Research progress of biosensors for detection of SARS-CoV-2 variants based on ACE2

Development of Ni-ZnO-ACE-2 peptide hybrids as electrochemical devices for SARS-CoV-2 spike protein detection

Owing to fast SARS-CoV-2 mutations, biosensors employing antibodies as biorecognition elements have presented problems with sensitivity and accuracy. To face these challenges, antibodies can be replaced with the human angiotensin converting enzyme 2 (ACE-2), where it has been shown that the affinity between ACE-2 and the receptor binding domain (RBD) increases with the emergence of new variants. Herein, we report on Ni-doped ZnO nanorod electrochemical biosensors employing an ACE-2 peptide (IEEQAKTFLDKFNHEAEDLFYQS-NH2) as a biorecognition element for detecting Spike (S) Wild-Type (WT) protein. The electrode was fully characterized in terms of electrochemical and physical properties. The sensor showed high cross reactivity with Spike protein B.1.1.7 and Spike protein B.1.351. Still, there was no cross reactivity with the Nucleocapsid protein WT, showing that the biosensor can identify ancestral WT S protein and S protein variants of concern. The device exhibited a LOD of 60.13 ng mL-1 across an S protein WT concentration range from 200 ng mL-1 to 1000 ng mL-1 and a LOQ of 182.22 ng mL-1. The calculated sensitivity and specificity were 88.88 and 100 %, respectively. These results proved that the Ni-ZnO sensor has promising prospects for SARS-CoV-2 detection and diagnosis of other viruses, employing peptides as biorecognition elements.

An ACE2 PET imaging agent derived from 18F/Cl exchange of MLN-4760 under phase transfer catalysis

BACKGROUND

Angiotensin-converting enzyme-2 (ACE2) acts as a key regulatory molecule and important therapeutic target in the pathological remodeling of numerous organs and diseases. In this study, a rapid, simple, and efficient synthetic route with a catalytic, 18F-for-Cl (18F/Cl) exchange scheme was designed for the preparation of 18F-labeled MLN-4760, and its targeting ability was investigated in a humanized ACE2 mouse model.

RESULTS

A novel 18F-labeled MLN-4760 radioligand, abbreviated as 18F-MLN-4760, was successfully synthesized by the 18F/Cl exchange-labeling, and was purified by SepPak C18 columns with a radiochemical yield of 30% and a radiochemical purity of 29.89%. Target distribution of 18F-MLN-4760 in several organs with high ACE2 expression was observed by PET imaging with good stability over 120 min. The biodistribution data showed that the uptake of 18F-MLN-4760 in ACE2-overexpressing organs and tissues was highly specific, and immunohistochemistry confirmed the same results of ACE2 expression and biodistribution in the major organs (heart, liver, lungs, and kidneys). There was a good correlation between the uptake in the organs with high ACE2 expression and ACE2 expression levels (r = 0.935).

CONCLUSION

18F-MLN-4760 was successfully synthesized via 18F/Cl exchange under phase transfer catalysis, and served as a potential probe for ACE2-targeted PET imaging.

Discrimination between Covid-19 positive and negative blood serum based on excitation-emission matrix fluorescence spectroscopy and chemometrics

The outbreak of the disease caused by the SARS-CoV-2 virus (Covid-19) has resulted in a global health emergency that has caused millions of deaths in recent years. The control of the pandemic was significantly impacted by the availability of inputs and qualified labor to correctly diagnose the population. The challenges faced by numerous countries in conducting this extensive diagnosis, utilizing methods such as RT-PCR, emphasize the necessity for alternative testing strategies that are less reliant on expensive raw materials and can be implemented on a larger scale. This paper proposes a methodology for classifying blood serum samples as either positive or negative for Covid-19 infection using excitation-emission matrix (EEM) fluorescence spectroscopy associated with multivariate analysis. The proposed methodology uses EEM spectra of samples diagnosed by the reference method (RT-PCR) to train and validate classification models. Two approaches were tested: the first using PARAFAC and the second by unfolding the excitation-emission matrices. The DD-SIMCA model performed best in the PARAFAC approach, with an error rate of 0.05, sensitivity of 0.98 and specificity of 0.96. The PLS-DA and PCA-DA models in the second approach effectively distinguished between classes. The PCA-DA model performed the best with an error rate of 0.06 and sensitivity and specificity of 0.94. Fluorescence spectroscopy was found to be effective in analyzing serum samples and obtaining discrimination models to determine if a patient is infected with SARS-CoV-2. The findings are encouraging and could aid in the development of an inexpensive and reliable auxiliary diagnostic method.

Development of a unit conversion tool for five quantitative anti-spike assays and agreement analysis of three qualitative anti-nucleocapsid assays for SARS-CoV-2

Commercially available assays for measuring severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) anti-spike (S) or anti-nucleocapsid (N) antibodies differ in units, making results comparisons challenging. This study aimed to develop conversion equations between five quantitative anti-S antibody tests and to assess the agreement over time between three qualitative anti-N antibody tests. Blood samples from 24 216 vaccinated healthcare workers in Hiroshima Prefecture, Japan, were analyzed for anti-S antibodies using five quantitative tests (Abbott, Fujirebio, Ortho, Sysmex, Roche) and for anti-N antibodies using three qualitative tests (Abbott, Sysmex, Roche). Geometric mean regression was performed to establish equations for converting measured values between the five quantitative tests. Fleiss κ statistic was used to assess the agreement between the three qualitative tests. A strong correlation (Pearson's coefficient r > 0.9) was found for each pair of the five quantitative tests measuring anti-S antibodies, enabling the development of equations to convert values between each pair. Using these equations, which are based on the original output unit of each test, values obtained from one test can be transformed to be equivalent to the corresponding values in another test. For the three tests for anti-N antibodies, the agreement was substantial in the total sample (Fleiss' κ, 0.74) and moderate among those with self-reported past coronavirus disease 2019 (COVID-19) infection (Fleiss' κ, 0.39). The agreement decreased with time after infection. Reduced agreement between anti-N antibodies tests over time suggests caution in comparing seroepidemiological studies of COVID-19 exposure based on anti-N antibodies measurement. The findings could help improve antibody measurement systems and inform public health decision-makers.

Rapid Detection of SARS-CoV-2 Variants Using an Angiotensin-Converting Enzyme 2-Based Surface-Enhanced Raman Spectroscopy Sensor Enhanced by CoVari Deep Learning Algorithms

An integrated approach combining surface-enhanced Raman spectroscopy (SERS) with a specialized deep learning algorithm to rapidly and accurately detect and quantify SARS-CoV-2 variants is developed based on an angiotensin-converting enzyme 2 (ACE2)-functionalized AgNR@SiO2 array SERS sensor. SERS spectra with concentrations of different variants were collected using a portable Raman system. After appropriate spectral preprocessing, a deep learning algorithm, CoVari, is developed to predict both the viral variant species and concentrations. Using a 10-fold cross-validation strategy, the model achieves an average accuracy of 99.9% in discriminating between different virus variants and R2 values larger than 0.98 for quantifying viral concentrations of the three viruses, demonstrating the high quality of the detection. The limit of detection of the ACE2 SERS sensor is determined to be 10.472, 11.882, and 21.591 PFU/mL for SARS-CoV-2, SARS-CoV-2 B1, and CoV-NL63, respectively. The feature importance of virus classification and concentration regression in the CoVari algorithm are calculated based on a permutation algorithm, which showed a clear correlation to the biochemical origins of the spectra or spectral changes. In an unknown specimen test, classification accuracy can achieve >90% for concentrations larger than 781 PFU/mL, and the predicted concentrations consistently align with actual values, highlighting the robustness of the proposed algorithm. Based on the CoVari architecture and the output vector, this algorithm can be generalized to predict both viral variant species and concentrations simultaneously for a broader range of viruses. These results demonstrate that the SERS + CoVari strategy has the potential for rapid and quantitative detection of virus variants and potentially point-of-care diagnostic platforms.

Cutting-edge biorecognition strategies to boost the detection performance of COVID-19 electrochemical biosensors: A review

Electrochemical biosensors are known for their high sensitivity, selectivity, and low cost. Recently, they have gained significant attention and became particularly important as promising tools for the detection of COVID-19 biomarkers, since they offer a rapid and accurate means of diagnosis. Biorecognition strategies are a crucial component of electrochemical biosensors and determine their specificity and sensitivity based on the interaction of biological molecules, such as antibodies, enzymes, and DNA, with target analytes (e.g., viral particles, proteins and genetic material) to create a measurable signal. Different biorecognition strategies have been developed to enhance the performance of electrochemical biosensors, including direct, competitive, and sandwich binding, alongside nucleic acid hybridization mechanisms and gene editing systems. In this review article, we present the different strategies used in electrochemical biosensors to target SARS-CoV-2 and other COVID-19 biomarkers, as well as explore the advantages and disadvantages of each strategy and highlight recent progress in this field. Additionally, we discuss the challenges associated with developing electrochemical biosensors for clinical COVID-19 diagnosis and their widespread commercialization.

Unraveling the Dynamics of SARS-CoV-2 Mutations: Insights from Surface Plasmon Resonance Biosensor Kinetics

Surface Plasmon Resonance (SPR) technology is known to be a powerful tool for studying biomolecular interactions because it offers real-time and label-free multiparameter analysis with high sensitivity. This article summarizes the results that have been obtained from the use of SPR technology in studying the dynamics of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) mutations. This paper will begin by introducing the working principle of SPR and the kinetic parameters of the sensorgram, which include the association rate constant (ka), dissociation rate constant (kd), equilibrium association constant (KA), and equilibrium dissociation constant (KD). At the end of the paper, we will summarize the kinetic data on the interaction between angiotensin-converting enzyme 2 (ACE2) and SARS-CoV-2 obtained from the results of SPR signal analysis. ACE2 is a material that mediates virus entry. Therefore, understanding the kinetic changes between ACE2 and SARS-CoV-2 caused by the mutation will provide beneficial information for drug discovery, vaccine development, and other therapeutic purposes.

Low-Cost Biosensor Technologies for Rapid Detection of COVID-19 and Future Pandemics

Many systems have been designed for the detection of SARS-CoV-2, which is the virus that causes COVID-19. SARS-CoV-2 is readily transmitted, resulting in the rapid spread of disease in human populations. Frequent testing at the point of care (POC) is a key aspect for controlling outbreaks caused by SARS-CoV-2 and other emerging pathogens, as the early identification of infected individuals can then be followed by appropriate measures of isolation or treatment, maximizing the chances of recovery and preventing infectious spread. Diagnostic tools used for high-frequency testing should be inexpensive, provide a rapid diagnostic response without sophisticated equipment, and be amenable to manufacturing on a large scale. The application of these devices should enable large-scale data collection, help control viral transmission, and prevent disease propagation. Here we review functional nanomaterial-based optical and electrochemical biosensors for accessible POC testing for COVID-19. These biosensors incorporate nanomaterials coupled with paper-based analytical devices and other inexpensive substrates, traditional lateral flow technology (antigen and antibody immunoassays), and innovative biosensing methods. We critically discuss the advantages and disadvantages of nanobiosensor-based approaches compared to widely used technologies such as PCR, ELISA, and LAMP. Moreover, we delineate the main technological, (bio)chemical, translational, and regulatory challenges associated with developing functional and reliable biosensors, which have prevented their translation into the clinic. Finally, we highlight how nanobiosensors, given their unique advantages over existing diagnostic tests, may help in future pandemics.

Development of practical techniques for simultaneous detection and distinction of current and emerging SARS-CoV-2 variants

Countless individuals have fallen victim to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and have generated antibodies, reducing the risk of secondary infection in the short term. However, with the emergence of mutated strains, the probability of subsequent infections remains high. Consequently, the demand for simple and accessible methods for distinguishing between different variants is soaring. Although monitoring viral gene sequencing is an effective approach for differentiating between various types of SARS-CoV-2 variants, it may not be easily accessible to the general public. In this article, we provide an overview of the reported techniques that use combined approaches and adaptable testing methods that use editable recognition receptors for simultaneous detection and distinction of current and emerging SARS-CoV-2 variants. These techniques employ straightforward detection strategies, including tests capable of simultaneously identifying and differentiating between different variants. Furthermore, we recommend advancing the development of uncomplicated protocols for distinguishing between current and emerging variants. Additionally, we propose further development of facile protocols for the differentiation of existing and emerging variants.

Machine Learning Techniques for Effective Pathogen Detection Based on Resonant Biosensors

We describe a machine learning (ML) approach to processing the signals collected from a COVID-19 optical-based detector. Multilayer perceptron (MLP) and support vector machine (SVM) were used to process both the raw data and the feature engineering data, and high performance for the qualitative detection of the SARS-CoV-2 virus with concentration down to 1 TCID50/mL was achieved. Valid detection experiments contained 486 negative and 108 positive samples, and control experiments, in which biosensors without antibody functionalization were used to detect SARS-CoV-2, contained 36 negative samples and 732 positive samples. The data distribution patterns of the valid and control detection dataset, based on T-distributed stochastic neighbor embedding (t-SNE), were used to study the distinguishability between positive and negative samples and explain the ML prediction performance. This work demonstrates that ML can be a generalized effective approach to process the signals and the datasets of biosensors dependent on resonant modes as biosensing mechanism.

68Ga-cyc-DX600 PET/CT in ACE2-targeted tumor imaging

PURPOSE

For the tumor-specific ACE2 expression, this research aimed to establish and verify ACE2-targeted PET imaging in differentiating tumors with distinct ACE2 expression.

METHODS

68Ga-cyc-DX600 was synthesized as tracer of ACE2 PET. NOD-SCID mice were used to prepare the subcutaneous tumor models with HEK-293 or HEK-293T/hACE2 cells to verify ACE2 specificity, with other kinds of tumor cells to evaluate the diagnostic efficiency for ACE2 expression, additionally, immunohistochemical analysis and western blot were used to certify the findings on ACE2 PET, which was then performed on four cancer patients and compared with FDG PET.

RESULTS

The metabolic clearance of 68Ga-cyc-DX600 was initially completed in 60 min, realizing an ACE2-dependent and organ-specific background of ACE2 PET; meanwhile, tracer uptake of subcutaneous tumor models was of a definite dependence on ACE2 expression (r = 0.903, p < 0.05), and the latter served as the primary factor when ACE2 PET was used for the differential diagnosis of ACE2-related tumors. In pre-clinical practice, a comparable tumor-to-background ratio was acquired in ACE2 PET of a lung cancer patient at 50 and 80 min post injection; the quantitative values of ACE2 PET and FDG PET were negatively correlated (r =  - 0.971 for SUVmax, p = 0.006; r =  - 0.994 for SUVmean, p = 0.001) in an esophageal cancer patient, no matter the primary lesion or metastasis.

CONCLUSIONS

68Ga-cyc-DX600 PET was an ACE2-specific imaging for the differential diagnosis of tumors and added complementary value to conventional nuclear medicine diagnosis, such as FDG PET on glycometabolism.

Next-generation nanophotonic-enabled biosensors for intelligent diagnosis of SARS-CoV-2 variants

Constantly mutating SARS-CoV-2 is a global concern resulting in COVID-19 infectious waves from time to time in different regions, challenging present-day diagnostics and therapeutics. Early-stage point-of-care diagnostic (POC) biosensors are a crucial vector for the timely management of morbidity and mortalities caused due to COVID-19. The state-of-the-art SARS-CoV-2 biosensors depend upon developing a single platform for its diverse variants/biomarkers, enabling precise detection and monitoring. Nanophotonic-enabled biosensors have emerged as 'one platform' to diagnose COVID-19, addressing the concern of constant viral mutation. This review assesses the evolution of current and future variants of the SARS-CoV-2 and critically summarizes the current state of biosensor approaches for detecting SARS-CoV-2 variants/biomarkers employing nanophotonic-enabled diagnostics. It discusses the integration of modern-age technologies, including artificial intelligence, machine learning and 5G communication with nanophotonic biosensors for intelligent COVID-19 monitoring and management. It also highlights the challenges and potential opportunities for developing intelligent biosensors for diagnosing future SARS-CoV-2 variants. This review will guide future research and development on nano-enabled intelligent photonic-biosensor strategies for early-stage diagnosing of highly infectious diseases to prevent repeated outbreaks and save associated human mortalities.

SARS-CoV-2 multi-variant rapid detector based on graphene transistor functionalized with an engineered dimeric ACE2 receptor

Reliable point-of-care (POC) rapid tests are crucial to detect infection and contain the spread of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). The emergence of several variants of concern (VOC) can reduce binding affinity to diagnostic antibodies, limiting the efficacy of the currently adopted tests, while showing unaltered or increased affinity for the host receptor, angiotensin converting enzyme 2 (ACE2). We present a graphene field-effect transistor (gFET) biosensor design, which exploits the Spike-ACE2 interaction, the crucial step for SARS-CoV-2 infection. Extensive computational analyses show that a chimeric ACE2-Fragment crystallizable (ACE2-Fc) construct mimics the native receptor dimeric conformation. ACE2-Fc functionalized gFET allows in vitro detection of the trimeric Spike protein, outperforming functionalization with a diagnostic antibody or with the soluble ACE2 portion, resulting in a sensitivity of 20 pg/mL. Our miniaturized POC biosensor successfully detects B.1.610 (pre-VOC), Alpha, Beta, Gamma, Delta, Omicron (i.e., BA.1, BA.2, BA.4, BA.5, BA.2.75 and BQ.1) variants in isolated viruses and patient's clinical nasopharyngeal swabs. The biosensor reached a Limit Of Detection (LOD) of 65 cps/mL in swab specimens of Omicron BA.5. Our approach paves the way for a new and reusable class of highly sensitive, rapid and variant-robust SARS-CoV-2 detection systems.