engleski

Data-Independent and Label-Free Approaches for Absolute Quantitative Mass Spectrometry Proteomics

For years much effort was put into constructing comprehensive protein catalogs, i.e. proteomes. Such in-depth proteomic studies, supported by Human Protein Atlas discoveries, unexpectedly demonstrated the extent to which diverse cellular systems have similar proteomes. Although fundamental, proteomes don’t provide necessary data for describing and explaining complex biological mechanisms and dynamic processes. Therefore, tissue or cellular identity as well as dynamic nature of the proteome under physiological and experimentally manipulated conditions are primarily determined by the abundance at which constituent proteins are expressed. The maturation of mass spectrometry (MS) – based quantitative proteomics in recent years has brought a variety of technologies, each with their own strengths and weaknesses. For those unfamiliar with the evolving landscape it can be difficult to match the experiment at hand with the best tool for the job. This presentation will briefly introduce you to quantitative proteomic MS-based techniques that can easily be grouped into (1) label-free and (2) metabolic or chemical labeling approaches. Concisely, the most commonly used MS data acquisition strategies namely, data-dependent analysis (DDA) and data-independent analysis (DIA) as well as main differences between them, will be mentioned. In the main part of this presentation benefits and weaknesses of the label-free approach and data-independent acquisition strategies for absolute quantitative MS proteomics of complex biological systems and large-scale studies will be addressed in more detail. Method application examples on both in vivo models and clinical specimens will be presented. Special attention will be given to bottom-up sample preparation strategies and their importance in assessing reproducible and comparable label-free absolute quantitative proteomic MS data.

mass spectrometry, medicine

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