In the confocal Raman spectra of skin dermis, the band area in the spectral region of proline and hydroxyproline varies according to the age and health condition of the volunteers, classified as healthy young women, healthy elderly women, and diabetic elderly women. Another observation refers to the intensity variation and negative Raman shift of the amide I band. To understand these effects, we adopted a model system using the DFT/B3LYP:3-21G procedure, considering the amino acid chain formed by glycine, hydroxyproline, proline, and alanine, which interacts with two and six water molecules. Through these systems, polarizability variations were analyzed to correlate its values with the observed Raman intensities of the three groups of volunteers and to assign the vibrational spectra of the skin dermis. As a way to correlate other experimental trends, we propose a model of chemical reaction of water interchange between the bonding amino acids, in which water molecules are attached with glucose by hydrogen bonds. The theoretical results are in accordance with the observed experimental trends.
KEYWORDS: Statistical analysis, Skin, Raman spectroscopy, Confocal microscopy, In vivo imaging, Biological research, Principal component analysis, Data analysis, Spectroscopy, Analytical research
The analysis of biological systems by spectroscopic techniques involves the evaluation of hundreds to thousands of variables. Hence, different statistical approaches are used to elucidate regions that discriminate classes of samples and to propose new vibrational markers for explaining various phenomena like disease monitoring, mechanisms of action of drugs, food, and so on. However, the technical statistics are not always widely discussed in applied sciences. In this context, this work presents a detailed discussion including the various steps necessary for proper statistical analysis. It includes univariate parametric and nonparametric tests, as well as multivariate unsupervised and supervised approaches. The main objective of this study is to promote proper understanding of the application of various statistical tools in these spectroscopic methods used for the analysis of biological samples. The discussion of these methods is performed on a set of in vivo confocal Raman spectra of human skin analysis that aims to identify skin aging markers. In the Appendix, a complete routine of data analysis is executed in a free software that can be used by the scientific community involved in these studies.
The aging process involves the reduction in the production of the major components of skin tissue. During intrinsic aging and photoaging processes, in dermis of human skin, fibroblasts become senescent and have decreased activity, which produce low levels of collagen. Moreover, there is accumulation of advanced glycation end products (AGEs). AGEs have incidence in the progression of age-related diseases, principally in diabetes mellitus and in Alzheimer's diseases. AGEs causes intracellular damage and/or apoptosis leading to an increase of the free radicals, generating a crosslink with skin proteins and oxidative stress. The aim of this study is to detect AGEs markers on human skin by in vivo Confocal Raman spectroscopy. Spectra were obtained by using a Rivers Diagnostic System, 785 nm laser excitation and a CCD detector from the skin surface down to 120 μm depth. We analyzed the confocal Raman spectra of the skin dermis of 30 women volunteers divided into 3 groups: 10 volunteers with diabetes mellitus type II, 65–80 years old (DEW); 10 young healthy women, 20–33 years old (HYW); and 10 elderly healthy women, 65–80 years old (HEW). Pentosidine and glucosepane were the principally identified AGEs in the hydroxyproline and proline Raman spectral region (1000–800 cm–1), in the 1.260–1.320 cm–1 region assignable to alpha-helical amide III modes, and in the Amide I region. Pentosidine and glucosepane calculated vibrational spectra were performed through Density Functional Theory using the B3LYP functional with 3-21G basis set. Difference between the Raman spectra of diabetic elderly women and healthy young women, and between healthy elderly women and healthy young women were also obtained with the purpose of identifying AGEs Raman bands markers. AGEs peaks and collagen changes have been identified and used to quantify the glycation process in human skin.
Raman spectroscopy has been applied to the analysis of biological samples for the last 12 years providing detection of changes occurring at the molecular level during the pathological transformation of the tissue. The potential use of this technology in cancer diagnosis has shown encouraging results for the in vivo, real-time and minimally invasive diagnosis. Confocal Raman technics has also been successfully applied in the analysis of skin aging process providing new insights in this field. In this paper it is presented the latest biomedical applications of Raman spectroscopy in our laboratory. It is shown that Raman spectroscopy (RS) has been used for biochemical and molecular characterization of thyroid tissue by micro-Raman spectroscopy and gene expression analysis. This study aimed to improve the discrimination between different thyroid pathologies by Raman analysis. A total of 35 thyroid tissues samples including normal tissue (n=10), goiter (n=10), papillary (n=10) and follicular carcinomas (n=5) were analyzed. The confocal Raman spectroscopy allowed a maximum discrimination of 91.1% between normal and tumor tissues, 84.8% between benign and malignant pathologies and 84.6% among carcinomas analyzed. It will be also report the application of in vivo confocal Raman spectroscopy as an important sensor for detecting advanced glycation products (AGEs) on human skin.
Accumulation of AGEs [Advanced Glycation End – products] occurs slowly during the human aging process. However, its formation is accelerated in the presence of diabetes mellitus. In this paper, we perform a noninvasive analysis of glycation effect on human skin by in vivo confocal Raman spectroscopy. This technique uses a laser of 785 nm as excitation source and, by the inelastic scattering of light, it is possible to obtain information about the biochemical composition of the skin. Our aim in this work was to characterize the aging process resulting from the glycation process in a group of 10 Health Elderly Women (HEW) and 10 Diabetic Elderly Women (DEW). The Raman data were collected from the dermis at a depth of 70-130 microns. Through the theory of functional density (DFT) the bands positions of hydroxyproline, proline and AGEs (pentosidine and glucosepane) were calculated by using Gaussian 0.9 software. A molecular interpretation of changes in type I collagen was performed by the changes in the vibrational modes of the proline (P) and hydroxyproline (HP). The data analysis shows that the aging effects caused by glycation of proteins degrades type I collagen differently and leads to accelerated aging process.
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