Detection threshold of single SPIO-labelled cells with FIESTA

Imaging Research Laboratories, Robarts Research Institute, London, Canada.
Magnetic Resonance in Medicine (Impact Factor: 3.57). 02/2005; 53(2):312-20. DOI: 10.1002/mrm.20356
Source: PubMed


MRI of superparamagnetic iron oxide (SPIO)-labeled cells has become a valuable tool for studying the in vivo trafficking of transplanted cells. Cellular detection with MRI is generally considered to be orders of magnitude less sensitive than other techniques, such as positron emission tomography (PET), single photon emission-computed tomography (SPECT), or optical fluorescence microscopy. However, an analytic description of the detection threshold for single SPIO-labeled cells and the parameters that govern detection has not been adequately provided. In the present work, the detection threshold for single SPIO-labeled cells and the effect of resolution and SNR were studied for a balanced steady-state free precession (SSFP) sequence (3D-FIESTA). Based on the results from both theoretical and experimental analyses, an expression that predicts the minimum detectable mass of SPIO (m(c)) required to detect a single cell against a uniform signal background was derived: m(c) = 5v/(K(fsl) x SNR), where v is the voxel volume, SNR is the image signal-to-noise ratio, and K(fsl) is an empirical constant measured to be 6.2 +/- 0.5 x 10(-5) microl/pgFe. Using this expression, it was shown that the sensitivity of MRI is not very different from that of PET, requiring femtomole quantities of SPIO iron for detection under typical micro-imaging conditions (100 microm isotropic resolution, SNR = 60). The results of this work will aid in the design of cellular imaging experiments by defining the lower limit of SPIO labeling required for single cell detection at any given resolution and SNR.

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Available from: Chris V Bowen, Apr 15, 2014
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    • "lung, bones) or in the presence of haemorrhages (because of the paramagnetism of de-oxy haemoglobin). Furthermore, the IONP-labelled cells generated contrast is linearly correlated to the cells number only at low iron concentrations [6] [7]. *Address correspondence to this author at the Department of Molecular Biotechnology and Health Sciences, University of Torino, Torino, Italy, Via Nizza 52, 10126 – Torino, Italy; Tel: +39-0116706452; Fax: +39-0116706487; E-mail: "
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    • "In addition, iron-labeled cell quantification is difficult. We, and others, have shown that the contrast generated by iron-labeled cells increases with the amount of iron/voxel but that this is only linear at low iron loadings; the change in contrast reaches a saturation plateau at higher iron loadings.6,7 When quantifying the presence of iron-labeled stem cells over time, most studies measure the “signal void volume”8,9 or the “number of black pixels”,10,11 and present the change relative to the first imaging time point. "
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    ABSTRACT: Mesenchymal stem cells (MSC) are used to restore deteriorated cell environments. There is a need to specifically track these cells following transplantation in order to evaluate different methods of implantation, to follow their migration within the body, and to quantify their accumulation at the target. Cellular magnetic resonance imaging (MRI) using fluorine-based nanoemulsions is a great means to detect these transplanted cells in vivo because of the high specificity for fluorine detection and the capability for precise quantification. This technique, however, has low sensitivity, necessitating improvement in MR sequences. To counteract this issue, the balanced steady-state free precession (bSSFP) imaging sequence can be of great interest due to the high signal-to-noise ratio (SNR). Furthermore, it can be applied to obtain 3D images within short acquisition times. In this paper, bSSFP provided accurate quantification of samples of the perfluorocarbon Cell Sense-labeled cells in vitro. Cell Sense was internalized by human MSC (hMSC) without adverse alterations in cell viability or differentiation into adipocytes/osteocytes. The bSSFP sequence was applied in vivo to track and quantify the signals from both Cell Sense-labeled and iron-labeled hMSC after intramuscular implantation. The fluorine signal was observed to decrease faster and more significantly than the volume of iron-associated voids, which points to the advantage of quantifying the fluorine signal and the complexity of quantifying signal loss due to iron.
    International Journal of Nanomedicine 04/2014; 9(1):1731-9. DOI:10.2147/IJN.S59127 · 4.38 Impact Factor
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    • "Obviously MRI does not reach microscopic resolution; (Heyn et al., 2005; Martin, 2011), for in vivo experiments, the detection limit is in the range between 100 and 500 cells (Heyn et al., 2005; Muja and Bulte, 2009). This is relevant insofar, as final diagnosis is based on the cellular (type and shape) and molecular information (surface epitopes expressed by the cells) derived from histology. "
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    ABSTRACT: THE DIAGNOSIS OF A SUSPECTED TUMOR LESION FACES TWO BASIC PROBLEMS: detection and identification of the specific type of tumor. Radiological techniques are commonly used for the detection and localization of solid tumors. Prerequisite is a high intrinsic or enhanced contrast between normal and neoplastic tissue. Identification of the tumor type is still based on histological analysis. The result depends critically on the sampling sites, which given the inherent heterogeneity of tumors, constitutes a major limitation. Non-invasive in vivo imaging might overcome this limitation providing comprehensive three-dimensional morphological, physiological, and metabolic information as well as the possibility for longitudinal studies. In this context, magnetic resonance based techniques are quite attractive since offer at the same time high spatial resolution, unique soft tissue contrast, good temporal resolution to study dynamic processes and high chemical specificity. The goal of this paper is to review the role of magnetic resonance techniques in characterizing tumor tissue in vivo both at morphological and physiological levels. The first part of this review covers methods, which provide information on specific aspects of tumor phenotypes, considered as indicators of malignancy. These comprise measurements of the inflammatory status, neo-vascular physiology, acidosis, tumor oxygenation, and metabolism together with tissue morphology. Even if the spatial resolution is not sufficient to characterize the tumor phenotype at a cellular level, this multiparametric information might potentially be used for classification of tumors. The second part discusses mathematical tools, which allow characterizing tissue based on the acquired three-dimensional data set. In particular, methods addressing tumor heterogeneity will be highlighted. Finally, we address the potential and limitation of using MRI as a tool to provide in vivo tissue characterization.
    Frontiers in Genetics 01/2014; 4:298. DOI:10.3389/fgene.2013.00298
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