Micro-Raman spectroscopy of lipid halo and dense-core amyloid plaques: aging process characterization in the Alzheimer's disease APPswePS1ΔE9 mouse model.
Emerson A FonsecaLucas LafetaJoão Luiz CamposRenan S CunhaAlexandre BarbosaMarco Aurélio Romano SilvaRafael VieiraLeandro M MalardAdo JorioPublished in: The Analyst (2021)
The deposition of amyloid plaques is considered one of the main microscopic features of Alzheimer's disease (AD). Since plaque formation can precede extensive neurodegeneration and it is the main clinical manifestation of AD, it constitutes a relevant target for new treatment and diagnostic approaches. Micro-Raman spectroscopy, a label-free technique, is an accurate method for amyloid plaque identification and characterization. Here, we present a high spatial resolution micro-Raman hyperspectral study in transgenic APPswePS1ΔE9 mouse brains, showing details of AD tissue biochemical and histological changes without staining. First we used stimulated micro-Raman scattering to identify the lipid-rich halo surrounding the amyloid plaque, and then proceeded with spontaneous (conventional) micro-Raman spectral mapping, which shows a cholesterol and sphingomyelin lipid-rich halo structure around dense-core amyloid plaques. The detailed images of this lipid halo relate morphologically well with dystrophic neurites surrounding plaques. Principal Component Analysis (PCA) of the micro-Raman hyperspectral data indicates the feasibility of the optical biomarkers of AD progression with the potential for discriminating transgenic groups of young adult mice (6-month-old) from older ones (12-month-old). Frequency-specific PCA suggests that plaque-related neurodegeneration is the predominant change captured by Raman spectroscopy, and the main differences are highlighted by vibrational modes associated with cholesterol located majorly in the lipid halo.
Keyphrases
- raman spectroscopy
- label free
- coronary artery disease
- fatty acid
- mouse model
- high resolution
- young adults
- optical coherence tomography
- cognitive decline
- physical activity
- magnetic resonance imaging
- machine learning
- magnetic resonance
- type diabetes
- adipose tissue
- deep learning
- metabolic syndrome
- climate change
- insulin resistance
- electronic health record
- low density lipoprotein
- convolutional neural network
- human health
- molecular dynamics
- mild cognitive impairment
- risk assessment
- combination therapy
- big data