Surface-Enhanced Electrochemiluminescence Imaging for Multiplexed Immunoassays of Cancer Markers in Exhaled Breath Condensates.
Li DingShaohua XuYueyue HuangYuanyuan YaoYueliang WangLifen ChenYanbo ZengLei LiZhenyu LinLonghua GuoPublished in: Analytical chemistry (2022)
Recently we have demonstrated that the surface plasmon of noble metal nanoparticles can effectively enhance the ECL intensity of Ru(bpy) 3 2+ , and we named this detection principle as surface-enhanced electrochemiluminescence (SEECL-I). However, SEECL based on photomultiplier tube (PMT) detection can only detect one target at a time, which is not suitable for multiple targets detection. In this work, we combined our previous developed SEECL with a bioimaging device to develop a novel multiplexed immunassay for simultaneous and fast analysis of cancer markers. A core-shell nanocomposite consisted of gold-silicon dioxide nanoparticles doped with Ru(bpy) 3 2+ (Au@SiO 2 -Ru) with strong ECL emission was employed as ECL label due to the localized surface plasmon resonance (LSPR) of AuNPs, which can significantly enhance the ECL emission of Ru(bpy )3 2+ . The ECL signals from the 4 × 4 electrode arrays were collected using the constant potential method (current-time curve method) imaging with a sCOMS camera. As a proof-of-concept application, we demonstrated the use of the proposed SEECL-I for simultaneous detection of carcinoembryonic antigen (CEA), neuron specific enolase (NSE), and squamous cell carcinoma antigen (SCC) in exhaled breath condensates (EBCs) with low detection limit (LOD) of 0.17, 0.33, and 0.33 pg/mL (S/N = 3), respectively. The results demonstrated that the proposed SEECL-I strategy can provide a high sensitivity, fast analysis, and high-throughput platform for clinical diagnosis of cancer markers in EBCs.
Keyphrases
- loop mediated isothermal amplification
- high throughput
- papillary thyroid
- squamous cell carcinoma
- energy transfer
- real time pcr
- label free
- quantum dots
- sensitive detection
- high resolution
- lymph node metastasis
- machine learning
- high intensity
- young adults
- radiation therapy
- gold nanoparticles
- deep learning
- convolutional neural network
- highly efficient