Dual-target recognition sandwich assay based on core-shell magnetic mesoporous silica nanoparticles for sensitive...

A novel dual-target recognition sandwich strategy for selective capture and detection of MCF-7 breast cancer cells based on core-shell magnetic mesoporous silica (Fe₃O₄@nSiO₂@mSiO₂@apt) nanoparticles was developed. Fe₃O₄@nSiO₂@mSiO₂@apt nanoparticles, which were prepared by a layer-by-layer method and were used for the first time to capture cancer cells, have large surface areas, particularly accessible mesochannels, and good biocompatibility, enabling aptamers to be compactly anchored onto the surface of the core-shell magnetic nanoparticles. A mucin 1 protein (MUC1)-targeted Fe₃O₄@nSiO₂@mSiO₂@apt nanoparticle was used as an affinity magnetic isolate material to capture target MCF-7 cells selectively and to reduce interference through affinity interaction between the anti-MUC1 aptamer and the MUC1 protein over-expressed on the surface of the MCF-7 cells. Meanwhile, a folate receptor (FR)-targeted affinity fluorescent probe (FA-BSA-FITC) was developed by coupling folic acid and FITC to the surface of BSA, enabling high sensitivity, selective fluorescent labeling of FR over-expressed MCF-7 cells. A dual-target recognition sandwich assay was developed based on the MUC1-targeted magnetic nanoparticles and the FR-targeted fluorescent probes. Under optimum conditions, a quantitative assay of MCF-7 cells was achieved with a dynamic range of 10²-10⁵ cells/mL (R² = 0.9991). This assay showed high specificity and sensitivity to the target MCF-7 cells. Finally, the proposed strategy could be extended to detect MCF-7 cells in human plasma and whole blood with a recovery range of 86.1-104.0% and a RSD range of 1.2-8.4%, respectively. This indicates that the dual-target recognition method developed in this research exhibits good selectivity, anti-interference capability, and reliability even in plasma and whole blood samples and is more suitable for complex samples than previous targeted assays. Therefore, the approach proposed here may have great potential for early breast cancer diagnosis.