If we have learned anything from the dozens of collaborations at our lab, it is that the term Proteomics is actually very confusing to the outside world. Indeed, we measure peptides but what we actually report on, the proteins, is merely inferred. In a time where productivity is key, automating this inference as much as possible has become a goal in its own right. Yet, only human intervention can assure that the most correct or least ambiguous outcome is reported. Thus, here we will argue that proteomics is an “emergent” – not “emerging” – field. And, that facilitating human inspection of the visualized data is required to fill the gap between what is measured and what can be concluded in terms of potential biomarkers or biology. To illustrate this point, we will look at histones, five complexly modified low molecular weight proteins that are often used to normalize entire proteomes.
Studying histone modifications is an intrinsically peptide-centric approach. This is how we got to realize that inferring protein abundance is extremely hard and in some cases impossible. In this webinar we will follow a few of those peptides on their journey through the Progenesis QI for proteomics workflow including peak reviewing, QC metrics, conflict resolving and spectral library matching. Illustrating that no automation process to date is able to anticipate the complexity of protein abundance. In short, the final list of potential biomarkers should always be manually inspected and visualized in order to save time and money in the downstream validation process.
During this webinar attendees will:
Dr. Maarten Dhaenens
Dr. Dhaenens heads the proteomics department of the Laboratory of Pharmaceutical Biotechnology at Ghent University, Belgium. After a few years in immunology, he came across a histone clipping event that would change the course of his career. Over the past five years, his interest increasingly shifted towards the mass spectrometry-based study of histone epigenetic dynamics, the so-called “histone code”. Especially the promise of Data-independent acquisition (HDMSE and SWATH) herein assures that this line of research with the team of 5 PhD students will continue for the time to come.