3D-Imaging and Quantification of Magnetic Nanoparticle Uptake by Living Cells

Magnetic particle imaging (MPI) is a non-invasive, non-ionizing imaging technique for the visualization and quantification of magnetic nanoparticles (MNPs). The technique is especially suitable for cell imaging as it offers zero background contribution from the surrounding tissue, high sensitivity, and good spatial and temporal resolutions. Previous studies have demonstrated that the dynamic magnetic behaviour of MNPs changes during cellular binding and internalization. In this study, we demonstrate how this information is encoded in the MPI imaging signal. Through MPI imaging we are able to discriminate between free and cell-bound MNPs in reconstructed images. This technique was used to image and quantify the changes that occur in-vitro when free MNPs come into contact with cells and undergo cellularuptake over time. The quantitative MPI results were verified by a phenanthroline assay. The results showed a mean relative difference of 23.8% for the quantification of cell-bound MNPs. The insights gained from such observations provide a new window into fundamental biological processes and associated pathological changes occurring at a cellular level. This technique could therefore offer new opportunities for the early diagnosis of inflammatory diseases.


INTRODUCTION
Magnetic particle imaging (MPI) is a non-invasive technique capable of determining the spatial distribution of magnetic nanoparticles (MNPs) both in-vivo and in-vitro [1]. MPI achieves imaging and quantification detecting the non-linear dynamic magnetic response of MNPs exposed to multiple superimposed static and dynamic magnetic fields. No background signals are generated by bone or tissue. The technique uses non-ionizing radiation and non-toxic nanoparticles to prevent tissue damage. MPI shows great potential for different biomedical applications, such as angiography, stem cell tracking, diagnosis of inflammatory diseases and cancer [2]- [11]. In inflammation-associated diseases, including cancer, MNPs accumulate preferentially in diseased tissue as a result of leaky vasculature thereby enabling imaging with MRI and MPI [12]- [16]. In diseased tissue, MNPs accumulate mainly in macrophages [17], [18]. These phagocytic cells are a hallmark of tissue inflammation and their quantity is considered a marker of the severity of the disease [19]- [21].
The magnetic response generated by MNPs is severely influenced by their local environment. In this respect, the changing magnetic properties of MNPs induced by their interaction with cells are a relevant factor for MPI. Previous studies using magnetic particle spectroscopy (MPS), have described and quantified the changes that occur in the dynamic magnetization of certain MNP systems upon interaction with living cells [22]- [26]. These effects may be caused by a variety of factors including the aggregation of particles in the surrounding solution, within the extra-cellular matrix or within various intracellular compartments. "Sizefiltering" during cellular uptake and an increase in dipole-dipole interactions caused by a lower separation and mobility of the MNPs may also be influencing this phenomenon [25]- [28]. Typically, these signal changes lead to a deterioration of MPI images, resulting in lower sensitivity, decreased spatial resolution and more imaging artifacts [26], [28]- [30]. However, if the unique magnetic signal patterns induced by the interaction of MNPs with cells are known, they can be incorporated into the image reconstruction to negate or minimize these negative effects. Additionally, this enables the possibility to search and separate these unique signal patterns generated by MNPs in specific environmental conditions. Such a technique, known as multi-color or multicontrast MPI, has been previously demonstrated by separating the signals of different MNP systems under varying parameters such as temperature or viscosity [30]- [32].
The aim of this work was to test the potential of imaging the internalization of MNPs into living cells with MPI. It was hypothesized, that based on different magnetic signals, the particle distributions of free vs cell-bound MNPs can be separated using MPI image reconstruction. Furthermore, due to the high temporal resolution of MPI, the processes involved in the cellular uptake of MNPs were imaged and quantified.

MPI acquisition
All MPI measurements were performed on a commercial, preclinical MPI scanner (Bruker MPI 25/50 FF) based on the field-free point (FFP) approach. The FFP is generated by a static magnetic gradient field with gradient strengths 0.6/0.6/1.2 T/m (x/y/zdirection). By superimposing oscillating magnetic drive fields with field amplitudes of 12/12/12 mT and frequencies 24.5/26.0/25.3 kHz the FFP is moved on a Lissajous trajectory through the field of view (FOV) of size 40/40/20 mm. Based on these magnetic field settings and the nonlinear dynamic magnetic properties of MNPs, higher harmonics of the excitation frequencies are generated and measured by a gradiometric receive coil [33]. The spectral patterns of the MPI signal ( ) depend on the MNP system, the environmental conditions and the spatial distribution of the MNPs within the FOV. A 3D image reconstruction P-59 3 of the MNP distribution ( ) is performed by solving the following least-squares problem using the Kaczmarz algorithm with Tikhonov regularization (regularization parameter ): The SF ( ) contains information on how the environmental conditions and spatial position affect the measured MPI signal . The SFs used in this study were acquired experimentally by measuring small reference samples of MNPs with fixed environmental conditions at multiple positions all over the scanner's FOV. For this approach to be accurate, it is necessary that all environmental conditions influencing the MPI signal during a measurement are the same as the conditions during the SF acquisition. If the environmental conditions of the MNPs change, it must be considered in the image reconstruction process. This can be achieved by acquiring multiple SFs ( and ) of MNPs at different environmental conditions. Thus, a combined SF can be used to reconstruct multiple particle distributions ( and ) of MNPs at given environmental conditions by adapting the least squares problem: Assuming that changes in the dynamic magnetization induced by interactions of cells and MNPs can be described with a simple two-state system, two SFs were acquired under same field settings with a fluid (8 μL Synomag, iron oxide MNP, product nr. 103-02-301, LOT: 05218103-03 iron concentration c(Fe)=2.75 mg/ml, micromod Nanopartikeltechnologie GmbH Rostock) diluted in phosphate-buffered saline (PBS)) and cell sample (2 ⋅ 10 6 THP-1 cells incubated with Synomag), respectively. This enables the reconstruction of two particle distributions representing the free and cell-bound MNP distributions. The choice of reconstruction parameters heavily influences the quantitative and qualitative MPI images. Therefore, we varied the reconstruction parameters over a certain range, taking a priori knowledge of the particle distributions into account to determine an optimal parameter set. A detailed description of this procedure, and a list of all measurement and reconstruction parameters used in this study can be found in the supplement.
The cell sample for system function acquisition was prepared as follows: 2·10 6 THP-1 cells suspended in 2 mL of RPMI containing 1% FCS were treated for 3 hours with 0.5 mM Synomag. After incubation cells were centrifuged for 3 min at 200 g and pellets were washed three times with 1 mL PBS to remove all unbound MNPs. After washing, resuspended cells were filled into a cubic 2 mm³ container (Bruker Biospin, Germany). Centrifugation for 3 min at 200 g led to the sedimentation of the cells in the lower chamber of the container. After SF acquisition the iron content of the pellet was quantified using the 1,10-phenanthroline-based iron assay as previously described [15].
For the colorimetric quantification of Synomag uptake similar conditions as for the quantification with MPI were used. As for each frame.  during the required washing and vacuum evaporation steps associated with the phenanthroline-based iron assay. Hence, the amount of free MNPs was underestimated.

Figure 1: Reconstructions as a function of time of free (a) and cell-bound (b) MNPs in contact with THP-1 cells. The first column displays the reconstruction before injection of the cells (number of cells displayed on the left), in which only
The deviation of the total iron amount determined by MPI increased over time and correlated with the number of injected cells.
These deviations can only partly be attributed to technical issues, such as heating of hardware components and thermal drifts of the background signal, which caused small deviations below 5% in the control experiment (as determined from injection of PBS without cells). The impact of thermal drifts could be minimized by an advanced background signal removal technique [34], [35].
We assume the main reason for the deviation was caused by MNPs whose magnetic properties deviate from the states "free MNP" and "cell-bound MNPs", which we used in this proof-of-principle study. The detection of signals generated by additional states leads to quantification errors and also explains the imaging artifacts occurring at the edges of the FOV after 10 minutes in some of the reconstructed images [30], [36]. We have previously shown, that MNPs undergo multiple changes of magnetic behaviour during cellular uptake [23], [24]. At initial contact, MNPs are interacting with the pericellular matrix, a highly hydrated mesh consisting mainly of complex carbohydrates, such as glycosaminoglycans. This is followed by the initiation of a process known as endocytosis. Endocytosis is the process of internalizing extracellular substances which are then processed by a variety of intracellular vesicles such as endosomes and lysosomes. Later, MNPs are increasingly packed densely inside endosomes and finally degraded in the acidic environment of the endolysosomes [37]. All these stages seemed to be characterized by a distinct magnetic signature that can theoretically each be registered by a separate SF acquisition [23], [24]. The creation of such a SF matrix would complicate the image reconstruction from a mathematical point of view but could be adapted to the respective experimental research question.
A different approach to include multiple states in the MPI image reconstruction would be to take the reconstructed signals gained from a two-state system as presented here. The respective MPI signals could then be correlated with a priori knowledge of the measured sample to generate a calibration curve of the cellular uptake process. A similar approach was presented for viscosity quantification using multi-contrast MPI [32].
It was shown, that MPI can not only be used to image and quantify the spatial distribution of MNPs, but also to extract functional information about the environmental conditions such as the binding state based on the simple assumption of a two-state model.
Overall imaging the cellular uptake process was possible for cell numbers down to 2.5·10 5 with a temporal resolution of 2.15 s.
This technique can be adapted for other cell types and MNP systems. This would permit for nanoscale imaging of MNP cellular internalization without damaging or destroying the cells. Further improvements can be achieved by adapting the reconstruction process and by extending the two-state model as discussed above.

CONCLUSION
This study demonstrates the potential of MPI to separate the signals of free and cell-bound MNPs, within a mixed sample. This technique can be used to image and quantify the uptake of MNPs into cells starting directly from initial MNP-cell contact thereby providing information about the dynamics of cellular uptake. It is assumed that the uptake dynamics of MNPs in vivo correlates with pathological changes of diseased tissue, in particular with the permeability of the vasculature and the infiltration of immune cells. Therefore, further development of this technique is anticipated to result in highly interesting opportunities for future diagnostic applications.

Reconstruction parameters
A decisive role for MPI image reconstruction is the choice of the reconstruction parameters. In general, the MPI imaging equation is formulated as an underdetermined linear system. Therefore, the existence of a unique solution cannot be guaranteed. Hence, a least-squares problem is formulated to guarantee a solution, which is solved by the Kaczmarz algorithm with Tikhonov regularization. However, the number of frequency components used in the reconstruction, the number of iterations and the regularization parameter are variable and might have severe influence on the qualitative and quantitative values of the reconstructed images. In most MPI studies these parameters are chosen manually. Here, we performed a reconstruction parameter study in combination with a priori knowledge of the particle distribution to determine a full set of reconstruction parameters. The final reconstruction parameters are then used for the reconstructions of all measurement data to allow a quantitative comparison. In the following the procedure will be explained.
A time frame before injection of the cells was chosen for which the quantitative particle distribution is known. After background correction and truncation of the frequency components (>60 kHz), multiple image reconstructions were performed using the same measurement data with varying reconstruction parameters. The number of frequency components (FCs) was varied between 25 − 2000, sorted by the SNR of the SF, the relative regularization parameter (see [1]) was varied between 5 ⋅ 10 −5 − 5 ⋅ 10 0 and the number of iterations between 1 − 1000. The intensities in a region of interest (ROI) around the nominal sample position were summed up and converted into the respective iron masses. Since the particle distribution is known, the difference of the reconstructed iron mass and the nominal iron mass can be calculated Δ = , − , for the free and cell-bound particle distributions respectively, which is visualized as a function of the reconstruction parameters in figure S1. The reconstruction parameters will be chosen to minimize Δ for both particle distributions. The final parameters are listed in the table below.
P-59 11 Figure S1: Visualization of reconstructed iron masses of free and cell-bound particle distributions determined in a ROI for the in-vitro study.
Based on a priori knowledge of the nominal particle distributions, the difference to the reconstructed iron mass was calculated and is minimized to determine the final reconstruction parameters used for all measurements.

MPI reconstruction parameters in-vitro study
Parameter Value/description