Optimizing SPION Labeling for Single-Cell Magnetic Microscopy

Abstract

This study explores the correlation between the iron mass on cell surfaces and the resultant magnetic field. Human colorectal cancer cells (HT29 line) were labeled with varying concentrations of superparamagnetic iron oxide nanoparticles (SPIONs) and imaged via an NV center widefield magnetic microscope. To assess the labeling efficacy, a convolutional neural network trained on simulated magnetic dipole data was utilized to reconstruct key labeling parameters on a cell-by-cell basis, including cell diameter, sensor proximity, and the iron mass associated with surface-bound SPIONs. Our analysis provided quantitative metrics for these parameters across a range of labeling concentrations. The findings indicated that increasing the SPION concentration enhances both the cell-surface iron mass and magnetic field strength, demonstrating a saturation effect. This methodology offers a coherent framework for the quantitative, high-throughput characterization of magnetically labeled cells, presenting significant implications for the fields of cell biology and magnetic sensing applications.