Catching COVID: Engineering Peptide-Modified Surface-Enhanced Raman Spectroscopy Sensors for SARS-CoV-2

Detection strategies of infectious diseases via peptide-based electrochemical biosensors

Infectious diseases have threatened human life for as long as humankind has existed. One of the most crucial aspects of fighting against these infections is diagnosis to prevent disease spread. However, traditional diagnostic methods prove insufficient and time-consuming in the face of a pandemic. Therefore, studies focusing on detecting viruses causing these diseases have increased, with a particular emphasis on developing rapid, accurate, specific, user-friendly, and portable electrochemical biosensor systems. Peptides are used integral components in biosensor fabrication for several reasons, including various and adaptable synthesis protocols, long-term stability, and specificity. Here, we discuss peptide-based electrochemical biosensor systems that have been developed over the last decade for the detection of infectious diseases. In contrast to other reports on peptide-based biosensors, we have emphasized the following points i) the synthesis methods of peptides for biosensor applications, ii) biosensor fabrication approaches of peptide-based electrochemical biosensor systems, iii) the comparison of electrochemical biosensors with other peptide-based biosensor systems and the advantages and limitations of electrochemical biosensors, iv) the pros and cons of peptides compared to other biorecognition molecules in the detection of infectious diseases, v) different perspectives for future studies with the shortcomings of the systems developed in the past decade.

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.

Advances in nanobiosensors during the COVID-19 pandemic and future perspectives for the post-COVID era

The unprecedented threat of the highly contagious virus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes exponentially increased infections of coronavirus disease 2019 (COVID-19), highlights the weak spots of the current diagnostic toolbox. In the midst of catastrophe, nanobiosensors offer a new opportunity as an alternative tool to fill a gap among molecular tests, rapid antigen tests, and serological tests. Nanobiosensors surpass the potential of antigen tests because of their enhanced sensitivity, thus enabling us to see antigens as stable and easy-to-access targets. During the first three years of the COVID-19 pandemic, a substantial number of studies have reported nanobiosensors for the detection of SARS-CoV-2 antigens. The number of articles on nanobiosensors and SARS-CoV-2 exceeds the amount of nanobiosensor research on detecting previous infectious diseases, from influenza to SARS-CoV and MERS-CoV. This unprecedented publishing pace also implies the significance of SARS-CoV-2 and the present pandemic. In this review, 158 studies reporting nanobiosensors for detecting SARS-CoV-2 antigens are collected to discuss the current challenges of nanobiosensors using the criteria of point-of-care (POC) diagnostics along with COVID-specific issues. These advances and lessons during the pandemic pave the way for preparing for the post-COVID era and potential upcoming infectious diseases.

The Label-Free Detection and Identification of SARS-CoV-2 Using Surface-Enhanced Raman Spectroscopy and Principal Component Analysis

The World Health Organization (WHO) declared in a May 2023 announcement that the COVID-19 illness is no longer categorized as a Public Health Emergency of International Concern (PHEIC); nevertheless, it is still considered an actual threat to world health, social welfare and economic stability. Consequently, the development of a convenient, reliable and affordable approach for detecting and identifying SARS-CoV-2 and its emerging new variants is crucial. The fingerprint and signal amplification characteristics of surface-enhanced Raman spectroscopy (SERS) could serve as an assay scheme for SARS-CoV-2. Here, we report a machine learning-based label-free SERS technique for the rapid and accurate detection and identification of SARS-CoV-2. The SERS spectra collected from samples of four types of coronaviruses on gold nanoparticles film, fabricated using a Langmuir-Blodgett self-assembly, can provide more spectroscopic signatures of the viruses and exhibit low limits of detection (<100 TCID50/mL or even <10 TCID50/mL). Furthermore, the key Raman bands of the SERS spectra were systematically captured by principal component analysis (PCA), which effectively distinguished SARS-CoV-2 and its variant from other coronaviruses. These results demonstrate that the combined use of SERS technology and PCA analysis has great potential for the rapid analysis and discrimination of multiple viruses and even newly emerging viruses without the need for a virus-specific probe.

From the lab to the field: handheld surface enhanced Raman spectroscopy (SERS) detection of viral proteins

Translating sensors from the lab benchtop to a readily available point-of-need setting is desirable for many fields, including medicine, agriculture, and industry. However, this transition generally suffers from loss of sensitivity, high background signals, and other issues which can impair reproducibility. Here we adapt a label-free surface-enhanced Raman spectroscopy (SERS) sensor for SARS-CoV-2 antigens from a lab-based assay to a handheld device. Utilizing a peptide capture molecule, which we previously employed for a surface-based assay, we optimize a simpler and more cost-efficient nanoparticle-based assay. This new assay allows for the direct detection of these viral antigens by SERS, now with the advantages of robustness and portability. We highlight considerations for nanoparticle modification conditions and warn against methods which can interfere with accurate detection. The comparison of these two assays will help guide further development of SERS-based sensors into devices that can be easily used in point-of-care settings, such as by emergency room nurses, farmers, or quality control technicians.

A point-of-care microfluidic biosensing system for rapid and ultrasensitive nucleic acid detection from clinical samples

Rapid and ultrasensitive point-of-care RNA detection plays a critical role in the diagnosis and management of various infectious diseases. The gold-standard detection method of reverse transcription-quantitative polymerase chain reaction (RT-qPCR) is ultrasensitive and accurate yet limited by the lengthy turnaround time (1-2 days). On the other hand, an antigen test offers rapid at-home detection (typically ~15 min) but suffers from low sensitivity and high false-negative rates. An ideal point-of-care diagnostic device would combine the merits of PCR-level sensitivity and rapid sample-to-result workflow comparable to antigen testing. However, the existing detection platforms typically possess superior sensitivity or rapid sample-to-result time, but not both. This paper reports a point-of-care microfluidic device that offers ultrasensitive yet rapid detection of viral RNA from clinical samples. The device consists of a microfluidic chip for precisely manipulating small volumes of samples, a miniaturized heater for viral lysis and ribonuclease inactivation, a Cas13a-electrochemical sensor for target preamplification-free and ultrasensitive RNA detection, and a smartphone-compatible potentiostat for data acquisition. As demonstrations, the devices achieve the detection of heat-inactivated SARS-CoV-2 samples with a limit of detection down to 10 aM within 25 minutes, which is comparable to the sensitivity of RT-PCR and rapidness of an antigen test. The platform also successfully distinguishes all nine positive unprocessed clinical SARS-CoV-2 nasopharyngeal swab samples from four negative samples within 25 minutes of sample-to-result time. Together, this device provides a point-of-care solution that can be deployed in diverse settings beyond laboratory environments for rapid and accurate detection of RNA from clinical samples. The device can potentially be expandable to detect other viral targets, such as human immunodeficiency virus self-testing and Zika virus, where rapid and ultrasensitive point-of-care detection is required.

Magnetic polyphosphazene@Au particles as substrates for multiple-detection of immunoproteins by surface-enhanced Raman spectroscopy

Au coated magnetic polyphosphazene (MPCTP) composite particles (MPCTP@Au) were fabricated with sensitive SERS activity. The MPCTP particles were generated by coating polyphosphazene on Fe₃O₄ nanoparticles through precipitation polycondensation of hexachlorocyclotriphosphazene and phloroglucinol. MPCTP@Au composite particles were obtained by deposition of Au nanoparticles on MPCTP by the reduction of HAuCl₄. The size and the thickness of the Au shell can be controlled by varying the amount of HAuCl₄. The magnetic core endowed the composite particles with good magnetic responsiveness, which allowed the analyte to be enriched and separated from the complex matrix, and significantly simplifying the sample pretreatment procedure. The SERS activity of MPCTP@Au composite particles were evaluated by DTNB as model Raman reporter, and the limits of detection (LOD) of DTNB was 10^-8 mol/L. A high efficient SERS immunoassay system based on the MPCTP@Au substrates for the detection of immunoproteins was developed. Human IgG and rabbit IgG were quantitatively determinated simultaneously by this immunoassay system. The quantitative determination of the immunoglobulin G (IgG) was achieved and the LOD of human IgG, rabbit IgG and the mixture of human IgG and rabbit IgG were as low as 10 fg/mL, 100 pg/mL and 1 ng/mL, respectively. The results showed that the MPCTP@Au composite particles have broad application prospects as high performance SERS active substrates for immunoprotein analysis.

Raman and fourier transform infrared spectroscopy techniques for detection of coronavirus (COVID-19): a mini review

Coronavirus pandemic has been a huge jeopardy to human health in various systems since it outbroke, early detection and prevention of further escalation has become a priority. The current popular approach is to collect samples using the nasopharyngeal swab method and then test for RNA using the real-time polymerase chain reaction, which suffers from false-positive results and a longer diagnostic time scale. Alternatively, various optical techniques, namely, optical sensing, spectroscopy, and imaging shows a great promise in virus detection. In this mini review, we briefly summarize the development progress of vibrational spectroscopy techniques and its applications in the detection of SARS-CoV family. Vibrational spectroscopy techniques such as Raman spectroscopy and infrared spectroscopy received increasing appreciation in bio-analysis for their speediness, accuracy and cost-effectiveness in detection of SARS-CoV. Further, an account of emerging photonics technologies of SARS-CoV-2 detection and future possibilities is also explained. The progress in the field of vibrational spectroscopy techniques for virus detection unambiguously show a great promise in the development of rapid photonics-based devices for COVID-19 detection.

Recent development of surface-enhanced Raman scattering for biosensing

Surface-Enhanced Raman Scattering (SERS) technology, as a powerful tool to identify molecular species by collecting molecular spectral signals at the single-molecule level, has achieved substantial progresses in the fields of environmental science, medical diagnosis, food safety, and biological analysis. As deepening research is delved into SERS sensing, more and more high-performance or multifunctional SERS substrate materials emerge, which are expected to push Raman sensing into more application fields. Especially in the field of biological analysis, intrinsic and extrinsic SERS sensing schemes have been widely used and explored due to their fast, sensitive and reliable advantages. Herein, recent developments of SERS substrates and their applications in biomolecular detection (SARS-CoV-2 virus, tumor etc.), biological imaging and pesticide detection are summarized. The SERS concepts (including its basic theory and sensing mechanism) and the important strategies (extending from nanomaterials with tunable shapes and nanostructures to surface bio-functionalization by modifying affinity groups or specific biomolecules) for improving SERS biosensing performance are comprehensively discussed. For data analysis and identification, the applications of machine learning methods and software acquisition sources in SERS biosensing and diagnosing are discussed in detail. In conclusion, the challenges and perspectives of SERS biosensing in the future are presented.

Chemical conjugation to differentiate monosaccharides by Raman and surface enhanced Raman spectroscopy

Sugars play important roles in numerous biological processes, from providing energy to modifying proteins to alter their function. Glycosylation, the attachment of a sugar residue to a protein, is the most common post translational modification. Identifying the glycans on a protein is a useful tool both for pharmaceutical development as well as probing the proteome and glycome further. Sugars, however, are difficult analytes to probe due to their isomeric nature. In this work, Raman spectroscopy and surface enhanced Raman spectroscopy (SERS) are used to identify different monosaccharide species based on the vibrational modes of these isomeric analytes. The weak scattering of the sugars was overcome through conjugation with phenylboronic acid to provide a larger Raman scattering cross section and induce slight changes in the observed spectra associated with the structure of the monosaccharides. Spontaneous Raman, SERS in flow, and static SERS detection were performed in order to discriminate between arabinose, fructose, galactose, glucose, mannose, and ribose, as well as provide a method for identification and quantification for these sugar conjugates.

Label-free detection and discrimination of respiratory pathogens based on electrochemical synthesis of biomaterials-mediated plasmonic composites and machine learning analysis

Seasonal outbreaks of respiratory viral infections remain a global concern, with increasing morbidity and mortality rates recorded annually. Timely and false responses contribute to the widespread of respiratory pathogenic diseases owing to similar symptoms at an early stage and subclinical infection. The prevention of emerging novel viruses and variants is also a big challenge. Reliable point-of-care diagnostic assays for early infection diagnosis play a critical role in the response to threats of epidemics or pandemics. We developed a facile method for specifically identifying different viruses based on surface-enhanced Raman spectroscopy (SERS) with pathogen-mediated composite materials on Au nanodimple electrodes and machine learning (ML) analyses. Virus particles were trapped in three-dimensional plasmonic concave spaces of the electrode via electrokinetic preconcentration, and Au films were simultaneously electrodeposited, leading to the acquisition of intense and in-situ SERS signals from the Au-virus composites for ultrasensitive SERS detection. The method was useful for rapid detection analysis (<15 min), and the ML analysis for specific identification of eight virus species, including human influenza A viruses (i.e., H1N1 and H3N2 strains), human rhinovirus, and human coronavirus, was conducted. The highly accurate classification was achieved using the principal component analysis-support vector machine (98.9%) and convolutional neural network (93.5%) models. This ML-associated SERS technique demonstrated high feasibility for direct multiplex detection of different virus species for on-site applications.

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.

Plasmonic nanorod probes' journey inside plant cells for in vivo SERS sensing and multimodal imaging

Nanoparticle-based platforms are gaining strong interest in plant biology and bioenergy research to monitor and control biological processes in whole plants. However, in vivo monitoring of biomolecules using nanoparticles inside plant cells remains challenging due to the impenetrability of the plant cell wall to nanoparticles beyond the exclusion limits (5-20 nm). To overcome this physical barrier, we have designed unique bimetallic silver-coated gold nanorods (AuNR@Ag) capable of entering plant cells, while conserving key plasmonic properties in the near-infrared (NIR). To demonstrate cellular internalization and tracking of the nanorods inside plant tissue, we used a comprehensive multimodal imaging approach that included transmission electron microscopy (TEM), confocal fluorescence microscopy, two-photon luminescence (TPL), X-ray fluorescence microscopy (XRF), and photoacoustics imaging (PAI). We successfully acquired SERS signals of nanorods in vivo inside plant cells of tobacco leaves. On the same leaf samples, we applied orthogonal imaging methods, TPL and PAI techniques for in vivo imaging of the nanorods. This study first demonstrates the intracellular internalization of AuNR@Ag inside whole plant systems for in vivo SERS analysis in tobacco cells. This work demonstrates the potential of this nanoplatform as a new nanotool for intracellular in vivo biosensing for plant biology.

Label-Free Saliva Test for Rapid Detection of Coronavirus Using Nanosensor-Enabled SERS

The recent COVID-19 pandemic has highlighted the inadequacies of existing diagnostic techniques and the need for rapid and accurate diagnostic systems. Although molecular tests such as RT-PCR are the gold standard, they cannot be employed as point-of-care testing systems. Hence, a rapid, noninvasive diagnostic technique such as Surface-enhanced Raman scattering (SERS) is a promising analytical technique for rapid molecular or viral diagnosis. Here, we have designed a SERS- based test to rapidly diagnose SARS-CoV-2 from saliva. Physical methods synthesized the nanostructured sensor. It significantly increased the detection specificity and sensitivity by ~ten copies/mL of viral RNA (~femtomolar concentration of nucleic acids). Our technique combines the multiplexing capability of SERS with the sensitivity of novel nanostructures to detect whole virus particles and infection-associated antibodies. We have demonstrated the feasibility of the test with saliva samples from individuals who tested positive for SARS-CoV-2 with a specificity of 95%. The SERS-based test provides a promising breakthrough in detecting potential mutations that may come up with time while also preparing the world to deal with other pandemics in the future with rapid response and very accurate results.

SARS-CoV-2 proteins monitored by long-range surface plasmon field-enhanced Raman scattering with hybrid bowtie nanoaperture arrays and nanocavities

The identification of biomacromolecules by using surface-enhanced Raman scattering (SERS) remains a challenge because of the near-field effect of traditional substrates. Long-range surface plasmon resonance (LRSPR) is a special type of surface optical phenomenon that provides higher electromagnetic field enhancement and longer penetration depth than conventional surface plasmon resonance. To break the limit of SERS detection distance and obtain a SERS substrate with increased enhancement ability, a bowtie nanoaperture array was sandwiched between two symmetric dielectric environments. Then, an Au mirror was inserted to form a metal-insulator-metal configuration. Finite-difference time-domain simulations revealed that numerous hybrid modes can be provided by this novel configuration (denoted as long-range SERS [LR-SERS] substrate). In particular, the LRSPR mode can be excited and reach the maximum value through the regulation of the polarizations of the incident light and the geometrical parameters of the LR-SERS substrate. The optimized LR-SERS substrate was then applied to detect SARS-CoV-2 spike (S) and nucleocapsid (N) proteins. This substrate displayed ultralow detection limits of ∼9.2 and ∼11.3 pg mL^-1 for the S and N proteins, respectively. Moreover, with the help of principal component analysis and receiver operating characteristic methods, our fabricated sensors exhibited excellent selectivity and hold great potential for the diagnosis of SARS-CoV-2 proteins in real samples.

Achieving broad availability of SARS-CoV-2 detections via smartphone-based analysis

With the development of COVID-19, widely available tests are in great demand. Naked-eye SARS-CoV-2 test kits have recently been developed as home tests, but their sensitivity and accuracy are sometimes limited. Smartphones can convert various signals into digital information, potentially improving the sensitivity and accuracy of these home tests. Herein, we summarize smartphone-based detections for SARS-CoV-2. Optical detections of non-nucleic acids using various sensors and portable imaging systems, as well as nucleic acid analyses based on LAMP, CRISP, CATCH, and biosensors are discussed. Furthermore, different electrochemical detections were compared. We show results obtained using relatively complex equipment, complicated programming procedures, or custom smartphone apps, and describe methods for obtaining information with only simple setups and free software on smartphones. Then, the combined costs of typical smartphone-based detections are evaluated. Finally, the prospect of improving smartphone-based strategies to achieve broad availability of SARS-CoV-2 detection is proposed.

Ultrasensitive photoelectrochemical biosensing platform based target-triggered biocatalytic precipitation reactions on a flower-like Bi2O2S super-structured photoanode

Herein, we reported a novel photoelectrochemical immunoassay method based on a target-triggered on/off signal of the ultra-structured Bi₂O₂S (BOS) photoanode system for the sensitive testing of carcinoembryonic antigens (CEAs) in serum samples. Well-defined three-dimensional sheet-like self-assembled flower-like Bi₂O₂S superstructures were obtained using a time-controlled hydrothermal method. Such well-shaped multifaceted surfaces were considered to be good laser cavity mirror surfaces for multifaceted reflection and refraction of excitation light in the material. An elegant enzyme biocatalytic strategy was introduced into the constructed detection model to sensitively detect CEAs. The substrate 4-chloro-1-naphthol (4-CN) was oxidized to 4-chloro-hexadienone (4-CD) under the formation of target-triggered immune complexes against mAb₁ and peroxidase-modified mAb₂. Subsequently, 4-CD produced by the biocatalytic precipitation reaction was transferred to the photoanodes of Bi₂O₂S nanoflowers (BOS NFs) to burst their photoelectric signals, thus achieving the quantification of CEAs. Through optimization of the conditions of the immunization protocol, a good negative photocurrent response to the target CEA was found in the wide range of 0.02-50 ng mL^-1 with a detection limit of 11.2 pg mL^-1. Impressively, the reported biocatalytic PEC sensing strategy on superstructures is comparable, or superior, to the gold standard ELISA kit in terms of sensitivity and the target response range. This study presents a target-mediated PEC immunoassay for biocatalytic precipitation based on a self-assembled superstructure of Bi₂O₂S, providing a fresh scheme for the analysis of disease-related markers.

A Four-Channel Surface Plasmon Resonance Sensor Functionalized Online for Simultaneous Detections of Anti-SARS-CoV-2 Antibody, Free Viral Particles, and Neutralized Viral Particles

Current tests for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) detect either the constituent nucleic acids/proteins of the viral particles or antibodies specific to the virus, but cannot provide information about viral neutralization by an antibody and the efficacy of an antibody. Such information is important about individuals' vulnerability to severe symptoms or their likelihood of showing no symptoms. We immobilized online SARS-CoV-2 spike (S1) protein and angiotensin-converting enzyme 2 (ACE2) into separate surface plasmon resonance (SPR) channels of a tris-nitrilotriacetic acid (tris-NTA) chip to simultaneously detect the anti-S1 antibody and viral particles in serum samples. In addition, with a high-molecular-weight-cutoff filter, we separated the neutralized viral particles from the free antibody molecules and used a sensing channel immobilized with Protein G to determine antibody-neutralized viral particles. The optimal density of probe molecules in each fluidic channel can be precisely controlled through the closure and opening of the specific ports. By utilizing the high surface density of ACE2, multiple assays can be carried out without regenerations. These three species can be determined with a short analysis time (<12 min per assay) and excellent sensor-to-sensor/cycle-to-cycle reproducibility (RSD < 5%). When coupled with an autosampler, continuous assays can be performed in an unattended manner at a single chip for up to 6 days. Such a sensor capable of assaying serum samples containing the three species at different levels provides additional insights into the disease status and immunity of persons being tested, which should be helpful for containing the SARS-CoV-2 spread during the era of incessant viral mutations.

Point-of-care CRISPR/Cas biosensing technology: A promising tool for preventing the possible COVID-19 resurgence caused by contaminated cold-chain food and packaging

The ongoing coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused great public health concern and has been a global threat due to its high transmissibility and morbidity. Although the SARS-CoV-2 transmission mainly relies on the person-to-person route through the respiratory droplets, the possible transmission through the contaminated cold-chain food and packaging to humans has raised widespread concerns. This review discussed the possibility of SARS-CoV-2 transmission via the contaminated cold-chain food and packaging by tracing the occurrence, the survival of SARS-CoV-2 in the contaminated cold-chain food and packaging, as well as the transmission and outbreaks related to the contaminated cold-chain food and packaging. Rapid, accurate, and reliable diagnostics of SARS-CoV-2 is of great importance for preventing and controlling the COVID-19 resurgence. Therefore, we summarized the recent advances on the emerging clustered regularly interspaced short palindromic repeats (CRISPR)/Cas system-based biosensing technology that is promising and powerful for preventing the possible COVID-19 resurgence caused by the contaminated cold-chain food and packaging during the COVID-19 pandemic, including CRISPR/Cas system-based biosensors and their integration with portable devices (e.g., smartphone, lateral flow assays, microfluidic chips, and nanopores). Impressively, this review not only provided an insight on the possibility of SARS-CoV-2 transmission through the food supply chain, but also proposed the future opportunities and challenges on the development of CRISPR/Cas system-based detection methods for the diagnosis of SARS-CoV-2.

Freestanding Laser-Induced Graphene Ultrasensitive Resonative Viral Sensors

Early and reliable detection of an infectious viral disease is critical to accurately monitor outbreaks and to provide individuals and health care professionals the opportunity to treat patients at the early stages of a disease. The accuracy of such information is essential to define appropriate actions to protect the population and to reduce the likelihood of a possible pandemic. Here, we show the fabrication of freestanding laser-induced graphene (FLIG) flakes that are highly sensitive sensors for high-fidelity viral detection. As a case study, we show the detection of SARS-CoV-2 spike proteins. FLIG flakes are nonembedded porous graphene foams ca. 30 μm thick that are generated using laser irradiation of polyimide and can be fabricated in seconds at a low cost. Larger pieces of FLIG were cut forming a cantilever, used as suspended resonators, and characterized for their electromechanics behavior. Thermomechanical analysis showed FLIG stiffness comparable to other porous materials such as boron nitride foam, and electrostatic excitation showed amplification of the vibrations at frequencies in the range of several kilo-hertz. We developed a protocol for aqueous biological sensing by characterizing the wetting dynamic response of the sensor in buffer solution and in water, and devices functionalized with COVID-19 antibodies specifically detected SARS-CoV-2 spike protein binding, while not detecting other viruses such as MS2. The FLIG sensors showed a clear mass-dependent frequency response shift of ∼1 Hz/pg, and low nanomolar concentrations could be detected. Ultimately, the sensors demonstrated an outstanding limit of detection of 2.63 pg, which is equivalent to as few as ∼5000 SARS-CoV-2 viruses. Thus, the FLIG platform technology can be utilized to develop portable and highly accurate sensors, including biological applications where the fast and reliable protein or infectious particle detection is critical.

In Situ Surface-Enhanced Raman Scattering Detection of a SARS-CoV-2 Biomarker Using Flexible and Transparent Polydimethylsiloxane Films with Embedded Au Nanoplates

Coronavirus disease 2019 (COVID-19) remains an ongoing issue worldwide and continues to disrupt daily life. Transmission of infection primarily occurs through secretions when in contact with infected individuals, but more recent evidence has shown that fomites are also a source of virus transmission, especially in cold-chain logistics. Traditional nucleic acid testing for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) contamination in cold-chain logistics is time-consuming and inaccurate because of the multiplex sampling sites. Surface-enhanced Raman spectroscopy (SERS) provides a rapid, sensitive, and label-free detection route for various molecules, including viruses, through the identification of the characteristic peaks of their outer membrane proteins. In this study, we embedded arbitrarily orientated gold nanoplates (Au NPLs) in polydimethylsiloxane (PDMS) elastomer and used it as biosensor for the ultrasensitive detection of the SARS-CoV-2 spike protein in cold-chain logistics. This transparent and flexible substrate can be wrapped onto arbitrary surfaces and permits light penetration into the underlying contact surface, enabling in situ and point-of-care SERS diagnostics. The developed assay displayed high reproducibility (8.7%) and a low detection limit of 6.8 × 10-9 g mL-1, indicating its potential to serve as a promising approach with increased accuracy and sensitivity for the detection of the S protein.

Model of the SARS-CoV-2 Virus for Development of a DNA-Modified, Surface-Enhanced Raman Spectroscopy Sensor with a Novel Hybrid Plasmonic Platform in Sandwich Mode

The recent severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has posed a great challenge for the development of ultra-fast methods for virus identification based on sensor principles. We created a structure modeling surface and size of the SARS-CoV-2 virus and used it in comparison with the standard antigen SARS-CoV-2—the receptor-binding domain (RBD) of the S-protein of the envelope of the SARS-CoV-2 virus from the Wuhan strain—for the development of detection of coronaviruses using a DNA-modified, surface-enhanced Raman scattering (SERS)-based aptasensor in sandwich mode: a primary aptamer attached to the plasmonic surface—RBD-covered Ag nanoparticle—the Cy3-labeled secondary aptamer. Fabricated novel hybrid plasmonic structures based on “Ag mirror-SiO2-nanostructured Ag” demonstrate sensitivity for the detection of investigated analytes due to the combination of localized surface plasmons in nanostructured silver surface and the gap surface plasmons in a thin dielectric layer of SiO2 between silver layers. A specific SERS signal has been obtained from SERS-active compounds with RBD-specific DNA aptamers that selectively bind to the S protein of synthetic virion (dissociation constants of DNA-aptamer complexes with protein in the range of 10 nM). The purpose of the study is to systematically analyze the combination of components in an aptamer-based sandwich system. A developed virus size simulating silver particles adsorbed on an aptamer-coated sensor provided a signal different from free RBD. The data obtained are consistent with the theory of signal amplification depending on the distance of the active compound from the amplifying surface and the nature of such a compound. The ability to detect the target virus due to specific interaction with such DNA is quantitatively controlled by the degree of the quenching SERS signal from the labeled compound. Developed indicator sandwich-type systems demonstrate high stability. Such a platform does not require special permissions to work with viruses. Therefore, our approach creates the promising basis for fostering the practical application of ultra-fast, amplification-free methods for detecting coronaviruses based on SARS-CoV-2.

Human ACE2 Peptide-Attached Plasmonic-Magnetic Heterostructure for Magnetic Separation, Surface Enhanced Raman Spectroscopy Identification, and Inhibition of Different Variants of SARS-CoV-2 Infections

The emergence of Alpha, Beta, Gamma, Delta, and Omicron variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for several million deaths up to now. Because of the huge amount of vaccine escape mutations in the spike (S) protein for different variants, the design of material for combating SARS-CoV-2 is very important for our society. Herein, we report on the design of a human angiotensin converting enzyme 2 (ACE2) peptide-conjugated plasmonic-magnetic heterostructure, which has the capability for magnetic separation, identification via surface enhanced Raman spectroscopy (SERS), and inhibition of different variant SARS-CoV-2 infections. In this work, plasmonic-magnetic heterostructures were developed using the initial synthesis of polyethylenimine (PEI)-coated Fe3O4-based magnetic nanoparticles, and then gold nanoparticles (GNPs) were grown onto the surface of the magnetic nanoparticles. Experimental binding data between ACE2-conjugated plasmonic-magnetic heterostructures and spike-receptor-binding domain (RBD) show that the Omicron variant has maximum binding ability, and it follows Alpha < Beta < Gamma < Delta < Omicron. Our finding shows that, due to the high magnetic moment (specific magnetization 40 emu/g), bioconjugated heterostructures are capable of effective magnetic separation of pseudotyped SARS-CoV-2 bearing the Delta variant spike from an infected artificial nasal mucus fluid sample using a simple bar magnet. Experimental data show that due to the formation of huge "hot spots" in the presence of SARS-CoV-2, Raman intensity for the 4-aminothiolphenol (4-ATP) Raman reporter was enhanced sharply, which has been used for the identification of separated virus. Theoretical calculations using finite-difference time-domain (FDTD) simulation indicate that, due to the "hot spots" formation, a six orders of magnitude Raman enhancement can be observed. A concentration-dependent inhibition efficiency investigation using a HEK293T-human cell line indicates that ACE2 peptide-conjugated plasmonic-magnetic heterostructures have the capability of complete inhibition of entry of different variants and original SARS-CoV-2 pseudovirions into host cells.

A non-invasive ultrasensitive diagnostic approach for COVID-19 infection using salivary label-free SERS fingerprinting and artificial intelligence

Clinical diagnostics for SARS-CoV-2 infection usually comprises the sampling of throat or nasopharyngeal swabs that are invasive and create patient discomfort. Hence, saliva is attempted as a sample of choice for the management of COVID-19 outbreaks that cripples the global healthcare system. Although limited by the risk of eliciting false-negative and positive results, tedious test procedures, requirement of specialized laboratories, and expensive reagents, nucleic acid-based tests remain the gold standard for COVID-19 diagnostics. However, genetic diversity of the virus due to rapid mutations limits the efficiency of nucleic acid-based tests. Herein, we have demonstrated the simplest screening modality based on label-free surface enhanced Raman scattering (LF-SERS) for scrutinizing the SARS-CoV-2-mediated molecular-level changes of the saliva samples among healthy, COVID-19 infected and COVID-19 recovered subjects. Moreover, our LF-SERS technique enabled to differentiate the three classes of corona virus spike protein derived from SARS-CoV-2, SARS-CoV and MERS-CoV. Raman spectral data was further decoded, segregated and effectively managed with the aid of machine learning algorithms. The classification models built upon biochemical signature-based discrimination method of the COVID-19 condition from the patient saliva ensured high accuracy, specificity, and sensitivity. The trained support vector machine (SVM) classifier achieved a prediction accuracy of 95% and F1-score of 94.73%, and 95.28% for healthy and COVID-19 infected patients respectively. The current approach not only differentiate SARS-CoV-2 infection with healthy controls but also predicted a distinct fingerprint for different stages of patient recovery. Employing portable hand-held Raman spectrophotometer as the instrument and saliva as the sample of choice will guarantee a rapid and non-invasive diagnostic strategy to warrant or assure patient comfort and large-scale population screening for SARS-CoV-2 infection and monitoring the recovery process.

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

Currently, the coronavirus disease 2019 (COVID-19) pandemic is ravaging the world, causing serious crisis in economy and human health. The top priority is the detection and drug development of the novel coronavirus. The gold standard for real-time diagnosis of coronavirus disease is the reverse transcription-polymerase chain reaction (RT-PCR), which is usually operatively complex and time-consuming. Biosensors are known for their low cost and rapid detection, which are developing rapidly in detecting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The current study showed that the spike protein of SARS-CoV-2 will bind to angiotensin-converting hormone 2 (ACE2) to mediate the entry of the virus into cells. Interestingly, the affinity between ACE2 and SARS-CoV-2 spike protein increases with the mutation of the virus. Using ACE2 as a biosensor recognition receptor to detect SARS-CoV-2 will effectively avoid the decline of detection accuracy and false negative caused by variants. In fact, due to the variation of the virus, it may even lead to enhanced detection performance. In addition, ACE2-specific drugs to prevent SARS-CoV-2 from entering cells will be effectively evaluated using the biosensors even with virus mutations. Here, we reviewed the biosensors for rapid detection of SARS-CoV-2 by ACE2 and discussed the advantages of ACE2 as an antibody for the detection of SARS-CoV-2 variants. The review also discussed the value of ACE2-based biosensors for screening for drugs that modulate the interaction between ACE2 and SARS-CoV-2.

Development of novel spectroscopic and machine learning methods for the measurement of periodic changes in COVID-19 antibody level

In this research, blood samples of 47 patients infected by COVID were analyzed. The samples were taken on the 1st, 3rd and 6th month after the detection of COVID infection. Total antibody levels were measured against the SARS-CoV-2 N antigen and surrogate virus neutralization by serological methods. To differentiate COVID patients with different antibody levels, Fourier Transform InfraRed (FTIR) and Raman spectroscopy methods were used. The spectroscopy data were analyzed by multivariate analysis, machine learning and neural network methods. It was shown, that analysis of serum using the above-mentioned spectroscopy methods allows to differentiate antibody levels between 1 and 6 months via spectral biomarkers of amides II and I. Moreover, multivariate analysis showed, that using Raman spectroscopy in the range between 1317 cm^-1 and 1432 cm^-1, 2840 cm^-1 and 2956 cm^-1 it is possible to distinguish patients after 1, 3, and 6 months from COVID with a sensitivity close to 100%.

A review of applications of surface-enhanced raman spectroscopy laser for detection of biomaterials and a quick glance into its advances for COVID-19 investigations

Surface-enhanced Raman spectroscopy (SERS) is one of the most sensitive analytical tools. In some cases, it is possible to record a high-quality SERS spectrum in which even a single molecule is involved. Therefore, SERS is considered a significantly promising option as an alternative to routine analytical techniques used in food, environmental, biochemical, and medical analyzes. In this review, the definitive applications of SERS developed to identify biochemically important species (especially medical and biological) from the simplest to the most complex are briefly discussed. Moreover, the potential capability of SERS for being used as an alternative to routine methods in diagnostic and clinical cases is demonstrated. In addition, this article describes how SERS-based sensors work, addresses its advancements in the last 20 years, discusses its applications for detecting Coronavirus Disease 2019 (COVID-19), and finally describes future works. The authors hope that this article will be useful for researchers who want to enter this amazing field of research.

Modeling insights into SARS-CoV-2 respiratory tract infections prior to immune protection

Mechanistic insights into human respiratory tract (RT) infections from SARS-CoV-2 can inform public awareness as well as guide medical prevention and treatment for COVID-19 disease. Yet the complexity of the RT and the inability to access diverse regions pose fundamental roadblocks to evaluation of potential mechanisms for the onset and progression of infection (and transmission). We present a model that incorporates detailed RT anatomy and physiology, including airway geometry, physical dimensions, thicknesses of airway surface liquids (ASLs), and mucus layer transport by cilia. The model further incorporates SARS-CoV-2 diffusivity in ASLs and best-known data for epithelial cell infection probabilities, and, once infected, duration of eclipse and replication phases, and replication rate of infectious virions. We apply this baseline model in the absence of immune protection to explore immediate, short-term outcomes from novel SARS-CoV-2 depositions onto the air-ASL interface. For each RT location, we compute probability to clear versus infect; per infected cell, we compute dynamics of viral load and cell infection. Results reveal that nasal infections are highly likely within 1-2 days from minimal exposure, and alveolar pneumonia occurs only if infectious virions are deposited directly into alveolar ducts and sacs, not via retrograde propagation to the deep lung. Furthermore, to infect just 1% of the 140 m2 of alveolar surface area within 1 week, either 103 boluses each with 106 infectious virions or 106 aerosols with one infectious virion, all physically separated, must be directly deposited. These results strongly suggest that COVID-19 disease occurs in stages: a nasal/upper RT infection, followed by self-transmission of infection to the deep lung. Two mechanisms of self-transmission are persistent aspiration of infected nasal boluses that drain to the deep lung and repeated rupture of nasal aerosols from infected mucosal membranes by speaking, singing, or cheering that are partially inhaled, exhaled, and re-inhaled, to the deep lung.

Surface-enhanced Raman scattering-based immunoassay for severe acute respiratory syndrome coronavirus 2

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has affected humans worldwide for over a year now. Although various tests have been developed for the detection of SARS-CoV-2, advanced sensing methods are required for the diagnosis, screening, and surveillance of coronavirus disease 2019 (COVID-19). Here, we report a surface-enhanced Raman scattering (SERS)-based immunoassay involving an antibody pair, SERS-active hollow Au nanoparticles (NPs), and magnetic beads for the detection of SARS-CoV-2. The selected antibody pair against the SARS-CoV-2 antigen, along with the magnetic beads, facilitates the accurate direct detection of the virus. The hollow Au NPs exhibit strong, reproducible SERS signals, allowing sensitive quantitative detection of SARS-CoV-2. This assay had detection limits of 2.56 fg/mL for the SARS-CoV-2 antigen and 3.4 plaque-forming units/mL for the SARS-CoV-2 lysates. Furthermore, it facilitated the identification of SARS-CoV-2 in human nasopharyngeal aspirates and diagnosis of COVID-19 within 30 min using a portable Raman device. Thus, this assay can be potentially used for the diagnosis and prevention of COVID-19.

Applications of surface-enhanced Raman spectroscopy in environmental detection

As the human population grows, the anthropogenic impacts from various agricultural and industrial processes produce unwanted contaminants in the environment. The accurate, sensitive and rapid detection of such contaminants is vital for human health and safety. Surface-enhanced Raman spectroscopy (SERS) is a valuable analytical tool with wide applications in environmental contaminant monitoring. The aim of this review is to summarize recent advancements within SERS research as it applies to environmental detection, with a focus on research published or accessible from January 2021 through December 2021 including early-access publications. Our goal is to provide a wide breadth of information that can be used to provide background knowledge of the field, as well as inform and encourage further development of SERS techniques in protecting environmental quality and safety. Specifically, we highlight the characteristics of effective SERS nanosubstrates, and explore methods for the SERS detection of inorganic, organic, and biological contaminants including heavy metals, pharmaceuticals, plastic particles, synthetic dyes, pesticides, viruses, bacteria and mycotoxins. We also discuss the current limitations of SERS technologies in environmental detection and propose several avenues for future investigation. We encourage researchers to fill in the identified gaps so that SERS can be implemented in a real-world environment more effectively and efficiently, ultimately providing reliable and timely data to help and make science-based strategies and policies to protect environmental safety and public health.

SERS/TERS Characterization of New Potential Therapeutics: The Influence of Positional Isomerism, Interface Type, Oxidation State of Copper, and Incubation Time on Adsorption on the Surface of Copper(I) and (II) Oxide Nanoparticles

The aim of this study was to investigate how the oxidation state of copper (Cu(I) vs Cu(II)), the nature of the interface (solid/aqueous vs solid/air), positional isomerism, and incubation time affect the functionalization of the surface of copper oxide nanostructures by [(butylamino)(pyridine)methyl]phenylphosphinic acid (PyPA). For this purpose, 2-, 3-, and 4-isomers of PyPA and the nanostructures were synthesized. The nanostructure were characterized by UV-visible spectroscopy (UV–vis), scanning electron microscopy (SEM), Raman spectroscopy (RS), and X-ray diffraction (XRD) analysis, which proved the formation of spherical Cu₂O nanoparticles (Cu₂ONPs; 1500–600 nm) and leaf-like CuO nanostructures (CuONSs; 80–180/400–700 nm, width/length). PyPA isomers were deposited on the surface of NSs, and adsorption was investigated by surface-enhanced Raman scattering (SERS) and tip-enhanced Raman scattering (TERS). The changes of adsorption on the surface of copper oxide NSs caused by the above-mentioned factors were described and the enhancement factor on this substrate was calculated.