Raman spectroscopy of blood plasma samples from breast cancer patients at different stages.

Raman spectroscopy was employed for the characterization of blood plasma samples from patients at different stages of breast cancer. Blood plasma samples taken from clinically diagnosed breast cancer patients were compared with healthy controls using multivariate data analysis techniques (principal components analysis - PCA) to establish Raman spectral features which can be considered spectral markers of breast cancer development. All the stages of the disease can be differentiated from normal samples. It is also found that stage 2 and 3 are biochemically similar, but can be differentiated from each other by PCA. The Raman spectral data of the stage 4 is found to be biochemically distinct, but very variable between patients. Raman spectral features associated with DNA and proteins were identified, which are exclusive to patient plasma samples. Moreover, there are several other spectral features which are strikingly different in the blood plasma samples of different stages of breast cancer. In order to further explore the potential of Raman spectroscopy as the basis of a minimally invasive screening technique for breast cancer diagnosis and staging, PCA-Factorial Discriminant Analysis (FDA) was employed to classify the Raman spectral datasets of the blood plasma samples of the breast cancer patients, according to different stages of the disease, yielding promisingly high values of sensitivity and specificity for all stages.

Raman spectral analysis for rapid screening of dengue infection.

Infection with the dengue virus is currently clinically detected according to different biomarkers in human blood plasma, commonly measured by enzyme linked immunosorbent assays, including non-structural proteins (Ns1), immunoglobulin M (IgM) and immunoglobulin G (IgG). However, there is little or no mutual correlation between the biomarkers, as demonstrated in this study by a comparison of their levels in samples from 17 patients. As an alternative, the label free, rapid screening technique, Raman spectroscopy has been used for the characterisation/diagnosis of healthy and dengue infected human blood plasma samples. In dengue positive samples, changes in specific Raman spectral bands associated with lipidic and amino acid/protein content are observed and assigned based on literature and these features can be considered as markers associated with dengue development. Based on the spectroscopic analysis of the current, albeit limited, cohort of samples, Principal Components Analysis (PCA) coupled Factorial Discriminant Analysis, yielded values of 97.95% sensitivity and 95.40% specificity for identification of dengue infection. Furthermore, in a comparison of the normal samples to the patient samples which scored low for only one of the biomarker tests, but high or medium for either or both of the other two, PCA-FDA demonstrated a sensitivity of 97.38% and specificity of 86.18%, thus providing an unambiguous screening technology.

The combination of four analytical methods to explore skeletal muscle metabolomics: Better coverage of metabolic pathways or a marketing argument?

OBJECTIVES: Metabolomics is an emerging science based on diverse high throughput methods that are rapidly evolving to improve metabolic coverage of biological fluids and tissues. Technical progress has led researchers to combine several analytical methods without reporting the impact on metabolic coverage of such a strategy. The objective of our study was to develop and validate several analytical techniques (mass spectrometry coupled to gas or liquid chromatography and nuclear magnetic resonance) for the metabolomic analysis of small muscle samples and evaluate the impact of combining methods for more exhaustive metabolite covering. DESIGN AND METHODS: We evaluated the muscle metabolome from the same pool of mouse muscle samples after 2 metabolite extraction protocols. Four analytical methods were used: targeted flow injection analysis coupled with mass spectrometry (FIA-MS/MS), gas chromatography coupled with mass spectrometry (GC-MS), liquid chromatography coupled with high-resolution mass spectrometry (LC-HRMS), and nuclear magnetic resonance (NMR) analysis. We evaluated the global variability of each compound i.e., analytical (from quality controls) and extraction variability (from muscle extracts). We determined the best extraction method and we reported the common and distinct metabolites identified based on the number and identity of the compounds detected with low analytical variability (variation coefficient<30%) for each method. Finally, we assessed the coverage of muscle metabolic pathways obtained. RESULTS: Methanol/chloroform/water and water/methanol were the best extraction solvent for muscle metabolome analysis by NMR and MS, respectively. We identified 38 metabolites by nuclear magnetic resonance, 37 by FIA-MS/MS, 18 by GC-MS, and 80 by LC-HRMS. The combination led us to identify a total of 132 metabolites with low variability partitioned into 58 metabolic pathways, such as amino acid, nitrogen, purine, and pyrimidine metabolism, and the citric acid cycle. This combination also showed that the contribution of GC-MS was low when used in combination with other mass spectrometry methods and nuclear magnetic resonance to explore muscle samples. CONCLUSION: This study reports the validation of several analytical methods, based on nuclear magnetic resonance and several mass spectrometry methods, to explore the muscle metabolome from a small amount of tissue, comparable to that obtained during a clinical trial. The combination of several techniques may be relevant for the exploration of muscle metabolism, with acceptable analytical variability and overlap between methods However, the difficult and time-consuming data pre-processing, processing, and statistical analysis steps do not justify systematically combining analytical methods.

Chemotherapeutic efficiency of drugs in vitro: Comparison of doxorubicin exposure in 3D and 2D culture matrices.

The interest in the use of 3D matrices for in vitro analysis, with a view to increasing the relevance of in vitro studies and reducing the dependence on in vivo studies, has been growing in recent years. Cells grown in a 3D in vitro matrix environment have been reported to exhibit significantly different properties to those in a conventional 2D culture environment. However, comparison of 2D and 3D cell culture models have recently been noted to result in differing responses of cytotoxic assays, without any associated change in viability. The effect was attributed to differing conversion rates and effective concentrations of the resazurin assay in 2D and 3D environments, rather than differences in cellular metabolism. In this study, the efficacy of a chemotherapeutic agent, doxorubicin, is monitored and compared in conventional 2D and 3D collagen gel exposures of immortalized human cervical cells. Viability was monitored with the aid of the Alamar Blue assay and drug internalisation was verified using confocal microscopy. Drug uptake and retention within the collagen matrix was monitored by absorption spectroscopy. The viability studies showed apparent differences between the 2D and 3D culture systems, the differences attributed in part to the physical transition from 2D to a 3D environment causing alterations to dye resazurin uptake and conversion rates. The use of 3D culture matrices has widely been interpreted to result in "reduced" toxicity or cellular "resistance" to the chemotherapeutic agent. The results of this study show that the reduced efficiency of the drug to cells grown in the 3D environment can be accounted for by a sequential reduction of the effective concentration of the test compound and assay. This is due to absorption within the collagen gel inducing a higher uptake of both drug and assay thereby influencing the toxic impact of the drug and conversion rate of resazurin, and. The increased effective surface area of the cell exposed to the drug and assay in the 3D environment. The effect was noted to be higher after shorter exposure periods and should be accounted for in in vitro 2D and 3D culture environment comparisons.

Cellular discrimination using in vitro Raman micro spectroscopy: the role of the nucleolus.

Raman micro spectroscopy has attracted considerable attention over the last few years to explore its possible clinical applications as a non-invasive powerful label-free in vitro screening tool in cancer diagnosis and monitoring, subcellular analysis of biochemical processes, drug uptake, mode of action and mechanisms of interaction as well as toxicity of, for example, chemotherapeutic agents. However, in order to evaluate accurately the potential of Raman micro spectroscopy for such applications it is essential to optimise measurement and data processing protocols associated with subcellular analysis. To this end, in vitro differentiation of cell lines is a basic proof of concept for the potential of the technique, and although many studies have indicated successful differentiation based on Raman micro spectroscopy, it is important, as the measurement and processing techniques are improved, to establish the biochemical and subcellular basis of that discrimination. In this study, Raman micro spectroscopy is used to compare and differentiate normal and cancer cells from human lung origin, A549 adenocarcinoma cell line, Calu-1 epidermoid non-small-cell and BEAS-2B normal immortalized bronchial epithelium cell line. Spectra were taken from the three subcellular compartments, cytoplasm, nucleus and nucleolus and Principal Components Analysis was used to compare the spectral profiles between the cell lines and, coupled to Linear Discriminant Analysis, to explore the optimum sensitivity and specificity of discrimination. To support the analysis, Raman micro spectroscopy was coupled with Flow Cytometry, Confocal Laser Scanning Microscopy and Atomic Force Microscopy. While all subcellular regions can be employed to differentiate the normal and cancer cell lines, optimum discrimination sensitivity and specificity is achieved using the spectra from the nucleolar region alone. Notably, only the nucleolar spectral profiles differentiate the two cancer cell lines. The results point to the importance of the nucleolar regions in diagnostic applications of Raman microscopy as well as further applications in subcellular analysis of cytological processes.

Novel alginate-based nanocarriers as a strategy to include high concentrations of hydrophobic compounds in hydrogels for topical application.

The cutaneous penetration of hydrophobic active molecules is of foremost concern in the dermatology and cosmetic formulation fields. The poor solubility in water of those molecules limits their use in hydrophilic forms such as gels, which are favored by patients with chronic skin disease. The aim of this work is to design a novel nanocarrier of hydrophobic active molecules and to determine its potential as an ingredient of a topical form. The nanocarrier consists of an oily core surrounded by a protective shell of alginate, a natural polysaccharide isolated from brown algae. These calcium alginate-based nanocarriers (CaANCs) were prepared at room temperature and without the use of organic solvent by an accelerated nanoemulsification-polymer crosslinking method. The size (hydrodynamic diameter ~200 nm) and surface charge (zeta potential ~ - 30 mV) of the CaANCs are both compatible with their application on skin. CaANCs loaded with a fluorescent label were stable in model hydrophilic galenic forms under different storage conditions. Curcumin was encapsulated in CaANCs with an efficiency of ~95%, fully retaining its antioxidant activity. The application of the curcumin-loaded CaANCs on excised human skin led to a significant accumulation of the active molecules in the upper layers of the skin, asserting the potential of these nanocarriers in active pharmaceutical and cosmetic ingredients topical delivery.

Raman micro spectroscopy for in vitro drug screening: subcellular localisation and interactions of doxorubicin.

Vibrational spectroscopy, including Raman micro spectroscopy, has been widely used over the last few years to explore potential biomedical applications. Indeed, Raman micro spectroscopy has been demonstrated to be a powerful non-invasive tool in cancer diagnosis and monitoring. In confocal microscopic mode, the technique is also a molecularly specific analytical tool with optical resolution which has potential applications in subcellular analysis of biochemical processes, and therefore as an in vitro screening tool of the efficacy and mode of action of, for example, chemotherapeutic agents. In order to demonstrate and explore the potential in this field, established, model chemotherapeutic agents can be valuable. In this study paper, Raman micro spectroscopy coupled with confocal microscopy were used for the localization and tracking of the commercially available drug, doxorubicin (DOX), in the intracellular environment of the lung cancer cell line, A549. Cytotoxicity assays were employed to establish clinically relevant drug doses for 24 h exposure, and Confocal Laser Scanning Fluorescence Microscopy was conducted in parallel with Raman micro spectroscopy profiling to confirm the drug internalisation and localisation. Multivariate statistical analysis, consisting of PCA (principal components analysis) was used to highlight doxorubicin interaction with cancer cells and spectral variations due to its effects before and after DOX spectral features subtraction from nuclear and nucleolar spectra, were compared to non-exposed control spectra. Results show that Raman micro spectroscopy is not only able to detect doxorubicin inside cells and profile its specific subcellular localisation, but, it is also capable of elucidating the local biomolecular changes elicited by the drug, differentiating the responses in different sub cellular regions. Further analysis clearly demonstrates the early apoptotic effect in the nuclear regions and the initial responses of cells to this death process, demonstrating the potential of the technique to monitor the mechanisms of action and response on a molecular level, with subcellular resolution.

Cell viability assessment using the Alamar blue assay: a comparison of 2D and 3D cell culture models.

Comparisons of 2D and 3D cell culture models in literature have indicated differences in cellular morphology and metabolism, commonly attributed the better representation of in vivo conditions of the latter cell culture environment. Thus, interest in the use of 3D collagen gels for in vitro analysis has been growing. Although comparative studies to date have indicated an enhanced resistance of cells on collagen matrices against different toxicants, in the present study it is demonstrated that non-adapted protocols can lead to misinterpretation of results obtained from classical colorimetric dye-based cytotoxic assays. Using the well established Alamar blue assay, the study demonstrates how the transfer from 2D substrates to 3D collagen matrices can affect the uptake of the resazurin itself, affecting the outcome of the assay. Using flow cytometry, it is demonstrated that the cell viability is unaffected when cells are grown on collagen matrices, thus the difference seen in the fluorescence is a result of a dilution of the resazurin dye in the collagen matrix, and an increased uptake rate due to the larger cell surface exposed to the surrounding environment, facilitating more effective diffusion through the cellular membrane. The results are supported by a rate equation based simulation, verifying that differing uptake kinetics can result in apparently different cell viability. Finally, this work highlights the feasibility to apply classical dye-based assays on collagen based 3D cell culture models. However, the diffusion and bioavailability of test substances in 3D matrices used in in vitro toxicological assays must be considered and adaption of the protocols is necessary for direct comparison with the traditional 2D models. Moreover, the observations made based on the resazurin dye can be applied to drugs or nanoparticles which freely diffuse through the collagen matrices, thus affecting the effective concentration exposed to the cells.

Raman spectroscopic mapping for the analysis of solar radiation induced skin damage.

The effects of simulated solar irradiation of an artificial skin model have been examined using Raman spectroscopy and the results are compared with cytotoxicological and histological profiling. Samples exposed for times varying between 30 minutes and 240 minutes were incubated post exposure for a period of 96 hours. The cytotoxicological response as measured by the MTT [3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide] assay demonstrated a ~50% loss of viability of the artificial tissue after 120 minutes exposure. Histological staining of tissue sections showed considerable loss of cellular content in the epidermal layer at this endpoint. Raman spectroscopic mapping of tissue sections, coupled with K-means cluster analysis (KMCA) clearly identified the dermal and stratum corneum layers and differentiated further substructures of the epidermis. Post irradiation, a significant loss of DNA features in the basal layer was apparent in the results of the KMCA. Principal Components Analysis (PCA) of layers identified by the KMCA post exposure compared with controls indicated a significant increase in the lipidic signatures of the stratum corneum. In the dermal layer, little photodamage was observed, but a similar increase in lipidic signatures in the basal layer was accompanied by a decrease in DNA and protein contributions. The spectral profiles of the photodamage to the basal layer as identified by PCA are consistent over the exposure periods of 30-240 minutes, but an examination of the evolution of features associated with specific biochemical components indicated DNA damage and loss of lipidic signatures at the early exposure times, whereas changes in protein signatures appeared to evolve over longer periods. In comparison to the cytotoxicological responses, the study demonstrates that Raman spectroscopy can identify biochemical changes as a result of solar exposure at time points significantly earlier than changes in tissue viability are observed.

Understanding the molecular information contained in principal component analysis of vibrational spectra of biological systems.

K-means clustering followed by Principal Component Analysis (PCA) is employed to analyse Raman spectroscopic maps of single biological cells. K-means clustering successfully identifies regions of cellular cytoplasm, nucleus and nucleoli, but the mean spectra do not differentiate their biochemical composition. The loadings of the principal components identified by PCA shed further light on the spectral basis for differentiation but they are complex and, as the number of spectra per cluster is imbalanced, particularly in the case of the nucleoli, the loadings under-represent the basis for differentiation of some cellular regions. Analysis of pure bio-molecules, both structurally and spectrally distinct, in the case of histone, ceramide and RNA, and similarly in the case of the proteins albumin, collagen and histone, show the relative strong representation of spectrally sharp features in the spectral loadings, and the systematic variation of the loadings as one cluster becomes reduced in number. The more complex cellular environment is simulated by weighted sums of spectra, illustrating that although the loading becomes increasingly complex; their origin in a weighted sum of the constituent molecular components is still evident. Returning to the cellular analysis, the number of spectra per cluster is artificially balanced by increasing the weighting of the spectra of smaller number clusters. While it renders the PCA loading more complex for the three-way analysis, a pair wise analysis illustrates clear differences between the identified subcellular regions, and notably the molecular differences between nuclear and nucleoli regions are elucidated. Overall, the study demonstrates how appropriate consideration of the data available can improve the understanding of the information delivered by PCA.

Imaging live cells grown on a three dimensional collagen matrix using Raman microspectroscopy.

Three dimensional collagen gels have been used as matrices for the imaging of live cells by Raman spectroscopy. The study is conducted on a human lung adenocarcinoma (A549) and a spontaneously immortalized human epithelial keratinocyte (HaCaT) cell line. The lateral resolution of the system has been estimated to be <1.5 µm making it possible to access the subcellular organization. Using K-means clustering analysis, it is shown that the different subcellular compartments of individual cells can be identified and differentiated. The biochemical specificity of the information contained in the Raman spectra allows the visualization of differences in the molecular signature of the different sub-cellular structures. Furthermore, to enhance the chemical information obtained from the spectra, principal component analysis has been employed, allowing the identification of spectral windows with a high variability. The comparison between the loadings calculated and spectra from pure biochemical compounds enables the correlation of the variations observed with the molecular content of the different cellular compartments.

Three dimensional collagen gels as a cell culture matrix for the study of live cells by Raman spectroscopy.

Three dimensional collagen gels are evaluated as matrices for the study of live cells by Raman spectroscopy. The study is conducted on a human lung adenocarcinoma (A549) and a spontaneously immortalized human epithelial keratinocyte (HaCaT) cell line. It is demonstrated, using the Alamar Blue assay, that both cell models exhibit enhanced viability in collagen matrices compared to quartz substrates, commonly used for Raman spectroscopy. Using principal component analysis, it is shown that the Raman spectral analysis of cells in collagen matrices is minimally contaminated by substrate contributions and the cell to cell spectral variations are greatly reduced compared to those measured on quartz substrates. Furthermore, the spectral measurements are seen to have little contribution from the cell culture medium, implying that cultures can be kept viable over prolonged measurement or mapping procedures.

Detection of pathological aortic tissues by infrared multispectral imaging and chemometrics.

Processing of multispectral images is becoming an important issue, especially in terms of data mining for disease diagnosis. We report here an original image analysis procedure developed in order to compare 42 infrared multispectral images acquired on human ascending aortic healthy and pathological tissues. Each image contained about 2500 infrared absorption spectra, each composed of 1641 variables (wavenumbers). To process this large data set, we have restricted the spectral window used to the 1800-950 cm(-1) spectral range and selected 100 spectra from the aortic media, which is the most altered part of the aortic tissue in aneurysms. Prior to this selection, a spectral quality test was performed to eliminate 'bad' spectra. Our data set was first subjected to a discriminant analysis, which allowed separation of aortic tissues in two groups corresponding respectively to normal and aneurysmal states. Then a K-means analysis, based on 20 groups, allowed reconstruction of infrared images using false-colours and discriminated between pathological and healthy tissues. These results demonstrate the usefulness of such data processing methods for the analysis and comparison of a set of spectral images.

Analysis of structural changes in normal and aneurismal human aortic tissues using FTIR microscopy.

Aortic aneurisms are frequently asymptomatic but can induce dramatic complications. The diagnosis is only based on the aortic diameter and not on a structural and compositional basis. In this preliminary study, we propose infrared microspectroscopy to nondestructively probe normal and aneurismal human aortas. Spectra from 19 human ascending aortic biopsies (10 normal and 9 aneurismal) were acquired using infrared microspectroscopy. A 1500 x 150 microm(2) area of each 7-microm thick cryosection was investigated using a 30-microm spatial resolution with a total of about 200 spectra per sample. Spectral differences between normal and aneurismal tissues were mainly located in spectral regions related to proteins, such as elastin and collagen, and proteoglycans (1750-1000 cm(-1)). Tissue heterogeneity and sample classification have been evaluated using hierarchical cluster analysis of individual or mean spectra and their second derivative. Using spectral range related to proteins, 100% of good classification was obtained whereas the proteoglycan spectral range was less discriminant. This in vitro study demonstrates the potential of such technique to differentiate between normal and aneurismal aortas using selected spectral ranges. Future investigations will be focused on these specific spectral regions to determine the role of elastin and collagen in the discrimination of normal and pathological aortas.

In-vitro analysis of normal and aneurismal human ascending aortic tissues using FT-IR microspectroscopy.

FTIR microspectroscopy has shown to be a proven tool in the investigation of many tissue types. We have used this spectroscopic approach to analyse structural differences between normal and aneurismal aortic tissues and also aortas from patients with congenital anomalies like aortic bicuspid valves. Spectral analysis showed important variations in amide I and II regions, related to changes in alpha-helix and beta-sheet secondary structure of proteins that seem to be correlated to structural modifications of collagen and elastin. These proteins are the major constituents of the aortic wall associated to smooth muscular cells. The amide regions have thus been identified as a marker of structural modifications related to these proteins whose modifications can be associated to a given aortic pathological situation. Both univariate (total absorbance image and band ratio) and multivariate (principal components analysis) analyses of the spectral information contained in the infrared images have been performed. Differences between tissues have been identified by these two approaches and allowed to separate each group of aortic tissues. However, with univariate band ratio analysis, the pathological group was found to be composed of samples from aneurismal aortas associated or not with an aortic bicuspid valve. In contrast, PCA was able to separate these two types of aortic pathologies. For other groups, PCA and band ratio analysis can differentiate between normal, aneurismal, and none dilated aortas from patients with a bicuspid aortic valve.