Name: resolving water, proteins, and lipids from in vivo confocal raman spectra of stratum corneum through a chemometric approach
Uploaded: 2019-09-26T15:00:00
Description: 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

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
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	<li>Include subjects in compliance with the following criteria.
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		<li>Include subjects between the...

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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. 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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. 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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. 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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>

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 o...

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;

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	<tbody>
		<tr height="33">
			<td height="33" style="height:34px;width:319px;">Bovine Serum Albumin</td>
			<td style="width:360px;">Sigma-Aldrich</td>
			<td style="width:136px;"></td>
			<td style="width:240px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:319px;">Cholesterol</td>
			<td style="width:360px;">Sigma-Aldrich</td>
			<td style="width:136px;"></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:319px;">Cholesterol 3-sulfate sodium</td>
			<td style="width:360px;">Sigma-Aldrich</td>
			<td style="width:136px;"></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:319px;">D-Erythro-Dihydrosphingosine</td>
			<td style="width:360px;">Sigma-Aldrich</td>
			<td style="width:136px;"></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:319px;">DI water</td>
			<td style="width:360px;"></td>
			<td style="width:136px;"></td>
			<td>Purified with Milipore(18.2M&#937;)</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:319px;">Gen2-SCA skin analyzer</td>
			<td style="width:360px;">River Diagnostics, Rotterdam, The Netherlands</td>
			<td style="width:136px;">Gen2</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:319px;">Matlab 2018b</td>
			<td style="width:360px;">Mathwork</td>
			<td style="width:136px;">2018b</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:319px;">N-behenoyl-D-erythro-sphingosine</td>
			<td style="width:360px;">Avanti Polar Lipids, Inc.</td>
			<td style="width:136px;"></td>
			<td></td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:319px;">N-Lignoceroyl-D-erythro-sphinganine(ceramide)</td>
			<td style="width:360px;">Avanti Polar Lipids, Inc.</td>
			<td style="width:136px;"></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:319px;">Oleic Acid</td>
			<td style="width:360px;">Sigma-Aldrich</td>
			<td style="width:136px;"></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:319px;">Palmitic Acid</td>
			<td style="width:360px;">Sigma-Aldrich</td>
			<td style="width:136px;"></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:319px;">Palmitoleic Acid</td>
			<td style="width:360px;">Sigma-Aldrich</td>
			<td style="width:136px;"></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:319px;">PLS_Toolbox version 8.2</td>
			<td style="width:360px;">Eigenvector Research Inc.</td>
			<td align="right" style="width:136px;">8.2</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:319px;">RiverICon</td>
			<td style="width:360px;">River Diagnostics, Rotterdam, The Netherlands</td>
			<td style="width:136px;">version 3.2</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:319px;">Squalene</td>
			<td style="width:360px;">Sigma-Aldrich</td>
			<td style="width:136px;"></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:319px;">Stearic Acid</td>
			<td style="width:360px;">Sigma-Aldrich</td>
			<td style="width:136px;"></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 i...

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 ...

Direct measurement of water, proteins, and lipids with depth resolution in human subjects is very important for skin-related diseases, for the characterization of skincare product performance. This method, along with the subsequent analysis, leverages chemometrics to extract chemical information. The main advantage of this technique is that it allows for the clinical Raman data set to be collected by trained instrument operators that lack technical expertise to identify, exclude, and remediate all sources of spectroscopic artifacts.The resulting data set can then be processed to identify outliers that need to be excluded from the date prior to analysis. During the data analysis, a key challenge is the removal of the outliers, and identification of the number of the key components in the data set. The approach show in this video leverages of prior knowledge of the clinical data set and chemometrics approach to successfully extract the water, protein and lipid with depth resolution.Demonstrating the procedure will be Li Yang, technician from our C&T lab. To begin, have the subject place a marked lesion body site or control site in close contact with the imaging window of the in vivo confocal Raman instrument. Be sure that they cover the whole window to avoid the impact of room light on imaging.Then, open the software and move the focus until a spectrum similar to the one shown here, is seen. Afterwards, move the focus 10 microns away from skin surface. Collect data for 26 steps with a two-micron step size in the frequency region shown here, using an exposure time of one second.Measure eight replicates for each area, lasting up to 15 minutes in total. First, use the command window and MATLAB to change the file extension of the collected data from ric to mat. Then load the mat file to the MATLAB software platform, as shown here.Correct the data set's baseline using the automatic weighted least squares method, by going to the PLS_Workspace window and right-clicking the imported data set, scrolling to Analyze, selecting Other Tools and clicking on Preprocessing. In the window that pops up, click on Show. Then, scroll down the Available Methods toolbar to the Automatic Weighted Leased Squares Baseline filtering, and select Add.Next, click on Ok to set the options and to apply preprocessing to the data. Save this as Spectra_baseline. Next, return to the command window and replace the data using the baseline corrected result.Now, go to the Text Editor and run the program as shown here. This will sum up the values between 2910 and 2965 inverse centimeters to obtain the intensity values under each Raman spectrum from the 26 consecutive steps measurement, and store them in an Excel file. In MATLAB, go to the Workspace and set the path for the data in Depth_save, as shown.Next, use the process described here to interpolate the instrument offset value from 26 to 260 using the linspace function in MATLAB. This process will interpolate the intensity value from 26 to 260 using the spline method, leveraging the newly generated 260 position values. Additionally, it will use the 260 position and intensity values as x and y inputs for the polyfit function respectively, setting the degree value to 20.Then, it will use the output coefficients and the 260 extended position values as the input for polyval, to obtain the final 260 intensity values. Next, it will calculate the mean intensity and find the point in the curve which is closest to the mean intensity. It will also change the depth value according to the skin surface, in the known two-micron step size.Now, run the program. Load the Raman spectra data set after removal of the outer skin spectra into the PLS_Toolbox software, under the MATLAB platform and right-click the data set to choose Analyze and then select PCA. Next, click on Choose Preprocessing.Select Normalize as the preprocessing approach. Then, choose none for the cross-validation. Then, build the model using the three components for the PCA decomposition analysis.Now, remove the cover on the in vivo Ramen instrument collection window, and collect the room light spectra in the high frequency region using the same parameters used for the reference material's data collection. Identify the room light effect factor through comparison with the room light background. Now, review the scores and remove the spectra with the significantly higher corresponding score value than normal.This means removing score values of more than 99.8%of the whole data set, which is 0.16 in this study. Save the resulting calibration X-block data. Finally, go to the PLS_Workspace Browser and edit the new file.Select the Row Labels and go to Hard Delete Excluded to permanently delete the excluded data before re-saving the file. Begin by correcting the Raman spectra baseline using the same just shown. Next, perform the PCA analysis on the preprocessed data set.Plot the eigenvalues in logarithmic scale along with the number of components by clicking on the Choose Component button, and select log(eigenvalues)as the y value. To perform multivariate curve resolution analysis, first use the data selection button to load the data set into the MCR_main software. Manually choose the number of components and set the component number to between three and eight.Then, under the Initial Estimation tab, click on the Pure button. Next, select Concentration and click the Do button. Once the screen refreshes, click the Ok button, and then on Continue to move to the next page.Now, click Continue, and under Implementation, apply fnnls. Then, select six from the drop-down menu for the number of species with non-negativity profiles, and click Continue. On the next page, choose the same parameters and click Continue.To determine the location at the skin surface, the area under the protein's Raman peak was integrated to obtain the depth profile of the protein signal. 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.A total of 30, 862 Raman spectra were collected with the data collection protocol described in this video. This large spectral data set contains 20%spectral outliers. Proper identification and removal of the outlier spectra is important to achieve a proper data set.Here, one can see the contribution from room lights, superimposed on a reference spectra of Roomlight. Principle component analysis was performed on the preprocessed confocal Raman data set, and the eigenvalue, along with the number of factors used, are plotted here. A significant decrease in the eigenvalue was observed for factor nine.This observation suggests investigating models with the number of principle components varying between three and eight factors for inclusion in the multivariate curve resolution model. When attempting this procedure, it is critical to start a depth profile above the skin surface, to accurately determine the location of the skin surface. This methodology enables the investigation into the impact of skincare products on key skin components, including water, proteins and lipids.

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

Research

JoVE Journal

Chemistry

Untargeted Metabolomics from Biological Sources Using Ultraperformance Liquid Chromatography-High Resolution Mass Spectrometry (UPLC-HRMS)

Here we present a workflow to analyze the metabolic profiles for biological samples of interest including; cells, serum, or tissue. The sample is first separated into polar and non-polar fractions by a liquid-liquid phase extraction, and partially purified to facilitate downstream analysis. Both aqueous (polar metabolites) and organic (non-polar metabolites) phases of the initial extraction are processed to survey a broad range of metabolites. Metabolites are separated by different liquid chromatography methods based upon their partition properties. In this method, we present microflow ultra-performance (UP)LC methods, but the protocol is scalable to higher flows and lower pressures. Introduction into the mass spectrometer can be through either general or compound optimized source conditions. Detection of a broad range of ions is carried out in full scan mode in both positive and negative mode over a broad m/z range using high resolution on a recently calibrated instrument. Label-free differential analysis is carried out on bioinformatics platforms. Applications of this approach include metabolic pathway screening, biomarker discovery, and drug development.

Untargeted Metabolomics from Biological Sources Using Ultraperformance Liquid Chromatography-High Resolution Mass Spectrometry UPLC-HRMS

Untargeted metabolomics provides a hypothesis generating snapshot of a metabolic profile. This protocol will demonstrate the extraction and analysis of metabolites from cells, serum, or tissue. A range of metabolites are surveyed using liquid-liquid phase extraction, microflow ultraperformance liquid chromatography/high-resolution mass spectrometry (UPLC-HRMS) coupled to differential analysis software.

Here we present a workflow to analyze the metabolic profiles for biological samples of interest including; cells, serum, or tissue. The sample is first ...

A Simple and Rapid Protocol for Measuring Neutral Lipids in Algal Cells Using Fluorescence

Algae are considered excellent candidates for renewable fuel sources due to their natural lipid storage capabilities. Robust monitoring of algal fermentation processes and screening for new oil-rich strains requires a fast and reliable protocol for determination of intracellular lipid content. Current practices rely largely on gravimetric methods to determine oil content, techniques developed decades ago that are time consuming and require large sample volumes. In this paper, Nile Red, a fluorescent dye that has been used to identify the presence of lipid bodies in numerous types of organisms, is incorporated into a simple, fast, and reliable protocol for measuring the neutral lipid content of Auxenochlorella protothecoides, a green alga. The method uses ethanol, a relatively mild solvent, to permeabilize the cell membrane before staining and a 96 well micro-plate to increase sample capacity during fluorescence intensity measurements. It has been designed with the specific application of monitoring bioprocess performance. Previously dried samples or live samples from a growing culture can be used in the assay.

A simple protocol to determine the neutral lipid content of algal cells using a Nile Red staining procedure is described. This time-saving technique offers an alternative to traditional gravimetric-based lipid quantification protocols. It has been designed for the specific application of monitoring bioprocess performance.

Algae are considered excellent candidates for renewable fuel sources due to their natural lipid storage capabilities. Robust monitoring of algal ...

An Inverse Analysis Approach to the Characterization of Chemical Transport in Paints

The ability to directly characterize chemical transport and interactions that occur within a material (i.e., subsurface dynamics) is a vital component in understanding contaminant mass transport and the ability to decontaminate materials. If a material is contaminated, over time, the transport of highly toxic chemicals (such as chemical warfare agent species) out of the material can result in vapor exposure or transfer to the skin, which can result in percutaneous exposure to personnel who interact with the material. Due to the high toxicity of chemical warfare agents, the release of trace chemical quantities is of significant concern. Mapping subsurface concentration distribution and transport characteristics of absorbed agents enables exposure hazards to be assessed in untested conditions. Furthermore, these tools can be used to characterize subsurface reaction dynamics to ultimately design improved decontaminants or decontamination procedures. To achieve this goal, an inverse analysis mass transport modeling approach was developed that utilizes time-resolved mass spectroscopy measurements of vapor emission from contaminated paint coatings as the input parameter for calculation of subsurface concentration profiles. Details are provided on sample preparation, including contaminant and material handling, the application of mass spectrometry for the measurement of emitted contaminant vapor, and the implementation of inverse analysis using a physics-based diffusion model to determine transport properties of live chemical warfare agents including distilled mustard (HD) and the nerve agent VX.

In this paper, a procedure for quantifying the mass transport parameters of chemicals in various materials is presented. This process involves employing an inverse-analysis based diffusion model to vapor emission profiles recorded by real-time, mass spectrometry in high vacuum.

The ability to directly characterize chemical transport and interactions that occur within a material (i.e., subsurface dynamics) is a vital component in ...

Water in Oil Emulsions: A New System for Assembling Water-soluble Chlorophyll-binding Proteins with Hydrophobic Pigments

Chlorophylls (Chls) and bacteriochlorophylls (BChls) are the primary cofactors that carry out photosynthetic light harvesting and electron transport. Their functionality critically depends on their specific organization within large and elaborate multisubunit transmembrane protein complexes. In order to understand at the molecular level how these complexes facilitate solar energy conversion, it is essential to understand protein-pigment, and pigment-pigment interactions, and their effect on excited dynamics. One way of gaining such understanding is by constructing and studying complexes of Chls with simple water-soluble recombinant proteins. However, incorporating the lipophilic Chls and BChls into water-soluble proteins is difficult. Moreover, there is no general method, which could be used for assembly of water-soluble proteins with hydrophobic pigments. Here, we demonstrate a simple and high throughput system based on water-in-oil emulsions, which enables assembly of water-soluble proteins with hydrophobic Chls. The new method was validated by assembling recombinant versions of the water-soluble chlorophyll binding protein of Brassicaceae plants (WSCP) with Chl a. We demonstrate the successful assembly of Chl a using crude lysates of WSCP expressing E. coli cell, which may be used for developing a genetic screen system for novel water-soluble Chl-binding proteins, and for studies of Chl-protein interactions and assembly processes.

This manuscript describes a simple and high-throughput method for assembling water-soluble proteins with hydrophobic pigments that is based on water-in-oil emulsions. We demonstrate the effectiveness of the method in the assembly of native chlorophylls with four variants of recombinant water-soluble-chlorophyll binding proteins (WSCPs) of Brassica plants expressed in E. coli.

Chlorophylls (Chls) and bacteriochlorophylls (BChls) are the primary cofactors that carry out photosynthetic light harvesting and electron transport. ...

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 ...

Preparation of Biopolymer Aerogels Using Green Solvents

Although the first reports on aerogels made by Kistler1 in the 1930s dealt with aerogels from both inorganic oxides (silica and others) and biopolymers (gelatin, agar, cellulose), only recently have biomasses been recognized as an abundant source of chemically diverse macromolecules for functional aerogel materials. Biopolymer aerogels (pectin, alginate, chitosan, cellulose, etc.) exhibit both specific inheritable functions of starting biopolymers and distinctive features of aerogels (80-99% porosity and specific surface up to 800 m2/g). This synergy of properties makes biopolymer aerogels promising candidates for a wide gamut of applications such as thermal insulation, tissue engineering and regenerative medicine, drug delivery systems, functional foods, catalysts, adsorbents and sensors. This work demonstrates the use of pressurized carbon dioxide (5 MPa) for the ionic cross linking of amidated pectin into hydrogels. Initially a biopolymer/salt dispersion is prepared in water. Under pressurized CO2 conditions, the pH of the biopolymer solution is lowered to 3 which releases the crosslinking cations from the salt to bind with the biopolymer yielding hydrogels. Solvent exchange to ethanol and further supercritical CO2 drying (10 - 12 MPa) yield aerogels. Obtained aerogels are ultra-porous with low density (as low as 0.02 g/cm3), high specific surface area (350 - 500 m2/g) and pore volume (3 - 7 cm3/g for pore sizes less than 150 nm).

A new way for the production of biopolymer-based aerogels by carbon dioxide (CO2) induced gelation is shown. The technique utilizes pressurized carbon dioxide (5 MPa) for the production of biopolymer hydrogels and supercritical CO2 (12 MPa) to convert gels into aerogels. The only solvents needed besides CO2 are water and ethanol.

Although the first reports on aerogels made by Kistler1 in the 1930s dealt with aerogels from both inorganic oxides (silica and others) and biopolymers ...

Laboratory Production of Biofuels and Biochemicals from a Rapeseed Oil through Catalytic Cracking Conversion

The work is based on a reported study which investigates the processability of canola oil (bio-feed) in the presence of bitumen-derived heavy gas oil (HGO) for production of transportation fuels through a fluid catalytic cracking (FCC) route. Cracking experiments are performed with a fully automated reaction unit at a fixed weight hourly space velocity (WHSV) of 8 hr-1, 490-530 &#176;C, and catalyst/oil ratios of 4-12 g/g. When a feed is in contact with catalyst in the fluid-bed reactor, cracking takes place generating gaseous, liquid, and solid products. The vapor produced is condensed and collected in a liquid receiver at -15 &#176;C. The non-condensable effluent is first directed to a vessel and is sent, after homogenization, to an on-line gas chromatograph (GC) for refinery gas analysis. The coke deposited on the catalyst is determined in situ by burning the spent catalyst in air at high temperatures. Levels of CO2 are measured quantitatively via an infrared (IR) cell, and are converted to coke yield. Liquid samples in the receivers are analyzed by GC for simulated distillation to determine the amounts in different boiling ranges, i.e., IBP-221 &#176;C (gasoline), 221-343 &#176;C (light cycle oil), and 343 &#176;C+ (heavy cycle oil). Cracking of a feed containing canola oil generates water, which appears at the bottom of a liquid receiver and on its inner wall. Recovery of water on the wall is achieved through washing with methanol followed by Karl Fischer titration for water content. Basic results reported include conversion (the portion of the feed converted to gas and liquid product with a boiling point below 221 &#176;C, coke, and water, if present) and yields of dry gas (H2-C2's, CO, and CO2), liquefied petroleum gas (C3-C4), gasoline, light cycle oil, heavy cycle oil, coke, and water, if present.

This paper presents an experimental method to produce biofuels and biochemicals from canola oil mixed with a fossil-based feed in the presence of a catalyst at mild temperatures. Gaseous, liquid, and solid products from a reaction unit are quantified and characterized. Conversion and individual product yields are calculated and reported.

The work is based on a reported study which investigates the processability of canola oil (bio-feed) in the presence of bitumen-derived heavy gas oil ...

A Novel Technique for Raman Analysis of Highly Radioactive Samples Using Any Standard Micro-Raman Spectrometer

A novel approach for the Raman measurement of nuclear materials is reported in this paper. It consists of the enclosure of the radioactive sample in a tight capsule that isolates the material from the atmosphere. The capsule can optionally be filled with a chosen gas pressurized up to 20 bars. The micro-Raman measurement is performed through an optical-grade quartz window. This technique permits accurate Raman measurements with no need for the spectrometer to be enclosed in an alpha-tight containment. It therefore allows the use of all options of the Raman spectrometer, like multi-wavelength laser excitation, different polarizations, and single or triple spectrometer modes. Some examples of measurements are shown and discussed. First, some spectral features of a highly radioactive americium oxide sample (AmO2) are presented. Then, we report the Raman spectra of neptunium oxide (NpO2) samples, the interpretation of which is greatly improved by employing three different excitation wavelengths, 17O doping, and a triple mode configuration to measure the anti-stokes Raman lines. This last feature also allows the estimation of the sample surface temperature. Finally, data that were measured on a sample from Chernobyl lava, where phases are identified by Raman mapping, are shown.

We present a technique for Raman spectroscopic analysis of highly radioactive samples compatible with any standard micro-Raman spectrometer, without any radioactive contamination of the instrument. We also show some applications using actinide compounds and irradiated fuel materials.

A novel approach for the Raman measurement of nuclear materials is reported in this paper. It consists of the enclosure of the radioactive sample in a ...

Observation and Analysis of Blinking Surface-enhanced Raman Scattering

From a single molecule at a silver nanoaggregate junction, blinking surface-enhanced Raman scattering (SERS) is observed. Here, a protocol is presented on how to prepare the SERS-active silver nanoaggregate, record a video of certain blinking spots in the microscopic image, and analyze the blinking statistics. In this analysis, a power law reproduces the probability distributions for bright events relative to their duration. The probability distributions for dark events are fitted by a power law with an exponential function. The parameters of the power law represent molecular behavior in both bright and dark states. The random walk model and the speed of the molecule across the entire silver surface can be estimated. It is difficult to estimate even when using averages, autocorrelation functions, and super-resolution SERS imaging. In the future, power law analyses should be combined with spectral imaging, because the origins of blinking cannot be confirmed by this analysis method alone.

This protocol describes the analysis of blinking surface-enhanced Raman scattering due to the random walk of a single molecule on a silver surface using power laws.

From a single molecule at a silver nanoaggregate junction, blinking surface-enhanced Raman scattering (SERS) is observed. Here, a protocol is presented on ...

Vibrational Spectra of a N719-Chromophore/Titania Interface from Empirical-Potential Molecular-Dynamics Simulation, Solvated by a Room Temperature Ionic Liquid

The accurate molecular simulation prediction of vibrational spectra, and other structural, energetic and spectral characteristics, of photo-active metal-oxide surfaces in contact with light-absorbing dyes is an ongoing thorny and elusive challenge in physical chemistry. With this in mind, a molecular-dynamics (MD) simulation were performed by using optimized empirical potentials for a well-representative and prototypical dye-sensitized solar cell (DSC) solvated by a widely-studied room temperature ionic liquid (RTIL), in the guise of a [bmim]+[NTf2]- RTIL solvating an N719-sensitizing dye adsorbed onto 101 anatase-titania. In doing so, important insights were gleaned into how using a RTIL as the electrolytic hole acceptor modulates the dynamical and vibrational properties of a N719 dye, estimating the spectra for the DSC photo-active interface via Fourier transformation of mass-weighted velocity autocorrelation functions from MD. The acquired vibrational spectra were compared with the experiment spectra and those sampled from ab initio molecular dynamics (AIMD); in particular, various empirical-potential spectra generated from MD provide insight into how partial-charge charge parameterization of the ionic liquid affects vibrational spectra prediction. In any event, careful fitting of empirical force-field models has been shown to be an effective tool in handling DSC vibrational properties, when validated by AIMD and an experiment.

A dye-sensitized solar cell was solvated by RTILs; using optimized empirical potentials, a molecular dynamics simulation was applied to compute vibrational properties. The obtained vibrational spectra were compared with experiment and ab initio molecular dynamics; various empirical potential spectra show how partial-charge charge parameterization of the ionic liquid affects vibrational spectra prediction.