Subtyping of Magnetically Isolated Breast Cancer Cells Using Magnetic Force Microscopy
Abstract
Circulating tumor cell (CTC) which recently arisen as potential sources for monitoring and characterizing non-haematologic cancers and their metastatic derivatives. Immunomagnetic microbeads and magnetic nanoparticles (MNPs) have been extensively explored to isolate CTCs from blood samples. However, MNPs attached on the membrane protein are interrupted further analysis to distinguish the cancer subtype by consumption or blocking the target surface marker. Here, an MNP-mediated analysis method for surface marker expression profile by magnetic force microscopy (MFM) is described. Two MNPs, zinc ferrite and iron oxide, are showed distinct phase shift (-16.5° and -3.7°, respectively) signal on the MFM images. The antibody conjugated MNPs are successfully isolated target cells without giving damage to the cell. The MFM image of MNP decorated cells show clear differences between two breast cancer cell lines, MCF-7 and SK-BR-3, which proof the cancer subtyping property using MFM method. To confirmation of the surface marker consumption during the cell isolation, antibody-conjugated quantum dots and drug-loaded oleosome are treated on the cells, thereby MNP decorated cells are survived. This newly developed MFM analysis method provides a new direction to utilize the MNP for the surface marker expression phenotypes.
Read the paper
Related studies
- Quantum dots: synthesis, bioapplications, and toxicity
- A Guide to In Silico Drug Design
- Quantum Computing for Healthcare: A Review
- Automated in Silico Design of Homogeneous Catalysts
- Identification of Novel 3-Hydroxy-pyran-4-One Derivatives as Potent HIV-1 Integrase Inhibitors Using in silico Structure-Based Combinatorial Library Design Approach
- In silico structural elucidation of RNA-dependent RNA polymerase towards the identification of potential Crimean-Congo Hemorrhagic Fever Virus inhibitors
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.