Frequency Mixing Magnetic Detection Scanner for Imaging Magnetic Particles in Planar Samples

Development and optimization of a frequency mixing sensor for adjacent samples quantitative detection on a lateral flow assay

Frequency-mixing technology has been widely used to precisely identify magnetic nanoparticles in applications of quantitative biomedical detection in recent years. Examples include immune adsorption, lateral flow assays (LFAs), and biomagnetic imaging. However, the signals of magnetic response generated by adjacent magnetic samples interfere with each other owing to the small spacing between them in applications involving multi-sample detection (such as the LFA and multiplexing detection). Such signal interference prevents the biosensor from obtaining characteristic peaks related to the concentration of adjacent biomarkers from the magnetic response signals. Mathematical and physical models of the structure of sensors based on frequency-mixing techniques were developed. The theoretical model was verified and its key parameters were optimized by using simulations. A new frequency-mixing magnetic sensor structure was then designed and developed based on the model, and the key technical problem of signal crosstalk between adjacent samples was structurally solved. Finally, standard cards with stable magnetic properties were used to evaluate the performance of the sensor, and strips of the gastrin-17 (G-17) LFA were used to evaluate its potential for use in clinical applications. The results show that the minimum spacing between samples required by the optimized sensor to accurately identify them was only about 4-5 mm, and the minimum detectable concentration of G-17 was 11 pg mL-1 . This is a significant reduction in the required spacing between samples for multiplexing detection. The optimized sensor also has the potential for use in multi-channel synchronous signal acquisition, and can be used to detect synchronous magnetic signals in vivo.

Comparative Modeling of Frequency Mixing Measurements of Magnetic Nanoparticles Using Micromagnetic Simulations and Langevin Theory

Dual frequency magnetic excitation of magnetic nanoparticles (MNP) enables enhanced biosensing applications. This was studied from an experimental and theoretical perspective: nonlinear sum-frequency components of MNP exposed to dual-frequency magnetic excitation were measured as a function of static magnetic offset field. The Langevin model in thermodynamic equilibrium was fitted to the experimental data to derive parameters of the lognormal core size distribution. These parameters were subsequently used as inputs for micromagnetic Monte-Carlo (MC)-simulations. From the hysteresis loops obtained from MC-simulations, sum-frequency components were numerically demodulated and compared with both experiment and Langevin model predictions. From the latter, we derived that approximately 90% of the frequency mixing magnetic response signal is generated by the largest 10% of MNP. We therefore suggest that small particles do not contribute to the frequency mixing signal, which is supported by MC-simulation results. Both theoretical approaches describe the experimental signal shapes well, but with notable differences between experiment and micromagnetic simulations. These deviations could result from Brownian relaxations which are, albeit experimentally inhibited, included in MC-simulation, or (yet unconsidered) cluster-effects of MNP, or inaccurately derived input for MC-simulations, because the largest particles dominate the experimental signal but concurrently do not fulfill the precondition of thermodynamic equilibrium required by Langevin theory.

Construction of 3D-rendering imaging of an ischemic rat brain model using the planar FMMD technique

Occlusion of the major cerebral artery usually results in brain hypoxic-ischemic injury, which evokes neuroinflammation and microglial activation. Activated microglia are considered a source of multiple neurotoxic factors, such as reactive oxygen species (ROS), in the central nervous system (CNS). We herein present a 3D-rendering brain imaging technique in an experimental rodent model of cerebral ischemia based on 2D magnetic images of superparamagnetic iron oxide nanoparticles (SPIONs) using the planar frequency mixing magnetic detection (p-FMMD) technique. A rat model of cerebral ischemia was established by unilateral middle cerebral artery occlusion with reperfusion (MCAO/R) injury. 2,3,5-Triphenyltetrazolium chloride (TTC) staining was performed to demonstrate the irreversibly damaged ischemic brain tissues, and double immunofluorescent labeling of OX6 (activated microglial marker) and ethidium (ROS marker) was conducted to confirm ROS generation in the activated microglia in the infarcted brain region. The ischemic brain sections treated with OX6-conjugated SPIONs were scanned using our p-FMMD system, yielding 2D images on the basis of the nonlinear magnetic characteristics inherent in SPIONs. The p-FMMD signal images representing microglia activation show an infarct ratio of 44.6 ± 7.1% compared to the contralateral counterpart, which is smaller than observed by TTC (60.9 ± 4.9%) or magnetic resonance imaging (MRI, 65.7 ± 2.7%). Furthermore, we developed a 3D-rendering brain imaging process based on the 2D p-FMMD signal images. The 3D reconstructed model showed a decreased ratio of coincidence of the ischemic regions compared with MRI models. In this study, we successfully conducted a feasibility test on whether our p-FMMD technology, a technique for signaling and imaging based on the nonlinearity of SPIONs, can be used to visualize the ischemic brain region in real time by detecting activated microglia in an MCAO/R animal model. Therefore, our method might allow for a different approach to analyze the pathophysiology of ischemic stroke through molecular imaging. Furthermore, we propose that this magnetic particle imaging (MPI) technique that detects the nonlinear magnetization properties of SPIONs could be applied not only to a stroke model but also to various types of pathophysiological studies as a new bioimaging tool.