Antibody detection assays are essential for evaluating immunity of individuals against a given virus, and this has been particularly relevant during the COVID‐19 pandemic. Current serology assays either require a laboratory setting and take >1 hr (i.e., enzyme‐linked immunosorbent assay [ELISA]) or are rapid but only qualitative in nature and cannot accurately track antibody levels over time (i.e., lateral flow assay [LFA]). Therefore, there is a need for development of a rapid and simple but also quantitative assay that can evaluate antibody levels in patients accurately over time. We have developed an assay that uses a split nanoluciferase fused to the spike or nucleocapsid proteins of the SARS‐CoV‐2 virus to enable luminescent‐based detection of spike‐ or nucleocapsid‐binding antibodies in serum, plasma, and whole blood samples. The resulting approach is simple, rapid, and quantitative and is highly amenable to low‐/medium‐throughput scale using plate‐based assays, high‐throughput scale using robotics, and point‐of‐care applications. In this article, we describe how to perform the assay in a laboratory setting using a plate reader or liquid‐handling robotics and in a point‐of‐care setting using a handheld, battery‐powered luminometer. Together, these assays allow antibody detection to be easily performed in multiple settings by simplifying and reducing assay time in a laboratory or clinical environment and by allowing for antibody detection in point‐of‐care, nonlaboratory settings. © 2022 Wiley Periodicals LLC.
Protein-based lateral flow assays for COVID-19 detection
Abstract To combat the enduring and dangerous spread of COVID-19, many innovations to rapid diagnostics have been developed based on proteinprotein interactions of the SARS-CoV-2 spike and nucleocapsid proteins to increase testing accessibility. These antigen tests have most prominently been developed using the lateral flow assay (LFA) test platform which has the benefit of administration at point-of-care, delivering quick results, lower cost, and does not require skilled personnel. However, they have gained criticism for an inferior sensitivity. In the last year, much attention has been given to creating a rapid LFA test for detection of COVID-19 antigens that can address its high limit of detection while retaining the advantages of rapid antibodyantigen interaction. In this review, a summary of these proteinprotein interactions as well as the challenges, benefits, and recent improvements to protein based LFA for detection of COVID-19 are discussed.
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- NSF-PAR ID:
- 10281588
- Date Published:
- Journal Name:
- Protein Engineering, Design and Selection
- Volume:
- 34
- ISSN:
- 1741-0126
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Basic Protocol : SARS‐CoV‐2 antibody detection using the split‐luciferase assay on a medium‐throughput scale with a laboratory luminometerAlternate Protocol 1 : High‐throughput‐based protocol for SARS‐CoV‐2 antibody detection using a robotic platformAlternate Protocol 2 : Point‐of‐care‐based protocol for SARS‐CoV‐2 antibody detection using a handheld luminometerSupport Protocol : Determining positive/negative cutoffs for test samples and standardizing the assay between days -
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.more » « less
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null (Ed.)The newly discovered Coronavirus Disease 2019 (COVID-19) has been globally spreading and causing hundreds of thousands of deaths around the world as of its first emergence in late 2019. The rapid outbreak of this disease has overwhelmed health care infrastructures and arises the need to allocate medical equipment and resources more efficiently. The early diagnosis of this disease will lead to the rapid separation of COVID-19 and non-COVID cases, which will be helpful for health care authorities to optimize resource allocation plans and early prevention of the disease. In this regard, a growing number of studies are investigating the capability of deep learning for early diagnosis of COVID-19. Computed tomography (CT) scans have shown distinctive features and higher sensitivity compared to other diagnostic tests, in particular the current gold standard, i.e., the Reverse Transcription Polymerase Chain Reaction (RT-PCR) test. Current deep learning-based algorithms are mainly developed based on Convolutional Neural Networks (CNNs) to identify COVID-19 pneumonia cases. CNNs, however, require extensive data augmentation and large datasets to identify detailed spatial relations between image instances. Furthermore, existing algorithms utilizing CT scans, either extend slice-level predictions to patient-level ones using a simple thresholding mechanism or rely on a sophisticated infection segmentation to identify the disease. In this paper, we propose a two-stage fully automated CT-based framework for identification of COVID-19 positive cases referred to as the “COVID-FACT”. COVID-FACT utilizes Capsule Networks, as its main building blocks and is, therefore, capable of capturing spatial information. In particular, to make the proposed COVID-FACT independent from sophisticated segmentations of the area of infection, slices demonstrating infection are detected at the first stage and the second stage is responsible for classifying patients into COVID and non-COVID cases. COVID-FACT detects slices with infection, and identifies positive COVID-19 cases using an in-house CT scan dataset, containing COVID-19, community acquired pneumonia, and normal cases. Based on our experiments, COVID-FACT achieves an accuracy of 90.82 % , a sensitivity of 94.55 % , a specificity of 86.04 % , and an Area Under the Curve (AUC) of 0.98, while depending on far less supervision and annotation, in comparison to its counterparts.more » « less
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null (Ed.)Abstract Dr. Deborah Birx, the White House Coronavirus Task Force coordinator, told NBC News on “Meet the Press” that “[T]he U.S. needs a ‘breakthrough’ in coronavirus testing to help screen Americans and get a more accurate picture of the virus’ spread.” We have been involved with biopathogen detection since the 2001 anthrax attacks and were the first to detect anthrax in real-time. A variation on the laser spectroscopic techniques we developed for the rapid detection of anthrax can be applied to detect the Severe Acute Respiratory Syndrome-Corona Virus-2 (SARS-CoV-2 virus). In addition to detecting a single virus, this technique allows us to read its surface protein structure. In particular, we have been conducting research based on a variety of quantum optical approaches aimed at improving our ability to detect Corona Virus Disease-2019 (COVID-19) viral infection. Indeed, the detection of a small concentration of antibodies, after an infection has passed, is a challenging problem. Likewise, the early detection of disease, even before a detectible antibody population has been established, is very important. Our team is researching both aspects of this problem. The paper is written to stimulate the interest of both physical and biological scientists in this important problem. It is thus written as a combination of tutorial (review) and future work (preview). We join Prof. Federico Capasso and Editor Dennis Couwenberg in expressing our appreciation to all those working so heroically on all aspects of the COVID-19 problem. And we thank Drs. Capasso and Couwenberg for their invitation to write this paper.more » « less
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This work presents a proof-of-concept demonstration of a novel inductive transducer, the femtoMag, that can be integrated with a lateral-flow assay (LFA) to provide detection and quantification of molecular biomarkers. The femtoMag transducer is manufactured using a low-cost printed circuit board (PCB) technology and can be controlled by relatively inexpensive electronics. It allows rapid high-precision quantification of the number (or amount) of superparamagnetic nanoparticle reporters along the length of an LFA test strip. It has a detection limit of 10−10 emu, which is equivalent to detecting 4 ng of superparamagnetic iron oxide (Fe3O4) nanoparticles. The femtoMag was used to quantify the hCG pregnancy hormone by quantifying the number of 200 nm magnetic reporters (superparamagnetic Fe3O4 nanoparticles embedded into a polymer matrix) immuno-captured within the test line of the LFA strip. A sensitivity of 100 pg/mL has been demonstrated. Upon further design and control electronics improvements, the sensitivity is projected to be better than 10 pg/mL. Analysis suggests that an average of 109 hCG molecules are needed to specifically bind 107 nanoparticles in the test line. The ratio of the number of hCG molecules in the sample to the number of reporters in the test line increases monotonically from 20 to 500 as the hCG concentration increases from 0.1 ng/mL to 10 ng/mL. The low-cost easy-to-use femtoMag platform offers high-sensitivity/high-precision target analyte quantification and promises to bring state-of-the-art medical diagnostic tests to the point of care.more » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1093/protein/gzab010
