Quantitative Proteomics Claire E Eyers Simon Gaskell Bruno Domon by Claire E Eyers, Simon Gaskell, Bruno Domon, Phil Wright, Birgit Schilling, Erik Soderblom, Art Moseley, Kathryn Lilley, Christina Ludwig, Yishai Levin, Hannah Johnson, Wolf Lehmann, Rob Beynon, Claire Mills, Angus Lamond, Christophe Borchers, Adam Hawkrid 9781849738088, 1849738084 instant download after payment.

As a component of post-genome science, the field of proteomics has assumed great prominence in recent years. Whereas quantitative analyses focussed initially on relative quantification, a greater emphasis is now placed on absolute quantification and consideration of proteome dynamics. Coverage of the topic of quantitative proteomics requires consideration both of the analytical fundamentals of quantitative mass spectrometry and the specific demands of the problem being addressed. Quantitative Proteomics aims to outline the state of the art in mass spectrometry-based quantitative proteomics, describing recent advances and current limitations in the instrumentation used, together with the various methods employed for generating high quality data. Details on both strategies describing how stable isotope labelling can be applied and methods for performing quantitative analysis of proteins in a label-free manner are given. The utility of these strategies to understanding cellular protein dynamics are then exemplified with chapters looking at spatial proteomics, dynamics of protein function as determined by quantifying changes in protein post-translational modification and protein turnover. Finally, a key application of these techniques to biomarker discovery and validation is presented, together with the rapidly developing area of quantitative analysis of protein-based foodstuffs. This exemplary book will be essential reading for analytical and biological mass spectrometrists working in proteomics research, as well as those undertaking either fundamental or clinical-based investigations with an interest in understanding protein dynamics and/or biomarker assessment.