Quality control in quantitative proteomics: a case study for the human CSF proteome

Proteome studies using mass spectrometry (MS)-based quantification is a main approach for 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 immense complexity of MS data it is often neglected. Here, we practically address the quality assessment of quantitative MS data describing key steps for the evaluation including the levels: raw data, identification and quantification. With this, four independent datasets from cerebrospinal fluid, an important biofluid for neurodegenerative diseases biomarker studies, were assessed demonstrating that already sample processing-based differences are reflected on all three levels but with a varying impact on the quality of the quantitative data.