2.1.

Materials

Back and belly skin from Yucatan white, hairless pigs was received from Sinclair Bioresources (Auxvasse, Missouri). All tissues were harvested only as waste byproducts from animals aged three to six months. Tissues were only rinsed with water prior to shipment and were not frozen. A Padgett Electric Dermatome (Plainsboro, New Jersey) was used to isolate the top 500 μm of the skin. Pieces of the dermatomed skin, $\sim 1{\mathrm{cm}}^2$, were placed between a glass microscope slide and No. 1.5 glass cover slip (VWR, Radnor, Pennsylvania) for SRS transmission microscopy. $\text{Oleic acid}\text{-}{\mathrm{d}}_{34}$ (Sigma-Aldrich, St. Louis, Missouri) was applied directly to the skin surface using a positive displacement pipette (Gilson, Middleton, Wisconsin) to deliver 2 μL onto a $1\text{-}{\mathrm{cm}}^2$ treatment area.

2.2.

SRS Microscopic Imaging

The details of SRS have been previously described.^13,21 The skin sample is mounted on the stage of a modified upright laser scanning microscope (BX81, Olympus, Pittsburgh, Pennsylvania) configured for transmission illumination. Two overlapping laser beams are focused on the sample with either a UPLSAPO20X $20\times$ air objective or a UPLSAPO60XW $60\times 1.2\text{-}\mathrm{NA}$ water immersion objective (Olympus). The 1064-nm Stokes beam source was an $\mathrm{Nd}:{\mathrm{YVO}}_4$ laser (picoTRAIN, High-Q, Watertown, Massachusetts). Part of the 1064-nm output is used to pump a tunable optical parametric oscillator (Levante Emerald, APE-Berlin, Berlin, Germany). The pump beam was tuned to 816.0, 810.6, or 781.3 nm for skin imaging, corresponding to Raman shifts of ca. 2850, 2950, or $3340{\mathrm{cm}}^{-1}$, respectively. The main contributors from skin at these vibrational frequencies can be assigned to vibrational modes from lipids, proteins, and water²² (Table 1). The pump laser was tuned to 869.2 nm to image the $\nu (\mathrm{C}\text{-}\mathrm{D})$ vibrational mode at $2105{\mathrm{cm}}^{-1}$ after topical application of $\text{oleic acid}\text{-}{\mathrm{d}}_{34}$, ensuring that only the applied material would be visible in the SRS image stack. Image integration time was typically 4 to $8\mu \text{s}/\text{pixel}$. The beams were scanned in two dimensions with a pair of galvanometer mirrors (Olympus fluosview FV-300). The transmitted light was collected with a 1.4-NA oil condenser lens (Nikon, Melville, New York) and detected by a large-area photodiode (FDS1010, Thorlabs, Newton, New Jersey). Images were collected parallel to the skin surface. Image stacks were obtained through sample stage movements controlled by the microscope software, with a step size of 2 μm over depth ranges of 60 to 100 μm. An image stack at a single wavelength can be acquired in 30 to 60 s.

2.3.

SRS Image Analysis

The three associated image stacks (lipid, protein, and water) were combined into a four-dimensional (4-D) array (X,Y,Z, wavelength) in MATLAB® (version 2009a, MathWorks, Natick, Massachusetts). The region of interest (ROI) (cell cluster and/or canyon) was designated on the water image using a rectangular area selection tool that records the pixel location values for all selected points. Depth profiles for each pixel location in the ROI are extracted from the 4-D array for each wavelength. The water profiles for clusters and canyons were computed using the method previously reported for in vivo confocal Raman spectroscopic measurements.²³ Each water profile was normalized against the corresponding protein depth profile with a calibration factor of 1. The SRS technique minimizes background response [typically $<1\%$ of the peak $\nu ({\mathrm{CH}}_2)$ intensity¹⁵], but a small profile is present in the image response. The background signal is neither spectroscopic nor resonant in origin. The magnitude of this response will change slightly at different regions of the Raman spectrum, but since the wavelengths we are using are very near to each other, the ratio method used for analysis will cancel out background effects. Additionally, the method proposed by Caspers et al. is, at best, an approximation due to their use of overlapping spectral bands for protein and water.²³ A pure response is, therefore, never achieved, so any calculation for the constant (R) can only ever yield an approximate value because it is dependent on the calculated values for water and protein. We used the same equation but replaced $\mathrm{R}=2$ with the arbitrary value of $\mathrm{R}=1$. This maintains the same profile shape but changes the absolute scale. Depth profile plots are presented as relative water content. The water content approximation, therefore, was modeled after the Caspers method as $(\text{percent relative water content})=\{(\mathrm{W}/\mathrm{P})/[(\mathrm{W}/\mathrm{P})+\mathrm{R}]\}*100$, where $\mathrm{W}$ and $\mathrm{P}$ are the intensities from the water and protein images, respectively, and $\mathrm{R}=1$. $\mathrm{W}$ and $\mathrm{P}$ represent single-wavelength intensities from the SRS images, whereas the confocal in vivo Raman spectroscopic method employs integrated wavelength ranges. The water content values reported here are averaged over all pixels in the ROI. Lipid content values are also generated by a similar protocol, with the lipid intensity L replacing water intensity $\mathrm{W}$ in the above equation.

Cross-sections calculated across canyon regions were generated by selecting a rectangular ROI of the water image stack and averaging across the planes to form a single water depth profile. The resulting images are displayed using a relative normalized scale from 0 to 255, corresponding to the gray-scale range of the original images. The red false color indicates high water content and the blue false color represents low water content. The overlaid 3-D surface contour images of $\text{oleic acid}\text{-}{\mathrm{d}}_{34}$ and the skin protein microstructure were generated in Amira (version 5.4, Visage Imaging, San Diego, California). False colors were selected to maximize contrast between the two images in the 3-D rendering.

3.1.

Skin Microstructure

An example of the cluster and canyon structure of porcine skin is shown in an SRS water ($3340{\mathrm{cm}}^{-1}$) image collected near the skin surface at $20\times$ magnification (Fig. 1). In SRS images, the intensity of each pixel corresponds to the amount of the chemical component(s) with a Raman signal at that vibrational frequency. In this data, there is some contribution in each of the single-wavelength images to components other than the labeled chemical component. Every effort was made to minimize overlap with the other component images so as to maximize image purity. The idea here is not to present absolute quantitation of the component content from the images, but rather to show that differences in chemical information can be obtained in a relatively straightforward manner. The water ($3340{\mathrm{cm}}^{-1}$) image shows a clear distinction between high-intensity water-rich cellular regions (cell clusters), such as region 2 in Fig. 1, and low-intensity water-deficient canyons separating the clusters (region 3). Multiple cell clusters and canyons are observed at this lower magnification. More detailed examination of the changes in skin microstructure as a function of depth can be accomplished by collecting multiple image stacks at $60\times$ magnification. The unique and varying architecture of the epidermal layers is represented in SRS images selected from different depths in three image stacks acquired at the higher $60\times$ magnification (Fig. 2). The three columns show images taken at the frequencies corresponding to protein ($2950{\mathrm{cm}}^{-1}$), lipid ($2850{\mathrm{cm}}^{-1}$), and water ($3340{\mathrm{cm}}^{-1}$), while the three rows portray depths of 6, 18, and 28 μm below the surface. Near the surface of the skin, the corneocytes aggregate into clusters ranging from 100 to 250 μm wide [Figs. 2(a) to 2(c)]. Lipid, protein, and water content appear to be distributed across the visible areas, as evidenced by the approximately uniform SRS intensities for each of the three frequencies. The dark spots in these images (marked “D” in the first row of images) are optical shadows from air pockets between the skin surface and coverslip. The corneocyte clusters are separated by invaginations 10 to 25 μm wide, features previously designated as canyons.¹⁷

Fig. 1

Stimulated Raman scattering (SRS) images show the organization of corneocytes into clusters and canyons. A single SRS image acquired at $3340{\mathrm{cm}}^{-1}$ at $20\times$ magnification from porcine skin. Pixel intensity corresponds to water content, with cell clusters appearing bright, separated by the dark regions defining canyons. Multiple cell clusters can be seen at this lower magnification. $\text{Scale bar}=50\mu \text{m}$.

Fig. 2

Changes in chemical distribution are visible by collecting depth stacks at multiple wavelengths. SRS images were acquired at $60\times$ magnification from pig skin. The rows correspond to focal planes 6 μm [(a) to (c)], 18 μm [(d) to (f)], and 28 μm [(g) to (i)] below the skin surface. The columns from left to right represent protein, lipid, and water content, respectively, according to the band assignments given in Table 1. The dark spots labeled “D” in (a) and (c) are due to optical shadows from air pockets between the skin surface and coverslip. The arrows in (e) designate one canyon in the image. Fine lines can also be seen within the canyon. The arrow in (h) points to one of the visible cell membranes in the cluster. $\text{Scale bar}=50\mu \text{m}$.

The transition from the stratum corneum into the viable epidermis is discernible in images collected 18 μm below the surface [Figs. 2(d) to 2(f)]. At this depth, the distinct boundaries between the clusters are visible, such as between the arrows in Fig. 2(e). The average canyon width in these images is $21.2\pm 4.4\mu \text{m}$. The signal intensity in the protein and lipid images is highest along the canyon walls, as evidenced by the bright lines outlining the canyons [Figs. 2(d) and 2(e)]. Fine lines are visible between the canyons, as can be seen between the arrows in the lipid ($2850{\mathrm{cm}}^{-1}$) image [Fig. 2(e)]. These are the edges of stratum corneum corneocytes following the contours of the canyon, as has previously been proposed.¹⁷ The water signal is significantly weaker in the canyons than in the cluster regions. In the cluster regions, nuclei are visible as dark ovals in the protein and lipid images [Figs. 2(d) and 2(e)], and as bright ovals in the water image [Fig. 2(f)]. Around this depth, the distinctive polygonal shape of the cells is revealed as the lipid-rich cell wall membranes appear in the lipid ($2850{\mathrm{cm}}^{-1}$) image [Fig. 2(e)]. The chemical specificity of SRS imaging allows these cell walls to be visualized, whereas they are often missed in other optical techniques, such as autofluorescence.^13,17

Looking deeper into the epidermis at 28 μm below the surface, the canyons become progressively narrower [Figs. 2(g) to 2(i)]. The water ($3340{\mathrm{cm}}^{-1}$) image is dark between the cell clusters, indicating any water that may be present in the canyons is below the limit of detection. The cell walls are brightly illuminated in the lipid ($2850{\mathrm{cm}}^{-1}$), showing the cell shape becoming rounder [Fig. 2(h)]. The density of visible nuclei has increased in this layer.

3.2.

Depth Profiles

Measured SRS signals are proportional to molecular concentration, providing a distinct advantage over nonlinear techniques, such as CARS.⁸ This linear dependence is a requirement for quantitative examination of the spatial distribution of chemical species within skin substructures. The relative water content spanning one intercluster canyon was averaged through all images of the water ($3340{\mathrm{cm}}^{-1}$) depth stack at each pixel location from the $65\times 15$ pixel region designated as region 1 in the $20\times$ water image (Fig. 1). The average water content values are plotted as a false color map, distinctly showing the boundary between the water-rich cells (displayed in red) and the water-free canyon (in blue) (Fig. 3). The red false color indicates high water content and the blue false color represents low water content. This visual representation can be used to measure the distance between the two clusters. For this example, the widest gap—the spacing between the two bright red regions—is $\sim 30\text{pixels}$, corresponding to a width of $\sim 37\mu \text{m}$. The water map gives an indication of the varying canyon width as a function of depth. The weaker water signals shown in yellow and green correspond to narrowing of the canyon at a depth of $\sim 20\mu \text{m}$. The water content of each pixel is averaged through the entire stack. The pixels shown in yellow and light blue are located in areas that are open canyons near the surface, but viable cells deeper in the skin.

Fig. 3

Analysis of SRS water depth stack shows water resides exclusively within the cell clusters. The color map calculated from region 1 of the SRS water image in Fig. 1 indicates the relative water content at each pixel through the complete 60 μm of the depth stack. Red regions are high in water content and correspond to the location of the cells. The water profile on the right aggregates the calculated values across each row of pixels.

This technique provides detailed depth information about the hydration of skin and the distribution of water throughout the substructures. The mean water signal intensity was calculated throughout the image depth stack for the cluster and canyon features designated as regions 2 and 3 in Fig. 1. Water depth profiles are generated from the intensities in the protein ($2950{\mathrm{cm}}^{-1}$) and water ($3340{\mathrm{cm}}^{-1}$) images, as described above. These depth profiles further demonstrate that the water content of the skin resides predominantly within the cellular areas and not in the canyons [Fig. 4(a)]. The distribution of water within cell clusters, shown here as a function of depth, is in agreement with water profiles measured using in vivo confocal Raman spectroscopy in human skin.¹⁰ In healthy skin, the water content is $\sim 30\%$ in the stratum corneum. There is a sharp rise in water content located at the boundary between the stratum corneum and viable cells, eventually reaching a plateau of 60 to 70%. We observed the same sigmoidal profile of water as a function of depth in SRS image data [Fig. 4(b)] as seen in single-point in vivo confocal Raman microspectroscopy data.

Fig. 4

Depth profiles show water distribution increasing in the lower layers of the skin [(a) and (b)], while lipid material resides in the canyon near the skin surface [(c) and (d)]. The water profiles shown in (a) were normalized against protein content and calculated for the cluster and canyons regions designated 2 and 3 in Fig. 1, while (b) shows the mean plot of the analysis of three different images. The sharp increase in water content in the cell clusters at 20 μm corresponds to the transition from the stratum corneum to the viable cells of epidermis. The lipid profiles shown in (c) were also normalized against protein content and calculated for the cluster and canyon regions designated 2 and 3 in Fig. 1, while (d) shows the mean plots of the analysis of three different images. The high levels of lipid signal from the canyon region between 5 and 10 μm below the skin surface confirms the presence of lipid along the canyons. The error bars in (b) and (d) represent 95% confidence interval of the mean at each point.

This same analytical approach can be employed to generate lipid depth profiles. Using the same ROI 2 and 3 from Fig. 1, evaluation of the lipid ($2850{\mathrm{cm}}^{-1}$) image stack shows that there is a significant accumulation of lipid material near the top of the canyon [Fig. 4(c)]. These canyon lipids are in a discrete volume, as evidenced by the sharp maximum in the lipid profile, in contrast to the relatively even distribution in the cell clusters. Lipid levels in all areas start to decline 15 μm below the skin surface [Fig. 4(d)]. It is important to consider that the signal at $2850{\mathrm{cm}}^{-1}$ is not exclusively from skin lipids and may include some contribution from protein as well.

3.3.

Active Penetration

Canyons can potentially play a role in the delivery of materials into the viable skin cells depending upon the compactness, thickness, and ultrastructure of stratum corneum that forms the canyon walls. We are beginning to explore possible penetration pathways by evaluating the diffusion of $\text{oleic acid}\text{-}{\mathrm{d}}_{34}$ topically applied to the porcine skin surface. Figure 5 shows a 3-D space model rendered from two SRS image stacks collected after application of oleic acid. The canyon and cluster structure of the skin is colored orange in the 3-D image obtained from the protein signal in the SRS stack. A separate oleic acid image stack was obtained for the C-D stretching mode at ca. $2105{\mathrm{cm}}^{-1}$ 25 min after treatment. The oleic acid surface contour map is superimposed in white against the canyon structure, showing the deuterated material residing in the canyon and coating the canyon walls. This example shows penetration of materials deep into the canyons in a short period of time.

Fig. 5

Three-dimensional (3-D) surface plot shows $\text{oleic acid}\text{-}{\mathrm{d}}_{34}$ filling the canyons. The shape of a canyon structure is visualized by rendering the protein SRS image stack as a 3-D surface plot, shown in orange. The $\nu (\mathrm{CD})$ stretching mode rendering is overlaid in white onto the protein image, showing the ${\mathrm{d}}_{34}\text{-}\text{oleic acid}$ remains within the canyon 25 min after application. The oleic acid film sitting on the surface of the clusters was excluded from the rendering to improve the visibility of the canyon material. The 3-D volume represented in these images is ca. $630\times 630\times 50{\mu \text{m}}^3$.