A fingerprint of amyloid plaques in a bitransgenic animal model of Alzheimer's disease obtained by statistical unmixing analysis of hyperspectral Raman data

Applications of Raman spectroscopy in the diagnosis and monitoring of neurodegenerative diseases

Raman scattering is an inelastic light scattering that occurs in a manner reflective of the molecular vibrations of molecular structures and chemical conditions in a given sample of interest. Energy changes in the scattered light can be assessed to determine the vibration mode and associated molecular and chemical conditions within the sample, providing a molecular fingerprint suitable for sample identification and characterization. Raman spectroscopy represents a particularly promising approach to the molecular analysis of many diseases owing to clinical advantages including its instantaneous nature and associated high degree of stability, as well as its ability to yield signal outputs corresponding to a single molecule type without any interference from other molecules as a result of its narrow peak width. This technology is thus ideally suited to the simultaneous assessment of multiple analytes. Neurodegenerative diseases represent an increasingly significant threat to global public health owing to progressive population aging, imposing a severe physical and social burden on affected patients who tend to develop cognitive and/or motor deficits beginning between the ages of 50 and 70. Owing to a relatively limited understanding of the etiological basis for these diseases, treatments are lacking for the most common neurodegenerative diseases, which include Alzheimer's disease, Parkinson's disease, Huntington's disease, and amyotrophic lateral sclerosis. The present review was formulated with the goal of briefly explaining the principle of Raman spectroscopy and discussing its potential applications in the diagnosis and evaluation of neurodegenerative diseases, with a particular emphasis on the research prospects of this novel technological platform.

Unraveling Molecular Composition in Biological Samples-Systematic Evaluation of Statistical Methods for the Analysis of Hyperspectral Raman Data

Recently, confocal Raman microscopy has gained popularity in biomedical research for studying tissues in healthy and diseased state due to its ability to acquire chemically selective data in a noninvasive approach. However, biological samples, such as brain tissue, are inherently difficult to analyze due to the superposition of molecules in the Raman spectra and low variation of spectral features within the sample. The analysis is further impeded by pathological hallmarks, for example beta-amyloid (Aβ) plaques in Alzheimer's disease, which are often solely characterized by subtle shifts in the respective Raman peaks. To unravel the underlying molecular information, convoluted statistical procedures are inevitable. Unfortunately, such statistical methods are often inadequately described, and most natural scientists lack knowledge of their appropriate use, causing unreproducible results and stagnation in the application of hyperspectral Raman imaging. Therefore, we have set out to provide a comprehensive guide to address these challenges with the example of a complex hyperspectral data set of brain tissue samples with Aβ plaques. Our study encompasses established as well as novel statistical methods, including univariate analysis, principal component analysis, cluster analysis, spectral unmixing, and 2D correlation spectroscopy, and critically compares the outcomes of each analysis. Moreover, we transparently demonstrate the effect of preprocessing decisions like denoising and scaling techniques, providing valuable insights into implications of spectral quality for data evaluation. Thereby, this study provides a comprehensive evaluation of analysis approaches for complex hyperspectral Raman data, laying out a blueprint for elucidating meaningful information from biological samples in chemical imaging.

A review: Exploring the metabolic and structural characterisation of beta pleated amyloid fibril in human tissue using Raman spectrometry and SAXS

Amyloidosis is a deleterious condition caused by abnormal amyloid fibril build-up in living tissues. To date, 42 proteins that are linked to amyloid fibrils have been discovered. Amyloid fibril structure variation can affect the severity, progression rate, or clinical symptoms of amyloidosis. Since amyloid fibril build-up is the primary pathological basis for various neurodegenerative illnesses, characterization of these deadly proteins, particularly utilising optical techniques have been a focus. Spectroscopy techniques provide significant non-invasive platforms for the investigation of the structure and conformation of amyloid fibrils, offering a wide spectrum of analyses ranging from nanometric to micrometric size scales. Even though this area of study has been intensively explored, there still remain aspects of amyloid fibrillization that are not fully known, a matter hindering progress in treating and curing amyloidosis. This review aims to provide recent updates and comprehensive information on optical techniques for metabolic and proteomic characterization of β-pleated amyloid fibrils found in human tissue with thorough literature analysis of publications. Raman spectroscopy and SAXS are well established experimental methods for study of structural properties of biomaterials. With suitable models, they offer extended information for valid proteomic analysis under physiologically relevant conditions. This review points to evidence that despite limitations, these techniques are able to provide for the necessary output and proteomics indication in order to extrapolate the aetiology of amyloid fibrils for reliable diagnostic purposes. Our metabolic database may also contribute to elucidating the nature and function of the amyloid proteome in development and clearance of amyloid diseases.

Non-Perturbative Identification and Subtyping of Amyloidosis in Human Kidney Tissue with Raman Spectroscopy and Machine Learning

Amyloids are proteins with characteristic beta-sheet secondary structures that display fibrillary ultrastructural configurations. They can result in pathologic lesions when deposited in human organs. Various types of amyloid protein can be routinely identified in human tissue specimens by special stains, immunolabeling, and electron microscopy, and, for certain forms of amyloidosis, mass spectrometry is required. In this study, we applied Raman spectroscopy to identify immunoglobulin light chain and amyloid A amyloidosis in human renal tissue biopsies and compared the results with a normal kidney biopsy as a control case. Raman spectra of amyloid fibrils within unstained, frozen, human kidney tissue demonstrated changes in conformation of protein secondary structures. By using t-distributed stochastic neighbor embedding (t-SNE) and density-based spatial clustering of applications with noise (DBSCAN), Raman spectroscopic data were accurately classified with respect to each amyloid type and deposition site. To the best of our knowledge, this is the first time Raman spectroscopy has been used for amyloid characterization of ex vivo human kidney tissue samples. Our approach, using Raman spectroscopy with machine learning algorithms, shows the potential for the identification of amyloid in pathologic lesions.

In Vitro Diagnosis and Visualization of Cerebral Ischemia/Reperfusion Injury in Rats and Protective Effects of Ferulic Acid by Raman Biospectroscopy and Machine Learning

Ischemic stroke is a major cause of mortality with complicated pathophysiological mechanisms, and hematoxylin and eosin (HE) staining is a histochemical diagnosis technique heavily relying on subjective observation. In this study, we developed a noninvasive assay using Raman spectroscopy for in vitro diagnosis and visualization of cerebral ischemia/reperfusion injury and protective effects of ferulic acid. By establishing a middle cerebral artery occlusion (MCAO) model in Sprague-Dawley male rats, we found effective interventions by ferulic acid using the neurological function score and HE staining. Raman spectra of neuronal and neuroglial cells exhibited significant intensity changes of protein, nucleotide, lipid, and carbohydrate at 780, 814, 1002, 1012, 1176, 1224, 1402, 1520, 1586, 1614, and 1752 cm^-1. Cluster vector analysis highlighted the alterations at 1002, 1080, 1298, 1430, 1478, 1508, 1586, and 1676 cm^-1. To evaluate the levels of neuron injury and intervention performance, a random forest model was developed on Raman spectral data and achieved satisfactory accuracy (0.9846), sensitivity (0.9679-0.9932), and specificity (0.9945-0.9989), ranking peaks around 1002 cm^-1 as key fingerprint for classification. Spectral phenylalanine-to-tryptophan ratio was the biomarker to visualize neuronal injury and intervention performance of ferulic acid with a resolution of 1 μm. Our results unravel the biochemical changes in neuronal cells with cerebral ischemia/reperfusion injury and ferulic acid treatment, and prove Raman spectroscopy coupled with machine learning as a power tool to classify neuron viability and evaluate the intervention performance in pharmacological research.

Effects of multi-mode physical stimulation on APP/PS1 Alzheimer's disease model mice

Some researchers and clinics have reported that non-drug treatments for Alzheimer disease (AD) such as electrical stimulation, light stimulation, music stimulation, laser stimulation, and transcranial magnetic stimulation may have beneficial treatment effects. Following these findings, in this study, we performed multimodel physical stimulation on APP/PS1 mice using visible light, music with a γ rhythm, and an infrared laser. And the effects of physical stimulation on APP/PS1 mice were evaluated by behavioral analysis, the content of amyloid (Aβ40 and Aβ42), and NISSL staining of hippocampal tissue slices. The results of subsequent behavioral and tissue analyses showed that the multi-model physical stimulations could relieve APP/PS1 mice's dementia symptoms, such as the behavior ability, the content of Aβ40 and Aβ42 in the hippocampal tissue suspension, and Nissl staining for hippocampal tissue analyses.

Micro-Raman spectroscopy of lipid halo and dense-core amyloid plaques: aging process characterization in the Alzheimer's disease APPswePS1ΔE9 mouse model

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.

Role of Retinal Amyloid-β in Neurodegenerative Diseases: Overlapping Mechanisms and Emerging Clinical Applications

Amyloid-β (Aβ) accumulations have been identified in the retina for neurodegeneration-associated disorders like Alzheimer's disease (AD), glaucoma, and age-related macular degeneration (AMD). Elevated retinal Aβ levels were associated with progressive retinal neurodegeneration, elevated cerebral Aβ accumulation, and increased disease severity with a decline in cognition and vision. Retinal Aβ accumulation and its pathological effects were demonstrated to occur prior to irreversible neurodegeneration, which highlights its potential in early disease detection and intervention. Using the retina as a model of the brain, recent studies have focused on characterizing retinal Aβ to determine its applicability for population-based screening of AD, which warrants a further understanding of how Aβ manifests between these disorders. While current treatments directly targeting Aβ accumulations have had limited results, continued exploration of Aβ-associated pathological pathways may yield new therapeutic targets for preserving cognition and vision. Here, we provide a review on the role of retinal Aβ manifestations in these distinct neurodegeneration-associated disorders. We also discuss the recent applications of retinal Aβ for AD screening and current clinical trial outcomes for Aβ-associated treatment approaches. Lastly, we explore potential future therapeutic targets based on overlapping mechanisms of pathophysiology in AD, glaucoma, and AMD.

Nonlinear and vibrational microscopy for label-free characterization of amyloid-β plaques in Alzheimer's disease model

Multimodal optical imaging was used for characterization of amyloid-β plaques in mouse brain tissues. We obtained high-resolution images for different biomarkers and investigated vibrational fingerprints that could be used for diagnostic purposes.

Label-free vibrational imaging of different Aβ plaque types in Alzheimer's disease reveals sequential events in plaque development

The neuropathology of Alzheimer’s disease (AD) is characterized by hyperphosphorylated tau neurofibrillary tangles (NFTs) and amyloid-beta (Aβ) plaques. Aβ plaques are hypothesized to follow a development sequence starting with diffuse plaques, which evolve into more compact plaques and finally mature into the classic cored plaque type. A better molecular understanding of Aβ pathology is crucial, as the role of Aβ plaques in AD pathogenesis is under debate. Here, we studied the deposition and fibrillation of Aβ in different plaque types with label-free infrared and Raman imaging. Fourier-transform infrared (FTIR) and Raman imaging was performed on native snap-frozen brain tissue sections from AD cases and non-demented control cases. Subsequently, the scanned tissue was stained against Aβ and annotated for the different plaque types by an AD neuropathology expert. In total, 160 plaques (68 diffuse, 32 compact, and 60 classic cored plaques) were imaged with FTIR and the results of selected plaques were verified with Raman imaging. In diffuse plaques, we detect evidence of short antiparallel β-sheets, suggesting the presence of Aβ oligomers. Aβ fibrillation significantly increases alongside the proposed plaque development sequence. In classic cored plaques, we spatially resolve cores containing predominantly large parallel β-sheets, indicating Aβ fibrils. Combining label-free vibrational imaging and immunohistochemistry on brain tissue samples of AD and non-demented cases provides novel insight into the spatial distribution of the Aβ conformations in different plaque types. This way, we reconstruct the development process of Aβ plaques in human brain tissue, provide insight into Aβ fibrillation in the brain, and support the plaque development hypothesis.

Advances in nanomedicines for diagnosis of central nervous system disorders

In spite of a great improvement in medical health services and an increase in lifespan, we have witnessed a skyrocket increase in the incidence of central nervous system (CNS) disorders including brain tumors, neurodegenerative diseases (Alzheimer's disease, Parkinson's disease, amyotrophic lateral sclerosis, Huntington's disease), ischemic stroke, and epilepsy, which have seriously undermined the quality of life and substantially increased economic and societal burdens. Development of diagnostic methods for CNS disorders is still in the early stage, and the clinical outcomes suggest these methods are not ready for the challenges associated with diagnosis of CNS disorders, such as early detection, specific binding, sharp contrast, and continuous monitoring of therapeutic interventions. Another challenge is to overcome various barrier structures during delivery of diagnostic agents, especially the blood-brain barrier (BBB). Fortunately, utilization of nanomaterials has been pursued as a potential and promising strategy to address these challenges. This review will discuss anatomical and functional structures of BBB and transport mechanisms of nanomaterials across the BBB, and special emphases will be placed on the state-of-the-art advances in the development of nanomedicines from a variety of nanomaterials for diagnosis of CNS disorders. Meanwhile, current challenges and future perspectives in this field are also highlighted.

Biochemical Characterization of Mouse Retina of an Alzheimer's Disease Model by Raman Spectroscopy

The presence of biomarkers characteristic for Alzheimer’s disease in the retina is a controversial topic. Raman spectroscopy offers information on the biochemical composition of tissues. Thus, it could give valuable insight into the diagnostic value of retinal analysis. Within the present study, retinas of a double transgenic mouse model, that expresses a chimeric mouse/human amyloid precursor protein and a mutant form of human presenilin 1, and corresponding control group were subjected to ex vivo Raman imaging. The Raman data recorded on cross sections of whole eyes highlight the layered structure of the retina in a label-free manner. Based on the Raman information obtained from en face mounted retina samples, a discrimination between healthy and Alzheimer’s disease retinal tissue can be done with an accuracy of 85.9%. For this a partial least squares-linear discriminant analysis was applied. Therefore, although no macromolecular changes in form of, i.e., amyloid beta plaques, can be noticed based on Raman spectroscopy, subtle biochemical changes happening in the retina could lead to Alzheimer’s disease identification.