Multidimensional Chromatography, what is the synergy with untargeted metabolomics profiling?

Pierre-Hugues Stefanuto
Multidimensional Chromatography, what is the synergy with untargeted metabolomics profiling?

In this webinar, I will discuss the new development of multidimensional chromatography and the synergy with metabolomics. This presentation will be broadcasted in the context of the Multidimensional Chromatography Workshop held in Liège last January (http://multidimensionalchromatography.com). During this event, four focus group discussions were organized: 1) data processing for untargeted screening, 2) minimum reporting information for QC and compound validation, 3) hyphenation of MDGC with high-resolution MS, 4) and the general acceptance of MDC techniques. I will illustrate these topics through some ongoing medical research articulated around volatile organic compound (VOC) measurements in human breath and in vitro in metabolomic applications.

Pierre-Hugues Stefanuto obtained is PhD in analytical chemistry in 2016 from Liège University, Belgium. He conducted his research in the group of Prof. Jef Focant on the development of analytical strategies for the characterization of complex volatile mixtures from life sciences. He worked on the development of analytical approaches for different type of samples including clinical, forensic, and food matrices. Following his PhD, he spent one year in the group of Prof. J. Hill at Dartmouth College, USA. During this period, he focused his research on small molecules metabolomics for clinical applications. Since 2018, he has been lecturer and research scientist at Liège University, where he is developing multimodal analytical strategies for the characterization of clinical samples.
In his research, he is developing full analytical workflow mostly based on multidimensional chromatographic approaches coupled with mass spectrometry. His main interest is the implementation of robust analytical protocols based on QA/QC and identification validation. He is then combining information from different types of sample through advanced data processing to obtain a clearer picture of their composition.