Detection of biogenic magnetic nanoparticles in rapidly dividing tumor cells by the nonlinear magnetization method

Abstract

Introduction: Genetically encoded nanoplatforms–bacterial nanocompartments (encapsulins) have demonstrated a remarkable capacity for innovation in the fields of biomedicine and biotechnology. These platforms have found novel applications in a variety of approaches, including magnetic resonance imaging (MRI), transmission electron microscopy (TEM), and high-resolution microscopy, among others. Particular attention has been given to the encapsulin system of the bacterium Quasibacillus thermotolerans (Qt). Divalent iron has been found to sequester within Qt shells, resulting in the formation of biogenic magnetic ferric oxide nanoparticles (MNPs) with T2 contrast properties. Recent studies have led to the successful obtaining of mammalian cells that stably express Qt genes and are capable of producing MNPs. These cells can be detected in vitro and in vivo using both MRI and the nonlinear magnetization method (magnetic particle quantification (MPQ) method). The objective of this study was to investigate the advantages and limitations of labeling mammalian cells with the Qt encapsulins. Methods: A rat C6 glioma cell line was engineered to express a red fluorescent protein (RFP) as an optical tag and a Qt nanocompartment as a magnetic tag by lentiviral transduction. The generated C6-RFP-Qt cells were characterized by inductively coupled plasma mass spectrometry (ICP-MS) and Perls staining as well as using the MPQ technique, fluorescent microscopy, and optical tomography. The in vivo study was conducted using severe combined immunodeficient (SCID) mice. Results: A prominent in vivo model of glioblastoma multiforme has undergone substantial enhancement. The magnetic signal retention time in C6-RFP-Qt cells was first estimated by the MPQ technique. Discussion: The findings indicated the potential for real-time monitoring of magnetic signal amplitude during cell proliferation process utilizing the MPQ method. The approach employed constitutes a simple yet more sensitive alternative to conventional methods for studying MNPs.