2. Úvod
  3. Science
  4. Research Support
  5. Core Facilities
  6. Untargeted Metabolomics


The Untargeted Metabolomics Embedded Core Facility provides state-of-the-art LC-MS measurements and support in metabolomic data processing and interpretation to IOCB groups. Untargeted metabolomic analysis aims at comprehensive profiling of small molecules present within a biological sample, e.g., cell cultures, blood samples or plant tissues.

How to start your metabolomic analysis

To fully assess your research goals and to understand your research objectives, each study begins with a one-on-one consultation. After that we can discuss the sample preparation protocol and plan the LC-MS measurements.

Provided services

Sample preparation
The users are expected to prepare their own samples for analysis. We offer the following support:

  • Standardized sample preparation protocols for diverse biological matrices. We can provide solvents and consumables (e.g., sample vials or plates) compatible with our LC-MS system.
  • Support for the development of custom sample preparation protocols.
  • Training on sample preparation.
  • Access to and training on sample homogenization equipment (e.g., TissueLyser).

LC-MS analysis
The core facility is equipped with a high resolution mass spectrometer (Orbitrap ID-X, Thermo Fisher Scientific) coupled to a biocompatible ultra-high performance liquid chromatography system (Vanquish, Thermo Fisher Scientific). Additionally, a DAD detector (UV and VIS) can be used. Standard LC separation columns (reverse phase and HILIC) are available. The configuration of the LC-MS system can be adjusted based on the requirements of each project.

Data analysis
Untargeted LC-MS raw data requires extensive data processing to extract relevant information. Accordingly, we provide:

  • Raw data quality assessment (e.g., accuracy/reproducibility, artifact removal)
  • Standardized data analysis pipelines for:
    1. LC-MS feature (compounds represented by their m/z and retention time) detection and alignment across samples/injections
    2. Metabolite annotation based on spectral library matching (commercial, public, or in-house-developed) and/or in-silico structure prediction
    3. Univariate and multivariate statistical analysis
    4. Molecular networking to identify analogs and derivatives through MS/MS spectral similarity
  • Support with visualization and interpretation of the results
  • Preparation of publication-ready outputs

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