Raman hyperspectroscopy of saliva and machine learning for Sjögren's disease diagnostics.

Sjögren's disease is an autoimmune disorder affecting exocrine glands, causing dry eyes and mouth and other morbidities. Polypharmacy or a history of radiation to the head and neck can also lead to dry mouth. Sjogren's disease is often underdiagnosed due to its non-specific symptoms, limited awareness among healthcare professionals, and the complexity of diagnostic criteria, limiting the ability to provide therapy early. Current diagnostic methods suffer from limitations including the variation in individuals, the absence of a single diagnostic marker, and the low sensitivity and specificity, high cost, complexity, and invasiveness of current procedures. Here we utilized Raman hyperspectroscopy combined with machine learning to develop a novel screening test for Sjögren's disease. The method effectively distinguished Sjögren's disease patients from healthy controls and radiation patients. This technique shows potential for development of a single non-invasive, efficient, rapid, and inexpensive medical screening test for Sjögren's disease using a Raman hyper-spectral signature.

In Situ Stability Test for mRNA Vaccines Based on Deep-UV Resonance Raman Spectroscopy.

Deep-UV resonance Raman spectroscopy has been shown to offer great potential for probing the in situ stability of mRNA vaccines. In this study, a vaccine model was subjected to controlled degradation using RNase A or through aging at room temperature. The degradation of mRNA was confirmed by using a cell transfection test and by gel electrophoresis. Under both settings, DUVRR spectroscopy successfully revealed the mRNA degradation signs of the vaccine model.

Diagnosing COVID-19 in nasopharyngeal secretion through Raman spectroscopy: a feasibility study.

Since the beginning of the COVID-19 pandemic, the scientific community has sought to develop fast and accurate techniques for detecting the SARS-CoV-2 virus. Raman spectroscopy is a promising technique for diagnosing COVID-19 through serum samples. In the present study, the diagnosis of COVID-19 through nasopharyngeal secretion has been proposed. Raman spectra from nasopharyngeal secretion samples (15 Control, negative and 12 COVID-19, positive, assayed by immunofluorescence antigen test) were obtained in triplicate in a dispersive Raman spectrometer (830 nm, 350 mW), accounting for a total of 80 spectra. Using principal component analysis (PCA) the main spectral differences between the Control and COVID-19 samples were attributed to N and S proteins from the virus in the COVID-19 group. Features assigned to mucin (serine, threonine and proline amino acids) were observed in the Control group. A binary model based on partial least squares discriminant analysis (PLS-DA) differentiated COVID-19 versus Control samples with accuracy of 91%, sensitivity of 80% and specificity of 100%. Raman spectroscopy has a great potential for becoming a technique of choice for rapid and label-free evaluation of nasopharyngeal secretion for COVID-19 diagnosis.

A novel Raman spectroscopic method for detecting traces of blood on an interfering substrate.

Traces of body fluids discovered at a crime scene are a primary source of DNA evidence. Raman spectroscopy is a promising universal technique for identifying biological stains for forensic purposes. The advantages of this method include the ability to work with trace amounts, high chemical specificity, no need for sample preparation and the nondestructive nature. However, common substrate interference limits the practical application of this novel technology. To overcome this limitation, two approaches called "Reducing a spectrum complexity" (RSC) and "Multivariate curve resolution combined with the additions method" (MCRAD) were investigated for detecting bloodstains on several common substrates. In the latter approach, the experimental spectra were "titrated" numerically with a known spectrum of a targeted component. The advantages and disadvantages of both methods for practical forensics were evaluated. In addition, a hierarchical approach to reduce the possibility of false positives was suggested.

Identification and highly selective differentiation of organic gunshot residues utilizing their elemental and molecular signatures.

Firearm related evidence is of great significance to forensic science. In recent years, many researchers have focused on exploring the probative value of organic gunshot residue (OGSR) evidence, which is often bolstered by many factors including recoverability. In addition, OGSR analysis has shown the potential to achieve differentiation between OGSRs generated from various ammunition brands and/or calibers. Raman spectroscopy is a vibrational spectroscopic technique which has been used in the past for gunshot residue analysis-including OGSR specifically. Raman spectroscopy is a nondestructive, highly-selective, simple, and rapid technique which provides molecular information about samples. LIBS or Laser-Induced Breakdown Spectroscopy is a simple, robust, and rapid analytical method which requires minimal to no sample preparation and a small amount of sample for analysis. LIBS provides information on the elemental compositions of samples. In this study, Raman spectroscopy and LIBS were used together in sequence in an attempt to achieve the specific identification and characterization of OGSR particles from ammunition types which were closely related. The main goal was to determine if this method had the potential to differentiate between various ammunition types of the same caliber and produced by the same manufacturer, and generated under identical firing conditions. High-resolution optical microscopy documented the OGSR particles' morphologies and Raman spectroscopy was used to identify particles as OGSRs. Finally, LIBS analysis of the OGSR particles was carried out. Advanced chemometric techniques were shown to allow for very successful differentiation between the OGSR samples analyzed.

A Novel Method for Detecting Duchenne Muscular Dystrophy in Blood Serum of mdx Mice.

Duchenne muscular dystrophy (DMD) is the most common form of muscular dystrophy, typically affecting males in infancy. The disease causes progressive weakness and atrophy of skeletal muscles, with approximately 20,000 new cases diagnosed yearly. Currently, methods for diagnosing DMD are invasive, laborious, and unable to make accurate early detections. While there is no cure for DMD, there are limited treatments available for managing symptoms. As such, there is a crucial unmet need to develop a simple and non-invasive method for accurately detecting DMD as early as possible. Raman spectroscopy with chemometric analysis is shown to have the potential to fill this diagnostic need.

Spontaneous Refolding of Amyloid Fibrils from One Polymorph to Another Caused by Changes in Environmental Hydrophobicity.

Here, we report a new phenomenon in which lysozyme fibrils formed in a solution of acetic acid spontaneously refold to a different polymorph through a disassembled intermediate upon the removal of acetic acid. The structural changes were revealed and characterized by deep-UV resonance Raman spectroscopy, nonresonance Raman spectroscopy, intrinsic tryptophan fluorescence spectroscopy, and atomic force microscopy. A PPII-like structure with highly solvent-exposed tryptophan residues predominates the intermediate aggregates before refolding to polymorph II fibrils. Furthermore, the disulfide (SS) bonds undergo significant rearrangements upon the removal of acetic acid from the lysozyme fibril environment. The main SS bond conformation changes from gauche-gauche-trans in polymorph I to gauche-gauche-gauche in polymorph II. Changing the hydrophobicity of the fibril environment was concluded to be the decisive factor causing the spontaneous refolding of lysozyme fibrils from one polymorph to another upon the removal of acetic acid. Potential biological implications of the discovered phenomenon are discussed.

Label-free laser spectroscopy for respiratory virus detection: A review.

Infectious diseases are among the most severe threats to modern society. Current methods of virus infection detection based on genome tests need reagents and specialized laboratories. The desired characteristics of new virus detection methods are noninvasiveness, simplicity of implementation, real-time, low cost and label-free detection. There are two groups of methods for molecular biomarkers' detection and analysis: (i) a sample physical separation into individual molecular components and their identification, and (ii) sample content analysis by laser spectroscopy. Variations in the spectral data are typically minor. It requires the use of sophisticated analytical methods like machine learning. This review examines the current technological level of laser spectroscopy and machine learning methods in applications for virus infection detection.

Surface-enhanced Raman spectroscopy for drug discovery: peptide-RNA binding.

The ever-growing demand for new drugs highlights the need to develop novel cost- and time-effective techniques for drug discovery. Surface-enhanced Raman spectroscopy (SERS) is an emerging ultrasensitive and label-free technique that allows for the efficient detection and characterization of molecular interactions. We have recently developed a SERS platform for detecting a single protein molecule linked to a gold substrate (Almehmadi et al. Scientific Reports 2019). In this study, we extended the approach to probe the binding of potential drugs to RNA targets. To demonstrate the proof of concept, two 16-amino acid residue peptides with close primary structures and different binding affinities to the RNA CUG repeat related to myotonic dystrophy were tested. Three-microliter solutions of the RNA repeat with these peptides at nanomolar concentrations were probed using the developed approach, and the binding of only one peptide was demonstrated. The SER spectra exhibited significant fluctuations along with a sudden strong enhancement as spectra were collected consecutively from individual spots. Principal component analysis (PCA) of the SER spectral datasets indicated that free RNA repeats could be differentiated from those complexed with a peptide with 100% accuracy. The developed SERS platform provides a novel opportunity for label-free screening of RNA-binding peptides for drug discovery. Schematic representation of the SERS platform for drug discovery developed in this study.

Detection and identification of drug traces in latent fingermarks using Raman spectroscopy.

Recent advancements in analytical techniques have greatly contributed to the analysis of latent fingermarks' (LFMs) "touch chemistry" and identification of materials that a suspect might have come into contact with. This type of information about the FM donor is valuable for criminal investigations because it narrows the pool of suspects. It is estimated that at least 30 million people around the world take over-the-counter and prescription nonsteroidal anti-inflammatory drugs (NSAIDs) for pain relief, headaches and arthritis every day. The daily use of such drugs can lead to an increased risk of their abuse. In the present study, Raman spectroscopy combined with multivariate statistical analysis was used for the detection and identification of drug traces in LFMs when NSAID tablets of aspirin, ibuprofen, diclofenac, ketoprofen and naproxen have been touched. Partial least squares discriminant analysis of Raman spectra showed an excellent separation between natural FMs and all NSAID-contaminated FMs. The developed classification model was externally validated using FMs deposited by a new donor and showed 100% accuracy on a FM level. This proof-of-concept study demonstrated the great potential of Raman spectroscopy in the chemical analysis of LFMs and the detection and identification of drug traces in particular.

Improved folding of recombinant protein via co-expression of exogenous chaperones.

Expression of heterologous genes in Escherichia coli is a routine technology for recombinant protein production, but the predictable recovery of properly folded and uniformly bioactive material remains a challenge. Misfolded proteins typically accumulate as insoluble inclusion bodies, and a variety of strategies have been employed in efforts to increase the yield of soluble product. One technique is the overexpression of E. coli protein chaperones during recombinant protein induction, in an effort to increase the folding capacity of the bacterial host. We have developed an alternative approach, by supplementing the host protein folding machinery with chaperones from other species. Extremophiles have evolved under conditions (extremes of temperature, salinity, pressure, and/or pH) that make them attractive candidates for possessing chaperones with novel folding activities. The green fluorescent protein (GFP) of Aequorea victoria, which is predominantly insoluble under typical recombinant expression culture conditions, was employed as an in vivo indicator of protein folding activity for chaperone homologs from a variety of extremophiles. For a subset of the chaperones tested, co-expression with GFP promoted an increase in both fluorescence signal intensity as well as the amount of GFP recovered in the soluble protein fraction. Several archaeal chaperones were also found to be able to refold soluble Lyt_Orn C40 peptidase from inclusion bodies in vitro. In particular, Pf Cpn(MA), a mutant chaperonin which exhibited significant refolding activity, is also shown to deconstruct the morphology and structure of inclusion bodies (Kurouski et al., 2012). Hence, the simple and rapid GFP assay provides a tool to screen for extremophilic chaperones that exhibit folding activity under E. coli growth conditions, and suggests that increasing the repertoire of heterologous chaperones might provide a partial but general solution to the problem of recombinant protein insolubility.

Post deposition aging of bloodstains probed by steady-state fluorescence spectroscopy.

Blood is one of the most common body fluids discovered at crime scenes involving violent actions. It is one of the most important types of forensic evidence since it allows for the identification of the individual providing that there is a match with a known DNA profile. Determining the time since deposition (TSD) can assist investigators in establishing when the crime occurred or if a bloodstain present is actually related to the investigated event. To develop a forensically sound method for determining the TSD of a bloodstain, it is necessary to understand the underlying biochemical mechanisms occurring during aging. As biochemical processes occurring in blood are necessary for the continued survival of living organisms, they are important subjects of human biology and biomedicine and are well understood. However, the biochemistry of bloodstain aging ex vivo is primarily of interest to forensic scientists and has not yet been thoroughly researched. This preliminary study utilizes steady-state fluorescence spectroscopy to probe the changes in fluorescence properties of peripheral and menstrual blood up to 24-h post deposition. Peripheral and menstrual blood exhibited similar kinetic changes over time, assigned to the presence of the fluorophores: tryptophan, nicotinamide adenine dinucleotide (NADH), and flavins in both biological fluids. The biochemical mechanism of blood aging ex vivo is discussed.

Vibrational Spectroscopy for Detection of Diabetes: A Review.

Type II diabetes mellitus (T2DM) is a metabolic disorder that is characterized by chronically elevated glucose caused by insulin resistance. Although T2DM is manageable through insulin therapy, the disorder itself is a risk factor for much more dangerous diseases including cardiovascular disease, kidney disease, retinopathy, Alzheimer's disease, and more. T2DM affects 450 million people worldwide and is attributed to causing over four million deaths each year. Current methods for detecting diabetes typically involve testing a person's glycated hemoglobin levels as well as blood sugar levels randomly or after fasting. However, these methods can be problematic due to an individual's levels differing on a day-to-day basis or being affected by diet or environment, and due to the lack of sensitivity and reliability within the tests themselves. Vibrational spectroscopic methods have been pursued as a novel method for detecting diabetes accurately and early in a minimally invasive manner. This review summarizes recent research, since 2015, which has used infrared or Raman spectroscopy for the purpose of developing a fast and accurate method for diagnosing diabetes. Based on critical evaluation of the reviewed work, vibrational spectroscopy has the potential to improve and revolutionize the way diabetes is diagnosed, thereby allowing for faster and more effective treatment of the disorder.

Towards development of a novel screening method for identifying Alzheimer's disease risk: Raman spectroscopy of blood serum and machine learning.

There is an urgent clinical need for a fast and effective method for diagnosing Alzheimer's disease (AD). The identification of AD in its most initial stages, at which point treatment could provide maximum therapeutic benefits, is not only likely to slow down disease progression but to also potentially provide a cure. However, current clinical detection is complicated and requires a combination of several methods based on significant clinical manifestations due to widespread neurodegeneration. As such, Raman spectroscopy with machine learning is investigated as a novel alternative method for detecting AD in its earliest stages. Here, blood serum obtained from rats fed either a standard diet or a high-fat diet was analyzed. The high-fat diet has been shown to initiate a pre-AD state. Partial least squares discriminant analysis combined with receiver operating characteristic curve analysis was able to separate the two rat groups with 100% accuracy at the donor level during external validation. Although further work is necessary, this research suggests there is a potential for Raman spectroscopy to be used in the future as a successful method for identifying AD early on in its progression, which is essential for effective treatment of the disease.

Determining the stages of cellular differentiation using deep ultraviolet resonance Raman spectroscopy.

Cellular differentiation is a fundamental process in which one cell type changes into one or more specialized cell types. Cellular differentiation starts at the beginning of embryonic development when a simple zygote begins to transform into a complex multicellular organism composed of various cell and tissue types. This process continues into adulthood when adult stem cells differentiate into more specialized cells for normal growth, regeneration, repair, and cellular turnover. Any abnormalities associated with this fundamental process of cellular differentiation are linked to life-threatening conditions, including degenerative diseases and cancers. Detection of undifferentiated and different stages of differentiated cells can be used for disease diagnosis but is often challenging due to the laborious procedures, expensive tools, and specialized technical skills which are required. Here, a novel approach, called deep ultraviolet resonance Raman spectroscopy, is used to study various stages of cellular differentiation using a well-known myoblast cell line as a model system. These cells proliferate in the growth medium and spontaneously differentiate in differentiation medium into myocytes and later into myotubes. The cellular and molecular characteristics of these cells mimic very well actual muscle tissue in vivo. We have found that undifferentiated myoblast cells and myoblast cells differentiated at three different stages are able to be easily separated using deep ultraviolet resonance Raman spectroscopy in combination with chemometric techniques. Our study has a great potential to study cellular differentiation during normal development as well as to detect abnormal cellular differentiation in human pathological conditions in future studies.

Discrimination of menstrual and peripheral blood traces using attenuated total reflection Fourier transform-infrared (ATR FT-IR) spectroscopy and chemometrics for forensic purposes.

Body fluid traces can provide highly valuable clues in forensic investigations. In particular, bloodstains are a common occurrence in criminal investigation, and the discrimination of menstrual and peripheral blood is a crucial step for casework involving rape and sexual assault. Most of the current protocols require the detection of characteristic menstrual blood components using sophisticated procedures that need to be performed in a laboratory. The present study uses attenuated total reflection Fourier transform-infrared (ATR FT-IR) spectroscopy as a nondestructive technique for discriminating menstrual and peripheral blood traces. This method incorporates statistical analysis and was evaluated by internal and external validation testing. A partial least squares discriminant analysis (PLSDA) classification model was created for differentiating the two types of blood in a binary manner. Excellent separation between menstrual and peripheral blood samples was achieved during internal validation. External validation resulted in 100% accuracy for predicting a sample as peripheral or menstrual blood. This study demonstrates that ATR FT-IR spectroscopy combined with chemometrics is a reliable approach for rapid and nondestructive discrimination of menstrual and peripheral bloodstains. It offers a significant advantage to forensic science due to the availability of portable instruments and the potential for bloodstain analysis at a crime scene. Graphical abstract.

Analysis of individual red blood cells for Celiac disease diagnosis.

The field of medical diagnostics has endeavored to explore single species of biomolecules for sensitive and informative disease diagnostic applications. Here, Raman hyperspectroscopy is used to analyze red blood cells for identifying Celiac disease (CD). CD is a common autoimmune disorder which affects approximately 1% of the population. The ingestion of gluten by an individual with CD will result in the body initiating a violent immune response which causes severe damage to the small intestine. If the disease goes undiagnosed, substantial long-term health complications ranging in severity can arise. It is thus crucial to identify the disease as early on as possible to prevent additional problems from manifesting. However, current methods for detecting CD are expensive, invasive, and laborious. It was therefore the goal of this study to develop a better method for diagnosing CD which is noninvasive, inexpensive, accurate and definitive. Raman hyperspectroscopy was used to investigate individual red blood cells from donors with CD and from healthy controls who follow a gluten-free diet. Partial least squares discriminant analysis (PLS-DA) was used to evaluate the collected Raman spectral data for diagnostic purposes. Receiver operating characteristic (ROC) curve analysis was applied to evaluate the performance of the PLS-DA prediction algorithm, resulting in 100% successful external validation of the developed method at the donor level. Raman hyperspectroscopy in combination with chemometric analysis is shown herein to successfully evaluate red blood cells for the accurate detection of CD in a noninvasive, simple, and cost-effective manner.

Raman spectroscopy and machine learning for biomedical applications: Alzheimer's disease diagnosis based on the analysis of cerebrospinal fluid.

Current Alzheimer's disease (AD) diagnostics is based on clinical assessments, imaging and neuropsychological tests that are efficient only at advanced stages of the disease. Early diagnosis of AD will provide decisive opportunities for preventive treatment and development of disease-modifying drugs. Cerebrospinal fluid (CSF) is in direct contact with the human brain, where the deadly pathological process of the disease occurs. As such, the CSF biochemical composition reflects specific changes associated with the disease and is therefore the most promising body fluid for AD diagnostic test development. Here, we describe a new method to diagnose AD based on CSF via near infrared (NIR) Raman spectroscopy in combination with machine learning analysis. Raman spectroscopy is capable of probing the entire biochemical composition of a biological fluid at once. It has great potential to detect small changes specific to AD, even at the earliest stages of pathogenesis. NIR Raman spectra were measured of CSF samples acquired from 21 patients diagnosed with AD and 16 healthy control (HC) subjects. Artificial neural networks (ANN) and support vector machine discriminant analysis (SVM-DA) statistical methods were used for differentiation purposes, with the most successful results allowing for the differentiation of AD and HC subjects with 84% sensitivity and specificity. Our classification models show high discriminative power, suggesting the method has a great potential for AD diagnostics. The reported Raman spectroscopic examination of CSF can complement current clinical tests, making early AD detection fast, accurate, and inexpensive. While this study shows promise using a small sample set, further method validation on a larger scale is required to indicate the true strength of the approach.

Probing menstrual bloodstain aging with fluorescence spectroscopy.

Menstrual blood (MB) is a common and important type of forensic evidence, especially in sexual assault cases. MB is composed of peripheral blood (PB), vaginal fluid, and endometrial cells of the uterine wall. In forensic investigations, the differentiation of MB and PB can determine whether the blood present is a result of tissue damage from an assault or a natural cause and thus help to reconstruct the event. Understanding how menstrual blood changes is necessary to develop a method for bloodstain aging. Fluorescence spectroscopy, a promising spectroscopic method for bloodstain analysis, was used to probe the biochemical changes that occur over time in menstrual bloodstains. It was found that steady-state fluorescence spectra underwent significant changes over first nine hours post deposition. The underlying mechanism of fluorescence changes was proposed to involve the kinetic transformation of three fluorophores: tryptophan, nicotinamide adenine dinucleotide and flavins.