Enhanced imaging of glycan expressing cancer cells using poly(glycidyl methacrylate)-grafted silica nanospheres labeled with quantum dots.

Enhanced imaging of glycan expressing cancer cells using poly(glycidyl methacrylate)-grafted silica nanospheres labeled with quantum dots.

Yang, Yaqiong;Qian, Xiaoping;Zhang, Lihui;Miao, Wenjun;Ming, Dengming;Jiang, Ling;Huang, He;
analytica chimica acta 2020 Vol. 1095 pp. 138-145
198
yang2020enhancedanalytica

Abstract

Glycosylation on the cell surface contains abundant biological information, and detecting the glycan on cell surfaces can offer critical insight into biology and diseases. Here, a signal amplification strategy for the sensitive detection of glycan expression on the cell surface was proposed. In this approach, glycans on the cell surface were detected with poly(glycidyl methacrylate)-grafted silica nanosphere labeled with quantum dots (QDs) and biotin through the specific affinity reaction of avidin-biotin on the cancer cells. Glycans on the cell surface were first labeled via selective oxidization of sialyl groups into aldehydes by periodate. Aniline-catalyzed hydrazone ligation with biotin hydrazide was then used for the specific recognition to avidin. The nanoprobe was fabricated with "living" SiO nanoparticles with alkyl bromide groups on their surfaces. They were then subsequently grafted with poly(glycidyl methacrylate) (PGMA) brushes via the successive surface-initiated atom transfer radical polymerization. The CdTe QDs and biotin were immobilized through a ring-open reaction with epoxy groups in the PGMA brushes to obtain QDs/biotin-polymer brush-functionalized silica nanosphere (SiO-PGMA-QDs/biotin). Enhanced sensitivity could be achieved by an increase in CdTe QDs loading per assay event, because of the large number of surface functional epoxy groups offered by the PGMA. As a result, fluorescence signal increased versus the unamplified method. This method successfully demonstrates a simple, specific, and potent method to detect glycans on the cell surface.

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ID: 104548
Ref Key: yang2020enhancedanalytica
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