Raman spectroscopy represents an analytical, non-destructive, and dynamic method to evaluate the permeation of actives in the skin layers.

Type I and IV collagens are important constituents of the skin. Type I collagen is found in all dermal layers in high proportion, while type IV collagen is localized in the basement membrane of the dermo-epidermal junction (DEJ). These proteins are strongly altered during aging or cancer progression. Although they possess amino acid compositions which, are close, they present also important structural differences inducing specific physicochemical properties. Raman spectroscopy is based on a nondestructive interaction of the light with the matter. This technique permits to probe the intrinsic molecular composition of the samples without staining or particular preparation. The aim of our research is to study the correlation between the molecular conformations of type I and IV collagens and their Raman features. We showed that signals specific of each protein can be revealed and that they translate structural differences between the two collagens. From this collagens spectral characterization, the analysis of skin sections also permitted to identify spectral markers of dermis, epidermis, and epidermis/dermis interface. These preliminary results represent basic data for further studies, particularly to probe skin molecular alterations induced by chronologic aging.

Raman spectroscopy has proven its potential for the analysis of cell constituents and processes. However, sample preparation methods compatible with clinical practice must be implemented for collection of accurate spectral information. This study aims at assessing, using micro-Raman imaging, the effects of some routinely used fixation methods such as formalin-fixation, formalin-fixation/air drying, cytocentrifugation, and air drying on intracellular spectral information. Data were compared with those acquired from single living cells. In parallel to these spectral information, cell morphological modifications that accompany sample preparation were compared. Spectral images of isolated cells were first analyzed in an unsupervised way using hierarchical cluster analysis (HCA), which allowed delimitation of the cellular compartments. The resulting nuclei cluster centers were compared and revealed at the molecular level that fixation induced changes in spectral information assigned to nucleic acids and proteins. In a second approach, a supervised fitting procedure using model spectra of DNA, RNA, and proteins, chemically extracted from living cells, revealed very small modifications at the level of the localization and quantification of these macromolecules. Finally, HCA and principal components analysis (PCA) performed on individual spectra randomly selected from the nuclear regions showed that formalin-fixation and cytocentrifugation are sample preparation methods that have little impact on the biochemical information as compared to living conditions. Any step involving cell air drying seems to accentuate the spectral deviations from the other preparation methods. It is therefore important in a future context of spectral cytology to take into account these variations.

Histopathology remains the gold standard method for colon cancer diagnosis. Novel complementary approaches for molecular level diagnosis of the disease are need of the hour. Infrared (IR) imaging could be a promising candidate method as it probes the intrinsic chemical bonds present in a tissue, and provides a "spectral fingerprint" of the biochemical composition. To this end, IR spectral histopathology, which combines IR imaging and data processing techniques, was employed on seventy seven paraffinized colon tissue samples (48 tumoral and 29 non-tumoral) in the form of tissue arrays. To avoid chemical deparaffinization, a digital neutralization of the spectral interference of paraffin was implemented. Clustering analysis was used to partition the spectra and construct pseudo-colored images, for assigning spectral clusters to various tissue structures (normal epithelium, malignant epithelium, connective tissue etc.). Based on the clustering results, linear discriminant analysis was then used to construct a stringent prediction model which was applied on samples without a priori histopathological information. The predicted spectral images not only revealed common features representative of the colonic tissue biochemical make-up, but also highlighted additional features like tumor budding and tumor-stroma association in a label-free manner. This novel approach of IR spectral imaging on paraffinized tissues showed 100% sensitivity and allowed detection and differentiation of normal and malignant colonic features based purely on their intrinsic biochemical features. This non-destructive methodology combined with multivariate statistical image analysis appears as a promising tool for colon cancer diagnosis and opens up the way to the concept of numerical spectral histopathology.

Innovative diagnostic methods are the need of the hour that could complement conventional histopathology for cancer diagnosis. In this perspective, we propose a new concept based on spectral histopathology, using IR spectral micro-imaging, directly applied to paraffinized colon tissue array stabilized in an agarose matrix without any chemical pre-treatment. In order to correct spectral interferences from paraffin and agarose, a mathematical procedure is implemented. The corrected spectral images are then processed by a multivariate clustering method to automatically recover, on the basis of their intrinsic molecular composition, the main histological classes of the normal and the tumoral colon tissue. The spectral signatures from different histological classes of the colonic tissues are analyzed using statistical methods (Kruskal-Wallis test and principal component analysis) to identify the most discriminant IR features. These features allow characterizing some of the biomolecular alterations associated with malignancy. Thus, via a single analysis, in a label-free and nondestructive manner, main changes associated with nucleotide, carbohydrates, and collagen features can be identified simultaneously between the compared normal and the cancerous tissues. The present study demonstrates the potential of IR spectral imaging as a complementary modern tool, to conventional histopathology, for an objective cancer diagnosis directly from paraffin-embedded tissue arrays.

IR-MSP imaging provides a new type of histology, independent of visual morphology, presenting tremendous possibilities for discovery and clinical monitoring of cancer markers.

Identifying early cellular events in response to a chemotherapy drug treatment, in particular at low doses that will destroy the highest possible number of cancer cells, is an important issue in patient management. In this study, we employed Fourier transform infrared spectroscopy as a potential tool to access such information. We used as model the non-small cell lung cancer cell line, Calu-1. They were exposed to cytostatic doses (0.1 to 100 nM for 24, 48 and 72 h) of gemcitabine, an anti-tumour drug, currently used in treatment of lung cancer patients. In these conditions, inhibition of cell proliferation ranges from weak (< or = 5%), to moderate (approximately 23%), to high (82-95%) without affecting cell viability. Following drug treatment as a function of doses and incubation times, the spectra of cell populations and of individual cells were acquired using a bench-top IR source and a synchrotron infrared microscope. It is demonstrated that spectral cell response to gemcitabine is detectable at sublethal doses and that effects observed on cell populations are similar to those from single cells. Using cluster analysis, spectra could be classified in two main groups: a first group that contains spectra of cells exhibiting a weak or moderate proliferation rate and a second group with spectra from cells presenting a high growth inhibition. These results are promising since they show that effects of subtoxic doses can also be monitored at the single-cell level with the clinical implications that this may have in terms of patient benefit and response to chemotherapy.

Malignant melanoma (MM) is the most severe tumor affecting the skin and accounts for three quarters of all skin cancer deaths. Raman spectroscopy is a promising nondestructive tool that has been increasingly used for characterization of the molecular features of cancerous tissues. Different multivariate statistical analysis techniques are used in order to extract relevant information that can be considered as functional spectroscopic descriptors of a particular pathology. Paraffin embedding (waxing) is a highly efficient process used to conserve biopsies in tumor banks for several years. However, the use of non-dewaxed formalin-fixed paraffin-embedded tissues for Raman spectroscopic investigations remains very restricted, limiting the development of the technique as a routine analytical tool for biomedical purposes. This is due to the highly intense signal of paraffin, which masks important vibrations of the biological tissues. In addition to being time consuming and chemical intensive, chemical dewaxing methods are not efficient and they leave traces of the paraffin in tissues, which affects the Raman signal. In the present study, we use independent component analysis (ICA) on Raman spectral images collected on melanoma and nevus samples. The sources obtained from these images are then used to eliminate, using non-negativity constrained least squares (NCLS), the paraffin contribution from each individual spectrum of the spectral images of nevi and melanomas. Corrected spectra of both types of lesion are then compared and classified into dendrograms using hierarchical cluster analysis (HCA).