Joint multi-field T1 quantification for fast field-cycling MRI

Acknowledgment This article is based upon work from COST Action CA15209, supported by COST (European Cooperation in Science and Technology). Oliver Maier is a Recipient of a DOC Fellowship (24966) of the Austrian Academy of Sciences at the Institute of Medical Engineering at TU Graz. The authors would like to acknowledge the NVIDIA Corporation Hardware grant support.Peer reviewedPublisher PD

Fast field-cycling magnetic resonance detection of intracellular ultra-small iron oxide particles in vitro: Proof-of-concept.

PurposeInflammation is central in disease pathophysiology and accurate methods for its detection and quantification are increasingly required to guide diagnosis and therapy. Here we explored the ability of Fast Field-Cycling Magnetic Resonance (FFC-MR) in quantifying the signal of ultra-small superparamagnetic iron oxide particles (USPIO) phagocytosed by J774 macrophage-like cells as a proof-of-principle.MethodsRelaxation rates were measured in suspensions of J774 macrophage-like cells loaded with USPIO (0-200 μg/ml Fe as ferumoxytol), using a 0.25 T FFC benchtop relaxometer and a human whole-body, in-house built 0.2 T FFC-MR prototype system with a custom test tube coil. Identical non-imaging, saturation recovery pulse sequence with 90° flip angle and 20 different evolution fields selected logarithmically between 80 μT and 0.2 T (3.4 kHz and 8.51 MHz proton Larmor frequency [PLF] respectively). Results were compared with imaging flow cytometry quantification of side scatter intensity and USPIO-occupied cell area. A reference colorimetric iron assay was used.ResultsThe T1 dispersion curves derived from FFC-MR were excellent in detecting USPIO at all concentrations examined (0-200 μg/ml Fe as ferumoxytol) vs. control cells, p ≤ 0.001. FFC-NMR was capable of reliably detecting cellular iron content as low as 1.12 ng/µg cell protein, validated using a colorimetric assay. FFC-MR was comparable to imaging flow cytometry quantification of side scatter intensity but superior to USPIO-occupied cell area, the latter being only sensitive at exposures ≥ 10 µg/ml USPIO.ConclusionsWe demonstrated for the first time that FFC-MR is capable of quantitative assessment of intra-cellular iron which will have important implications for the use of USPIO in a variety of biological applications, including the study of inflammation

Multicomponent analysis of T1 relaxation in bovine articular cartilage at low magnetic fields

European Union’s Horizon 2020 Research and Innovation Programme; Grant/Award number 668119 (project “IDentIFY”).Peer reviewedPublisher PD

Biophysical Characteristics Reveal Neural Stem Cell Differentiation Potential

Distinguishing human neural stem/progenitor cell (huNSPC) populations that will predominantly generate neurons from those that produce glia is currently hampered by a lack of sufficient cell type-specific surface markers predictive of fate potential. This limits investigation of lineage-biased progenitors and their potential use as therapeutic agents. A live-cell biophysical and label-free measure of fate potential would solve this problem by obviating the need for specific cell surface markers

Extraction of dielectric properties of multiple populations from dielectrophoretic collection spectrum data

In this paper we show how simplifying assumptions can be used to extract useful data from the dielectrophoretic collection spectrum, in particular for the cytoplasm, and hence determine the properties of multiple populations of cells within a sample. Specifically, the observation of the frequencies of onset of dielectric dispersion allows the identification and enumeration of populations of cells according to cytoplasmic conductivity, with particular relevance to the determination of the action of drugs for high-throughput screening applications

Extraction of dielectric properties of multiple populations from dielectrophoretic collection spectrum data

In this paper we show how simplifying assumptions can be used to extract useful data from the dielectrophoretic collection spectrum, in particular for the cytoplasm, and hence determine the properties of multiple populations of cells within a sample. Specifically, the observation of the frequencies of onset of dielectric dispersion allows the identification and enumeration of populations of cells according to cytoplasmic conductivity, with particular relevance to the determination of the action of drugs for high-throughput screening applications

Rapid, automated measurement of dielectrophoretic forces using DEP-activated microwells.

Dielectrophoresis (DEP) is a physical effect that generates a force on polarisable particles experiencing a non-homogeneous electric field; studying the effect as a function of frequency allows the determination of the electrical properties of that particle, i.e. the electrical permittivity and conductivity. In the past, DEP-based techniques applied to the measurement of one or several cells at a time have been subject to many sources of noise, which result in an ambiguous or inaccurate result. However, improvements are possible by generating more information from the experiments. In this paper, we present a rapid automated system that measures the DEP spectrum from a large population of cells with a low level of noise using the microwell electrodes, based on a method of analysis that provides additional information about the electrical properties of the cells and a new theoretical approach was developed to obtain accurate, bias-free results in <5 min

Rapid, automated measurement of dielectrophoretic forces using DEP-activated microwells.

Dielectrophoresis (DEP) is a physical effect that generates a force on polarisable particles experiencing a non-homogeneous electric field; studying the effect as a function of frequency allows the determination of the electrical properties of that particle, i.e. the electrical permittivity and conductivity. In the past, DEP-based techniques applied to the measurement of one or several cells at a time have been subject to many sources of noise, which result in an ambiguous or inaccurate result. However, improvements are possible by generating more information from the experiments. In this paper, we present a rapid automated system that measures the DEP spectrum from a large population of cells with a low level of noise using the microwell electrodes, based on a method of analysis that provides additional information about the electrical properties of the cells and a new theoretical approach was developed to obtain accurate, bias-free results in <5 min