Automated Data Collection and SERS

Surface-enhanced Raman scattering (SERS) is becoming highly popular as an analytical technique. The use of automated collection and analysis for SERS as well as the potential for multi-sample high-throughput analysis using the DXR Raman microscope are discussed in this application note.

In order to illustrate two different approaches to automated SERS sampling, two samples were analyzed, which are:

• A sample set comprising microRNA, which are small RNA molecules in the size range of 21 to 25 nucleotides in length
• A second sample set comprising 12 samples of ink on paper

Automating Data Acquisition and Management

Thermo Scientific OMNIC Array Automation Software is sued for the automated collection and analysis of multiple samples.

The salient features of Array Automation are:

• The movement of the motorized stage of a DXR Raman microscope or the well-plate accessory of the DXR Raman microscope is controlled by the software and the stage movement is coordinated with the spectral data collection of the samples.
• The software includes templates for a number of multi-sample platforms such as a 96-well plate.
• It is also possible to create new templates using this software.
• Several options are also included for data collection
• Several parameters for grid collection are also available
• For irregular samples or samples that do not fill the entire analysis area two options are proposed for optimal data collection: the software either searches for the strongest signal using a defined grid or manually searches the sample and focuses on each sample location before collection

Metrics available for analysis in array automation are:

• Area above baseline
• Peak area
• Correlation
• Peak area ratio
• Cluster analysis
• Peak height
• Group analysis
• Peak height ratio
• Height above baseline
• Principal components
• Multivariate curve resolution
• Quantitative result

The primary focus of this application note is the use of Array Automation for collecting large data sets for multiple SERS samples such as microRNA.

Experimental

microRNA Sample

The experimental procedure is listed below:

• meRIDIAN microRNA samples were used for the microRNA part of the project.
• Lyophilized samples arrived in reaction tubes and were stored at -80°C till required
• RNase-free water is added to the reaction vial to form the sample solutions
• The vials are shaken to ensure that the dissolution of the entire sample in water.
• The final solution concentration was 1 μg/μl as not all the samples had the same amount of microRNA.
• Each solution was separated into several aliquots and then frozen at -80 °C until required.
• In order to collect SERS spectra, a DXR Raman microscope provided with a 780 nm laser, brightfield/darkfield illumination, 20× microscope objective, and a motorized microscope stage was used.
• The samples were analyzed using 1 mW of laser power.
• The laser power control was very essential as it made sure that samples are not damaged by the laser during collection and the CCD detector is not saturated by the signal. For preparation of the DXR/SERS analysis kit was used for the microRNA sample preparation.
• 2μl of one of the aqueous microRNA solutions with 2 μl of the 70 nm gold colloid with the help of a micropipette.
• Two μl of the resulting solution was deposited onto one of the 12 spots of the gold slide. Before data collection, the samples were air-dried.
• A part of the DXR SERS analysis kit, a verification solution, was used in a preliminary test to validate that the combination of laser wavelength and gold colloid particle size offers a useful SERS response.

Figure 1. Array Automation set-up window showing the template for a 12-spot slide, the 6 spots on the left side of the slide (A1-A3 and B1-B3) have been selected for a multi-spectrum grid collection

The OMNIC software and the Array Automation software were used for data collection and analysis. The procedure is listed below:

• A software template for the 12-position gold slide was designed and used for spectral analysis.
• A set of spectra was collected for each sample spot, with the collection set up and run using the Array Automation software.
• Figure 1 shows a screen capture of the set-up window for Array Automation showing the template for a 12-spot slide, with 6 spots selected for a grid collection. Using a 50 μm step from point to point, a square grid 650 μm on every side is collected. This generated 169 spectra per spot on the slide.

Ink Analysis Sample

The procedure followed for the ink analysis section of the project is as follows:

• Twelve different ink sources or pens and one red ink, three blue inks and eight black inks were collected for preparation of the samples.
• A paper template was created matching the size and dimensions of the company’s 12-spot microscope slide.
• Deposition of samples was done on specific spots by just writing on the paper.
• The template was attached to a microscope slide by taping along the edges.
• Using the standard citrate LEE and Meisel method, a silver colloid solution was prepared for SERS analysis.
• In a series of three microliter aliquots, a total of 12 μl of silver colloid was applied to each ink spot with specific time set between each application for samples to dry. Untreated samples were prepared and analyzed for comparison.
• With the help of the DXR Raman microscope, spectra of the treated and untreated samples were collected this time with a 532 nm laser 10 x objective, bright/dark field illumination and motorized microscope stage. A 2 mW laser power was used with a 25 μm slit aperture. For each sampling location 30 one second scans were collected.
• Due to the high background influence, a 0.2 mW laser power was used combined with 30 scans 0.2 seconds long
• The 12-spot microscope slide template in Array Automation was used, with a 13 × 13 step grid per sample spot with a 50 μm step between sampling locations, resulting in a total of 169 spectra over a sample area of 650 x 650 μm, resulting in more than 2000 spectra being collected for each slide

Results and Discussion

microRNA Samples

Figure 2 shows the average SERS spectra of three of the microRNA samples that were analyzed. In Array Automation it is possible to display or analyze each individual spectrum from a grid or the software can collect all the spectra for a sample spot and create an average, here, individual spectra were collected.

Figure 2. Average SERS spectra of selected microRNA samples (spectra were corrected using the fluorescence correction algorithm)

Figure 2 shows there are a lot of similarities between the spectra but there are spectral differences between the samples. Certain spectral similarities are expected since the samples are made of similar nucleotides, however the arrangements of the nucleotides are what lead to significant spectral differences.

Figure 3 shows the results of a 12-spot analysis and how Array Automation displays all the data points, with the squares color coded based on an analysis algorithm. One sample set was designated as a known set and one set was designated as an unknown set. The sample layout is shown in Figure 4.

Figure 3. Example of Array Automation spectral collection results, the large grid shown to the right is an expansion of the data for spot B3

Figure 4. Showing the layout of microRNA samples, samples designated as knowns are on the left side, and samples designated as unknowns are on the right side, spots are labeled with the particular microRNA sample number

A spectral library was created using the standard OMNIC software. The spectra from the unknown samples were then run against the library of knowns. Figure 5 shows the result of one of the library searches.

Figure 5. MicroRNA library search results for spot B4, which was correctly identified as microRNA sample #11 (Match % 98.71)

Firstly there is a high percentage match from the library search and secondly the spectra of the two samples match visually. Sample to sample reproducibility is very essential for any good analytical method.

Ink on Paper Analysis

There were two objectives for the ink analysis, which are:

• To use the spectra from the ink samples to prove that SERS is useful in the analysis of inks on paper.
• To develop a method for discrimination of all the inks especially those with a similar composition such as one black ink versus another.

Figure 6 shows the Array Automation results for analyzing 12 ink samples on paper treated with silver colloid. Also analyzed was a set of untreated ink samples. Every spot comprises 169 spectra in a 13 x 13 grid. For SERS analysis, it is possible to average out dead spots or hot spots so as not to skew the results.

Figure 6. Array Automation results for the silver treated ink on paper SERS analysis

Figure 7 shows the comparison of the spectral averages of an untreated ink sample versus a SERS ink sample. All the studied inks showed a similar response, a result of a dual mechanism of fluorescence reduction and signal enhancement.

Figure 7. Spectral comparison of a Raman and SERS analysis of a black ink on paper, shown on the same intensity scale to illustrate the signal enhancement from SERS

Figure 8 shows the average spectra for the treated and untreated red ink samples. Spectra for both SERS and regular Raman ink samples were loaded into the TQ Analyst software. TQ Analyst is used for complex data analysis, especially large sets of data. It can be used for quantitative analysis, qualitative analysis, and large calibration sets.

Data for ten of the ink samples were used for the chemometric analysis. A qualitative discriminant analysis method was constructed using the spectra. For effective comparison of the SERS and regular Raman data, the same parameters were used for both data sets.

Figure 8. Spectral comparison of the Raman and SERS spectra of the red ink on paper

Figure 9 shows a principal component score plot for the untreated ink sample data, as can be seen there is not a clear separation of the various samples. The SERS spectra were analyzed using the same parameters, and a plot of the resulting principal component scores is shown in Figure 10.

Figure 9. Principal component score plot for the untreated ink on paper results (Raman). There were 750 misclassified spectra out of 1690, 44.38% of the spectra

Figure 10. Principal component score plot for the silver treated ink on paper results (SERS). There were 54 misclassified spectra out of 1690, only 3.20% of the spectra

Automation

The most critical factor for any type of throughput analysis is the ability to automate data collection. SERS data collection has been restricted to single samples that need hands-on work of the analyst for sample swapping or analysis of new areas of the same sample.

As observed from the results of the two experiments, SERS can be applied to different types of analysis, with the help of different SERS substrates. Array Automation enables the collection of a large amount of data that can be used to build spectral averages, for statistical models, or other types of data analysis. For microRNA analysis Array Automation is used for the development of a diagnostic method where samples are tested for the presence of a specific microRNA related to a disease, and for forensic analysis. The software is also useful to collect ink data to the help in the identification of different inks used on a forged document.

Conclusion

Automated data collection by integrating the DXR Raman microscope with SERS substrate and Array Automation software add on for OMNIC moves SERS from a single sample analysis method to an automated high throughput analytical technique with a number of potential future applications. The work shown in this note demonstrates the ability of a user to prepare up to 12 samples per slide, place the slide into the instrument, collect from one up to 169 spectra per sample, and then analyze that data with a variety of tools. This can all be done with one instrument and one suite of software tools.

This information has been sourced, reviewed and adapted from materials provided by Thermo Fisher Scientific – Materials & Structural Analysis.

For more information on this source, please visit Thermo Fisher Scientific – Materials & Structural Analysis.