Line-field Confocal Optical Coherence Tomography (LC-OCT) is a non-invasive optical technique for imaging the skin in vivo in depth and at high resolution. Confocal Raman Microspectroscopy (CRM) is a label-free technique that provides point-wise information on the molecular content of the analyzed sample. We present the development of a prototype platform allowing the co-localized acquisition of LC-OCT images and Raman spectra on ex vivo skin samples with an accuracy of ± 20 μm. We present results obtained on healthy skin as well as preliminary results obtained on a basal cell carcinoma surgery, aiming at identifying molecular tumor markers.
Dermatologists need to combine different clinically relevant characteristics for a better understanding of skin health. These characteristics are usually measured by different techniques, and some of them are highly time consuming. Therefore, a predicting model based on Raman spectroscopy and partial least square (PLS) regression was developed as a rapid multiparametric method. The Raman spectra collected from the five uppermost micrometers of 11 healthy volunteers were fitted to different skin characteristics measured by independent appropriate methods (transepidermal water loss, hydration, pH, relative amount of ceramides, fatty acids, and cholesterol). For each parameter, the obtained PLS model presented correlation coefficients higher than R2=0.9. This model enables us to obtain all the aforementioned parameters directly from the unique Raman signature. In addition to that, in-depth Raman analyses down to 20 μm showed different balances between partially bound water and unbound water with depth. In parallel, the increase of depth was followed by an unfolding process of the proteins. The combinations of all these information led to a multiparametric investigation, which better characterizes the skin status. Raman signal can thus be used as a quick response code (QR code). This could help dermatologic diagnosis of physiological variations and presents a possible extension to pathological characterization.
Raman spectroscopy coupled with K-means clustering analysis (KMCA) is employed to elucidate the biochemical structure of human skin tissue sections and the effects of tissue processing. Both hand and thigh sections of human cadavers were analyzed in their unprocessed and formalin-fixed, paraffin-processed (FFPP), and subsequently dewaxed forms. In unprocessed sections, KMCA reveals clear differentiation of the stratum corneum (SC), intermediate underlying epithelium, and dermal layers for sections from both anatomical sites. The SC is seen to be relatively rich in lipidic content; the spectrum of the subjacent layers is strongly influenced by the presence of melanin, while that of the dermis is dominated by the characteristics of collagen. For a given anatomical site, little difference in layer structure and biochemistry is observed between samples from different cadavers. However, the hand and thigh sections are consistently differentiated for all cadavers, largely based on lipidic profiles. In dewaxed FFPP samples, while the SC, intermediate, and dermal layers are clearly differentiated by KMCA of Raman maps of tissue sections, the lipidic contributions to the spectra are significantly reduced, with the result that respective skin layers from different anatomical sites become indistinguishable. While efficient at removing the fixing wax, the tissue processing also efficiently removes the structurally similar lipidic components of the skin layers. In studies of dermatological processes in which lipids play an important role, such as wound healing, dewaxed samples are therefore not appropriate. Removal of the lipids does however accentuate the spectral features of the cellular and protein components, which may be more appropriate for retrospective analysis of disease progression and biochemical analysis using tissue banks.
In the last few years, Raman spectroscopy has been increasingly used for the characterization of normal and pathological tissues. A new Raman system, constituted of optic fibers bundle coupled to an axial Raman spectrometer (Horiba Jobin Yvon SAS), was developed for in vivo investigations. Here, we present in vivo analysis on two tissues: human skin and esophagus mucosa on a rat model. The skin is a directly accessible organ, representing a high diversity of lesions and cancers. Including malignant melanoma, basal cell carcinoma and the squamous cell carcinoma, skin cancer is the cancer with the highest incidence worldwide. Several Raman investigations were performed to discriminate and classify different types of skin lesions, on thin sections of biopsies. Here, we try to characterize in vivo the different types of skin cancers in order to be able to detect them in their early stages of development and to define precisely the exeresis limits. Barrett's mucosa was also studied by in vivo examination of rat's esophagus. Barrett's mucosa, induced by gastro-esophageal reflux, is a pretumoral state that has to be carefully monitored due to its high risk of evolution in adenocarcinoma. A better knowledge of the histological transformation of esophagus epithelium in a Barrett's type will lead to a more efficient detection of the pathology for its early diagnosis. To study these changes, an animal model (rats developing Barrett's mucosa after duodenum - esophagus anastomosis) was used. Potential of vibrational spectroscopy for Barrett's mucosa identification is assessed on this model.
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