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.

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

Raman spectroscopy is used for the localization and tracking of chemotherapeutic drug, doxorubicin, in the intracellular environment of lung cancer cell line. Results show the potential of the technique to monitor the mechanisms of action and response on a molecular level, with subcellular resolution.

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

Raman micro spectroscopy is employed to discriminate between cell lines. Results show the importance of the nuclear sub-cellular organelle, the nucleoli, to differentiate between cancer cell lines with high specificity and sensitivity.

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.

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.

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.