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1.IntroductionIn the vibrational spectra of biological molecules, indelible “fingerprints” can be found for the occurrence (and frequency) of a number of otherwise hidden natural phenomena. The discovery and rationalization of such spectral “fingerprints” represents a socially useful and fascinating practice, but also pose formidable challenges to spectroscopists. Unlike simpler inorganic structures, an explicit description of the irreducible phonon representation for complicated organic molecules is a task of enormous cumbersomeness, which can by no means be accomplished without the help of computational programs. Even an “elementary” protein structure possesses a number of distinct vibrational modes (i.e., in the order of ),1 which makes the task of unfolding such a structural complexity one of the hardest challenges in modern science. On the positive side, however, there is the high sensitivity of vibrational spectroscopy to structural features. This method can be exceptionally sensitive to even quite small variations of bond strength, i.e., in the order of 0.02%.2 Such variations can promptly be resolved by probing with a spectral resolution better than , and modern spectroscopes usually possess spectral resolutions at least 1 order of magnitude better than the above threshold. In other words, Raman spectroscopy provides us with a suitably high sensitivity for monitoring subtle bond modifications and molecular distortions with high accuracy. From an analytical point of view, the main consequence of the complexity of biomolecules has been that Raman spectra have often been treated as a mere matricial sequence of mathematical data, with little emphasis being placed on the interpretation of the physical origin of individual bands in the recorded spectra. Such a statistically driven approach could be quite useful in locating spectral differences. However, despite the huge piece of information potentially contained in the Raman spectra, such spectroscopic “cryptograms” have, most of the time, remained unfolded with only a small (qualitative) part of their contents being physically interpreted. On the other hand, several research groups have continued to intensely work on basic interpretations of the Raman spectra emitted from biological samples.3–9 Such basic approaches have enabled clarifying a number of structural features in organic molecules, including the interpretation of Raman bands and their spectral positions from saccharides,5 lipids3,4,10 and carotenoids.9 The presence of mono-unsaturated acyl groups has also been related to the first derivative value of scattering intensities measured at , which relate to the vibrational stretching of cis and trans bonds in lipids.10 Moreover, classification overviews of Raman spectra for biomolecules have recently become available.11 Specifically regarding model protein structures, Raman bands represent vibrational modes of both the peptide backbone and its side chains. Spectral positions, intensities and polarizations of the Raman bands result in being quite sensitive to protein secondary, tertiary and quaternary structures, in addition to side-chain orientations and local environments. In a number of favorable cases, the Raman spectrum has provided a straightforward signature of the protein three-dimensional (3-D) structure, intramolecular dynamics and intermolecular interactions.12 Specifically related to skin, its constituent organic molecules generally display in the Raman spectrum according to the corresponding model structures, but significant compositional and structural variations can be expected with progressing age. Collagen fibers are composed of collagen types I and III in a ratio that depends on their location being either in the papillary or in the reticular dermis. In young people, collagen fibers in the region of papillary dermis take the form of densely packed and irregularly arranged networks, whereas in the reticular dermis, their morphology appears coarser and with loosely arranged and intertwined bundles. Upon aging, the amount of collagen fibers increases, packing becomes denser, and the stereological arrangement is less twisted.13,14 Moreover, elastic fibers consist of an amorphous fraction ( of the mature fiber), which is exclusively composed of elastin and a microfibrillar component consisting of nanostructured fibrils.15 Superficial microfibril bundles gradually thicken and merge with increasingly large amounts of amorphous elastin as the papillary dermis changes into reticular dermis. However, with increasing age, the concentration of elastin fibers in the papillary dermis decreases. Accordingly, fibers from the skin of older individuals lose some of their elasticity, thicken (fold) into agglutinated masses and unravel.14 While the anatomical details of structural skin modifications with aging are well studied and documented, methods for finding such features in the recorded Raman spectra are yet at their early development. Consequences of aging processes are obviously the changes in the biochemical structure of tissue, which should also be seen in its Raman spectrum. The challenge, therefore, shifts now to how to translate such qualitative notions into a quantitative spectroscopic algorithm capable of assessing human age to a degree of precision. Building upon previous outputs of different approaches to Raman evaluations of biological samples, we challenged, in this paper, the establishment of parameters for the quantitative assessment of human age from skin samples belonging to cadaveric donors. This study was mainly motivated by the need in the field of forensic pathology to determine within an improved degree of accuracy the age of human subjects lacking specific identity information. Spectroscopic outputs were rationalized and discussed in terms of selected parameters, including the degree of protein folding and the degree of lipid crystallization. Some other parameters—such as the fractional ratio between -helix and -sheet, the presence of sphingomyelin in the ceramide structure, and the content of collagen versus lipids—were also noticed. It should be stated at the outset of our awareness that this study lacks statistical relevance, which has been a direct consequence of the cadaveric origin of the studied samples and the need to examine the skin sample within a narrow interval of time (i.e., week) since the date of decease and the necessity of preliminary clearing up ethical issues with respect to donors. Nevertheless, specific care was taken in obtaining “standard” spectra for each donor with averaging on a large number of acquisitions on each sample. In other words, the validity of the shown concepts relies on the basic assumption that if an age representing parameter (i.e., a “natural clock”) actually exists in the vibrational behavior of skin tissue, this should be independent of individual classes. The practical possibility of retrieving age information from skin samples is a confirmation that sensitive and selective “fingerprints” of natural aging exist in the intermolecular interactions and dynamics of the constituent phases of human skin. Further studies are presently ongoing for obtaining a statistical validation of the proposed parameters and procedures. 2.Experimental ProceduresA series of cadaveric skin samples was obtained from donors (human patients deceased at different ages spanning from a few months to 62 years old) upon preliminary clearance of ethical procedures at the Department of Forensic Medicine of the Graduate School of Medical Science of Kyoto Prefectural University of Medicine. Details of the cadaver samples available for this study are provided in Table 1. The samples were taken from abdominal locations in the body unexposed to solar irradiation. Skin samples were typically wide (cut by hand) and encompassed the full thickness of the skin structure, from the stratum corneum to the hypodermis. All the skin samples investigated in this study belonged to the same race, showing similar color (pale white skin, with some yellow tones) on the skin surface. No special medical treatments were performed on the skin samples before and after they were extracted from the human bodies. More importantly, all the donors examined in this study died in a healthy situation and their pathological records did not show specific items either related to liver or other diseases specifically impacting on protein and lipid structures. Neither the infant nor other donors received medical therapy that might have induced changes in the molecular structure of their skin. Raman experiments were conducted on the skin samples within less than 1 week after the date of the patient’s decease. The skin samples were preserved at in a freezer before Raman analysis. Immediately before the Raman experiments, skin samples were thawed and kept on ice. Prior to Raman spectroscopic characterizations, each sample was divided into smaller specimens and hematoxylin and eosin (H&E) stain histology and optical microscopy were applied on the cross sections of one specimen for each sample in order to preliminarily assess the location of different zones of epidermis and dermis from morphological features. As a spectroscopic reference, a sample of skin type I collagen was also investigated, which was purchased from Sigma-Aldrich, Co.16 Table 1Information on the human cadaver samples investigated in this study.
All the Raman spectroscopic experiments described in this paper were carried out in backscattering optical probe configuration with using a triple monochromator (T-64000, Horiba/Jobin-Yvon, Kyoto, Japan) equipped with a liquid nitrogen-cooled charge coupled device. The excitation source in the present experiments used a 532-nm diode-pumped solid-state laser (SOC JUNO, Showa Optronics Co. Ltd., Tokyo, Japan) operating with a power of 200 mW. An objective lens with a numerical aperture of 0.5 was used both to focus the laser beam on the sample surface and to collect the scattered Raman light. All the experiments described in this paper were conducted with a pinhole aperture of and by employing an objective lens with a magnification of . All the experiments were conducted at room temperature with a relative humidity of 68%. Each spectrum at a measurement location was collected for five scans with the accumulation time of each scan being 1 min. For each studied sample, several tens of spectra were collected on the top skin surface and an average spectrum could be obtained with a statistical validity using commercial software (LabSpec Ver. 4.02, Jobin-Ivon/Horiba, Tokyo, Japan). On the cross section of each skin sample, spectral line scans were also performed, starting from the stratum corneum to 800 μm deep in the depth direction. Spectral Raman lines were analyzed using a commercially available software package (Origin 9.1, OriginLab Co., Northampton, Massachusetts, United States). Fitting was performed according to Gaussian-Lorentzian functions after subtracting the baseline. 3.Experimental Results3.1.Labeling the Raman Spectrum of Human SkinSimilar to the case of other soft tissues, the Raman spectrum of skin is dominated by the vibrational bands of its structural proteins, amino acids and lipids. Figure 1 shows a Raman spectrum detected by our microprobe equipment with focusing on the stratum corneum of a sample (top-view spectrum) from a 3-month-old donor. Raman spectra from the healthy skin of infants are seldom found in the published literature. For this reason, we have considered this spectrum as a “reference” one since it was almost unaffected by environmental effects. In this study, it is used for a preliminary screening of the emitted Raman bands and to label them according to the published literature. The Raman spectrum in Fig. 1 has arbitrarily been divided into a low-frequency zone (250 to ) and a high-frequency zone (2800 to ). In these two spectral zones, a total of 20 bands could be distinctly observed, as labeled in Fig. 1 (bands 1 to 20). Table 2 summarizes the observed Raman bands and their physical origin in the two noticed spectral zones reported in literature.17–61 Bands associated with vibrations of amide bonds in polypeptide chains were observed, with the amide I emission being dominated by stretching vibrations and the amide III band by stretching and bending vibrations. The former emission (band 13) at (here often observed, in agreement with other authors, at a slightly higher frequency of )23,31,62 is typical of mammalian keratins with mainly α-helical conformation.18,19,63 On the other hand, the latter emission presented two maxima: one at (band 10, assigned to nonpolar fragments with high proline content forming collagen triple helix) and the other at (overlapped to band 8 already assigned to tryptophan and phenylalanine) from polar fragments of collagen characterized by a low proline content.24,31,32 Band 9, which appears as a low-frequency shoulder to the band 10, is most likely an overlap of vibrational modes from the adenine and cytosine belonging to the -sheet structure of amide III (reported at )33 and lipids (reported at ).34 The strong emission detected at (band 11) and the weaker but clearly detectable band at (band 14) can both be assigned to vibrational modes in lipids (with a contribution from proteins, especially in the former band).17–19,26,32,64 In particular, the former band arises from scissoring and bending,35 whereas the latter one mainly represents the stretching mode in lipids and phospholipid molecules.36,37 Table 2Assignment of Raman bands in skin and skin collagen samples: v, stretching mode; vs, symmetric stretch; vas, asymmetric stretch; δ, bending mode; and w, wagging mode.
In the high-frequency zone, a broad overlapping emission, contributed by at least five relatively strong bands (bands 15 to 19), could be detected, in addition to a rather weak and isolated band centered at higher frequencies (band 20). In this broad emission, the sub-band labeled as band 15 was centered at around and represented the symmetric stretching of lipids in the liquid state.38 However, band 15 is likely to be overlapped by the symmetric stretching band of collagen centered at .39 The strongest sub-band in this spectral zone was centered at (band 16). This band has been assigned to symmetric stretching in lipids.38,40 However, band 16 seemed to show a low-frequency shoulder at around , also of protein origin, which we did not explicitly label here. This sub-band shoulder was related to symmetric stretching in collagen.41 Overlapping effects between lipids and collagen will be discussed in more detail in the following sections. Bands 17 and 18 are seen as two consecutive shoulder sub-bands to the overall emission toward higher frequencies. The origin of both these bands, which are located at 2928 and , respectively, is probably also a composite one with components from both lipids and collagen. However, the former band corresponds to symmetric stretching (i.e., due primarily to proteins),66 whereas the latter one hits a frequency related to their asymmetric stretching.42 Band 19 is weaker than the other bands composing the overall high-frequency Raman emission, but clearly appears as a more separate sub-band. Its location is at , which corresponds to asymmetric stretching of groups in lipids, fatty and unsaturated acids.40,43 Finally, a quite weak but resolvable band was observed at . We labeled it as band 20, and it was tentatively assigned to symmetric stretching.44 It has been reported that in tissue, the spectral range from 3200 to is occupied by a broad band peaking at , which is associated with stretching vibrations of tissue-bound water and stretching vibrations of proteins.17,18,32 Conversely, the presence of unbound water (i.e., tetrahedral water clusters) in skin is represented by a band located at in the Raman spectrum.67 The status of hydration of skin has been estimated through the intensity ratio between the protein stretching band at (which we observed at ) and the water stretching band at .67 Raman spectra from skin samples before and after sunlight exposure revealed a total fraction of water higher by in the latter sample as compared to the former one.17 We also observed water-related features in our skin samples. However, although it has been recognized that the water content in skin generally increases with age,68 we ruled out a priori the possibility of using the hydration ratio as a meaningful parameter for age assessments because of the strong environmental effects on the hydration state of skin and the fact that each skin layer might show a different hydration level.69,70 We failed in observing a reliable trend in hydration levels independent of environmental effects and depending on age, probably also because the Raman probe reached different structures while in-depth penetrating samples from donors with different ages, thus giving different intensity ratios. On the other hand, an important hint in this work was that skin-aging processes involve conformational changes in structural proteins. In the Raman spectra of skin samples from older individuals and/or of skin exposed to sunlight, the maxima of the amide I and III bands were systematically detected at spectral positions shifted toward lower frequencies as compared to the spectrum of young and/or unexposed skin samples. Moreover, reduced intensities were generally found and a shift occurred of the stretching band in the aliphatic side chains of amino acids toward lower frequencies. This latter spectral perturbation was interpreted as the consequence of structural changes in protein folding.70,71 3.2.Average Raman Spectra as a Function of Donors’ AgePrior to Raman spectroscopic characterizations, H&E stain histology and optical microscopy were applied on the cross sections of all samples in order to preliminarily assess the location of different zones of epidermis and dermis from morphological features. Figure 2 shows a micrograph of the investigated histological section with the H&E stain of the sample from the 3-month-old infant donor. Labels show the protocol for the line-scan Raman spectroscopy characterization in various regions of the skin at increasing depths. Similar line scans were performed on all the investigated samples from donors of different age. In this section, we compare typical Raman spectra collected as a function of patient age from the stratum corneum (i.e., at depth from the sample surface, ), from a deeper zone in the epidermis zone (just below the stratum corneum, ; simply referred to as “epidermis,” henceforth) and from a zone further in depth, which was thought to preponderantly be part of the dermis in all the investigated samples (). It should be noted at the outset that the thickness of various zones along the depth of skin significantly varies with location in the body and with age. We have minimized the former difference by sampling always from the same part of the donors’ body (abdomen). However, the latter difference is itself a part of our assessments and yet stems from our sampling. Figures 3Fig. 4Fig. 5Fig. 6–7 show average spectra collected on skin cross-section samples of a 3-month-old infant, 15-, 17-, 35-, and 62-year-old donors, respectively. The recorded spectra showed a significant degree of complexity and there were a large number of features coming out from a comparison of average Raman spectra collected at different locations and from donors with different ages. Table 2 lists all the Raman bands discussed in the remainder of this paper, together with their physical origin. In the following 10 points, we attempt to rationalize the main features that came to light from the recorded Raman spectra. The main findings can be summarized as follows:
According to the spectral characterizations shown above, we monitored the following spectral features:
More information about the variation of these five factors along the in-depth axis on cross sections of the investigated skin samples is available in the Appendix, and the dependence of these parameters on age will be further discussed in the following section. 4.Discussion4.1.Subtracting from Skin Spectra the Contribution of Standard CollagenThe Raman spectrum of dermis is dominated by collagen, which in turn constitutes 70% of the dry weight and 90% of the total protein content. Important contributions to the Raman spectrum of skin are also expected in the stratum corneum and in the epidermis zones. Of the about 20 types of collagen existing in the human body, skin consists of type I and 15% type III, while the remaining 5% is predominantly type IV collagen.87–89 Figure 8 shows the Raman spectrum of skin (type I) collagen purchased from Sigma-Aldrich Co, which was collected under exactly the same experimental conditions as the spectra of skin as shown in Figs. 3–7. On the low-frequency side of the spectrum, as shown in Fig. 8(a), most of the bands could be labeled, similar to what was detectable in the skin samples (cf. Table 2). The relatively strong intensity of the vibration bands (, , and ) in skin collagen suggests that the contribution of collagen to their overall intensity in skin samples is not negligible as compared to the respective contributions in lipids. Only two additional bands (i.e., bands 1* and 3*) remained to be assigned; these bands were not obviously detectable in the skin samples as they appeared on the low-frequency side of the collagen spectrum. Band 1* was centered at around and could be related to amide III disordered structures ( stretching and wagging), whereas band 3*, at , was again related to α-helix amide III ( bending mode).65 Regarding the high-frequency side of the Raman spectrum of pure collagen, we have collected six distinct bands (labeled as 15, , 17–19 and L) with an overall morphology comparable to that found in skin spectra. However, non-negligible shifts (toward lower or higher wavenumbers) could be found for some bands. Interestingly, band L was quite weak, thus suggesting a preponderant contribution of lipids over proteins. Moreover, band 16, observable in skin, could not be found in the collagen spectrum. The important implication in these findings is that band 15 and band as observed in the skin sample, which were assigned to symmetric stretching of units in lipids embedded in phases with different physical states, also exist in collagen with the same physical origin, but appearing at shifted frequencies. In an attempt to better visualize this complex spectroscopic situation, we normalized the high-frequency side of the skin spectra to the respective intensity maxima along the cross-section scan (in Figs. 3–7), and from them we subtracted the contribution of the similarly normalized collagen spectrum (Fig. 8). The results of this procedure are shown in Fig. 9 for spectra belonging to the youngest and to the oldest donors in this study [spectra from the 3-month- and 62-year-old donors in (a) and (b), respectively]. In the plots, the Raman intensity difference, , is plotted as a function of spectral locations, where and represent the normalized intensities of skin and collagen spectra, respectively. In each figure, three plots are given which correspond to different depths along the -axis, namely (stratum corneum), (epidermis), and (dermis). In the plots, positive and negative values for correspond to a preponderance of lipids or collagen contributions to the skin spectra, respectively. For better clarity, the spectral location of these bands, belonging to collagen, is also shown in insets of both figures. The following conclusions could be drawn as the main outputs of the spectral subtraction procedure:
4.2.Possible Spectroscopic Parameters for Evaluating Human AgeOur attempts “to decode” skin spectra in search for a possible natural “clock” in the vibrational behavior of skin have brought us several hints concerning the evolution of the chemical and physical nature of proteins and lipids with age. Figure 10 shows plots of different selected parameters as a function of donors’ ages. As previously discussed, the intensity ratios and can be considered as representative of the fractional ratio between the α-helix and β-sheet, and of the fractional ratio between disordered and ordered structures (in peptides and proteins), respectively. The plots in Figs. 10(a) and 10(b) display these (average) parameters as a function of donors’ ages. In the averaging process, we have excluded the stratum corneum in order to minimize environmental effects. The trend for in the former plot, when averaged on different areas of the cross section of the samples, showed a minimum for the 17-year-old donor. This clearly nonmonotonic nature was more pronounced in the epidermis than in the stratum corneum. We have similarly found nonmonotonic plots (not shown here) for other parameters [e.g., the sphingomyelin ratio, ], as proposed above. On the other hand, the plot of versus donors’ ages remarkably fitted to a high degree of precision with an exponential decrease in donors’ ages. According to our data, the algorithm relating the Raman intensity ratio to age, , can be given as The notion of folding and misfolding in peptides and proteins is a well-known one in the field of biophysics.90–92 In order to become functionally active, newly made (or “nascent”) protein chains must assemble into a “fold,” namely a well defined 3-D pattern. An amino acid sequence specifies the information that specifies the fold, which is a process thermodynamically driven by the hydrophobic effect. Structural rearrangement gives rise to the correct amino acid packing that corresponds to the most stable and active state in healthy subjects. This task is completed within intervals of time between milliseconds and many minutes, depending on protein size. On the other hand, band 13, in the region at around , is associated with the triple-stranded helix stabilized in collagen by a large number of interchain hydrogen bonds.93 Fourier transform infrared spectroscopy data by Federman et al.94 have assessed band displacements from the 1652 toward , which indicates the rupture of the triple helix molecule within the collagen macromolecule due to degradation of collagen type IV by the metalloproteinase trypsin. In other words, the plot in Fig. 10(c), which shows the spectral displacements of band 13 as a function of age, is another aspect of protein changes related to aging. It should be noted that in the case of collagen, newly synthesized premature collagen is imported into the lumen of endoplasmic reticulum and folded and modified during transportation through the Golgi apparatus and is then secreted as a mature form. During this protein maturation, misfolded proteins are subjected to degradation. Mature proteins are also degraded when damaged. This protein metabolism can be affected by both age and environmental stress. The protein state in the skin should thus be the result of both synthesis and degradation, which is altered in an age-dependent manner, so young skin contains a higher amount of fresh collagen than old. In conclusion, our data in Fig. 10(b) simply show that the amount of proteins in skin, which are yet to assembly into folds, is largest in infants and gradually reduces with aging. This circumstance is thus the spectroscopic representation of the fact that the dermis of newborns contains less mature collagen than adults. Moreover, Fig. 10(c) shows that stabilization of the triple helix is maximized at intermediate ages, a behavior that is in good agreement with the maximization of contents testified by the concurrent minimum of the intensity ratio, [Fig. 10(a)]. In other words, as far as proteins are concerned, Raman spectroscopic data have provided us with a consistent picture of structural evolution with age which is in agreement with well-established concepts in biophysics of the aging process. Regarding lipids, we show here another spectroscopic plot which apparently contained characteristics of remarkable precision with respect to the evolution with increasing age. This plot is shown in Fig. 10(d) and represents the relationship found between the first two maxima appearing in the high-frequency zone investigated at around 2885 and . In epidermis and dermis, the presence of more crystallized and ordered structures (i.e., a decreasing trend for the ratio) through aging corresponds to a general and well-established notion in biophysics.95–97 We plotted, in Fig. 10(d), both the ratio of to and the ratio of to . Both plots show a similar trend of exponential increase with increasing age, although the former plot gives a more distinct separation between young and old donors. Note also that the former plot could be preferable to the latter one because it just represents the ratio between the first two maxima in correspondence of the lower foot of the high-frequency zone of the skin spectrum; thus, it does not require any spectral deconvolution procedure to be calculated. Interestingly, a plot that simply uses the intensity of the deconvoluted band 16 to band 15 leads to the opposite trend (i.e., a decreasing ratio with age; cf. Figs. 11 and 12 in Appendix). This finding, together with the observation of the remarkably low intensity of band L in collagen, suggests the preponderance of the lipid contribution (i.e., cholesterol, phospholipids and creatine) of band L in skin samples. Following the definition given by Gaber and Peticolas,38 we defined the order parameter for lateral interaction, , according to the following equation: which refers to lipids in fully crystalline and fully liquid states when equal to 1 and 0, respectively. This parameter, given in the inset of Fig. 10(d), reflects the intermolecular structure of lipids and decreases in the order from lamellar liquid, hexagonal liquid crystal and orthorhombic crystal state. According to our data, an empirical equation that represents the Raman intensity ratio, , as a function of age, , can be drawn, as follows: or, alternatively, in terms of the order parameter, :4.3.Needs for the Raman Probe in Forensic Assessments of Human AgeA reliable estimation of age at death, which is a main element in the identification of bodies of unidentified origin, is also one of the main challenges in forensic sciences. For example, official data from Kyoto Prefecture98 report an average number of persistently unidentified human bodies per year in the last 12 years. Such a large number of cases for a limited geographical area could give an idea of the severity of this problem from the social side. A number of anthropological techniques have been put forward to estimate the age at death in children and adults, but they are obviously insufficient in a number of cases. These techniques include long-bone length, epiphyseal fusion, dental eruption, and lengths of diaphysis at birth for infant and juvenile remains; eruption of third molars, fusion of the spheno-occipital synchondrosis, pubic symphysis, auricular surface, cranial sutures, sternal rib ends (costal cartilage), maxillary suture closure, tooth-root translucency, and formation of osteoarthritis characteristics in adults (for a complete review of these methods, see Ref. 99). However, two main shortcomings appear in applying these methodologies: (1) an increasingly lower accuracy for adult subjects and (2) the absolute necessity of specific references depending on population. In order to overcome these deficiencies, new methodologies have been developed, which have so far included both biochemical and chemical methods. The former methods basically consist of screening the natural processes of aging, thus including different biochemical changes that lead to alterations in cells and tissues. Biochemical methods have so far relied on forensic analyses of hair, drugs in hair blood, and semen, based on infrared spectroscopy, chromatography, ultraviolet, and mass spectrophotometry.100 The chemical methods, on the other hand, involve manipulation and modifications of molecules or accumulation of selected products as, for example, modifications that take place in DNA and chromosomes. Among the chemical methods, the most accurate technique today is considered to be aspartic acid racemization in noncollagenous bone proteins or tooth enamel,101,102 although other techniques are being concurrently used depending on the forensic context under examination.103 In this paper, we have described a Raman approach to age evaluation on skin samples which basically belongs to the biochemical type of analyses since it requires no sample manipulation except for the extraction of a skin sample from the ventral part of the body. Although Raman spectroscopy could, in principle, also be applied in situ without any physical extraction of a skin sample from the body, our study has clearly shown how crucial it is to obtain cross-sectional skin samples in order to retrieve more detailed spectral information on protein and lipid structures and to avoid biases of environmental nature unavoidably contained in the stratum corneum (i.e., on the top surface) of the skin sample. Although less established as compared to radiographic analyses,104,105 Raman spectroscopy has been widely used in different branches of forensic science.106,107 However, studies using the Raman method for age assessments are few and are based on phenomenological approaches.108,109 In Ref. 109, the authors used a correlation between the variability of Raman spectra and the stages of dentinal evolution with advancing age and remarkably obtained predictions of a correct age, with a mean error of years. The main benefit of this method consisted of minimal and nondestructive sample preparation, which in turn led to an efficient age prediction for any age group. From this viewpoint, our study of skin presents a similar advantage but also necessitates more detailed and physics-driven Raman analyses of organic molecules. The very least outputs of our study are a complete labeling of Raman bands in cadaveric samples of skin (including an infant), their spectral deconvolution and their different trends with age as a function of in-depth abscissa. Despite the limited number of cadaveric samples investigated, which unavoidably limits the nature of this investigation to a proof-of-concept study, the Raman response of proteins and lipids in skin samples seemed to remarkably obey precise patterns, consistent with general biophysical concepts. Thus, Raman spectroscopy seems to provide a reliable and more straightforward path to age determination as compared to other spectroscopic techniques involving electrons, microscopic forces or neutrons.110–113 5.ConclusionThe aim of this work was to establish a correlation between age and Raman spectra retrieved from human skin. We explored both top-surface and cross-sectional Raman responses in skin samples from cadaveric donors of different ages and found the latter spectra more meaningful and richer in structural details than the former ones, regarding both proteins and lipids. Clear correlations could be found between the relative intensity of selected Raman bands and the stages of protein folding and evolution of lipid crystallization with advancing age. Protein folding seems to be a more sensitive parameter for infants and young patients, while lipid crystallization follows more sensitive variations with age for patients in their adulthood. It is probable that an algorithm combining both these two spectral parameters could improve the precision of age estimation. Moreover, there were hints for the presence of additional “biological clocks” in the Raman spectrum of skin. However, the complexity of the retrieved spectra poses considerable challenges to additional findings. Although a limited number of only five cadaveric donors that were available for our experiments necessarily confines our data into a mere proof-of-concept frame, the degrees of protein folding and lipid crystallization seemed to represent precise predictors of biological age. While we hope that our findings will stimulate other researchers to prove the newly stated concepts, additional experiments are going on in our laboratory to enlarge the number of donors every time they become available to us. The proposed Raman method should especially be suitable for those kinds of situations where traditional methods fail and a prompt and minimally invasive evaluation of age is needed. AppendicesAppendixProfiles along the in-depth axis on cross sections of the skin samples. Figures 11 and 12 show line-scan plots of these spectroscopic parameters as a function of distance, , from the surface of the skin sample. Plots are displayed for samples from the 3-month- and 62 year-old donors in Fig. 11, whereas Fig. 12 compares the results obtained from samples belonging to the 15-year- and 35-year-old donors. For brevity’s sake, data from the 17-year-old donor were not explicitly shown, since they were very similar to those collected on the sample from the 15-year-old sample. As discussed in the manuscript, the intensity ratios and [displayed in Figs. 11(a) and 11(e), and 12(a) and 12(e)] were considered as representative of the fractional ratio between α-helix and β-sheet and of the fractional ratio between disordered and ordered structures (in peptides and proteins), respectively. A comparison between the youngest and the oldest donors in this study [i.e., as displayed in Figs. 11(a) and 11(e)] revealed a similar in-depth distribution for the ratio , while the trends for the ratio were quite different. Leaving aside data from the stratum corneum (strongly affected by environmental effects), we examined data collected in the epidermis and dermis region. In these zones, the ratio was almost constant at for the infant sample, while it was in the sample from the old donor. This finding was interpreted with the presence of a more crystallized and ordered structure in the older donor as compared with the infant donor. Looking at the samples from the donors of intermediate age [in Figs. 12(a) and 12(e)], the trend of a decreasing ratio (i.e., of an increased crystallinity and structural order) in both epidermis and dermis with increasing age seemed to be confirmed. Moreover, the ratio also experienced lower values at intermediate ages as compared to both samples from the youngest and oldest donors. The ratio was monitored [cf. Figs. 11(b), 11(f), 12(b), and 12(f)] because it was thought to represent the content of sphingomyelin as compared to the overall amount of lipids in the sample. The amount of sphingomyelin showed a trend that was not monotonic with donors’ ages. It was highest in the deep portions of the sample from the 35-year-old donor, while the minimum value was detected in the infant sample. Sphingomyelin (also referred to as ceramide phosphorylcholine) consists of a ceramide unit with an attachment of phosphorylcholine moiety. Although sphingolipids are the main polar lipid constituents of milk, they represent an important but minor nutrient for infants, which justifies the low level found for this sphingolipid in the sample from the infant donor. Holleran et al.114 have shown that sphingolipids, including sphingomyelin, represent of the lipids located in the stratum corneum and are a major element of the epidermal permeability barrier. Moreover, alterations in epidermal barrier function lead to a rapid increase in cholesterol and fatty acid synthesis, which parallels the early stages of the repair process. Sphingolipid synthesis was also found to increase during barrier repair but with a delayed response in comparison to cholesterol and fatty acid synthesis.115 Since a deficiency of any of these species will result in abnormal membrane structures with a reduced capacity to impede trans-epidermal water flux,116 our findings here might simply show that such a protective capacity in skin is maximized in the medium age of humans, initially increasing from infant age and then again turning back to low levels for advanced ages. Figures 11(c), 11(g), 12(c), and 12(g) show the line-scan trends retrieved for the Raman intensity ratio , which is representative of the physical state of lipids, namely liquid versus crystalline state. A comparison between the youngest and oldest donors [Figs. 11(c) and (g)] shows a clearly positive gradient up to high values of this intensity ratio (i.e., up to 5 to 6) as a function of in-depth abscissa, , in the former sample, versus a conspicuously constant (and low) value in the latter sample. Samples from donors from intermediate ages [Figs. 12(c), and 12(g)] also showed intermediate trends with conspicuously constant values in large portions of both the epidermis and dermis. High ratios represent a preponderance of crystalline lipids over liquid ones, which conceivably accompany the increase in age in humans. Interestingly, a similar trend could be obtained from retrieving the spectral shifts, , for band 13 as a function of the abscissa, [cf. Figs. 11(d), 11(h), 12(d), and 12(h)]. A gradual increase as a function of the abscissa, , was found for the infant donor versus a lesser increase up to lower frequencies in the oldest donor. Intermediate trends appeared in samples from donors of intermediate ages. As discussed in the paper, the shift of band 13 toward higher frequencies has been considered to represent changes in the molecular geometry of amide I due to degradation of collagen triple helix chains and their dissociation into simple or double strings. Higher frequencies, such as those being observed in younger donors, thus might represent a higher fraction of unfolded structures in young skin samples, as also suggested by the higher values of the ratio. On the other hand, shifts toward low wavenumbers have been interpreted as the effect of the squeezing out of water (and a consequent reinforcement of hydrogen bonds) between adjacent chains in a study of keratin fibers under strain by Paquin and Colomban.65 This interpretation leads us to consider a higher chain packing in adult skin samples, which is also a reasonable output when considering the chemistry of skin aging. ReferencesW. J. Netzer and F. U. Hartl,
“Recombination of protein domains facilitated by co-translational folding in eukaryotes,”
Nature, 388 343
–349
(1997). http://dx.doi.org/10.1038/41024 NATUAS 0028-0836 Google Scholar
A. Barth and C. Zscherp,
“What vibrations tell about proteins?,”
Q. Rev. Biophys., 35 369
–430
(2002). http://dx.doi.org/10.1017/S0033583502003815 QURBAW 0033-5835 Google Scholar
J. J. L. Lippert and W. L. Peticolas,
“Laser Raman investigation of the effect of cholesterol on conformational changes in dipalmitoyl lecithin multilayers,”
Biochim. Biophys. Acta, 282 8
–17
(1972). http://dx.doi.org/10.1073/pnas.68.7.1572 BBACAQ 0006-3002 Google Scholar
Y. M. Weng et al.,
“Structural analysis of triacylglycerols and edible oils by near-infrared Fourier transform Raman spectroscopy,”
Appl. Spectrosc., 57 413
–418
(2003). http://dx.doi.org/10.1366/00037020360625952 APSPA4 0003-7028 Google Scholar
M. F. Mrozek and M. J. Weaver,
“Detection and identification of aqueous saccharides by using surface-enhanced Raman spectroscopy,”
Anal. Chem., 74 4069
–4075
(2002). http://dx.doi.org/10.1021/ac020115g ANCHAM 0003-2700 Google Scholar
R. J. Weesie et al.,
“Semiempirical and Raman spectroscopic studies of carotenoids,”
Biospectroscopy, 5 19
–33
(1999). http://dx.doi.org/10.1002/(ISSN)1520-6343 BIOSFS 1075-4261 Google Scholar
K. Maquelin et al.,
“Identification of medically relevant microorganisms by vibrational spectroscopy,”
J. Microbiol. Methods, 51 255
–271
(2002). http://dx.doi.org/10.1016/S0167-7012(02)00127-6 JMIMDQ 0167-7012 Google Scholar
K. De Gussem et al.,
“Raman spectroscopic study of Lactarius spores (Russulales, Fungi),”
Spectrochim. Acta A, 61 2896
–2908
(2005). http://dx.doi.org/10.1016/j.saa.2004.10.038 SAMCAS 1386-1425 Google Scholar
H. G. M. Edwards et al.,
“Non-destructive analysis of pigments and other organic compounds in lichens using Fourier-transform Raman spectroscopy: a study of Antarctic epilithic lichens,”
Spectrochim. Acta A, 59 2301
–2309
(2003). http://dx.doi.org/10.1016/S1386-1425(03)00073-8 SAMCAS 1386-1425 Google Scholar
V. Baeten,
“Raman spectroscopy in lipid analysis,”
Lipid Technol., 22
(2), 36
–38
(2010). http://dx.doi.org/10.1002/lite.200900082 LITEEI Google Scholar
J. De Gelder et al.,
“Reference database of Raman spectra of biological molecules,”
J. Raman Spectrosc., 38 1133
–1147
(2007). http://dx.doi.org/10.1002/(ISSN)1097-4555 JRSPAF 0377-0486 Google Scholar
G. J ThomasJr.,
“Raman spectroscopy of protein and nucleic acid assemblies,”
Annu. Rev. Biophys. Biomol. Struct., 28 1
–27
(1999). http://dx.doi.org/10.1146/annurev.biophys.28.1.1 ABBSE4 1056-8700 Google Scholar
C. Raschke and P. Elsner,
“Skin aging: a brief summary of characteristic changes,”
Textbook of Aging Skin, 37
–44 Springer-Verlag, Berlin
(2010). Google Scholar
F. H. Silver, Mechanosensing and Mechanochemical Transduction in Extracellular Matrix Biological Chemical Engineering and Physiological Aspects, Springer, New York
(2006). Google Scholar
J. Rosenbloom, W. R. Abrams and R. Mecham,
“Extracellular matrix 4: the elastic fiber,”
FASEB J., 7
(13), 1208
–1218
(1993). FAJOEC 0892-6638 Google Scholar
M. Gniadecka et al.,
“Structure of water proteins and lipids in intact human skin hair and nail,”
J. Invest. Dermatol., 110 393
–398
(1998). http://dx.doi.org/10.1046/j.1523-1747.1998.00146.x JIDEAE 0022-202X Google Scholar
W. Akhtar and H. G. M. Edwards,
“Fourier-transform Raman spectroscopy of mammalian and avian keratotic biopolymers,”
Spectrochim. Acta A, 53 81
–90
(1997). http://dx.doi.org/10.1016/S1386-1425(97)83011-9 SAMCAS 1386-1425 Google Scholar
H. G. M. Edwards, A. C. Williams and B.W. Barry,
“Potential applications of FT-Raman spectroscopy for dermatological diagnostics,”
J. Mol. Struct., 347 358
–379
(1995). http://dx.doi.org/10.1016/0022-2860(95)08560-I JMOSB4 0022-2860 Google Scholar
R. Manoharan, Y. Wang and M. S. Feld,
“Histochemical analysis of biological tissues using Raman spectroscopy,”
Spectrochim. Acta, 52 215
–249
(1996). http://dx.doi.org/10.1016/0584-8539(95)01573-6 SPACA5 0038-6987 Google Scholar
N. Stone et al.,
“Near-infrared Raman spectroscopy for the classification of epithelial pre-cancers and cancers,”
J. Raman Spectrosc., 33 564
–573
(2002). http://dx.doi.org/10.1002/(ISSN)1097-4555 JRSPAF 0377-0486 Google Scholar
W. T. Cheng et al.,
“Micro-Raman spectroscopy used to identify and grade human skin pilomatrixoma,”
Microsc. Res. Tech., 68
(2), 75
–79
(2005). http://dx.doi.org/10.1002/(ISSN)1097-0029 MRTEEO 1059-910X Google Scholar
R. Dong et al.,
“Temperature-dependent Raman spectra of collagen and DNA,”
Spectrochim. Acta A, 60 557
–561
(2004). http://dx.doi.org/10.1016/S1386-1425(03)00262-2 SAMCAS 1386-1425 Google Scholar
W. T. Cheng et al.,
“Micro-Raman spectroscopy used to identify and grade human skin pilomatrixoma,”
Microsc. Res. Tech., 68
(2), 75
–79
(2005). http://dx.doi.org/10.1002/(ISSN)1097-0029 MRTEEO 1059-910X Google Scholar
M. Polomska et al.,
“Fourier transform near infrared Raman spectroscopy in studies on connective tissue,”
Acta Phys. Pol. A, 118
(1), 136
–140
(2010). ATPLB6 0587-4246 Google Scholar
M. G. Shim and B. C. Wilson,
“Development of an in vivo Raman spectroscopic system for diagnostic applications,”
J. Raman Spectrosc., 28 131
–142
(1997). http://dx.doi.org/10.1002/(ISSN)1097-4555 JRSPAF 0377-0486 Google Scholar
H. G. M. Edwards et al.,
“NIR-FT Raman spectroscopy as a diagnostic probe for mummified skin and nails,”
Vibr. Spectrosc., 28 3
–15
(2002). http://dx.doi.org/10.1016/S0924-2031(01)00141-2 VISPEK 0924-2031 Google Scholar
S. M. Ali et al.,
“Raman spectroscopic analysis of human skin tissue sections ex-vivo: evaluation of the effects of tissue processing and dewaxing,”
J. Biomed. Opt., 18
(6), 061202
(2013). http://dx.doi.org/10.1117/1.JBO.18.6.061202 JBOPFO 1083-3668 Google Scholar
Z. Huang et al.,
“Near-infrared Raman spectroscopy for optical diagnosis of lung cancer,”
Int. J. Cancer, 107 1047
–1052
(2003). http://dx.doi.org/10.1002/(ISSN)1097-0215 IJCNAW 1097-0215 Google Scholar
Z. Huang et al.,
“Raman spectroscopy in combination with background near-infrared autofluorescence enhances the in vivo assessment of malignant tissues,”
Photochem. Photobiol., 81 1219
–1226
(2005). http://dx.doi.org/10.1562/2005-02-24-RA-449 PHCBAP 0031-8655 Google Scholar
F. S. Parker, Application of Infrared Raman and Resonance Raman Spectroscopy in Biochemistry, Plenum Press, New York
(1983). Google Scholar
T. M. Greve, K. B. Andersen and O. F. Nielsen,
“ATR-FTIR FT- NIR and near-FT-Raman spectroscopic studies of molecular composition in human skin in vivo and pig ear skin in vitro,”
Spectroscopy, 22
(6), 437
–457
(2008). SPECET 0887-6703 Google Scholar
P. R. T. Jess et al.,
“Early detection of cervical neoplasia by Raman spectroscopy,”
Int. J. Cancer, 121 2723
–2728
(2007). http://dx.doi.org/10.1002/(ISSN)1097-0215 IJCNAW 1097-0215 Google Scholar
R. K. Dukor,
“Vibrational spectroscopy in the detection of cancer,”
Handbook of Vibrational Spectroscopy, 3335
–3359 John Wiley & Sons, Ltd, Chichester
(2002). Google Scholar
D. P. Lau et al.,
“Raman spectroscopy for optical diagnosis in the larynx: preliminary findings,”
Lasers Surg. Med., 37 192
–200
(2005). http://dx.doi.org/10.1002/(ISSN)1096-9101 LSMEDI 0196-8092 Google Scholar
N. Stone et al.,
“Raman spectroscopy for identification of epithelial cancers,”
Faraday Discuss., 126 141
–157
(2004). http://dx.doi.org/10.1039/b304992b FDISE6 0301-7249 Google Scholar
R. E. Kast et al.,
“Raman spectroscopy can differentiate malignant tumors from normal breast tissue and detect early neoplastic changes in a mouse model,”
Biopolymer, 89 134
–141
(2008). http://dx.doi.org/10.1002/(ISSN)1097-0282 BIPMAA 0006-3525 Google Scholar
B. P. Gaber and W. L. Peticolas,
“On the quantitative interpretation of biomembrane structure by Raman spectroscopy,”
Biochim. Biophys. Acta, 465 260
–268
(1977). http://dx.doi.org/10.1016/0005-2736(77)90078-5 BBACAQ 0006-3002 Google Scholar
R. Calheiros et al.,
“Antioxidant phenolic esters with potential anticancer activity: a Raman spectroscopic study,”
J. Raman Spectrosc., 39 95
–107
(2008). http://dx.doi.org/10.1002/jrs.v39:1 JRSPAF 0377-0486 Google Scholar
G. Shetty et al.,
“Raman spectroscopy: evaluation of biochemical changes in carcinogenesis of oesophagus,”
Br. J. Cancer, 94 1460
–1464
(2006). http://dx.doi.org/10.1038/sj.bjc.6603102 BJCAAI 0007-0920 Google Scholar
H. Schultz and M. Baranska,
“Identification and qualification of valuable plant substances by IR and Raman spectroscopy,”
Vib. Spectrosc., 43 13
–25
(2007). http://dx.doi.org/10.1016/j.vibspec.2006.06.001 VISPEK 0924-2031 Google Scholar
R. Agarwal, P. Tandon and V. D. Gupta,
“Phonon dispersion in poly(dimethylsilane),”
J. Organomet. Chem., 691 2902
–2908
(2006). http://dx.doi.org/10.1016/j.jorganchem.2006.02.032 JORCAI 0022-328X Google Scholar
C. Krafft et al.,
“Near-infrared Raman spectroscopy of human brain lipids,”
Spectrochim Acta A Mol. Biomol. Spectrosc., 61
(7), 1529
–1535
(2005). SPACA5 0038-6987 Google Scholar
G. I. Dovbeshka et al.,
“FTIR spectroscopy studies of nucleic acid damage,”
Talanta, 53 233
–246
(2000). http://dx.doi.org/10.1016/S0039-9140(00)00462-8 TLNTA2 0039-9140 Google Scholar
M. Gniadecka et al.,
“Diagnosis of basal cell carcinoma by Raman spectroscopy,”
J. Raman Spectrosc., 28 125
–129
(1997). http://dx.doi.org/10.1002/(ISSN)1097-4555 JRSPAF 0377-0486 Google Scholar
R. Heckel et al.,
“Characteristic infrared spectroscopic patterns in the protein bands of human breast cancer tissue,”
Vib. Spectrosc., 27 165
–173
(2001). http://dx.doi.org/10.1016/S0924-2031(01)00134-5 VISPEK 0924-2031 Google Scholar
S. Sigurdsson et al.,
“Detection of skin cancer by classification of Raman spectra,”
IEEE Trans. Biomed. Eng., 51 1784
–1793
(2004). http://dx.doi.org/10.1109/TBME.2004.831538 IEBEAX 0018-9294 Google Scholar
A. Singha et al.,
“Quantitative analysis of hydrogenated diamond-like carbon films by visible Raman spectroscopy,”
J. Appl. Phys., 100 1
–8
(2006). http://dx.doi.org/10.1063/1.2219983 JAPIAU 0021-8979 Google Scholar
A. R. Viehoever et al.,
“Organotypic raft cultures as an effective in vitro tool for understanding Raman spectral analysis of tissue,”
Photochem. Photobiol., 78 517
–524
(2003). http://dx.doi.org/10.1562/0031-8655(2003)078<0517:ORCAAE>2.0.CO;2 PHCBAP 0031-8655 Google Scholar
H. P. Wang, H. C. Wang and Y. J. Huang,
“Microscopic FTIR studies of lung cancer cells in pleural fluid,”
Sci. Total Environ., 204 283
–287
(1997). http://dx.doi.org/10.1016/S0048-9697(97)00180-0 STENDL 0048-9697 Google Scholar
Y. G. Hu, A. G. Shen and T. Jiang,
“Classification of normal and malignant human gastric mucosa tissue with confocal Raman microspectroscopy and wavelet analysis,”
Petrochem. Acta A Mol. Biomol. Spectrosc., 69 378
–382
(2008). http://dx.doi.org/10.1016/j.saa.2007.04.009 Google Scholar
G. I. Dovbeshka et al.,
“Surface enhanced IR absorption of nucleic acids from tumor cells: FTIR reflectance study,”
Biopolymer, 67 470
–486
(2002). http://dx.doi.org/10.1002/(ISSN)1097-0282 BIPMAA 0006-3525 Google Scholar
Y. Fukuyama et al.,
“A study on the differences between oral squamous cell carcinimas and normal oral mucosas measured by Fourier transform infrared spectroscopy,”
Biospectroscopy, 5 117
–126
(1999). http://dx.doi.org/10.1002/(ISSN)1520-6343 BIOSFS 1075-4261 Google Scholar
Q. B. Li et al.,
“Diagnosis of gastric inflammation and malignancy in endoscopic biopsies based on Fourier transform infrared spectroscopy,”
Clin. Chem., 51 346
–350
(2005). http://dx.doi.org/10.1373/clinchem.2004.037986 CLCHAU 0009-9147 Google Scholar
J. W. Chan et al.,
“Micro-Raman spectroscopy detects individual neoplastic and normal hematopoietic cells,”
Biophys. J., 90 648
–656
(2006). http://dx.doi.org/10.1529/biophysj.105.066761 BIOJAU 0006-3495 Google Scholar
R. J. Lakshimi et al.,
“Tissue Raman spectroscopy for the study of radiation damage: brain irradiation of mice,”
Radiat. Res., 157 175
–182
(2002). http://dx.doi.org/10.1667/0033-7587(2002)157[0175:TRSFTS]2.0.CO;2 RAREAE 0033-7587 Google Scholar
T. Richter et al.,
“Identification of tumor tissue by FTIR spectroscopy in combination with position emission tomography,”
Vib. Spectrosc., 28 103
–110
(2002). http://dx.doi.org/10.1016/S0924-2031(01)00149-7 VISPEK 0924-2031 Google Scholar
E. O. Faolain et al.,
“A study examining the effects of tissue processing on human tissue sections using vibrational spectroscopy,”
Vib. Spectrosc., 38 121
–127
(2005). http://dx.doi.org/10.1016/j.vibspec.2005.02.013 VISPEK 0924-2031 Google Scholar
M. Huleihel et al.,
“Novel optical method for study of viral carcinogenesis in vitro,”
J. Biochem. Biophys. Methods, 50 111
–121
(2002). http://dx.doi.org/10.1016/S0165-022X(01)00177-4 JBBMDG 0165-022X Google Scholar
J. A. Bouwstra et al.,
“Phase behavior of lipid mixtures based on human ceramides: coexistence of crystalline and liquid phases,”
J. Lipid Res., 42
(11), 1759
–1770
(2001). JLPRAW 0022-2275 Google Scholar
F. Damien and M. Boncheva,
“The extent of orthorhombic lipid phases in the stratum corneum determines the barrier efficiency of human skin in vivo,”
J. Invest. Dermatol., 130 611
–614
(2010). http://dx.doi.org/10.1038/jid.2009.272 JIDEAE 0022-202X Google Scholar
F. M. Lyng et al.,
“Vibrational spectroscopy for cervical cancer pathology from biochemical analysis to diagnostic tool,”
Exp. Mol. Pathol., 82 121
–129
(2007). http://dx.doi.org/10.1016/j.yexmp.2007.01.001 EXMPA6 0014-4800 Google Scholar
A. N. C. Anigbogu et al.,
“Fourier transform Raman spectroscopy of interactions between the penetration enhancer dimethyl sulfoxide and human stratum corneum,”
Int. J. Pharm., 125 265
–282
(1995). http://dx.doi.org/10.1016/0378-5173(95)00141-5 IJPHDE 0378-5173 Google Scholar
L. Knudsen et al.,
“Natural variations and reproducibility of in vivo near-infrared Fourier transform Raman spectroscopy of normal human skin,”
J. Raman Spectrosc., 33 574
–579
(2002). http://dx.doi.org/10.1002/(ISSN)1097-4555 JRSPAF 0377-0486 Google Scholar
R. Paquin and P. Colomban,
“Nanomechanics of single keratin fibres: a Raman study of the transition and water effect,”
J. Raman Spectrosc., 38 504
–514
(2007). http://dx.doi.org/10.1002/(ISSN)1097-4555 JRSPAF 0377-0486 Google Scholar
N. J. Kline and P. J. Treado,
“Raman chemical imaging of breast tissue,”
J. Raman Spectrosc., 28 119
–124
(1997). http://dx.doi.org/10.1002/(ISSN)1097-4555 JRSPAF 0377-0486 Google Scholar
M. Gniadecka, O. F. Nielsen and H. C. Wulf,
“Water content and structure in malignant and benign skin tumours,”
J. Mol. Struct., 661–662 405
–410
(2003). http://dx.doi.org/10.1016/j.molstruc.2003.08.030 JMOSB4 0022-2860 Google Scholar
N. Nakagawa and M. Matsumoto, and Sakai,
“In vivo measurement of the water content in the dermis by confocal Raman spectroscopy,”
Skin Res. Technol., 16 137
–141
(2010). http://dx.doi.org/10.1111/srt.2010.16.issue-2 0909-752X Google Scholar
S. Fendel and B. Schrader,
“Investigation of skin and skin lesions by NIR-FT-Raman spectroscopy,”
Fresenius J. Anal. Chem., 360 609
–613
(1998). http://dx.doi.org/10.1007/s002160050767 FJACES 0937-0633 Google Scholar
M. Egawa, T. Hirao and M. Takahashi,
“In vivo estimation of stratum corneum thickness from water concentration profiles obtained with Raman spectroscopy,”
Acta Dermatol. Venereol., 87
(1), 4
–8
(2007). http://dx.doi.org/10.2340/00015555-0183 ADVEA4 0001-5555 Google Scholar
S. Olsztynska-Janus et al.,
“Spectroscopic techniques in the study of human tissues and their components. Part II: Raman spectroscopy,”
Acta Bioeng. Biomech., 14
(4), 121
–133
(2012). 1509-409X Google Scholar
P. J. Caspers et al.,
“In vivo confocal Raman microspectroscopy of the skin: Non-invasive determination of molecular concentration profiles,”
J. Invest. Dermatol., 116 434
–442
(2001). http://dx.doi.org/10.1046/j.1523-1747.2001.01258.x JIDEAE 0022-202X Google Scholar
T. Oda et al.,
“Effect of oral intake of ceramide-containing acetic acid bacteria on skin barrier function,”
Antiaging Med., 7
(5), 50
–54
(2010). JAMEF8 1094-5458 Google Scholar
V. Renugopalakrishnan et al.,
“Non-uniform triple helical structure in chick skin type I collagen on thermal denaturation: Raman spectroscopic study,”
Z. Naturforsch., C53
(5–6), 383
–388
(1998). ZNTFA2 0372-9516 Google Scholar
M. G. Tosato et al.,
“Raman spectroscopic investigation of the effects of cosmetic formulations on the constituents and properties of human skin,”
Photomed. Laser Surg., 30 85
–91
(2012). http://dx.doi.org/10.1089/pho.2011.3059 PLDHA8 1549-5418 Google Scholar
J.-H. Choi and M. Cho,
“Calculations of intermode coupling constants and simulations of amide I, II, and III vibrational spectra of dipeptides,”
Chem. Phys., 361 168
–175
(2009). http://dx.doi.org/10.1016/j.chemphys.2009.05.016 CMPHC2 0301-0104 Google Scholar
M. Gniadecka et al.,
“Water and protein structure in photoaged and chronically aged skin,”
J. Invest. Dermatol., 111 1129
–1132
(1998). http://dx.doi.org/10.1046/j.1523-1747.1998.00430.x JIDEAE 0022-202X Google Scholar
K. R. Feingold,
“The importance of lipids in cutaneous function,”
J. Lipid Res., 48 2529
–2530
(2007). http://dx.doi.org/10.1194/jlr.E700004-JLR200 JLPRAW 0022-2275 Google Scholar
E. J. Kim et al.,
“UV decreases the synthesis of free fatty acids and triglycerides in the epidermis of human skin in vivo, contributing to development of skin photoaging,”
J. Dermatol. Sci., 57 19
–26
(2010). http://dx.doi.org/10.1016/j.jdermsci.2009.10.008 JDSCEI 0923-1811 Google Scholar
R. Ghadially et al.,
“The aged epidermal permeability barrier: Structural, functional, and lipid biochemical abnormalities in humans and a senescent murine model,”
J. Clin. Invest., 95 2281
–2290
(1995). http://dx.doi.org/10.1172/JCI117919 JCINAO 0021-9738 Google Scholar
S. Krimm and J. Bandekar,
“Vibrational spectroscopy and conformation of peptides, polypeptides, and proteins,”
Adv. Protein Chem., 38 181
–365
(1986). http://dx.doi.org/10.1016/S0065-3233(08)60528-8 APCHA2 0065-3233 Google Scholar
A. M. Herrero,
“Raman spectroscopy a promising technique for quality assessment of meat and fish: a review,”
Food Chem., 107 1642
–1651
(2008). http://dx.doi.org/10.1016/j.foodchem.2007.10.014 FOCHDJ 0308-8146 Google Scholar
E. C. Y. Li-Chan,
“The applications of Raman spectroscopy in food science,”
Trends Food Sci.Technol., 7 361
–370
(1996). http://dx.doi.org/10.1016/S0924-2244(96)10037-6 TFTEEH 0924-2244 Google Scholar
M. Bouraoui, S. Nakai and E. C. Y. Li-Chan,
“In situ investigation of protein structure in Pacific whiting surimi and gels using Raman spectroscopy,”
Food Res. Int., 30 65
–72
(1997). http://dx.doi.org/10.1016/S0963-9969(97)00020-3 FORIEU 0963-9969 Google Scholar
A. J. P. Alix, G. Pedanou and M. Berjot,
“Determination of the quantitative secondary structure of proteins by using some parameters of the Raman amide I band,”
J. Mol. Struct., 174 159
–164
(1988). http://dx.doi.org/10.1016/0022-2860(88)80151-0 JMOSB4 0022-2860 Google Scholar
J.-H. Shao et al.,
“Evaluation of structural changes in raw and heated meat batters prepared with different lipids using Raman spectroscopy,”
Food Res. Int., 44 2955
–2961
(2011). http://dx.doi.org/10.1016/j.foodres.2011.07.003 FORIEU 0963-9969 Google Scholar
P. J. Caspers et al.,
“In vitro and in vivo spectroscopy of human skin,”
Biospectroscopy, 4 S31
–S39
(1998). http://dx.doi.org/10.1002/(ISSN)1520-6343 BIOSFS 1075-4261 Google Scholar
A. Tfayli et al.,
“Discriminating nevus and melanoma on paraffin-embedded skin biopsies using FTIR microspectroscopy,”
Biochim. Biophys. Acta, 1724 262
–269
(2005). http://dx.doi.org/10.1016/j.bbagen.2005.04.020 BBACAQ 0006-3002 Google Scholar
F. H. Silver, J. W. Freeman and D. DeVore,
“Viscoelastic properties of human skin and processed dermis,”
Skin Res. Technol., 7 18
–23
(2001). http://dx.doi.org/10.1034/j.1600-0846.2001.007001018.x 0909-752X Google Scholar
V. N. Uversky, C. J. Oldfield and A. K. Dunker,
“Intrinsically disordered proteins in human diseases: introducing the D2 concept,”
Annu. Rev. Biophys., 37 215
–246
(2008). http://dx.doi.org/10.1146/annurev.biophys.37.032807.125924 ARBNCV 1936-122X Google Scholar
A. L. Fink,
“Protein aggregation: folding aggregates, inclusion bodies and amyloid,”
Folding Des., 3
(1), R9
–R23
(1998). http://dx.doi.org/10.1016/S1359-0278(98)00002-9 FODEFH 1359-0278 Google Scholar
P. Leandro and C. M. Gomes,
“Protein misfolding in conformational disorders: rescue of folding defects and chemical chaperoning,”
Mini Rev. Med. Chem., 8 901
–911
(2008). http://dx.doi.org/10.2174/138955708785132783 1389-5575 Google Scholar
E. Ly et al.,
“Polarized Raman microspectroscopy can reveal structural changes of peritumoral dermis in basal cell carcinoma,”
Appl. Spectrosc., 62 1088
–1094
(2008). http://dx.doi.org/10.1366/000370208786049187 APSPA4 0003-7028 Google Scholar
S. Federman, L. M. Miller and I. Sagi,
“Following matrix metalloproteinases activity near the cell boundary by infrared micro-spectroscopy,”
Matrix Biol., 21 567
–577
(2002). http://dx.doi.org/10.1016/S0945-053X(02)00089-6 MTBOEC 0945-053X Google Scholar
B. D. McKersie and J. E. Thompson,
“Lipid crystallization in senescent membranes from cotyledons,”
Plant Physiol., 59 803
–807
(1977). http://dx.doi.org/10.1104/pp.59.5.803 PLPHAY 0032-0889 Google Scholar
D. M. Small,
“Progression and regression of atherosclerotic lesions. Insights from lipid physical biochemistry,”
Arterioscler. Thromb. Vasc. Biol., 8 103
–129
(1988). http://dx.doi.org/10.1161/01.ATV.8.2.103 ATVBFA 1079-5642 Google Scholar
K. Matsuzaki,
“Physicochemical interactions of amyloid with lipid bilayers,”
Biochem. Biophys. Acta Biomembr., 1768
(8), 1935
–1942
(2007). http://dx.doi.org/10.1016/j.bbamem.2007.02.009 0005-2736 Google Scholar
H. M. Garvin et al.,
“Developments in forensic anthropology: age-at-death estimation,”
10 in A Companion to Forensic Anthropology, 202
–223 1st ed.Wiley-Blackwell Publishing Ltd., Chichester, West Sussex, United Kingdom
(2012). Google Scholar
P. H. Whitehead,
“Biochemical techniques in forensic science,”
Trends Biochem. Sci., 10
(8), 299
–302
(1985). http://dx.doi.org/10.1016/0968-0004(85)90167-7 TBSCDB 0167-7640 Google Scholar
S. Ritz, A. Turzynski and H. W. Schutz,
“Estimation of age at death based on aspartic acid racemization in noncolagenous bone proteins,”
Forensic Sci. Int., 69
(2), 149
–159
(1994). http://dx.doi.org/10.1016/0379-0738(94)90251-8 FSINDR 0379-0738 Google Scholar
P. M. Helfman and J. L. Bada,
“Aspartic acid racemization in tooth enamel from living humans,”
Proc. Nat. Acad. Sci. U. S. A., 72
(8), 2891
–2894
(1975). http://dx.doi.org/10.1073/pnas.72.8.2891 1091-6490 Google Scholar
E. Baccino and A. Schmitt,
“Determination of adult age at death in the forensic context,”
Forensic Anthropology and Medicine: Complementary Sciences from Recovery to Cause of Death, 259
–280 Humana Press Inc., Totowa, New Jersey, United States
(2006). Google Scholar
S. I. Kvaal et al.,
“Age estimation of adults from dental radiographs,”
Forensic Sci. Int., 74 175
–185
(1995). http://dx.doi.org/10.1016/0379-0738(95)01760-G FSINDR 0379-0738 Google Scholar
T. H. Hsiao, H. P. Chang and K. M. Liu,
“Sex determination by discriminant function analysis of lateral radiographic cephalometry,”
J. Forensic Sci., 41
(5), 792
–795
(1996). JFSCAS 0022-1198 Google Scholar
A. H. Kuptsov,
“Applications of Fourier transform Raman spectroscopy in forensic science,”
J. Forensic Sci., 39
(2), 305
–318
(1994). JFSCAS 0022-1198 Google Scholar
M. Clayboum and M. Ansell,
“Using Raman spectroscopy to solve crime: inks, questioned documents and fraud,”
Sci. Justice, 40
(4), 261
–271
(2000). http://dx.doi.org/10.1016/S1355-0306(00)71996-4 SJUSFE 1355-0306 Google Scholar
A. Osmani, O. Gamulin and M. Vodanovic,
“Age estimation of teeth with Raman spectrometry: preliminary study,”
Bull. Int. Assoc. Paleodontol., 8
(1), 137
–143
(2014). 1846-6273 Google Scholar
P. Tramini et al.,
“A method of age estimation using Raman microspectrometry imaging of the human dentin,”
Forensic Sci. Int., 118
(1), 1
–9
(2001). http://dx.doi.org/10.1016/S0379-0738(00)00352-2 FSINDR 0379-0738 Google Scholar
F. Kosa, A. Antal and I. Farkas,
“Electron probe microanalysis of human teeth for the determination of individual age,”
Med. Sci. Law, 30
(2), 109
–114
(1990). MDSLA6 0025-8024 Google Scholar
S. Strasser et al.,
“Age determination of blood spots in forensic medicine by force spectroscopy,”
Forensic Sci. Int., 170 8
–14
(2007). http://dx.doi.org/10.1016/j.forsciint.2006.08.023 FSINDR 0379-0738 Google Scholar
K. L. Spalding et al.,
“Forensics: age written in teeth by nuclear tests,”
Nature, 437 333
–334
(2005). http://dx.doi.org/10.1038/437333a NATUAS 0028-0836 Google Scholar
B. A. Buchholz and K. L. Spalding,
“Year of birth determination using radiocarbon dating of dental enamel,”
Surf. Interfaces Anal., 42 398
–401
(2010). http://dx.doi.org/10.1002/sia.v42:5 SIANDQ 0142-2421 Google Scholar
W. M. Holleran et al.,
“Serine-palmitoyl transferase activity in cultured human keratinocytes,”
J. Lipid Res., 31
(9), 1655
–1661
(1990). JLPRAW 0022-2275 Google Scholar
W. M. Holleran et al.,
“Regulation of epidermal sphingolipid synthesis by permeability barrier function,”
J. Lipid Res., 32
(7), 1151
–1158
(1991). JLPRAW 0022-2275 Google Scholar
W. M. Holleran et al.,
“Sphingolipids are required for mammalian epidermal barrier function: inhibition of sphingolipid synthesis delays barrier recovery after acute penetration,”
J. Clin. Invest., 88 1338
–1345
(1991). http://dx.doi.org/10.1172/JCI115439 JCINAO 0021-9738 Google Scholar
BiographyGiuseppe Pezzotti is full tenured professor and leader of the Ceramic Physics Laboratory at the Kyoto Institute of Technology, Japan. He graduated summa cum laude in mechanical engineering from Rome University “La Sapienza,” Italy, he holds three doctoral degrees in materials engineering, solid state physics, and medical sciences, and is author/coauthor of 570 scientific papers, 1 book, 13 book chapters, and 8 patents, including a world patent on nanoscale stress microscopy in the scanning electron microscope. Marco Boffelli is a third year PhD student in material science at Kyoto Institute of Technology. He joined professor Pezzotti’s research group in 2012 after receiving his diploma in material science from the Ca' Foscari University in Venice in the same year. His research interest includes ceramic biomaterials synthesis and characterization mainly with Raman spectroscopy, scanning electron microscopy, and cathodoluminescence. Daisuke Miyamori is a lecturer in the Department of Forensic Medicine, Kyoto Prefectural University of Medicine (KPUM). He graduated from Kyushu University. He was working at the emergency department of Shonankamakura Tokushukai Hospital before he got his position at KPUM. He is a licensed medical doctor and specialized in emergency medicine. Takeshi Uemura is a lecturer in the Department of Forensic Medicine, Kyoto Prefectural University of Medicine (KPUM). He graduated from Toho University and the Graduate School of Pharmaceutical Science, Chiba University. He was working at Arizona University before he got his position at KPUM. He has a pharmacist license. He has been doing his main research about polyamine. Here, he is studying the relationship between polyamine and age. Yoshinori Marunaka, MD, PhD, is professor and chairperson in the Departments of Molecular Cell Physiology and Bio-Ionomics, Kyoto Prefectural University of Medicine, Japan. He is director and professor at the Japan Institute for Food Education and Health and St. Agnes’ University. He is the president-elect of the Physiological Society of Japan, and Editor-in-Chief of the Journal of Physiological Sciences. His current research focuses on roles of H+ and Cl- in cell function, including cell growth, neurite elongation, diabetes mellitus etc. Wenliang Zhu got his PhD in material sciences in Kyoto Institute of Technology in 2005 and is now a lecturer in the Medical School of Osaka University. His main research field is on the development and application of high resolution photo-/electrostimulated spectroscopies for quantitative analysis of structural, mechanical, and chemical properties in single crystalline and polycrystalline ceramics. Hiroshi Ikegaya is a professor and chairman of the Department of Forensic Medicine, Kyoto Prefectural University of Medicine (KPUM). He graduated from Miyazaki Medical College and Graduate School of Medicine, The University of Tokyo. He was working at National Research Institute of Police Science before he got his position at KPUM. He has a medical doctorate and architect license. He has been doing his main research about development of individual identification methods. |