Raman spectroscopy‐based detection of RNA viruses in saliva: A preliminary report

Several non‐invasive Raman spectroscopy‐based assays have been reported for rapid and sensitive detection of pathogens. We developed a novel statistical model for the detection of RNA viruses in saliva, based on an unbiased selection of a set of 65 Raman spectral features that mostly attribute to the RNA moieties, with a prediction accuracy of 91.6% (92.5% sensitivity and 88.8% specificity). Furthermore, to minimize variability and automate the downstream analysis of the Raman spectra, we developed a GUI‐based analytical tool “RNA Virus Detector (RVD).” This conceptual framework to detect RNA viruses in saliva could form the basis for field application of Raman Spectroscopy in managing viral outbreaks, such as the ongoing COVID‐19 pandemic. ( http://www.actrec.gov.in/pi-webpages/AmitDutt/RVD/RVD.html ).     

Therapeutic prognosis of prostate cancer using surface-enhanced Raman scattering of patient urine and multivariate statistical analysis

Surface-enhanced Raman scattering (SERS) is highly sensitive and label-free analytical technique based on Raman spectroscopy aided by field-multiplying plasmonic nanostructures. We report the use of SERS measurements of patient urine in conjunction with biostatistical algorithms to assess the treatment response of prostate cancer (PCa) in 12 recurrent (Re) and 63 nonrecurrent (NRe) patient cohorts. Multiple Raman spectra are collected from each urine sample using monodisperse silver nanoparticles (AgNPs) for Raman signal enhancement. Genetic algorithms-partial least squares-linear discriminant analysis (GA-PLS-LDA) was employed to analyze the Raman spectra. Comprehensive GA-PLS-LDA analyses of these Raman spectral features (p = 3.50 × 10⁻¹⁶ ) yield an accuracy of 86.6%, sensitivity of 86.0%, and specificity 87.1% in differentiating the Re and NRe cohorts. Our study suggests that SERS combined with multivariate GA-PLS-LDA algorithm can potentially be used to detect and monitor the risk of PCa relapse and to aid with decision-making for optimal intermediate secondary therapy to recurred patients.     

A computationally efficient Monte-Carlo model for biomedical Raman spectroscopy

Monte Carlo (MC) modeling is a valuable tool to gain fundamental understanding of light-tissue interactions, provide guidance and assessment to optical instrument designs, and help analyze experimental data. It has been a major challenge to efficiently extend MC towards modeling of bulk-tissue Raman spectroscopy (RS) due to the wide spectral range, relatively sharp spectral features, and presence of background autofluorescence. Here, we report a computationally efficient MC approach for RS by adapting the massively-parallel Monte Carlo eXtreme (MCX) simulator. Simulation efficiency is achieved through “isoweight,” a novel approach that combines the statistical generation of Raman scattered and Fluorescence emission with a lookup-table-based technique well-suited for parallelization. The MC model uses a graphics processor to produce dense Raman and fluorescence spectra over a range of 800 − 2000 cm⁻¹ with an approximately 100× increase in speed over prior RS Monte Carlo methods. The simulated RS signals are compared against experimentally collected spectra from gelatin phantoms, showing a strong correlation.     

Supervised learning methods for the recognition of melanoma cell lines through the analysis of their Raman spectra

Malignant melanoma is an aggressive form of skin cancer, which develops from the genetic mutations of melanocytes – the most frequent involving BRAF and NRAS genes. The choice and the effectiveness of the therapeutic approach depend on tumour mutation; therefore, its assessment is of paramount importance. Current methods for mutation analysis are destructive and take a long time; instead, Raman spectroscopy could provide a fast, label-free and non-destructive alternative. In this study, confocal Raman microscopy has been used for examining three in vitro melanoma cell lines, harbouring different molecular profiles and, in particular, specific BRAF and NRAS driver mutations. The molecular information obtained from Raman spectra has served for developing two alternative classification algorithms based on linear discriminant analysis and artificial neural network. Both methods provide high accuracy (≥90%) in discriminating all cell types, suggesting that Raman spectroscopy may be an effective tool for detecting molecular differences between melanoma mutations.     

Effect of hemolysis on Fourier transform infrared and Raman spectra of blood plasma

Hemolysis is a very common phenomenon and is referred as the release of intracellular components from red blood cells to the extracellular fluid. Hemolyzed samples are often rejected in clinics due to the interference of hemoglobin and intracellular components in laboratory measurements. Plasma and serum based vibrational spectroscopy studies are extensively applied to generate spectral biomarkers for various diseases. However, no studies have reported the effect of hemolysis in blood based vibrational spectroscopy studies. This study was undertaken to evaluate the effect of hemolysis on infrared and Raman spectra of blood plasma. In this study, prostate cancer plasma samples (n = 30) were divided into three groups (nonhemolyzed, mildly hemolyzed, and moderately hemolyzed) based on the degree of hemolysis and FTIR and Raman spectra were recorded using high throughput (HT)‐FTIR and HT‐Raman spectroscopy. Discrimination was observed between the infrared and Raman spectra of nonhemolyzed and hemolyzed plasma samples using principal component analysis. A classical least square fitting analysis showed differences in the weighting of pure components in nonhemolyzed and hemolyzed plasma samples. Therefore, it is worth to consider the changes in spectral features due to hemolysis when comparing the results within and between experiments.     

Blood plasma resonance Raman spectroscopy combined with multivariate analysis for esophageal cancer detection

We herein report a novel, reliable and inexpensive method for detecting esophageal cancer using blood plasma resonance Raman spectroscopy combined with multivariate analysis methods. The blood plasma samples were divided into late stage cancer group (n = 164), early stage cancer group (n = 35) and normal group (n = 135) based on clinical pathological diagnosis. Using a specially designed quartz capillary tube as sample holder, we obtained higher quality resonance Raman spectra of blood plasma than existing method. The study demonstrated that the carotenoids levels in blood plasma were reduced in esophageal cancer patients. The area under the receiver operating characteristic curve (and 95% confidence interval) calculated by wavenumber selection and principal component analysis combined with linear discriminant analysis (PC-LDA) algorithm were 0.894 (0.858-0.929), 0.901 (0.841-0.960) and 0.871 (0.799-0.942) for differentiating late cancer from normal, late cancer from early cancer, and early cancer from normal respectively. The contribution from the two carotenoids wavenumber regions of 1155 and 1515 cm⁻¹ were more than 84.2%. The results show that the plasma carotenoids could be a potential biomarker for screening esophageal cancer using resonance Raman spectroscopy combined with wavenumber selection and PC-LDA algorithms.      

Classification of pathogenic bacteria by Raman spectroscopy combined with variational auto-encoder and deep learning

Rapid and early identification of pathogens is critical to guide antibiotic therapy. Raman spectroscopy as a noninvasive diagnostic technique provides rapid and accurate detection of pathogens. Raman spectrum of single cells serves as the “fingerprint” of the cell, revealing its metabolic characteristics. Rapid identification of pathogens can be achieved by combining Raman spectroscopy and deep learning. Traditional classification techniques frequently require lots of data for training, which is time costing to collect Raman spectra. For trace samples and strains that are difficult to culture, it is difficult to provide an accurate classification model. In order to reduce the number of samples collected and improve the accuracy of the classification model, a new pathogen detection method integrating Raman spectroscopy, variational auto-encoder (VAE), and long short-term memory network (LSTM) is proposed in this paper. We collect the Raman signals of pathogens and input them to VAE for training. VAE will generate a large number of Raman spectral data that cannot be distinguished from the real spectrum, and the signal-to-noise ratio is higher than that of the real spectrum. These spectra are input into the LSTM together with the real spectrum for training, and a good classification model is obtained. The results of the experiments reveal that this method not only improves the average accuracy of pathogen classification to 96.9% but also reduces the number of Raman spectra collected from 1000 to 200. With this technology, the number of Raman spectra collected can be greatly reduced, so that strains that are difficult to culture or trace can be rapidly identified.     

Organelle specific simultaneous Raman/green fluorescence protein microspectroscopy for living cell physicochemical studies

We demonstrate a novel bio‐spectroscopic technique, “simultaneous Raman/GFP microspectroscopy”. It enables organelle specific Raman microspectroscopy of living cells. Fission yeast, Schizosaccharomyces pombe, whose mitochondria are green fluorescence protein (GFP) labeled, is used as a test model system. Raman excitation laser and GFP excitation light irradiate the sample yeast cells simultaneously. GFP signal is monitored in the anti‐Stokes region where interference from Raman scattering is negligibly small. Of note, 13 568 Raman spectra measured from different points of 19 living yeast cells are categorized according to their GFP fluorescence intensities, with the use of a two‐component multivariate curve resolution with alternate least squares (MCR‐ALS) analysis in the anti‐Stokes region. This categorization allows us to know whether or not Raman spectra are taken from mitochondria. Raman spectra specific to mitochondria are obtained by an MCR‐ALS analysis in the Stokes region of 1389 strongly GFP positive spectra. Two mitochondria specific Raman spectra have been obtained. The first one is dominated by protein Raman bands and the second by lipid Raman bands, being consistent with the known molecular composition of mitochondria. In addition, the second spectrum shows a strong band of ergosterol at 1602 cm^?1, previously reported as “Raman spectroscopic signature of life of yeast.”     

Raman spectroscopy of blood serum for Alzheimer's disease diagnostics: specificity relative to other types of dementia

The key moment for efficiently and accurately diagnosing dementia occurs during the early stages. This is particularly true for Alzheimer's disease (AD). In this proof‐of‐concept study, we applied near infrared (NIR) Raman microspectroscopy of blood serum together with advanced multivariate statistics for the selective identification of AD. We analyzed data from 20 AD patients, 18 patients with other neurodegenerative dementias (OD) and 10 healthy control (HC) subjects. NIR Raman microspectroscopy differentiated patients with more than 95% sensitivity and specificity. We demonstrated the high discriminative power of artificial neural network (ANN) classification models, thus revealing the high potential of this developed methodology for the differential diagnosis of AD. Raman spectroscopic, blood‐based tests may aid clinical assessments for the effective and accurate differential diagnosis of AD, decrease the labor, time and cost of diagnosis, and be useful for screening patient populations for AD development and progression.

Multivariate data analysis of blood serum Raman spectra allows for the differentiation between patients with Alzheimer's disease, other types of dementia and healthy individuals.     

Raman spectroscopy of human skin for kidney failure detection

The object of this paper is in vivo study of skin spectral-characteristics in patients with kidney failure by conventional Raman spectroscopy in near infrared region. The experimental dataset was subjected to discriminant analysis with the projection on latent structures (PLS-DA). Application of Raman spectroscopy to investigate the forearm skin in 85 adult patients with kidney failure (90 spectra) and 40 healthy adult volunteers (80 spectra) has yielded the accuracy of 0.96, sensitivity of 0.94 and specificity of 0.99 in terms of identifying the target subjects with kidney failure. The autofluorescence analysis in the near infrared region identified the patients with kidney failure among healthy volunteers of the same age group with specificity, sensitivity, and accuracy of 0.91, 0.84, and 0.88, respectively. When classifying subjects by the presence of kidney failure using the PLS-DA method, the most informative Raman spectral bands are 1315 to 1330, 1450 to 1460, 1700 to 1800 cm⁻¹. In general, the performed study demonstrates that for in vivo skin analysis, the conventional Raman spectroscopy can provide the basis for cost-effective and accurate detection of kidney failure and associated metabolic changes in the skin.     

Fingerprint‐to‐CH stretch continuously tunable high spectral resolution stimulated Raman scattering microscope

Stimulated Raman scattering (SRS) microscopy is a label‐free method generating images based on chemical contrast within samples, and has already shown its great potential for high‐sensitivity and fast imaging of biological specimens. The capability of SRS to collect molecular vibrational signatures in bio‐samples, coupled with the availability of powerful statistical analysis methods, allows quantitative chemical imaging of live cells with sub‐cellular resolution. This application has substantially driven the development of new SRS microscopy platforms. Indeed, in recent years, there has been a constant effort on devising configurations able to rapidly collect Raman spectra from samples over a wide vibrational spectral range, as needed for quantitative analysis by using chemometric methods. In this paper, an SRS microscope which exploits spectral shaping by a narrowband and rapidly tunable acousto‐optical tunable filter (AOTF) is presented. This microscope enables spectral scanning from the Raman fingerprint region to the Carbon‐Hydrogen (CH)‐stretch region without any modification of the optical setup. Moreover, it features also a high enough spectral resolution to allow resolving Raman peaks in the crowded fingerprint region. Finally, application of the developed SRS microscope to broadband hyperspectral imaging of biological samples over a large spectral range from 800 to 3600 cm^?1, is demonstrated.     

A comparative analysis of different biofluids using Raman spectroscopy to determine disease activity in ANCA-associated vasculitis

Identifying persistent or relapsing disease in anti-neutrophil cytoplasmic autoantibody- associated vasculitis (AAV) remains a clinical challenge with an unmet need for a reliable biomarker of multisystem disease. In this study, we confirm for the first time that Raman spectroscopy offers a novel cost-effective candidate biomarker to discriminate active disease from remission in AAV with excellent accuracy. Spectrochemical interrogation of plasma and serum samples demonstrated equal ability to discriminate disease activity with good group separation on PC1 direction and a high degree of accuracy on validation testing using blind predictive modelling: F-score 80% for plasma (specificity 93.3%, sensitivity 70%, AUC 0.95) and 80% for serum (specificity 80%, sensitivity 80%, AUC 0.92). Similar findings were seen on analysis of paired remission samples following successful remission-induction therapy. A larger study with longitudinal data is required to validate these findings with the potential to aid patient care.     

A representation learning approach for recovering scatter-corrected spectra from Fourier-transform infrared spectra of tissue samples

Infrared spectra obtained from cell or tissue specimen have commonly been observed to involve a significant degree of scattering effects, often Mie scattering, which probably overshadows biochemically relevant spectral information by a nonlinear, nonadditive spectral component in Fourier transform infrared (FTIR) spectroscopic measurements. Correspondingly, many successful machine learning approaches for FTIR spectra have relied on preprocessing procedures that computationally remove the scattering components from an infrared spectrum. We propose an approach to approximate this complex preprocessing function using deep neural networks. As we demonstrate, the resulting model is not just several orders of magnitudes faster, which is important for real-time clinical applications, but also generalizes strongly across different tissue types. Using Bayesian machine learning approaches, our approach unveils model uncertainty that coincides with a band shift in the amide I region that occurs when scattering is removed computationally based on an established physical model. Furthermore, our proposed method overcomes the trade-off between computation time and the corrected spectrum being biased towards an artificial reference spectrum.     

Quantitative determination of concentration profiles of skin components and topically applied oils by tailored multivariate curve resolution-alternating least squares using in vivo confocal Raman micro-spectroscopy

The main components of the stratum corneum (SC), water, lipids, and proteins, are non-homogeneously distributed throughout the depth. The quantitative determination of their concentration profiles and penetration depth of topically applied substances are urgent topics of dermatological and cosmetic research. Confocal Raman micro-spectroscopy has distinct advantages when determining semi-quantitative concentrations of SC components and topically applied substances non-invasively and in vivo. In this work, we applied a tailored multivariate curve resolution-alternating least squares (tMCR-ALS) method to analyze Raman spectra of the SC in the 2000–4000 cm⁻¹ region for quantitatively determining the concentrations of water, lipids, proteins, and topically applied oils using substance-related spectral loadings which were allowed to change depth-dependently from the SC's surface toward its bottom. tMCR-ALS makes matching of depth-dependent signal attenuation, that is, the normalization on keratin, unnecessary and requires only a few additional experiments for calibration – Raman spectra of the pure materials and their densities.     

Exploration research on the fusion of multimodal spectrum technology to improve performance of rapid diagnosis scheme for thyroid dysfunction

The spectral fusion by Raman spectroscopy and Fourier infrared spectroscopy combined with pattern recognition algorithms is utilized to diagnose thyroid dysfunction serum, and finds the spectral segment with the highest sensitivity to further advance diagnosis speed. Compared with the single infrared spectroscopy or Raman spectroscopy, the proposal can improve the detection accuracy, and can obtain more spectral features, indicating greater differences between thyroid dysfunction and normal serum samples. For discriminating different samples, principal component analysis (PCA) was first used for feature extraction to reduce the dimension of high‐dimension spectral data and spectral fusion. Then, support vector machine (SVM), back propagation neural network, extreme learning machine and learning vector quantization algorithms were employed to establish the discriminant diagnostic models. The accuracy of spectral fusion of the best analytical model PCA‐SVM, single Raman spectral accuracy and single infrared spectral accuracy is 83.48%, 78.26% and 80%, respectively. The accuracy of spectral fusion is higher than the accuracy of single spectrum in five classifiers. And the diagnostic accuracy of spectral fusion in the range of 2000 to 2500 cm^?1 is 81.74%, which greatly improves the sample measure speed and data analysis speed than analysis of full spectra. The results from our study demonstrate that the serum spectral fusion technique combined with multivariate statistical methods have great potential for the screening of thyroid dysfunction.     

Sub millimetre flexible fibre probe for background and fluorescence free Raman spectroscopy

Using the shifted-excitation Raman difference spectroscopy technique and an optical fibre featuring a negative curvature excitation core and a coaxial ring of high numerical aperture collection cores, we have developed a portable, background and fluorescence free, endoscopic Raman probe. The probe consists of a single fibre with a diameter of less than 0.25 mm packaged in a sub-millimetre tubing, making it compatible with standard bronchoscopes. The Raman excitation light in the fibre is guided in air and therefore interacts little with silica, enabling an almost background free transmission of the excitation light. In addition, we used the shifted-excitation Raman difference spectroscopy technique and a tunable 785 nm laser to separate the fluorescence and the Raman spectrum from highly fluorescent samples, demonstrating the suitability of the probe for biomedical applications. Using this probe we also acquired fluorescence free human lung tissue data.     

Raman microspectroscopic study for the detection of oral field cancerisation using brush biopsy samples

Field cancerisation (FC) is potentially an underlying cause of poor treatment outcomes of oral squamous cell carcinoma (OSCC). To explore the phenomenon using Raman microspectroscopy, brush biopsies from the buccal mucosa, tongue, gingiva and alveolus of healthy donors (n = 40) and from potentially malignant lesions (PML) of Dysplasia Clinic patients (n = 40) were examined. Contralateral normal samples (n = 38) were also collected from the patients. Raman spectra were acquired from the nucleus and cytoplasm of each cell, and subjected to partial least squares‐discriminant analysis (PLS‐DA). High discriminatory accuracy for donor and PML samples was achieved for both cytopalmic and nuclear data sets. Notably, contralateral normal (patient) samples were also accurately discriminated from donor samples and contralateral normal samples from patients with multiple lesions showed a similar spectral profile to PML samples, strongly indicating a FC effect. These findings support the potential of Raman microspectroscopy as a screening tool for PML using oral exfoliated cells.     

Profiling antibiotic resistance in Escherichia coli strains displaying differential antibiotic susceptibilities using Raman spectroscopy

The rapid identification of antibiotic resistant bacteria is important for public health. In the environment, bacteria are exposed to sub-inhibitory antibiotic concentrations which has implications in the generation of multi-drug resistant strains. To better understand these issues, Raman spectroscopy was employed coupled with partial least squares-discriminant analysis to profile Escherichia coli strains treated with sub-inhibitory concentrations of antibiotics. Clear differences were observed between cells treated with bacteriostatic (tetracycline and rifampicin) and bactericidal (ampicillin, ciprofloxacin, and ceftriaxone) antibiotics for 6 hr: First, atomic force microscopy revealed that bactericidal antibiotics cause extensive cell elongation whereas short filaments are observed with bacteriostatic antibiotics. Second, Raman spectral analysis revealed that bactericidal antibiotics lower nucleic acid to protein (I₈₁₂/I₈₃₀) and nucleic acid to lipid ratios (I₁₄₈₃/I₁₄₅₂) whereas the opposite is seen with bacteriostatic antibiotics. Third, the protein to lipid ratio (I₂₉₃₆/I₂₈₈₅ and I₂₉₃₆/I₂₈₅₀) is a Raman stress signature common to both the classes. These signatures were validated using two mutants, Δlon and ΔacrB, that exhibit relatively high and low resistance towards antibiotics, respectively. In addition, these spectral markers correlated with the emergence of phenotypic antibiotic resistance. Overall, this study demonstrates the efficacy of Raman spectroscopy to identify resistance in bacteria to sub-lethal concentrations of antibiotics.     

Identification of circulating biomarkers of Crohn's disease and spondyloarthritis using Fourier transform infrared spectroscopy

Crohn's disease (CD) and spondyloarthritis (SpA) are two inflammatory diseases sharing many common features (genetic polymorphism, armamentarium). Both diseases lack diagnostic markers of certainty. While the diagnosis of CD is made by a combination of clinical, and biological criteria, the diagnosis of SpA may take several years to be confirmed. Based on the hypothesis that CD and SpA alter the biochemical profile of plasma, the objective of this study was to evaluate the analytical capability of Fourier transform infrared spectroscopy (FTIR) in identifying spectral biomarkers. Plasma from 104 patients was analyzed. After data processing of the spectra by Extended Multiplicative Signal Correction and linear discriminant analysis, we demonstrated that it was possible to distinguish CD and SpA from controls with an accuracy of 97% and 85% respectively. Spectral differences were mainly associated with proteins and lipids. This study showed that FTIR analysis is efficient to identify plasma biosignatures specific to CD or SpA.