Principal Component Linear Discriminant Analysis Research Articles

Confocal Raman microspectral analysis and imaging of the drug response of osteosarcoma to cisplatin.

Confocal Raman microspectral analysis and imaging were used to elucidate the drug response of osteosarcoma (OS) to cisplatin. Raman spectral data were obtained from OS cells that were untreated (UT group) and treated with 20 µM (20T group) and 40 µM (40T group) cisplatin for 24 hours. Statistical analysis of the changes in specific Raman signals was performed using a one-way ANOVA and multiple Tukey's honest significant difference (HSD) post hoc tests. Principal component analysis-linear discriminant analysis (PCA-LDA) was used to highlight the featured cellular drug responses based on the obtained spectral information. For spectral imaging analysis, k-means cluster analysis (KCA) was adopted to clarify the effect of cisplatin dose changes on the subcellular structure and its biochemical composition. The results suggest that the major biochemical changes induced by cisplatin in OS cells undergoing apoptosis are reduced protein and nucleic acid content. Through univariate analysis, the changes in the distribution of nucleic acids in OS cells induced by different doses of cisplatin were obtained. The combination of Raman spectroscopy and multivariate analysis shows that cisplatin mainly acts on the nucleus and causes changes in the secondary structure of proteins. These results indicate that Raman imaging technology has the potential to offer the basis of dose optimization for personalized cancer treatment by helping to understand in vitro cellular drug interactions.

Applicability of Raman spectroscopy on porcine parvovirus and porcine circovirus type 2 detection

Porcine parvovirus (PPV) is one of the major infectious causes of reproductive failure of swine. This disease is characterized by embryonic and fetal infection and death, responsible for important economic losses. PPV is also implicated as a trigger in the development of post-weaning multisystemic wasting syndrome (PMWS) caused by Porcine circovirus type 2 (PCV2). Their detection is PCR-based, which is quite sensitive and specific, but laborious, costly and time-demanding. Therefore, this study aimed to assess Raman spectroscopy (RS) as a diagnostic tool for PPV and PCV2 due to its label-free properties and unique ability to search and identify molecular fingerprints. Briefly, swine testis (ST) cells were inoculated with PPV or PCV2 and in vitro cultured (37 °C, 5% CO2) for four days. Fixed cells were then submitted to RS investigation using a 633 nm laser. A total of 225 spectra centered at 1300 cm−1 was obtained for each sample (5 spectra/cell; 15 cells/replicate; 3 replicates) of PPV-, PCV2-infected and uninfected (control) ST cells. Clear statistical discrimination between samples from both virus-infected cells was achieved with a Principal Component – Linear Discriminant Analysis (PCA-LDA) model, reaching sensitivity rates from 95.55% to 97.77%, respectively to PCV2- and PPV-infected cells. These results were then submitted to a Leave-One-Out (LOO) validation algorithm resulting in 99.97% of accuracy. Extensive band assignment was analyzed and compiled for better understanding of PPV and PCV2 virus-cell interaction, demonstrating that specific protein, lipids and DNA/RNA bands are the most important assignments related to discrimination of virus-infected from uninfected cells. In conclusion, these results represent promising bases for RS application on PCV2 and PPV detection for future diagnostic applications.

On the discrimination of soil samples by derivative diffuse reflectance UV–vis-NIR spectroscopy and chemometric methods

The derivative diffuse reflectance UV–vis-NIR spectroscopy combined with the multivariate methods are utilized for the discrimination and classification of the soil samples collected from the north-western part of India. The acquired spectra reveal the presence of different organic and inorganic minerals such as humic acid, fulvic acid, hematite, etc. in varying amounts. The differentiation/segregation among soil samples is achieved by peak comparison and chemometric methods like clustering algorithm and principal component analysis (PCA). Among these, the PCA method gives clear discrimination of soil samples. The developed PCA model is further validated by analyzing unknown samples for the prediction to their respective clusters significantly. Principal component linear discriminant analysis (PC-LDA) based discriminant model is developed to classify the unknown soil samples to its respective groups. PC-LDA based model reveals 95 % accurate clustering of the soil by the leave-one-out cross-validation approach.

Repeated double cross-validation applied to the PCA-LDA classification of SERS spectra: a case study with serum samples from hepatocellular carcinoma patients

Intense label-free surface-enhanced Raman scattering (SERS) spectra of serum samples were rapidly obtained on Ag plasmonic paper substrates upon 785 nm excitation. Spectra from the hepatocellular carcinoma (HCC) patients showed consistent differences with respect to those of the control group. In particular, uric acid was found to be relatively more abundant in patients, while hypoxanthine, ergothioneine, and glutathione were found as relatively more abundant in the control group. A repeated double cross-validation (RDCV) strategy was applied to optimize and validate principal component analysis-linear discriminant analysis (PCA-LDA) models. An analysis of the RDCV results indicated that a PCA-LDA model using up to the first four principal components has a good classification performance (average accuracy was 81%). The analysis also allowed confidence intervals to be calculated for the figures of merit, and the principal components used by the LDA to be interpreted in terms of metabolites, confirming that bands of uric acid, hypoxanthine, ergothioneine, and glutathione were indeed used by the PCA-LDA algorithm to classify the spectra.

Stromal vapors for real-time molecular guidance of breast-conserving surgery

Achieving radical tumor resection while preserving disease-free tissue during breast-conserving surgery (BCS) remains a challenge. Here, mass spectrometry technologies were used to discriminate stromal tissues reported to be altered surrounding breast tumors, and build tissue classifiers ex vivo. Additionally, we employed the approach for in vivo and real-time classification of breast pathology based on electrosurgical vapors. Breast-resected samples were obtained from patients undergoing surgery at MUMC+. The specimens were subsequently sampled ex vivo to generate electrosurgical vapors analyzed by rapid evaporative ionization mass spectrometry (REIMS). Tissues were processed for histopathology to assign tissue components to the mass spectral profiles. We collected a total of 689 ex vivo REIMS profiles from 72 patients which were analyzed using multivariate statistical analysis (principal component analysis-linear discriminant analysis). These profiles were classified as adipose, stromal and tumor tissues with 92.3% accuracy with a leave-one patient-out cross-validation. Tissue recognition using this ex vivo-built REIMS classification model was subsequently tested in vivo on electrosurgical vapors. Stromal and adipose tissues were classified during one BCS. Complementary ex vivo analyses were performed by REIMS and by desorption electrospray ionization mass spectrometry (DESI-MS) to study the potential of breast stroma to guide BCS. Tumor border stroma (TBS) and remote tumor stroma (RTS) were classified by REIMS and DESI-MS with 86.4% and 87.8% accuracy, respectively. We demonstrate the potential of stromal molecular alterations surrounding breast tumors to guide BCS in real-time using REIMS analysis of electrosurgical vapors.

Novel Quantitative Analysis Using Optical Imaging (VELscope) and Spectroscopy (Raman) Techniques for Oral Cancer Detection.

Simple SummaryOur study aims to develop a novel quantitative analysis method that can increase the oral cancer detection rate for screening oral cancer. We used two different optical techniques, a light-based detection technique (VELScope) and a vibrational spectroscopic technique (Raman spectroscopy). First, we analyzed and evaluated the performance of these two techniques individually using PCA–LDA, and PCA–QDA classifiers. The PCA–LDA of Raman spectroscopy had 82.9% accuracy, 80% sensitivity, and 85.7% specificity, while the region of interests on the autofluorescence images were differentiated with 90% accuracy, 100% sensitivity, and 80% specificity. Afterward, we combined both techniques and evaluated their performance. The combination of two optical techniques can differentiate the cancer and normal groups with 97.14% accuracy, 100% sensitivity, and 94.3% specificity. The main advantage of our study is that we can confirm our results by using two different techniques that are completely independent of each other. That is the reason that the combination of two techniques can increase the sensitivity and specificity.In this study, we developed a novel quantitative analysis method to enhance the detection capability for oral cancer screening. We combined two different optical techniques, a light-based detection technique (visually enhanced lesion scope) and a vibrational spectroscopic technique (Raman spectroscopy). Materials and methods: Thirty-five oral cancer patients who went through surgery were enrolled. Thirty-five cancer lesions and thirty-five control samples with normal oral mucosa (adjacent to the cancer lesion) were analyzed. Thirty-five autofluorescence images and 70 Raman spectra were taken from 35 cancer and 35 control group cryopreserved samples. The normalized intensity and heterogeneity of the 70 regions of interest (ROIs) were calculated along with 70 averaged Raman spectra. Linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA) were used with principal component analysis (PCA) to differentiate the cancer and control groups (normal). The classifications rates were validated using two different validation methods, leave-one-out cross-validation (LOOCV) and k-fold cross-validation. Results: The cryopreserved normal and tumor tissues were differentiated using the PCA–LDA and PCA–QDA models. The PCA–LDA of Raman spectroscopy (RS) had 82.9% accuracy, 80% sensitivity, and 85.7% specificity, while ROIs on the autofluorescence images were differentiated with 90% accuracy, 100% sensitivity, and 80% specificity. The combination of two optical techniques differentiated cancer and normal group with 97.14% accuracy, 100% sensitivity, and 94.3% specificity. Conclusion: In this study, we combined the data of two different optical techniques. Furthermore, PCA–LDA and PCA–QDA quantitative analysis models were used to differentiate tumor and normal groups, creating a complementary pathway for efficient tumor diagnosis. The error rates of RS and VELcope analysis were 17.10% and 10%, respectively, which was reduced to 3% when the two optical techniques were combined.

Wavelength-dependent label-free identification of isolated nontuberculous mycobacteria using surface-enhanced Raman spectroscopy

We investigated the effect of Raman excitation wavelengths on the surface-enhanced Raman spectroscopy (SERS)-based identification of isolated nontuberculous mycobacteria (NTM). The SERS spectra with 3 commonly used excitation wavelengths, 532, 638, and 785 nm, were compared across 6 representative NTM species that primarily cause human NTM infections in Korea and the United States; these species were identified. The statistical differences among NTM SERS spectra at each Raman excitation wavelength were verified using 1-way analysis of variance, and the 6 NTM species were identified using principal components-linear discriminant analysis with leave-one-out cross validation. The identification accuracies with aromatic amino acid biomarkers were 99.3%, 91.3%, and 90.7% for 532, 638, and 785 nm, respectively. We believe that the proposed SERS protocol with aromatic amino acid biomarkers at the 532-nm Raman excitation wavelength will enable fast and accurate identification of NTM compared to previous identification methods.

Surface-enhanced Raman scattering for the diagnosis of ulcerative colitis: will it change the rules of the game?

Ulcerative colitis (UC) is a relapsing-remitting inflammatory bowel disease that requires numerous costly invasive investigations which lead to physical and psychological patient discomfort. We need a non-invasive technological approach that would significantly improve its diagnosis. Surface-enhanced Raman scattering (SERS) is a growing technique that can provide a molecular diagnostic fingerprint in just a few minutes, without the need for prior sample preparation. The aim of this pilot in vivo study was to prove that multivariate analysis of SER spectra collected on plasma samples could be employed for non-invasive diagnosis of UC. Plasma samples were collected from healthy subjects (n = 35) and patients with UC (n = 28). SERS spectra were acquired using a 785-nm excitation laser line and a solid plasmonic substrate developed in our laboratory using an original procedure described in the literature. The classification accuracy yielded by SERS was assessed by principal component analysis-linear discriminant analysis (PCA-LDA) and partial least squares discriminant analysis (PLS-DA). PCA-LDA differentiated UC samples from those of healthy subjects with a sensitivity of 86%, a specificity of 92%, and an accuracy of 89%, the AUC being 0.96. The PLS-DA analysis resulted in a sensitivity of 89%, a specificity of 94%, an accuracy of 92%, and an AUC value of 0.92. Several spectral bands were associated with UC: 376-420, 440-513, 686-715, 919-939, 1035-1062, 1083-1093, 1120-1132, 1148-1156, 1191-1211, 1234-1262, 1275-1294, 1382-1405, 1511-1526, and 1693-1702cm-1. Changes in plasma levels of amino acids, proteins, lipids, and other compounds were noted using SERS in patients with UC. Multivariate analysis of SER spectra collected on a solid plasmonic substrate represents a promising alternative to diagnosing UC, as it is non-invasive, easy to use, and fast.

Rapid Authentication of 100% Italian Durum Wheat Pasta by FT-NIR Spectroscopy Combined with Chemometric Tools.

Italy is the country with the largest durum wheat pasta production and consumption. The mandatory labelling for pasta indicating the country of origin of wheat has made consumers more aware about the consumed pasta products and is influencing their choice towards 100% Italian wheat pasta. This aspect highlights the need to promote the use of domestic wheat as well as to develop rapid methodologies for the authentication of pasta. A rapid, inexpensive, and easy-to-use method based on infrared spectroscopy was developed and validated for authenticating pasta made with 100% Italian durum wheat. The study was conducted on pasta marketed in Italy and made with durum wheat cultivated in Italy (n = 176 samples) and on pasta made with mixtures of wheat cultivated in Italy and/or abroad (n = 185 samples). Pasta samples were analyzed by Fourier transform-near infrared (FT-NIR) spectroscopy coupled with supervised classification models. The good performance results of the validation set (sensitivity of 95%, specificity and accuracy of 94%) obtained using principal component-linear discriminant analysis (PC-LDA) clearly demonstrated the high prediction capability of this method and its suitability for authenticating 100% Italian durum wheat pasta. This output is of great interest for both producers of Italian pasta pointing toward authentication purposes of their products and consumer associations aimed to preserve and promote the typicity of Italian products.

<p><strong><em>Linaria semialata</em> and <em>L. amethystea </em>subsp.<em> aedoi</em>,<em> </em>two new taxa of <em>L.</em> sect. <em>Supinae </em>(<em>Plantaginaceae</em>) from Southeastern Iberian Peninsula</strong></p>

Two new taxa of the genus Linaria are described, illustrated and compared, through a morphometric analysis, to morphologically similar species from L. sect. Supinae: L. amethystea, L. diffusa, L. intricata, and L. orbensis. A Principal Component Analysis and Linear Discriminant Analysis were carried out in order to find out which morphological characters were the most important to discriminate these species. Morphometric analyses revealed that discrimination of species were more related to their particular morphological character combinations than to a private character. Two taxa are described as new to science, by providing a detailed description, illustrations, scanning-electron micrographs and an identification key.

Label-free detection of high-risk human papillomaviruses infection using Raman spectroscopy and multivariate analysis

Persistent infection with high-risk human papillomavirus (HR-HPV) is considered as the leading cause of pre-cervical cancer and cancer. Currently, HPV testing is expensive, time-consuming, and requires experienced technicians. Here, we report a method for label-free detection of HR-HPV by recording and analyzing Raman spectroscopy (RS) of cervical exfoliated cell specimens. A total of 315 Raman spectra (165 normal, 150 HR-HPV) were recorded from 63 subjects (33 normal samples, 30 HR-HPV positive patients). Mean Raman spectra differed significantly between normal and HR-HPV positive groups. The differences showed an increase in the relative contents of hemoglobin and tryptophan, and new peaks appeared due to cell-level infection in HR-HPV positive group. Furthermore, a principal component analysis-linear discriminant analysis diagnostic model was developed and applied on the Raman spectra of HR-HPV positive patients as well as normal samples, and satisfactory classification results were obtained. Raman diagnostic performance was evaluated through the leave-one-spectrum-out cross-validation (LOSOCV) or leave-one-patient-out cross-validation (LOPOCV) method. The diagnostic accuracy, sensitivity and specificity of LOSOCV and LOPOCV were 99.4%, 99.4%, 99.3% and 98.4%, 100%, 96.7%, respectively. Interestingly, the use of LOSOCV or LOPOCV method yielded similar classification results. The results showed that the use of multiple data points from the same sample would not lead to biases necessarily impacting the reliability of the classification models. Furthermore, our exploratory work demonstrated that RS combined with multivariate analysis might provide a new method for clinical detection of patients with HR-HPV infection.

Novel nondestructive NMR method aided by artificial neural network for monitoring the flavor changes of garlic by drying

Flavor changes of garlic during drying process were monitored using LF-NMR combined with partial least squares (PLS) and back-propagation artificial neural network (BP-ANN). Results show that with elapsed drying time, the free water (A23) and total moisture content (A) of garlic decrease with different drying conditions. Correspondingly, the sulfide of main flavor components in garlic was significantly reduced, but alcohol and acid components increased slightly and the overall aromatic flavor showed a downward trend, which was consistent with the GC-MS volatile component detection results. Electronic nose sensors S2, S5, S8, S10 were determined as feature sensors by principal component analysis (PCA) and linear discriminant analysis (LDA). The univariate linear model of NMR parameters and electronic nose characteristic sensors show high correlation. Furthermore, ANN and PLS garlic flavor prediction model were established, although the PLS model was not as good as the BP-ANN model (RP 2 of 0.9713 and 0.9975) to monitor flavor changes, it also yields relatively accurate prediction performance with RP 2 of 0.9418 and 0.9633 for mid-shortwave infrared drying and microwave vacuum drying, respectively.

Unveiling dose- and time-dependent osteosarcoma cell responses to the γ-secretase inhibitor, DAPT, by confocal Raman microscopy.

Using confocal Raman micro-spectroscopy, this study aims to elucidate the cellular responses of the γ-secretase inhibitor, N-[N-(3,5-difluorophenacetyl)-L-alanyl]-S-phenylglycine t-butyl ester (DAPT), in osteosarcoma (OS) cells in a dose- and time-dependent manner. The K7M2 murine OS cell line was treated with different DAPT doses (0, 10, 20, and 40 μM) for 24 and 48 hours before investigations. Significant compositional changes (nucleic acids, protein and lipid) after DAPT treatment were addressed, which testified inhibitory effect of DAPT on the growth of OS cells. Moreover, both partial least squares-discriminant analysis (PLS-DA) and principal component analysis-linear discriminant analysis (PCA-LDA) analyses revealed governing composition variations among groups by distinguishing their spectral characteristics. Furthermore, by adopting leave-one-out cross validation method, it is shown that PLS-DA exhibited more classification capacity than PCA-LDA algorithm. Hence, by understanding the DAPT-based cellular variations, the achieved results provided an experimental foundation to establish new DAPT-based anticancer therapeutic strategies, and preclinical Raman analytical methodologies on drug-cell interactions.

Iterative taxonomic study of Pareiorhaphis hystrix (Siluriformes, Loricariidae) suggests a single, yet phenotypically variable, species in south Brazil.

Pareiorhaphis hystrix is a widely distributed species, occurring in the upper and middle Uruguay River and in the Taquari River basin, Patos Lagoon system, southern Brazil. Morphological variation has been detected throughout the distribution of P. hystrix, and this work seeks to test the conspecific nature of populations in several occurrence areas. Specimens from six areas in the Uruguay River basin and three in the Taquari River basin were compared. Variance analysis (ANOVA) was performed for the meristic data, and Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) were conducted for morphometric data. Molecular analyses used coI, cytb, 12S and 16S mitochondrial genes, examining nucleotide diversity, haplotype diversity, genetic distance, and delimitation of possible multiple species through the Generalized Mixed Yule Coalescent (GMYC) method. Phylogenetic relationships of studied populations were also investigated through Bayesian inference. While PCA indicated a tendency of overlap between areas, ANOVA and LDA detected a subtle differentiation between populations from the two hydrographic basins. Yet, both latter analyses recovered the population from Pelotas River, a tributary to Uruguay River, as more similar to populations from Taquari River, which is congruent to morphological observations of anterior abdominal plates. The molecular data indicated a nucleotide diversity lower than the haplotypic diversity, suggestive of recent expansion. The concatenated haplotype network points to slight differentiation between areas, with each locality presenting unique and non-shared haplotypes, although with few mutational steps in general. The species delimitation by coalescence analysis suggested the presence of a variable number of OTUs depending on the inclusion or exclusion of an outgroup. In general, the morphological data suggest a subtle variation by river basin, while the genetic data indicates a weak population structuration by hydrographic areas, especially the Chapecó and Passo Fundo rivers. However, there is still not enough differentiation between the specimens to suggest multiple species. The iterative analyses indicate that Pareiorhaphis hystrix is composed of a single, although variable, species.

Geographical Origin Discrimination of Monofloral Honeys by Direct Analysis in Real Time Ionization-High Resolution Mass Spectrometry (DART-HRMS).

An untargeted method using direct analysis in real time and high resolution mass spectrometry (DART-HRMS) combined to multivariate statistical analysis was developed for the discrimination of two monofloral (chestnut and acacia) honeys for their geographical origins—i.e., Italy and Portugal for chestnut honey and Italy and China for acacia honey. Principal Component Analysis, used as an unsupervised approach, showed samples of clusterization for chestnut honey samples, while overlapping regions were observed for acacia honeys. Three supervised statistical approaches, such as Principal Components—Linear Discriminant Analysis, Partial Least Squares—Discriminant Analysis and k-nearest neighbors, were tested on the dataset gathered and relevant performances were compared. All tested statistical approaches provided comparable prediction abilities in cross-validation and external validation with mean values falling between 89.2–98.4% for chestnut and between 85.8–95.0% for acacia honey. The results obtained herein indicate the feasibility of the DART-HRMS approach in combination with chemometrics for the rapid authentication of honey’s geographical origin.

Data fusion by ratio modulation of stable isotope, multi-element, and fatty acids to improve geographical traceability of lamb

There is a growing enthusiasm among consumers for high value lamb, especially the lamb with certain geographical indications, which may result in possible fraudulent labeling due to the opportunity for large economic profits. On such a basis, the aim of this study was to improve the discrimination rate of geographical traceability of lamb. To achieve this objective, lamb samples obtained from four different regions from China and New Zealand were analyzed. The chemical profiles of lamb samples from different origins were characterized by stable isotope analysis, multi-element determination, and fatty-acid profile, and further subjected to a series of statistical analyses, including principal component analysis, hierarchical cluster analysis, and principal component analysis-linear discriminant analysis. Compared with the result obtained by using each method individually, data fusion by ratio modulation of multi-technique clearly improved the discrimination performance for origin traceability. A satisfactory discrimination rate of 100% was obtained after ratio modulation of these techniques. Comprehensively, this study offers proof of concept for a promising method that could be used for lamb provenance authentication and fraud detection.

Changes in Amino Acid and Acylcarnitine Plasma Profiles for Distinguishing Patients with Multiple Sclerosis from Healthy Controls.

McDonald criteria and magnetic resonance imaging (MRI) are used for the diagnosis of multiple sclerosis (MS); nevertheless, it takes a considerable amount of time to make a clinical decision. Amino acid and fatty acid metabolic pathways are disturbed in MS, and this information could be useful for diagnosis. The aim of our study was to find changes in amino acid and acylcarnitine plasma profiles for distinguishing patients with multiple sclerosis from healthy controls. We have applied a targeted metabolomics approach based on tandem mass-spectrometric analysis of amino acids and acylcarnitines in dried plasma spots followed by multivariate statistical analysis for discovery of differences between MS (n = 16) and control (n = 12) groups. It was found that partial least square discriminant analysis yielded better group classification as compared to principal component linear discriminant analysis and the random forest algorithm. All the three models detected noticeable changes in the amino acid and acylcarnitine profiles in the MS group relative to the control group. Our results hold promise for further development of the clinical decision support system.

Solid Plasmonic Substrates for Breast Cancer Detection by Means of SERS Analysis of Blood Plasma.

Surface enhanced Raman spectroscopy (SERS) represents a promising technique in providing specific molecular information that could have a major impact in biomedical applications, such as early cancer detection. SERS requires the presence of a suitable plasmonic substrate that can generate enhanced and reproducible diagnostic relevant spectra. In this paper, we propose a new approach for the synthesis of such a substrate, by using concentrated silver nanoparticles purified using the Tangential Flow Filtration method. The capacity of our substrates to generate reproducible and enhanced Raman signals, in a manner that can allow cancer detection by means of Multivariate Analysis (MVA) of Surface Enhanced Raman (SER) spectra, has been tested on blood plasma samples collected from 35 healthy donors and 29 breast cancer patients. All the spectra were analyzed by a combined Principal Component-Linear Discriminant Analysis. Our results facilitated the discrimination between healthy donors and breast cancer patients with 90% sensitivity, 89% specificity and 89% accuracy. This is a direct consequence of substrates’ ability to generate diagnostic relevant spectral information by performing SERS measurements on pristine blood plasma samples. Our results suggest that this type of solid substrate could be employed for the detection of other types of cancer or other diseases by means of MVA-SERS procedure.

Raman Spectroscopy: An Exploratory Study to Identify Post-Radiation Cell Survival.

Resistance to radiotherapy has been an impediment in the treatment of cancer, and the inability to detect it at an early stage further exacerbates the prognosis. We have assessed the feasibility of Raman spectroscopy as a rapid assay for predicting radiosensitivity of cancer cells in comparison to the conventional biological assays. Cell lines derived from breast adenocarcinoma (MCF7), gingivobuccal squamous cell carcinoma (ITOC-03), and human embryonic kidney (HEK293) were subjected to varying doses of ionizing radiation. Cell viability of irradiated cells was assessed at different time points using MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide) assay and Raman spectroscopy, and colony-forming capability was evaluated by clonogenic assay. Radiosensitivity observed using MTT assay was limited by the finding of similar cell viability in all the three cell lines 24 h post-irradiation. However, cell survival assessed using clonogenic assay and principal component linear discriminant analysis (PC-LDA) classification of Raman spectra showed correlating patterns. Irradiated cells showed loss of nucleic acid features and enhancement of 750 cm-1 peak probably attributing to resonance Raman band of cytochromes in all three cell lines. PC-LDA analysis affirmed MCF7 to be a radioresistant cell line as compared to ITOC-03 and HEK293 to be the most radiosensitive cell line. Raman spectroscopy is shown to be a rapid and alternative assay for identification of radiosensitivity as compared to the gold standard clonogenic assay.

Filter-Membrane-Based Ultrafiltration Coupled with Surface-Enhanced Raman Spectroscopy for Potential Differentiation of Benign and Malignant Thyroid Tumors from Blood Plasma.

ObjectiveThe objective of this study is to evaluate the performance and feasibility of surface-enhanced Raman spectroscopy coupled with a filter membrane and advanced multivariate data analysis on identifying and differentiating benign and malignant thyroid tumors from blood plasma.Patients and MethodsWe proposed a membrane filter SERS technology for the differentiation between benign thyroid tumor and thyroid cancer. That is to say, by using filter membranes with optimal pore size, the blood plasma samples from thyroid tumor patients were pretreated with the macromolecular proteins being filtered out prior to SERS measurement. The SERS spectra of blood plasma ultrafiltrate obtained using filter membranes from 102 patients with thyroid tumors (70 thyroid cancers and 32 benign thyroid tumors) were then analyzed and compared. Two multivariate statistical analyses, principal component analysis-linear discriminate analysis (PCA-LDA) and Lasso-partial least squares-discriminant analysis (Lasso-PLS-DA), were performed on the SERS spectral data after background subtraction and normalization, as well as the first derivative processing, to analyze and compare the differential diagnosis of benign thyroid tumors and thyroid cancer.ResultsSERS measurements were performed in blood plasma acquired from a total of 102 thyroid tumor patients (benign thyroid tumor N=32; thyroid cancer N=70). By using filter membranes, the macromolecular proteins in blood plasma were effectively filtered out to yield high-quality SERS spectra. 84.3% discrimination accuracy between benign and malignant thyroid tumor was achieved using PCA-LDA method, while Lasso-PLS-DA yields a discrimination accuracy of 90.2%.ConclusionOur results demonstrate that SERS spectroscopy, coupled with ultrafiltration and multivariate analysis has the potential of providing a non-invasive, rapid, and objective detection and differentiation of benign and malignant thyroid tumors.