Smart electrochemical sensing of amyloid-beta to manage total Alzheimer’s diseases

Need for Alzheimer’s disease progression monitoring: Alzheimer’s disease (AD) is an irreversible progressive brain disorder that causes severe and incurable neuro-impairment. The World Health Organization estimates that 55 million people are affected by AD dementia by 2020 which may exceed 78 million by 2030 and 139 in 2050. The estimated cost to manage AD is above US$ 1.3 trillion, which will further increase to US$ 2.8 trillion by 2030. According to Alzheimer’s Diseases International and Bright Focus Foundation, the world gets a new AD patient every 3 seconds, 60% of the dementia population belongs to low and middle-income countries, and this will increase to 71% by 2050.

The associated health risk factors (especially in the elderly population) and non-availability of therapy is continuously making AD more severe, as discussed by Ashton et al. (2021), Kaushik et al. (2016a, b), Le et al. (2022), and Tiwari et al. (2019). In clinical practices, AD diagnosis is based on clinical history and in-vivo brain imaging using magnetic resonance imaging, single-photon emission computed tomography, and metabolic positron emission tomography techniques. Neuro-psychological, cognitive, and neurological tests are also in practice to diagnose and understand AD progression. Unfortunately, these methods are time-consuming and expensive, and accuracy is limited to only ~85%. Recently, the detection of soluble biomarkers related to AD progression has also been explored for AD diagnostics applications (Kaushik et al., 2016a, b; Tiwari et al., 2019; Ashton et al., 2021). Such biomarkers involved tau and amyloid-beta (Aβ) protein levels in biofluids such as cerebrospinal fluid, saliva, or plasma (Kaushik et al., 2016a; Tiwari et al., 2019; Ashton et al., 2021). As per the amyloid hypothesis, Aβ (peptides with 1–40 and 1–42 amino acids residue) accumulation in AD brain-which cleaved out of the neuronal-expressed amyloid precursor protein, has been investigated as a major causative agent for AD progression. Irregular Aβ_1–40 & Aβ_1–42 levels cause neurotoxicity and induce oxidative stress in the AD brain. This results in neurodegeneration and finally causes dementia. Therefore, establishing Aβ_1–40 & Aβ_1–42 detection approaches that not only can perform AD diagnostic efficiently but also at point-of-care (POC) applications will be worth exploring. It is suggested that such sensing tools can also be tuned according to patient profiling which will be useful to manage AD in a personalized manner.

Exploring Aβ_1–40 & Aβ_1–42 biosensing to tackle total AD: Various nano-enabled magnetic, optical, and electrical systems have been explored Aβ_1–40 & Aβ_1–42 sensing using specific monoclonal antibodies and aptamers as Aβ specific bio-recognizing elements, supported by the reports of Abbasi et al. (2021), Carneiro et al. (2017), Le et al. (2022), Qin et al. (2021), Sharma et al. (2022), Xue et al. (2019), and Yu et al. (2015). However, efficient AD monitoring analytical tools for rapid AD diagnostics acceptably and affordably are not available yet. The level of both Aβ_1–40 & Aβ_1–42 varies in the brain because they can cross the blood-brain barrier to move to the periphery (Ashton et al., 2021). The changing level of Aβ confuses selecting an appropriate biomarker for AD monitoring. Therefore, the detection of two or more markers monitoring can give a better idea of AD diagnostics (Kaushik et al., 2016a, b; Tiwari et al., 2019; Ashton et al., 2021) – towards total AD management. In this direction, recently the detection of both Aβ_1–40 & Aβ_1–42 to measure total Aβ and the ratio of Aβ_1–40 to Aβ_1–42 to improve the accuracy of early-stage AD diagnostics. The outcomes of this approach are useful to establish a better prediction of an individual than based on a single biomarker-based analysis. Enzyme-linked immunosorbent assays and real-time polymerase chain reaction are the main laboratory techniques in practice to detect Aβ_1–40 & Aβ_1–42 concentration in clinics, discussed by Tiwari et al. (2019). However, no facility to detect Aβ_1–40 & Aβ_1–42 for AD monitoring is available for field use which can be promoted in POC applications. Therefore, there is a scope to explore nano-enabled chips technology for developing electrochemical sensing (ES) to detect Aβ (at low and pathological levels), especially at POC applications, useful for AD progression, as illustrated in Figure 1.

Figure 1.

An approach of total AD management using 2D@Chip@MP-based ES for sensing Aβ_1–40 & Aβ_1–42 detection selectively, rapidly, and at low levels.

Created with Microsoft PowerPoint and some of the components of Figure 1 are adopted from the open source: https://www.freepik.com/. 2D: Two dimensional; Aβ: amyloid beta; AD: Alzheimer’s disease; ES: electrochemical sensing; MP: miniaturized potentiostat.

Addressing challenges via sensing of Aβ proteins at POC: The electrochemical biosensing system has the potential to be miniaturized to perform sensing of Aβ_1–40 or Aβ_1–42, but not at the same time, to generate bioinformatics needed for efficient AD disease management. Besides outstanding performance, the electrochemical immunosensing (E-IS) of Aβ_1–40 & Aβ_1–42 is facing the issue of electrical properties variation (batch to batch), lacking scaling-up production, and not being optimized for point-of-location testing. Therefore, developing E-IS of reduced form factors, suitable for large-scale production and performing diagnostics at POC is the need for next-generation sensing technology. Therefore, ES supported by a 2D nanotechnology-based chip is emerging and useful for the selective detection of a biomarker at a low level. Such 2D/Nano@Chip systems can be miniaturized by adopting an appropriate fabrication approach and interfacing of chips with smart electronics [mainly miniaturized potentiostat (MP) operated using smartphone] using the Internet of medical things to establish and perform biosensing at POC applications as reported and demonstrated by Kaushik et al. (2021), Kaushik and Mostafavi (2022), and Manickam et al. (2022). Such generated informatics can be more valuable once analyzed using artificial intelligence, as reported by Kaushik et al. (2021), Kaushik and Mostafavi (2022), and Manickam et al. (2022) for prediction and decision-making strategies. Therefore, ES supported by artificial intelligence and Internet of medical things is projected as the future of AD management due to timely sensing outcomes which could support therapy decisions and optimization. Currently, a few ES systems are available for Aβ_1–40 & Aβ_1–42 (Yu et al., 2015; Carneiro et al., 2017; Xue et al., 2019; Abbasi et al., 2021; Qin et al., 2021; Sharma et al., 2022) detection but their validation using real samples needs more focused research. Besides, some available ES are detecting Aβ_1–40 & Aβ_1–42 (Yu et al., 2015) at the same time but are far from adopting them as a part of AD management. These research developments are at an early stage and more detailed studies are suggested before recommending them for clinical application. A nano-enabled ES approach can be established in such scenarios for selective detection of Aβ_1–40 & Aβ_1–42 protein structure using specific antibodies. However, none of such platforms detects both the markers at the same time and lacks performance at POC diagnostics.

It is anticipated that an efficient 2D@Nano@Chip-based (reported by Kaushik et al., 2021; Kaushik and Mostafavi, 2022; Manickam et al., 2022) ES technology is urgently required to detect Aβ_1–40 & Aβ_1–42 in bio-fluids for total AD diagnostics and progression understanding. Our research group has previously developed an ES methodology for Aβ_1–40 detection at the pM level but lacks interfacing with MP and smartphone to perform sensing at POC application. Therefore, there is considerable scope to upgrade state-of-art E-IS strategies to meet the expectation of managing AD management via detecting Aβ efficiently. One such upgradation is introducing 2D materials like Mxenes, electrically reduced graphene oxide, etc. will be advantageous, as a desired sensing platform to generate suitable functionality for higher loading of bio-active molecules (such as DNA, Aptamers, and antibody), identical electrical properties in every batch, and chip fabrication using electrophoretic deposition (suitable for large scale). In this direction, we are proposing to upgrade our state-of-the-art ES technology via introducing high-performance 2D@Chip as a sensing platform (suitable for scaling up with control over electrical and surface properties) for rapid (20–30 minutes) detection of βA_1–40 & βA_1–42 selectively for efficient AD diagnostics. Such suggested sensing can be performed at POC as the developed 2D@Nano@chip can be interfaced with a MP, which can be operated using a smartphone, as proposed by (Kaushik et al., 2021; Kaushik and Mostafavi, 2022).

In the future, it is suggested to develop a 2D@Chip@M-P@Sm-based electrochemical Aβ_1–40 & Aβ_1–42 sensing systems, suggested for total AD diagnostics. We propose 2D@Chip (highly electroactive surface with tunable surface chemistry) modified miniaturized electrodes, fabricated using electrophoretic deposition, for the immobilization of specific anti-Aβ monoclonal antibodies, aptamers, or peptides for selective detecting both Aβ_1–40 & Aβ_1–42 (at pM level within 20 minutes) separately and simultaneously. The low-level production or variation of Aβ (at pM levels) in the AD brain affects neuronal dysfunction of AD patients and its monitoring is essential for AD management. Thus, developing a 2D@Chip@M-P@Sm platform can be promoted as an efficient analytical tool for detecting Aβ_1–40 & Aβ_1–42 in the physiological range (µM) along with at low level (pM or sub pM level) is an urgent need for early-stage AD diagnostics, therapy efficacy assessment, and risks assessment understanding. Although, available analytical methods such as neuroimaging (magnetic resonance imaging, positron emission tomography, and single-photon emission computed tomography), enzyme-linked immunosorbent assays, and PCR are available in clinical practice for Aβ detection for AD management (Kaushik et al., 2016a, b; Tiwari et al., 2019). But these approaches are time-consuming, need expertise, exhibit detection limit at the nM level, and are not suitable for POC testing which is seriously required to manage AD for the elderly population. Besides, these state-of-art methods are limited to the laboratory only and are unable to detect low levels of Aβ (pM). Therefore, the development of a cost-effective and sensitive E-IS system to detect Aβ_1–40 & Aβ_1–42 rapidly (within 20 minutes) ranging from pM to µM level at POC is suggested in this editorial. However, this perspective proposed the need for well-coordinated efforts involving multi-institutional and multi-investigators for developing well-calibrated and well-validated ES for understanding, monitoring, and managing total AD.

Ajeet Kaushik acknowledges Florida Polytechnic University, Lakeland, USA for providing support and facilities.