Compound Discoverer Software

The software presents complex sample set results as easy-to-navigate compound tables, with all detailed information linked in related tables. These customizable tables allow personalized layouts to be quickly created, saved, and applied. Chromatographic, MS1, and MSn data are easy to review along with results from various search and identification tools.

Loadings and variance plots are also available and, as with all plots displaying individual compound data, the loadings plot is linked to all other tables and views to allow for quick navigation and compound selection.

Data visualization can be a powerful tool to find compounds of interest. Here a custom plot is used to display the results of a PFAS workflow calculating the mass defect to carbon ratio versus the mass to carbon number, leveraging the zero-mass defect value of fluorine, to highlight potential PFAS compounds. Custom visualization and plotting in Compound Discoverer Software can be used in many ways to allow you to focus on compounds of interest.

Custom plotting and graphing tools can be used to query and evaluate the results of your analysis enabling the assessment of chromatographic method performance, instrument reproducibility, or method suitability. Virtually any aspect of your results can be quickly plotted and graphed to allow queries such as how frequently the desired adduct precursor was selected and used for fragmentation or how the area of a select group of key compounds changed between two sample matrices.

Using pooled QC samples, which are analyzed throughout data acquisition, allows for the correction of batch-effects over time. Correction for each compound is performed individually, and multiple methods are available. The displayed correction plot utilizes SERRF QC (Fan et. al). In addition, QC-based correction per compound can be performed based upon a peer-reviewed methodology published by Dunn et. al. in Nature Protocols. Compound Discoverer Software provides the capability to view the impact of any changes to the data pre- and post- correction. In addition, it also supports several general signal normalization tools.

Internal standards can be a useful tool to assess chromatographic performance across a large analytical batch. Compound Discoverer Software supports the definition of custom internal standard QC sets and extracts the data for quick visualization of performance across the run to enable an assessment of performance quickly. Assess the reproducibility of retention time, mass accuracy, area, or many other critical parameters across the batch with simple to use displays.

Demonstrating the connectivity, the data points highlighted by blue circles in the volcano plot (bottom right) are selected within the compound table (bottom left). Selecting any compound in any plot automatically updates all plots to show the relevant data. The interconnected tools enable you to rapidly identify differences and the compounds or groups of compounds responsible for those differences. Apply filters based on any observable criteria from CSV to fold-change to checked status to quickly filter through complex data.

From volcano plots from differential analysis (left), S-plots from partial least squares discriminant analysis (middle), and hierarchical clustering analysis (right), it is easy to visualize complex data sets and determine what is statistically different. Each plot is active, so data points selected in the plot can be marked in the results tables and vice versa, helping determine the cause of observed differences or similarities and tracking compounds in complex data sets.

The software leverages both online and offline library tools for comprehensive data analysis. Online, the mzCloud Spectral Library hosts millions of high-quality curated HRAM MSn spectra on tens of thousands of compounds. Offline libraries, available in the mzVault application format, can assist with PFAS analysis, extractables and leachables, and more. In addition, a compatible version of the NIST Tandem MS library is also available.

The integration of LipidSearch into Compound Discoverer Software allows for the detection and annotation of more potential lipids. A database of lipid classes including 96 subclasses along with predicted fragmentation provides a broad range of coverage as well as annotations for the fragmentation spectra of detected lipids. The lipid grade and ID scoring from LipidSearch are included with results giving more information to make decision during data review.

Chemical database searches can yield on overwhelming number of possible candidates. Compound Discoverer Software offers additional tools to help to refine and rank candidate lists, including mzLogic. By leveraging real fragmentation data from mzCloud, mzLogic analyzes common fragment ions and substructures in unknown spectra, matching them with database candidates. This powerful feature effectively filters out less likely candidates, allowing you to focus only on the most relevant. Here, for an unknown with 53 possible candidates, mzLogic quickly helps to focus on the most relevant.

Another tool offered for helping to reduce the complexity of multiple candidate proposals, either from a database search or from an assessment of possible metabolism or chemical degradation, is FISh scoring. Here, the list of possible candidate structures is used to generate in silico fragmentation data with a score given to each candidate based on its ability to annotate the unknown compounds’ fragmentation spectra. This helps you to separate the most likely candidates from among all possibilities.

Mapping observed data onto biological pathways can also provide useful interpretive information, especially when combined with experimental information such as fold-change or stable isotope label incorporation. Internal tools like Metabolika facilitate mapping onto provided or user custom biological pathways. In addition, information can be mapped onto BioCyc (user subscription required). Data from the results can be directly mapped onto the pathways. Shown here is the average 13C label incorporation, but multiple data mapping options are available.

The fully interactive Molecular Networks visualization browser allows you to view your data in a different way. Identified compounds are shown by nodes (circles) and when a relationship is identified, the nodes are connected. Selecting a node (compound) or connection (transformation) displays pertinent information (right) about the identified compound and the relevant transformation(s). All the visualized data can be interactively filtered using thresholds, data quality information or text search for specific compounds or transformations.

Chemical analysis of PFAS can be a challenge given the complexity of real environmental and treatment samples. New visualization tools, such as the PFAS plot, help to determine potential suspect compounds by leveraging the unique nature of fluorine and heavily fluorinated compounds. By plotting their mass defect to carbon ratio against their mass to carbon ratio, compounds of potential PFAS interest are separated from the bulk of normal organic molecules. When combined with a PFAS score calculated from multiple diagnostic points, this approach further refines and prioritizes high-interest targets.

Even more tools are available for the analysis of PFAS contaminants. Given their often polymeric nature, PFAS compounds undergo a common series of losses such as a single CF2 group. These internal neutral losses can quickly be detected and along with visualization tools help in the determination of compounds of interest.  Additional scoring based on common PFAS fragments (class-based fragmentation) and annotation using extensive built-in PFAS databases further refines the analysis. All of this combined helps to focus on potential PFAS contaminants and gain more confidence in their assignment.

Stable isotope flux experiments are a powerful method to assess the biological impact of various changes. The software supports fully untargeted stable isotope label experiments, helping to uncover unknown or unexpected alterations. Average and relative exchange is calculated as well as total label number incorporated on a per compound basis — all of which can be mapped onto biological pathways to enhance understanding of the changes.

Isotope label incorporation can also be visualized per compound to see how much label has been incorporated and how extensive the labeling is for a given compound across the experiment. Here the incorporation of 13C label into phenylalanine is nearly complete over the course of this experiment with all nine carbons labeled in the majority of observed compound.

Examples of two GC-based workflow trees: The first is an EI workflow that can be used to find biomarkers through statistical analysis and identify unknown compounds via library search, and the second is a CI workflow that can identify unknown compounds of interest through molecular formula determination and structural elucidation of MS/MS spectra.

The above shows GC-EI compound identification in the result view. On the upper right-hand side, a mirror plot shows the deconvoluted spectrum and the library spectrum. Highlighted in the second level table under Library Search Results are total score, delta mass of molecular ion and RI delta: Total score is a composite score that includes contribution from the HRF score and SI score; delta mass is the mass accuracy of the molecular ion if it is present in the deconvoluted spectrum; RI delta is the difference between the library RI and calculated RI. Based on the total score “94.9,” the less than 1 ppm delta mass of the molecular ion, and the RI delta value of one, there is very high confidence in this identification.

A suite of statistical visualization including PCA, PLS-DA, Variance, Loadings, and Volcano Plot are available to discover the real differences between your sample groups within your GC- Orbitrap mass spectrometer data. In addition, identified compounds can be mapped against biological pathways both within Compound Discoverer Software (Metabolika) and against BioCyc, including mapping relevant information such as fold change.​