Label-free imaging of amyloid plaques in Alzheimer's disease with stimulated Raman scattering microscopy

Advanced vibrational microscopes for life science

Providing molecular fingerprint information, vibrational spectroscopic imaging opens a new window to decipher the function of biomolecules in living systems. While classic vibrational microscopes based on spontaneous Raman scattering or mid-infrared absorption offer rich insights into sample composition, they have very small cross sections or poor spatial resolution. Nonlinear vibrational microscopy, based on coherent Raman scattering or optical photothermal detection of vibrational absorption, overcomes these barriers and enables high-speed and high-sensitivity imaging of chemical bonds in live cells and tissues. Here, we introduce various modalities, including their principles, strengths, weaknesses and data mining methods to the life sciences community. We further provide a guide for prospective users and an outlook on future technological advances.

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.

High-Throughput Volumetric Mapping Facilitated by Active Tissue SHRINK

Comprehensive visualization of tissue architecture in large organs such as the brain is crucial for understanding functional relationships across key tissue regions. However, the large size of whole organs makes it challenging to image their entirety with subcellular resolution, often requiring prolonged imaging sessions, volume reconstruction, and compromises in spatial coverage. Here, Scalable Hydrogel-embedded Rapid Imaging of tissue NetworK (SHRINK) is reported to address this challenge through active tissue shrinkage and clearing. Utilizing the identified hydrogel network to preserve the spatial pattern of proteins in situ and remove the uncrosslinked biomolecules to create space, it is shown that SHRINK isotropically drives the reduction of sample sizes down to 16% of their original volume while maintaining high cellular and tissue-level integrity in a reversible manner. The size reduction and the corresponding 3D concentrating of the biomolecules render a more than sixfold enhancement for throughput and signal respectively, which addresses a key bottleneck for the stimulated Raman scattering (SRS) microscopy, ideal for 3D, label-free and super-multiplex tissue mapping. It is further demonstrated that SHRINK-SRS achieves organ-scale mapping of brain, intestine, heart, and kidney tissues. SHRINK offers a powerful approach to overcome traditional imaging barriers, enabling rapid and detailed visualization of large organs.

Intelligent Bi-Dimensional Skin Biopsies of Rheumatoid Arthritis Based on Raman Spectral Imaging and Machine Learning

Rheumatoid arthritis (RA) is one of the most common autoimmune diseases worldwide, characterized by its progressive and irreversible nature. Early diagnosis is crucial for delaying disease progression and optimizing treatment strategies. Existing diagnostic methods face limitations in asymptomatic screening and often rely on subjective judgment by experienced rheumatologists, restricting their application in early screening and clinical diagnosis. To address these challenges, we developed an innovative approach for intelligent bidimensional skin biopsies, employing Raman spectroscopy for direct spectral scanning and imaging of affected joint skin. This method enables preliminary RA diagnosis after a brief skin surface scan. It generates high-resolution three-dimensional Raman images of the affected skin within 13 min, providing rapid and reliable diagnostic support. Furthermore, Raman data are analyzed and classified using multiple artificial intelligence algorithms, such as naive Bayes, linear discriminant analysis, decision trees, k-nearest neighbors, random forests, and support vector machines, achieving high-accuracy RA differentiation. The design significantly enhances diagnostic precision and speed, enabling nonspecialists to accurately diagnose RA. Extensive experimental data validated the method's 100% diagnostic accuracy. This approach provides a novel and effective tool for early RA screening and demonstrates potential applications in other autoimmune and dermatological diseases.

Monitoring photobiomodulation of amyloid-β aggregation in 3D cultured cells using label-free nonlinear optical imaging

The accumulation of beta-amyloid (Aβ) peptide aggregates, commonly known as plaques, is considered a key hallmark in the development of Alzheimer's disease (AD). Recently, low-level light therapy (LLLT), also referred to as photobiomodulation (PBM), has emerged as a promising treatment approach for AD. Previous studies have shown that PBM reduces Aβ load primarily by enhancing the clearance capabilities of glia cells. However, it remains unclear whether PBM can directly reduce the formation of Aβ plaques in neuronal cells independent of the glia cell effect. In this study, we employed three-dimensional (3D) cultured HEK 293 APPsw cells as an AD model to investigate the impact of PBM on Aβ aggregation. We demonstrated that label-free two-photon excited fluorescence (TPEF) imaging and second harmonic generation (SHG) imaging are effective tools for monitoring Aβ aggregation in 3D cell models. The TPEF imaging results and subsequent quantification revealed that PBM, particularly with low-level near-infrared light from an 808 nm laser (compared to 1064, 1210, and 1470 nm lasers), significantly reduced Aβ aggregation, specifically plaques formation, in the 3D cultured cells, with the effect found to be dose-dependent. Moreover, a comprehensive analysis of protein expression in the 3D cultured cells revealed that PBM induces overexpression of the LRP1 receptor, which mediates Aβ degradation and thus leads to the reduction of Aβ aggregation. This study highlights the use of label-free nonlinear optical imaging to monitor Aβ aggregation in AD progression and provides novel insights into the effects of PBM on Aβ plaque formation in AD models.

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.

Coherent Raman microscopy visualizes ongoing cellular senescence through amide I peak shifts originating from β sheets in disordered nucleolar proteins

Cellular senescence occurs through the accumulation of many kinds of stresses. Senescent cells in tissues also cause various age-related disorders. Therefore, detecting them without labeling is beneficial for medical research and developing diagnostic methods. However, existing biomarkers have limitations of requiring fixation and labeling, or their molecular backgrounds are uncertain. Coherent anti-Stokes Raman scattering (CARS) spectroscopic imaging is a novel option because it can assess and visualize molecular structures based on their molecular fingerprint. Here, we present a new label-free method to visualize cellular senescence using CARS imaging in nucleoli. We found the peak of the nucleolar amide I band shifted to a higher wavenumber in binuclear senescent cells, which reflects changes in the protein secondary structure from predominant α-helices to β-sheets originating from amyloid-like aggregates. Following this, we developed a procedure that can visualize the senescent cells by providing the ratios and subtractions of these two components. We also confirmed that the procedure can visualize nucleolar aggregates due to unfolded/misfolded proteins produced by proteasome inhibition. Finally, we found that this method can help visualize the nucleolar defects in naïve cells even before binucleation. Thus, our method is beneficial to evaluate ongoing cellular senescence through label-free imaging of nucleolar defects.

Advances and Prospects in Liquid Biopsy Techniques for Malignant Tumor Diagnosis and Surveillance

Liquid biopsy technology provides invaluable support for the early diagnosis of tumors and surveillance of disease course by detecting tumor-related biomarkers in bodily fluids. Currently, liquid biopsy techniques are mainly divided into two categories: biomarker and label-free. Biomarker liquid biopsy techniques utilize specific antibodies or probes to identify and isolate target cells, exosomes, or molecules, and these techniques are widely used in clinical practice. However, they have certain limitations including dependence on tumor markers, alterations in cell biological properties, and high cost. In contrast, label-free liquid biopsy techniques directly utilize physical or chemical properties of cells, exosomes, or molecules for detection and isolation. These techniques have the advantage of not needing labeling, not impacting downstream analysis, and low detection cost. However, most are still in the research stage and not yet mature. This review first discusses recent advances in liquid biopsy techniques for early tumor diagnosis and disease surveillance. Several current techniques are described in detail. These techniques exploit differences in biomarkers, size, density, deformability, electrical properties, and chemical composition in tumor components to achieve highly sensitive tumor component identification and separation. Finally, the current research progress is summarized and the future research directions of the field are discussed.

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.

Strategies for measuring concentrations and forms of amyloid-β peptides

Alzheimer's disease (AD) is affecting more and more people worldwide without the effective treatment, while the existed pathological mechanism has been confirmed barely useful in the treatment. Amyloid-β peptide (Aβ), a main component of senile plaque, is regarded as the most promising target in AD treatment. Aβ clearance from AD brain seems to be a reliably therapeutic strategy, as the two exited drugs, GV-971 and aducanumab, are both developed based on it. However, doubt still exists. To exhaustive expound on the pathological mechanism of Aβ, rigorous analyses on the concentrations and aggregation forms are essential. Thus, it is attracting broad attention these years. However, most of the sensors have not been used in pathological studies, as the lack of the bridge between analytical chemist and pathologists. In this review, we made a brief introduce on Aβ-related pathological mechanism included in β-amyloid hypothesis to elucidate the detection conditions of sensor methods. Furthermore, a summary of the sensor methods was made, which were based on Aβ concentrations and form detections that have been developed in the past 10 years. As the greatest number of the sensors were built on fluorescent spectroscopy, electrochemistry, and Roman spectroscopy, detailed elucidation on them was made. Notably, the aggregation process is another important factor in revealing the progress of AD and developing the treatment methods, so the sensors on monitoring Aβ aggregation processes were also summarized.

Large field-of-view metabolic profiling of murine brain tissue following morphine incubation using label-free multiphoton microscopy

BACKGROUND

Although the effects on neural activation and glucose consumption caused by opiates such as morphine are known, the metabolic machinery underlying opioid use and misuse is not fully explored. Multiphoton microscopy (MPM) techniques have been developed for optical imaging at high spatial resolution. Despite the increased use of MPM for neural imaging, the use of intrinsic optical contrast has seen minimal use in neuroscience.

NEW METHOD

We present a label-free, multimodal microscopy technique for metabolic profiling of murine brain tissue following incubation with morphine sulfate (MSO4). We evaluate two- and three-photon excited autofluorescence, and second and third harmonic generation to determine meaningful intrinsic contrast mechanisms in brain tissue using simultaneous label-free, autofluorescence multi-harmonic (SLAM) microscopy.

RESULTS

Regional differences quantified in the cortex, caudate, and thalamus of the brain demonstrate region-specific changes to metabolic profiles measured from FAD intensity, along with brain-wide quantification. While the overall intensity of FAD signal significantly decreased after morphine incubation, this metabolic molecule accumulated near the nucleus accumbens.

COMPARISON WITH EXISTING METHODS

Histopathology requires tissue fixation and staining to determine cell type and morphology, lacking information about cellular metabolism. Tools such as fMRI or PET imaging have been widely used, but lack cellular resolution. SLAM microscopy obviates the need for tissue preparation, permitting immediate use and imaging of tissue with subcellular resolution in its native environment.

CONCLUSIONS

This study demonstrates the utility of SLAM microscopy for label-free investigations of neural metabolism, especially the intensity changes in FAD autofluorescence and structural morphology from third-harmonic generation.

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.

Seeing is Believing: Developing Multimodal Metabolic Insights at the Molecular Level

This outlook explores how two different molecular imaging approaches might be combined to gain insight into dynamic, subcellular metabolic processes. Specifically, we discuss how matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) and stimulated Raman scattering (SRS) microscopy, which have significantly pushed the boundaries of imaging metabolic and metabolomic analyses in their own right, could be combined to create comprehensive molecular images. We first briefly summarize the recent advances for each technique. We then explore how one might overcome the inherent limitations of each individual method, by envisioning orthogonal and interchangeable workflows. Additionally, we delve into the potential benefits of adopting a complementary approach that combines both MSI and SRS spectro-microscopy for informing on specific chemical structures through functional-group-specific targets. Ultimately, by integrating the strengths of both imaging modalities, researchers can achieve a more comprehensive understanding of biological and chemical systems, enabling precise metabolic investigations. This synergistic approach holds substantial promise to expand our toolkit for studying metabolites in complex environments.

Multi-molecular hyperspectral PRM-SRS microscopy

Lipids play crucial roles in many biological processes. Mapping spatial distributions and examining the metabolic dynamics of different lipid subtypes in cells and tissues are critical to better understanding their roles in aging and diseases. Commonly used imaging methods (such as mass spectrometry-based, fluorescence labeling, conventional optical imaging) can disrupt the native environment of cells/tissues, have limited spatial or spectral resolution, or cannot distinguish different lipid subtypes. Here we present a hyperspectral imaging platform that integrates a Penalized Reference Matching algorithm with Stimulated Raman Scattering (PRM-SRS) microscopy. Using this platform, we visualize and identify high density lipoprotein particles in human kidney, a high cholesterol to phosphatidylethanolamine ratio inside granule cells of mouse hippocampus, and subcellular distributions of sphingosine and cardiolipin in human brain. Our PRM-SRS displays unique advantages of enhanced chemical specificity, subcellular resolution, and fast data processing in distinguishing lipid subtypes in different organs and species.

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.

Time-resolved vibrational dynamics: Novel opportunities for sensing and imaging

The evolution of time-resolved spectroscopies has resulted in significant advancements across numerous scientific disciplines, particularly those concerned with molecular electronic states. However, the intricacy of molecular vibrational spectroscopies, which provide comprehensive molecular-level information within complex structures, has presented considerable challenges due to the ultrashort dephasing time. Over recent decades, an increasing focus has been placed on exploring the temporal progression of bond vibrations, thereby facilitating an improved understanding of energy redistribution within and between molecules. This review article focuses on an array of time-resolved detection methodologies, each distinguished by unique technological attributes that offer exclusive capabilities for investigating the physical phenomena propelled by molecular vibrational dynamics. In summary, time-resolved vibrational spectroscopy emerges as a potent instrument for deciphering the dynamic behavior of molecules. Its potential for driving future progress across fields as diverse as biology and materials science is substantial, marking a promising future for this innovative tool.

Exploring the effect of photobiomodulation and gamma visual stimulation induced by 808 nm and visible LED in Alzheimer's disease mouse model

Although photobiomodulation (PBM) and gamma visual stimulatqion (GVS) have been overwhelmingly explored in the recent time as a possible light stimulation (LS) means of Alzheimer's disease (AD) therapy, their effects have not been assessed at once. In our research, the AD mouse model was stimulated using light with various parameters [continuous wave (PBM) or 40 Hz pulsed visible LED (GVS) or 40 Hz pulsed 808 nm LED (PBM and GVS treatment)]]. The brain slices collected from the LS treated AD model mice were evaluated using (i) fluorescence microscopy to image thioflavine-S labeled amy-loid-β (Aβ) plaques (the main hallmark of AD), or (ii) two-photon excited fluorescence (TPEF) imaging of unlabeled Aβ plaques, showing that the amount of Aβ plaques was reduced after LS treatment. The imaging results correlated well with the results of Morris water maze (MWM) test, which demonstrated that the spatial learning and memory abilities of LS treated mice were noticeably higher than those of untreated mice. The LS effect was also assessed by in vivo nonlinear optical imaging, revealing that the cerebral amyloid angiopathy decreased spe-cifically as a result of 40 Hz pulsed 808 nm irradiation, on the contrary, the angiopathy reversed after visible 40 Hz pulsed light treatment. The obtained results provide useful reference for further optimization of the LS (PBM or GVS) parameters to achieve efficient phototherapy of AD.

Quantum optical tomography based on time-resolved and mode-selective single-photon detection by femtosecond up-conversion

We developed an optical time-of-flight measurement system using a time-resolved and mode-selective up-conversion single-photon detector for acquiring tomographic images of a mouse brain. The probe and pump pulses were spectrally carved from a 100-femtosecond mode-locked fiber laser at 1556 nm using 4f systems, so that their center wavelengths were situated at either side of the phase matching band separated by 30 nm. We demonstrated a sensitivity of 111 dB which is comparable to that of shot-noise-limited optical coherence tomography and an axial resolution of 57 μm (a refractive index of 1.37) with 380 femtosecond probe and pump pulses whose average powers were 1.5 mW and 30 μW, respectively. The proposed technique will open a new way of non-contact and non-invasive three-dimensional structural imaging of biological specimens with ultraweak optical irradiation.

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.

Combination of deep learning and 2D CARS figures for identification of amyloid-β plaques

In vivo imaging and accurate identification of amyloid-β (Aβ) plaque are crucial in Alzheimer's disease (AD) research. In this work, we propose to combine the coherent anti-Stokes Raman scattering (CARS) microscopy, a powerful detection technology for providing Raman spectra and label-free imaging, with deep learning to distinguish Aβ from non-Aβ regions in AD mice brains in vivo. The 1D CARS spectra is firstly converted to 2D CARS figures by using two different methods: spectral recurrence plot (SRP) and spectral Gramian angular field (SGAF). This can provide more learnable information to the network, improving the classification precision. We then devise a cross-stage attention network (CSAN) that automatically learns the features of Aβ plaques and non-Aβ regions by taking advantage of the computational advances in deep learning. Our algorithm yields higher accuracy, precision, sensitivity and specificity than the results of conventional multivariate statistical analysis method and 1D CARS spectra combined with deep learning, demonstrating its competence in identifying Aβ plaques. Last but not least, the CSAN framework requires no prior information on the imaging modality and may be applicable to other spectroscopy analytical fields.

Long-lived SERS Matrix for Real-Time Biochemical Detection Using "Frozen" Transition State

For the long-time tracking of biological events, maintaining the bioactivity of the analytes during the detection process is essential. Here, we show a versatile surface-enhanced Raman Scattering (SERS) platform, termed a superwettable-omniphobic lubricous porous SERS (SOLP-SERS) substrate. The SOLP-SERS substrate could generate a three-dimensional liquid "hotspots" matrix with an ultra-long lifetime (tens of days) by confining tiny amounts of liquids within the gaps between nanoparticles. Then, the analytes are trapped in the uniform liquid "hotspots", whose bioactivity can be well maintained over a long period of time during SERS detection. Limits of detection down to femtomolar levels were achieved for various molecules. More importantly, SERS signals were uniform within the substrate and remained stable for more than 30 days. As a proof-of-concept experiment, the dynamic detection of the polymerization of Aβ peptides into amyloids was monitored by the SOLP-SERS substrate within 48 h. Moreover, the exosomes secreted by breast cancer cells, an important biomarker of cancer, were also measured. These results demonstrate that the SOLP-SERS platform will provide new insights into the development of real-time biochemical sensors with ultrahigh sensitivity.

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.

Impact of seed amplification assay and surface-enhanced Raman spectroscopy combined approach on the clinical diagnosis of Alzheimer's disease

BACKGROUND

The current diagnosis of Alzheimer's disease (AD) is based on a series of analyses which involve clinical, instrumental and laboratory findings. However, signs, symptoms and biomarker alterations observed in AD might overlap with other dementias, resulting in misdiagnosis.

METHODS

Here we describe a new diagnostic approach for AD which takes advantage of the boosted sensitivity in biomolecular detection, as allowed by seed amplification assay (SAA), combined with the unique specificity in biomolecular recognition, as provided by surface-enhanced Raman spectroscopy (SERS).

RESULTS

The SAA-SERS approach supported by machine learning data analysis allowed efficient identification of pathological Aβ oligomers in the cerebrospinal fluid of patients with a clinical diagnosis of AD or mild cognitive impairment due to AD.

CONCLUSIONS

Such analytical approach can be used to recognize disease features, thus allowing early stratification and selection of patients, which is fundamental in clinical treatments and pharmacological trials.

Mid-infrared chemical imaging of intracellular tau fibrils using fluorescence-guided computational photothermal microscopy

Amyloid proteins are associated with a broad spectrum of neurodegenerative diseases. However, it remains a grand challenge to extract molecular structure information from intracellular amyloid proteins in their native cellular environment. To address this challenge, we developed a computational chemical microscope integrating 3D mid-infrared photothermal imaging with fluorescence imaging, termed Fluorescence-guided Bond-Selective Intensity Diffraction Tomography (FBS-IDT). Based on a low-cost and simple optical design, FBS-IDT enables chemical-specific volumetric imaging and 3D site-specific mid-IR fingerprint spectroscopic analysis of tau fibrils, an important type of amyloid protein aggregates, in their intracellular environment. Label-free volumetric chemical imaging of human cells with/without seeded tau fibrils is demonstrated to show the potential correlation between lipid accumulation and tau aggregate formation. Depth-resolved mid-infrared fingerprint spectroscopy is performed to reveal the protein secondary structure of the intracellular tau fibrils. 3D visualization of the β-sheet for tau fibril structure is achieved.

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.

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.

Stimulated Raman Scattering Microscopy Enables Gleason Scoring of Prostate Core Needle Biopsy by a Convolutional Neural Network

Focal therapy (FT) has been proposed as an approach to eradicate clinically significant prostate cancer while preserving the normal surrounding tissues to minimize treatment-related toxicity. Rapid histology of core needle biopsies is essential to ensure the precise FT for localized lesions and to determine tumor grades. However, it is difficult to achieve both high accuracy and speed with currently available histopathology methods. Here, we demonstrated that stimulated Raman scattering (SRS) microscopy could reveal the largely heterogeneous histologic features of fresh prostatic biopsy tissues in a label-free and near real-time manner. A diagnostic convolutional neural network (CNN) built based on images from 61 patients could classify Gleason patterns of prostate cancer with an accuracy of 85.7%. An additional 22 independent cases introduced as external test dataset validated the CNN performance with 84.4% accuracy. Gleason scores of core needle biopsies from 21 cases were calculated using the deep learning SRS system and showed a 71% diagnostic consistency with grading from three pathologists. This study demonstrates the potential of a deep learning-assisted SRS platform in evaluating the tumor grade of prostate cancer, which could help simplify the diagnostic workflow and provide timely histopathology compatible with FT treatment.

SIGNIFICANCE

A platform combining stimulated Raman scattering microscopy and a convolutional neural network provides rapid histopathology and automated Gleason scoring on fresh prostate core needle biopsies without complex tissue processing.

Histological diagnosis of unprocessed breast core-needle biopsy via stimulated Raman scattering microscopy and multi-instance learning

Core-needle biopsy (CNB) plays a vital role in the initial diagnosis of breast cancer. However, the complex tissue processing and global shortage of pathologists have hindered traditional histopathology from timely diagnosis on fresh biopsies. In this work, we developed a full digital platform by integrating label-free stimulated Raman scattering (SRS) microscopy with weakly-supervised learning for rapid and automated cancer diagnosis on un-labelled breast CNB. Methods: We first compared the results of SRS imaging with standard hematoxylin and eosin (H&E) staining on adjacent frozen tissue sections. Then fresh unprocessed biopsy tissues were imaged by SRS to reveal diagnostic histoarchitectures. Next, weakly-supervised learning, i.e., the multi-instance learning (MIL) model was conducted to evaluate the ability to differentiate between benign and malignant cases, and compared with the performance of supervised learning model. Finally, gradient-weighted class activation mapping (Grad-CAM) and semantic segmentation were performed to spatially resolve benign/malignant areas with high efficiency. Results: We verified the ability of SRS in revealing essential histological hallmarks of breast cancer in both thin frozen sections and fresh unprocessed biopsy, generating histoarchitectures well correlated with H&E staining. Moreover, we demonstrated that weakly-supervised MIL model could achieve superior classification performance to supervised learnings, reaching diagnostic accuracy of 95% on 61 biopsy specimens. Furthermore, Grad-CAM allowed the trained MIL model to visualize the histological heterogeneity within the CNB. Conclusion: Our results indicate that MIL-assisted SRS microscopy provides rapid and accurate diagnosis on histologically heterogeneous breast CNB, and could potentially help the subsequent management of patients.

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.

Copper homeostasis and cuproptosis in health and disease

As an essential micronutrient, copper is required for a wide range of physiological processes in virtually all cell types. Because the accumulation of intracellular copper can induce oxidative stress and perturbing cellular function, copper homeostasis is tightly regulated. Recent studies identified a novel copper-dependent form of cell death called cuproptosis, which is distinct from all other known pathways underlying cell death. Cuproptosis occurs via copper binding to lipoylated enzymes in the tricarboxylic acid (TCA) cycle, which leads to subsequent protein aggregation, proteotoxic stress, and ultimately cell death. Here, we summarize our current knowledge regarding copper metabolism, copper-related disease, the characteristics of cuproptosis, and the mechanisms that regulate cuproptosis. In addition, we discuss the implications of cuproptosis in the pathogenesis of various disease conditions, including Wilson's disease, neurodegenerative diseases, and cancer, and we discuss the therapeutic potential of targeting cuproptosis.

Lipid-driven condensation and interfacial ordering of FUS

Protein condensation into liquid-like structures is critical for cellular compartmentalization, RNA processing, and stress response. Research on protein condensation has primarily focused on membraneless organelles in the absence of lipids. However, the cellular cytoplasm is full of lipid interfaces, yet comparatively little is known about how lipids affect protein condensation. Here, we show that nonspecific interactions between lipids and the disordered fused in sarcoma low-complexity (FUS LC) domain strongly affect protein condensation. In the presence of anionic lipids, FUS LC formed lipid-protein clusters at concentrations more than 30-fold lower than required for pure FUS LC. Lipid-triggered FUS LC clusters showed less dynamic protein organization than canonical, lipid-free FUS LC condensates. Lastly, we found that phosphatidylserine membranes promoted FUS LC condensates having β sheet structures, while phosphatidylglycerol membranes initiated unstructured condensates. Our results show that lipids strongly influence FUS LC condensation, suggesting that protein-lipid interactions modulate condensate formation in cells.

Raman Spectroscopy Techniques for the Investigation and Diagnosis of Alzheimer's Disease

Alzheimer's disease (AD) is the most common neurodegenerative disorder, resulting in memory loss, cognitive decline, bodily function impairment, and finally death. The growing number of people suffering from AD increasingly urges the development of effective early diagnosis and monitoring techniques. Here, we review the most recent developments in the field of Raman-based techniques, which have shown a significant potential in identifying AD by detecting specific biomarkers in biological fluids, as well as in providing fundamental insights into key molecules involved in the disease progression or in the analysis of histological specimens of patients with AD. These techniques comprise spontaneous and resonant Raman spectroscopies, exploit plasmon- or fiber- enhanced effects, such as surface-, tip- or fiber- enhanced Raman spectroscopies, or involve non-linear techniques like coherent Raman scattering. The scientific efforts employed up to now as well as the rapid technological advancements in optical detection instruments (spectrometers, lasers, substrates for analysis, etc.) and the diffusion of advanced data processing methods suggest a leading role of Raman techniques in the perspective of a preclinical or clinical detection of AD.

Tracking Molecular Diffusion across Biomaterials' Interfaces Using Stimulated Raman Scattering

The determination of molecular diffusion across biomaterial interfaces, including those involving hydrogels and tissues remains important, underpinning the understanding of a broad range of processes including, for example, drug delivery. Current techniques using Raman spectroscopy have previously been established as a method to quantify diffusion coefficients, although when using spontaneous Raman spectroscopy, the signal can be weak and dominated by interferences such as background fluorescence (including biological autofluoresence). To overcome these issues, we demonstrate the use of the stimulated Raman scattering technique to obtain measurements in soft tissue samples that have good signal-to-noise ratios and are largely free from fluorescence interference. As a model illustration of a small metabolite/drug molecule being transported through tissue, we use deuterated (d⁷-) glucose and monitor the Raman C-D band in a spectroscopic region free from other Raman bands. The results show that although mass transport follows a diffusion process characterized by Fick's laws within hydrogel matrices, more complex mechanisms appear within tissues.

Bringing Vibrational Imaging to Chemical Biology with Molecular Probes

As an emerging optical imaging modality, stimulated Raman scattering (SRS) microscopy provides invaluable opportunities for chemical biology studies using its rich chemical information. Through rapid progress over the past decade, the development of Raman probes harnessing the chemical biology toolbox has proven to play a key role in advancing SRS microscopy and expanding biological applications. In this perspective, we first discuss the development of biorthogonal SRS imaging using small tagging of triple bonds or isotopes and highlight their unique advantages for metabolic pathway analysis and microbiology investigations. Potential opportunities for chemical biology studies integrating small tagging with SRS imaging are also proposed. We next summarize the current designs of highly sensitive and super-multiplexed SRS probes, as well as provide future directions and considerations for next-generation functional probe design. These rationally designed SRS probes are envisioned to bridge the gap between SRS microscopy and chemical biology research and should benefit their mutual development.

Instant diagnosis of gastroscopic biopsy via deep-learned single-shot femtosecond stimulated Raman histology

Gastroscopic biopsy provides the only effective method for gastric cancer diagnosis, but the gold standard histopathology is time-consuming and incompatible with gastroscopy. Conventional stimulated Raman scattering (SRS) microscopy has shown promise in label-free diagnosis on human tissues, yet it requires the tuning of picosecond lasers to achieve chemical specificity at the cost of time and complexity. Here, we demonstrate that single-shot femtosecond SRS (femto-SRS) reaches the maximum speed and sensitivity with preserved chemical resolution by integrating with U-Net. Fresh gastroscopic biopsy is imaged in <60 s, revealing essential histoarchitectural hallmarks perfectly agreed with standard histopathology. Moreover, a diagnostic neural network (CNN) is constructed based on images from 279 patients that predicts gastric cancer with accuracy >96%. We further demonstrate semantic segmentation of intratumor heterogeneity and evaluation of resection margins of endoscopic submucosal dissection (ESD) tissues to simulate rapid and automated intraoperative diagnosis. Our method holds potential for synchronizing gastroscopy and histopathological diagnosis.

Diagnosis of gastric cancer currently requires gastroscopic biopsy, which requires time and expertize to perform. Here, the authors demonstrate a femto-SRS imaging method which showed high accuracy in diagnosing gastric cancer without the need for pathologistbased diagnosis.

Correlative imaging to resolve molecular structures in individual cells: Substrate validation study for super-resolution infrared microspectroscopy

Light microscopy has been a favorite tool of biological studies for almost a century, recently producing detailed images with exquisite molecular specificity achieving spatial resolution at nanoscale. However, light microscopy is insufficient to provide chemical information as a standalone technique. An increasing amount of evidence demonstrates that optical photothermal infrared microspectroscopy (O-PTIR) is a valuable imaging tool that can extract chemical information to locate molecular structures at submicron resolution. To further investigate the applicability of sub-micron infrared microspectroscopy for biomedical applications, we analyzed the contribution of substrate chemistry to the infrared spectra acquired from individual neurons grown on various imaging substrates. To provide an example of correlative immunofluorescence/O-PTIR imaging, we used immunofluorescence to locate specific organelles for O-PTIR measurement, thus capturing molecular structures at the sub-cellular level directly in cells, which is not possible using traditional infrared microspectroscopy or immunofluorescence microscopy alone.

Quantitative Stimulated Raman Scattering Microscopy: Promises and Pitfalls

Since its first demonstration, stimulated Raman scattering (SRS) microscopy has become a powerful chemical imaging tool that shows promise in numerous biological and biomedical applications. The spectroscopic capability of SRS enables identification and tracking of specific molecules or classes of molecules, often without labeling. SRS microscopy also has the hallmark advantage of signal strength that is directly proportional to molecular concentration, allowing for in situ quantitative analysis of chemical composition of heterogeneous samples with submicron spatial resolution and subminute temporal resolution. However, it is important to recognize that quantification through SRS microscopy requires assumptions regarding both system and sample. Such assumptions are often taken axiomatically, which may lead to erroneous conclusions without proper validation. In this review, we focus on the tacitly accepted, yet complex, quantitative aspect of SRS microscopy. We discuss the various approaches to quantitative analysis, examples of such approaches, challenges in different systems, and potential solutions. Through our examination of published literature, we conclude that a scrupulous approach to experimental design can further expand the powerful and incisive quantitative capabilities of SRS microscopy.

Raman spectroscopy and multivariate analysis as potential tool to follow Alzheimer's disease progression

Raman spectroscopy is an emerging tool in the research and diagnosis of different diseases, including neurodegenerative disorders. In this work, blood serum samples collected from healthy controls and dementia patients were analysed by Raman spectroscopy to develop a classification model for the diagnosis of dementia of Alzheimer’s type (DAT). Raman spectra were processed by means of multivariate tools for multivariate analysis. Lower concentration levels of carotenoids were detected in blood serum from patients, which allowed for a good discrimination with respect to controls, such as 93% of correct predictions on the test set with random forest. We also hypothesize that carotenoid levels might be informative about the severity and progression of the disease, since the intensity of carotenoid signals decreased from the early stage to more severe patients. These encouraging results suggest the possibility to use Raman spectroscopy for the analysis of alternative biofluids (e.g. saliva) and the unobtrusive diagnosis of other neurodegenerative disorders.

Label-free Super-resolution Imaging Enabled by Vibrational Imaging of Swelled Tissue and Analysis

The universal utilization of fluorescence microscopy, especially super-resolution microscopy, has greatly advanced knowledge about modern biology. Conversely, the requirement of fluorophore labeling in fluorescent techniques poses significant challenges, such as photobleaching and non-uniform labeling of fluorescent probes and prolonged sample processing. In this protocol, the detailed working procedures of vibrational imaging of swelled tissue and analysis (VISTA) are presented. VISTA circumvents obstacles associated with fluorophores and achieves label-free super-resolution volumetric imaging in biological samples with spatial resolution down to 78 nm. The procedure is established by embedding cells and tissues in hydrogel, isotropically expanding the hydrogel sample hybrid, and visualizing endogenous protein distributions by vibrational imaging with stimulated Raman scattering microscopy. The method is demonstrated on both cells and mouse brain tissues. Highly correlative VISTA and immunofluorescence images were observed, validating the protein origin of imaging specificities. Exploiting such correlation, a machine learning-based image-segmentation algorithm was trained to achieve multi-component prediction of nuclei, blood vessels, neuronal cells, and dendrites from label-free mouse brain images. The procedure was further adapted to investigate pathological poly-glutamine (polyQ) aggregates in cells and amyloid-beta (Aβ) plaques in brain tissues with high throughput, justifying its potential for large-scale clinical samples.

Rapid Biomarker Screening of Alzheimer's Disease by Interpretable Machine Learning and Graphene-Assisted Raman Spectroscopy

The study of Alzheimer's disease (AD), the most common cause of dementia, faces challenges in terms of understanding the cause, monitoring the pathogenesis, and developing early diagnoses and effective treatments. Rapid and accurate identification of AD biomarkers in the brain is critical to providing key insights into AD and facilitating the development of early diagnosis methods. In this work, we developed a platform that enables a rapid screening of AD biomarkers by employing graphene-assisted Raman spectroscopy and machine learning interpretation in AD transgenic animal brains. Specifically, we collected Raman spectra on slices of mouse brains with and without AD and used machine learning to classify AD and non-AD spectra. By contacting monolayer graphene with the brain slices, the accuracy was increased from 77% to 98% in machine learning classification. Further, using a linear support vector machine (SVM), we identified a spectral feature importance map that reveals the importance of each Raman wavenumber in classifying AD and non-AD spectra. Based on this spectral feature importance map, we identified AD biomarkers including Aβ and tau proteins and other potential biomarkers, such as triolein, phosphatidylcholine, and actin, which have been confirmed by other biochemical studies. Our Raman-machine learning integrated method with interpretability will facilitate the study of AD and can be extended to other tissues and biofluids and for various other diseases.

Protein and lipid mass concentration measurement in tissues by stimulated Raman scattering microscopy

Cell mass and chemical composition are important aggregate cellular properties that are especially relevant to physiological processes, such as growth control and tissue homeostasis. Despite their importance, it has been difficult to measure these features quantitatively at the individual cell level in intact tissue. Here, we introduce normalized Raman imaging (NoRI), a stimulated Raman scattering (SRS) microscopy method that provides the local concentrations of protein, lipid, and water from live or fixed tissue samples with high spatial resolution. Using NoRI, we demonstrate that protein, lipid, and water concentrations at the single cell are maintained in a tight range in cells under the same physiological conditions and are altered in different physiological states, such as cell cycle stages, attachment to substrates of different stiffness, or by entering senescence. In animal tissues, protein and lipid concentration varies with cell types, yet an unexpected cell-to-cell heterogeneity was found in cerebellar Purkinje cells. The protein and lipid concentration profile provides means to quantitatively compare disease-related pathology, as demonstrated using models of Alzheimer’s disease. This demonstration shows that NoRI is a broadly applicable technique for probing the biological regulation of protein mass, lipid mass, and water mass for studies of cellular and tissue growth, homeostasis, and disease.

High-Resolution Digital Panorama of Multiple Structures in Whole Brain of Alzheimer's Disease Mice

Simultaneously visualizing Amyloid-β (Aβ) plaque with its surrounding brain structures at the subcellular level in the intact brain is essential for understanding the complex pathology of Alzheimer's disease, but is still rarely achieved due to the technical limitations. Combining the micro-optical sectioning tomography (MOST) system, whole-brain Nissl staining, and customized image processing workflow, we generated a whole-brain panorama of Alzheimer's disease mice without specific labeling. The workflow employed the steps that include virtual channel splitting, feature enhancement, iso-surface rendering, direct volume rendering, and feature fusion to extract and reconstruct the different signals with distinct gray values and morphologies. Taking advantage of this workflow, we found that the denser-distribution areas of Aβ plaques appeared with relatively more somata and smaller vessels, but show a dissimilar distributing pattern with nerve tracts. In addition, the entorhinal cortex and adjacent subiculum regions present the highest density and biggest diameter of plaques. The neuronal processes in the vicinity of these Aβ plaques showed significant structural alternation such as bending or abrupt branch ending. The capillaries inside or adjacent to the plaques were observed with abundant distorted micro-vessels and abrupt ending. Depicting Aβ plaques, somata, nerve processes and tracts, and blood vessels simultaneously, this panorama enables us for the first time, to analyze how the Aβ plaques interact with capillaries, somata, and processes at a submicron resolution of 3D whole-brain scale, which reveals potential pathological effects of Aβ plaques from a new cross-scale view. Our approach opens a door to routine systematic studies of complex interactions among brain components in mouse models of Alzheimer's disease.

Fiber-Enhanced Stimulated Raman Scattering and Sensitive Detection of Dilute Solutions

Stimulated Raman scattering (SRS) is known to gain coherent amplification of molecular vibrations that allow for rapid and label-free chemical imaging in the microscopy setting. However, the tightly focused laser spot has limited the detection sensitivity, partly due to the tiny interaction volume. Here, we report the use of metal-lined hollow-core fiber (MLHCF) to improve the sensitivity of SRS in sensing dilute solutions by extending the light-matter interaction volume through the fiber waveguide. With a focusing lens (100 mm FL) and 320 μm diameter fiber, we demonstrated an optimum enhancement factor of ~20 at a fiber length of 8.3 cm. More importantly, the MLHCF exhibited a significantly suppressed cross-phase modulation (XPM) background, enabling the detection of ~0.7 mM DMSO in water. Furthermore, the relationship between fiber length and SRS signal could be well explained theoretically. The fiber-enhanced SRS (FE-SRS) method may be further optimized and bears potential in the sensitive detection of molecules in the solution and gas phases.

Screening of Alzheimer's Disease With Multiwavelength Stokes Polarimetry in a Mouse Model

The minimum histological criterion for the diagnostics of Alzheimer's disease (AD) in tissue is the presence of senile plaques and neurofibrillary tangles in specific brain locations. The routine procedure of morphological analysis implies time-consuming and laborious steps including sectioning and staining of formalin-fixed paraffin-embedded (FFPE) tissue. We developed a multispectral Stokes polarimetric imaging approach that allows characterization of FFPE brain tissue samples to discern the stages of AD progression without sectioning and staining the tissue. The Stokes polarimetry approach is highly sensitive to structural alterations of brain tissue, particularly to the changes in light scattering and birefringence. We present the results of the label-free non-destructive screening of FFPE mouse brain tissue and show several polarization metrics that demonstrate statistically significant differences for tissues at different stages of AD.

Raman Spectroscopy in Prostate Cancer: Techniques, Applications and Advancements

Optical techniques are widely used tools in the visualisation of biological species within complex matrices, including biopsies, tissue resections and biofluids. Raman spectroscopy is an emerging analytical approach that probes the molecular signature of endogenous cellular biomolecules under biocompatible conditions with high spatial resolution. Applications of Raman spectroscopy in prostate cancer include biopsy analysis, assessment of surgical margins and monitoring of treatment efficacy. The advent of advanced Raman imaging techniques, such as stimulated Raman scattering, is creating opportunities for real-time in situ evaluation of prostate cancer. This review provides a focus on the recent preclinical and clinical achievements in implementing Raman-based techniques, highlighting remaining challenges for clinical applications. The research and clinical results achieved through in vivo and ex vivo Raman spectroscopy illustrate areas where these evolving technologies can be best translated into clinical practice.