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

Clearance of interstitial fluid (ISF) and CSF (CLIC) group-part of Vascular Professional Interest Area (PIA), updates in 2022-2023. Cerebrovascular disease and the failure of elimination of Amyloid-β from the brain and retina with age and Alzheimer's disease: Opportunities for therapy

This editorial summarizes advances from the Clearance of Interstitial Fluid and Cerebrospinal Fluid (CLIC) group, within the Vascular Professional Interest Area (PIA) of the Alzheimer's Association International Society to Advance Alzheimer's Research and Treatment (ISTAART). The overarching objectives of the CLIC group are to: (1) understand the age-related physiology changes that underlie impaired clearance of interstitial fluid (ISF) and cerebrospinal fluid (CSF) (CLIC); (2) understand the cellular and molecular mechanisms underlying intramural periarterial drainage (IPAD) in the brain; (3) establish novel diagnostic tests for Alzheimer's disease (AD), cerebral amyloid angiopathy (CAA), retinal amyloid vasculopathy, amyloid-related imaging abnormalities (ARIA) of spontaneous and iatrogenic CAA-related inflammation (CAA-ri), and vasomotion; and (4) establish novel therapies that facilitate IPAD to eliminate amyloid β (Aβ) from the aging brain and retina, to prevent or reduce AD and CAA pathology and ARIA side events associated with AD immunotherapy.

Current Trends of Raman Spectroscopy in Clinic Settings: Opportunities and Challenges

Early clinical diagnosis, effective intraoperative guidance, and an accurate prognosis can lead to timely and effective medical treatment. The current conventional clinical methods have several limitations. Therefore, there is a need to develop faster and more reliable clinical detection, treatment, and monitoring methods to enhance their clinical applications. Raman spectroscopy is noninvasive and provides highly specific information about the molecular structure and biochemical composition of analytes in a rapid and accurate manner. It has a wide range of applications in biomedicine, materials, and clinical settings. This review primarily focuses on the application of Raman spectroscopy in clinical medicine. The advantages and limitations of Raman spectroscopy over traditional clinical methods are discussed. In addition, the advantages of combining Raman spectroscopy with machine learning, nanoparticles, and probes are demonstrated, thereby extending its applicability to different clinical phases. Examples of the clinical applications of Raman spectroscopy over the last 3 years are also integrated. Finally, various prospective approaches based on Raman spectroscopy in clinical studies are surveyed, and current challenges are discussed.

Protein Corona Formation and Aggregation of Amyloid β 1-40-Coated Gold Nanocolloids

Amyloid fibrillogenesis is a pathogenic protein aggregation process that occurs through a highly ordered process of protein-protein interactions. To better understand the protein-protein interactions involved in amyloid fibril formation, we formed nanogold colloid aggregates by stepwise additions of ∼2 nmol of amyloid β 1-40 peptide (Aβ1-40) at pH ∼3.7 and ∼25 °C. The processes of protein corona formation and building of gold colloid [diameters (d) of 20 and 80 nm] aggregates were confirmed by a red-shift of the surface plasmon resonance (SPR) band, λpeak, as the number of Aβ1-40 peptides [N(Aβ1-40)] increased. The normalized red-shift of λpeak, Δλ, was correlated with the degree of protein aggregation, and this process was approximated as the adsorption isotherm explained by the Langmuir-Freundlich model. As the coverage fraction (θ) was analyzed as a function of ϕ, which is the N(Aβ1-40) per total surface area of nanogold colloids available for adsorption, the parameters for explaining the Langmuir-Freundlich model were in good agreement for both 20 and 80 nm gold, indicating that ϕ could define the stage of the aggregation process. Surface-enhanced Raman scattering (SERS) imaging was conducted at designated values of ϕ and suggested that a protein-gold surface interaction during the initial adsorption stage may be dependent on the nanosize. The 20 nm gold case seems to prefer a relatively smaller contacting section, such as a -C-N or C═C bond, but a plane of the benzene ring may play a significant role for 80 nm gold. Regardless of the size of the particles, the β-sheet and random coil conformations were considered to be used to form gold colloid aggregates. The methodology developed in this study allows for new insights into protein-protein interactions at distinct stages of aggregation.

Raman spectroscopy of brain and skin tissue in a minipig model of Huntington's disease

We applied Raman spectroscopy to brain and skin tissues from a minipig model of Huntington's disease. Differences were observed between measured spectra of tissues with and without Huntington's disease, for both brain tissue and skin tissue. There are linked to changes in the chemical composition between tissue types. Using machine learning we correctly classified 96% of test spectra as diseased or wild type, indicating that the test would have a similar accuracy when used as a diagnostic tool for the disease. This suggests the technique has great potential in the rapid and accurate diagnosis of Huntington's and other neurodegenerative diseases in a clinical setting.

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.

Shifted-excitation Raman difference spectroscopy for improving in vivo detection of nasopharyngeal carcinoma

A strong fluorescence background is one of the common interference factors of Raman spectroscopic analysis in biological tissue. This study developed an endoscopic shifted-excitation Raman difference spectroscopy (SERDS) system for real-time in vivo detection of nasopharyngeal carcinoma (NPC) for the first time. Owing to the use of the SERDS method, the high-quality Raman signals of nasopharyngeal tissue could be well extracted and characterized from the complex raw spectra by removing the fluorescence interference signals. Significant spectral differences relating to proteins, phospholipids, glucose, and DNA were found between 42 NPC and 42 normal tissue sites. Using linear discriminant analysis, the diagnostic accuracy of SERDS for NPC detection was 100%, which was much higher than that of raw Raman spectroscopy (75.0%), showing the great potential of SERDS for improving the accurate in vivo detection of NPC.

Harnessing the power of Raman spectroscopic imaging for ophthalmology

Eye diseases can cause great inconvenience to people's daily life; therefore, it is necessary to study the causes of ocular diseases and related physiological processes. Raman spectroscopic imaging (RSI) is a non-destructive, non-contact detection technique with the advantages of label-free, non-invasive and highly specific. Compared with other mature imaging technologies, RSI can provide real-time molecular information and high-resolution imaging at relatively low cost, making it very suitable for quantitative detection of biological molecules. RSI can reflect the overall situation of the sample, revealing the content distribution of the same substance in different areas of the sample. This review focuses on the recent advances in ophthalmology, with particular emphasis on the powerful use of RSI techniques, as well as its combination with other imaging techniques. Finally, we prospect the wider application and future potential of RSI approaches in ophthalmology.

Accuracy of Raman spectroscopy in the diagnosis of Alzheimer's disease

Objective

To systematically evaluate the accuracy of Raman spectroscopy in the diagnosis of Alzheimer's disease.

Methods

Databases including Web of Science, PubMed, The Cochrane Library, EMbase, CBM, CNKI, Wan Fang Data, and VIP were electronically searched for studies on Raman spectroscopy in diagnosis of Alzheimer's disease from inception to November 2022. Two reviewers independently screened the literature, extracted data, and assessed the risk of bias in the included studies. Then, meta-analysis was performed using Meta-Disc1.4 and Stata 16.0 software.

Results

A total of eight studies were finally included. The pooled sensitivity of Raman spectroscopy was 0.86 [95% CI (0.80–0.91)], specificity was 0.87 [95% CI (0.79–0.92)], positive likelihood ratio was 5.50 [95% CI (3.55–8.51)], negative likelihood ratio was 0.17 [95% CI (0.09–0.34)], diagnosis odds ratio and area under the curve of SROC were 42.44 [95% CI (19.80–90.97)] and 0.931, respectively. Sensitivity analysis was carried out after each study was excluded one by one, and the results showed that pooled sensitivity and specificity had no significant change, indicating that the stability of the meta-analysis results was great.

Conclusions

Our findings indicated that Raman spectroscopy had high accuracy in the diagnosis of AD, though it still did not rule out the possibility of misdiagnosis and missed diagnosis. Limited by the quantity and quality of the included studies, the above conclusions need to be verified by more high-quality studies.

Review: Emerging Eye-Based Diagnostic Technologies for Traumatic Brain Injury

The study of ocular manifestations of neurodegenerative disorders, Oculomics, is a growing field of investigation for early diagnostics, enabling structural and chemical biomarkers to be monitored overtime to predict prognosis. Traumatic brain injury (TBI) triggers a cascade of events harmful to the brain, which can lead to neurodegeneration. TBI, termed the "silent epidemic" is becoming a leading cause of death and disability worldwide. There is currently no effective diagnostic tool for TBI, and yet, early-intervention is known to considerably shorten hospital stays, improve outcomes, fasten neurological recovery and lower mortality rates, highlighting the unmet need for techniques capable of rapid and accurate point-of-care diagnostics, implemented in the earliest stages. This review focuses on the latest advances in the main neuropathophysiological responses and the achievements and shortfalls of TBI diagnostic methods. Validated and emerging TBI-indicative biomarkers are outlined and linked to ocular neuro-disorders. Methods detecting structural and chemical ocular responses to TBI are categorised along with prospective chemical and physical sensing techniques. Particular attention is drawn to the potential of Raman spectroscopy as a non-invasive sensing of neurological molecular signatures in the ocular projections of the brain, laying the platform for the first tangible path towards alternative point-of-care diagnostic technologies for TBI.

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.

Laser tweezers Raman spectroscopy combined with machine learning for diagnosis of Alzheimer's disease

Alzheimer's disease (AD) is a common nervous system disease to affect mostly elderly people over the age of 65 years. However, the diagnosis of AD is mainly depend on the imaging examination, clinical assessments and neuropsychological tests, which may get error diagnosis results and are not able to detect early AD. Here, a rapid, non-invasive, and high accuracy diagnostic method for AD especially early AD is provided based on the laser tweezers Raman spectroscopy (LTRS) combined with machine learning algorithms. AD platelets from different 3xTg-AD transgenic rats at different stages of disease are captured to collect high signal-to-noise ratio Raman signals without contact by LTRS, which is then combined with partial least squares discriminant analysis (PLS-DA), support vector machine (SVM) and principal component analysis (PCA)-canonical discriminate function (CDA) for classification. The results show that the normal and diseased platelets at 3-, 6- and 12-month AD are successfully distinguished and the accuracy is 91%, 68% and 97% respectively, which demonstrates the suggested method can provide a precise detection for AD diagnosis at early, middle and advanced stages.

Multimodal retinal imaging to detect and understand Alzheimer's and Parkinson's disease

Retinal neurodegeneration and visual dysfunctions have been reported in a majority of Alzheimer's and Parkinson's patients, and, in light of the quest for novel biomarkers for these neurodegenerative proteinopathies, the retina has been receiving increasing attention as an organ for diagnosing, monitoring, and understanding disease. Thinning of retinal layers, abnormalities in vasculature, and protein deposition can be imaged at unprecedented resolution, which offers a unique systems biology view on the cellular and molecular changes underlying these pathologies. It makes the retina not only a promising target for biomarker development, but it also suggests that novel fundamental insights into the pathophysiology of Alzheimer's and Parkinson's disease can be obtained by studying the retina-brain axis.

The Progress of Label-Free Optical Imaging in Alzheimer's Disease Screening and Diagnosis

As the major neurodegenerative disease of dementia, Alzheimer's disease (AD) has caused an enormous social and economic burden on society. Currently, AD has neither clear pathogenesis nor effective treatments. Positron emission tomography (PET) and magnetic resonance imaging (MRI) have been verified as potential tools for diagnosing and monitoring Alzheimer's disease. However, the high costs, low spatial resolution, and long acquisition time limit their broad clinical utilization. The gold standard of AD diagnosis routinely used in research is imaging AD biomarkers with dyes or other reagents, which are unsuitable for in vivo studies owing to their potential toxicity and prolonged and costly process of the U.S. Food and Drug Administration (FDA) approval for human use. Furthermore, these exogenous reagents might bring unwarranted interference to mechanistic studies, causing unreliable results. Several label-free optical imaging techniques, such as infrared spectroscopic imaging (IRSI), Raman spectroscopic imaging (RSI), optical coherence tomography (OCT), autofluorescence imaging (AFI), optical harmonic generation imaging (OHGI), etc., have been developed to circumvent this issue and made it possible to offer an accurate and detailed analysis of AD biomarkers. In this review, we present the emerging label-free optical imaging techniques and their applications in AD, along with their potential and challenges in AD diagnosis.

Label-free Raman spectroscopy: A potential tool for early diagnosis of diabetic keratopathy

Diabetes has become a major public health problem worldwide, and the incidence of diabetes has been increasing progressively. Diabetes is prone to cause various complications, among which diabetic keratopathy (DK) emphasizes the significant impact on the cornea. The current diagnosis of DK lacks biochemical markers that can be used for early and non-invasive screening and detection. In contrast, in this study, Raman spectroscopy, which demonstrates non-destructive, label-free features, especially the unique advantage of providing molecular fingerprint information for target substances, were utilized to interrogate the intrinsic information of the corneal tissues from normal and diabetic mouse models, respectively. Visually, the Raman spectral response derived from the biochemical components and biochemical differences between the two groups were compared. Moreover, multivariate analysis methods such as principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were carried out for advanced statistical analysis. PCA yields a diagnostic results of 57.4% sensitivity, 89.2% specificity, 74.8% accuracy between the diabetic group and control group; Moreover, PLS-DA was employed to enhance the diagnostic ability, showing 76.1% sensitivity, 86.1% specificity, and 87.6% accuracy between the diabetic group and control group. Our proof-of-concept results show the potential of Raman spectroscopy-based techniques to help explore the underlying pathogenesis of DK disease and thus be further expanded for potential applications in the early screening of diabetic diseases.

Surface Plasmon Localization-Based Super-resolved Raman Microscopy

We investigate label-free measurement of molecular distribution by super-resolved Raman microscopy using surface plasmon (SP) localization. Localized SP was formed with plasmonic nanopost arrays (PNAs) for measurement of the molecular distribution in HeLa cells. Compared with conventional Raman microscopy on gold thin films, PNAs induce a localized near-field, which allows for the enhancement of the peak signal-to-noise ratio by as much as 4.5 dB in the Raman shifts. Super-resolved distributions of aromatic amino acids and lipids (C-C stretching and C-H₂ twist mode) as targets in HeLa cells were obtained after image reconstruction. Results show almost 4-fold improvement on average in the lateral precision over conventional diffraction-limited Raman microscopy images. Combined with axial imaging in an evanescent field, the results suggest an improvement in optical resolution due to superlocalized light volume by more than an order of magnitude over that of conventional diffraction-limited Raman microscopy.