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

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.