Optimizing SPION Labeling for Single-Cell Magnetic Microscopy

TL;DR

This paper presents a new high-throughput method for quantitatively analyzing SPION labeling on cells using magnetic microscopy and neural network reconstruction, revealing how iron mass correlates with magnetic field strength.

Contribution

It introduces a combined magnetic imaging and neural network approach for precise, cell-by-cell quantification of SPION labeling, advancing single-cell magnetic characterization techniques.

Findings

Increased SPION concentration raises surface iron mass and magnetic field strength.

The method achieves high-throughput, quantitative analysis of cell labeling.

A saturation effect was observed at higher SPION concentrations.

Abstract

This study explores the correlation between iron mass on cell surfaces and the resultant magnetic field. Human colorectal cancer cells (HT29 line) were labeled with varying concentrations of SPIONs and imaged via a 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 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.