Exploration of Raman exfoliated cytology for oral and cervical cancers

Exfoliated cell are one of the most patient compliant specimens, hence are workhorse of successful screening programs in cancers like cervix. Raman exfoliative cytology (REC), even on cell pellets using Raman microprobe, has demonstrated ∼80% specificity, comparable to conventional screening programs. Objective of this study is to further evaluate Raman spectroscopy, which is sensitive to biochemical composition, vis-a-vis conventional morphology based cytology that differentiates cell-type but not the origin. Exploration of this kind further corroborates utility and efficacy of REC as screening tool, also can pave way to prospective multi-cancer screening by a multi-group model. Therefore, the present study explores REC in a stringent two-cancer, four-group model for classification of 44 oral [29, tumor (OT), 15 normal (ON)] and 66 cervical (38 tumor (CT), 28 normal (CN)] exfoliated cell specimens. Spectra were acquired from cell pellets using Raman microprobe. Spectra were analyzed in two approaches, spectra-wise and subject-wise to circumvent inherent heterogeneity due to co-localization of normal cells in malignant specimens. In the first approach, PCA and LDA yielded a classification efficiency of 82% for OT and 77% for CT. A slightly higher classification (OT, 86% and CT, 84%) was achieved for the subject-wise approach. Thus findings of this study demonstrate similar efficacy and specificity to that of individual cancer-based models reported earlier. These findings are quite encouraging especially for resource deprived and under privileged countries where these two cancers are more prevalent and could be useful for prospective centralized REC facility.     

Raman microspectroscopic chemical mapping and chemometric classification for the identification of gunshot residue on adhesive tape

A novel approach utilizing automated Raman microspectroscopic mapping for gunshot residue (GSR) detection was investigated. A well-established technique for GSR recovery (tape lifting) was utilized for GSR particle collection. Uncontaminated samples of the substrate (tape), organic GSR (OGSR), and inorganic GSR (IGSR) particles were characterized to generate three respective Raman spectroscopic training sets. Automated Raman mapping was used to rapidly collect spectra over areas of the tape substrate populated with GSR particles. Raman spectra collected from the maps were classified against the training sets via partial least squares discriminant analysis (PLS-DA) to determine if GSR was present. We report the application of Raman chemical mapping as a proof of concept for the positive detection of GSR particles of varying morphologies. The estimated size of GSR particles, which could be readily detected by this method, is about 3.4 μm. The efficiency of the classification was quantitated with rates of true positives and negatives. Validation studies scrutinizing the practicality of this approach as a viable tool for potential forensics investigations are currently in progress.

Effect of normal variations on disease classification of Raman spectra from cervical tissue

In this paper, we examine how variations in normal tissue can influence disease classification of Raman spectra. Raman spectra from normal areas may be affected by previous disease or proximity to areas of dysplasia. Spectra were acquired in vivo from 172 patients and classified into five tissue categories: true normal (no history of disease), previous disease normal (history of disease, current normal diagnosis), adjacent normal (disease on cervix, spectra acquired from visually normal area), low grade, and high grade. Taking into account the various "normal" states of the tissue before statistical analysis led to a disease classification accuracy of 97%. These results indicate that abnormal changes significantly affect Raman spectra, even when areas are histopathologically normal. The sensitivity of Raman spectroscopy to subtle biochemical differences must be considered in order to successfully implement it in a clinical setting for diagnosing cervical dysplasia and cancer.     

Investigation of the influence of high-risk human papillomavirus on the biochemical composition of cervical cancer cells using vibrational spectroscopy

The main aetiology of cervical cancer is infection with high-risk human papillomavirus (HPV). Cervical cancer is almost 100% curable if detected in the early stages. Thus, information about the presence and levels of HPV in patient samples has high clinical value. As current screening methods, such as the Pap smear test, are highly subjective and in many cases show low sensitivity and specificity, new supportive techniques are desirable to improve the quality of cervical cancer screening. In this study, vibrational spectroscopic techniques (Raman and Fourier Transform Infra Red absorption) have been applied to the investigation of four cervical cancer cell lines: HPV negative C33A, HPV-18 positive HeLa with 20-50 integrated HPV copies per cell, HPV-16 positive SiHa with 1-2 integrated HPV strands per cell and HPV-16 positive CaSki containing 60-600 integrated HPV copies per cell. Results show that vibrational spectroscopic techniques can discriminate between the cell lines and elucidate cellular differences originating from proteins, nucleic acids and lipids. Similarities between C33A and SiHa cells were exhibited in the Raman and infrared spectra and were confirmed by Principal Component Analysis (PCA). Analysis of the biochemical composition of the investigated cells, with the aid of PCA, showed a clear discrimination between the C33A-SiHa group and HeLa and CaSki cell lines indicating the potential of vibrational spectroscopic techniques as a support to current methods for cervical cancer screening.     

Raman endoscopy for in vivo differentiation between benign and malignant ulcers in the stomach

The aim of this study was to evaluate the clinical utility of an image-guided Raman endoscopy technique for in vivo differential diagnosis of benign and malignant ulcerous lesions in the stomach. A rapid-acquisition image-guided Raman endoscopy system with 785 nm laser excitation has been developed to acquire in vivo gastric tissue Raman spectra within 0.5 s during clinical gastroscopic examinations. A total of 1102 in vivo Raman spectra were acquired from 71 gastric patients, in which 924 Raman spectra were from normal tissue, 111 Raman spectra were from benign ulcers whereas 67 Raman spectra were from ulcerated adenocarcinoma. There were distinctive spectral differences in Raman spectra among normal mucosa, benign ulcers and malignant ulcers, particularly in the spectral ranges of 800-900, 1000-1100, 1245-1335, 1440-1450 and 1500-1800 cm(-1), which primarily contain signals related to proteins, DNA, lipids and blood. The malignant ulcerous lesions showed Raman signals to be mainly associated with abnormal nuclear activity and decrease in lipids as compared to benign ulcers. Partial least squares-discriminant analysis (PLS-DA) was employed to generate multi-class diagnostic algorithms for classification of Raman spectra of different gastric tissue types. The PLS-DA algorithms together with leave-one tissue site-out, cross validation technique yielded diagnostic sensitivities of 90.8%, 84.7%, 82.1%, and specificities of 93.8%, 94.5%, 95.3%, respectively, for classification of normal mucosa, benign and malignant ulcerous lesions in the stomach. This work demonstrates that image-guided Raman endoscopy technique associated with PLS-DA diagnostic algorithms has for the first time promising clinical potential for rapid, in vivo diagnosis and detection of malignant ulcerous gastric lesions at the molecular level.     

In and ex vivo breast disease study by Raman spectroscopy

In this work, Raman spectra in the 900?C1,800?cm^?1 wavenumber region of in vivo and ex vivo breast tissues of both healthy mice (normal) and mice with induced mammary gland tumors (abnormal) were measured. In the case of the in vivo tissues, the Raman spectra were collected for both transcutaneous (with skin) and skin-removed tissues. To identify the spectral differences between normal and cancer breast tissue, the paired t-test was carried out for each wavenumber using the whole spectral range from both groups. Quadratic discriminate analysis based on principal component analysis (PCA) was also used to determine and evaluate differences in the Raman spectra for the various samples as a basis for diagnostic purposes. The differences in the Raman spectra of the samples were due to biochemical changes at the molecular, cellular and tissue levels. The sensitivity and specificity of the classification scheme based on the differences in the Raman spectra obtained by PCA were evaluated using the receiver operating characteristic (ROC) curve. The in vivo transcutaneous normal and abnormal tissues were correctly classified based on their measured Raman spectra with a discriminant proportion of 73%, while the in vivo skin-removed normal and abnormal tissues were correctly classified again based on their measured Raman spectra with a discriminant proportion of 86%. This result reveals a strong influence due to the skin of the breast, which decreased the specificity by 11%. Finally, the results from ex vivo measurements gave the highest specificity and sensitivity: 96 and 97%, respectively, as well as a largest percentage for correct discrimination: 94%. Now that the important bands have been experimentally determined in this and other works, what remains is for first principles molecular-level simulations to determine whether the changes are simply due to conformational changes, due to aggregation, due to changes in the environment, or complex interactions of all of the above.     

Near infrared Raman spectroscopic mapping of native brain tissue and intracranial tumors

This study assessed the diagnostic potential of Raman spectroscopic mapping by evaluating its ability to distinguish between normal brain tissue and the human intracranial tumors gliomas and meningeomas. Seven Raman maps of native specimens were collected ex vivo by a Raman spectrometer with 785 nm excitation coupled to a microscope with a motorized stage. Variations within each Raman map were analyzed by cluster analysis. The dependence of tissue composition on the tissue type in cluster averaged Raman spectra was shown by linear combinations of reference spectra. Normal brain tissue was found to contain higher levels of lipids, intracranial tumors have more hemoglobin and lower lipid to protein ratios, meningeomas contain more collagen with maximum collagen content in normal meninges. One sample was studied without freezing. Whereas tumor regions did not change significantly, spectral changes were observed in the hemoglobin component after snap freezing and thawing to room temperature. The results constitute a basis for subsequent Raman studies to develop classification models for diagnosis of brain tissue.     

Transcutaneous in vivo Raman spectroscopy: Detection of age-related changes in mouse breast

The risk of female breast cancer increases as age progresses. This can be explained by Pike's model, which suggests that the process of aging in breast is not uniform. ‘Breast ageing’ is most rapid during menarche, slows with each pregnancy, slows further during perimenopause, and is least after the menopause. In this study, the feasibility of using transcutaneous in vivo Raman spectroscopy to detect age-related changes in mouse breast and its effect on tumor detection were explored. Spectra acquired transcutaneously from breast of 2 (menarche), 4–6 (mid reproductive phase), 10–12 (perimenopause), 13–15 month (menopause) old mice and frank breast tumors were analyzed using principal component-linear discriminant analysis (PC-LDA). A classification efficiency of ∼80% was achieved for different age groups. Further, it was observed that the number of misclassifications among age groups increase as age progresses. For example, 3% spectra from menarche misclassify with other age groups, while 19–28% from mid-reproductive, perimenopause and menopause misclassify with each other. Misclassifications between groups indicate homogeneity in tissues. Thus, results suggest that menarche breast is biochemically distinct while breast during mid-reproductive, perimenopause and menopause is relatively homogenous. This probably indicates that the rate of aging is rapid during menarche, but slows down during other phases. Thus, spectroscopic data correlate with Pike's model. Although sensitive to age-related changes, RS could classify tumors with 95% efficiency. Overall, results suggest possibility of distinguishing age-related changes using RS without affecting ability to classify tumors.     

Effect of Cold Atmospheric Plasma Jet and Gamma Radiation Treatments on Gingivobuccal Squamous Cell Carcinoma and Breast Adenocarcinoma Cells

Surgery, chemotherapy and radiotherapy, the conventional treatment modalities of cancer though successful are limited by presence of residual tumor cells, toxic side-effects and treatment resistance, thus raising the need for investigating other novel approaches. Here, we have used a cold atmospheric plasma (CAP) jet and assessed the in vitro efficacy in gingivobuccal squamous cell carcinoma (GB-SCC) – ITOC-03, breast adenocarcinoma—MCF7 and HEK293 cells. Cells lines were subjected to varying doses of ionizing radiation (0, 2, 4, 6, 8 Gy) and CAP jet treatment (0, 60, 180, 240, 300 s). CAP jet treatment showed time dependent increase in H₂O₂ and NO₂^? concentration. Cell viability assay showed potent effect of CAP jet on all three cell lines in comparison to radiation treatment, while helium gas treatment showed minimal inhibitory effect. Irradiated, CAP jet and helium gas treated cells showed loss of nucleic acid features, 788 cm^?1 and 1340 cm^?1 in Raman spectra, indicating DNA damage. Principal Component Analysis (PCA) showed distinct classification of CAP-treated and control cells, while Principal Component – Linear Discriminant Analysis (PC-LDA) based classification of Raman spectra showed ITOC-03 and HEK293 cells to be sensitive to CAP jet and radiation treatment in comparison to MCF7 cells. Collectively, cell viability assay and Raman spectroscopy have shown potent effect of CAP jet in GB-SCC and breast adenocarcinoma cells.

     

Raman spectroscopic identification of single bacterial cells at different stages of their lifecycle

Early, rapid, and reliable bacterial identification is of great importance in natural environments and in medical situations. Numerous studies have shown that Raman spectroscopy can be used to differentiate between different bacteria under controlled laboratory conditions. However, individual bacteria within a population exhibit macromolecular and metabolic heterogeneity over their lifetime. Therefore it is important to be able to identify and classify specific bacteria at different time points of the growth cycle. In this study, four species of bacteria were used to explore the capability of confocal Raman spectroscopy as a tool for the identification of (and discrimination between) diverse bacterial species at various growth time points. The results show that bacterial cells from different growth time points (as well as from a random growth phase) can be discriminated among the four species using principal component analysis (PCA). The results also show that bacteria selected from different growth phases can be classified with the help of a prediction model based on principal component and linear discriminant analysis (PC-LDA). These findings demonstrate that Raman spectroscopy with the application of a PC-LDA model rooted in chemotaxonomic analysis has potential for rapid sensing of microbial cells in environmental and clinical studies.     

基于显微共聚焦拉曼光谱技术快速检测3种梭菌中肉毒梭菌

提出了一种基于显微共聚焦拉曼光谱技术的肉毒梭菌快速鉴别方法.利用共聚焦显微拉曼光谱技术(CRM)采集了肉毒梭菌、艰难梭菌和产气荚膜梭菌的拉曼光谱,比较了3种梭菌的平均拉曼光谱,采用基线校正、标准正态变换、Savitzky-Golay 5点平滑和最大最小值归一化预处理后,借助主成分分析(PCA)降维并提取特征变量,对样本...     

Experimental and quantum chemical study on the vibrational spectroscopy of N-methylphenothiazines: part 1

In this work the authors deal with the vibrational spectroscopy of three derivatives of phenothiazine: the 10-methyl-10H-phenothiazine, the 10 methyl-10H-phenothiazine-3-carbaldehyde and the 10-methyl-10H-phenothiazine-3-yl-methanol. The authors investigated the vibrational spectroscopic behaviour of the phenothiazine skeleton and dealt with the aldehyde and the alcohol substituent effect on the vibrational spectroscopic and structural properties of these skeleton. The infrared and Raman spectra of the compounds have been recorded in condensed state. The Gaussian 98 program package was applied with the ab initio HF method since in this case beside the infrared also the Raman spectoroscopic properties appear in the output file. On the basis of the calculated force constants and geometric parameters, normal coordinate analysis was applied for the interpretation of the experimental vibrational spectra. Problems arose with the choice of the internal coordinates of the molecules. Full interpretations of the vibrational fundamentals of the compounds are presented. The relative mean deviations between the measured and calculated frequencies were below 1%.     

Discrimination of natural gas-related bacteria by means of micro-Raman spectroscopy

Methane-oxidizing bacteria (MOB) are a unique group of gram-negative bacteria that are proved to be biological indicator for gas prospecting since they utilize methane as a sole source of carbon and energy. Herein the feasibility of a novel and efficient gas prospecting method using Raman spectroscopy is studied. Confocal Raman spectroscopy is utilized to establish a Raman database of 11 species of methanotrophs and other closely related bacteria with similar morphology that generally coexist in the upper soil of natural gas. After strict and consistent spectral preprocessing, Raman spectra from the whole cell area are analyzed using the combination of principal component analysis (PCA) and Mahalanobis distance (MD) that allow unambiguous classification of the different cell types with an accuracy of 95.91%. The discrimination model based on multivariate analysis is further evaluated by classifying Raman spectra from independently cultivated bacteria, and achieves an overall accuracy of 94.04% on species level. Our approach using Raman spectroscopy in combination with statistical analysis of various gas reservoirs related bacteria provides rapid distinction that can potentially play a vital role in gas exploration.     

A microfluidic system enabling Raman measurements of the oxygenation cycle in single optically trapped red blood cells

Using a lab-on-a-chip approach we demonstrate the possibility of selecting a single cell with certain properties and following its dynamics after an environmental stimulation in real time using Raman spectroscopy. This is accomplished by combining a micro Raman set-up with optical tweezers and a microfluidic system. The latter gives full control over the media surrounding the cell, and it consists of a pattern of channels and reservoirs defined by electron beam lithography that is moulded into rubber silicon (PDMS). Different buffers can be transported through the channels using electro-osmotic flow, while the resonance Raman response of an optically trapped red blood cell (RBC) is simultaneously registered. This makes it possible to monitor the oxygenation cycle of the cell in real time and to investigate effects like photo-induced chemistry caused by the illumination. The experimental set-up has high potential for in vivo monitoring of cellular drug response using a variety of spectroscopic probes.     

Raman spectroscopy in combination with background near-infrared autofluorescence enhances the in vivo assessment of malignant tissues

The diagnostic ability of optical spectroscopy techniques, including near-infrared (NIR) Raman spectroscopy, NIR autofluorescence spectroscopy and the composite Raman and NIR autofluorescence spectroscopy, for in vivo detection of malignant tumors was evaluated in this study. A murine tumor model, in which BALB/c mice were implanted with Meth-A fibrosarcoma cells into the subcutaneous region of the lower back, was used for this purpose. A rapid-acquisition dispersive-type NIR Raman system was employed for tissue Raman and NIR autofluorescence spectroscopic measurements at 785-nm laser excitation. High-quality in vivo NIR Raman spectra associated with an autofluorescence background from mouse skin and tumor tissue were acquired in 5 s. Multivariate statistical techniques, including principal component analysis (PCA) and linear discriminant analysis (LDA), were used to develop diagnostic algorithms for differentiating tumors from normal tissue based on their spectral features. Spectral classification of tumor tissue was tested using a leave-one-out, cross-validation method, and the receiver operating characteristic (ROC) curves were used to further evaluate the performance of diagnostic algorithms derived. Thirty-two in vivo Raman, NIR fluorescence and composite Raman and NIR fluorescence spectra were analyzed (16 normal, 16 tumors). Classification results obtained from cross-validation of the LDA model based on the three spectral data sets showed diagnostic sensitivities of 81.3%, 93.8% and 93.8%; specificities of 100%, 87.5% and 100%; and overall diagnostic accuracies of 90.6%, 90.6% and 96.9% respectively, for tumor identification. ROC curves showed that the most effective diagnostic algorithms were from the composite Raman and NIR autofluorescence techniques.     

Evaluation of a spectral searching algorithm for the comparison of Raman band positions

Raman spectroscopic identification of unknown materials involves often the comparison of the spectrum of the unknown spectrum with previously recorded reference spectra or data from literature. However, when spectra with many Raman bands or spectra of mixtures are involved, searching can be quite complex. Different chemometrical approaches have been proposed, but these have some drawbacks. Therefore, in this paper a novel approach is proposed, which is based on a multivariate comparison of Raman band positions. Different similarity measures can be used and are evaluated with spectra of test samples that were recorded on different spectrometers, using different laser wavelengths. Moreover, this study evaluates the performances of this algorithm for identifying different compounds in mixtures, by using an iterative approach.     

Characterization of lipid extracts from brain tissue and tumors using Raman spectroscopy and mass spectrometry

Brain tissue is characterized by high lipid content. The amount of lipids decreases, and its composition changes in the most frequent primary brain tumor, the glioma. Scope of the current paper was to extract quantitatively lipids from porcine and human brain tissue as well as from five human gliomas using a modified protocol according to Folch. The lipid extracts were studied by Raman spectroscopy with 785 nm excitation and by mass spectrometry with electron impact ionization. Porcine and human brain tissues have similar water and lipid content and show similar Raman and mass spectra. In contrast, gliomas are characterized by increased water content and decreased lipid content. Elevated phosphatidylcholine to cholesterol ratios in lipid extracts of gliomas were indicated by Raman bands of the choline group and cholesterol. Due to its higher sensitivity, mass spectrometry detected increased levels of cholesterol ester relative to cholesterol in lipid extracts of gliomas. For comparison, thin tissue sections were prepared from the glioma specimens before lipid extraction; infrared spectroscopic images were recorded and analyzed by a supervised classification model. This study demonstrates how to improve the analysis of brain tumors and to complement the diagnosis of brain pathologies using a multimodal approach.     

Classification of nasopharyngeal cell lines (C666-1, CNE2, NP69) via Raman spectroscopy and decision tree

Researchers have demonstrated that Raman spectroscopy can be used for characterization of tumor cells with excellent spatial resolution. However, performance evaluation of different algorithms in classifying multiclass of Raman spectra has not been reported yet. In this work, we present Raman spectra of nasopharyngeal carcinoma and nasopharyngeal normal cell lines. Combined with student’s t-test and several multivariate approaches, including decision tree, support vector classification, and linear discriminant analysis, our work shows that the relative content of two histological abnormality sensitive bands at 1449 and 1658 cm⁻¹ in tumor cells is significantly different from that of normal cells (p = 0.0132), and can be a biomarker to classify these cells. This difference is confirmed by importance analyses in the decision tree model. Furthermore, performances of statistical methods are compared with one another to explore the ability in classification. Results show that the decision tree can be more capable for classification between tumorous and normal cell lines with sensitivity and specificity of 99.0% and 96.9%, respectively. Findings of this work further support our previous work and indicate that the decision tree performs more robustly in cell classification. Our work will prove helpful to the early diagnosis of nasopharyngeal carcinoma, and will indicate the decision tree to be the primary algorithm in tumor-cell classification.     

二维相关分析光谱技术

给出推导二维相关光谱和推广至普遍意义上的数学过程,其物理含义和二维相关光谱的性质及其解释规则。以应用实例给出了该光谱技术在分析结构、相互作用等相关性方面的独特性质。还给出紫外可见二维相关光谱。