Development of a Label-Free Immunosensor for Clusterin Detection as an Alzheimer's Biomarker

An immobilized antibody-based affinity grid strategy for on-grid purification of target proteins enables high-resolution cryo-EM

In cryo-electron microscopy (cryo-EM), sample preparation poses a critical bottleneck, particularly for rare or fragile macromolecular assemblies and those suffering from denaturation and particle orientation distribution issues related to air-water interface. In this study, we develop and characterize an immobilized antibody-based affinity grid (IAAG) strategy based on the high-affinity PA tag/NZ-1 antibody epitope tag system. We employ Pyr-NHS as a linker to immobilize NZ-1 Fab on the graphene oxide or carbon-covered grid surface. Our results demonstrate that the IAAG grid effectively enriches PA-tagged target proteins and overcomes preferred orientation issues. Furthermore, we demonstrate the utility of our IAAG strategy for on-grid purification of low-abundance target complexes from cell lysates, enabling atomic resolution cryo-EM. This approach greatly streamlines the purification process, reduces the need for large quantities of biological samples, and addresses common challenges encountered in cryo-EM sample preparation. Collectively, our IAAG strategy provides an efficient and robust means for combined sample purification and vitrification, feasible for high-resolution cryo-EM. This approach holds potential for broader applicability in both cryo-EM and cryo-electron tomography (cryo-ET).

Colorimetric Aptasensor for the Visual and Microplate Determination of Clusterin in Human Urine Based on Aggregation Characteristics of Gold Nanoparticles

Clusterin has the potential to become the biomarker of multiple diseases, but its clinical quantitative detection methods are limited, which restricts its research progress as a biomarker. A rapid and visible colorimetric sensor for clusterin detection based on sodium chloride-induced aggregation characteristic of gold nanoparticles (AuNPs) was successfully constructed. Unlike the existing methods based on antigen-antibody recognition reactions, the aptamer of clusterin was used as the sensing recognition element. The aptamer could protect AuNPs from aggregation caused by sodium chloride, but clusterin bound with aptamer detached it from AuNPs, thereby inducing aggregation again. Simultaneously, the color change from red in the dispersed state to purple gray in the aggregated state made it possible to preliminarily judge the concentration of clusterin by observation. This biosensor showed a linear range of 0.02-2 ng/mL and good sensitivity with a detection limit of 5.37 pg/mL. The test results of clusterin in spiked human urine confirmed that the recovery rate was satisfactory. The proposed strategy is helpful for the development of label-free point-of-care testing equipment for clinical testing of clusterin, which is cost-effective and feasible.

Revolutionizing the Early Detection of Alzheimer's Disease through Non-Invasive Biomarkers: The Role of Artificial Intelligence and Deep Learning

Alzheimer's disease (AD) is now classified as a silent pandemic due to concerning current statistics and future predictions. Despite this, no effective treatment or accurate diagnosis currently exists. The negative impacts of invasive techniques and the failure of clinical trials have prompted a shift in research towards non-invasive treatments. In light of this, there is a growing need for early detection of AD through non-invasive approaches. The abundance of data generated by non-invasive techniques such as blood component monitoring, imaging, wearable sensors, and bio-sensors not only offers a platform for more accurate and reliable bio-marker developments but also significantly reduces patient pain, psychological impact, risk of complications, and cost. Nevertheless, there are challenges concerning the computational analysis of the large quantities of data generated, which can provide crucial information for the early diagnosis of AD. Hence, the integration of artificial intelligence and deep learning is critical to addressing these challenges. This work attempts to examine some of the facts and the current situation of these approaches to AD diagnosis by leveraging the potential of these tools and utilizing the vast amount of non-invasive data in order to revolutionize the early detection of AD according to the principles of a new non-invasive medicine era.

Antibody-receptor bioengineering and its implications in designing bioelectronic devices

Antibodies play a crucial role in the defense mechanism countering pathogens or foreign antigens in eukaryotes. Its potential as an analytical and diagnostic tool has been exploited for over a century. It forms immunocomplexes with a specific antigen, which is the basis of immunoassays and aids in developing potent biosensors. Antibody-based sensors allow for the quick and accurate detection of various analytes. Though classical antibodies have prolonged been used as bioreceptors in biosensors fabrication due to their increased fragility, they have been engineered into more stable fragments with increased exposure of their antigen-binding sites in the recent era. In biosensing, the formats constructed by antibody engineering can enhance the signal since the resistance offered by a conventional antibody is much more than these fragments. Hence, signal amplification can be observed when antibody fragments are utilized as bioreceptors instead of full-length antibodies. We present the first systematic review on engineered antibodies as bioreceptors with the description of their engineering methods. The detection of various target analytes, including small molecules, macromolecules, and cells using antibody-based biosensors, has been discussed. A comparison of the classical polyclonal, monoclonal, and engineered antibodies as bioreceptors to construct highly accurate, sensitive, and specific sensors is also discussed.

Nanoparticle-based colorimetric sensors to detect neurodegenerative disease biomarkers

Neurodegenerative disorders (NDDs) are progressive, incurable health conditions that primarily affect brain cells, and result in loss of brain mass and impaired function. Current sensing technologies for NDD detection are limited by high cost, long sample preparation, and/or require skilled personnel. To overcome these limitations, optical sensors, specifically colorimetric sensors, have garnered increasing attention towards the development of a cost-effective, simple, and rapid alternative approach. In this review, we evaluate colorimetric sensing strategies of NDD biomarkers (e.g. proteins, neurotransmitters, bio-thiols, and sulfide), address the limitations and challenges of optical sensor technologies, and provide our outlook on the future of this field.

Novel Trends in Electrochemical Biosensors for Early Diagnosis of Alzheimer's Disease

BACKGROUND

Alzheimer's disease (AD) is a multifactorial progressive and irreversible neurodegenerative disorder affecting mainly the population over 65 years of age. It is becoming a global health and socioeconomic problem, and the current number of patients reaching 30-50 million people will be three times higher over the next thirty years.

OBJECTIVE

Late diagnosis caused by decades of the asymptomatic phase and invasive and cost-demanding diagnosis are problems that make the whole situation worse. Electrochemical biosensors could be the right tool for less invasive and inexpensive early diagnosis helping to reduce spend sources- both money and time.

METHOD

This review is a survey of the latest advances in the design of electrochemical biosensors for the early diagnosis of Alzheimer's disease. Biosensors are divided according to target biomarkers.

CONCLUSION

Standard laboratory methodology could be improved by analyzing a combination of currently estimated markers along with neurotransmitters and genetic markers from blood samples, which make the test for AD diagnosis available to the wide public.

A Concise Overview of Biosensing Technologies for the Detection of Alzheimer's Disease Biomarkers

Alzheimer's disease (AD) is a brain-linked pathophysiological condition with neuronal degeneration, cognition dysfunctions, and other debilitations. Due to the growing prevalence of AD, there is a highly commended tendency to accelerate and develop analytical technologies for easy, cost-effective, and sensitive detection of AD biomarkers. In the last decade, remarkable advancements have been achieved on the gate to the progression of biosensors, predominantly optical and electrochemical, to detect AD biomarkers. Biosensors are commanding analytical devices that can conduct biological responses on transducers into measurable signals. These analytical devices can assist the case finding and management of AD. This review focuses on up-to-date developments, contests, and tendencies regarding AD biosensing principally, emphasizing the exclusive possessions of nanomaterials.

Early Detection of Alzheimer's Disease Based on Clinical Trials, Three-Dimensional Imaging Data, and Personal Information Using Autoencoders

Background:

A timely diagnosis of Alzheimer's disease (AD) is crucial to obtain more practical treatments. In this article, a novel approach using Auto-Encoder Neural Networks (AENN) for early detection of AD was proposed.

Method:

The proposed method mainly deals with the classification of multimodal data and the imputation of missing data. The data under study involve the MiniMental State Examination, magnetic resonance imaging, positron emission tomography, cerebrospinal fluid data, and personal information. Natural logarithm was used for normalizing the data. The Auto-Encoder Neural Networks was used for imputing missing data. Principal component analysis algorithm was used for reducing dimensionality of data. Support Vector Machine (SVM) was used as classifier. The proposed method was evaluated using Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Then, 10fold crossvalidation was used to audit the detection accuracy of the method.

Results:

The effectiveness of the proposed approach was studied under several scenarios considering 705 cases of ADNI database. In three binary classification problems, that is AD vs. normal controls (NCs), mild cognitive impairment (MCI) vs. NC, and MCI vs. AD, we obtained the accuracies of 95.57%, 83.01%, and 78.67%, respectively.

Conclusion:

Experimental results revealed that the proposed method significantly outperformed most of the stateoftheart methods.

Graphene FET Sensors for Alzheimer's Disease Protein Biomarker Clusterin Detection

We report on the fabrication and characterisation of graphene field-effect transistor (GFET) biosensors for the detection of Clusterin, a prominent protein biomarker of Alzheimer’s disease (AD). The GFET sensors were fabricated on Si/SiO₂ substrate using photolithographic patterning and metal lift-off techniques with evaporated chromium and sputtered gold contacts. Raman Spectroscopy was performed on the devices to determine the quality of the graphene. The GFETs were annealed to improve their performance before the channels were functionalized by immobilising the graphene surface with linker molecules and anti-Clusterin antibodies. Concentration of linker molecules was also independently verified by absorption spectroscopy using the highly collimated micro-beam light of Diamond B23 beamline. The detection was achieved through the binding reaction between the antibody and varying concentrations of Clusterin antigen from 1 to 100 pg/mL, as well as specificity tests using human chorionic gonadotropin (hCG), a glycoprotein risk biomarker of certain cancers. The GFETs were characterized using direct current (DC) 4-probe electrical resistance (4-PER) measurements, which demonstrated a limit of detection of the biosensors to be ∼ 300 fg/mL (4 fM). Comparison with back-gated Dirac voltage shifts with varying concentration of Clusterin show 4-PER measurements to be more accurate, at present, and point to a requirement for further optimisation of the fabrication processes for our next generation of GFET sensors. Thus, we have successfully fabricated a promising set of GFET biosensors for the detection of Clusterin protein biomarker. The developed GFET biosensors are entirely generic and also have the potential to be applied to a variety of other disease detection applications such as Parkinson’s, cancer, and cardiovascular.

Enlightening the advancements in electrochemical bioanalysis for the diagnosis of Alzheimer's disease and other neurodegenerative disorders

Neurodegenerative disorders (NDD), and particularly Alzheimer's disease (AD), are one of the greatest challenges facing our current medicine and society because of its increasing incidence and the high burden imposed both on patients' families and health systems. Despite this, their accurate diagnosis, mostly conducted by cerebrospinal fluid (CSF) analysis or neuroimaging techniques, costly, time-consuming, and unaffordable for most of the population, remains a complex task. In this situation, electrochemical biosensors are flourishing as promising alternative tools for the simple, fast, and low-cost diagnosis of NDD/AD. This review article provides the relevant clinical details of NDD/AD along with the closely related genetic (genetic mutations, polymorphisms of ApoE and specific miRNAs) and proteomic (amyloid-β peptides, total and phosphorylated tau protein) biomarkers circulating mostly in CSF. In addition, the article systematically enlightens a general view of the electrochemical affinity biosensors (mostly aptasensors and immunosensors) reported in the past two years for the determination of such biomarkers. The different developed strategies, analytical performances and applications are comprehensively discussed. Recent advancements in signal amplification methodologies involving smart designs and the use of nanomaterials and rational surface chemistries, as well as the challenges that must be struggled and the prospects in electrochemical affinity biosensing to bring more accessibility to NDD/AD diagnosis, prognosis, and follow-up, are also pointed out.

A label-free biosensor based on graphene and reduced graphene oxide dual-layer for electrochemical determination of beta-amyloid biomarkers

A label-free biosensor is developed for the determination of plasma-based Aβ_1–42 biomarker in Alzheimer’s disease (AD). The platform is based on highly conductive dual-layer of graphene and electrochemically reduced graphene oxide (rGO). The modification of dual-layer with 1-pyrenebutyric acid N-hydroxysuccinimide ester (Pyr-NHS) is achieved to facilitate immobilization of H31L21 antibody. The effect of these modifications were studied with morphological, spectral and electrochemical techniques. The response of the biosensor was evaluated using differential pulse voltammetry (DPV). The data was acquired at a working potential of ~ 180 mV and a scan rate of 50 mV s⁻¹. A low limit of detection (LOD) of 2.398 pM is achieved over a wide linear range from 11 pM to 55 nM. The biosensor exhibits excellent specificity over Aβ_1–40 and ApoE ε4 interfering species. Thus, it provides a viable tool for electrochemical determination of Aβ_1–42. Spiked human and mice plasmas were used for the successful validation of the sensing platform in bio-fluidic samples. The results obtained from mice plasma analysis concurred with the immunohistochemistry (IHC) and magnetic resonance imaging (MRI) data obtained from brain analysis.

Graphical abstract

Emphasis Learning, Features Repetition in Width Instead of Length to Improve Classification Performance: Case Study-Alzheimer's Disease Diagnosis

In the past decade, many studies have been conducted to advance computer-aided systems for Alzheimer's disease (AD) diagnosis. Most of them have recently developed systems concentrated on extracting and combining features from MRI, PET, and CSF. For the most part, they have obtained very high performance. However, improving the performance of a classification problem is complicated, specifically when the model's accuracy or other performance measurements are higher than 90%. In this study, a novel methodology is proposed to address this problem, specifically in Alzheimer's disease diagnosis classification. This methodology is the first of its kind in the literature, based on the notion of replication on the feature space instead of the traditional sample space. Briefly, the main steps of the proposed method include extracting, embedding, and exploring the best subset of features. For feature extraction, we adopt VBM-SPM; for embedding features, a concatenation strategy is used on the features to ultimately create one feature vector for each subject. Principal component analysis is applied to extract new features, forming a low-dimensional compact space. A novel process is applied by replicating selected components, assessing the classification model, and repeating the replication until performance divergence or convergence. The proposed method aims to explore most significant features and highest-preforming model at the same time, to classify normal subjects from AD and mild cognitive impairment (MCI) patients. In each epoch, a small subset of candidate features is assessed by support vector machine (SVM) classifier. This repeating procedure is continued until the highest performance is achieved. Experimental results reveal the highest performance reported in the literature for this specific classification problem. We obtained a model with accuracies of 98.81%, 81.61%, and 81.40% for AD vs. normal control (NC), MCI vs. NC, and AD vs. MCI classification, respectively.

Search for biomarkers of Alzheimer's disease: Recent insights, current challenges and future prospects

Due to the trend of prolonged lifespan leading to higher incidence of age-related diseases, the demand for reliable biomarkers of dementia rises. In this review, we present novel biomarkers of high potential, especially those found in blood, urine or saliva, which could lead to a more comfortable patient experience and better time- and cost-effectivity, compared to the currently used diagnostic methods. We focus on biomarkers that might allow for the detection of Alzheimer's disease before its clinical manifestations. Such biomarkers might be helpful for better understanding the etiology of the disease and identifying its risk factors. Moreover, it could be a base for developing new treatment or at least help to prolong the presymptomatic stage in patients suffering from Alzheimer's disease. As potential candidates, we present, for instance, neurofilament light in both cerebrospinal fluid and blood plasma or amyloid β in plasma. Above all, we provide an overview of different approaches to the diagnostics, analyzing patient's biofluids as a whole using molecular spectroscopy. Infrared and Raman spectroscopy and especially chiroptical methods provide information not only on the chemical composition, but also on molecular structure. Therefore, these techniques are promising for the diagnostics of Alzheimer's disease, as the accumulation of amyloid β in abnormal conformation is one of the hallmarks of this disease.

Embedded Disposable Functionalized Electrochemical Biosensor with a 3D-Printed Flow Cell for Detection of Hepatic Oval Cells (HOCs)

Hepatic oval cells (HOCs) are considered the progeny of the intrahepatic stem cells that are found in a small population in the liver after hepatocyte proliferation is inhibited. Due to their small number, isolation and capture of these cells constitute a challenging task for immunosensor technology. This work describes the development of a 3D-printed continuous flow system and exploits disposable screen-printed electrodes for the rapid detection of HOCs that over-express the OV6 marker on their membrane. Multiwall carbon nanotube (MWCNT) electrodes have a chitosan film that serves as a scaffold for the immobilization of oval cell marker antibodies (anti-OV6-Ab), which enhance the sensitivity of the biomarker and makes the designed sensor specific for oval cells. The developed sensor can be easily embedded into the 3D-printed flow cell to allow cells to be exposed continuously to the functionalized surface. The continuous flow is intended to increase capture of most of the target cells in the specimen. Contact angle measurements were performed to characterize the nature and quality of the modified sensor surface, and electrochemical measurements (cyclic voltammetry (CV) and square wave voltammetry (SWV)) were performed to confirm the efficiency and selectivity of the fabricated sensor to detect HOCs. The proposed method is valuable for capturing rare cells and could provide an effective tool for cancer diagnosis and detection.