Diabetes, obesity, heart disease and ageing have all been strongly associated with oxidative stress. For heart disease in particular, the resultant oxidative protein damage caused has been directly linked to dysregulation of myocardial redox signalling leading development and progression of heart disease. Despite promising research, the translation to clinical practice of promising redox-based therapies is currently hampered by several factors. Recent advances in MS-based proteomics have helped address some of these.
In this study, Tomin et al. applied a novel, combinatorial MS-based approach, utilising state-of-the-art IonOpticks Aurora Series nanoUHPLC columns to comprehensively map the global and peptide-specific differential oxidation and differential protein expression of failing human hearts.
Combining cysteine redox proteome analysis, glutathione status and quantitative proteomics from the same samples, the authors were able to generate the most comprehensive redox and proteomics profiling of failing human heart tissue to date.
Significantly, similar observations were also obtained in an in vitro system of cardiomyocytes cultured under different oxygen concentrations – suggesting a major role of local oxygen concentrations in proteome remodelling.
Read the full paper
Mass Spectrometry-Based Redox and Protein Profiling of Failing Human Hearts.
Int. J. Mol. Sci. 11 Feb 2021. doi: https://doi.org/10.3390/ijms22041787
Tamara Tomin, Matthias Schittmayer, Simon Sedej, Heiko Bugger, Johannes Gollmer, Sophie Honeder, Barbara Darnhofer, Laura Liesinger, Andreas Zuckermann, Peter P. Rainer, and Ruth Birner-Gruenberger.
Commentary by Andrew Webb, PhD.
About the author
Andrew has over 15 years’ experience in the field of chromatography and mass spectrometry. As the Head of Research at IonOpticks, he works closely with the team to test, refine and develop cutting edge techniques to support higher quality outputs and analytics from MS instruments. Andrew also leads the Proteomics Research Laboratory at the Walter and Eliza Hall Institute of Medical Research.
Your data is only as good as your columns.