Attenuated total reflection Fourier-transform infrared spectroscopy reveals environment specific phenotypes in clonal Japanese knotweed.

BACKGROUND: Japanese knotweed (Reynoutria japonica var. japonica), a problematic invasive species, has a wide geographical distribution. We have previously shown the potential for attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy and chemometrics to segregate regional differentiation between Japanese knotweed plants. However, the contribution of environment to spectral differences remains unclear. Herein, the response of Japanese knotweed to varied environmental habitats has been studied. Eight unique growth environments were created by manipulation of the red: far-red light ratio (R: FR), water availability, nitrogen, and micronutrients. Their impacts on plant growth, photosynthetic parameters, and ATR-FTIR spectral profiles, were explored using chemometric techniques, including principal component analysis (PCA), linear discriminant analysis, support vector machines (SVM) and partial least squares regression. Key wavenumbers responsible for spectral differences were identified with PCA loadings, and molecular biomarkers were assigned. Partial least squared regression (PLSR) of spectral absorbance and root water potential (RWP) data was used to create a predictive model for RWP. RESULTS: Spectra from plants grown in different environments were differentiated using ATR-FTIR spectroscopy coupled with SVM. Biomarkers highlighted through PCA loadings corresponded to several molecules, most commonly cell wall carbohydrates, suggesting that these wavenumbers could be consistent indicators of plant stress across species. R: FR most affected the ATR-FTIR spectra of intact dried leaf material. PLSR prediction of root water potential achieved an R2 of 0.8, supporting the potential use of ATR-FTIR spectrometers as sensors for prediction of plant physiological parameters. CONCLUSIONS: Japanese knotweed exhibits environmentally induced phenotypes, indicated by measurable differences in their ATR-FTIR spectra. This high environmental plasticity reflected by key biomolecular changes may contribute to its success as an invasive species. Light quality (R: FR) appears critical in defining the growth and spectral response to environment. Cross-species conservation of biomarkers suggest that they could function as indicators of plant-environment interactions including abiotic stress responses and plant health.

Non-invasive diagnostic test for lung cancer using biospectroscopy and variable selection techniques in saliva samples.

Lung cancer is one of the most commonly occurring malignant tumours worldwide. Although some reference methods such as X-ray, computed tomography or bronchoscope are widely used for clinical diagnosis of lung cancer, there is still a need to develop new methods for early detection of lung cancer. Especially needed are approaches that might be non-invasive and fast with high analytical precision and statistically reliable. Herein, we developed a swab "dip" test in saliva whereby swabs were analysed using attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy harnessed to principal component analysis-quadratic discriminant analysis (QDA) and variable selection techniques employing successive projections algorithm (SPA) and genetic algorithm (GA) for feature selection/extraction combined with QDA. A total of 1944 saliva samples (56 designated as lung-cancer positive and 1888 designed as controls) were obtained in a lung cancer-screening programme being undertaken in North-West England. GA-QDA models achieved, for the test set, sensitivity and specificity values of 100.0% and 99.1%, respectively. Three wavenumbers (1422 cm-1, 1546 cm-1 and 1578 cm-1) were identified using the GA-QDA model to distinguish between lung cancer and controls, including ring C-C stretching, CN adenine, Amide II [δ(NH), ν(CN)] and νs(COO-) (polysaccharides, pectin). These findings highlight the potential of using biospectroscopy associated with multivariate classification algorithms to discriminate between benign saliva samples and those with underlying lung cancer.

Attenuated total reflection Fourier-transform infrared spectroscopy for the prediction of hormone concentrations in plants.

Plant hormones are important in the control of physiological and developmental processes including seed germination, senescence, flowering, stomatal aperture, and ultimately the overall growth and yield of plants. Many currently available methods to quantify such growth regulators quickly and accurately require extensive sample purification using complex analytic techniques. Herein we used ultra-performance liquid chromatography-high-resolution mass spectrometry (UHPLC-HRMS) to create and validate the prediction of hormone concentrations made using attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectral profiles of both freeze-dried ground leaf tissue and extracted xylem sap of Japanese knotweed (Reynoutria japonica) plants grown under different environmental conditions. In addition to these predictions made with partial least squares regression, further analysis of spectral data was performed using chemometric techniques, including principal component analysis, linear discriminant analysis, and support vector machines (SVM). Plants grown in different environments had sufficiently different biochemical profiles, including plant hormonal compounds, to allow successful differentiation by ATR-FTIR spectroscopy coupled with SVM. ATR-FTIR spectral biomarkers highlighted a range of biomolecules responsible for the differing spectral signatures between growth environments, such as triacylglycerol, proteins and amino acids, tannins, pectin, polysaccharides such as starch and cellulose, DNA and RNA. Using partial least squares regression, we show the potential for accurate prediction of plant hormone concentrations from ATR-FTIR spectral profiles, calibrated with hormonal data quantified by UHPLC-HRMS. The application of ATR-FTIR spectroscopy and chemometrics offers accurate prediction of hormone concentrations in plant samples, with advantages over existing approaches.

Serum-based ATR-FTIR spectroscopy combined with multivariate analysis for the diagnosis of pre-diabetes and diabetes.

Diabetes mellitus (DM) is a metabolic disease with an increasing prevalence that is causing worldwide concern. The pre-diabetes stage is the only reversible stage in the patho-physiological process towards DM. Due to the limitations of traditional methods, the diagnosis and detection of DM and pre-diabetes are complicated, expensive, and time-consuming. Therefore, it would be of great benefit to develop a simple, rapid and inexpensive diagnostic test. Herein, the infrared (IR) spectra of serum samples from 111 DM patients, 111 pre-diabetes patients and 333 healthy volunteers were collected using attenuated total reflection Fourier-transform IR (ATR-FTIR) spectroscopy and this was combined with the multivariate analysis of principal component analysis linear discriminant analysis (PCA-LDA) to develop a discriminant model to verify the diagnostic potential of this approach. The study found that the accuracy of the test model established by ATR-FTIR spectroscopy combined with PCA-LDA was 97%, and the sensitivity and specificity were 100% and 100% in the control group, 94% and 98% in the pre-diabetes group, and 91% and 98% in the DM group, respectively. This indicates that this method can effectively diagnose DM and pre-diabetes, which has far-reaching clinical significance.

Combined exposure of polystyrene microplastics and benzo[a]pyrene in rat: Study of the oxidative stress effects in the liver.

Microplastics (MPs) and benzo[a]pyrene (B[a]P) are prevalent environmental pollutants. Numerous studies have extensively reported their individual adverse effects on organisms. However, the combined effects and mechanisms of exposure in mammals remain unknown. Thus, this study aims to investigate the potential effects of oral administration of 0.5µm polystyrene (PS) MPs (1 mg/mL or 5 mg/mL), B[a]P (1 mg/mL or 5 mg/mL) and combined (1 mg/mL or 5 mg/mL) on 64 male SD rats by gavage method over 6-weeks. The results demonstrate that the liver histopathological examination showed that the liver lobules in the combined (5 mg/kg) group had blurred and loose boundaries, liver cord morphological disorders, and significant steatosis. The levels of AST, ALT, TC, and TG in the combined dose groups were significantly higher than those in the other groups, the combined (5 mg/kg) group had the lowest levels of antioxidant enzymes and the highest levels of oxidants. The expression of Nrf2 was lowest and the expression of P38, NF-κB, and TNF-α was highest in the combined (5 mg/kg) group. In conclusion, these findings indicate that the combination of PSMPs and B[a]P can cause the highest levels of oxidative stress and elicit markedly enhanced toxic effects, which cause severe liver damage.

MALDI(+) FT-ICR Mass Spectrometry (MS) Combined with Machine Learning toward Saliva-Based Diagnostic Screening for COVID-19.

Rapid identification of existing respiratory viruses in biological samples is of utmost importance in strategies to combat pandemics. Inputting MALDI FT-ICR MS (matrix-assisted laser desorption/ionization Fourier-transform ion cyclotron resonance mass spectrometry) data output into machine learning algorithms could hold promise in classifying positive samples for SARS-CoV-2. This study aimed to develop a fast and effective methodology to perform saliva-based screening of patients with suspected COVID-19, using the MALDI FT-ICR MS technique with a support vector machine (SVM). In the method optimization, the best sample preparation was obtained with the digestion of saliva in 10 µL of trypsin for 2 h and the MALDI analysis, which presented a satisfactory resolution for the analysis with 1 M. SVM models were created with data from the analysis of 97 samples that were designated as SARS-CoV-2 positives versus 52 negatives, confirmed by RT-PCR tests. SVM1 and SVM2 models showed the best results. The calibration group obtained 100% accuracy, and the test group 95.6% (SVM1) and 86.7% (SVM2). SVM1 selected 780 variables and has a false negative rate (FNR) of 0%, while SVM2 selected only two variables with a FNR of 3%. The proposed methodology suggests a promising tool to aid screening for COVID-19.

Noninvasive Diagnostic for COVID-19 from Saliva Biofluid via FTIR Spectroscopy and Multivariate Analysis.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused the worst global health crisis in living memory. The reverse transcription polymerase chain reaction (RT-qPCR) is considered the gold standard diagnostic method, but it exhibits limitations in the face of enormous demands. We evaluated a mid-infrared (MIR) data set of 237 saliva samples obtained from symptomatic patients (138 COVID-19 infections diagnosed via RT-qPCR). MIR spectra were evaluated via unsupervised random forest (URF) and classification models. Linear discriminant analysis (LDA) was applied following the genetic algorithm (GA-LDA), successive projection algorithm (SPA-LDA), partial least squares (PLS-DA), and a combination of dimension reduction and variable selection methods by particle swarm optimization (PSO-PLS-DA). Additionally, a consensus class was used. URF models can identify structures even in highly complex data. Individual models performed well, but the consensus class improved the validation performance to 85% accuracy, 93% sensitivity, 83% specificity, and a Matthew's correlation coefficient value of 0.69, with information at different spectral regions. Therefore, through this unsupervised and supervised framework methodology, it is possible to better highlight the spectral regions associated with positive samples, including lipid (∼1700 cm-1), protein (∼1400 cm-1), and nucleic acid (∼1200-950 cm-1) regions. This methodology presents an important tool for a fast, noninvasive diagnostic technique, reducing costs and allowing for risk reduction strategies.

Automated Computational Detection of Disease Activity in ANCA-Associated Glomerulonephritis Using Raman Spectroscopy: A Pilot Study.

Biospectroscopy offers the ability to simultaneously identify key biochemical changes in tissue associated with a given pathological state to facilitate biomarker extraction and automated detection of key lesions. Herein, we evaluated the application of machine learning in conjunction with Raman spectroscopy as an innovative low-cost technique for the automated computational detection of disease activity in anti-neutrophil cytoplasmic autoantibody (ANCA)-associated glomerulonephritis (AAGN). Consecutive patients with active AAGN and those in disease remission were recruited from a single UK centre. In those with active disease, renal biopsy samples were collected together with a paired urine sample. Urine samples were collected immediately prior to biopsy. Amongst those in remission at the time of recruitment, archived renal tissue samples representative of biopsies taken during an active disease period were obtained. In total, twenty-eight tissue samples were included in the analysis. Following supervised classification according to recorded histological data, spectral data from unstained tissue samples were able to discriminate disease activity with a high degree of accuracy on blind predictive modelling: F-score 95% for >25% interstitial fibrosis and tubular atrophy (sensitivity 100%, specificity 90%, area under ROC 0.98), 100% for necrotising glomerular lesions (sensitivity 100%, specificity 100%, area under ROC 1) and 100% for interstitial infiltrate (sensitivity 100%, specificity 100%, area under ROC 0.97). Corresponding spectrochemical changes in paired urine samples were limited. Future larger study is required, inclusive of assigned variables according to novel non-invasive biomarkers as well as the application of forward feature extraction algorithms to predict clinical outcomes based on spectral features.

The evolving role of MUC16 (CA125) in the transformation of ovarian cells and the progression of neoplasia.

MUC16 (the cancer antigen CA125) is the most commonly used serum biomarker in epithelial ovarian cancer, with increasing levels reflecting disease progression. It is a transmembrane glycoprotein with multiple isoforms, undergoing significant changes through the metastatic process. Aberrant glycosylation and cleavage with overexpression of a small membrane-bound fragment consist MUC16-related mechanisms that enhance malignant potential. Even MUC16 knockdown can induce an aggressive phenotype but can also increase susceptibility to chemotherapy. Variable MUC16 functions help ovarian cancer cells avoid immune cytotoxicity, survive inside ascites and form metastases. This review provides a comprehensive insight into MUC16 transformations and interactions, with description of activated oncogenic signalling pathways, and adds new elements on the role of its differential glycosylation. By following the journey of the molecule from pre-malignant states to advanced stages of disease it demonstrates its behaviour, in relation to the phenotypic shifts and progression of ovarian cancer. Additionally, it presents proposed differences of MUC16 structure in normal/benign conditions and epithelial ovarian malignancy.

Ultrarapid On-Site Detection of SARS-CoV-2 Infection Using Simple ATR-FTIR Spectroscopy and an Analysis Algorithm: High Sensitivity and Specificity.

There is an urgent need for ultrarapid testing regimens to detect the severe acute respiratory syndrome coronavirus 2 [SARS-CoV-2] infections in real-time within seconds to stop its spread. Current testing approaches for this RNA virus focus primarily on diagnosis by RT-qPCR, which is time-consuming, costly, often inaccurate, and impractical for general population rollout due to the need for laboratory processing. The latency until the test result arrives with the patient has led to further virus spread. Furthermore, latest antigen rapid tests still require 15-30 min processing time and are challenging to handle. Despite increased polymerase chain reaction (PCR)-test and antigen-test efforts, the pandemic continues to evolve worldwide. Herein, we developed a superfast, reagent-free, and nondestructive approach of attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy with subsequent chemometric analysis toward the prescreening of virus-infected samples. Contrived saliva samples spiked with inactivated γ-irradiated COVID-19 virus particles at levels down to 1582 copies/mL generated infrared (IR) spectra with a good signal-to-noise ratio. Predominant virus spectral peaks are tentatively associated with nucleic acid bands, including RNA. At low copy numbers, the presence of a virus particle was found to be capable of modifying the IR spectral signature of saliva, again with discriminating wavenumbers primarily associated with RNA. Discrimination was also achievable following ATR-FTIR spectral analysis of swabs immersed in saliva variously spiked with virus. Next, we nested our test system in a clinical setting wherein participants were recruited to provide demographic details, symptoms, parallel RT-qPCR testing, and the acquisition of pharyngeal swabs for ATR-FTIR spectral analysis. Initial categorization of swab samples into negative versus positive COVID-19 infection was based on symptoms and PCR results (n = 111 negatives and 70 positives). Following training and validation (using n = 61 negatives and 20 positives) of a genetic algorithm-linear discriminant analysis (GA-LDA) algorithm, a blind sensitivity of 95% and specificity of 89% was achieved. This prompt approach generates results within 2 min and is applicable in areas with increased people traffic that require sudden test results such as airports, events, or gate controls.

Regional differences in clonal Japanese knotweed revealed by chemometrics-linked attenuated total reflection Fourier-transform infrared spectroscopy.

BACKGROUND: Japanese knotweed (R. japonica var japonica) is one of the world's 100 worst invasive species, causing crop losses, damage to infrastructure, and erosion of ecosystem services. In the UK, this species is an all-female clone, which spreads by vegetative reproduction. Despite this genetic continuity, Japanese knotweed can colonise a wide variety of environmental habitats. However, little is known about the phenotypic plasticity responsible for the ability of Japanese knotweed to invade and thrive in such diverse habitats. We have used attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy, in which the spectral fingerprint generated allows subtle differences in composition to be clearly visualized, to examine regional differences in clonal Japanese knotweed. RESULTS: We have shown distinct differences in the spectral fingerprint region (1800-900 cm- 1) of Japanese knotweed from three different regions in the UK that were sufficient to successfully identify plants from different geographical regions with high accuracy using support vector machine (SVM) chemometrics. CONCLUSIONS: These differences were not correlated with environmental variations between regions, raising the possibility that epigenetic modifications may contribute to the phenotypic plasticity responsible for the ability of R. japonica to invade and thrive in such diverse habitats.

Mid-infrared spectral classification of endometrial cancer compared to benign controls in serum or plasma samples.

This study demonstrates a discrimination of endometrial cancer versus (non-cancerous) benign controls based on mid-infrared (MIR) spectroscopy of dried plasma or serum liquid samples. A detailed evaluation was performed using four discriminant methods (LDA, QDA, kNN or SVM) to execute the classification task. The discriminant methods used in the study comprised methods that are widely used in the statistics (LDA and QDA) and machine learning literature (kNN and SVM). Of particular interest, is the impact of discrimination when presented with spectral data from a section of the bio-fingerprint region (1430 cm-1 to 900 cm-1) in contrast to the more extended bio-fingerprint region used here (1800 cm-1 to 900 cm-1). Quality metrics used were the misclassification rate, sensitivity, specificity, and Matthew's correlation coefficient (MCC). For plasma (with spectral data ranging from 1430 cm-1 to 900 cm-1), the best performing classifier was kNN, which achieved a sensitivity, specificity and MCC of 0.865 ± 0.043, 0.865 ± 0.023 and 0.762 ± 0.034, respectively. For serum (in the same wavenumber range), the best performing classifier was LDA, achieving a sensitivity, specificity and MCC of 0.899 ± 0.023, 0.763 ± 0.048 and 0.664 ± 0.067, respectively. For plasma (with spectral data ranging from 1800 cm-1 to 900 cm-1), the best performing classifier was SVM, with a sensitivity, specificity and MCC of 0.993 ± 0.010, 0.815 ± 0.000 and 0.815 ± 0.010, respectively. For serum (in the same wavenumber range), QDA performed best achieving a sensitivity, specificity and MCC of 0.852 ± 0.023, 0.700 ± 0.162 and 0.557 ± 0.012, respectively. Our findings demonstrate that even when a section of the bio-fingerprint region has been removed, good classification of endometrial cancer versus non-cancerous controls is still maintained. These findings suggest the potential of a MIR screening tool for endometrial cancer screening.

Detection of ovarian cancer (± neo-adjuvant chemotherapy effects) via ATR-FTIR spectroscopy: comparative analysis of blood and urine biofluids in a large patient cohort.

Ovarian cancer remains the most lethal gynaecological malignancy, as its timely detection at early stages remains elusive. Attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy of biofluids has been previously applied in pilot studies for ovarian cancer diagnosis, with promising results. Herein, these initial findings were further investigated by application of ATR-FTIR spectroscopy in a large patient cohort. Spectra were obtained by measurements of blood plasma and serum, as well as urine, from 116 patients with ovarian cancer and 307 patients with benign gynaecological conditions. A preliminary chemometric analysis revealed significant spectral differences in ovarian cancer patients without previous chemotherapy (n = 71) and those who had received neo-adjuvant chemotherapy-NACT (n = 45), so these groups were compared separately with benign controls. Classification algorithms with blind predictive model validation demonstrated that serum was the best biofluid, achieving 76% sensitivity and 98% specificity for ovarian cancer detection, whereas urine exhibited poor performance. A drop in sensitivities for the NACT ovarian cancer group in plasma and serum indicates the potential of ATR-FTIR spectroscopy to identify chemotherapy-related spectral changes. Comparisons of regression coefficient plots for identification of biomarkers suggest that glycoproteins (such as CA125) are the main classifiers for ovarian cancer detection and responsible for smaller differences in spectra between NACT patients and benign controls. This study confirms the capacity of biofluids' ATR-FTIR spectroscopy (mainly blood serum) to diagnose ovarian cancer with high accuracy and demonstrates its potential in monitoring response to chemotherapy, which is reported for the first time. ATR-FTIR spectroscopy of blood serum achieves good segregation of ovarian cancers from benign controls, with attenuation of differences following neo-adjuvant chemotherapy.

A comparative analysis of different biofluids towards ovarian cancer diagnosis using Raman microspectroscopy.

Biofluids, such as blood plasma or serum, are currently being evaluated for cancer detection using vibrational spectroscopy. These fluids contain information of key biomolecules, such as proteins, lipids, carbohydrates and nucleic acids, that comprise spectrochemical patterns to differentiate samples. Raman is a water-free and practically non-destructive vibrational spectroscopy technique, capable of recording spectrochemical fingerprints of biofluids with minimum or no sample preparation. Herein, we compare the performance of these two common biofluids (blood plasma and serum) together with ascitic fluid, towards ovarian cancer detection using Raman microspectroscopy. Samples from thirty-eight patients were analysed (n = 18 ovarian cancer patients, n = 20 benign controls) through different spectral pre-processing and discriminant analysis techniques. Ascitic fluid provided the best class separation in both unsupervised and supervised discrimination approaches, where classification accuracies, sensitivities and specificities above 80% were obtained, in comparison to 60-73% with plasma or serum. Ascitic fluid appears to be rich in collagen information responsible for distinguishing ovarian cancer samples, where collagen-signalling bands at 1004 cm-1 (phenylalanine), 1334 cm-1 (CH3CH2 wagging vibration), 1448 cm-1 (CH2 deformation) and 1657 cm-1 (Amide I) exhibited high statistical significance for class differentiation (P < 0.001). The efficacy of vibrational spectroscopy, in particular Raman spectroscopy, combined with ascitic fluid analysis, suggests a potential diagnostic method for ovarian cancer. Raman microspectroscopy analysis of ascitic fluid allows for discrimination of patients with benign gynaecological conditions or ovarian cancer.

Spectrochemical determination of effects on rat liver of binary exposure to benzo[a]pyrene and 2,2',4,4'-tetrabromodiphenyl ether.

Benzo[a]pyrene (B[a]P) and polybrominated diphenyl ethers (PBDEs) are persistent environmental contaminants. The effects in organisms of exposures to binary mixtures of such contaminants remain obscure. Attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy is a label-free, non-destructive analytical technique allowing spectrochemical analysis of macromolecular components, and alterations thereof, within tissue samples. Herein, we employed ATR-FTIR spectroscopy to identify biomolecular changes in rat liver post-exposure to B[a]P and BDE-47 (2,2',4,4'-tetrabromodiphenyl ether) congener mixtures. Our results demonstrate that significant separation occurs between spectra of tissue samples derived from control versus exposure categories (accuracy = 87%; sensitivity = 95%; specificity = 79%). Additionally, there is significant spectral separation between exposed categories (accuracy = 91%; sensitivity = 98%; specificity = 90%). Segregation between control and all exposure categories were primarily associated with wavenumbers ranging from 1600 to 1700 cm-1 . B[a]P and BDE-47 alone, or in combination, induces liver damage in female rats. However, it is suggested that binary exposure apparently attenuates the toxic effects in rat liver of the individual contaminants. This is supported by morphological observations of liver tissue architecture on hematoxylin and eosin (H&E)-stained liver sections. Such observations highlight the difficulties in predicting the endpoint effects in target tissues of exposures to mixtures of environmental contaminants.

Improving data splitting for classification applications in spectrochemical analyses employing a random-mutation Kennard-Stone algorithm approach.

MOTIVATION: Data splitting is a fundamental step for building classification models with spectral data, especially in biomedical applications. This approach is performed following pre-processing and prior to model construction, and consists of dividing the samples into at least training and test sets; herein, the training set is used for model construction and the test set for model validation. Some of the most-used methodologies for data splitting are the random selection (RS) and the Kennard-Stone (KS) algorithms; here, the former works based on a random splitting process and the latter is based on the calculation of the Euclidian distance between the samples. We propose an algorithm called the Morais-Lima-Martin (MLM) algorithm, as an alternative method to improve data splitting in classification models. MLM is a modification of KS algorithm by adding a random-mutation factor. RESULTS: RS, KS and MLM performance are compared in simulated and six real-world biospectroscopic applications using principal component analysis linear discriminant analysis (PCA-LDA). MLM generated a better predictive performance in comparison with RS and KS algorithms, in particular regarding sensitivity and specificity values. Classification is found to be more well-equilibrated using MLM. RS showed the poorest predictive response, followed by KS which showed good accuracy towards prediction, but relatively unbalanced sensitivities and specificities. These findings demonstrate the potential of this new MLM algorithm as a sample selection method for classification applications in comparison with other regular methods often applied in this type of data. AVAILABILITY AND IMPLEMENTATION: MLM algorithm is freely available for MATLAB at https://doi.org/10.6084/m9.figshare.7393517.v1.

Raman spectroscopy as a potential diagnostic tool to analyse biochemical alterations in lung cancer.

Patient survival remains poor even after diagnosis in lung cancer cases, and the molecular events resulting from lung cancer progression remain unclear. Raman spectroscopy could be used to noninvasively and accurately reveal the biochemical properties of biological tissues on the basis of their pathological status. This study aimed at probing biomolecular changes in lung cancer, using Raman spectroscopy as a potential diagnostic tool. Herein, biochemical alterations were evident in the Raman spectra (region of 600-1800 cm-1) in normal and cancerous lung tissues. The levels of saturated and unsaturated lipids and the protein-to-lipid, nucleic acid-to-lipid, and protein-to-nucleic acid ratios were significantly altered among malignant tissues compared to normal lung tissues. These biochemical alterations in tissues during neoplastic transformation have profound implications in not only the biochemical landscape of lung cancer progression but also cytopathological classification. Based on this spectroscopic approach, classification methods including k-nearest neighbour (kNN) and support vector machine (SVM) were successfully applied to cytopathologically diagnose lung cancer with an accuracy approaching 99%. The present results indicate that Raman spectroscopy is an excellent tool to biochemically interrogate and diagnose lung cancer.

A three-dimensional discriminant analysis approach for hyperspectral images.

Raman hyperspectral imaging is a powerful technique that provides both chemical and spatial information of a sample matrix being studied. The generated data are composed of three-dimensional (3D) arrays containing the spatial information across the x- and y-axis, and the spectral information in the z-axis. Unfolding procedures are commonly employed to analyze this type of data in a multivariate fashion, where the spatial dimension is reshaped and the spectral data fits into a two-dimensional (2D) structure and, thereafter, common first-order chemometric algorithms are applied to process the data. There are only a few algorithms capable of working with the full 3D array. Herein, we propose new algorithms for 3D discriminant analysis of hyperspectral images based on a three-dimensional principal component analysis linear discriminant analysis (3D-PCA-LDA) and a three-dimensional discriminant analysis quadratic discriminant analysis (3D-PCA-QDA) approach. The analysis was performed in order to discriminate simulated and real-world data, comprising benign controls and ovarian cancer samples based on Raman hyperspectral imaging, in which 3D-PCA-LDA and 3D-PCA-QDA achieved far superior performance than classical algorithms using unfolding procedures (PCA-LDA, PCA-QDA, partial lest squares discriminant analysis [PLS-DA], and support vector machines [SVM]), where the classification accuracies improved from 66% to 83% (simulated data) and from 50% to 100% (real-world dataset) after employing the 3D techniques. 3D-PCA-LDA and 3D-PCA-QDA are new approaches for discriminant analysis of hyperspectral images multisets to provide faster and superior classification performance than traditional techniques.

Applying Raman Microspectroscopy to Evaluate the Effects of Nutrient Cations on Alkane Bioavailability to Acinetobacter baylyi ADP1.

Contamination with petroleum hydrocarbons causes extensive damage to ecological systems. On oil-contaminated sites, alkanes are major components; many indigenous bacteria can access and/or degrade alkanes. However, their ability to do so is affected by external properties of the soil, including nutrient cations. This study used Raman microspectroscopy to study how nutrient cations affect alkanes' bioavailability to Acinetobacter baylyi ADP1 (a known degrader). Treated with Na, K, Mg, and Ca at 10 mM, A. baylyi was exposed to seven n-alkanes (decane, dodecane, tetradecane, hexadecane, nonadecane, eicosane, and tetracosane) and one alkane mixture (mineral oil). Raman spectral analysis indicated that bioavailability of alkanes varied with carbon chain lengths, and additional cations altered the bacterial response to n-alkanes. Sodium significantly increased the bacterial affinity toward decane and dodecane, and K and Mg enhanced the bioavailability of tetradecane and hexadecane. In contrast, the bacterial response was inhibited by Ca for all alkanes. Similar results were observed in mineral oil exposure. Our study employed Raman spectral assay to offer a deep insight into how nutrient cations affect the bioavailability of alkanes, suggesting that nutrient cations can play a key role in influencing the harmful effects of hydrocarbons and could be optimized to enhance the bioremediation strategy.

Establishing spectrochemical changes in the natural history of oesophageal adenocarcinoma from tissue Raman mapping analysis.

Raman spectroscopy is a fast and sensitive technique able to identify molecular changes in biological specimens. Herein, we report on three cases where Raman microspectroscopy was used to distinguish normal vs. oesophageal adenocarcinoma (OAC) (case 1) and Barrett's oesophagus vs. OAC (cases 2 and 3) in a non-destructive and highly accurate fashion. Normal and OAC tissues were discriminated using principal component analysis plus linear discriminant analysis (PCA-LDA) with 97% accuracy (94% sensitivity and 100% specificity) (case 1); Barrett's oesophagus vs. OAC tissues were discriminated with accuracies ranging from 98 to 100% (97-100% sensitivity and 100% specificity). Spectral markers responsible for class differentiation were obtained through the difference-between-mean spectrum for each group and the PCA loadings, where C-O-C skeletal mode in ß-glucose (900 cm-1), lipids (967 cm-1), phosphodioxy (1296 cm-1), deoxyribose (1456 cm-1) and collagen (1445, 1665 cm-1) were associated with normal and OAC tissue differences. Phenylalanine (1003 cm-1), proline/collagen (1066, 1445 cm-1), phospholipids (1130 cm-1), CH2 angular deformation (1295 cm-1), disaccharides (1462 cm-1) and proteins (amide I, 1672/5 cm-1) were associated with Barrett's oesophagus and OAC tissue differences. These findings show the potential of using Raman microspectroscopy imaging for fast and accurate diagnoses of oesophageal pathologies and establishing subtle molecular changes predisposing to adenocarcinoma in a clinical setting. Graphical abstract Graphical abstract demonstrating how oesophageal tissue is processed through Raman mapping analysis in order to detect spectral differences between stages of oesophageal transformation to adenocarcinoma.