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

Sensitivity-Enhancing Strategies of Graphene Field-Effect Transistor Biosensors for Biomarker Detection

The ultrasensitive recognition of biomarkers plays a crucial role in the precise diagnosis of diseases. Graphene-based field-effect transistors (GFET) are considered the most promising devices among the next generation of biosensors. GFET biosensors possess distinct advantages, including label-free, ease of integration and operation, and the ability to directly detect biomarkers in liquid environments. This review summarized recent advances in GFET biosensors for biomarker detection, with a focus on interface functionalization. Various sensitivity-enhancing strategies have been overviewed for GFET biosensors, from the perspective of optimizing graphene synthesis and transfer methods, refinement of surface functionalization strategies for the channel layer and gate electrode, design of biorecognition elements and reduction of nonspecific adsorption. Further, this review extensively explores GFET biosensors functionalized with antibodies, aptamers, and enzymes. It delves into sensitivity-enhancing strategies employed in the detection of biomarkers for various diseases (such as cancer, cardiovascular diseases, neurodegenerative disorders, infectious viruses, etc.) along with their application in integrated microfluidic systems. Finally, the issues and challenges in strategies for the modulation of biosensing interfaces are faced by GFET biosensors in detecting biomarkers.

Graphene transistor-based biosensors for rapid detection of SARS-CoV-2

Field-effect transistor (FET) biosensors use FETs to detect changes in the amount of electrical charge caused by biomolecules like antigens and antibodies. COVID-19 can be detected by employing these biosensors by immobilising bio-receptor molecules that bind to the SARS-CoV-2 virus on the FET channel surface and subsequent monitoring of the changes in the current triggered by the virus. Graphene Field-effect Transistor (GFET)-based biosensors utilise graphene, a two-dimensional material with high electrical conductivity, as the sensing element. These biosensors can rapidly detect several biomolecules including the SARS-CoV-2 virus, which is responsible for COVID-19. GFETs are ideal for real-time infectious illness diagnosis due to their great sensitivity and specificity. These graphene transistor-based biosensors could revolutionise clinical diagnostics by generating fast, accurate data that could aid pandemic management. GFETs can also be integrated into point-of-care (POC) diagnostic equipment. Recent advances in GFET-type biosensors for SARS-CoV-2 detection are discussed here, along with their associated challenges and future scope.

Neurodiagnostic and neurotherapeutic potential of graphene nanomaterials

Graphene has emerged as a highly promising nanomaterial for a variety of advanced technologies, including batteries, energy, electronics, and biotechnologies. Its recent contribution to neurotechnology is particularly noteworthy because its superior conductivity, chemical resilience, biocompatibility, thermal stability, and scalable nature make it well-suited for measuring brain activity and plasticity in health and disease. Graphene-mediated compounds are microfabricated in two central methods: chemical processes with natural graphite and chemical vapor deposition of graphene in a film form. They are widely used as biosensors and bioelectronics for neurodiagnostic and neurotherapeutic purposes in several brain disorders, such as Parkinson's disease, stroke, glioma, epilepsy, tinnitus, and Alzheimer's disease. This review provides an overview of studies that have demonstrated the technical advances of graphene nanomaterials in neuroscientific and clinical applications. We also discuss current limitations and future demands in relation to the clinical application of graphene, highlighting its potential technological and clinical significance for treating brain disorders. Our review underscores the potential of graphene nanomaterials as powerful tools for advancing the understanding of the brain and developing new therapeutic strategies.

Challenges for Field-Effect-Transistor-Based Graphene Biosensors

Owing to its outstanding physical properties, graphene has attracted attention as a promising biosensor material. Field-effect-transistor (FET)-based biosensors are particularly promising because of their high sensitivity that is achieved through the high carrier mobility of graphene. However, graphene-FET biosensors have not yet reached widespread practical applications owing to several problems. In this review, the authors focus on graphene-FET biosensors and discuss their advantages, the challenges to their development, and the solutions to the challenges. The problem of Debye screening, in which the surface charges of the detection target are shielded and undetectable, can be solved by using small-molecule receptors and their deformations and by using enzyme reaction products. To address the complexity of sample components and the detection mechanisms of graphene-FET biosensors, the authors outline measures against nonspecific adsorption and the remaining problems related to the detection mechanism itself. The authors also introduce a solution with which the molecular species that can reach the sensor surfaces are limited. Finally, the authors present multifaceted approaches to the sensor surfaces that provide much information to corroborate the results of electrical measurements. The measures and solutions introduced bring us closer to the practical realization of stable biosensors utilizing the superior characteristics of graphene.

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.

Synthesis and Functionalization of Graphene Materials for Biomedical Applications: Recent Advances, Challenges, and Perspectives

Since its discovery in 2004, graphene is increasingly applied in various fields owing to its unique properties. Graphene application in the biomedical domain is promising and intriguing as an emerging 2D material with a high surface area, good mechanical properties, and unrivalled electronic and physical properties. This review summarizes six typical synthesis methods to fabricate pristine graphene (p-G), graphene oxide (GO), and reduced graphene oxide (rGO), followed by characterization techniques to examine the obtained graphene materials. As bare graphene is generally undesirable in vivo and in vitro, functionalization methods to reduce toxicity, increase biocompatibility, and provide more functionalities are demonstrated. Subsequently, in vivo and in vitro behaviors of various bare and functionalized graphene materials are discussed to evaluate the functionalization effects. Reasonable control of dose (<20 mg kg^-1 ), sizes (50-1000 nm), and functionalization methods for in vivo application are advantageous. Then, the key biomedical applications based on graphene materials are discussed, coupled with the current challenges and outlooks of this growing field. In a broader sense, this review provides a comprehensive discussion on the synthesis, characterization, functionalization, evaluation, and application of p-G, GO, and rGO in the biomedical field, highlighting their recent advances and potential.

Advancement in Analytical Techniques for Determining the Activity of β-Site Amyloid Precursor Protein Cleaving Enzyme 1

Alzheimer's disease (AD) is a degenerative disease of the central nervous system. The pathogenesis is still not fully clear. One of the main histopathological manifestations is senile plaques formed by β-amyloid (Aβ) accumulation. Aβ is generated from the sequential proteolysis of amyloid precursor protein (APP) by β-secretase [i.e. β-site APP cleaving enzyme 1 (BACE1)] and γ-secretase, with a rate-limiting step controlled by BACE1 activity. Therefore, inhibiting BACE1 activity has become a potential therapeutic strategy for AD. The development of reliable detection methods for BACE1 activity plays an important role in early diagnosis of AD and evaluation of the therapeutic effect of new drugs for AD. This article has reviewed the recent advances in BACE1 activity detection techniques. The challenges of applying these analysis techniques to early clinical diagnosis of AD and development trends of the detection techniques have been prospected.

Rapid Biomarker Screening of Alzheimer's Disease by Interpretable Machine Learning and Graphene-Assisted Raman Spectroscopy

The study of Alzheimer's disease (AD), the most common cause of dementia, faces challenges in terms of understanding the cause, monitoring the pathogenesis, and developing early diagnoses and effective treatments. Rapid and accurate identification of AD biomarkers in the brain is critical to providing key insights into AD and facilitating the development of early diagnosis methods. In this work, we developed a platform that enables a rapid screening of AD biomarkers by employing graphene-assisted Raman spectroscopy and machine learning interpretation in AD transgenic animal brains. Specifically, we collected Raman spectra on slices of mouse brains with and without AD and used machine learning to classify AD and non-AD spectra. By contacting monolayer graphene with the brain slices, the accuracy was increased from 77% to 98% in machine learning classification. Further, using a linear support vector machine (SVM), we identified a spectral feature importance map that reveals the importance of each Raman wavenumber in classifying AD and non-AD spectra. Based on this spectral feature importance map, we identified AD biomarkers including Aβ and tau proteins and other potential biomarkers, such as triolein, phosphatidylcholine, and actin, which have been confirmed by other biochemical studies. Our Raman-machine learning integrated method with interpretability will facilitate the study of AD and can be extended to other tissues and biofluids and for various other diseases.

Electrochemical Detection of Alzheimer’s Disease Biomarker, β-Secretase Enzyme (BACE1), With One-Step Synthesized Reduced Graphene Oxide

β-Secretase1 (BACE1) catalyzes the rate-limiting step in the generation of amyloid-β peptides, that is, the principal component involved in the pathology of Alzheimer’s disease (AD). Recent research studies show correlation between blood and cerebrospinal fluid (CSF) levels of BACE1 with the pathophysiology of AD. In this study, we report one-step synthesized reduced graphene oxide (rGO), activated via carbodiimide chemistry, conjugated with BACE1 antibody (Ab), and immobilized on fluorine-doped tin oxide (FTO) electrodes for rapid detection of BACE1 antigen (Ag) for AD diagnosis. The synthesis and fabrication steps were characterized using different types of spectroscopic, X-ray analytic, microscopic, and voltametric techniques. Various parameters including nanomaterial/Ab concentration, response time, pH, temperature, and rate of scan were standardized for maximum current output using the modified electrode. Final validation was performed via detection of BACE1 Ag ranging from 1 fM to 1 µM, with a detection limit of 0.64 fM in buffer samples and 1 fM in spiked serum samples, as well as negligible cross-reactivity with neurofilament Ag in buffer, spiked serum, and spiked artificial CSF. The proposed immunosensor gave a quick result in 30 s, and good repeatability and storage stability for a month, making it a promising candidate for sensitive, specific, and early diagnosis of AD. Thus, the fabricated electrochemical biosensor for BACE-1 detection improves detection performance compared to existing sensors as well as reduces detection time and cost, signifying its potential in early diagnosis of AD in clinical samples.

Detection of Glial Fibrillary Acidic Protein in Patient Plasma Using On-Chip Graphene Field-Effect Biosensors, in Comparison with ELISA and Single-Molecule Array

Glial fibrillary acidic protein (GFAP) is a discriminative blood biomarker for many neurological diseases, such as traumatic brain injury. Detection of GFAP in buffer solutions using biosensors has been demonstrated, but accurate quantification of GFAP in patient samples has not been reported, yet in urgent need. Herein, we demonstrate a robust on-chip graphene field-effect transistor (GFET) biosensing method for sensitive and ultrafast detection of GFAP in patient plasma. Patients with moderate–severe traumatic brain injuries, defined by the Mayo classification, are recruited to provide plasma samples. The binding of target GFAP with the specific antibodies that are conjugated on a monolayer GFET device triggers the shift of its Dirac point, and this signal change is correlated with the GFAP concentration in the patient plasma. The limit of detection (LOD) values of 20 fg/mL (400 aM) in buffer solution and 231 fg/mL (4 fM) in patient plasma have been achieved using this approach. In parallel, for the first time, we compare our results to the state-of-the-art single-molecule array (Simoa) technology and the classic enzyme-linked immunosorbent assay (ELISA) for reference. The GFET biosensor shows competitive LOD to Simoa (1.18 pg/mL) and faster sample-to-result time (<15 min), and also it is cheaper and more user-friendly. In comparison to ELISA, GFET offers advantages of total detection time, detection sensitivity, and simplicity. This GFET biosensing platform holds high promise for the point-of-care diagnosis and monitoring of traumatic brain injury in GP surgeries and patient homes.