Förderzeitraum 2011
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In Vivo Imaging of Stepwise Vessel Occlusion in Cerebral Photothrombosis of Mice by \(^{19}\)F MRI
(2011)
Background
\(^{19}\)F magnetic resonance imaging (MRI) was recently introduced as a promising technique for in vivo cell tracking. In the present study we compared \(^{19}\)F MRI with iron-enhanced MRI in mice with photothrombosis (PT) at 7 Tesla. PT represents a model of focal cerebral ischemia exhibiting acute vessel occlusion and delayed neuroinflammation.
Methods/Principal Findings
Perfluorocarbons (PFC) or superparamagnetic iron oxide particles (SPIO) were injected intravenously at different time points after photothrombotic infarction. While administration of PFC directly after PT induction led to a strong \(^{19}\)F signal throughout the entire lesion, two hours delayed application resulted in a rim-like \(^{19}\)F signal at the outer edge of the lesion. These findings closely resembled the distribution of signal loss on T2-weighted MRI seen after SPIO injection reflecting intravascular accumulation of iron particles trapped in vessel thrombi as confirmed histologically. By sequential administration of two chemically shifted PFC compounds 0 and 2 hours after illumination the different spatial distribution of the \(^{19}\)F markers (infarct core/rim) could be visualized in the same animal. When PFC were applied at day 6 the fluorine marker was only detected after long acquisition times ex vivo. SPIO-enhanced MRI showed slight signal loss in vivo which was much more prominent ex vivo indicative for neuroinflammation at this late lesion stage.
Conclusion
Our study shows that vessel occlusion can be followed in vivo by \(^{19}\)F and SPIO-enhanced high-field MRI while in vivo imaging of neuroinflammation remains challenging. The timing of contrast agent application was the major determinant of the underlying processes depicted by both imaging techniques. Importantly, sequential application of different PFC compounds allowed depiction of ongoing vessel occlusion from the core to the margin of the ischemic lesions in a single MRI measurement.
Super-resolution fluorescence imaging based on inglemolecule localization relies critically on the availability of efficient processing algorithms to distinguish, identify, and localize emissions of single fluorophores. In multiple current applications, such as threedimensional, time-resolved or cluster imaging, high densities of fluorophore emissions are common. Here, we provide an analytic tool to test the performance and quality of localization microscopy algorithms and demonstrate that common algorithms encounter difficulties for samples with high fluorophore density. We demonstrate that, for typical single-molecule localization microscopy methods such as dSTORM and the commonly used rapidSTORM scheme, computational precision limits the acceptable density of concurrently active fluorophores to 0.6 per square micrometer and that the number of successfully localized fluorophores per frame is limited to 0.2 per square micrometer.
Understanding a complex network’s structure holds the key to understanding its function. The physics community has contributed a multitude of methods and analyses to this cross-disciplinary endeavor. Structural features exist on both the microscopic level, resulting from differences between single node properties, and the mesoscopic level resulting from properties shared by groups of nodes. Disentangling the determinants of network structure on these different scales has remained a major, and so far unsolved, challenge. Here we show how multiscale generative probabilistic exponential random graph models combined with efficient, distributive message-passing inference techniques can be used to achieve this separation of scales, leading to improved detection accuracy of latent classes as demonstrated on benchmark problems. It sheds new light on the statistical significance of motif-distributions in neural networks and improves the link-prediction accuracy as exemplified for gene-disease associations in the highly consequential Online Mendelian Inheritance in Man database.