Issue 3, 2024

Fluorescence and colorimetric dual-mode multienzyme cascade nanoplatform based on CuNCs/FeMn-ZIF-8/PCN for detection of sarcosine

Abstract

It is critical to develop a highly efficient and sensitive method for detecting the biomarker sarcosine (SA) of prostate cancer due to its importance for men's health. In our work, a fluorescence (FL) and colorimetric dual-mode multienzyme cascade nanoplatform for SA detection was designed and constructed. CuNCs/FeMn-ZIF-8/PCN nanocomposites with high FL properties and peroxidase-like activity were successfully prepared by encapsulating copper nanoclusters (CuNCs) into FeMn-ZIF-8 and then loaded onto P-doped graphitic carbon nitride (PCN). Furthermore, the nanocomposites served as carriers for the immobilization of sarcosine oxidase (SOX) to construct a high-efficiency dual-mode multienzyme cascade nanoplatform CuNCs/SOX@FeMn-ZIF-8/PCN for the detection of SA. The intermediate H2O2 generated in the cascade caused the FL quenching of nanocomposites and the discoloration of 3,3′,5,5′-tetramethylbenzidin. The linear ranges for SA detection in the dual-mode system were 1–100 μM (FL) and 1–200 μM (colorimetric), with detection limits of 0.34 and 0.59 μM, respectively. This nanoplatform exhibited notable repeatability, specificity, and stability, making it suitable for detecting sarcosine in real human urine samples. Therefore, this dual-mode multienzyme cascade nanoplatform would have a potential applicative prospect for detecting SA and other biomarkers in real clinical samples.

Graphical abstract: Fluorescence and colorimetric dual-mode multienzyme cascade nanoplatform based on CuNCs/FeMn-ZIF-8/PCN for detection of sarcosine

Supplementary files

Article information

Article type
Paper
Submitted
15 Nov 2023
Accepted
20 Dec 2023
First published
03 Jan 2024

Analyst, 2024,149, 935-946

Fluorescence and colorimetric dual-mode multienzyme cascade nanoplatform based on CuNCs/FeMn-ZIF-8/PCN for detection of sarcosine

Y. Wu, C. Ke, Z. Song, H. Zhu, H. Guo, H. Sun and M. Liu, Analyst, 2024, 149, 935 DOI: 10.1039/D3AN01984E

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