Multimodal, label-free fluorescence and Raman imaging of amyloid deposits in snap-frozen Alzheimer's disease human brain tissue

Harnessing Photo-Energy Conversion in Nanomaterials for Precision Theranostics

The rapidly advancing field of theranostics aims to integrate therapeutic and diagnostic functionalities into a single platform for precision medicine, enabling the simultaneous treatment and monitoring of diseases. Photo-energy conversion-based nanomaterials have emerged as a versatile platform that utilizes the unique properties of light to activate theranostics with high spatial and temporal precision. This review provides a comprehensive overview of recent developments in photo-energy conversion using nanomaterials, highlighting their applications in disease theranostics. The discussion begins by exploring the fundamental principles of photo-energy conversion in nanomaterials, including the types of materials used and various light-triggered mechanisms, such as photoluminescence, photothermal, photoelectric, photoacoustic, photo-triggered SERS, and photodynamic processes. Following this, the review delves into the broad spectrum of applications of photo-energy conversion in nanomaterials, emphasizing their role in the diagnosis and treatment of major diseases, including cancer, neurodegenerative disorders, retinal degeneration, and osteoarthritis. Finally, the challenges and opportunities of photo-energy conversion-based technologies for precision theranostics are discussed, aiming to advance personalized medicine.

Machine Learning Interpretation of Optical Spectroscopy Using Peak-Sensitive Logistic Regression

Optical spectroscopy, a noninvasive molecular sensing technique, offers valuable insights into material characterization, molecule identification, and biosample analysis. Despite the informativeness of high-dimensional optical spectra, their interpretation remains a challenge. Machine learning methods have gained prominence in spectral analyses, efficiently unveiling analyte compositions. However, these methods still face challenges in interpretability, particularly in generating clear feature importance maps that highlight the spectral features specific to each class of data. These limitations arise from feature noise, model complexity, and the lack of optimization for spectroscopy. In this work, we introduce a machine learning algorithm─logistic regression with peak-sensitive elastic-net regularization (PSE-LR)─tailored for spectral analysis. PSE-LR enables classification and interpretability by producing a peak-sensitive feature importance map, achieving an F1-score of 0.93 and a feature sensitivity of 1.0. Its performance is compared with other methods, including k-nearest neighbors (KNN), elastic-net logistic regression (E-LR), support vector machine (SVM), principal component analysis followed by linear discriminant analysis (PCA-LDA), XGBoost, and neural network (NN). Applying PSE-LR to Raman and photoluminescence (PL) spectra, we detected the receptor-binding domain (RBD) of SARS-CoV-2 spike protein in ultralow concentrations, identified neuroprotective solution (NPS) in brain samples, recognized WS2 monolayer and WSe2/WS2 heterobilayer, analyzed Alzheimer's disease (AD) brains, and suggested potential disease biomarkers. Our findings demonstrate PSE-LR's utility in detecting subtle spectral features and generating interpretable feature importance maps. It is beneficial for the spectral characterization of materials, molecules, and biosamples and applicable to other spectroscopic methods. This work also facilitates the development of nanodevices such as nanosensors and miniaturized spectrometers based on nanomaterials.

Oligomer sensitive in-situ detection and characterization of gold colloid aggregate formations observed within the hippocampus of the Alzheimer's disease rat

In order to better understand the dynamics governing the formation of pathological oligomers leading to Alzheimer's disease (AD) in a rat model the present study examined the protein aggregates accumulating on gold colloids in the hippocampus. Sections of the hippocampus of the Long Evans Cohen's AD(+) rat model were mixed with gold colloids and the resulting aggregates were examined by Surface Enhanced Raman Scattering (SERS) imaging. Compared to AD(-) rat tissues, the AD(+) rat hippocampal tissues produced a larger sized gold colloid aggregates. The SERS spectrum of each hippocampal section exhibited similar spectral patterns in the Amide I, II, and III band regions, but showed distinct spectral patterns in the region between 300 cm-1 - 1250 cm-1 in AD(+) rat tissues, respectively. Amyloid fibrils with a β-sheet conformation were previously reported to form gold colloid aggregates in mouse and human AD brain tissues. The gold colloid aggregates in the AD (+) rat hippocampal brain sections showed distinct morphological traits compared to those observed in AD(-) rats. This suggests that there is a spatial distribution of oligomer concentration in the hippocampus, which induces fibril formation to disrupt neuronal networks within the hippocampus and between other parts of the brain.

Label-free Aβ plaque detection in Alzheimer's disease brain tissue using infrared microscopy and neural networks

We present a novel method for the label-free detection of amyloid-beta (Aβ) plaques, the key hallmark of Alzheimer's disease, in human brain tissue sections. Conventionally, immunohistochemistry (IHC) is employed for the characterization of Aβ plaques, hindering subsequent analysis. Here, a semi-supervised convolutional neural network (CNN) is trained to detect Aβ plaques in quantum cascade laser infrared (QCL-IR) microscopy images. Laser microdissection (LMD) is then used to precisely extract plaques from snap-frozen, unstained tissue sections. Mass spectrometry-based proteomics reveals a loss of soluble proteins in IHC stained samples. Our method prevents this loss and provides a novel tool that expands the scope of molecular analysis methods to chemically native plaques. Insight into soluble plaque components will complement our understanding of plaques and their role in Alzheimer's disease.

A comparison of multivariate curve resolution with endmember extraction methods in hyperspectral Raman imaging

Hyperspectral Raman imaging not only offers spectroscopic fingerprints but also reveals morphological information such as spatial distributions in an analytical sample. However, the spectrum-per-pixel nature of hyperspectral imaging (HSI) results in a vast amount of data. Furthermore, HSI often requires pre- and post-processing steps to extract valuable chemical information. To derive pure spectral signatures and concentration abundance maps of the active spectroscopic compounds, both endmember extraction (EX) and Multivariate Curve Resolution (MCR) techniques are widely employed. The objective of this study is to carry out a systematic investigation based on Raman mapping datasets to highlight the similarities and differences between these two approaches in retrieving pure variables, and ultimately provide guidelines for pure variable extraction. Numerical simulations and Raman mapping experiments on a mixture of pharmaceutical powders and on a layered plastic foil sample were conducted to underscore the distinctions between MCR and EX algorithms (in particular Vertex Component Analysis, VCA) and their outputs. Both methods were found to perform well if the dataset contains pure pixels for each of the individual components. However, in cases where such pure pixels do not exist, only MCR was found to be capable of extracting the pure component spectra.

Structural diversity of Alzheimer-related protein aggregations revealed using photothermal ratio-metric micro-spectroscopy

The crucial link between pathological protein aggregations and lipids in Alzheimer's disease pathogenesis is increasingly recognized, yet its spatial dynamics remain challenging for labeling-based microscopy. Here, we demonstrate photothermal ratio-metric infrared spectro-microscopy (PRISM) to investigate the in situ structural and molecular compositions of pathological features in brain tissues at submicron resolution. By identifying the vibrational spectroscopic signatures of protein secondary structures and lipids, PRISM tracks the structural dynamics of pathological proteins, including amyloid and hyperphosphorylated Tau (pTau). Amyloid-associated lipid features in major brain regions were observed, notably the enrichment of lipid-dissociated plaques in the hippocampus. Spectroscopic profiling of pTau revealed significant heterogeneity in phosphorylation levels and a distinct lipid-pTau relationship that contrasts with the anticipated lipid-plaque correlation. Beyond in vitro studies, our findings provide direct visualization evidence of aggregate-lipid interactions across the brain, offering new insights into mechanistic and therapeutic research of neurodegenerative diseases.

Artificial Intelligence-Assisted Stimulated Raman Histology: New Frontiers in Vibrational Tissue Imaging

Frozen section biopsy, introduced in the early 1900s, still remains the gold standard methodology for rapid histologic evaluations. Although a valuable tool, it is labor-, time-, and cost-intensive. Other challenges include visual and diagnostic variability, which may complicate interpretation and potentially compromise the quality of clinical decisions. Raman spectroscopy, with its high specificity and non-invasive nature, can be an effective tool for dependable and quick histopathology. The most promising modality in this context is stimulated Raman histology (SRH), a label-free, non-linear optical process which generates conventional H&E-like images in short time frames. SRH overcomes limitations of conventional Raman scattering by leveraging the qualities of stimulated Raman scattering (SRS), wherein the energy gets transferred from a high-power pump beam to a probe beam, resulting in high-energy, high-intensity scattering. SRH's high resolution and non-requirement of preprocessing steps make it particularly suitable when it comes to intrasurgical histology. Combining SRH with artificial intelligence (AI) can lead to greater precision and less reliance on manual interpretation, potentially easing the burden of the overburdened global histopathology workforce. We review the recent applications and advances in SRH and how it is tapping into AI to evolve as a revolutionary tool for rapid histologic analysis.

Mapping cellular stress and lipid dysregulation in Alzheimer-related progressive neurodegeneration using label-free Raman microscopy

Aβ plaques are a main feature of Alzheimer's disease, and pathological alterations especially in their microenvironment have recently come into focus. However, a holistic imaging approach unveiling these changes and their biochemical nature is still lacking. In this context, we leverage confocal Raman microscopy as unbiased tool for non-destructive, label-free differentiation of progressive biomolecular changes in the Aβ plaque microenvironment in brain tissue of a murine model of cerebral amyloidosis. By developing a detailed approach, overcoming many challenges of chemical imaging, we identify spatially-resolved molecular signatures of disease-associated structures. Specifically, our study reveals nuclear condensation, indicating cellular degeneration, and increased levels of cytochrome c, showing mitochondrial dysfunction, in the vicinity of Aβ plaques. Further, we observe severe accumulation of especially unsaturated lipids. Thus, our study contributes to a comprehensive understanding of disease progression in the Aβ plaque microenvironment, underscoring the prospective of Raman imaging in neurodegenerative disorder research.

Identification of tauopathy-associated lipid signatures in Alzheimer's disease mouse brain using label-free chemical imaging

There is cumulative evidence that lipid metabolism plays a key role in the pathogenesis of various neurodegenerative disorders including Alzheimer's disease (AD). Visualising lipid content in a non-destructive label-free manner can aid in elucidating the AD phenotypes towards a better understanding of the disease. In this study, we combined multiple optical molecular-specific methods, Fourier transform infrared (FTIR) spectroscopic imaging, synchrotron radiation-infrared (SR-IR) microscopy, Raman and stimulated Raman scattering (SRS) microscopy, and optical-photothermal infrared (O-PTIR) microscopy with multivariate data analysis, to investigate the biochemistry of brain hippocampus in situ using a mouse model of tauopathy (rTg4510). We observed a significant difference in the morphology and lipid content between transgenic (TG) and wild type (WT) samples. Immunohistochemical staining revealed some degree of microglia co-localisation with elevated lipids in the brain. These results provide new evidence of tauopathy-related dysfunction in a preclinical study at a subcellular level.

A versatile Raman setup with time-gating and fast wide-field imaging capabilities

Raman spectroscopy is a well-established method for chemical identification, with a wide variety of applications. The two major limitations are that fluorescence can hamper detection, and that Raman imaging is slow; it typically takes multiple hours to measure even a small surface area. We have developed a multimodal setup that mitigates these limitations. The setup has a point-scanning mode that allows for time-gated as well as continuous Raman spectroscopy, and both modes use an 80 MHz, 532 nm excitation laser with up to 20 W of power. The fluorescence suppression capabilities of the setup were demonstrated by comparing time-gated to continuous detection of a Dracaena leaf. Raman bands showed a 4-8 times improvement in signal-to-background ratio, and one band that was invisible in the continuous measurement, became visible in the time-gated measurement. The setup also has a 4-band simultaneously detected wide-field mode. Using a set of beam splitters, the Raman signal from the sample is split. This signal is imaged onto four separate cameras, each with a specific band-pass filter. The wide-field data were processed using principal component analysis with k-means clustering. To illustrate the wide-field capabilities of the setup, a 1mm2 sample containing aspirin, caffeine and paracetamol was measured using 10 W excitation power. A 10-second measurement enabled identification of the compounds, and a 1-second measurement showed promising results. This brings the setup close to real-time imaging, showing great potential for applications in quality control or for measuring samples that change over time.

Multi-modal, Label-free, Optical Mapping of Cellular Metabolic Function and Oxidative Stress in 3D Engineered Brain Tissue Models

Brain metabolism is essential for the function of organisms. While established imaging methods provide valuable insights into brain metabolic function, they lack the resolution to capture important metabolic interactions and heterogeneity at the cellular level. Label-free, two-photon excited fluorescence imaging addresses this issue by enabling dynamic metabolic assessments at the single-cell level without manipulations. In this study, we demonstrate the impact of spectral imaging on the development of rigorous intensity and lifetime label-free imaging protocols to assess dynamically over time metabolic function in 3D engineered brain tissue models comprising human induced neural stem cells, astrocytes, and microglia. Specifically, we rely on multi-wavelength spectral imaging to identify the excitation/emission profiles of key cellular fluorophores within human brain cells, including NAD(P)H, LipDH, FAD, and lipofuscin. These enable development of methods to mitigate lipofuscin's overlap with NAD(P)H and flavin autofluorescence to extract reliable optical metabolic function metrics from images acquired at two excitation wavelengths over two emission bands. We present fluorescence intensity and lifetime metrics reporting on redox state, mitochondrial fragmentation, and NAD(P)H binding status in neuronal monoculture and triculture systems, to highlight the functional impact of metabolic interactions between different cell types. Our findings reveal significant metabolic differences between neurons and glial cells, shedding light on metabolic pathway utilization, including the glutathione pathway, OXPHOS, glycolysis, and fatty acid oxidation. Collectively, our studies establish a label-free, non-destructive approach to assess the metabolic function and interactions among different brain cell types relying on endogenous fluorescence and illustrate the complementary nature of information that is gained by combining intensity and lifetime-based images. Such methods can improve understanding of physiological brain function and dysfunction that occurs at the onset of cancers, traumatic injuries and neurodegenerative diseases.

Unveiling brain disorders using liquid biopsy and Raman spectroscopy

Brain disorders, including neurodegenerative diseases (NDs) and traumatic brain injury (TBI), present significant challenges in early diagnosis and intervention. Conventional imaging modalities, while valuable, lack the molecular specificity necessary for precise disease characterization. Compared to the study of conventional brain tissues, liquid biopsy, which focuses on blood, tear, saliva, and cerebrospinal fluid (CSF), also unveils a myriad of underlying molecular processes, providing abundant predictive clinical information. In addition, liquid biopsy is minimally- to non-invasive, and highly repeatable, offering the potential for continuous monitoring. Raman spectroscopy (RS), with its ability to provide rich molecular information and cost-effectiveness, holds great potential for transformative advancements in early detection and understanding the biochemical changes associated with NDs and TBI. Recent developments in Raman enhancement technologies and advanced data analysis methods have enhanced the applicability of RS in probing the intricate molecular signatures within biological fluids, offering new insights into disease pathology. This review explores the growing role of RS as a promising and emerging tool for disease diagnosis in brain disorders, particularly through the analysis of liquid biopsy. It discusses the current landscape and future prospects of RS in the diagnosis of brain disorders, highlighting its potential as a non-invasive and molecularly specific diagnostic tool.

Raman spectroscopy in the study of amyloid formation and phase separation

Neurodegenerative diseases, such as Alzheimer's and Parkinson's, share a common pathological feature of amyloid structure accumulation. However, the structure-function relationship between these well-ordered, β-sheet-rich, filamentous protein deposits and disease etiology remains to be defined. Recently, an emerging hypothesis has linked phase separation, a process involved in the formation of protein condensates, to amyloid formation, suggesting that liquid protein droplets serve as loci for amyloid initiation. To elucidate how these processes contribute to disease progression, tools that can directly report on protein secondary structural changes are needed. Here, we review recent studies that have demonstrated Raman spectroscopy as a powerful vibrational technique for interrogating amyloid structures; one that offers sensitivity from the global secondary structural level to specific residues. This probe-free technique is further enhanced via coupling to a microscope, which affords structural data with spatial resolution, known as Raman spectral imaging (RSI). In vitro and in cellulo applications of RSI are discussed, highlighting studies of protein droplet aging, cellular internalization of fibrils, and Raman imaging of intracellular water. Collectively, utilization of the myriad Raman spectroscopic methods will contribute to a deeper understanding of protein conformational dynamics in the complex cellular milieu and offer potential clinical diagnostic capabilities for protein misfolding and aggregation processes in disease states.

Imaging of intracellular protein aggregates through plasmon-assisted clusteroluminescence

The formation of clusters in non-aromatic molecules can give rise to unconventional luminescence or clusteroluminescence. Typically containing heteroatoms without extended conjugation or aromatic rings, these molecules have drawn much attention owing to the prospects of label-free biological imaging. However, their applications have been limited due to the lack of knowledge of the underlying mechanism. Herein, we have elucidated the mechanism of clusteroluminescence from proteins, which were explicitly aggregated using plasmonic silver nanoparticles. The nanoparticles promoted protein aggregation and induced nitrile formation on the surface, which, along with other lone-pair-containing heteroatoms, contributed to enhanced emission in the visible range. Remarkably, this makes imaging of proteins possible with visible excitations, as co-factor-lacking proteins generally undergo electronic transitions only in the ultraviolet range. Furthermore, the inherent protein-aggregating behaviour of plasmonic nanoparticles was harnessed for imaging of intracellular Huntingtin protein aggregates overexpressed in HeLa cells through clusteroluminescence. Significant plasmon-enhanced and red-shifted fluorescence emission was observed, which helped in the imaging and localization of the intracellular aggregates. Density functional theory calculations and transient absorbance spectroscopy were used to probe the molecular interactions at the protein-nanoparticle interface and the charge transfer states, further elucidating the role of nanoparticles and the emission mechanism. This technique thus opens alternate avenues for label-free fluorescence bioimaging.

Amyloids, amorphous aggregates and assemblies of peptides - Assessing aggregation

Amyloid and amorphous aggregates represent the two major categories of aggregates associated with diseases, and although exhibiting distinct features, researchers often treat them as equivalent, which demonstrates the need for more thorough characterization. Here, we compare amyloid and amorphous aggregates based on their biochemical properties, kinetics, and morphological features. To further decipher this issue, we propose the use of peptide self-assemblies as minimalistic models for understanding the aggregation process. Peptide building blocks are significantly smaller than proteins that participate in aggregation, however, they make a plausible means to bridge the gap in discerning the aggregation process at the more complex, protein level. Additionally, we explore the potential use of peptide-inspired models to research the liquid-liquid phase separation as a feasible mechanism preceding amyloid formation. Connecting these concepts can help clarify our understanding of aggregation-related disorders and potentially provide novel drug targets to impede and reverse these serious illnesses.

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.

Colocalization of β-Sheets and Carotenoids in Aβ Plaques Revealed with Multimodal Spatially Resolved Vibrational Spectroscopy

The aggregation of amyloid β(Aβ) peptides is at the heart of Alzheimer's disease development and progression. As a result, amyloid aggregates have been studied extensively in vitro, and detailed structural information on fibrillar amyloid aggregates is available. However, forwarding these structural models to amyloid plaques in the human brain is still a major challenge. The chemistry of amyloid plaques, particularly in terms of the protein secondary structure and associated chemical moieties, remains poorly understood. In this report, we use Raman microspectroscopy to identify the presence of carotenoids in amyloid plaques and demonstrate that the abundance of carotenoids is correlated with the overall protein secondary structure of plaques, specifically to the population of β-sheets. While the association of carotenoids with plaques has been previously identified, their correlation with the β structure has never been identified. To further validate these findings, we have used optical photothermal infrared (O-PTIR) spectroscopy, which is a spatially resolved technique that yields complementary infrared contrast to Raman. O-PTIR unequivocally demonstrates the presence of elevated β-sheets in carotenoid-containing plaques and the lack of β structure in noncarotenoid plaques. Our findings underscore the potential link between anti-inflammatory species as carotenoids to specific secondary structural motifs within Aβ plaques and highlight the possible role of chemically distinct plaques in neuroinflammation, which can uncover new mechanistic insights and lead to new therapeutic strategies for AD.

Probing Chemical Complexity of Amyloid Plaques in Alzheimer's Disease Mice using Hyperspectral Raman Imaging

One of the distinctive pathological features of Alzheimer's disease (AD) is the deposition of amyloid plaques within the brain of affected individuals. These plaques have traditionally been investigated using labeling techniques such as immunohistochemical imaging. However, the use of labeling can disrupt the structural integrity of the molecules being analyzed. Hence, it is imperative to employ label-free imaging methods for noninvasive examination of amyloid deposits in their native form, thereby providing more relevant information pertaining to AD. This study presents compelling evidence that label-free and nondestructive confocal Raman imaging is a highly effective approach for the identification and chemical characterization of amyloid plaques within cortical regions of an arcAβ mouse model of AD. Furthermore, this investigation elucidates how the spatial correlation of Raman signals can be exploited to identify robust Raman marker bands and discern proteins and lipids from amyloid plaques. Finally, this study uncovers the existence of distinct types of amyloid plaques in the arcAβ mouse brain, exhibiting significant disparities in terms of not only shape and size but also molecular composition.

Label-free autofluorescence and hyperspectral imaging of cerebral amyloid-β lesions in aged squirrel monkeys

The observation of amyloid-β (Aβ) lesions using autofluorescence in transgenic mice and human Alzheimer disease patients has been reported frequently. However, no reports verify the autofluorescence of spontaneous Aβ amyloidosis in animals, to our knowledge. We validated the autofluorescence of Aβ lesions in spontaneous squirrel monkey cases under label-free conditions; lesions had intense blue-white autofluorescence in fluorescence microscopy using excitation light at 400-440 nm. Thioflavin S staining and immunohistochemistry of the same specimens revealed that this blue-white autofluorescence was derived from Aβ lesions. Hyperspectral analysis of these lesions revealed a characteristic spectrum with bimodal peaks at 440 and 460 nm, as reported for Aβ lesions in mice. Principal component analysis using hyperspectral data specifically separated the Aβ lesions from other autofluorescent substances, such as lipofuscin. A non-labeled and mechanistic detection of Aβ lesions by hyperspectral imaging could provide valuable insights for developing early diagnostic techniques.

Label-free identification of protein aggregates using deep learning

Protein misfolding and aggregation play central roles in the pathogenesis of various neurodegenerative diseases (NDDs), including Huntington's disease, which is caused by a genetic mutation in exon 1 of the Huntingtin protein (Httex1). The fluorescent labels commonly used to visualize and monitor the dynamics of protein expression have been shown to alter the biophysical properties of proteins and the final ultrastructure, composition, and toxic properties of the formed aggregates. To overcome this limitation, we present a method for label-free identification of NDD-associated aggregates (LINA). Our approach utilizes deep learning to detect unlabeled and unaltered Httex1 aggregates in living cells from transmitted-light images, without the need for fluorescent labeling. Our models are robust across imaging conditions and on aggregates formed by different constructs of Httex1. LINA enables the dynamic identification of label-free aggregates and measurement of their dry mass and area changes during their growth process, offering high speed, specificity, and simplicity to analyze protein aggregation dynamics and obtain high-fidelity information.

Advanced Techniques for Detecting Protein Misfolding and Aggregation in Cellular Environments

Protein misfolding and aggregation, a key contributor to the progression of numerous neurodegenerative diseases, results in functional deficiencies and the creation of harmful intermediates. Detailed visualization of this misfolding process is of paramount importance for improving our understanding of disease mechanisms and for the development of potential therapeutic strategies. While in vitro studies using purified proteins have been instrumental in delivering significant insights into protein misfolding, the behavior of these proteins in the complex milieu of living cells often diverges significantly from such simplified environments. Biomedical imaging performed in cell provides cellular-level information with high physiological and pathological relevance, often surpassing the depth of information attainable through in vitro methods. This review highlights a variety of methodologies used to scrutinize protein misfolding within biological systems. This includes optical-based methods, strategies leaning on mass spectrometry, in-cell nuclear magnetic resonance, and cryo-electron microscopy. Recent advancements in these techniques have notably deepened our understanding of protein misfolding processes and the features of the resulting misfolded species within living cells. The progression in these fields promises to catalyze further breakthroughs in our comprehension of neurodegenerative disease mechanisms and potential therapeutic interventions.

Impacts of polypropylene microplastics on lipid profiles of mouse liver uncovered by lipidomics analysis and Raman spectroscopy

Microplastics (MPs) are emerging contaminants, and there are only limited studies reporting the impacts of some MPs on liver lipid metabolism in animals. In this study, we investigated the accumulation of polypropylene-MPs in mouse liver and unraveled the change in lipid metabolic profiles by both lipidomics and Raman spectroscopy. Polypropylene-MP exposure did not cause obvious health symptoms, but hematoxylin-eosin staining showed pathological changes that polypropylene-MPs induced lipid droplet accumulation in liver. Lipidomics results showed a significant change in lipid metabolic profiles and the most influenced categories were triglycerides, fatty acids, free fatty acids and lysophosphatidylcholine, implying the effects of polypropylene-MPs on the hemostasis of lipid droplet biogenesis and catabolism. Most altered lipids contained unsaturated bonds and polyunsaturated phospholipids, possibly affecting the fluidity and curvature of membrane surfaces. Raman spectroscopy confirmed that the major spectral alterations of liver tissues were related to lipids, evidencing the altered lipid metabolism and cell membrane components in the presence of polypropylene-MPs. Our findings firstly disclosed the impacts of polypropylene-MPs on lipid metabolisms in mouse liver and hinted at their detrimental disturbance on membrane properties, cellular lipid storage and oxidation regulation, helping our deeper understanding on the toxicities and corresponding risks of polypropylene-MPs to mammals.

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.

Label-Free Optical Metabolic Imaging in Cells and Tissues

Over the last half century, the autofluorescence of the metabolic cofactors NADH (reduced nicotinamide adenine dinucleotide) and FAD (flavin adenine dinucleotide) has been quantified in a variety of cell types and disease states. With the spread of nonlinear optical microscopy techniques in biomedical research, NADH and FAD imaging has offered an attractive solution to noninvasively monitor cell and tissue status and elucidate dynamic changes in cell or tissue metabolism. Various tools and methods to measure the temporal, spectral, and spatial properties of NADH and FAD autofluorescence have been developed. Specifically, an optical redox ratio of cofactor fluorescence intensities and NADH fluorescence lifetime parameters have been used in numerous applications, but significant work remains to mature this technology for understanding dynamic changes in metabolism. This article describes the current understanding of our optical sensitivity to different metabolic pathways and highlights current challenges in the field. Recent progress in addressing these challenges and acquiring more quantitative information in faster and more metabolically relevant formats is also discussed.

An Approach for In-Situ Detection of Gold Colloid Aggregates Amyloid Formations Within The Hippocampus of The Cohen's Alzheimer's Disease Rat Model By Surface Enhanced Raman Scattering Methods

BACKGROUND

Amyloid beta (Aβ) peptides, such as Aβ_1-40 or Aβ_1-42 are regarded as hallmark neuropathological biomarkers associated with Alzheimer's disease (AD). The formation of an aggregates by Aβ_1-40 or Aβ_1-42-coated gold nano-particles are hypothesized to contain conformation of Aβ oligomers, which could exist only at an initial stage of fibrillogenesis.

NEW METHOD

The attempt of in-situ detection of externally initiated gold colloid (ca. 80nm diameter) aggregates in the middle section of the hippocampus of the Long Evans Cohen's Alzheimer's disease rat model was conducted through the Surface Enhanced Raman Scattering (SERS) method.

RESULTS

The SERS spectral features contained modes associated with β-sheet interactions and a significant number of modes that were previously reported in SERS shifts for Alzheimer diseased rodent and human brain tissues; thereby, strongly implying a containment of amyloid fibrils. The spectral patterns were further examined and compared with those collected from in-vitro gold colloid aggregates which were formed from Aβ_1-40 - or Aβ_1-42 -coated 80nm gold colloid under pH ~4, pH ~7, and pH ~10, and the best matched datasets were found with that of the aggregates of Aβ_1-42 -coated 80nm gold colloid at ~pH 4.0. The morphology and physical size of this specific gold colloid aggregate was clearly different from those found in-vitro.

COMPARISON WITH EXISTING METHOD(S)

The amyloid fibril with a β-sheet conformation identified in previously reported in AD mouse/human brain tissues was involved in a formation of the gold colloid aggregates. However, to our surprise, best explanation for the observed SERS spectral features was possible with those in vitro Aβ_1-42 -coated 80nm gold colloid under pH ~4.

CONCLUSIONS

A formation of gold colloid aggregates was confirmed in the AD rat hippocampal brain section with unique physical morphology compared to those observed in in-vitro Aβ_1-42 or Aβ_1-40 mediated gold colloid aggregates. It was concluded that a β-sheet conformation identified in previously reported in AD mouse/human brain tissues was in volved in a formation of the gold colloid aggregates.

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.

β-Carotene, a Potent Amyloid Aggregation Inhibitor, Promotes Disordered Aβ Fibrillar Structure

The aggregation of amyloid beta (Aβ) into fibrillar aggregates is a key feature of Alzheimer’s disease (AD) pathology. β-carotene and related compounds have been shown to associate with amyloid aggregates and have direct impact on the formation of amyloid fibrils. However, the precise effect of β-carotene on the structure of amyloid aggregates is not known, which poses a limitation towards developing it as a potential AD therapeutic. In this report, we use nanoscale AFM-IR spectroscopy to probe the structure of Aβ oligomers and fibrils at the single aggregate level and demonstrate that the main effect of β-carotene towards modulating Aβ aggregation is not to inhibit fibril formation but to alter the secondary structure of the fibrils and promote fibrils that lack the characteristic ordered beta structure.

Raman Spectroscopy on Brain Disorders: Transition from Fundamental Research to Clinical Applications

Brain disorders such as brain tumors and neurodegenerative diseases (NDs) are accompanied by chemical alterations in the tissues. Early diagnosis of these diseases will provide key benefits for patients and opportunities for preventive treatments. To detect these sophisticated diseases, various imaging modalities have been developed such as computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET). However, they provide inadequate molecule-specific information. In comparison, Raman spectroscopy (RS) is an analytical tool that provides rich information about molecular fingerprints. It is also inexpensive and rapid compared to CT, MRI, and PET. While intrinsic RS suffers from low yield, in recent years, through the adoption of Raman enhancement technologies and advanced data analysis approaches, RS has undergone significant advancements in its ability to probe biological tissues, including the brain. This review discusses recent clinical and biomedical applications of RS and related techniques applicable to brain tumors and NDs.

Efficacy and Safety of a Brain-Penetrant Biologic TNF-α Inhibitor in Aged APP/PS1 Mice

Tumor necrosis factor alpha (TNF-α) plays a vital role in Alzheimer's disease (AD) pathology, and TNF-α inhibitors (TNFIs) modulate AD pathology. We fused the TNF-α receptor (TNFR), a biologic TNFI that sequesters TNF-α, to a transferrin receptor antibody (TfRMAb) to deliver the TNFI into the brain across the blood-brain barrier (BBB). TfRMAb-TNFR was protective in 6-month-old transgenic APP/PS1 mice in our previous work. However, the effects and safety following delayed chronic TfRMAb-TNFR treatment are unknown. Herein, we initiated the treatment when the male APP/PS1 mice were 10.7 months old (delayed treatment). Mice were injected intraperitoneally with saline, TfRMAb-TNFR, etanercept (non-BBB-penetrating TNFI), or TfRMAb for ten weeks. Biologic TNFIs did not alter hematology indices or tissue iron homeostasis; however, TfRMAb altered hematology indices, increased splenic iron transporter expression, and increased spleen and liver iron. TfRMAb-TNFR and etanercept reduced brain insoluble-amyloid beta (Aβ) 1-42, soluble-oligomeric Aβ, and microgliosis; however, only TfRMAb-TNFR reduced Aβ peptides, Thioflavin-S-positive Aβ plaques, and insoluble-oligomeric Aβ and increased plaque-associated phagocytic microglia. Accordingly, TfRMAb-TNFR improved spatial reference memory and increased BBB-tight junction protein expression, whereas etanercept did not. Overall, despite delayed treatment, TfRMAb-TNFR resulted in a better therapeutic response than etanercept without any TfRMAb-related hematology- or iron-dysregulation in aged APP/PS1 mice.

Label-free chemical imaging of cytochrome P450 activity by Raman microscopy

Although investigating drug modulation of cytochrome P450 (CYP) activity under physiological conditions is crucial in drug development to avoid severe adverse drug reactions, the current evaluation approaches that rely on the destructive and end-point analysis can be misleading due to invasive treatments and cellular heterogeneity. Here, we propose a non-destructive and high-content method for visualizing and quantifying intracellular CYP activity under drug administration by Raman microscopy. The redox-state and spin-state sensitive Raman measurement indicated that the induced CYPs in living hepatocytes were in oxidized and low-spin state, which is related to monooxygenase function of CYP. Moreover, glycogen depletion associated with CYP induction was simultaneously observed, indicating a relevant effect on glucose metabolism. By deciphering the overall changes in the biochemical fingerprints of hepatocytes, Raman microscopy offers a non-destructive and quantitative chemical imaging method to evaluate CYP activity at the single-cell level with the potential to facilitate future drug development schemes.

From Research to Diagnostic Application of Raman Spectroscopy in Neurosciences: Past and Perspectives

In recent years, Raman spectroscopy has been more and more frequently applied to address research questions in neuroscience. As a non-destructive technique based on inelastic scattering of photons, it can be used for a wide spectrum of applications including neurooncological tumor diagnostics or analysis of misfolded protein aggregates involved in neurodegenerative diseases. Progress in the technical development of this method allows for an increasingly detailed analysis of biological samples and may therefore open new fields of applications. The goal of our review is to provide an introduction into Raman scattering, its practical usage and also commonly associated pitfalls. Furthermore, intraoperative assessment of tumor recurrence using Raman based histology images as well as the search for non-invasive ways of diagnosis in neurodegenerative diseases are discussed. Some of the applications mentioned here may serve as a basis and possibly set the course for a future use of the technique in clinical practice. Covering a broad range of content, this overview can serve not only as a quick and accessible reference tool but also provide more in-depth information on a specific subtopic of interest.