The authors greatly acknowledge the financial support from the corporate function analytical and personal cleansing care department. We want to express our gratitude to analytical associate directors Ms. Jasmine Wang and Dr. Robb Gardner for their guidance and support and Ms. Li Yang for her help on data collection.

This study was approved by the institutional review committee of Beijing Children&#8217;s Hospital in compliance with the ethical guidelines of the 1975 Declaration of Helsinki. It was conducted according to ICH guidelines for Good Clinical Practice. The study took place from May to July 2015.
1. Collection of in vivo confocal Raman spectra from human subjects with atopic dermatitis
<ol>
	<li>Include subjects in compliance with the following criteria.
	<ol>
		<li>Include subjects between the ages of 4&#8211;18.</li>
		<li>Include subjects with mild to moderate atopic dermatitis (score of 2 or 3 according to the Physician&#8217;s global assessment) with active disease symptoms on 5%&#8211;30% of the body surface, with at least two lesions on the arms.</li>
		<li>Include subjects that are in good health, excluding symptoms directly related to the AD disease.</li>
		<li>Include subjects that provide written informed consent.</li>
		<li>Include subjects that have an individual topology angle (ITA) value higher than 40 at the testing location.</li>
	</ol>
	</li>
	<li>Exclude subjects that meet any of the following criteria.
	<ol>
		<li>Exclude subjects that are either currently participating or have previously participated in a clinical study at any test facility within the previous 4 weeks.</li>
		<li>Exclude subjects with cancer or that have been diagnosed or treated for cancer within 5 years prior to the study.</li>
		<li>Exclude diabetic subjects.</li>
		<li>Exclude subjects who have an immunologic or infectious disease that could place the subject at risk or interfere with the accuracy of the study results (i.e., hepatitis, tuberculosis, HIV, AIDS, lupus, or rheumatoid arthritis).</li>
		<li>Exclude subjects that have skin conditions that might interfere with instrumental measurements or will prevent the clear assessment of the skin only to atopic dermatitis. Examples include extremely dry skin, damaged skin, cuts, scratches, sunburn, birthmarks, tattoos, extensive scarring, rashes, excessive hair growth, or acne.</li>
		<li>Exclude subjects that use oral immunosuppressive drugs, antibiotics, or other systemic therapies within the past month, except for minor tranquilizers.</li>
		<li>Exclude subjects with any other medical conditions that, in the opinion of the investigator, precludes them from study participation.</li>
		<li>Exclude subjects with higher pigmentation in the testing area.</li>
	</ol>
	</li>
	<li>Label the lesion area of the atopic dermatitis study participant and mark with a 3 cm x 4 cm area on or near the lesion site as shown in Figure 1A.</li>
	<li>Label the non-lesion area with the same marker in the counterpart body site (e.g., left forearm vs. right forearm) as shown in Figure 1B.</li>
	<li>Place the marked body site in close contact with the window of the in vivo confocal Raman instrument as shown in Figure 2A and Figure 2B. Cover the whole window to avoid the impact of room light on the body site.</li>
	<li>Perform the Raman data collection. 
	NOTE: The instrument has a spectral resolution of 2 cm-1 and 50x microscopy objective (NA = 0.9 oil immersion), using a 671 nm laser with a power of 17 mW. Wavelength is calibrated using the spectrum of a built-in neon-argon lamp. The intensity calibration is done by measuring the spectrum of a NIST (National Institute of Standards) glass calibration standard.
	<ol>
		<li>Move the focus until a spectrum as illustrated in Figure 2C is observed, then move the focus away from the skin surface 10 &#956;m.</li>
		<li>Start the data collection for 26 steps with a 2 &#956;m step size in the 2,510 cm-1&#8211;4,000 cm-1 frequency region. Use an exposure time of 1 s and measure eight replicates for each area lasting ~10&#8211;15 min total.</li>
	</ol>
	</li>
</ol>
2. Collection of confocal Raman spectra from reference materials
<ol>
	<li>Place the reference materials, the major components in human skin stratum corneum19, on the window of the confocal Raman instrument (see Table of Materials: Bovine Serum Albumin (BSA), deionized water (DI water), ceramide, cholesterol, free fatty acid, and squalene).</li>
	<li>Collect the reference materials&#39;&#160;Raman spectra consecutively from the outside of the material to the material center using the same collection parameters as described above.</li>
	<li>Integrate the area under each Raman spectra between the range of 2,510&#8211;4,000 cm-1 and identify the top three maximum value points in these 26 measurements. Average the Raman spectra from those three points to obtain the final reference material spectra.</li>
</ol>
3. Removal of the outlier spectra through chemometrics analysis
<ol>
	<li>Determine the skin surface and remove the out-of-skin spectra.
	<ol>
		<li>Change the file extension from &#39;.ric&#39;&#160;to &#39;.mat&#39;&#160;and load the .mat file to the MATLAB software platform.</li>
		<li>Correct the baseline using Baseline(Automatic Weighted Least Squares) by right-clicking the imported dataset under Analyze | Other Tools | Preprocessing in the PLS_Toolbox software with default setting.</li>
		<li>Sum up the values between 2,910&#8211;2,965 cm-1 to obtain the intensity values under each Raman spectrum from the 26 consecutive steps measurement as shown in Figure 3A via the sum function in MATLAB.</li>
		<li>Interpolate the instrument offset value (value in the X-axis in Figure 3B) from 26&#8211;260 using the linspace function in MATLAB.</li>
		<li>Interpolate the intensity value from 26 to 260 using the spline method in MATLAB, leveraging the newly-generated 260 position values.</li>
		<li>Use MATLAB&#39;s polyfit and polyval functions to obtain the new set of intensity values with 260 points. First, use the 260 position and intensity values as X and Y inputs for the polyfit function, respectively. Set the degree value to 20. Then, use the output coefficients and the 260 extended position values as the input for polyval to obtain the final 260 intensity values.</li>
		<li>Use MATLAB&#39;s max and min functions to identify the maximum and minimum points from the newly interpolated 260 intensity values.</li>
		<li>Calculate the mean intensity value by dividing the sum of the maximum and minimum intensity value by two.</li>
		<li>Identify the intensity value from the 260 intensity values (calculated from step 3.1.6) that is closest to the mean intensity value and set its corresponding position value as skin surface. Set this position value as the zero point in the X-axis as illustrated in Figure 3B.</li>
		<li>Change all the other position values according to the zero point and the known 2 &#956;m step size.</li>
		<li>Remove all the spectra collected above the skin surface according to their position value.</li>
		<li>Import the rest of the data to PLS_Toolbox to create a dataset and rename it &#34;RamanData.mat&#34;.</li>
	</ol>
	</li>
	<li>Remove the outlier spectra with the room light effect.
	<ol>
		<li>Load the Raman spectra dataset (RamanData.mat) after removal of the out-of-skin spectra and implement the PCA analysis.</li>
		<li>Load the dataset into the PLS_Toolbox software under the MATLAB platform and right-click the dataset to choose Analyze | PCA.</li>
		<li>Select Normalize&#160;as the preprocessing approach and choose None for the cross validation.</li>
		<li>Use the three components for the PCA decomposition analysis as shown in Supplementary Figure 2.</li>
		<li>Remove the cover on the in vivo Raman instrument&#39;s collection window and collect the room light spectra in the high frequency region using the same parameters used for the reference materials data collection.</li>
		<li>Identify the room light effect factor through comparison with the room light background spectra as shown in Supplementary Figure 3.</li>
		<li>Remove the spectra with a significantly higher corresponding score value than normal (More than 99.8% of the score values of the whole dataset, which is 0.16 in this study).</li>
	</ol>
	</li>
</ol>
4. Selection of the number of the components in MCR decomposition analysis
<ol>
	<li>Correct the Raman spectra baseline using the same approach described above (section 3.1.2).</li>
	<li>Perform the PCA analysis on the preprocessed dataset as described above (section 3.2) except for selecting &#8220;PQN&#8221; &#8211; &#8220;Mean Center&#8221; rather than &#8220;Normalize&#8221;, and plot the eigenvalues in logarithmic scale along with the number of components (20) as the default number in the decomposition analysis by clicking the Choose Components button and select log(eigenvalues) as the Y value. Choose three to eight as the number of the components used for the MCR analysis.</li>
	<li>Perform MCR analysis.
	<ol>
		<li>Load the dataset into the MCR_main software20 through the Data Selection button.</li>
		<li>Choose the number of components (three to eight) by clicking the Determination of the number of components button.</li>
		<li>Click the Pure button under the Initial Estimation tab, select Concentration under the Direction of the variable selection tab, and click the Do button.</li>
		<li>Click the OK button and then the Continue button to the next page.</li>
		<li>Click Continue in the next page and then select fnnls and 6 under the Implementation and Nr. of species with non-negativity profiles tabs, respectively. Click the Continue button.</li>
		<li>Choose the same parameters as section 4.3.5 for this page and click Continue.</li>
	</ol>
	</li>
</ol>

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	<li>Caspers, P., Lucassen, G., Bruining, H., Puppels, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Automated+depth+-+scanning+confocal+Raman+microspectrometer+for+rapid+in+vivo+determination+of+water+concentration+profiles+in+human+skin.">Automated depth - scanning confocal Raman microspectrometer for rapid in vivo determination of water concentration profiles in human skin.</a> Journal of Raman Spectroscopy. 31 (8-9), 813-818 (2000).</li><li>Crowther, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Measuring+the+effects+of+topical+moisturizers+on+changes+in+stratum+corneum+thickness,+water+gradients+and+hydration+in+vivo.">Measuring the effects of topical moisturizers on changes in stratum corneum thickness, water gradients and hydration in vivo.</a> British Journal of Dermatology. 159 (3), 567-577 (2008).</li><li>Egawa, M., Tagami, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Comparison+of+the+depth+profiles+of+water+and+water-binding+substances+in+the+stratum+corneum+determined+in+vivo+by+Raman+spectroscopy+between+the+cheek+and+volar+forearm+skin:+effects+of+age,+seasonal+changes+and+artificial+forced+hydration.">Comparison of the depth profiles of water and water-binding substances in the stratum corneum determined in vivo by Raman spectroscopy between the cheek and volar forearm skin: effects of age, seasonal changes and artificial forced hydration.</a> British Journal of Dermatology. 158 (2), 251-260 (2008).</li><li>Crowther, J. M., Matts, P. J., Kaczvinsky, J. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Changes in Stratum Corneum Thickness, Water Gradients and Hydration by Moisturizers. , (2012).</li><li>Pudney, P. D., M&#233;lot, M., Caspers, P. J., Van, D. P. A., Puppels, G. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=An+in+vivo+confocal+Raman+study+of+the+delivery+of+trans+retinol+to+the+skin.">An in vivo confocal Raman study of the delivery of trans retinol to the skin.</a> Applied Spectroscopy. 61 (8), 804 (2007).</li><li>Mohammed, D., Matts, P., Hadgraft, J., Lane, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=In+vitro-in+vivo+correlation+in+skin+permeation.">In vitro-in vivo correlation in skin permeation.</a> Pharmaceutical Research. 31 (2), 394-400 (2014).</li><li>Hanlon, E., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Prospects+for+in+vivo+Raman+spectroscopy.">Prospects for in vivo Raman spectroscopy.</a> Physics in Medicine and Biology. 45 (2), 1 (2000).</li><li>Mohammed, D., Crowther, J. M., Matts, P. J., Hadgraft, J., Lane, M. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Influence+of+niacinamide+containing+formulations+on+the+molecular+and+biophysical+properties+of+the+stratum+corneum.">Influence of niacinamide containing formulations on the molecular and biophysical properties of the stratum corneum.</a> International Journal of Pharmaceutics. 441 (1-2), 192-201 (2013).</li><li>Boireau-Adamezyk, E., Baillet-Guffroy, A., Stamatas, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Age-dependent+changes+in+stratum+corneum+barrier+function.">Age-dependent changes in stratum corneum barrier function.</a> Skin Research and Technology. 20 (4), 409-415 (2014).</li><li>Pezzotti, G., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Raman+spectroscopy+of+human+skin:+looking+for+a+quantitative+algorithm+to+reliably+estimate+human+age.">Raman spectroscopy of human skin: looking for a quantitative algorithm to reliably estimate human age.</a> Journal of Biomedical Optics. 20 (6), 065008 (2015).</li><li>Mlitz, V., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Impact+of+filaggrin+mutations+on+Raman+spectra+and+biophysical+properties+of+the+stratum+corneum+in+mild+to+moderate+atopic+dermatitis.">Impact of filaggrin mutations on Raman spectra and biophysical properties of the stratum corneum in mild to moderate atopic dermatitis.</a> Journal of the European Academy of Dermatology and Venereology. 26 (8), 983-990 (2012).</li><li>Janssens, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Lipid+to+protein+ratio+plays+an+important+role+in+the+skin+barrier+function+in+patients+with+atopic+eczema.">Lipid to protein ratio plays an important role in the skin barrier function in patients with atopic eczema.</a> British Journal of Dermatology. 170 (6), 1248-1255 (2014).</li><li>Faiman, R., Larsson, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Assignment+of+the+C+H+stretching+vibrational+frequencies+in+the+Raman+spectra+of+lipids.">Assignment of the C H stretching vibrational frequencies in the Raman spectra of lipids.</a> Journal of Raman Spectroscopy. 4 (4), 387-394 (1976).</li><li>Edwards, H. G., Farwell, D. W., Williams, A. C., Barry, B. W., Rull, F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Novel+spectroscopic+deconvolution+procedure+for+complex+biological+systems:+vibrational+components+in+the+FT-Raman+spectra+of+ice-man+and+contemporary+skin.">Novel spectroscopic deconvolution procedure for complex biological systems: vibrational components in the FT-Raman spectra of ice-man and contemporary skin.</a> Journal of the Chemical Society, Faraday Transactions. 91 (21), 3883-3887 (1995).</li><li>Choe, C., Lademann, J., Darvin, M. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Lipid+organization+and+stratum+corneum+thickness+determined+in+vivo+in+human+skin+analyzing+lipid-keratin+peak+(2820-3030+cm-+1)+using+confocal+Raman+microscopy.">Lipid organization and stratum corneum thickness determined in vivo in human skin analyzing lipid-keratin peak (2820-3030 cm- 1) using confocal Raman microscopy.</a> Journal of Raman Spectroscopy. 47 (11), 1327-1331 (2016).</li><li>Stamatas, G. N., de Sterke, J., Hauser, M., von Stetten, O., van der Pol, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Lipid+uptake+and+skin+occlusion+following+topical+application+of+oils+on+adult+and+infant+skin.">Lipid uptake and skin occlusion following topical application of oils on adult and infant skin.</a> Journal of Dermatological Science. 50 (2), 135-142 (2008).</li><li>Choe, C., Lademann, J., Darvin, M. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Confocal+Raman+microscopy+for+investigating+the+penetration+of+various+oils+into+the+human+skin+in+vivo.">Confocal Raman microscopy for investigating the penetration of various oils into the human skin in vivo.</a> Journal of Dermatological Science. , (2015).</li><li>Zhang, L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+MCR+approach+revealing+protein,+water+and+lipid+depth+profile+in+atopic+dermatitis+patients'+stratum+corneum+via+in+vivo+confocal+Raman+spectroscopy.">A MCR approach revealing protein, water and lipid depth profile in atopic dermatitis patients' stratum corneum via in vivo confocal Raman spectroscopy.</a> Analytical Chemistry. , (2019).</li><li>Caspers, P. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> In vivo Skin Characterization by Confocal Raman Microspectroscopy. , (2003).</li><li>Jaumot, J., de Juan, A., Tauler, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=MCR-ALS+GUI+2.0:+New+features+and+applications.">MCR-ALS GUI 2.0: New features and applications.</a> Chemometrics and Intelligent Laboratory Systems. 140, 1-12 (2015).</li><li>Choe, C., Choe, S., Schleusener, J., Lademann, J., Darvin, M. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Modified+normalization+method+in+in+vivo+stratum+corneum+analysis+using+confocal+Raman+microscopy+to+compensate+nonhomogeneous+distribution+of+keratin.">Modified normalization method in in vivo stratum corneum analysis using confocal Raman microscopy to compensate nonhomogeneous distribution of keratin.</a> Journal of Raman Spectroscopy. , (2019).</li><li>Wise, B. M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Chemometrics+tutorial+for+PLS_Toolbox+and+Solo.">Chemometrics tutorial for PLS_Toolbox and Solo.</a> Eigenvector Research, Inc. 3905, 102-159 (2006).</li></ol>

The authors have nothing to disclose.

During the data collection, as described in section 2 and 3 of the protocol, each depth profile was collected in an area with contact between the instrument window and the skin by finding the darker areas from the microscopic images highlighted in the red circles in Figure 2C. Once these areas were located, it was critical to start the depth profile above the skin surface to accurately determine the location of the skin surface for the data analysis procedure. The location of the skin surface was subsequently used to determine the relative depth of each spectrum in the corresponding depth profile. As mentioned in section 1 of the protocol, starting the depth profile 10 &#956;m above the skin surface produces five data points outside the skin. This allows for successfully determining the locations of the maximum and minimum signal intensity on both sides of the skin surface. It is also important to avoid measuring locations that contain pen marks and higher pigmented areas such as freckles, because these areas produce a high fluorescence background signal. The selection of the exposure time is a balance between spectral quality and measurement duration. Longer exposure time improves signal-to-noise and significantly increases the overall measurement time. However, many subjects find it challenging to remain motionless for extended periods of time. This is extremely challenging for children, for example. Increasing laser power increases the signal-to-noise. However, too much power can damage the skin due to the absorption of the energy. The maximum permissible exposures, 17 mW laser power as defined by the Chinese national standard (GB 7247.1-2012), and the international laser safety standard (IEC 60285-1:2007; &#60;20 mW for 671 nm and &#60;30 mW for 785 nm), cannot be exceeded. Other safety precautions include ensuring that each subject is wearing eye protection prior to data acquisition, that body sites have an individual topology angle (ITA) value higher than 40, and avoiding areas with high skin pigmentation.
To determine the location of the skin surface, the area under the protein Raman peak (2,910-2,965 cm-1) was integrated to obtain the depth profile of the protein signal. The Raman spectra were first baseline-corrected using the automated weighted least square method from PLS_Toolbox prior to the integration of the peaks. The 26 data points from one depth profile were interpolated to 260 points using the linspace method for the instrument offset vaue (X-axis value in Figure 3A) and the spline method for the corresponding intensity value. The resulting data were interpolated onto a 20th order polynomial using the polyfit and polyval functions in MATLAB and the maximum and minimum points of the interpolated data were determined. The mean intensity value was calculated by dividing the sum of the maximum and minimum values by 2. The skin surface was defined as the location where the intensity value from the interpolated depth profile was closest to the mean intensity. The exact location of the skin surface does not need to coincide with an experimental data point. This method can only measure a limited depth of the skin due to the absorption and scattering of the beam21. Collecting spectroscopic data below ~50 &#956;m under the skin surface may require significant changes to the experimental parameters.
As described in section 3 of the protocol, after removal of outlier spectra with low signal-to-noise and high contribution from room lights, a small fraction of spectra containing cosmic rays remained in the data set. A comparison of the loading spectra generated before and after cosmic ray removal is shown in Supplementary Figure 4. A comparison of loading spectra shown in Supplementary Figure 4 indicates that the impact of a small number of spectra with cosmic rays was negligible. The three factors representing water, protein, and lipid were identical, and the additional three loadings associated with noise and spectral artifacts were also very similar. This might be attributed to a low occurrence of cosmic rays in the spectra (~0.25%) because the location of cosmic rays in the spectra are random.
The selection of the number of the components used in the MCR analysis is critical, because interpretation of the loadings&#39;&#160;shape in terms of the corresponding molecular species responsible for each loading significantly impacts both how the corresponding score values are used and overall method performance. As described in section 4 of the protocol, PCA was performed first to investigate the eigenvalue evolution associated with the increasing of the number of the components. This investigation was used to identify the number of the components that should be used in the following MCR analysis. Plotting the eigenvalue on a logarithmic scale can make this identification process easier than examining the raw eigenvalues, as shown in Figure 5A. Each eigenvalue is a representation of the variance that one component can capture. The larger the eigenvalue, the more variance this component can model in the spectra. Eigenvalues with similar size should be selected or eliminated together22. Following this guideline, two, five, and eight components were considered for the MCR analysis because components three, four, and five produce eigenvalues similar in size. A similar trend was also observed for components six, seven, and eight. Figure 5B is a plot of the difference in eigenvalues between &#39;n&#39;&#160;and &#39;n+1&#39;&#160;components showing local maxima after the second, fifth, and eighth components. Prior knowledge about the molecular composition of skin combined with the study design supports a minimum of three components required to model the high frequency Raman spectra. Therefore, multiple MCR models containing three to eight components were investigated and the loadings were compared to spectra from reference materials to identify the key components required for the final model.
Comparison of the loadings with Raman spectra from reference materials easily allows identifying and assigning two of the final MCR components to protein and water because they dominate the MCR loadings for all models tested and match the corresponding reference spectra, which are BSA and DI water. However, the expected spectroscopic properties of lipid in some of the MCR components was a weaker match to the lipid reference spectrum illustrated in MCR models that contain three and four components. In addition, residual protein peaks (2,840&#8211;3,000 cm-1) were observed in the MCR water loadings for all models tested below six components. Based on these observations, a six component MCR model was used in the final MCR analysis. Three of the six components were assigned to water, protein, and lipid by matching their loading spectrum to the corresponding reference spectrum. The interpretation and assignment of the lipid factor is based on comparison of the loading to Raman spectra of three representative ceramide materials, including N-behenoyl-D-erythro-sphingosine, N-Lignoceroyl-D-erythro-sphinganine, and D-Erythro-Dihyrosphingosine. The Raman spectra of other lipid materials in the stratum corneum were also examined. These materials include fatty acids (oleic, palmitic, palmitoleic, and stearic acid), cholesterol (cholesterol 3-sulfate sodium and cholesterol), and squalene, as shown in Supplementary Figure 5. The lipid factor used in the final MCR model was a strong match to ceramide spectra and consistent with other materials that contain long chain hydrocarbons. The other three MCR components were dominated by fluorescence and baseline artifacts and their corresponding score values were not used in any calculations. These three components are shown in Figure 7.
The overall analysis approach presented in this manuscript produces a final method with improved specificity and accuracy for measuring the key components in skin compared to other single peak or peak-fitting approaches. This methodology demonstrates that critical components can be extracted from a clinical dataset that contains a relatively small fraction of bad spectra. Future efforts are focused on the automation of this methodology into a software package to improve its efficiency and reduce the amount of technical expertise required for the analysis. Similar methodology is being developed for Raman spectra collected in the fingerprint region (400&#8211;1,800 cm-1) using a 785 nm laser source rather than the 671 nm laser incorporated into the same instrument.

In clinical studies, in vivo confocal Raman spectroscopy has shown its unique ability for determining stratum corneum thickness and water content1,2,3,4, and tracking the penetration of active materials topically applied to the skin5,6. As a noninvasive approach, confocal Raman spectroscopy detects molecular signals based on vibrational modes. Thus, labeling is not needed7. In vivo confocal Raman spectroscopy provides chemical information with depth resolution based on the confocal nature of the technique. This depth-dependent information is very useful in studying the effects of skin care products4,8, aging9,10, seasonal changes3, as well as skin barrier function diseases, such as atopic dermatitis11,12. There is a lot of information in the high frequency region of confocal Raman spectroscopy (2,500&#8211;4,000 cm-1), where water produces distinct peaks in the region between 3,250&#8211;3,550 cm-1. However, the Raman peaks of proteins and lipids, which are centered between approximately 2,800&#8211;3,000 cm-1, overlap each other because the signals are mainly produced from methylene (-CH2-) and methyl (-CH3) groups13. This overlapped information presents a technical challenge when obtaining relative amounts of individual molecular species. Peak fitting14,15 and selective peak position12,16 approaches have been used to resolve this challenge. However, it is difficult for these single peak-based methods to extract pure component information because multiple Raman peaks from the same component change simultaneously17. In our recent publication18, an MCR approach was proposed to elucidate the pure component information. Using this approach, three components (water, proteins, and lipids) were extracted from a large in vivo confocal Raman spectroscopic dataset.
The execution of large clinical studies can be demanding on individuals collecting in vivo spectroscopic data. In some cases, spectral acquisition can require operating equipment for many hours in a day and the study can extend up to weeks or months. Under these conditions, spectroscopic data may be generated by equipment operators that lack the technical expertise to identify, exclude, and correct for all sources of spectroscopic artifacts. The resulting data set may contain a small fraction of spectroscopic outliers that need to be identified and excluded from the data prior to analysis. This paper illustrates in detail a chemometric analysis process to &#34;clean up&#34;&#160;a clinical Raman dataset before analyzing the data with MCR. To successfully remove the outliers, the types of outliers and the potential cause for the generation of the outlier spectra need to be identified. Then, a specific approach can be developed to remove the targeted outliers. This requires prior knowledge of the dataset, including a detailed understanding about the data generation process and the study design. In this dataset, the majority of outliers are low signal-to-noise spectra and originate primarily from 1) spectra collected above the skin surface (6,208 out of 30,862), and 2) strong contribution to the spectrum from fluorescent room light (67 out of 30,862). Spectra collected above the skin surface produce a weak Raman response, as the laser focal point approaches the skin surface and is mostly in the instrument window below the skin. Spectra with a strong contribution from fluorescent room light are generated due to either instrument operator error or subject movement, which produces a condition where the confocal Raman collection window is not fully covered by the subject&#8217;s body site. Although these types of spectral artifacts could be identified and remediated during spectral acquisition by a spectroscopic expert at the time of data acquisition, the trained instrument operators used in this study were instructed to collect all data unless a catastrophic failure was observed. The task of identifying and excluding outliers is incorporated into the data analysis protocol. The protocol presented is developed to resolve this challenge. To address the low signal-to-noise spectra above the skin surface, the location of the skin surface needs to be determined first to allow removal of spectra collected above the skin surface. The location of the skin surface is defined as the depth where the Raman laser focal point is half in the skin and half out of the skin as illustrated in Supplemental Figure 1. After removing low signal-to-noise spectra, a principal component analysis (PCA) is implemented to extract the factor dominated by fluorescent room light peaks. These outliers are removed based on the score value of the corresponding factor.
This protocol provides detailed information for how six principal components are determined in the MCR process. This is done through a PCA analysis followed by spectral shape comparison between the loadings for models generated with a different number of principal components. The experimental process for data collection of reference materials as well as the human subjects is also explained in detail.&#160;

<table><tbody><tr><td>Bovine Serum Albumin</td><td>Sigma-Aldrich</td><td></td><td></td></tr><tr><td>Cholesterol</td><td>Sigma-Aldrich</td><td></td><td></td></tr><tr><td>Cholesterol 3-sulfate sodium</td><td>Sigma-Aldrich</td><td></td><td></td></tr><tr><td>D-Erythro-Dihydrosphingosine</td><td>Sigma-Aldrich</td><td></td><td></td></tr><tr><td>DI water</td><td></td><td></td><td>Purified with Milipore(18.2M&amp;Omega;)</td></tr><tr><td>Gen2-SCA skin analyzer</td><td>River Diagnostics, Rotterdam, The Netherlands</td><td>Gen2</td><td></td></tr><tr><td>Matlab 2018b</td><td>Mathwork</td><td>2018b</td><td></td></tr><tr><td>N-behenoyl-D-erythro-sphingosine</td><td>Avanti Polar Lipids, Inc.</td><td></td><td></td></tr><tr><td>N-Lignoceroyl-D-erythro-sphinganine(ceramide)</td><td>Avanti Polar Lipids, Inc.</td><td></td><td></td></tr><tr><td>Oleic Acid</td><td>Sigma-Aldrich</td><td></td><td></td></tr><tr><td>Palmitic Acid</td><td>Sigma-Aldrich</td><td></td><td></td></tr><tr><td>Palmitoleic Acid</td><td>Sigma-Aldrich</td><td></td><td></td></tr><tr><td>PLS_Toolbox version 8.2</td><td>Eigenvector Research Inc.</td><td>8.2</td><td></td></tr><tr><td>RiverICon</td><td>River Diagnostics, Rotterdam, The Netherlands</td><td>version 3.2</td><td></td></tr><tr><td>Squalene</td><td>Sigma-Aldrich</td><td></td><td></td></tr><tr><td>Stearic Acid</td><td>Sigma-Aldrich</td><td></td><td></td></tr></tbody></table>

resolving water, proteins, and lipids from in vivo confocal raman spectra of stratum corneum through a chemometric approach

Development of this in vivo confocal Raman spectroscopic method enables the direct measurement of water, proteins, and lipids with depth resolution in human subjects. This information is very important for skin-related diseases and characterizing skin care product performance. This protocol illustrates a method for confocal Raman spectra collection and the subsequent analysis of the spectral dataset leveraging chemometrics. The goal of this method is to establish a standard protocol for data collection and provide general guidance for data analysis. Preprocessing (e.g., removal of outlier spectra) is a critical step when processing large datasets from clinical studies. As an example, we provide guidance based on prior knowledge of a dataset to identify the types of outliers and develop specific strategies to remove them. A principal component analysis is performed, and the loading spectra are compared with spectra from reference materials to select the number of components used in the final multivariate curve resolution (MCR) analysis. This approach is successful for extracting meaningful information from a large spectral dataset.

In this clinical study, in vivo confocal Raman spectra were collected from 28 subjects from 4&#8211;18 years old. A total of 30,862 Raman spectra were collected with the data collection protocol mentioned above. This large spectral dataset contains 20% spectral outliers as shown in Figure 4A. The low signal-to-noise outlier spectra were removed after determining the skin surface, followed by the PCA to identify the spectra with room light features. The third factor in this PCA model is identified room light peaks. This is confirmed by comparison of the loading spectra of factor 3 with a spectrum of fluorescent room light collected separately at the study site using the same confocal Raman instrument (see Supplementary Figure 3). Figure 4B indicates that most of the outlier spectra were removed after this process.
PCA was performed on the preprocessed confocal Raman dataset and the eigenvalue along with the number of factors used are plotted in Figure 5. According to prior studies12,19, the model should include at least three components: water, protein, and lipid. A significant decrease in eigenvalue was observed for factor 9 as shown in Figure 5. This observation suggests investigating models with the number of principal components varying between three and eight factors for inclusion in the MCR model. MCR loadings that contain spectroscopic features most consistent with protein, water, and lipid are shown in Figure 6.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/60186/60186fig1.jpg" /> 
Figure 1. Illustration of the lesion and non-lesion mark on forearm. (A) A 3 cm x 4 cm marked area on a lesion site. (B) A 3 cm x 4 cm marked area on a non-lesion site. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/60186/60186fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/60186/60186fig2.jpg" /> 
Figure 2. Illustration of the confocal Raman data collection. (A) Confocal Raman instrument. (B) Spectra collection on the forearm of human subject. (C) A screen shot of determining the reference position for data collection. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/60186/60186fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/60186/60186fig3.jpg" /> 
Figure 3. Determining the skin surface. (A) Integration of the protein area under each Raman spectrum. (B) Setting the skin surface based on the maximum and minimum points. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/60186/60186fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/60186/60186fig4.jpg" /> 
Figure 4. Raman dataset spectra. (A) Confocal Raman spectra before removal of the outlier spectra. (B) Confocal Raman spectra after removal of the outlier spectra. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/60186/60186fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/60186/60186fig5.jpg" /> 
Figure 5. Determining the number of components from PCA analysis. (A) Eigenvalue on a logarithmic scale plotted as a function of the number of components used in PCA model. (B) Difference in eigenvalues between &#39;n&#39;&#160;and &#39;n + 1&#39;&#160;components <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/60186/60186fig5large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 6" class="xfigimg" src="/files/ftp_upload/60186/60186fig6.jpg" /> 
Figure 6. Comparison of the loading shape with the corresponding reference materials&#39;&#160;spectra with three to eight components in MCR model. (A) Protein, (B) water, and (C) lipid factors&#39;&#160;shape with three to eight components in the MCR model compared with BSA, water, and lipid reference materials&#39;&#160;spectra, respectively. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/60186/60186fig6large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 7" class="xfigimg" src="/files/ftp_upload/60186/60186fig7.jpg" /> 
Figure 7. The additional three loadings from the six component MCR model not used in the final model. These three MCR components are dominated by fluorescence and baseline artifacts. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/60186/60186fig7large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Supplemental Figure 1" src="/files/ftp_upload/60186/60186supfig1.jpg" /> 
Supplemental Figure 1. Illustration of the determination of the skin surface where the center of the laser focus touches the skin. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/60186/60186supfig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Supplemental Figure 2" src="/files/ftp_upload/60186/60186supfig2.jpg" /> 
Supplemental Figure 2. Illustration of the selection of three components in the PLS_Toolbox software PCA analysis. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/60186/60186supfig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Supplemental Figure 3" src="/files/ftp_upload/60186/60186supfig3.jpg" /> 
Supplemental Figure 3. Identification of the loading factor dominated by room light superimposed on a reference spectrum of room light. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/60186/60186supfig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Supplemental Figure 4" src="/files/ftp_upload/60186/60186supfig4.jpg" /> 
Supplemental Figure 4. Comparison of loadings from the MCR model before and after removal of cosmic rays. (A), (B), and (C) are the factors representing water, protein, and lipid, respectively. The additional loading factors not used in the final MCR model are d, e, and f. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/60186/60186supfig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Supplemental Figure 5" src="/files/ftp_upload/60186/60186supfig5.jpg" /> 
Supplemental Figure 5. Raman spectra of typical lipid materials in stratum corneum. (A) Cholesterol 3-sulfate sodium and cholesterol. (B) Oleic, palmitic, palmitoleic, and stearic acid. (C) Squalene. (D) N-behenoyl-D-erythro-sphingosine, N-Lignoceroyl-D-erythro-sphinganine, and D-Erythro-Dihyrosphingosine. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/60186/60186supfig5large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

Watch this Scientific Journal Video about Resolving Water, Proteins, and Lipids from In Vivo Confocal Raman Spectra of Stratum Corneum through a Chemometric Approach at JoVE.com

Resolving Water, Proteins, and Lipids from In Vivo Confocal Raman Spectra of Stratum Corneum through a Chemometric Approach

Here, we present a protocol for collection of confocal Raman spectra from human subjects in clinical studies combined with chemometric approaches for spectral outlier removal and the subsequent extraction of key features.

Development of this in vivo confocal Raman spectroscopic method enables the direct measurement of water, proteins, and lipids with depth resolution in ...

resolving-water-proteins-lipids-from-vivo-confocal-raman-spectra

Nanyang Technological University

Research

JoVE Journal

Chemistry

7.1K Views.  Beijing Innovative center. Directe meting van water, eiwitten en lipiden met diepteresolutie bij menselijke proefpersonen is erg belangrijk voor huidgerelateerde ziekten, voor de karakterisering van de prestaties van huidverzorgingsproducten. Deze methode, samen met de daaropvolgende analyse, maakt gebruik van chemometrie om chemische informatie te extraheren. Het belangrijkste voordeel van deze techniek is dat het mogelijk is voor de klinische Raman dataset worden verzameld door getrainde instrument exploitanten die g...

Video: Resolving Water, Proteins, and Lipids from In Vivo Confocal Raman Spectra of Stratum Corneum through a Chemometric Approach

Rejection of Fluorescence Background in Resonance and Spontaneous Raman Microspectroscopy

Raman spectroscopy is often plagued by a strong fluorescent background, particularly for biological samples. If a sample is excited with a train of ultrafast pulses, a system that can temporally separate spectrally overlapping signals on a picosecond timescale can isolate promptly arriving Raman scattered light from late-arriving fluorescence light. Here we discuss the construction and operation of a complex nonlinear optical system that uses all-optical switching in the form of a low-power optical Kerr gate to isolate Raman and fluorescence signals. A single 808 nm laser with 2.4 W of average power and 80 MHz repetition rate is split, with approximately 200 mW of 808 nm light being converted to &lt; 5 mW of 404 nm light sent to the sample to excite Raman scattering. The remaining unconverted 808 nm light is then sent to a nonlinear medium where it acts as the pump for the all-optical shutter. The shutter opens and closes in 800 fs with a peak efficiency of approximately 5%. Using this system we are able to successfully separate Raman and fluorescence signals at an 80 MHz repetition rate using pulse energies and average powers that remain biologically safe. Because the system has no spare capacity in terms of optical power, we detail several design and alignment considerations that aid in maximizing the throughput of the system. We also discuss our protocol for obtaining the spatial and temporal overlap of the signal and pump beams within the Kerr medium, as well as a detailed protocol for spectral acquisition. Finally, we report a few representative results of Raman spectra obtained in the presence of strong fluorescence using our time-gating system.

We discuss the construction and operation of a complex nonlinear optical
system that uses ultrafast all-optical switching to isolate Raman from fluorescence
signals. Using this system we are able to successfully separate Raman and fluorescence
signals utilizing pulse energies and average powers that remain biologically safe.

Raman spectroscopy is often plagued by a strong fluorescent background, particularly for biological samples. If a sample is excited with a train of ...

Bioengineering

Combining Raman Imaging and Multivariate Analysis to Visualize Lignin, Cellulose, and Hemicellulose in the Plant Cell Wall

The application of Raman imaging to plant biomass is increasing because it can offer spatial and compositional information on aqueous solutions. The analysis does not usually require extensive sample preparation; structural and chemical information can be obtained without labeling. However, each Raman image contains thousands of spectra; this raises difficulties when extracting hidden information, especially for components with similar chemical structures. This work introduces a multivariate analysis to address this issue. The protocol establishes a general method to visualize the main components, including lignin, cellulose, and hemicellulose within the plant cell wall. In this protocol, procedures for sample preparation, spectral acquisition, and data processing are described. It is highly dependent upon operator skill at sample preparation and data analysis. By using this approach, a Raman investigation can be performed by a non-specialist user to acquire high-quality data and meaningful results for plant cell wall analysis.

This protocol aims to present a general method to visualize lignin, cellulose, and hemicellulose in plant cell walls using Raman imaging and multivariate analysis.

The application of Raman imaging to plant biomass is increasing because it can offer spatial and compositional information on aqueous solutions. The ...

Biochemistry

Non-contact, Label-free Monitoring of Cells and Extracellular Matrix using Raman Spectroscopy

Non-destructive, non-contact and label-free technologies to monitor cell and tissue cultures are needed in the field of biomedical research.1-5 However, currently available routine methods require processing steps and alter sample integrity. Raman spectroscopy is a fast method that enables the measurement of biological samples without the need for further processing steps. This laser-based technology detects the inelastic scattering of monochromatic light.6 As every chemical vibration is assigned to a specific Raman band (wavenumber in cm-1), each biological sample features a typical spectral pattern due to their inherent biochemical composition.7-9 Within Raman spectra, the peak intensities correlate with the amount of the present molecular bonds.1 Similarities and differences of the spectral data sets can be detected by employing a multivariate analysis (e.g. principal component analysis (PCA)).10
Here, we perform Raman spectroscopy of living cells and native tissues. Cells are either seeded on glass bottom dishes or kept in suspension under normal cell culture conditions (37 &deg;C, 5% CO2) before measurement. Native tissues are dissected and stored in phosphate buffered saline (PBS) at 4 &deg;C prior measurements. Depending on our experimental set up, we then either focused on the cell nucleus or extracellular matrix (ECM) proteins such as elastin and collagen. For all studies, a minimum of 30 cells or 30 random points of interest within the ECM are measured. Data processing steps included background subtraction and normalization.

Raman spectroscopy is a suitable technique for the non-contact, label-free analysis of living cells, tissue-engineered constructs and native tissues. Source-specific spectral fingerprints can be generated and analyzed using multivariate analysis.

Non-destructive, non-contact and label-free technologies to monitor cell and tissue cultures are needed in the field of biomedical research.1-5 However, ...

In Situ Characterization of Hydrated Proteins in Water by SALVI and ToF-SIMS

This work demonstrates in situ characterization of protein biomolecules in the aqueous solution using the System for Analysis at the Liquid Vacuum Interface (SALVI) and time-of-flight secondary ion mass spectrometry (ToF-SIMS). The fibronectin protein film was immobilized on the silicon nitride (SiN) membrane that forms the SALVI detection area. During ToF-SIMS analysis, three modes of analysis were conducted including high spatial resolution mass spectrometry, two-dimensional (2D) imaging, and depth profiling. Mass spectra were acquired in both positive and negative modes. Deionized water was also analyzed as a reference sample. Our results show that the fibronectin film in water has more distinct and stronger water cluster peaks compared to water alone. Characteristic peaks of amino acid fragments are also observable in the hydrated protein ToF-SIMS spectra. These results illustrate that protein molecule adsorption on a surface can be studied dynamically using SALVI and ToF-SIMS in the liquid environment for the first time.

In Situ Characterization of Hydrated Proteins in Water by SALVI and ToF-SIMS

This work presents a protocol for liquid handling and sample introduction to a microchannel for in situ time-of-flight secondary ion mass spectrometry analysis of protein biomolecules in an aqueous solution.

This work demonstrates in situ characterization of protein biomolecules in the aqueous solution using the System for Analysis at the Liquid Vacuum ...

Single-throughput Complementary High-resolution Analytical Techniques for Characterizing Complex Natural Organic Matter Mixtures

Natural organic matter (NOM) is composed of a highly complex mixture of thousands of organic compounds which, historically, proved difficult to characterize. However, to understand the thermodynamic and kinetic controls on greenhouse gas (carbon dioxide [CO2] and methane [CH4]) production resulting from the decomposition of NOM, a molecular-level characterization coupled with microbial proteome analyses is necessary. Further, climate and environmental changes are expected to perturb natural ecosystems, potentially upsetting complex interactions that influence both the supply of organic matter substrates and the microorganisms performing the transformations. A detailed molecular characterization of the organic matter, microbial proteomics, and the pathways and transformations by which organic matter is decomposed will be necessary to predict the direction and magnitude of the effects of environmental changes. This article describes a methodological throughput for comprehensive metabolite characterization in a single sample by direct injection Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS), gas chromatography mass spectrometry (GC-MS), nuclear magnetic resonance (NMR) spectroscopy, liquid chromatography mass spectrometry (LC-MS), and proteomics analysis. This approach results in a fully-paired dataset which improves statistical confidence for inferring pathways of organic matter decomposition, the resulting CO2 and CH4 production rates, and their responses to environmental perturbation. Herein we present results of applying this method to NOM samples collected from peatlands; however, the protocol is applicable to any NOM sample (e.g., peat, forested soils, marine sediments, etc.).

This protocol describes a single throughput for complementary analytical and omics techniques culminating in a fully-paired characterization of natural organic matter and microbial proteomics in different ecosystems. This approach permits robust comparisons for identifying metabolic pathways and transformations important for describing greenhouse gas production and predicting responses to environmental change.

Natural organic matter (NOM) is composed of a highly complex mixture of thousands of organic compounds which, historically, proved difficult to ...

Environment

An Integrated Raman Spectroscopy and Mass Spectrometry Platform to Study Single-Cell Drug Uptake, Metabolism, and Effects

Cells are known to be inherently heterogeneous in their responses to drugs. Therefore, it is essential that single-cell heterogeneity is accounted for in drug discovery studies. This can be achieved by accurately measuring the plethora of cellular interactions between a cell and drug at the single-cell level (i.e., drug uptake, metabolism, and effect). This paper describes a single-cell Raman spectroscopy and mass spectrometry (MS) platform to monitor metabolic changes of cells in response to drugs. Using this platform, metabolic changes in response to the drug can be measured by Raman spectroscopy, while the drug and its metabolite can be quantified using mass spectrometry in the same cell. The results suggest that it is possible to access information about drug uptake, metabolism, and response at a single-cell level.

This protocol presents an integrated Raman spectroscopy-mass spectrometry (MS) platform that is capable of achieving single-cell resolution. Raman spectroscopy can be used to study cellular response to drugs, while MS can be used for targeted and quantitative analysis of drug uptake and metabolism.

Cells are known to be inherently heterogeneous in their responses to drugs. Therefore, it is essential that single-cell heterogeneity is accounted for in ...

Implementation of a Nonlinear Microscope Based on Stimulated Raman Scattering

Stimulated Raman scattering (SRS) microscopy uses near-infrared excitation light; therefore, it shares many multi-photon microscopic imaging properties. SRS imaging modality can be obtained using commercial laser-scanning microscopes by equipping with a non-descanned forward detector with proper bandpass filters and lock-in amplifier (LIA) detection scheme. A schematic layout of a typical SRS microscope includes the following: two pulsed laser beams, (i.e., the pump and probe directed in a scanning microscope), which must be overlapped in both space and time at the image plane, then focused by a microscope objective into the sample through two scanning mirrors (SMs), which raster the focal spot across an x-y plane. After interaction with the sample, transmitted output pulses are collected by an upper objective and measured by a forward detection system inserted in an inverted microscope. Pump pulses are removed by a stack of optical filters, whereas the probe pulses that are the result of the SRS process occurring in the focal volume of the specimen are measured by a photodiode (PD). The readout of the PD is demodulated by the LIA to extract the modulation depth. A two-dimensional (2D) image is obtained by synchronizing the forward detection unit with the microscope scanning unit. In this paper, the implementation of an SRS microscope is described and successfully demonstrated, as well as the reporting of label-free images of polystyrene beads with diameters of 3 &#181;m. It is worth noting that SRS microscopes are not commercially available, so in order to take advantage of these characteristics, the homemade construction is the only option. Since SRS microscopy is becoming popular in many fields, it is believed that this careful description of the SRS microscope implementation can be very useful for the scientific community.

In this manuscript, the implementation of a stimulated Raman scattering (SRS) microscope, obtained by the integration of an SRS experimental set-up with a laser scanning microscope, is described. The SRS microscope is based on two femtosecond (fs) laser sources, a Ti-Sapphire (Ti:Sa) and synchronized optical parametric oscillator (OPO).

Stimulated Raman scattering (SRS) microscopy uses near-infrared excitation light; therefore, it shares many multi-photon microscopic imaging properties. ...

Engineering

Ultrafast Time-resolved Near-IR Stimulated Raman Measurements of Functional &#960;-conjugate Systems

Femtosecond time-resolved stimulated Raman spectroscopy is a promising method of observing the structural dynamics of short-lived transients with near infrared (near-IR) transitions, because it can overcome the low sensitivity of spontaneous Raman spectrometers in the near-IR region. Here, we describe technical details of a femtosecond time-resolved near-IR multiplex stimulated Raman spectrometer that we have recently developed. A description of signal generation and optimization, measurement, data acquisition, and calibration and correction of recorded data is provided as well. We present an application of our spectrometer to analyze the excited-state dynamics of &#946;-carotene in toluene solution. A C=C stretch band of &#946;-carotene in the second lowest excited singlet (S2) state and the lowest excited singlet (S1) state is clearly observed in the recorded time-resolved stimulated Raman spectra. The femtosecond time-resolved near-IR stimulated Raman spectrometer is applicable to the structural dynamics of &#960;-conjugate systems from simple molecules to complex materials.

Details of signal generation and optimization, measurement, data acquisition, and data handling for a femtosecond time-resolved near-IR stimulated Raman spectrometer are described. A near infrared stimulated Raman study on the excited-state dynamics of &#946;-carotene in toluene is shown as a representative application.

Femtosecond time-resolved stimulated Raman spectroscopy is a promising method of observing the structural dynamics of short-lived transients with near ...

Differential Imaging of Biological Structures with Doubly-resonant Coherent Anti-stokes Raman Scattering (CARS)

Coherent Raman imaging techniques have seen a dramatic increase in activity over the past decade due to their promise to enable label-free optical imaging with high molecular specificity 1. The sensitivity of these techniques, however, is many orders of magnitude weaker than fluorescence, requiring milli-molar molecular concentrations 1,2. Here, we describe a technique that can enable the detection of weak or low concentrations of Raman-active molecules by amplifying their signal with that obtained from strong or abundant Raman scatterers. The interaction of short pulsed lasers in a biological sample generates a variety of coherent Raman scattering signals, each of which carry unique chemical information about the sample. Typically, only one of these signals, e.g. Coherent Anti-stokes Raman scattering (CARS), is used to generate an image while the others are discarded. However, when these other signals, including 3-color CARS and four-wave mixing (FWM), are collected and compared to the CARS signal, otherwise difficult to detect information can be extracted 3. For example, doubly-resonant CARS (DR-CARS) is the result of the constructive interference between two resonant signals 4. We demonstrate how tuning of the three lasers required to produce DR-CARS signals to the 2845 cm-1 CH stretch vibration in lipids and the 2120 cm-1 CD stretching vibration of a deuterated molecule (e.g. deuterated sugars, fatty acids, etc.) can be utilized to probe both Raman resonances simultaneously. Under these conditions, in addition to CARS signals from each resonance, a combined DR-CARS signal probing both is also generated. We demonstrate how detecting the difference between the DR-CARS signal and the amplifying signal from an abundant molecule's vibration can be used to enhance the sensitivity for the weaker signal. We further demonstrate that this approach even extends to applications where both signals are generated from different molecules, such that e.g. using the strong Raman signal of a solvent can enhance the weak Raman signal of a dilute solute.

Differential Imaging of Biological Structures with Doubly-resonant Coherent Anti-stokes Raman Scattering CARS

A combination of three single wavelength short-pulsed lasers is used to generate coherent anti-Stokes Raman scattering (CARS) and doubly-resonant CARS (DR-CARS). The difference between these signals provides enhanced sensitivity for otherwise difficult to detect coherent Raman signals, enabling imaging of weak Raman scatterers.

Coherent Raman imaging techniques have seen a dramatic increase in activity over the past decade due to their promise to enable label-free optical imaging ...

Biology

Multimodal Analysis of Bone Formation Using Explant Models

Comprehensive Characterization of Tissue Mineralization in an Ex Vivo Model

The extensive characterization of tissue mineralization in the context of bone regeneration represents a significant challenge, given the numerous modalities that are currently available for analysis. Here, we propose a workflow for a comprehensive evaluation of new bone formation using a relevant large animal osseous ex vivo explant. A bone defect (diameter = 3.75 mm; depth = 5.0 mm) is created in an explanted sheep femoral head and injected with a macroporous bone substitute loaded with a pro-osteogenic growth factor (bone morphogenetic protein 2 - BMP2). Subsequently, the explant is maintained in culture for a 28-day period, allowing cellular colonization and subsequent bone formation. To evaluate the quality and structure of newly mineralized tissue, the following successive methods are set up: (i) Characterization and high-resolution 3D images of the entire explant using micro-CT, followed by deep learning image analyses to enhance the discrimination of mineralized tissues; (ii) Nano-indentation to determine the mechanical properties of the newly formed tissue; (iii) Histological examinations, such as Hematoxylin/Eosin/Saffron (HES), Goldner's trichrome, and Movat's pentachrome to provide a qualitative assessment of mineralized tissue, particularly with regard to the visualization of the osteoid barrier and the presence of bone cells; (iv) Back-scattering scanning electron microscopy (SEM) mapping with internal reference to quantify the degree of mineralization and provide detailed insights into surface morphology, mineral composition, and bone-biomaterial interface; (v) Raman spectroscopy to characterize the molecular composition of the mineralized matrix and to provide insights into the persistence of BMP2 within the cement through the detection of peptide bonds. This multimodal analysis will provide an effective assessment of newly formed bone and comprehensive qualitative and quantitative insights into mineralized tissues. Through the standardization of these protocols, we aim to facilitate interstudy comparisons and improve the validity and reliability of research findings.

Author Spotlight: Advanced Techniques for Characterizing Tissue Mineralization in Bone Regeneration Research

The proposed protocol entails a global approach to assess bone formation in the context of bone regeneration using multimodal analyses. It aims to provide qualitative and quantitative information on new bone formation, enhancing the rigor and validity of basic and pre-clinical investigations.

Resolving Water, Proteins, and Lipids from In Vivo Confocal Raman Spectra of Stratum Corneum through a Chemometric Approach

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