In recent decades, mass spectrometry has moved more than ever before into the front line of protein-centered research. After being established at the qualitative level, the more challenging question of quantification of proteins and peptides using mass spectrometry has become a focus for further development. In this chapter, we discuss and review actual strategies and problems of the methods for the quantitative analysis of peptides, proteins, and finally proteomes by mass spectrometry. The common themes, the differences, and the potential pitfalls of the main approaches are presented in order to provide a survey of the emerging field of quantitative, mass spectrometry-based proteomics.

Solid tumor biopsies are the current standard for precision medicine. However, the procedure is invasive and not always feasible. In contrast, liquid biopsies, such as serum enriched for extracellular vesicles (EVs) represent a non-invasive source of cancer biomarkers. In this study, we compared two EV isolation methods in the context of the protein biomarker detection in inflammatory bowel disease (IBD) and colorectal cancer (CRC). Using serum samples of a healthy cohort as well as CRC and IBD patients, EVs were isolated by ultracentrifugation and ExoQuick™ in parallel. EV associated protein profiles were compared by multiplex-fluorescence two-dimensional difference gel electrophoresis (2D-DIGE) and subsequent identification by mass spectrometry. Validation of gelsolin (GSN) was performed using fluorescence-quantitative western blot. 2D-DIGE resolved 936 protein spots in all serum-enriched EVs isolated by ultracentrifugation or ExoQuick™. Hereof, 93 spots were differently expressed between isolation approaches. Higher levels of GSN in EVs obtained with ExoQuick™ compared to ultracentrifugation were confirmed by western blot (p = 0.0006). Although patient groups were distinguishable after both EV isolation approaches, sample preparation strongly influences EVs’ protein profile and thus impacts on inter-study reproducibility, biomarker identification and validation. The results stress the need for strict SOPs in EV research before clinical implementation can be reached.

Aqueous-saline human placenta extract (HPE) is known to possess antioxidant activity due to the high concentration of bioactive substances. This fact allows its application in clinical practice in order to treat oxidation-induced diseases. Extract antioxidant activity is mainly conditioned by proteins. Freezing of extracts has been shown to lead to their antioxidant activity increasing due to protein conformation changes. Different biological models are widely used in order to evaluate efficacy of novel antioxidants and mechanisms of their action. One such model appears to be erythrocytes under nitrite-induced oxidative stress. Nitrite is known to be able to penetrate erythrocyte membrane and to oxidize hemoglobin. In order to investigate whether HPE is able to decrease nitrite-induced oxidative injuries and to evaluate the protein contribution to this process, spectrophotometric and electron spin resonance (ESR) assays were used. Experimental data have revealed that antioxidant activity of extracts and of some of their fractions correlates with methemoglobin concentration lowering. Preliminary erythrocyte incubation with an extract fraction of 12 kDa allows preservation of the structural-dynamic cytosol state the closest to the control.

Proteomic studies using mass spectrometry (MS)-based quantification are a main approach to the discovery of new biomarkers. However, a number of analytical conditions in front and during MS data acquisition can affect the accuracy of the obtained outcome. Therefore, comprehensive quality assessment of the acquired data plays a central role in quantitative proteomics, though, due to the immense complexity of MS data, it is often neglected. Here, we address practically the quality assessment of quantitative MS data, describing key steps for the evaluation, including the levels of raw data, identification and quantification. With this, four independent datasets from cerebrospinal fluid, an important biofluid for neurodegenerative disease biomarker studies, were assessed, demonstrating that sample processing-based differences are already reflected at all three levels but with varying impacts on the quality of the quantitative data. Specifically, we provide guidance to critically interpret the quality of MS data for quantitative proteomics. Moreover, we provide the free and open source quality control tool MaCProQC, enabling systematic, rapid and uncomplicated data comparison of raw data, identification and feature detection levels through defined quality metrics and a step-by-step quality control workflow.