Hyperspectral Raman imaging of neuritic plaques and neurofibrillary tangles in brain tissue from Alzheimer's disease patients.
Ralph MichaelAufried LenferinkGijs F J M VrensenEllen GelpiRafael I BarraquerCees OttoPublished in: Scientific reports (2017)
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.
Keyphrases
- label free
- high resolution
- resting state
- functional connectivity
- white matter
- cognitive decline
- raman spectroscopy
- cerebral ischemia
- end stage renal disease
- optical coherence tomography
- endothelial cells
- newly diagnosed
- fatty acid
- computed tomography
- magnetic resonance imaging
- chronic kidney disease
- multiple sclerosis
- convolutional neural network
- magnetic resonance
- small molecule
- working memory
- mass spectrometry
- blood brain barrier
- deep learning
- machine learning
- high throughput
- brain injury
- fluorescence imaging