SpectralWorks
AnalyzerPro

 Data from analytical instruments needs to be processed before it can be analysed and interpreted. The key feature in AnalyzerPro is its ability to chromatographically deconvolute data. Deconvolution is defined as ’reversing the effects of convolution’. For chromatographic data the biggest problem is co-elution of chromatographic peaks as many components will not be completely separated by the column. Using AnalyzerPro we can determine which masses belong to which of the co-eluting peaks. The deconvolution step also allows the creation of target component lists. For comprehensive data analysis, automated library building is available in AnalyzerPro which includes all the components within an entire data set. This differs from a traditional target component list generated from standards in that the components do not need to be identified until later in the analysis. However, the traditional approach is also available in AnalyzerPro.

For example, a metabolomic study was conducted on the kidney tissue of a rat model for polycystic kidney disease. AnalyzerPro processed the data and library searched the found components against an in-house target component library of authentic metabolite standards. Components were also searched against the NIST mass spectral database for tentative identification of the unknowns. Tryptophan, the deconvoluted peak at the retention time 35.45 minutes with a base peak of m/z 202 was only found in the polycystic kidney tissue and not the healthy age-matched and sex-matched control animal kidney tissue. As with many metabolomic studies, there may be several interpretations of this finding which in turn may generate new hypotheses to test.

As shown below, AnalyzerPro is able to automatically determine component 505 (tryptophan) in the disease state samples in the presence of high levels of Docosane (a hydrocarbon added for RI calculation).

Figure 1. AnalyzerPro automatically determine tryptophan in high levels of docosane.

In each case, high quality library searchable spectra were obtained for each component with no prior knowledge of the sample. The following shows the extracted ion chromatograms for each of the masses of the components and their corresponding spectrum.

Figure 2. Spectrum and EICs of docosane

Figure 3. Spectrum and EICs of tryptophan

In this way, AnalyzerPro is able to generate library searchable spectra for closely eluting components.

 AnalyzerPro is fully compatible with the standard quantitation workflows as well as reporting requirements. The Quan Viewer has been optimized so that large data sets can be reviewed, checked and edited if necessary, in an extremely efficient manner to allow the user to focus on their results. This includes being able to visualize and edit calibration curves.

Figure 1. Calibration curve

It is possible to scroll through thumbnails of the chromatographic peaks to quickly identify integration results that may require modifying. The selected thumbnail is also displayed in a larger format which allows for easy drag-and-drop editing of a peak’s start and end points. The thumbnails can either show the compound, the internal standard or a combination of both. Modified quantitation results are flagged and saved with a time-stamp so that it is always possible to recognize modified results and return to the original results if necessary.

Figure 2. Quan components

The quality of the calibration data can be assessed with the retention time reproducibility displays in the Quan Viewer for both the compounds and the standards. These displays are color coded so that results can be quickly reviewed by exception.

In the example below for the pesticide 4,4-DDD, we are able to quickly determine that the first 6 samples are calibration standards and that they are all within the expected retention time window. Had any of the standards fallen outside the window, a red diamond with have been displayed instead of the green one. The two blue dots representing samples 7 and 8 indicate that these are unknown samples and in both instances contained a measurable amount of the pesticide.

Figure 3. Retention time review per component

In the case of the internal standards, we can immediately see that the retention times are running slightly earlier than expected but are still within tolerance. The red stars indicate that the responses for all the standards are lower than the user defined response limit.

Figure 4. Retention time review for each internal standard

We can visualize the responses for any of the internal standards across all the data set to ensure that the minimum and maximum responses are consistent. The average is represented by the dotted line so that you can effortlessly spot potential problems which can then be further investigated.

Figure 5. Review response factors for Internal standards

It is also possible to review the response factors for each compound, this shows the consistency of the results and is another display to assess the quality of the data.

Figure 6. Review response factors for component

The Quan viewer is a visualisation tool that allows you to quickly and easily QC your qantitation data including peaks areas and delimiters for all your Standards, QCs and Unknowns. The quantitation results can then be output using a number of reports depending on specific requirements.

Sample profiling using target component analysis is a useful tool for determining the relative amount of a target substance present in a complex mixture or to search for characteristic components of your samples. It is widely used in application areas such as environmental screening or in the forensic determination of drugs of abuse. It is also important to be able to determine non-target components in areas of product adulteration and AnalyzerPro is able to cover these workflows with ease.

AnalyzerPro provides a simple and accurate way to create your target component libraries from the components found in a sample. Retention time or retention index (RI) information as well as spectral, all NIST format library match information and meta data can all be captured for each component. Additional confirmation criteria using specified ion ratios for components can also be set. Target component analysis also captures reliable component abundance information (height and area) for each sample that is processed.

Figure 1. Create a target component library

Curation of target component libraries allows additional components to be added (appended) to the library, edited or deleted as required.

In the figure below, found components are shown in green and new non target components in blue. Target components that are not found are indicated by a red marker on the baseline.

Figure 2. Found targets, not found targets and unknown targets

Components can be visualized using the component ions, the base peak, selected ions or the sum of the component ions for visual confirmation of the peak deconvolution. This allows you to clearly see the deconvoluted chromatographic components that may be hidden in your data. In the example below, there are six components found over the retention time window. The component chromatograms for each of those components as well as the TIC are shown.

Figure 3. Chromatograms for the 6 found components and TIC

When processing samples, components can be quickly identified and reported as being found, not found or unknown. The unknown components are components that have been found in a sample which were not in the target component library. These components can subsequently be added to your library. When processing a sequence of samples, it is possible to automate this step such that when each new unknown component is detected it is added to the target library. In this way you can build up a comprehensive target library from all of your data files. This ’super’ library can then be used for profiling multiple samples using MatrixAnalyzer and its PCA visualization tool.

The use of MatrixAnalyzer to compare Multiple data files

MatrixAnalyzer allows you to view the individual target component abundances in each of your processed samples.

The data can be normalized to an internal standard component and/or to sample weight information if required. Results can be reported based on component abundance derived from height or area and from the sum of the component ions, specified component ions or base peak ion depending on specific requirements. The data can be viewed in a number of ways. A standard tabular view includes delta RT/RIs, average values, standard deviation, a component coverage percent and p-values. Further processing and reporting of principal component analysis is also available. MatrixAnalyzer is used extensively in metabolomics but the underlying workflow can be used in many other applications where sample differences are being investigated.

Class information can be incorporated in to your data processing sequence to assist with the interpretation of the PCA results and the component information including abundance can be exported to third party software for further processing. As an example, the PCA report shown below represents 12 derivatization batches for a mixture of metabolite standards. The PCA shows that batch 4 is clearly distinct from the other batches but with a total explained variance of 9.5% for PCs 1 and 2, the batches appear fairly similar.

PCA is used in many applications were differences between samples are being investigated. Additional visualization tools in MatrixAnalyzer allow for comparison of response and retention times for components across all samples and classes further assisting with the interpretation of the overall results.

Figure 1. PCA score plot from results with log transformation

Create a library of your sample using our unique ProfileAnalyzer

ProfileAnalyzer is an integrated data processing tool within AnalyzerPro which automates chromatographic matching for the analysis and identification of complex mixtures. It can be used in areas from clinical applications such as identifying inherited metabolomic disorders like urinary organic acidurias including phenylketonuria (PKU) to flavour and fragrance analysis where it can be used for product authentication or to detect counterfeit or adulterated products. ProfileAnalyzer turns found components over a chromatographic range into a spectragram which can be compared objectively against a library of spectragrams to provide an accurate assessment of one sample’s similarity to another.

A spectragram is created by taking the deconvolved components or components based on a targeted library and turning them into a spectrum-like display where the x-axis is either retention time, retention index or in the case of a targeted approach the name of the target component. The y-axis represents either the area or the height of the component. The unknown spectragram is compared against the best match or a specifically selected spectragram. A single spectragram is identical to the typical AnalyzerPro chromatographic display. For example:

Figure 1. Typical spectragram

After processing, ProfileAnalyzer graphically shows the comparison of the unknown sample against the entries in the spectragram library which it was searched against. A subsection of a fire debris analysis is shown using a targeted approach.

Figure 2. Spectragram comparison

In this instance, the top match shows a 73% confidence that our unknown sample matches the profile / spectragram of Gas 90. This is consistent with findings from an expert assessment of the sample. Below are the top ten results of the unknown compared against the fire debris spectragram library.

Table 1. Results from Unknown1.swx

• SpectralWorks AnalyzerPro Brochure
• SpectralWorks AnalyzerPro for Quantitation
• SpectralWorks AnalyzerPro in your Metabolomics Workflow
• Application Note : A GC-MS based longitudinal untargeted metabolomic analysis of plasma from the Lewis Polycystic Kidney rat model of nephronophthisis SpectralWorks AP
• Application Note : An Automated Metabolomics Data Processing Workflow to Distinguish Metabolite Profiles SpectralWorks AP
• Application Note : Deconvolution of Chemical Signals from Endangered African Wild Dogs SpectralWorks AP
• Application Note : FrogDataTutorial AP
• Application Note : GC-MS Based Untargeted Metabolomic Analysis of Kidney and Liver Tissue from the Lewis Polycystic Kidney Rat SpectralWorks AP
• Application Note : Optimization tissue extraction for GCMS metabolomic analysis reveals altered metabolism of barbiturates in a polycystic kidney disease model SpectralWorks AP
• Application Note : Using AnalyzerPro to Complete your Metabolomics Workflow SpectralWorks AP