Abstract
Quantifying the glycan expression status on cell surfaces is of vital importance for insight into the glycan function in biological processes and related diseases. Here we developed a versatile aptasensor for electrochemical quantification of cell surface glycan by taking advantage of the cell-specific aptamer, and the lectin-functionalized gold nanoparticles acting as both a glycan recognition unit and a signal amplification probe. To construct the aptasensor, amine-functionalized mucin 1 protein (MUC1) aptamer was first covalently conjugated to carboxylated-magnetic beads (MBs) using the succinimide coupling (EDC-NHS) method. On the basis of the specific recognition between aptamer and MUC1 protein that overexpressed on the surface of MCF-7 cells, the aptamer conjugated MBs showed a predominant capability for cell capture with high selectivity. Moreover, a lectin-based nanoprobe was designed by noncovalent assembly of concanavalin A (ConA) on gold nanoparticles (AuNPs). This nanoprobe incorporated the abilities of both the specific carbohydrate recognition and the signal amplification based on the gold-promoted reduction of silver ions. By coupling with electrochemical stripping analysis, the proposed sandwich-type cytosensor showed an excellent analytical performance for the ultrasensitive detection of MCF-7 cells and quantification of cell surface glycan. More importantly, taking advantage of Con A-gold nanoprobe catalyzed silver enhancement, the proposed method was further used for naked-eye tracking glycolytic inhibition in living cells. This aptasensor holds great promise as a new point-of-care diagnostic tool for analyzing glycan expression on living cells and further helps cancer diagnosis and treatment.
| Original language | English |
|---|---|
| Pages (from-to) | 937-945 |
| Number of pages | 9 |
| Journal | Biosensors and Bioelectronics |
| Volume | 89 |
| DOIs | |
| State | Published - 15 Mar 2017 |
Keywords
- Aptamer
- Electrochemical sensors
- Glycan analysis
- Gold nanoprobse
- Naked-eye detection