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. 2022 Sep 9;12(9):746.
doi: 10.3390/bios12090746.

Ultrasensitive and Rapid Detection of N-Terminal Pro-B-Type Natriuretic Peptide (NT-proBNP) Using Fiber Optic Nanogold-Linked Immunosorbent Assay

Affiliations

Ultrasensitive and Rapid Detection of N-Terminal Pro-B-Type Natriuretic Peptide (NT-proBNP) Using Fiber Optic Nanogold-Linked Immunosorbent Assay

Han-Long Liu et al. Biosensors (Basel). .

Abstract

The N-terminal pro-brain natriuretic peptide (NT-proBNP) is considered an important blood biomarker for heart failure. Herein, we report about a fiber optic nanogold-linked immunosorbent assay (FONLISA) method for the rapid, sensitive, and low-cost detection of NT-proBNP. The method is based on a sandwich immunoassay approach that uses two monoclonal NT-proBNP antibodies, a capture antibody (AbC), and a detection antibody (AbD). AbD is conjugated to a free gold nanoparticle (AuNP) to form the free AuNP@AbD conjugate, and AbC is immobilized on an unclad segment of an optical fiber. The detection of analyte (A), in this case NT-proBNP, is based on the signal change due to the formation of an AuNP@AbD-A-AbC complex on the fiber core surface, where a green light transmitted through the optical fiber will decrease in intensity due to light absorption by AuNPs via the localized surface plasmon resonance effect. This method provides a wide linear dynamic range of 0.50~5000 pg·mL-1 and a limit of detection of 0.058 pg·mL-1 for NT-proBNP. Finally, the method exhibits good correlation (r = 0.979) with the commercial central laboratory-based electrochemiluminescent immunoassay method that uses a Roche Cobas e411 instrument. Hence, our method is potentially a suitable tool for point-of-care testing.

Keywords: N-terminal pro-brain natriuretic peptide; fiber optic nanogold-linked immunosorbent assay; heart failure; localized surface plasmon resonance.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Schematic representation of the FONLISA strategy in the FOPPR sensing system for detection of NT-proBNP. The capture antibody (AbC, 29D12) is immobilized on the sensing region, and the detection antibody (AbD, 15C4) is conjugated to AuNP. When an analyte is sandwiched between the two antibodies, the green light generated from the LED (532 nm) will pass beneath the sensing region and be absorbed by AuNP to finally reach the PD.
Figure 2
Figure 2
(A) Schematic illustration of the experimental setup used for the biosensing system. (B) A photograph of the setup.
Figure 3
Figure 3
(A) Schematic of the process from citrate-capped AuNP to AuNP-SAM, and then to AuNP@AbD conjugate. (B) Corresponding UV-vis spectra. (C) TEM image of citrate-capped AuNPs. (D) Corresponding representative DLS results.
Figure 4
Figure 4
Calibration curves for quantitation of detection antibody and capture antibody immobilized on (A) AuNP and (B) sensing fiber, respectively. The black square data points represent the calibration points. The red circle and magenta triangle data points show the values obtained for the amount of antibody molecules immobilized on (A) AuNP and (B) sensing fiber, respectively. (C) Size estimation of an antibody.
Figure 5
Figure 5
Real-time sensorgram showing the NSA responses of a sensing fiber upon injection of an AuNP@AbD solution (~2.5 nM), and then a PBS solution.
Figure 6
Figure 6
(A) Sensorgram in response to sequential multiple injections of NT-proBNP standards with concentrations of 0.5, 5.0, 50, 500, and 5000 pg mL−1. (B) Corresponding standard calibration curve for NT-proBNP, with all calibration points denoted as black squares (n = 3). Sensor responses with serum-spiked NT-proBNP standards at concentrations of 1.0 × 10−12, 1.0 × 10−10, and 2.5 × 10−10 g mL−1 are denoted as red circles (n = 3). Red dashed line represents the BAL.
Figure 7
Figure 7
Correlation of results between FONLISA and Roche Cobas e411 for detection of NT-proBNP in blood plasma samples. The red line is the regression line, and the blue dashed lines are the 95% confidence intervals for the regression model.

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