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Plasma Proteome Profiling to detect and avoid sample-related biases in biomarker studies

MPG-Autoren
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Geyer,  Philipp E.
Mann, Matthias / Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Max Planck Society;

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Voytik,  Eugenia
Mann, Matthias / Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Max Planck Society;

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Treit,  Peter V.
Mann, Matthias / Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Max Planck Society;

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Doll,  Sophia
Mann, Matthias / Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Max Planck Society;

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Müller,  Johannes B.
Mann, Matthias / Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Max Planck Society;

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Bader,  Jakob Maximilian
Mann, Matthias / Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Max Planck Society;

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Mann,  Matthias
Mann, Matthias / Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Max Planck Society;

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Zitation

Geyer, P. E., Voytik, E., Treit, P. V., Doll, S., Kleinhempel, A., Niu, L., et al. (2019). Plasma Proteome Profiling to detect and avoid sample-related biases in biomarker studies. Embo Molecular Medicine, 11(11): e10427. doi:10.15252/emmm.201910427.


Zitierlink: https://hdl.handle.net/21.11116/0000-0005-51DA-A
Zusammenfassung
Plasma and serum are rich sources of information regarding an individual's health state, and protein tests inform medical decision making. Despite major investments, few new biomarkers have reached the clinic. Mass spectrometry (MS)-based proteomics now allows highly specific and quantitative readout of the plasma proteome. Here, we employ Plasma Proteome Profiling to define quality marker panels to assess plasma samples and the likelihood that suggested biomarkers are instead artifacts related to sample handling and processing. We acquire deep reference proteomes of erythrocytes, platelets, plasma, and whole blood of 20 individuals (> 6,000 proteins), and compare serum and plasma proteomes. Based on spike-in experiments, we determine sample quality-associated proteins, many of which have been reported as biomarker candidates as revealed by a comprehensive literature survey. We provide sample preparation guidelines and an online resource ( ) to assess overall sample-related bias in clinical studies and to prevent costly miss-assignment of biomarker candidates.