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Mladenovic Stokanic M, Simovic A, Jovanovic V, Radomirovic M, Udovicki B, Krstic Ristivojevic M, Djukic T, Vasovic T, Acimovic J, Sabljic L, Lukic I, Kovacevic A, Cujic D, Gnjatovic M, Smiljanic K, Stojadinovic M, Radosavljevic J, Stanic-Vucinic D, Stojanovic M, Rajkovic A, Cirkovic Velickovic T. Sandwich ELISA for the Quantification of Nucleocapsid Protein of SARS-CoV-2 Based on Polyclonal Antibodies from Two Different Species. Int J Mol Sci 2023; 25:333. [PMID: 38203504 PMCID: PMC10778659 DOI: 10.3390/ijms25010333] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Revised: 12/15/2023] [Accepted: 12/21/2023] [Indexed: 01/12/2024] Open
Abstract
In this study, a cost-effective sandwich ELISA test, based on polyclonal antibodies, for routine quantification SARS-CoV-2 nucleocapsid (N) protein was developed. The recombinant N protein was produced and used for the production of mice and rabbit antisera. Polyclonal N protein-specific antibodies served as capture and detection antibodies. The prototype ELISA has LOD 0.93 ng/mL and LOQ 5.3 ng/mL, with a linear range of 1.52-48.83 ng/mL. N protein heat pretreatment (56 °C, 1 h) decreased, while pretreatment with 1% Triton X-100 increased analytical ELISA sensitivity. The diagnostic specificity of ELISA was 100% (95% CI, 91.19-100.00%) and sensitivity was 52.94% (95% CI, 35.13-70.22%) compared to rtRT-PCR (Ct < 40). Profoundly higher sensitivity was obtained using patient samples mostly containing Wuhan-similar variants (Wuhan, alpha, and delta), 62.50% (95% CI, 40.59 to 81.20%), in comparison to samples mostly containing Wuhan-distant variants (Omicron) 30.00% (6.67-65.25%). The developed product has relatively high diagnostic sensitivity in relation to its analytical sensitivity due to the usage of polyclonal antibodies from two species, providing a wide repertoire of antibodies against multiple N protein epitopes. Moreover, the fast, simple, and inexpensive production of polyclonal antibodies, as the most expensive assay components, would result in affordable antigen tests.
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Affiliation(s)
- Maja Mladenovic Stokanic
- Centre of Excellence for Molecular Food Sciences, Department of Biochemistry, Faculty of Chemistry, University of Belgrade, Studentski trg 12-16, 11000 Belgrade, Serbia
| | - Ana Simovic
- Centre of Excellence for Molecular Food Sciences, Department of Biochemistry, Faculty of Chemistry, University of Belgrade, Studentski trg 12-16, 11000 Belgrade, Serbia
| | - Vesna Jovanovic
- Centre of Excellence for Molecular Food Sciences, Department of Biochemistry, Faculty of Chemistry, University of Belgrade, Studentski trg 12-16, 11000 Belgrade, Serbia
| | - Mirjana Radomirovic
- Centre of Excellence for Molecular Food Sciences, Department of Biochemistry, Faculty of Chemistry, University of Belgrade, Studentski trg 12-16, 11000 Belgrade, Serbia
| | - Bozidar Udovicki
- Department of Food Safety and Quality Management, Faculty of Agriculture, University of Belgrade, Nemanjina 6, Zemun, 11080 Belgrade, Serbia
| | - Maja Krstic Ristivojevic
- Centre of Excellence for Molecular Food Sciences, Department of Biochemistry, Faculty of Chemistry, University of Belgrade, Studentski trg 12-16, 11000 Belgrade, Serbia
| | - Teodora Djukic
- Institute of Medical Chemistry, Faculty of Medicine, University of Belgrade, Višegradska 26, 11000 Belgrade, Serbia
| | - Tamara Vasovic
- Centre of Excellence for Molecular Food Sciences, Department of Biochemistry, Faculty of Chemistry, University of Belgrade, Studentski trg 12-16, 11000 Belgrade, Serbia
| | - Jelena Acimovic
- Centre of Excellence for Molecular Food Sciences, Department of Biochemistry, Faculty of Chemistry, University of Belgrade, Studentski trg 12-16, 11000 Belgrade, Serbia
| | - Ljiljana Sabljic
- Institute for the Application of Nuclear Energy—INEP, University of Belgrade, Banatska 31b, Zemun, 11080 Belgrade, Serbia
| | - Ivana Lukic
- Institute of Virology, Vaccines, and Sera–TORLAK, Vojvode Stepe 458, 11152 Belgrade, Serbia
| | - Ana Kovacevic
- Institute of Virology, Vaccines, and Sera–TORLAK, Vojvode Stepe 458, 11152 Belgrade, Serbia
| | - Danica Cujic
- Institute for the Application of Nuclear Energy—INEP, University of Belgrade, Banatska 31b, Zemun, 11080 Belgrade, Serbia
| | - Marija Gnjatovic
- Institute for the Application of Nuclear Energy—INEP, University of Belgrade, Banatska 31b, Zemun, 11080 Belgrade, Serbia
| | - Katarina Smiljanic
- Centre of Excellence for Molecular Food Sciences, Department of Biochemistry, Faculty of Chemistry, University of Belgrade, Studentski trg 12-16, 11000 Belgrade, Serbia
| | - Marija Stojadinovic
- Centre of Excellence for Molecular Food Sciences, Department of Biochemistry, Faculty of Chemistry, University of Belgrade, Studentski trg 12-16, 11000 Belgrade, Serbia
| | - Jelena Radosavljevic
- Centre of Excellence for Molecular Food Sciences, Department of Biochemistry, Faculty of Chemistry, University of Belgrade, Studentski trg 12-16, 11000 Belgrade, Serbia
| | - Dragana Stanic-Vucinic
- Centre of Excellence for Molecular Food Sciences, Department of Biochemistry, Faculty of Chemistry, University of Belgrade, Studentski trg 12-16, 11000 Belgrade, Serbia
| | - Marijana Stojanovic
- Department of Molecular Biology, Institute for Biological Research “Siniša Stanković”, University of Belgrade, 142 Despot Stefan Blvd., 11000 Belgrade, Serbia
| | - Andreja Rajkovic
- Department of Food Safety and Quality Management, Faculty of Agriculture, University of Belgrade, Nemanjina 6, Zemun, 11080 Belgrade, Serbia
- Faculty of Bioscience Engineering, Ghent University, Coupure Links 653, geb. A, B-9000 Ghent, Belgium
| | - Tanja Cirkovic Velickovic
- Centre of Excellence for Molecular Food Sciences, Department of Biochemistry, Faculty of Chemistry, University of Belgrade, Studentski trg 12-16, 11000 Belgrade, Serbia
- Faculty of Bioscience Engineering, Ghent University, Coupure Links 653, geb. A, B-9000 Ghent, Belgium
- Serbian Academy of Sciences and Arts, Kneza Mihaila 35, 11000 Belgrade, Serbia
- Global Campus, Ghent University, 119-5 Songdomunwha-ro, Yeonsu-gu, Incheon 21985, Republic of Korea
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Lee S, Yoo YK, Han SI, Lee D, Cho SY, Park C, Lee D, Yoon DS, Lee JH. Advancing diagnostic efficacy using a computer vision-assisted lateral flow assay for influenza and SARS-CoV-2 detection. Analyst 2023; 148:6001-6010. [PMID: 37882491 DOI: 10.1039/d3an01189e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2023]
Abstract
Lateral flow assays (LFAs) have emerged as indispensable tools for point-of-care testing during the pandemic era. However, the interpretation of results through unassisted visual inspection by untrained individuals poses inherent limitations. In our study, we propose a novel approach that combines computer vision (CV) and lightweight machine learning (ML) to overcome these limitations and significantly enhance the performance of LFAs. By incorporating CV-assisted analysis into the LFA assay, we achieved a remarkable three-fold improvement in analytical sensitivity for detecting Influenza A and for SARS-CoV-2 detection. The obtained R2 values reached approximately 0.95, respectively, demonstrating the effectiveness of our approach. Moreover, the integration of CV techniques with LFAs resulted in a substantial amplification of the colorimetric signal specifically for COVID-19 positive patient samples. Our proposed approach, which incorporates a simple machine learning algorithm, provides substantial enhancements in assay sensitivity, improving diagnostic efficacy and accessibility of point-of-care testing without requiring significant additional resources. Moreover, the simplicity of the machine learning algorithm enables its standalone use on a mobile phone, further enhancing its practicality for point-of-care testing.
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Affiliation(s)
- Seungmin Lee
- Department of Electrical Engineering, Kwangwoon University, 20 Kwangwoon-ro, Nowon, Seoul 01897, Republic of Korea.
- School of Biomedical Engineering, Korea University, 145 Anam-ro, Seongbuk, Seoul 02841, Republic of Korea.
| | - Yong Kyoung Yoo
- Department of Electronic Engineering, Catholic Kwandong University, 24, Beomil-ro 579 beon-gil, Gangneung-si, Gangwon-do 25601, Republic of Korea
| | - Sung Il Han
- CALTH Inc., Changeop-ro 54, Seongnam, Gyeonggi 13449, Republic of Korea
| | - Dongho Lee
- CALTH Inc., Changeop-ro 54, Seongnam, Gyeonggi 13449, Republic of Korea
| | - Sung-Yeon Cho
- Vaccine Bio Research Institute, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Division of Infectious Diseases, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Chulmin Park
- Division of Infectious Diseases, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Dongtak Lee
- School of Biomedical Engineering, Korea University, 145 Anam-ro, Seongbuk, Seoul 02841, Republic of Korea.
- Center for Nanomedicine, Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women's hospital, Boston, MA 02115, USA
- Harvard Medical School, Boston, MA, 02115, USA
| | - Dae Sung Yoon
- School of Biomedical Engineering, Korea University, 145 Anam-ro, Seongbuk, Seoul 02841, Republic of Korea.
- Interdisciplinary Program in Precision Public Health, Korea University, Seoul 02841, South Korea
- Astrion Inc., Seoul 02841, Republic of Korea
| | - Jeong Hoon Lee
- Department of Electrical Engineering, Kwangwoon University, 20 Kwangwoon-ro, Nowon, Seoul 01897, Republic of Korea.
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