Analyzing normal proliferating,hypoxic and necrotic regions of T-47D human breast cancer spheroids using Raman spectroscopy

Raman spectroscopy is an advanced chemical analytical technique that has gained significant interest in cancer research, in particular early detection and monitoring of cancer, with added advantages of non-invasive and real-time diagnosis. Recently, studies have shown its sensitivity to monitor chemical changes during cancer progression. This information will lead to identification of chemical markers (molecular fingerprints of chemical composition) that can be used as biological markers. In this study, we used a tumor spheroid model that mimics the characteristics of a non-vascular in vitro tumor model, we used a combination of Raman and multivariate approach to identify chemical changes associated with normal proliferating, hypoxic and necrotic regions of T-47D human breast cancer spheroid model. The results provide evidence that lipids, amide I, III and nucleic acid contents differ significantly in normal, hypoxic and necrotic regions. Principal component analysis loading plots has suggested that normal proliferating region separated with low amide I and high-tryptophan content compared to hypoxic and necrotic regions. These differences observed in three regions might be useful in identification of new spectral markers associated stress faced by each region progressing toward necrosis.     

Raman spectroscopic diagnosis of breast cancers: evaluation of models

Breast cancer is the leading cancer affecting women. Conventional screening and diagnostic methods are shown to suffer from well‐described limitations. The aim of this study was to evaluate Raman spectroscopic models, which were developed earlier, by blinded tissue samples. We have recorded Raman spectra of 69 blinded tissue samples. In each sample, six or more spectra were recorded at different locations. Pretreated spectra were matched against normal, malignant and benign standard sets to obtain a match/mismatch status, which in turn was correlated with histopathology. Among 69 samples analyzed, 61 could be unambiguously diagnosed as 29 normal, 17 malignant and 15 benign, as their spectra matched with only one standard set and mismatched against the others. In the cases of the remaining tissue samples, matching them against pathological sets was the determining criteria. These samples were diagnosed as pathological since at least one of the spectra of these tissues had matched with pathological sets. Thus, we demonstrate a good correlation between histopathology and Raman spectroscopic diagnosis. Therefore, findings of the study further support the efficacy of Raman spectroscopic models that were developed by us. Prospectively, by developing models for as many pathological conditions as possible followed by rigorous validation, objective/unambiguous Raman spectroscopic diagnosis of breast pathologies can be realized. Copyright © 2008 John Wiley & Sons, Ltd.     

Assessment of ex vivo perfused liver health by Raman spectroscopy

Raman spectroscopy was applied in this research to monitor the overall health and degradation of porcine livers perfused ex vivo using the VasoWaveW® perfusion system. A novel Raman‐based diagnostic analysis was developed that enables near real‐time and label‐free monitoring of organ health during ex vivo perfusion designed to extend the useful life of the organ for transplantation. Multivariate statistical analysis of Raman spectra of organ perfusate fluid, using a combination of principal component analysis and linear discriminant analysis, proved to be an effective technique to assess the degradation properties of the livers. Three livers (with replicates) were perfused ex vivo under different pressures and temperatures and were compared with a 24‐h time‐course. Results indicated that perfusion pressure was a more significant factor in organ degradation than was temperature. In addition, a non‐linear degradation profile was identified for all three perfused livers, and this profile was different for individual livers, demonstrating the time‐dependent transition from its initial ‘healthy’ state towards a more ‘unhealthy’ degenerative state at 24 h. The Raman spectroscopy‐based approach described here has potential applications in perfusion and diagnostic instrumentation that can be used in near real‐time during organ transit and in operating rooms to help identify appropriately healthy organs for transplantation. Copyright © 2015 John Wiley & Sons, Ltd.     

一种基于拉曼光谱的石油产品快速分类方法

提出了一种基于拉曼光谱的石油产品快速分类方法.首先,利用经过谱图预处理的石油产品训练样本拉曼谱图构建模型知识库,计算各类别的特征拉曼谱图和类内阈值;其次,将石油产品测试样本的拉曼谱图经过相同的预处理,再计算其与各类别特征拉曼谱图的线性相关系数,若最大相关系数大于或等于最大相关系数对应类别的类内阈值,则该样本属于此类别....     

Raman spectroscopic analysis differentiates between breast cancer cell lines

Breast cancer incident rates are increasing in women worldwide with the highest incidence rates reported in developing countries. Major breast cancer screening approaches like mammography, ultrasound, clinical breast examination (CBE) and magnetic resonance imaging (MRI) are currently used but have their own limitations. Optical spectroscopy has attained great attention from biomedical researchers in recent years due to its non‐invasive and non‐destructive detection approach. Chemometrics is one of the powerful tools used in spectroscopic research to enhance its sensitivity. Raman spectroscopy, a vibrational spectroscopic approach, has been used to explore the chemical fingerprints of different biological tissues including normal and malignant types. This approach was used to characterize and differentiate two breast cancer and one normal breast cell lines (MDA‐MB‐436, MCF‐7 and MCF‐10A) using dispersive Raman spectroscopy. Raman spectra of the cell lines have revealed that basic differences in the concentration of biochemical compounds such as lipids, nucleic acids and protein Raman peaks were found to differ in intensity, and principal component analysis (PCA) was able to identify variations that lead to accurate and reliable separation of the three cell lines. Linear discriminant analysis (LDA) model of three cell lines was predicted with 100% sensitivity and 91% specificity. We have shown that a combination of Raman spectroscopy and chemometrics are capable of differentiation between breast cancer cell lines. These variations may be useful in identifying new spectral markers to differentiate different subtypes of breast cancer although this needs confirmation in a larger panel of cell lines as well as clinical material. Copyright © 2015 John Wiley & Sons, Ltd.     

Effect of supplementary manganese on the sporulation of Bacillus endospores analysed by Raman spectroscopy

It is a common practice in microbiology to induce and accelerate sporulation of spore‐forming bacteria by adding small amounts of divalent manganese to the cultivation medium. By micro‐Raman spectroscopy the effect of supplementary divalent manganese during the growth and sporulation of Bacillus spp. bacteria was studied. The spectral alterations in the Raman spectra of single endospores due to this cultivation parameter comprised slight alterations of the bands attributed to intracellular, abundantly present calcium dipicolinate (CaDPA). Those signals suffered a loss of intensity or partial band broadening because of the appearance of new weak signals next to them. Exclusively in Raman spectra of single B. sphaericuss endospores, the band at 1485 cm⁻¹ vanished. The theoretical spectra of CaDPA and manganese dipicolinate (MnDPA) were calculated and compared with the experimental spectra to prove the hypothesis that, while the overall intracellular DPA content decreased, an intracellular assembly of MnDPA in the endospores might also occur. Band shifts of the COO⁻ vibrations in the salt's spectra as well as in the endospore's spectra, and the decrease of the two CaDPA bands, confirmed this proposal. The appearance of the 1030 cm⁻¹ band in all Bacillus spectra as well as the disappearance of the 1485 cm⁻¹ band in the B. sphaericus spectra still needs to be clarified. With the help of two multivariate chemometric methods, these spectral alterations allowed discrimination between single endospores of different Bacillus strains cultivated on normal nutrient agar (NA) and those grown on NA with MnSO₄ · xH₂O addition. With these investigations, a possible strategy is shown to trace back the cultivation environment of matured single endospores. Utilizing the joint concept of micro‐Raman spectroscopy and chemometric analysis, the differentiation between natively grown endospores and those cultivated in a laboratory with the help of manganous salts as a common sporulation accelerator seems accomplishable. Copyright © 2009 John Wiley & Sons, Ltd.     

Assessment of robustness and transferability of classification models built for cancer diagnostics using Raman spectroscopy

Over recent years, Raman spectroscopy has been demonstrated as a prospective tool for application in cancer diagnostics. The use of Raman spectroscopy for this purpose relies on pattern recognition methods that have been developed to perform well on data achieved under laboratory conditions. However, the application of Raman spectroscopy as a routine clinical tool is likely to result in imperfect data due to instrument‐to‐instrument variation. Such corruption to the pure tissue spectral data is expected to negatively impact the classification performance of the diagnostic model. In this paper, we present a thorough assessment of the robustness of the Raman approach. This was achieved by perturbing a set of spectra in different ways, including various linear shifts, nonlinear shifts and random noise and using previously optimised classification models to predict the class membership of each spectrum in a testing set. The loss of predictive power with increased corruption was used to calculate a score, which allows an easy comparison of the model robustness. For this approach, three different types of classification models, including linear discriminant analysis (LDA), partial least square discriminant analysis (PLS‐DA) and support vector machine (SVM), built for lymph node diagnostics were the subject of the robustness testing. The results showed that a linear perturbation had the highest impact on the performance of all classification models. Among all linear corruption methods, a gradient y‐shift resulted in the highest performance loss. Thus, the factor most likely to affect the predictive outcome of models when using different systems is a gradient y‐shift. Copyright © 2010 John Wiley & Sons, Ltd.     

唾液表面增强拉曼光谱用于肺癌的诊断

表面增强拉曼光谱已经展示了其在低浓度体液的检测方面的优势。对21例肺癌患者和22例正常人的唾液进行了检测和区分。肺癌患者在多处波数位的峰强较正常人有所下降,只有少数峰强度上升且幅度较小。这些峰主要归属为蛋白质和核酸,表明肺癌患者唾液中这些成分的含量较正常人为少。主成分分析法(PCA)和线性辨别分析(LDA)被用于两组数据的降维和区分,所得结果准确度为84%,灵敏度为94%,特异性为81%。     

Different classification algorithms and serum surface enhanced Raman spectroscopy for noninvasive discrimination of gastric diseases

In this study, surface enhanced Raman spectroscopy (SERS) was used to investigate the spectral characteristics of blood serum for the purpose of diagnosing stomach diseases. SERS spectral data was collected from patients with atrophic gastritis, both pre‐operation and post‐operation gastric cancer, and from healthy individuals. Visual differences in the SERS spectra were observed between the four groups which indicate corresponding biomolecule concentration changes in blood. To further investigate the diagnostic ability of human serum, the spectral data was analyzed with three chemometric processes. These three methods extracted features and classified from the spectral data. Principal component analysis (PCA) was first performed to reduce the dimensionality of the original spectral data. Then, the classification methods support vector machine (SVM), linear discriminant analysis (LDA) and classification and regression tree (CART) were used for the evaluation of diagnostic ability. Accuracies of 96.5%, 88.8% and 87.1% were obtained for PCA‐SVM, PCA‐LDA and PCA‐CART, respectively. Copyright © 2016 John Wiley & Sons, Ltd.     

A novel method for urinary tract infection diagnosis and antibiogram using Raman spectroscopy

Urinary tract infection (UTI) diagnosis and antibiogram require a 48‐h waiting period using the standard conventional clinical methods. This long waiting period results in ineffective treatments, increased costs and, most importantly, in increased resistance to antibiotics. In this work, a novel method for classifying bacteria and determining their sensitivity to an antibiotic using Raman spectroscopy is described. Raman spectra belonging to three species of gram‐negative enterobacteria, which are most commonly responsible for UTIs, are classified with over 94% accuracy using novel feature extraction and discriminant analysis. Sensitivity to ciprofloxacin is also clearly evident as early as 2 h after treatment by differences in the Raman spectra of bacteria treated or not treated with this antibiotic. The proposed technique can become the basis for the development of new technology for UTI diagnosis and antibiogram with same day results, thus avoiding urine cultures and all undesirable consequences of current practice. Copyright © 2009 John Wiley & Sons, Ltd.     

Rapid screening of sildenafil and tadalafil adulterated in healthcare products by Micro‐Raman spectroscopy

Sildenafil and tadalafil are inhibitors of phosphodiesterase type 5, which are frequently added into healthcare products. The objective of this study was to evaluate the possibility of using micro‐Raman spectroscopy as a non‐destructive technique to screen for sildenafil and tadalafil in adulterated healthcare products. Using a viewing microscope, the suspect area of healthcare products was selected, which had a discernable crystal form or shape from the surrounding zone. Optimization of instrumental parameters of the Raman spectrometer was chosen to reduce the background fluorescence, and the Raman spectra were collected. The spectra collected were compared with the standard Raman spectra of pure sildenafil and tadalafil. Samples with an identifiable Raman signature to that of sildenafil or tadalafil could be confirmed using liquid chromatography–mass spectrometry (LC/MS). Additionally, wavelet denoising combined with similarity calculation was used to establish an automated approach for discrimination of adulterated healthcare products. Correlation coefficient was chosen for similarity calculation based on the spectra collected and the standard Raman spectra of pure sildenafil and tadalafil. We compared ten samples, secured by administrative authorities in Shanghai, to analyse and demonstrate the capabilities of our proposed method. We established six samples containing sildenafil or tadalafil warranting analysis using LC/MS. Thus, the use of micro Raman spectroscopy provides a quick, convenient and non‐destructive method for screening adulterated chemicals in healthcare products. Raman spectroscopy combined with similarity calculation requires little training after spectra library is developed, thus showing great promise to identify the adulterated healthcare products in the future. Copyright © 2012 John Wiley & Sons, Ltd.     

Characterisation and classification of automotive clear coats with Raman spectroscopy and chemometrics for forensic purposes

The clear coats from a collection of automotive paint samples of 139 vehicles, covering a range of Australian and international vehicle manufacturers and sold in Western Australia, were characterised using FT‐Raman spectroscopy. Principal component analysis (PCA) revealed 19 distinct classes that were associated with the vehicles' manufacturer and model, and in the case of Australian manufacturers, the years of manufacture. Linear discriminant analysis based on the PCA groupings gave excellent discrimination between the groups with 96.9% of the calibration set and 97.6% of the validation set being correctly classified. Although the sample set comprised only vehicles available in Australia, the methodology used is universal and hence applicable in any jurisdiction that is willing and able to generate a statistically significant data set and maintain and update it as new vehicles appear on the market. A FT‐Raman spectroscopy‐based database would rapidly provide information regarding vehicle origin and manufacture and hence generate investigative leads for questioned paint samples found at incident sites. Copyright © 2016 John Wiley & Sons, Ltd.     

Raman spectroscopy utilizing Fisher‐based feature selection combined with Support Vector Machines for the characterization of breast cell lines

Raman spectroscopy has the potential to significantly aid in the research and diagnosis of cancer. The information dense, complex spectra generate massive datasets in which subtle correlations may provide critical clues for biological analysis and pathological classification. Therefore, implementing advanced data mining techniques is imperative for complete, rapid and accurate spectral processing. Numerous recent studies have employed various data methods to Raman spectra for classification and biochemical analysis. Although, as Raman datasets from biological specimens are often characterized by high dimensionality and low sample numbers, many of these classification models are subject to overfitting. Furthermore, attempts to reduce dimensionality result in transformed feature spaces making the biological evaluation of significant and discriminative spectral features problematic. We have developed a novel data mining framework optimized for Raman datasets, called Fisher‐based Feature Selection Support Vector Machines (FFS‐SVM). This framework provides simultaneous supervised classification and user‐defined Fisher criterion‐based feature selection, reducing overfitting and directly yielding significant wavenumbers from the original feature space. Herein, we investigate five cancerous and non‐cancerous breast cell lines using Raman microspectroscopy and our unique FFS‐SVM framework. Our framework classification performance is then compared to several other frequently employed classification methods on four classification tasks. The four tasks were constructed by an unsupervised clustering method yielding the four different categories of cell line groupings (e.g. cancer vs non‐cancer) studied. FFS‐SVM achieves both high classification accuracies and the extraction of biologically significant features. The top ten most discriminative features are discussed in terms of cell‐type specific biological relevance. Our framework provides comprehensive cellular level characterization and could potentially lead to the discovery of cancer biomarker‐type information, which we have informally termed ‘Raman‐based spectral biomarkers’. The FFS‐SVM framework along with Raman spectroscopy will be used in future studies to investigate in‐situ dynamic biological phenomena. Copyright © 2013 John Wiley & Sons, Ltd.     

Use of thermo‐Raman spectroscopy and chemometric analysis to identify dehydration steps of hydrated inorganic samples—application to copper sulfate pentahydrate

In situ thermo‐Raman spectroscopy (TRS) measurements were performed in order to investigate solid‐phase transformation of the copper sulfate pentahydrate from room temperature up to 300 °C. Band‐target entropy minimization (BTEM), a blind‐source separation algorithm, was employed in order to identify and reconstruct the pure component spectra of the species involved in the dehydration process. In spite of low signal‐to‐noise ratio and elevated baseline spectral data, BTEM was successfully utilized to identify and reconstruct four pure component spectra of copper sulfate pentahydrate, trihydrate, monohydrate, and anhydrate, which were formed during this thermally induced process. Subsequent mapping of these four pure component spectral estimates back onto the preprocessed spectra yielded the relative concentrations of each individual species. Finally, the transition temperatures of each dehydration step could be unambiguously deduced from the obtained concentration profile. The current study shows that combined thermo‐Raman spectroscopy and chemometric analysis provides an effective tool to determine the dehydration temperatures as well as to identify the structures of each individual species involved in a solid‐phase dehydration process. Copyright © 2009 John Wiley & Sons, Ltd.     

Experimental and statistical analysis methods for peptide detection using surface‐enhanced Raman spectroscopy

Surface‐enhanced Raman spectroscopy (SERS) has the potential to make a significant impact in biology research due to its ability to provide information orthogonal to that obtained by traditional techniques such as mass spectrometry (MS). While SERS has been well studied for its use in chemical applications, detailed investigations with biological molecules are less common. In addition, a clear understanding of how methodology and molecular characteristics impact the intensity, the number of peaks, and the signal‐to‐noise of SERS spectra is largely missing. By varying the concentration and order of addition of the SERS‐enhancer salt (LiCl) with colloidal silver, we were able to evaluate the impact of these variables on peptide spectra using a quantitative measure of spectra quality based on the number of peaks and peak intensity. The LiCl concentration and order of addition that produced the best SERS spectra were applied to a panel of synthetic peptides with a range of charges and isoelectric points (pIs) where the pI was directly correlated with higher spectral quality. Those peptides with moderate to high pIs and spectra quality scores were differentiated from each other using the improved method and a hierarchical clustering algorithm. In addition, the same method and algorithm was applied to a set of highly similar phosphorylated peptides, and it was possible to successfully classify the majority of peptides on the basis of species‐specific peak differences. Copyright © 2008 John Wiley & Sons, Ltd.     

几种边界特征描述方法的比较研究

在图像处理与模式识别领域经常根据物体的边界来识别物体。对物体边界特征描述的三种主要方法,即基于极半径函数的傅里叶描述子、边界序列矩和改进的不变矩进行了比较研究,给出了实验结果和结论。     

玉米近红外光谱在仿生模式识别中的特征提取方法研究

近红外光谱特征提取是近红外光谱定性分析的关键步骤,其质量直接影响定性分析结果。采用漫透射方法测量8个玉米品种的近红外光谱,经预处理后,分别采用PCA,ICA,PLS-DA和小波分解四种方法对光谱进行特征提取,并使用仿生模式识别方法建立了8个玉米品种识别模型,最后使用测试集数据进行模型测试。结论如下:使用PLS-DA方法进行特征提取后建立的模型正确识别率优于使用PCA,ICA和小波分解特征提取后建立的模型。     

Local structure of hydroxy-peroxy apatite: A combined XRD, FT-IR, Raman, SEM, and solid-state NMR study

The synthesized hydroxyapatite (HAp) and hydroxy-peroxy apatite are studied using various techniques, such as X-ray powder diffraction, FT-IR and Raman spectroscopy, scanning electron microscopy, and solid-state NMR spectroscopy. The experimental data suggest that hydroxy-peroxy apatite contains a small amount of hydration of partially dehydroxylated hydroxyapatite phase and calcium hydroxide. The incorporation of peroxide ions into the lattice of HAp causes perturbations of the hydrogen environments and slight changes in its crystal morphology. The distance between H in some structural OH⁻ and adjacent O along the c-axis becomes longer instead of forming a hydrogen bond after the incorporation of peroxide ions.     

Raman Spectroscopy for Protein Analysis

Abstract

Proteins are essential parts of organisms and participate in virtually every process within cells. Raman spectroscopy can be a powerful tool for the characterization of modified amino acids and proteins. In addition to the potential for quantitative results, it offers the advantage of not requiring any sample preparation. This review focuses on applications for protein analysis, published in the period 2010–2014.