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Transition metal distribution in the brain and...
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Transition metal distribution in the brain and spinal cord of a dysmyelinated rodent model

Abstract

Transition metal concentrations in the central nervous system (CNS) are altered in neurodegenerative diseases such as Alzheimer’s, Parkinson’s and multiple sclerosis. A common symptom of these diseases is demyelination, which is the degradation of the myelin sheath that encapsulates the neurons in vertebrates. Transition metal concentrations were measured in Long Evans Shaker (LES) rodent model and compared to healthy age-matched controls to investigate the relationship between transition metals and myelination. Micro probe Synchrotron Radiation X-ray Fluorescence (µSRXRF) was used to measure concentrations of manganese (Mn), iron (Fe), copper (Cu), and zinc (Zn) in regions of grey matter and white matter in Shaker rodents and their age-matched Long Evans (LE) controls in the cerebellum and spinal cord. In the cerebellum, the concentrations of all elements were significantly increased in the white matter of the Shaker model, and decreased in the gray matter of the Shaker model in comparison to their age and region matched controls. In the spinal cord samples, concentrations of all metals were higher in white matter and grey matter of Shaker rat spinal cord compared to those in the control rat spinal cord. This study demonstrated that the sensitivity of µSRXRF is sufficient to discriminate between the elemental distributions of gray and white matter of the brain sections and spinal cords in the two groups. The observed significant increase of Mn, Fe, Zn and Cu in the white matter of the Shaker animals in the cerebellum and spinal cord compared to controls could be the result of astrocytic glial cells replacing the myelin in the CNS. Unlike other imaging techniques, the fine resolution of µSRXRF enables specific regions of gray matter structures namely, the molecular layer and the granule layer to be identified in the rat CNS, and their transition metal concentrations to be quantified. This work will further establish µSRXRF as a powerful analytic technique for compositional studies in brain sections from models of brain disease.

Authors

Al-Ebraheem A; Miller L; Geraki K; Desmond KL; Stanisz GJ; Bock NA; Farquharson MJ

Volume

1764

Publisher

AIP Publishing

Publication Date

August 30, 2016

DOI

10.1063/1.4961139

Conference proceedings

AIP Conference Proceedings

Issue

1

ISSN

0094-243X
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