Imaging Technique Shows Progress of Alzheimer's Disease

The researchers used 12 30×30 µm unstained samples of brain tissue to generate data matrices of 64 × 64 pixels, in which different tissue components, including proteins, lipids, water, and β-sheets were imaged at 0.47 µm spatial resolution.

Hierarchical cluster analysis was then performed to visualize regions with high Raman spectral similarity. The results showed that Raman images of proteins, lipids, water, and β-sheets matched classical brain morphology. Protein content was double, β-sheet content was X5.6 times, and Raman broadband autofluorescence was X2.4 times higher inside the plaques and tangles than in the surrounding tissue, while lipid content was equal. Broadband autofluorescence showed some correlation with protein content, and a better correlation with β-sheet content. The study was published on November 15, 2017, in Nature Scientific Reports.

“Compared to MRI, PET and CT imaging, Raman is able to detect areas smaller than cells with very high precision. In this way, it can be a very valuable extra technique,” concluded senior author Cees Otto, PhD, of the Medical Cell Biophysics group at UT. “The Raman images now show protein activity at neural cell level, but the sensitivity is high enough for detecting areas that are even smaller -- as is the case with the brain sample of the healthy person.”

Raman spectroscopy is an optical micro-spectroscopic method, in which sensitive and precise acquisition of spatial- and frequency-resolved light scattering allows the identification of groups of macromolecules with identical structural properties. Information identified by Raman spectra can be used to estimate of relative amounts of various tissue components, which can all be imaged simultaneously and used for numerical comparative analysis.

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