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MetaboScape provides new layers of insight into untargeted Metabolomics

The metabolome is the final manifestation of biochemical pathways. It shows an extremely large variety of structural classes as it includes the complete set of metabolites of all cellular processes.
Temporal and spatial changes in the metabolite composition reflect the outcome (phenotype) of interactions at the genomic, transcriptomic and proteomic level. Therefore, studying the phenome is a cornerstone to gaining deeper insights related to e.g. diseases, therapeutic interventions or environmental influences.

The aim of untargeted metabolomics is the global profiling of small molecule biomarkers that are characteristic for a particular physiological state. A major requirement is to quickly pinpoint and identify those compounds that change as a result of perturbation or disease.

Together with Bruker Compass® TASQ™ 2.0 software, both targeted validation and untargeted metabolomics experiments are supported. Double your level of insight by performing biochemical pathway-driven targeted analysis on the same data set.

Powerful T-ReX (Time aligned Region complete eXtraction) algorithm for feature extraction

MetaboScape 4.0 is able to process complementary data from Bruker's LC-QTOF-MS/MS, GC-APCI-QTOF, LC-TIMS-MS and MRMS systems:

 

  • T-ReX 2D: FIA-MRMS data for ‘MetaboTyping’
  • T-ReX 3D: LC-MS data e.g. from the Phenomics Workhorse
  • T-ReX 4D: LC-TIMS-MS data from timsTOF

Spectral libraries for de-replication of known compounds

Using the new releases of the Bruker HMDB Metabolite Library 2.0, the MetaboBASE Personal Library 2.0 or the MetaboBASE Plant Library, confidence in the de-replication of known compounds can be boosted.

Learn more about the libraries

As many as five important criteria for data quality are automatically matched and displayed visually using the “Annotation Quality Scoring” icon:

Annotation Quality Scoring
Annotation Quality Scoring

Identification of unknown compounds

Analytes that were selected for further evaluation in an untargeted metabolomics approach can be seamlessly annotated via automated formula generation and MetFrag in-silico fragmentation of structure candidates. The results can be set into a biological context via pathway mapping.

Additional new features of MetaboScape 4.0:

  • Processing of large batches of samples (> 1000 injections)
  • High throughput phenomics using FIA-MRMS approaches for > 200 samples /day
  • The 4D feature finder (T-ReX 4D) extracts collisional cross section values from timsTOF data
  • New workflow for lipidomics: export to SimLipid™ (third party software by PREMIER Biosoft) with a subsequent import of results
  • Calculation of theoretical CCS values for lipid structures
  • Use of isotopic fine structures in the formula calculation process

FIA-MRMS ‘MetaboTyping’

The chromatography free MRMS 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 untargeted 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 isotopic fidelity enables you to use the fine structures of ions. This adds another layer of confidence for compound ID in untargeted metabolomics.

MRMS data
Processing workflow of chromatography-free MRMS data (T-ReX 2D) (click to enlarge).

From high resolution accurate mass LC-MS Data to Biological Insight - the MetaboScape Workflow

With Bruker´s MetaboScape, you can quickly pinpoint and identify metabolite markers and use pathway mapping to set them in a biological context after the initial LC-MS/MS data acquisition from UHR-QqTOF instruments using the Instant Expertise™ routine:

  • Extract and combine all relevant information using the advanced T-Rex 3D algorithm
  • Quickly focus on relevant information in complex data set by supervised and non-supervised statistics like PCA, t-test, ANOVA, PLS and bucket correlation analysis
  • Automatically identify known target compounds and seamlessly annotate unknown compounds
  • Use pathway mapping to set identified metabolites in a biological context thereby turning LC-QTOF-MS/MS data into knowledge

 

 

Hypothesis and experimental design - Full scan high resolution LC-QTOF-MS/MS data aquisition

Full scan high resolution LC-QTOF-MS/MS data
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Non-targeted data extraction

T-ReX

Extract and combine all relevant Information- T-ReX

The new T-ReX 3D (Time aligned Region Complete eXtraction algorithm automatically extracts all relevant information, even from very complex LC-MS/ MS data sets. It combines ions belonging to the same compound into one feature, i.e. isotopes, charge states, adducts or fragments. Non-linear retention time alignment ensures data consistency even if chromatographic shifts between LC-MS runs occur.

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Data preprocessing

Sophisticated bucketing, filtering, scaling and normalization to match experimental designs

Sophisticated bucketing, filtering, scaling and normalization to match experimental designs

Region complete extraction by T-ReX 3D ensures features are not missed, which would result in “0” in the bucket table, a critical factor for subsequent statistical analyses of LC-MS/MS data. Different filtering, normalization and scaling options complete the set of data preprocessing tools - a prerequisite for large metabolomics studies.

De-replication / Known ID

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Automatic and confident identification of known compounds

Called de-replication, is essential to fully understand the biological context of metabolomics data. Combing complementary information acquired in positive and negative ionization modes generates deeper insights. Confidence in ID is provided by matching retention time, accurate mass, isotopic pattern information, and MS/MS spectral library spectra according to user definable threshold levels and graphical representation of the achieved “Annotation Quality”.

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Statistics

Seamless annotation of unknown compounds

Quickly identify relevant information in complex data sets

Using supervised and non-supervised statistics quickly focuses on the relevant information in your data set. Statistics include PCA, t-Test, ANOVA, PLS and bucket correlation analysis combined with dedicated views.

Unknown ID

Metaboscape workflow 6

Seamless annotation of compounds

Annotation of unknowns by automated molecular formula generation followed by structural assignment through public database queries and in-silico fragmentation of structure candidates.

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Pathway mapping

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Set your results into biological context

The mapping of identified metabolites on metabolic pathway maps using MetaboScape connects experimental data to biology. This can lead to a validated research question, or to formulating a novel hypothesis.

For Research Use Only. Not for Use in Clinical Diagnostic Procedures.