Progress regarding the context-of-use of tau as biomarker of Alzheimer's disease and other neurodegenerative diseases

Multimer Detection System: A Universal Assay System for Differentiating Protein Oligomers from Monomers

Depositions of protein aggregates are typical pathological hallmarks of various neurodegenerative diseases (NDs). For example, amyloid-beta (Aβ) and tau aggregates are present in the brain and plasma of patients with Alzheimer's disease (AD); α-synuclein in Parkinson's disease (PD), dementia with Lewy bodies (DLB), and multiple system atrophy (MSA); mutant huntingtin protein (Htt) in Huntington's disease (HD); and DNA-binding protein 43 kD (TDP-43) in amyotrophic lateral sclerosis (ALS), frontotemporal dementia (FTD), and limbic-predominant age-related TDP-43 encephalopathy (LATE). The same misfolded proteins can be present in multiple diseases in the form of mixed proteinopathies. Since there is no cure for all these diseases, understanding the mechanisms of protein aggregation becomes imperative in modern medicine, especially for developing diagnostics and therapeutics. A Multimer Detection System (MDS) was designed to distinguish and quantify the multimeric/oligomeric forms from the monomeric form of aggregated proteins. As the unique epitope of the monomer is already occupied by capturing or detecting antibodies, the aggregated proteins with multiple epitopes would be accessible to both capturing and detecting antibodies simultaneously, and signals will be generated from the oligomers rather than the monomers. Hence, MDS could present a simple solution for measuring various conformations of aggregated proteins with high sensitivity and specificity, which may help to explore diagnostic and treatment strategies for developing anti-aggregation therapeutics.

P-tau217 as a Reliable Blood-Based Marker of Alzheimer's Disease

Amyloid plaques and tau tangles are the hallmark pathologic features of Alzheimer's disease (AD). Traditionally, these changes are identified in vivo via cerebrospinal fluid (CSF) analysis or positron emission tomography (PET) scans. However, these methods are invasive, expensive, and resource-intensive. To address these limitations, there has been ongoing research over the past decade to identify blood-based markers for AD. Despite the challenges posed by their extremely low concentrations, recent advances in mass spectrometry and immunoassay techniques have made it feasible to detect these blood markers of amyloid and tau deposition. Phosphorylated tau (p-tau) has shown greater promise in reflecting amyloid pathology as evidenced by CSF and PET positivity. Various isoforms of p-tau, distinguished by their differential phosphorylation sites, have been recognized for their ability to identify amyloid-positive individuals. Notable examples include p-tau181, p-tau217, and p-tau235. Among these, p-tau217 has emerged as a superior and reliable marker of amyloid positivity and, thus, AD in terms of accuracy of diagnosis and ability for early prognosis. In this narrative review, we aim to elucidate the utility of p-tau217 as an AD marker, exploring its underlying basis, clinical diagnostic potential, and relevance in clinical care and trials.

Linking the Amyloid, Tau, and Mitochondrial Hypotheses of Alzheimer's Disease and Identifying Promising Drug Targets

Damage or loss of brain cells and impaired neurochemistry, neurogenesis, and synaptic and nonsynaptic plasticity of the brain lead to dementia in neurodegenerative diseases, such as Alzheimer's disease (AD). Injury to synapses and neurons and accumulation of extracellular amyloid plaques and intracellular neurofibrillary tangles are considered the main morphological and neuropathological features of AD. Age, genetic and epigenetic factors, environmental stressors, and lifestyle contribute to the risk of AD onset and progression. These risk factors are associated with structural and functional changes in the brain, leading to cognitive decline. Biomarkers of AD reflect or cause specific changes in brain function, especially changes in pathways associated with neurotransmission, neuroinflammation, bioenergetics, apoptosis, and oxidative and nitrosative stress. Even in the initial stages, AD is associated with Aβ neurotoxicity, mitochondrial dysfunction, and tau neurotoxicity. The integrative amyloid-tau-mitochondrial hypothesis assumes that the primary cause of AD is the neurotoxicity of Aβ oligomers and tau oligomers, mitochondrial dysfunction, and their mutual synergy. For the development of new efficient AD drugs, targeting the elimination of neurotoxicity, mutual potentiation of effects, and unwanted protein interactions of risk factors and biomarkers (mainly Aβ oligomers, tau oligomers, and mitochondrial dysfunction) in the early stage of the disease seems promising.

A Novel Deep Learning Radiomics Model to Discriminate AD, MCI and NC: An Exploratory Study Based on Tau PET Scans from ADNI

OBJECTIVE

We explored a novel model based on deep learning radiomics (DLR) to differentiate Alzheimer's disease (AD) patients, mild cognitive impairment (MCI) patients and normal control (NC) subjects. This model was validated in an exploratory study using tau positron emission tomography (tau-PET) scans.

METHODS

In this study, we selected tau-PET scans from the Alzheimer's Disease Neuroimaging Initiative database (ADNI), which included a total of 211 NC, 197 MCI, and 117 AD subjects. The dataset was divided into one training/validation group and one separate external group for testing. The proposed DLR model contained the following three steps: (1) pre-training of candidate deep learning models; (2) extraction and selection of DLR features; (3) classification based on support vector machine (SVM). In the comparative experiments, we compared the DLR model with three traditional models, including the SUVR model, traditional radiomics model, and a clinical model. Ten-fold cross-validation was carried out 200 times in the experiments.

RESULTS

Compared with other models, the DLR model achieved the best classification performance, with an accuracy of 90.76% ± 2.15% in NC vs. MCI, 88.43% ± 2.32% in MCI vs. AD, and 99.92% ± 0.51% in NC vs. AD.

CONCLUSIONS

Our proposed DLR model had the potential clinical value to discriminate AD, MCI and NC.

An unsupervised region of interest extraction model for tau PET images and its application in the diagnosis of Alzheimer's disease

BACKGROUND

Recently, tau positron-emission tomography (PET) images have been widely used for the diagnosis of Alzheimer's disease (AD). However, existing semi-quantitative uptake value ratios (SUVR) calculation is usually based on group analysis or specific brain regions from existing templates, which cannot detect individual heterogeneity. In this study, we proposed a novel deep learning model; called generative adversarial networks constrained multiple loss autoencoder for tau (GANCMLAE4TAU), to extract individual regions of interest (ROIs) of tau deposition.

METHODS

The basic framework of the proposed model is composed of two encoders, one decoder, and one discriminator. Tau PET images of 327 cognitive normal (CN) subjects from Alzheimer's Disease Neuroimaging Initiative (ADNI) were used to train the model and 29 CNs from Huashan Hospital were used as an external validation group. The other 57 AD patients and 83 CNs subjects from ADNI were used in the classification task. The Structural Similarity (SSIM), Peak Signal to Noise Ratio (PSNR) and Mean Square Error (MSE) were applied to validate the robustness of our model. In addition, we conducted a receiver operating characteristic curve (ROC) analysis for the SUVR of individual ROIs from the GANCMLAE4TAU model and compared it with SUVR of the whole brain and ROIs from the templates.

RESULTS

Our model achieved good SSIM (0.963±0.006), PSNR (35.960±3.458) and MSE (0.0004±0.0003). In ROC analysis, our model had the highest area under curve (AUC) (0.869, 0.809-0.929) in discriminating AD from CN subjects.

CONCLUSION

GANCMLAE4TAU could detect individual ROIs for tau PET images and had the potential to be developed as a novel diagnostic tool in the future. Clinical Relevance- This method can find individual ROIs of tau depositions, so as to achieve more accurate diagnosis of Alzheimer's disease.

A new generation of AD biomarkers: 2019 to 2021

Alzheimer's disease (AD) is the most common form of dementia and cases are rising worldwide. The effort to fight this disease is hampered by a lack of disease-modifying treatments and the absence of an early, accurate diagnostic tool. Neuropathology begins years or decades before symptoms occur and, upon onset of symptoms, diagnosis can take a year or more. Such delays postpone treatment and make research into the early stages of the disease difficult. Ideally, clinicians require a minimally invasive test that can detect AD in its early stages, before cognitive symptoms occur. Advances in proteomic technologies have facilitated the study of promising biomarkers of AD. Over the last two years (2019-2021) studies have identified and validated many species which can be measured in cerebrospinal fluid (CSF), plasma, or in both fluids, and which have a high predictive value for AD. We herein discuss proteins which have been highlighted as promising biomarkers of AD in the last two years, and consider implications for future research within the research framework of the amyloid (A), tau (T), neurodegeneration (N) scoring system. We review recently identified species of amyloid and tau which may improve diagnosis when used in combination with current measures such as amyloid-beta-42 (Aβ42), total tau (t-tau) and phosphorylated tau (p-tau). In addition, several proteins have been identified as likely proxies for neurodegeneration, including neurofilament light (NfL), synaptosomal-associated protein 25 (SNAP-25) and neurogranin (NRGN). Finally, proteins originating from diverse processes such as neuroinflammation, lipid transport and mitochondrial dysfunction could aid in both AD diagnosis and patient stratification.

Ultrasensitive techniques and protein misfolding amplification assays for biomarker-guided reconceptualization of Alzheimer's and other neurodegenerative diseases

INTRODUCTION

The clinical validation and qualification of biomarkers reflecting the complex pathophysiology of neurodegenerative diseases (NDDs) is a fundamental challenge for current drug discovery and development and next-generation clinical practice. Novel ultrasensitive detection techniques and protein misfolding amplification assays hold the potential to optimize and accelerate this process.

AREAS COVERED

Here we perform a PubMed-based state of the art review and perspective report on blood-based ultrasensitive detection techniques and protein misfolding amplification assays for biomarkers discovery and development in NDDs.

EXPERT OPINION

Ultrasensitive assays represent innovative solutions for blood-based assessments during the entire Alzheimer's disease (AD) biological and clinical continuum, for contexts of use (COU) such as prediction, detection, early diagnosis, and prognosis of AD. Moreover, cerebrospinal fluid (CSF)-based misfolding amplification assays show encouraging performance in detecting α-synucleinopathies in prodromal or at-high-risk individuals and may serve as tools for patients' stratification by the presence of α-synuclein pathology. Further clinical research will help overcome current methodological limitations, also through exploring multiple accessible bodily matrices. Eventually, integrative longitudinal studies will support precise definitions for appropriate COU across NDDs.

Novel blood test for early biomarkers of preeclampsia and Alzheimer's disease

A non-invasive and sensitive blood test has long been a goal for early stage disease diagnosis and treatment for Alzheimer's disease (AD) and other proteinopathy diseases. We previously reported that preeclampsia (PE), a severe pregnancy complication, is another proteinopathy disorder with impaired autophagy. We hypothesized that induced autophagy deficiency would promote accumulation of pathologic protein aggregates. Here, we describe a novel, sensitive assay that detects serum protein aggregates from patients with PE (n = 33 early onset and 33 late onset) and gestational age-matched controls (n = 77) as well as AD in both dementia and prodromal mild cognitive impairment (MCI, n = 24) stages with age-matched controls (n = 19). The assay employs exposure of genetically engineered, autophagy-deficient human trophoblasts (ADTs) to serum from patients. The aggregated protein complexes and their individual components, including transthyretin, amyloid β-42, α-synuclein, and phosphorylated tau231, can be detected and quantified by co-staining with ProteoStat, a rotor dye with affinity to aggregated proteins, and respective antibodies. Detection of protein aggregates in ADTs was not dependent on transcriptional upregulation of these biomarkers. The ROC curve analysis validated the robustness of the assay for its specificity and sensitivity (PE; AUC: 1, CI: 0.949-1.00; AD; AUC: 0.986, CI: 0.832-1.00). In conclusion, we have developed a novel, noninvasive diagnostic and predictive assay for AD, MCI and PE.