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
, 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.
Neuromelanin is a black-brownish pigment, present in so-called neuromelanin granules (NMGs) in dopaminergic neurons of the substantia nigra pars compacta. Besides neuromelanin, NMGs contain a variety ...of proteins, lipids, and metals. Although NMGs-containing dopaminergic neurons are preferentially lost in neurodegenerative diseases like Parkinson's disease and dementia with Lewy bodies, only little is known about the mechanism of NMG formation and the role of NMGs in health and disease. Thus, further research on the molecular characterization of NMGs is essential. Unfortunately, standard protocols for the isolation of proteins are based on density gradient ultracentrifugation and therefore require high amounts of human tissue. Thus, an automated laser microdissection (LMD)-based protocol is established here which allows the collection of NMGs and surrounding substantia nigra (SN) tissue using minimal amounts of tissue in an unbiased, automatized way. Excised samples are subsequently analyzed by mass spectrometry to decipher their proteomic composition. With this workflow, 2,079 proteins were identified of which 514 proteins were exclusively identified in NMGs and 181 in SN. The present results have been compared with a previous study using a similar LMD-based approach reaching an overlap of 87.6% for both proteomes, verifying the applicability of the revised and optimized protocol presented here. To validate current findings, proteins of interest were analyzed by targeted mass spectrometry, e.g., parallel reaction monitoring (PRM)-experiments.
