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Detection of carcinoembryonic antigen using aggregation-induced emission luminogens empowered triple-format biosensor
Yongfeng Lu1, Caihong Liu2, Yanling Liu3
1Department of Oncology & Cancer Institute, Sichuan Academy of Medical Sciences, Sichuan Provincial People's Hospital Chengdu, Sichuan 610072, China; School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China; Department of Laboratory Medicine and Sichuan Provincial Key Laboratory for Human Disease Gene Study, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu 610054, China.
Abstract:
Conventional fluorescent probes with weak fluorescence signals and aggregation-caused quenching effect limits in biomarkers detection, thus requiring many labeled target molecules to combine their output to achieve higher signal-to noise. Here, we harness a "immune-sandwich" based affinity sensor with development of ultrabright aggregation-induced emission luminogens (AIEgens) microspheres as signal reporter. The fabricated sensor can simultaneously permit triple detection formats by naked eye, spectrum, and computer vision counting (termed "NeSCV sensor"). This sensor demonstrates the ability to qualitatively and quantitatively screen for carcinoembryonic antigen (CEA) in serum samples from lung cancer patients and healthy controls. Specifically, CEA detection can be performed through three modes: (1) visual identification of aggregated immune-complex with naked eyes, (2) detection of dispersed immuno-complexes in solution using a spectrometer, and (3) analysis of drop-casted immuno-complexes on a solid substrate with a fluorescence microscope. The sensor exhibits a linear range from 1 fg/mL to 10 ng/mL, with a limit of quantification of 1 fg/mL. The NeSCV sensor surpasses the conventional enzyme-linked immunosorbent assay (ELISA), offering a limit of quantification that is nearly 7.8 × 104 times lower. The NeSCV sensor demonstrates high selectivity, accuracy and sensitivity in detecting serum samples from 28 lung cancer patients and 26 health controls with reduced serum volume and time requirements. A blind test conducted on an independent validation cohort yielded an accuracy rate of 90%, confirming the platform's high reliability and robustness. This sensor holds potential for early pathological identification, effective treatment monitoring, and advancing personalized medicine.
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