Three-dimensional organization of the perivascular glial limiting membrane and its relationship with the vasculature: a scanning electron microscope study.
ABSTRACT To examine the three-dimensional structure of the perivascular glial limiting membrane (Glm) and its relationship with the vasculature in rat/mouse cerebral cortices, serial ion-etched plastic sections were observed under the scanning electron microscope and their images were reconstructed. In the case of arterioles and venules close to the pial surface, cord-like principal processes predominantly formed the endfeet; whereas in the case of capillaries and venules, sheet-like secondary processes chiefly formed Glm. Moreover, it was found that several plate-like structures protruded from the basement membrane surrounding the arterioles to penetrate into the astrocytic somata. The perivascular Glm was formed by monolayers of astrocytic processes and/or somata irrespective of the types of blood vessel. However, the thickness of the perivascular Glm, varied greatly according to the type of blood vessel. The thickness of Glm decreased in the order of arterioles, venules and capillaries. The outer surface of the perivascular Glm was extremely irregular, and sheet-like processes arising from this Glm infiltrated into the surrounding neuropil.
The Journal of neuropsychiatry and clinical neurosciences 01/2014; 26(1):iv-4. DOI:10.1176/appi.neuropsych.13110351 · 2.34 Impact Factor
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ABSTRACT: Presently, there are no effective treatments for several diseases involving the CNS, which is protected by the blood-brain, blood-CSF and blood-arachnoid barriers. Traversing any of these barriers is difficult, especially for macromolecular drugs and particulates. However, there is significant experimental evidence that large molecules can be delivered to the CNS through the cerebro-spinal fluid (CSF). The flux of the interstitial fluid in the CNS parenchyma, as well as the macro flux of CSF in the leptomeningeal space, are believed to be generally opposite to the desirable direction of CNS-targeted drug delivery. On the other hand, the available data suggest that the layer of pia mater lining the CNS surface is not continuous, and the continuity of the leptomeningeal space (LMS) with the perivascular spaces penetrating into the parenchyma provides an unexplored avenue for drug transport deep into the brain via CSF. The published data generally do not support the view that macromolecule transport from the LMS to CNS is hindered by the interstitial and CSF fluxes. The data strongly suggest that leptomeningeal transport depends on the location and volume of the administered bolus and consists of four processes: (i) pulsation-assisted convectional transport of the solutes with CSF, (ii) active "pumping" of CSF into the periarterial spaces, (iii) solute transport from the latter to and within the parenchyma, and (iv) neuronal uptake and axonal transport. The final outcome will depend on the drug molecule behavior in each of these processes, which have not been studied systematically. The data available to date suggest that many macromolecules and nanoparticles can be delivered to CNS in biologically significant amounts (>1% of the administered dose); mechanistic investigation of macromolecule and particle behavior in CSF may result in a significantly more efficient leptomeningeal drug delivery than previously thought.Molecular Pharmaceutics 01/2013; 10(5). DOI:10.1021/mp300474m · 4.57 Impact Factor
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ABSTRACT: Neuroscientists are developing new imaging techniques and generating large volumes of data in an effort to understand the complex structure of the nervous system. The complexity and size of this data makes human interpretation a labor-intensive task. To aid in the analysis, new segmentation techniques for identifying neurons in these feature rich datasets are required. This paper presents a method for neuron boundary detection and nonbranching process segmentation in electron microscopy images and visualizing them in three dimensions. It combines both automated segmentation techniques with a graphical user interface for correction of mistakes in the automated process. The automated process first uses machine learning and image processing techniques to identify neuron membranes that deliniate the cells in each two-dimensional section. To segment nonbranching processes, the cell regions in each two-dimensional section are connected in 3D using correlation of regions between sections. The combination of this method with a graphical user interface specially designed for this purpose, enables users to quickly segment cellular processes in large volumes.Neuroinformatics 05/2012; DOI:10.1007/s12021-012-9149-y · 3.14 Impact Factor