2.1.

Tissue Samples

Eyelids from 24 month-old mice were excised and fixed in 2% paraformaldehyde in phosphate buffered (PBS) saline. Tissue was then embedded in optical cutting temperature (OCT) medium, snap frozen in liquid nitrogen and 25 μm thick sections cut using a Leica CM1850 Crytotome (Leica, Wetzlar, Germany). Tissue sections were placed on a glass microscope slides, immersed in PBS, covered with a No. 1 borosilicate coverslip, and sealed with epoxy glue. Experiments were performed at room temperature. Since the purpose of this study is to determine the presence of lipid changes in the gland structure, we have chosen to work with sectioned tissue samples that enable optimized imaging conditions. We note that, in principle, CARS imaging can be extended to examining fresh tissue and, with proper signal acquisition speeds, to studying tissue in vivo as well.

The wax esters stearyl stearate, behenyl stearate, palmityl palmitate, and the cholesterol esters cholesteryl linoleate, cholesteryl linolelaidate, cholesteryl stearate were purchased from Sigma-Aldrich and were used without further purification. A wax ester mixture was made by mixing the three wax esters in a 1:1:1 ratio. A similar 1:1:1 mixture was made with the cholesterol esters. These mixtures were used in Raman measurements to gain insight in the main vibrational signatures of wax esters and cholesterol esters.

2.2.

CARS Microscopy

Multimodal CARS microscopy was carried out on a modified inverted confocal microscope (Fluoview 300, Olympus). The lightsource consisted of an optical parametric oscillator (Levante Emerald OPO, APE, Berlin) pumped by the second harmonic of a Nd:vanadate picosecond mode-locked laser (PicoTrain, High-Q), which provided the pump (780 nm – 830 nm) and Stokes (1064 nm) beams for the CARS excitation process. The collinearly combined pump and Stokes beams were focused by a dry 20x, 0.70 NA objective lens (UplanSApo, Olympus) onto the sample. The CARS focal volume of this lens measures ∼0.5 μm laterally and ∼3.5 μm axially. CARS signals were registered in both the forward and epi-direction, filtered with two bandpass filters (650 nm, 40 nm bandwidth, Chroma) and detected with a photomultiplier tube (R3896, Hamamatsu) positioned in both the forward and epi-direction. Average power at the sample is less than 15 mW per beam and pixel dwell times were 4 μs. CARS images were typically averaged three times. A third photomultiplier tube detector in the epi-direction was used to monitor the pump induced SHG radiation. A dichroic filter (550 nm longpass filter, Semrock) and a bandpass filter (400 nm, 40 nm bandwidth, Thorlabs) were used to separate the SHG signal from the CARS radiation.

CARS spectral scanning was accomplished by scanning the wavelength of the OPO. Wavelength scanning is achieved by tuning the temperature of the periodically poled potassium titanyl phosphate crystal and adjusting the etalon setting. The results presented in this work were acquired by manually scanning the OPO. By tuning from high to low temperatures while keeping the power constant, the time separation between the acquisition of two subsequent images in the spectral scan was 10–20 sec. For each wavelength setting, a CARS image was taken, generating a three-dimensional data stack (x,y,ω). These data stacks were typically of dimension 512×512×50. The resulting data stacks were used for subsequent multivariate analysis. We verified that, at constant incident excitation power, the spectral dependence contained in the images is independent of the direction of the spectral scan. In addition, no sample damage was observed after completion of the spectral data stack acquisition.

2.3.

Raman Microspectroscopy

A frequency doubled Nd:vanadate laser (Verdi V5, Coherent) was used for Raman excitation at 532 nm. The laser light was coupled into the multimodal CARS microscope through the backport of the microscope frame (IX71 Olympus), and focused by the same objective lens. Average power at the sample was ∼5 mW. Epi-scattered light was directed to a holographic notch filter, spatially filtered and passed to a spectrometer (Shamrock, Andor) equipped with a CCD camera (iDus, Andor). The size of the Raman probing volume was ∼0.5 μm in the lateral dimension and ∼5 μm in the axial dimension. Switching between the CARS and Raman detection mode involved a simple turn of the carrousel. A spin cast rhodamine 6G layer on a coverslip was used to determine the spatial correlation between the galvanometric mirror pair and the Raman probing volume. To this end, the rhodamine layer was exposed to 532 nm excitation light to introduce a photo-bleached spot in the film. The spot was subsequently imaged through two-photon excited fluorescence induced by the mirror-scanned pump beam. The location of the photobleached spot corresponds to the location Raman probing volume, which was correlated with a precision of 0.5 μm to the position of the CARS excitation beams. Any position in the CARS image could be moved within the Raman probing volume with sub-micrometer precision.

2.4.

Principal Component Analysis

Principal component analysis (PCA) was used as an established multivariate data analysis method to extract information from the CARS images based on the variation among the CARS spectra. PCA is an unsupervised method that is well suited to distinguish recurring variations in a spectral data set. In the PCA method, the original data is expressed in a basis set that is based on the variance contained in the data.³⁴ The elements of this new set are called the principal components. Because there is typically a significant amount of repeated information in the spectral images, the data set can be satisfactorily described in terms of only a few principal components. Thus, the PCA reduces the multidimensional space spanned by the original N = xy spectral vectors, where x and y are the number of image pixels in the lateral dimensions, respectively, into a manageable set of principal components that describe the main spectral variances.

The datasets (x,y,ω) can be expressed as a single matrix, Y, with N-rows and s-columns. Each column corresponds to pixel intensities for measurement taken at distinct wavenumbers, whereas the spatial coordinate is unfolded into a single axis coordinate system. In our case, Y consists of 262,144 rows and 50 columns, representing 262,144 data points and its associated intensities at 50 different wavenumbers, respectively. Y is centered by subtracting the mean intensity for each column from the pixel intensities. Eigenvectors (V) and eigenvalues (D) are then computed by using MATLAB's Singular Value Decomposition built in function, SVD. The order of the eigenvectors is arranged so that the magnitude of the associated eigenvalue, which is proportional to the variance, is in decreasing order. Subsequently, the principal components matrix (Z) can be calculated from Z = Y V.

In this study, the first three principal components exhibited most of the variance in the measurement. The Z and V matrices can be truncated based on the selected principle components (p); in our case Z p had dimensions 262,144 × 3 and V p has dimensions 50×3. The reconstructed spectra, S, were then calculated as S = Zp Vp ^T followed by adding the mean intensity to each pixel that was subtracted earlier from the original datasets.

The resulting projected data, Z, is utilized to generate the RGB color map in the original coordinate system. The color map is particularly useful to group underlying spectra because different colors highlight the contribution(s) from particular principal component(s). The spectra for each color shown in the score map were constructed by averaging spectra for pixels represented by the same color; the spectra were normalized to the maximum average intensity. Computation was performed in MATLAB.

3.1.

CARS Microscopy of Meibomian Gland Lipids

Figure 1 shows representative CARS images of the lipid distribution in the meibomain gland. For these experiments, the Raman shift was tuned to the symmetric CH₂ stretching vibration at 2845 cm⁻¹. The CARS contrast resolves the major structural components of the gland. In Fig. 1a, a sagittal section along the long axis of the gland structure is shown. The meibocytes in the acini clusters can be clearly recognized at peripheral regions in the gland. A punctuate pattern is seen in the cytosolic space of the meibocytes, which corresponds to micrometer sized lipid droplets. Towards the center of the gland, the lipid droplet pattern becomes more diffuse and the cell nuclei become less distinct. This region of cellular degeneration is known as the ductule. Several ductules converge into the central duct of the gland, which exhibits a complete loss of cellular structure at the expense of an agglomerated pool of meibum lipid.

Fig. 1

CARS images of the lipid distrubution in the meibomian gland of a 24 month old mouse. Raman shift was set to 2845 cm⁻¹. (a) Sagittal cross section of the meibomian gland showing the acini clusters (ac), the converging ductules (dt) and part of the central duct (dc). (b) Transversal cross section of the meibomian gland. Scale bar is 50 mm.

Figure 1b shows a transverse cut through the gland. It can be clearly seen that the peripheral acini surround the region of the central duct. Figure 2a shows an acinus cluster. Individual meibocytes can be clearly distinguished. In addition, the larger intracellular droplets (>1 μm) can be sufficiently resolved, which enables a droplet specific analysis. The acini are separated by a collagen matrix, as shown in Fig. 2b, whereas no collagen fibers are seen inside the acini.

Fig. 2

(a) CARS image of a cluster of meibocytes in the acinus. (b) CARS image of several acini clusters. Lipids (red) are imaged with CARS at 2845 cm⁻¹ and collagen (blue) is imaged with SHG. Scale bar is 20 μm.

3.2.

Raman Spectroscopy of Duct and Acini Lipids

To investigate tentative differences in the lipid composition throughout the gland structure, we have performed Raman microspectroscopic measurements at selected locations in the gland. The locations for the Raman point measurements were determined from the CARS images generated on the same platform. Figure 3a shows the Raman spectrum in the CH stretching range measured in the lipid regions of the acini. The spectrum shown is the average of multiple point measurements (n = 10). In Fig. 3b, the spectrum attained from the central duct is shown. Although the lipid spectra of the duct and the acinus are nearly identical, the acinus spectrum exhibits additional spectral components in the ∼2940 cm⁻¹ range, as seen in the difference spectrum depicted in Fig. 3c. The residual spectrum indicates either that there are differences in the lipid composition of the duct and the acini or that there are additional cellular components probed in the acini. Because the Raman probing volume is larger than individual lipid droplets, spectral components from intracellular material other than lipid droplets may contribute to the spectrum. In the duct, on the other hand, the lipid pool is much larger than the probing volume, which excludes spectral contributions from tissue components other than the meibum lipids.

It is instructive to compare the meibum Raman spectrum with the spectra of several of its major constituents. The spectrum of the wax ester mixture and cholesterol ester mixture is shown in Figs. 4a and 4b, respectively. The wax ester spectrum is characterized by two relatively sharp peaks, which correspond to CH₂ stretching vibrations at 2846 cm⁻¹ and at 2886 cm⁻¹.³⁵ Figure 4c shows the sum of the two spectra. Although the resulting spectrum does not necessarily reflect the exact composition of meibum, the major features of the meibum lipid Raman spectrum are relatively well reproduced.

Fig. 4

(a) Raman spectrum of a 1:1:1 wax ester mixture of stearyl stearate, behenyl stearate, and palmityl palmitate. (b) Raman spectrum of a 1:1:1 cholesterol ester mixture of cholesteryl linoleate, cholesteryl linolelaidate, and cholesteryl stearate. (c) Normalized sum of spectrum (a) and spectrum (b).

Figures 5a and 5b show the Raman spectra of the duct and the acinus in the fingerprint region, respectively. For comparison, the spectra of the wax ester mixture and the cholesterol ester mixture are given in Figs. 5c and 5d, respectively. The main spectral signatures of the duct lipids are also seen in the acinus spectrum, indicating that the acinus spectrum is dominated by lipids with limited contributions from other cellular material. In particular, no residual contribution from the amide I vibration near 1650 cm⁻¹ is seen in the acinus spectrum, providing evidence that protein spectral components contribute very little to the spectrum. A weak, yet narrow, spectral component at 1585 cm⁻¹ is seen in the acinus spectrum that is absent in the lipid spectrum of the duct.

Fig. 5

Raman spectral point measurements in the meibomian gland in the fingerprint vibrational range. (a) Spectrum measured in the central duct. (b) Spectrum measured in the lipid regions of the acinus. Spectra were obtained by averaging 10 point-measurements at different locations in either the acinus or the central duct. (c) Spectrum of the wax ester mixture [as in Fig. 4(a)]. (d) Spectrum of the cholesterol ester mixture [as in Fig. 4(b)].

Comparing the duct and acini Raman spectra, it can be concluded that the acinus spectrum is dominated by contributions from lipids. Small yet clear differences are seen in the lipid spectra measured in the two regions, as evidenced by the additional components at 2940 cm⁻¹ and 1585 cm⁻¹ in the acinus spectrum. Since spectral differences are observed in both the fingerprint as well as in the CH stretching range of the vibrational spectrum, both regions of the spectrum qualify for CARS spectral scanning experiments. Since the expected vibrational contract relative to nonresonant background contributions is highest in the CH stretching region, we have chosen to perform CARS spectral scans in this higher energy range of the vibrational spectrum.

3.3.

CARS Spectral Imaging of Lipid Distribution

CARS spectral imaging enables an improved chemical analysis of lipid CARS images. In Fig. 6a, an area of the meibomian gland is visualized that comprises both acini clusters and the central duct. A spectral image was generated by scanning the Raman shift from 2790 cm⁻¹ to 3070 cm⁻¹ in approximately 6 cm⁻¹ steps. The spectral content of the areas marked in Fig. 6a are plotted in Figs. 6b and 6c. A characteristic CARS lipid spectrum is observed in the regions of the acini and the central duct. A comparison of the acini and duct spectra reveals a higher spectral density in the 2900 cm⁻¹ region of the acini spectrum, which is largely in line with the differences observed in the Raman spectra of the corresponding areas (Fig. 3). Note that the 2886 cm⁻¹ band of the Raman meibum spectra [Figs. 3a and 3b] is not clearly seen in the CARS spectrum as a result of spectral interferences with the nonresonant electronic background.

Fig. 3

Raman spectral point measurements in the meibomian gland in the CH₂ stretching vibrational range. (a) Spectrum measured in the lipid regions of the acinus. (b) Spectrum measured in central duct. (c) Difference spectrum (acinus-central duct). Spectra were obtained by averaging 10 point-measurements at different locations in either the acinus or the central duct.

Figure 6c shows the spectrum attained from the tissue matrix that surrounds the gland. The spectrum lacks the characteristic CH₂ stretching component of lipids, and peaks around 2900 cm⁻¹. This signature is reminiscent of CH stretching vibrations of protein structures, which is expected for a tissue region with a high concentration of structural protein. The rise of the spectrum beyond 3000 cm⁻¹ can be attributed to the red tail of the OH stretching vibration of water.

While insightful, the examination of a few selected regions does not do justice to the richness of information contained in the spectral CARS image. To more efficiently portray the spectral variation of the image, a multivariate analysis can be performed. In the following section we explore the use of a principal component analysis to classify the major spectral features within the CARS image.

3.4.

Principal Component Analysis

Figure 7a shows the score map composed of the first three principle components. The score map assigns a color to each pixel based on the score of each principle component (PC) at the pixel location. The PCA clearly identifies different regions in the spectral image. The extracellular matrix (magenta) is separated from the lipid rich regions of the acini and duct. The acinus (yellow) is spectrally discriminated from the areas of the ductule and central duct. Furthermore, within the ductal regions of the gland, two lipid populations are recognized based on their mutual spectral variance.

Fig. 7

Principal component analysis of the CARS spectral data stack shown in Fig. 6. (a) Score map of the first three principal components, represented in RGB colors. Scale bar is 50 μm. (b) Reconstructed averaged CARS spectra from the black (solid circles), cyan (open circles), and yellow (open triangles) lipid-rich regions of the score map. Spectra are offset for clarity. (c) Reconstructed averaged spectrum of the magenta colored region of the score map. Spectra are reconstructed using the first three principal components (p = 3).

The spectra associated with the main regions identified in Fig. 7a are given in Figs. 7b and 7c. These spectra were reconstructed using only the first three principal components. Figure 7b shows the averaged lipid spectra of the yellow, black, and cyan regions of the image. The spectra show much similarity to the extracted CARS spectra shown in Fig. 6b, indicating that the spectral reconstruction faithfully reproduces the main spectral signatures. The reconstructed spectra show small spectral differences among the different lipid-rich areas, which possibly reflect changes in the lipid composition within the gland structure. Figure 7c depicts the reconstructed spectrum of the tissue matrix, which shows much resemblance to the CARS spectrum shown in Fig. 6c.