Issue 14, 2016

Quantitative real-time detection of carcinoembryonic antigen (CEA) from pancreatic cyst fluid using 3-D surface molecular imprinting

Abstract

In this study, a sensitive, yet robust, biosensing system with real-time electrochemical readout was developed. The biosensor system was applied to the detection of carcinoembryonic antigen (CEA), which is a common marker for many cancers such as pancreatic, breast, and colon cancer. Real time detection of CEA during a medical procedure can be used to make critical decisions regarding further surgical intervention. CEA was templated on gold surface (RMS roughness ∼3–4 nm) coated with a hydrophilic self-assembled monolayer (SAM) on the working electrode of an open circuit potentiometric network. The subsequent removal of template CEA makes the biosensor capable of CEA detection based on its specific structure and conformation. The molecular imprinting (MI) biosensor was further calibrated using the potentiometric responses in solutions with known CEA concentrations and a detection limit of 0.5 ng ml−1 was achieved. Potentiometric sensing was then applied to pancreatic cyst fluid samples obtained from 18 patients when the cyst fluid was also evaluated using ELISA in a certified pathology laboratory. Excellent agreement was obtained between the quantitation of CEA obtained by both the ELISA and MI biosensor detection for CEA. A 3-D MI model, using the natural rms roughness of PVD gold layers, is presented to explain the high degree of sensitivity and linearity observed in those experiments.

Graphical abstract: Quantitative real-time detection of carcinoembryonic antigen (CEA) from pancreatic cyst fluid using 3-D surface molecular imprinting

Supplementary files

Article information

Article type
Paper
Submitted
22 Feb 2016
Accepted
27 Apr 2016
First published
10 May 2016

Analyst, 2016,141, 4424-4431

Quantitative real-time detection of carcinoembryonic antigen (CEA) from pancreatic cyst fluid using 3-D surface molecular imprinting

Y. Yu, Q. Zhang, J. Buscaglia, C. Chang, Y. Liu, Z. Yang, Y. Guo, Y. Wang, K. Levon and M. Rafailovich, Analyst, 2016, 141, 4424 DOI: 10.1039/C6AN00375C

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