MetaboScape^®

MetaboScape^® uses a unified workflow to process non-targeted analyses from Bruker's ESI & MALDI Imaging instruments, simplifying the number of steps and rapidly pinpointing and identifying biomarkers.

MetaboScape^® can be used across application areas, including discovery metabolomics, lipidomics, phenomics, foodomics, environmental and pharma and provides users with the flexibility to support workflows ranging from basic ID to advanced statistics.

• MetaboScape^®’s powerful T-ReX^® algorithm comprises retention time alignment, deisotoping and feature extraction to ensure robust data processing
• Target compounds can be automatically annotated using user defined Analyte Lists
• Unknown ID pipeline including library matching and in silico fragmentation to facilitate unknown ID
• Visualize relevant information in complex data sets using supervised and non-supervised statistics, including PCA, t-test, ANOVA, PLS and bucket correlation analyses
• Annotation Quality (AQ) scoring providing five indicators of data quality
• Pathway mapping to set identified metabolites in a biological context, thereby turning data into knowledge
• Identification of drug and xenobiotic metabolites using local metabolite prediction
• Batch correction to offset sample effects in large sample cohorts
• Time series plots to investigate changes in metabolites over time
• Dedicated lipidomics annotation tools, including rule based annotation, 4D Kendrick mass defect plot and CCSPredict
• To simplify the identification of knowns, MetaboScape^® supports the MetaboBASE^® Personal Library, HMDB Metabolite Library, the Bruker Sumner MetaboBASE^® Plant Library (including CCS values for >130 compounds), as well as custom libraries
• Customized data export to a file format suitable for import in GNPS (Global Natural Products Social Molecular Networking)
• Client-server architecture to enable rapid data processing and multiple users to share methods and access shared datasets
• Semi-targeted workflows in MetaboScape^® go hand in hand with targeted workflows for absolute quantification using TASQ^®

The aim of non-targeted profiling is to identify features that are characteristic of a particular physiological state or sample. As there is no single workflow to enable access to the dynamic temporal and spatial fingerprints for all compounds, there is a need to evaluate data from complementary platforms. MetaboScape^® addresses these needs by allowing for the evaluation of complementary data from both ESI and MALDI Imaging, as well as confidently assigning relevant markers in their biological context.

Identification of unknown compounds is supported through integrated tools, such as molecular formula determination based on accurate mass of precursors and fragments (SmartFormula3D), search of local and public databases (CompoundCrawler), in silico fragmentation to match theoretical to measured MS/MS (MetFrag) and MS/MS bucket matching for the identification of chemically related compounds.

Unknown ID pipeline:
A) SmartFormula3D limits possible precursor molecular formulea to typically one or a few candidates by automatically matching accurate mass and isotopic pattern fragment and precursor ion information.
B) Query of candidate formula in local and public databases returns possible structure candidates.
C) In silico fragmentation using implemented MetFrag functionality matches theoretical fragment structures to measured MS/MS peaks and scores most likely structure.
D) Optional MS/MS Bucket matching enables to assign compounds with similar MS/MS spectra for identification of further possibly unknown but likely structurally similar compounds.

The identification of drug metabolites is not only of great interest to pharmaceutical research but has gained increasing interest in metabolomics, phenomics, exposomics and non-target screening workflows. Here, metabolites of drugs or other xenobiotics like pesticides, toxics or narcotics are expected to occur, which may belong to the family of unidentified, so-called dark metabolome compounds.

MetaboScape^® supports a local BioTransformer¹-based metabolite prediction for assignment of these metabolism products both from liquid samples and directly from tissue using the SpatialOMx^® workflow. Additionally, changes in time of these metabolites can be tracked and semi-quantified by using integrated time series plots.

^{([1] Djoumbou-Feunang et al.; Journal of Cheminformatics 2019, 11:2).}

Rule based annotation routines in MetaboScape^® enable the identification of lipid species taking into consideration the Lipidomics Standards Initiative (LSI) guidelines. This Lipid Class (LC) annotation tool avoids this risk of over annotation and simplifies the automatic identification of lipid features.

MetaboScape^® can calculate and visualize Kendrick Mass Defects, turning complex mass spectral information into a compositional map with informative clustering of points based on lipid specific homologous repeating units (e.g. CH₂). The customizable 4D Kendrick mass defect plot allows for intuitive lipid ID validation. Various characteristics of the extracted features can be plotted in 4 dimensions (x-axis, y-Axis, color scale, and bubble size), allowing versatile applications.

• Plotting retention time vs m/z reveals the separation of different lipid classes using different colours for different lipid classes. Using this colour coding, you can easily spot annotations with obvious deviation in retention time or CCS relative to the rest of the same lipid class.
• Plotting m/z vs CCS, you can further interrogate lipid data by visualizing trends in CCS observed for lipids with differences in chain length and double bond numbers. These trends can be used to confirm lipid class IDs and can assist in the annotation of unknowns and help to remove false positives.
  
• Visualize the Kendrick Mass Defect with CH₂ specified as repeating unit allows to quickly investigate lipid species of a selected class for saturation and chain length consistency. In addition, the shown example for lipids annotated as Triacylglycerols (TGs) reveals the expected elution order using reversed phase chromatography, as well as the increasing trends in CCS value making full use of all 4 complementary dimensions.
  

A major requirement of metabolomics and lipidomics analyses is to quickly pinpoint and identify those compounds that change as a result of perturbation or disease. Matching retention time, precursor mass, isotopic pattern and MS/MS spectra are common criteria for accessing confidence in compound annotations. PASEF^® data acquisition on the timsTOF Pro provides hundreds of MS/MS events per second, resulting in a greater depth of fragment coverage in single analysis. Additionally, PASEF^® spectra benefit from ion mobility separation, therefore cleaner MS/MS spectra are obtained using an on-the-fly mobility filter. Each MS value is complemented with a collisional cross section (CCS) value to give a measure of the shape of the analyte, providing further confidence to ID.

In conjunction with SCiLS™ Lab software, T-ReX^®² empowers the SpatialOMx^®-based non-targeted profiling for processing and annotation of features, including drug metabolites, lipids and glycans. For the first time, map analytes spatially using this unique combination of T-ReX^®³ and combine it with CCS-enabled annotation of compounds to enable a higher confidence annotation of compounds acquired using MALDI Imaging on timsTOF fleX systems.  

The chromatography free MRMS aXelerate^® workflow provides higher sample throughput by omitting time-consuming chromatography in phenomics research. Compounds are accessible that are not readily detectable by LC-MS analysis, allowing targeted and non-targeted metabolomics approaches. The data extraction by T-ReX^® 2D in MetaboScape^® provides confidence in automatic annotation of the FIA MRMS data. The novel scimaX^® MRMS system can show its extreme performance with mass resolutions of >1 million and mass accuracies of <0.2 ppm. The ultra-high resolving power enables you to utilze isotopic fine structure for the unambiguous determination of elemental composition. This adds another layer of confidence for compound ID in non-targeted metabolomics.