Aptamer based surface plasma resonance assay for direct detection of neuron specific enolase and progastrin-releasing peptide (31-98). Issue 51 (29th September 2021)

• Record Type: Journal Article
• Authors: Sun, Linlin; Shen, Kemin; Zhang, Jianbin; Wan, Wenjuan; Cao, Wenjun; Wang, Zhijun; Guo, Chongzheng
• Is Part Of: RSC advances. Volume 11:Issue 51(2021)
• Page Start: 32135
• Page End: 32142
• Publication Date: 2021-09-29
• Subjects: Chemistry -- Periodicals; 540.5
• DOI: 10.1039/d1ra05041a
• Languages: English
• ISSNs: 2046-2069

Abstract

Abstract : Direct detection of neuron specific enolase and progastrin-releasing peptide (31-98) were achieved by measuring the target-induced SPR response of an aptamer-coated chip. Abstract : Neuron specific enolase (NSE) and progastrin-releasing peptide (31-98) (ProGRP31-98) are considered as reliable biomarkers of small cell lung cancer (SCLC). Sensitive determinations of NSE and ProGRP31-98 show great significance in disease surveillance, clinical diagnosis, efficacy evaluation and prognostic judgment. However, the conventional detection methods have the disadvantages of poor stability, tedious operation, and being very time consuming. Herein, we developed an aptamer-based surface plasmon resonance (SPR) assay in a direct format for NSE and ProGRP31-98 detection. The aptamer was loaded on a sensor chip and used as an affinity ligand. With sample injection, SPR signals increased due to the association of the target to the aptamer coated chip. Further dissociation and regeneration allowed this aptamer sensor chip to be used for the next sample analysis. We achieved sensitive detection of NSE and ProGRP31-98 by measuring the affinity binding-induced SPR responses. The detection limits for NSE and ProGRP31-98 were 3.9 nM and 15.6 nM, respectively. The aptamer sensor chip is stable and reusable, and has potential for diluted human serum analysis. This assay presents strengths in simplicity, rapidity, low material consumption, real time analysis and ease of implementing high throughput and automatic detection. It is promising for application in clinical disease-related biomarkers analysis.