned to a super-resolution imaging technique, stimulated emission depletion (STED) microscopy, which has a high enough precision to allow the discrimination of low- and high-molecular weight aggregates prepared in vitro. We used STED to analyze the structural organization of Aβ and tau in cerebrospinal fluid (CSF) from 36 AD patients, 11 patients with mild cognitive impairment (MCI), and 21 controls. We measured the numbers of aggregates in the CSF samples, and the aggregate sizes and intensities. These parameters enabled us to distinguish AD patients from controls with a specificity of ∼87% and a sensitivity of ∼79% . In addition, the aggregate parameters determined with STED microscopy correlated with the severity of cognitive impairment in AD patients. Finally, these parameters may be useful as predictive tools for MCI cases. The STED parameters of two MCI patients who developed AD during the course of the study, as well as of MCI patients whose Aβ ELISA values fall within the accepted range for AD, placed them close to the AD averages. We suggest that super-resolution imaging is a promising tool for AD diagnostics.