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Pradanas-González F, Peltomaa R, Lahtinen S, Luque-Uría Á, Rodríguez Y, Navarro-Villoslada F, Maragos CM, Soukka T, Moreno-Bondi MC, Benito-Peña E. Upconversion-Linked Immunosorbent Assay for the Biomimetic Detection of the Mycotoxin Cyclopiazonic Acid. Anal Chem 2024; 96:20115-20122. [PMID: 39644222 DOI: 10.1021/acs.analchem.4c05168] [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: 12/09/2024]
Abstract
The neurotoxin α-cyclopiazonic acid (CPA) is an emerging mycotoxin produced as a secondary metabolite by several fungi species (i.e., Penicillium spp. and Aspergillus spp.). CPA commonly contaminates maize, crops, cheese, and wine. In this work, CPA detection in foodstuff was accomplished by the innovative integration of two strategies: upconversion nanoparticles (UCNPs) and epitope-mimicking peptides, to develop a competitive upconversion-linked immunosorbent assay (ULISA). We have applied UCNPs (type NaYF4:Yb3+, Er3+) as background-free optical labels due to their anti-Stokes shift with excitation in the near-infrared region and emission in the ultraviolet-visible region. Moreover, a CPA epitope-mimicking cyclic peptide (A2) was used as a substitute for the toxin-conjugates traditionally applied to competitive assays. UCNPs were decorated with an anti-CPA fragment antigen-binding antibody (UCNP-Fab), and CPA detection was achieved through competition with a biotinylated CPA epitope-mimicking cyclic peptide (A2-biotin, ACNWWDLTLC-GGGSK (Biotin)-NH2), anchored to a streptavidin-coated microtiter plate, for antibody binding. The ULISA platform offers ultrasensitive detection of CPA (limit of detection of 1.3 pg mL-1 and IC50 value of 15 pg mL-1), and no cross-reactivity was observed with other coproduced mycotoxins. These results substantially outperformed the analytical features of conventional heterogeneous immunoassays based on enzymatic detection. Additionally, the use of advanced computational tools, such as MOE and Alphafold AI, proved advantageous in elucidating the molecular interactions between the antibody and the epitope, providing insights that enhance the rational design of immunoassays. The proposed ULISA was applied to detect CPA in spiked maize samples, and the results were validated by high-performance liquid chromatography coupled to a tandem mass spectrometry detector (HPLC-MS/MS).
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Affiliation(s)
- Fernando Pradanas-González
- Department of Analytical Chemistry, Faculty of Chemistry, Complutense University of Madrid, Ciudad Universitaria, 28040 Madrid, Spain
| | - Riikka Peltomaa
- Department of Life Technologies/Biotechnology, University of Turku, Kiinamyllynkatu 10, 20520 Turku, Finland
| | - Satu Lahtinen
- Department of Life Technologies/Biotechnology, University of Turku, Kiinamyllynkatu 10, 20520 Turku, Finland
| | - Álvaro Luque-Uría
- Department of Analytical Chemistry, Faculty of Chemistry, Complutense University of Madrid, Ciudad Universitaria, 28040 Madrid, Spain
| | - Yoel Rodríguez
- Department of Natural Sciences, Hostos Community College of The City University of New York, 500 Grand Concourse, Bronx, New York, New York 10451, United States
- Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, 1425 Madison Avenue, New York, New York 10029, United States
| | - Fernando Navarro-Villoslada
- Department of Analytical Chemistry, Faculty of Chemistry, Complutense University of Madrid, Ciudad Universitaria, 28040 Madrid, Spain
| | - Chris M Maragos
- Mycotoxin Prevention and Applied Microbiology Research Unit, National Center for Agricultural Utilization Research, Agricultural Research Service, USDA, 1815 N University Street, Peoria, Illinois 61604, United States
| | - Tero Soukka
- Department of Life Technologies/Biotechnology, University of Turku, Kiinamyllynkatu 10, 20520 Turku, Finland
| | - María C Moreno-Bondi
- Department of Analytical Chemistry, Faculty of Chemistry, Complutense University of Madrid, Ciudad Universitaria, 28040 Madrid, Spain
| | - Elena Benito-Peña
- Department of Analytical Chemistry, Faculty of Chemistry, Complutense University of Madrid, Ciudad Universitaria, 28040 Madrid, Spain
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Wu J, Wu J, Wei W, Zhang Y, Chen Q. Upconversion Nanoparticles Based Sensing: From Design to Point-of-Care Testing. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2024; 20:e2311729. [PMID: 38415811 DOI: 10.1002/smll.202311729] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/16/2023] [Revised: 01/29/2024] [Indexed: 02/29/2024]
Abstract
Rare earth-doped upconversion nanoparticles (UCNPs) have achieved a wide range of applications in the sensing field due to their unique anti-Stokes luminescence property, minimized background interference, excellent biocompatibility, and stable physicochemical properties. However, UCNPs-based sensing platforms still face several challenges, including inherent limitations from UCNPs such as low quantum yields and narrow absorption cross-sections, as well as constraints related to energy transfer efficiencies in sensing systems. Therefore, the construction of high-performance UCNPs-based sensing platforms is an important cornerstone for conducting relevant research. This work begins by providing a brief overview of the upconversion luminescence mechanism in UCNPs. Subsequently, it offers a comprehensive summary of the sensors' types, design principles, and optimized design strategies for UCNPs sensing platforms. More cost-effective and promising point-of-care testing applications implemented based on UCNPs sensing systems are also summarized. Finally, this work addresses the future challenges and prospects for UCNPs-based sensing platforms.
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Affiliation(s)
- Jizhong Wu
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, 212013, P.R. China
- Department of Biomedical Engineering, Faculty of Engineering, National University of Singapore, Singapore, 117583
| | - Jiaxi Wu
- Department of Biomedical Engineering, Faculty of Engineering, National University of Singapore, Singapore, 117583
| | - Wenya Wei
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, 212013, P.R. China
- College of Ocean Food and Biological Engineering, Jimei University, Xiamen, 361021, P.R. China
| | - Yong Zhang
- Department of Biomedical Engineering, City University of Hong Kong, Kowloon, 999077, Hong Kong
| | - Quansheng Chen
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, 212013, P.R. China
- College of Ocean Food and Biological Engineering, Jimei University, Xiamen, 361021, P.R. China
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