13.1. 2020, Laboratory of Translational Metabolism

Laboratory of Translational Metabolism

PhD project: Comprehensive analytical methods and bioinformatics tools for mass spectrometry-based metabolomics

Untargeted metabolomics and lipidomics methods focus on the analysis of all the detectable metabolites in a sample, including chemical unknowns. Coupling liquid chromatography to mass spectrometry (LC-MS) is the preferred technique in metabolomics and lipidomics permitting effective compound separations and detection. One of the most challenging aspects of metabolomics research is the identification of unknown metabolites. Unknown metabolites represent up to 80% of all detected signals from untargeted mass spectrometry-based profiling. Such a big obstacle in biomedical and biology research hinders meaningful biochemical and pathway interpretations.

The aim of the PhD project is to increase the coverage of spectral libraries used for metabolite annotation, to apply and optimize in-silico software for prediction of ‘unknown’ metabolites, and to evaluate and apply new bioinformatics tools for visualization and interpretation of the metabolome data.

Candidate’s profile (requirements):

We are seeking outstanding self-motivated candidates with master's degree or equivalent in biochemistry, bioinformatics, analytical chemistry or related fields, or those expecting to obtain their degree this year. Candidates should be fluent in English. Experience with LC-MS analysis, data processing as well as statistical analysis is advantage.

Supervisor: Assoc. Prof. Tomas Cajka, Ph.D. (tomas.cajka@fgu.cas.cz)

Relevant publications:

T. Cajka, O. Fiehn: Toward merging untargeted and targeted methods in mass spectrometry-based metabolomics and lipidomics. Analytical Chemistry 88 (2016) 524–545. (doi: 10.1021/acs.analchem.5b04491)
T. Cajka, J.T. Smilowitz, O. Fiehn: Validating quantitative untargeted lipidomics across nine liquid chromatography–high-resolution mass spectrometry platforms. Analytical Chemistry 89 (2017) 12360–12368. (doi: 10.1021/acs.analchem.7b03404)
H. Tsugawa, T. Cajka, T. Kind, Y. Ma, B. Higgins, K. Ikeda, M. Kanazawa, J. VanderGheynst, O. Fiehn, M. Arita: MS-DIAL: data-independent MS/MS deconvolution for comprehensive metabolome analysis. Nature Methods 12 (2015) 523–526. (doi: 10.1038/nmeth.3393)
H. Tsugawa, T. Kind, R. Nakabayashi, D. Yukihira, W. Tanaka, T. Cajka, K. Saito, O. Fiehn, M. Arita: Hydrogen rearrangement rules: Computational MS/MS fragmentation and structure elucidation using MS-FINDER software. Analytical Chemistry 88 (2016) 7946–7958 (doi: 10.1021/acs.analchem.6b00770)