Digital plasmonic immunosorbent assay for dynamic imaging detection of protein binding

Zhang, Wei; Dang, Tang; Liu, Ying; Liang, Jiawei; Xu, Hao; Liu, Gang Logan; Hu, Wenjun

doi:10.1016/j.snb.2021.130711

Cited by 9 publications

(5 citation statements)

References 41 publications

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“…This suggests that the main change in the microscopic image of the LSPR may be in the color of the image. In our previous studies [23][24][25][26], we determined that, by increasing the concentration of the molecules, the changes in the images were mainly in terms of the R . In this study, we found that the gray value histograms for the R channel of the sensor image shifted to the left (Figure 2b), and when AuNP-Ab was not added, although the frequency of pixels with different gray values would change, it was negligible compared to the AuNP-Ab added (Figure S1b, Figure 2c).…”

Section: Discussionmentioning

confidence: 96%

Applying Machine Learning with Localized Surface Plasmon Resonance Sensors to Detect SARS-CoV-2 Particles

et al. 2022

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The sudden outbreak of COVID-19 rapidly developed into a global pandemic, which caused tens of millions of infections and millions of deaths. Although SARS-CoV-2 is known to cause COVID-19, effective approaches to detect SARS-CoV-2 using a convenient, rapid, accurate, and low-cost method are lacking. To date, most of the diagnostic methods for patients with early infections are limited to the detection of viral nucleic acids via polymerase chain reaction (PCR), or antigens, using an enzyme-linked immunosorbent assay or a chemiluminescence immunoassay. This study developed a novel method that uses localized surface plasmon resonance (LSPR) sensors, optical imaging, and artificial intelligence methods to directly detect the SARS-CoV-2 virus particles without any sample preparation. The virus concentration can be qualitatively and quantitatively detected in the range of 125.28 to 106 vp/mL through a few steps within 12 min with a limit of detection (LOD) of 100 vp/mL. The accuracy of the SARS-CoV-2 positive or negative assessment was found to be greater than 97%, and this was demonstrated by establishing a regression machine learning model for the virus concentration prediction (R2 > 0.95).

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Section: Discussionmentioning

confidence: 96%

Applying Machine Learning with Localized Surface Plasmon Resonance Sensors to Detect SARS-CoV-2 Particles

et al. 2022

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“…The topic of our work is focused on the detection of biomarkers for the early screening of human diseases. Therefore, the human immunoglobulin G (IgG, a biomarker antibody of immune diseases [30] ), the human PSA (a biomarker of the prostate cancer [31] ), the human cardiac troponin I-C (cTnI-C, a biomarker of the myocardial damage [24] ), and the human c-reactive protein (CRP, a biomarker of the cardiovascular inflammation [32] ) are selected as controls. The PBS sample is used as the blank control, and the control sample of the mixture including the AFP, the IgG, the PSA, the cTnI-C, and the CRP is employed to emulate complex components in the human serum.…”

Section: Repeatability and Specificity Of The Immunoprobementioning

confidence: 99%

Optimally Configured Optical Fiber Near‐Field Enhanced Plasmonic Resonance Immunoprobe for the Detection of Alpha‐Fetoprotein

et al. 2023

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The detection of trace biomarkers is an important supplementary approach for early screening and diagnoses of tumors. An optical fiber near‐field enhanced plasmonic resonance immunoprobe is developed for the detection of the hepatocellular carcinoma biomarker, i.e., the alpha‐fetoprotein. Generic principles based on dispersion models and finite element analysis (FEA) models are developed to realize the optimized configuration of spectral characteristics of the immunoprobe. Dispersion models provide theoretical guidance for the design of the multilayer sensing structure from the perspective of the ray optics theory. FEA models provide theoretical guidance for the selection of coating materials from the perspective of the self‐defined dielectric constant ratio, i.e., the ratio of the real part to the imaginary part. The optimized configuration of the antibody coupling further improves the biosensing performance of the immunoprobe. The limit of detection (LOD) can reach down to 0.01 ng mL−1, which is one order of magnitude lower than those relevant reported works. Such a low LOD can more effectively avoid the accuracy degradation of detection results due to measurement errors. Human serum samples have also been detected, with the good precision achieved. This work shows promising prospects in applications of label‐free, low‐cost, rapid, and convenient early screening of tumors.

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“…The replica-molding technique was used for biosensor fabrication according to the methods described in our previous reports [24,25]. Firstly, the mold was photo-and ionetched to produce a tapered nanocup array on a silicon wafer containing nanopillars with a diameter, depth, and period of 200, 450, and 300 nm, respectively.…”

Section: Fabrication Of the Metaspr Chip And Biosensormentioning

confidence: 99%

A Metasurface Plasmonic Analysis Platform Combined with Gold Nanoparticles for Ultrasensitive Quantitative Detection of Small Molecules

Zhou¹,

Ji²,

Fan

et al. 2023

Biosensors

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Food safety related to drug residues in food has become a widespread public concern. Small-molecule drug residue analysis often relies on mass spectrometry, thin-layer chromatography, or enzyme-linked immunosorbent assays (ELISA). Some of these techniques have limited sensitivity and accuracy, while others are time-consuming, costly, and rely on specialized equipment that requires skilled operation. Therefore, the development of a sensitive, fast, and easy-to-operate biosensor could provide an accessible alternative to conventional small-molecule analysis. Here, we developed a nanocup array-enhanced metasurface plasmon resonance (MetaSPR) chip coupled with gold nanoparticles (AuNPs) (MSPRAN) to detect small molecules. As sulfamethazine drug residues in poultry eggs may cause health issues, we selected this as a model to evaluate the feasibility of using MSPRAN for small-molecule detection. The MSPRAN biosensor employed competitive immunoassay technology for sulfamethazine detection. The limit of detection was calculated as 73 pg/mL, with sensitivity approximately twice that of previously reported detection methods. Additionally, the recovery rate of the biosensor, tested in egg samples, was similar to that measured using ELISA. Overall, this newly developed MSPRAN biosensor platform for small-molecule detection provides fast and reliable results, facile operation, and is relatively cost-effective for application in food safety testing, environmental monitoring, or clinical diagnostics.

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Digital plasmonic immunosorbent assay for dynamic imaging detection of protein binding

Cited by 9 publications

References 41 publications

Applying Machine Learning with Localized Surface Plasmon Resonance Sensors to Detect SARS-CoV-2 Particles

Applying Machine Learning with Localized Surface Plasmon Resonance Sensors to Detect SARS-CoV-2 Particles

Optimally Configured Optical Fiber Near‐Field Enhanced Plasmonic Resonance Immunoprobe for the Detection of Alpha‐Fetoprotein

A Metasurface Plasmonic Analysis Platform Combined with Gold Nanoparticles for Ultrasensitive Quantitative Detection of Small Molecules

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