Hyperspectral Raman imaging of neuritic plaques and neurofibrillary tangles in brain tissue from Alzheimer's disease patients
Michael, Ralph (Institut Barraquer)
Lenferink, Aufried (University of Twente)
Vrensen, Gijs F. J. M. (Department of Ophthalmology, University of Leiden)
Gelpi, Ellen (Institut d'Investigacions Biomèdiques August Pi i Sunyer)
Barraquer, Rafael I. (Rafael Ignasi) (Universitat Internacional de Catalunya)
Otto, Cees (University of Twente)
Universitat Autònoma de Barcelona

Date:	2017
Abstract:	Neuritic plaques and neurofibrillary tangles are crucial morphological criteria for the definite diagnosis of Alzheimer's disease. We evaluated 12 unstained frontal cortex and hippocampus samples from 3 brain donors with Alzheimer's disease and 1 control with hyperspectral Raman microscopy on samples of 30 × 30 µm. Data matrices of 64 × 64 pixels were used to quantify different tissue components including proteins, lipids, water and beta-sheets for imaging at 0. 47 µm spatial resolution. Hierarchical cluster analysis was performed to visualize regions with high Raman spectral similarities. The Raman images of proteins, lipids, water and beta-sheets matched with classical brain morphology. Protein content was 2. 0 times, the beta-sheet content 5. 6 times and Raman broad-band autofluorescence was 2. 4 times higher inside the plaques and tangles than in the surrounding tissue. The lipid content was practically equal inside and outside. Broad-band autofluorescence showed some correlation with protein content and a better correlation with beta-sheet content. Hyperspectral Raman imaging combined with hierarchical cluster analysis allows for the identification of neuritic plaques and neurofibrillary tangles in unstained, label-free slices of human Alzheimer's disease brain tissue. It permits simultaneous quantification and distinction of several tissue components such as proteins, lipids, water and beta-sheets.
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Language:	Anglès
Document:	Article ; recerca ; Versió publicada
Published in:	Scientific reports, Vol. 7 (november 2017) , ISSN 2045-2322

DOI: 10.1038/s41598-017-16002-3
PMID: 29142266

11 p, 2.7 MB

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