GLAM: Glycogen-derived Lactate Absorption Map for visual analysis of dense and sparse surface reconstructions of rodent brain structures on desktop systems and virtual environments
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Magistretti, Pierre J.
KAUST DepartmentVisual Computing Center (VCC)
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Biological and Environmental Sciences and Engineering (BESE) Division
Computer Science Program
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AbstractHuman brain accounts for about one hundred billion neurons, but they cannot work properly without ultrastructural and metabolic support. For this reason, mammalian brains host another type of cells called “glial cells”, whose role is to maintain proper conditions for efficient neuronal function. One type of glial cell, astrocytes, are involved in particular in the metabolic support of neurons, by feeding them with lactate, one byproduct of glucose metabolism that they can take up from blood vessels, and store it under another form, glycogen granules. These energy-storage molecules, whose morphology resembles to spheres with a diameter ranging 10–80 nanometers roughly, can be easily recognized using electron microscopy, the only technique whose resolution is high enough to resolve them. Understanding and quantifying their distribution is of particular relevance for neuroscientists, in order to understand where and when neurons use energy under this form. To answer this question, we developed a visualization technique, dubbed GLAM (Glycogen-derived Lactate Absorption Map), and customized for the analysis of the interaction of astrocytic glycogen on surrounding neurites in order to formulate hypotheses on the energy absorption mechanisms. The method integrates high-resolution surface reconstruction of neurites, astrocytes, and the energy sources in form of glycogen granules from different automated serial electron microscopy methods, like focused ion beam scanning electron microscopy (FIB-SEM) or serial block face electron microscopy (SBEM), together with an absorption map computed as a radiance transfer mechanism. The resulting visual representation provides an immediate and comprehensible illustration of the areas in which the probability of lactate shuttling is higher. The computed dataset can be then explored and quantified in a 3D space, either using 3D modeling software or virtual reality environments. Domain scientists have evaluated the technique by either using the computed maps for formulating functional hypotheses or for planning sparse reconstructions to avoid excessive occlusion. Furthermore, we conducted a pioneering user study showing that immersive VR setups can ease the investigation of the areas of interest and the analysis of the absorption patterns in the cellular structures.
CitationAgus M, Boges D, Gagnon N, Magistretti PJ, Hadwiger M, et al. (2018) GLAM: Glycogen-derived Lactate Absorption Map for visual analysis of dense and sparse surface reconstructions of rodent brain structures on desktop systems and virtual environments. Computers & Graphics. Available: http://dx.doi.org/10.1016/j.cag.2018.04.007.
SponsorsWe thank Kalpana Kare for the technical support for the 3D reconstruction pipelines and coding; the KVL team for the support in the use of CAVE and for providing computing clusters to process the image stacks for 3D reconstructions; Graham Knott and the BioEM Facility at EPFL (Lausanne, Switzerland), and Elena Vezzoli from University of Milano (Milano, Italy), for providing the EM stacks that we used for extracting the 3D models. This work was supported by the KAUST Grant KAUST-EPFL Alliance for Integrative Modeling of Brain Energy Metabolism to Pierre Magistretti and Henry Markram.
JournalComputers & Graphics
Except where otherwise noted, this item's license is described as NOTICE: this is the author’s version of a work that was accepted for publication in Computers & Graphics. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Computers & Graphics, [(2018)] DOI: 10.1016/j.cag.2018.04.007. © 2018. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/