A point-of-care biosensor for rapid detection and differentiation of COVID-19 virus (SARS-CoV-2) and influenza virus using subwavelength grating micro-ring resonator
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Title: A point-of-care biosensor for rapid detection and differentiation of COVID-19 virus (SARS-CoV-2) and influenza virus using subwavelength grating micro-ring resonator
In the context of continued spread of coronavirus disease 2019 (COVID-19) caused by SARS-CoV-2 and the emergence of new variants, the demand for rapid, accurate, and frequent detection is increasing. Moreover, the new predominant strain, Omicron variant, manifests more similar clinical features to those of other common respiratory infections. The concurrent detection of multiple potential pathogens helps distinguish SARS-CoV-2 infection from other diseases with overlapping symptoms, which is significant for providing tailored treatment to patients and containing the outbreak. Here, we report a lab-on-a-chip biosensing platform for SARS-CoV-2 detection based on the subwavelength grating micro-ring resonator. The sensing surface is functionalized by specific antibody against SARS-CoV-2 spike protein, which could produce redshifts of resonant peaks by antigen–antibody combination, thus achieving quantitative detection. Additionally, the sensor chip is integrated with a microfluidic chip featuring an anti-backflow Y-shaped structure that enables the concurrent detection of two analytes. In this study, we realized the detection and differentiation of COVID-19 and influenza A H1N1. Experimental results indicate that the limit of detection of our device reaches 100 fg/ml (1.31 fM) within 15 min detecting time, and cross-reactivity tests manifest the specificity of the optical diagnostic assay. Furthermore, the integrated packaging and streamlined workflow facilitate its use for clinical applications. Thus, the biosensing platform presents a promising approach for attaining highly sensitive, selective, multiplexed, and quantitative point-of-care diagnosis and distinction between COVID-19 and influenza.
The sudden rise of the worldwide severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic in early 2020 has called into
drastic action measures to perform instant detection and reduce the rate of spread. Common clinical and nonclinical diagnostic testing
methods have been partially effective in satisfying the increasing demand for fast detection point-of-care (POC) methods to slow down
further spread. However, accurate point-of-risk diagnosis of this emerging viral infection is paramount as the need for simultaneous
standard operating procedures and symptom management of SARS-CoV-2 will be the norm for years to come. A sensitive, cost-effective
biosensor with mass production capability is crucial until a universal vaccination becomes available. Optical biosensors can provide a noninvasive,
extremely sensitive rapid detection platform with sensitivity down to 67 fg/ml (1 fM) concentration in a few minutes. These biosensors
can be manufactured on a mass scale (millions) to detect the COVID-19 viral load in nasal, saliva, urine, and serological samples,
even if the infected person is asymptotic. Methods investigated here are the most advanced available platforms for biosensing optical devices
that have resulted from the integration of state-of-the-art designs and materials. These approaches include, but are not limited to, integrated
optical devices, plasmonic resonance, and emerging nanomaterial biosensors. The lab-on-chip platforms examined here are suitable
not only for SARS-CoV-2 spike protein detection but also for other contagious virions such as influenza and Middle East respiratory syndrome
(MERS).
Pola, Cícero C; Rangnekar, Sonal V; Sheets, Robert; Szydłowska, Beata M; Downing, Julia R; Parate, Kshama W; Wallace, Shay G; Tsai, Daphne; Hersam, Mark C; Gomes, Carmen L; et al(
, 2D Materials)
Abstract Rapid, inexpensive, and easy-to-use coronavirus disease 2019 (COVID-19) home tests are key tools in addition to vaccines in the world wide fight to eliminate national and local shutdowns. However, currently available tests for SARS-CoV-2, the virus that causes COVID-19, are too expensive, painful, and irritating, or not sufficiently sensitive for routine, accurate home testing. Herein, we employ custom-formulated graphene inks and aerosol jet printing to create a rapid electrochemical immunosensor for direct detection of SARS-CoV-2 spike receptor-binding domain (RBD) in saliva samples acquired noninvasively. This sensor demonstrated limits of detection that are considerably lower than most commercial SARS-CoV-2 antigen tests (22.91 ± 4.72 pg ml −1 for spike RBD and 110.38 ± 9.00 pg ml −1 for spike S1) as well as fast response time (∼30 min), which was facilitated by the functionalization of printed graphene electrodes in a single-step with SARS-CoV-2 polyclonal antibody through the carbodiimide reaction without the need for nanoparticle functionalization or secondary antibody or metallic nanoparticle labels. This immunosensor presents a wide linear sensing range from 1 to 1000 ng ml −1 and does not react with other coexisting influenza viruses such as H1N1 hemagglutinin. By combining high-yield graphene ink synthesis, automated printing, high antigen selectivity, and rapid testing capability, this work offers a promising alternative to current SARS-CoV-2 antigen tests.
The COVID-19 pandemic has highlighted the urgent need for sensitive, affordable, and widely accessible testing at the point of care. Here we demonstrate a new, universal LFA platform technology using M13 phage conjugated with antibodies and HRP enzymes that offers high analytical sensitivity and excellent performance in a complex clinical matrix. We also report its complete integration into a sensitive chemiluminescence-based smartphone-readable lateral flow assay for the detection of SARS-CoV-2 nucleoprotein. We screened 84 anti-nucleoprotein monoclonal antibody pairs in phage LFA and identified an antibody pair that gave an LoD of 25 pg mL −1 nucleoprotein in nasal swab extract using a FluorChem gel documentation system and 100 pg mL −1 when the test was imaged and analyzed by an in-house-developed smartphone reader. The smartphone-read LFA signals for positive clinical samples tested ( N = 15, with known Ct) were statistically different ( p < 0.001) from signals for negative clinical samples ( N = 11). The phage LFA technology combined with smartphone chemiluminescence imaging can enable the timely development of ultrasensitive, affordable point-of-care testing platforms for SARS-CoV-2 and beyond.
Villanustre, Flavio; Chala, Arjuna; Dev, Roger; Xu, Lili; LexisNexis, Jesse Shaw; Furht, Borko; Khoshgoftaar, Taghi(
, Journal of Big Data)
Abstract This project is funded by the US National Science Foundation (NSF) through their NSF RAPID program under the title “Modeling Corona Spread Using Big Data Analytics.” The project is a joint effort between the Department of Computer & Electrical Engineering and Computer Science at FAU and a research group from LexisNexis Risk Solutions. The novel coronavirus Covid-19 originated in China in early December 2019 and has rapidly spread to many countries around the globe, with the number of confirmed cases increasing every day. Covid-19 is officially a pandemic. It is a novel infection with serious clinical manifestations, including death, and it has reached at least 124 countries and territories. Although the ultimate course and impact of Covid-19 are uncertain, it is not merely possible but likely that the disease will produce enough severe illness to overwhelm the worldwide health care infrastructure. Emerging viral pandemics can place extraordinary and sustained demands on public health and health systems and on providers of essential community services. Modeling the Covid-19 pandemic spread is challenging. But there are data that can be used to project resource demands. Estimates of the reproductive number (R) of SARS-CoV-2 show that at the beginning of the epidemic, each infected person spreads the virus to at least two others, on average (Emanuel et al. in N Engl J Med. 2020, Livingston and Bucher in JAMA 323(14):1335, 2020). A conservatively low estimate is that 5 % of the population could become infected within 3 months. Preliminary data from China and Italy regarding the distribution of case severity and fatality vary widely (Wu and McGoogan in JAMA 323(13):1239–42, 2020). A recent large-scale analysis from China suggests that 80 % of those infected either are asymptomatic or have mild symptoms; a finding that implies that demand for advanced medical services might apply to only 20 % of the total infected. Of patients infected with Covid-19, about 15 % have severe illness and 5 % have critical illness (Emanuel et al. in N Engl J Med. 2020). Overall, mortality ranges from 0.25 % to as high as 3.0 % (Emanuel et al. in N Engl J Med. 2020, Wilson et al. in Emerg Infect Dis 26(6):1339, 2020). Case fatality rates are much higher for vulnerable populations, such as persons over the age of 80 years (> 14 %) and those with coexisting conditions (10 % for those with cardiovascular disease and 7 % for those with diabetes) (Emanuel et al. in N Engl J Med. 2020). Overall, Covid-19 is substantially deadlier than seasonal influenza, which has a mortality of roughly 0.1 %. Public health efforts depend heavily on predicting how diseases such as those caused by Covid-19 spread across the globe. During the early days of a new outbreak, when reliable data are still scarce, researchers turn to mathematical models that can predict where people who could be infected are going and how likely they are to bring the disease with them. These computational methods use known statistical equations that calculate the probability of individuals transmitting the illness. Modern computational power allows these models to quickly incorporate multiple inputs, such as a given disease’s ability to pass from person to person and the movement patterns of potentially infected people traveling by air and land. This process sometimes involves making assumptions about unknown factors, such as an individual’s exact travel pattern. By plugging in different possible versions of each input, however, researchers can update the models as new information becomes available and compare their results to observed patterns for the illness. In this paper we describe the development a model of Corona spread by using innovative big data analytics techniques and tools. We leveraged our experience from research in modeling Ebola spread (Shaw et al. Modeling Ebola Spread and Using HPCC/KEL System. In: Big Data Technologies and Applications 2016 (pp. 347-385). Springer, Cham) to successfully model Corona spread, we will obtain new results, and help in reducing the number of Corona patients. We closely collaborated with LexisNexis, which is a leading US data analytics company and a member of our NSF I/UCRC for Advanced Knowledge Enablement. The lack of a comprehensive view and informative analysis of the status of the pandemic can also cause panic and instability within society. Our work proposes the HPCC Systems Covid-19 tracker, which provides a multi-level view of the pandemic with the informative virus spreading indicators in a timely manner. The system embeds a classical epidemiological model known as SIR and spreading indicators based on causal model. The data solution of the tracker is built on top of the Big Data processing platform HPCC Systems, from ingesting and tracking of various data sources to fast delivery of the data to the public. The HPCC Systems Covid-19 tracker presents the Covid-19 data on a daily, weekly, and cumulative basis up to global-level and down to the county-level. It also provides statistical analysis for each level such as new cases per 100,000 population. The primary analysis such as Contagion Risk and Infection State is based on causal model with a seven-day sliding window. Our work has been released as a publicly available website to the world and attracted a great volume of traffic. The project is open-sourced and available on GitHub. The system was developed on the LexisNexis HPCC Systems, which is briefly described in the paper.
Ning, Shupeng, Chang, Hao-Chen, Fan, Kang-Chieh, Hsiao, Po-Yu, Feng, Chenghao, Shoemaker, Devan, and Chen, Ray T. A point-of-care biosensor for rapid detection and differentiation of COVID-19 virus (SARS-CoV-2) and influenza virus using subwavelength grating micro-ring resonator. Retrieved from https://par.nsf.gov/biblio/10472261. Applied Physics Reviews 10.2 Web. doi:10.1063/5.0146079.
Ning, Shupeng, Chang, Hao-Chen, Fan, Kang-Chieh, Hsiao, Po-Yu, Feng, Chenghao, Shoemaker, Devan, & Chen, Ray T. A point-of-care biosensor for rapid detection and differentiation of COVID-19 virus (SARS-CoV-2) and influenza virus using subwavelength grating micro-ring resonator. Applied Physics Reviews, 10 (2). Retrieved from https://par.nsf.gov/biblio/10472261. https://doi.org/10.1063/5.0146079
Ning, Shupeng, Chang, Hao-Chen, Fan, Kang-Chieh, Hsiao, Po-Yu, Feng, Chenghao, Shoemaker, Devan, and Chen, Ray T.
"A point-of-care biosensor for rapid detection and differentiation of COVID-19 virus (SARS-CoV-2) and influenza virus using subwavelength grating micro-ring resonator". Applied Physics Reviews 10 (2). Country unknown/Code not available: AIP. https://doi.org/10.1063/5.0146079.https://par.nsf.gov/biblio/10472261.
@article{osti_10472261,
place = {Country unknown/Code not available},
title = {A point-of-care biosensor for rapid detection and differentiation of COVID-19 virus (SARS-CoV-2) and influenza virus using subwavelength grating micro-ring resonator},
url = {https://par.nsf.gov/biblio/10472261},
DOI = {10.1063/5.0146079},
abstractNote = {In the context of continued spread of coronavirus disease 2019 (COVID-19) caused by SARS-CoV-2 and the emergence of new variants, the demand for rapid, accurate, and frequent detection is increasing. Moreover, the new predominant strain, Omicron variant, manifests more similar clinical features to those of other common respiratory infections. The concurrent detection of multiple potential pathogens helps distinguish SARS-CoV-2 infection from other diseases with overlapping symptoms, which is significant for providing tailored treatment to patients and containing the outbreak. Here, we report a lab-on-a-chip biosensing platform for SARS-CoV-2 detection based on the subwavelength grating micro-ring resonator. The sensing surface is functionalized by specific antibody against SARS-CoV-2 spike protein, which could produce redshifts of resonant peaks by antigen–antibody combination, thus achieving quantitative detection. Additionally, the sensor chip is integrated with a microfluidic chip featuring an anti-backflow Y-shaped structure that enables the concurrent detection of two analytes. In this study, we realized the detection and differentiation of COVID-19 and influenza A H1N1. Experimental results indicate that the limit of detection of our device reaches 100 fg/ml (1.31 fM) within 15 min detecting time, and cross-reactivity tests manifest the specificity of the optical diagnostic assay. Furthermore, the integrated packaging and streamlined workflow facilitate its use for clinical applications. Thus, the biosensing platform presents a promising approach for attaining highly sensitive, selective, multiplexed, and quantitative point-of-care diagnosis and distinction between COVID-19 and influenza.},
journal = {Applied Physics Reviews},
volume = {10},
number = {2},
publisher = {AIP},
author = {Ning, Shupeng and Chang, Hao-Chen and Fan, Kang-Chieh and Hsiao, Po-Yu and Feng, Chenghao and Shoemaker, Devan and Chen, Ray T.},
}
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