Pawel Sadowski   Pawel Sadowski, Ph.D. completed his Ph.D. in the field of quantitative proteomics at the University of Cambridge. During subsequent post-doctoral appointments at the New York University and at the University of New South Wales in Sydney he applied label-free and label-based approaches to study biomarkers, protein-protein interactions and PTMs. In 2013 Pawel joined Queensland University of Technology in Brisbane to lead Proteomics and Small Molecule Mass Spectrometry Core in the QUT Central Analytical Research Facility. He supervises research that utilizes LCMS and GCMS instrumentation. Pawel’s interest lays in leveraging the power of recent advancements in data acquisition strategies for MS-based proteomics to drive clinical veterinary research into the next generation. More recently, he has started using quantitative mass spectrometry to study metabolites and other small molecules.

Teaching Old Dog New Tricks: Adaptation of Skyline to Analyze Untargeted Metabolomics Data Collected on GCMS Instrument

Processing untargeted metabolomics data collected on GCMS instruments comes with specific challenges and often requires specialized (or expensive) data analysis software. Moreover, metabolomics researchers tend to apply laboratory-specific GCMS data processing criteria depending on the functionalities available in their favorite software package which prevents others from being able to reproduce the results if they do not have access to the same tools. Read More
Having used Skyline for proteomics data analysis since its early days, we decided to leverage of its recent support for small molecules, and we have developed GCMS data analysis pipeline that utilizes identical criteria as for proteomics datasets and thus effectively we have standardized our quantitative mass spectrometry workflows across majority of our MS platforms and datasets. This Skyline-based pipeline has been subsequently deployed in another mass spectrometry facility in Queensland with positive feedback from its users. In our recent work we have successfully applied our new pipeline to understand the mechanism of sexual selection of fruit flies through GCMS-based metabolomics.