Plasma Proteome Profiling to detect and avoid sample‐related biases in biomarker studies
Abstract 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 quant...
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Online Access: | https://doi.org/10.15252/emmm.201910427 |
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doaj-7408b776a53943d6a2e3eb353fa0f1c52021-08-02T19:07:44ZengWileyEMBO Molecular Medicine1757-46761757-46842019-11-011111n/an/a10.15252/emmm.201910427Plasma Proteome Profiling to detect and avoid sample‐related biases in biomarker studiesPhilipp E Geyer0Eugenia Voytik1Peter V Treit2Sophia Doll3Alisa Kleinhempel4Lili Niu5Johannes B Müller6Marie‐Luise Buchholtz7Jakob M Bader8Daniel Teupser9Lesca M Holdt10Matthias Mann11Department of Proteomics and Signal Transduction Max Planck Institute of Biochemistry Martinsried GermanyDepartment of Proteomics and Signal Transduction Max Planck Institute of Biochemistry Martinsried GermanyDepartment of Proteomics and Signal Transduction Max Planck Institute of Biochemistry Martinsried GermanyDepartment of Proteomics and Signal Transduction Max Planck Institute of Biochemistry Martinsried GermanyInstitute of Laboratory Medicine University Hospital LMU Munich Munich GermanyNNF Center for Protein Research Faculty of Health Sciences University of Copenhagen Copenhagen DenmarkDepartment of Proteomics and Signal Transduction Max Planck Institute of Biochemistry Martinsried GermanyInstitute of Laboratory Medicine University Hospital LMU Munich Munich GermanyDepartment of Proteomics and Signal Transduction Max Planck Institute of Biochemistry Martinsried GermanyInstitute of Laboratory Medicine University Hospital LMU Munich Munich GermanyInstitute of Laboratory Medicine University Hospital LMU Munich Munich GermanyDepartment of Proteomics and Signal Transduction Max Planck Institute of Biochemistry Martinsried GermanyAbstract 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 ( www.plasmaproteomeprofiling.org) to assess overall sample‐related bias in clinical studies and to prevent costly miss‐assignment of biomarker candidates.https://doi.org/10.15252/emmm.201910427biomarker discoverymass spectrometryplasma proteomicssample qualitystudy design |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Philipp E Geyer Eugenia Voytik Peter V Treit Sophia Doll Alisa Kleinhempel Lili Niu Johannes B Müller Marie‐Luise Buchholtz Jakob M Bader Daniel Teupser Lesca M Holdt Matthias Mann |
spellingShingle |
Philipp E Geyer Eugenia Voytik Peter V Treit Sophia Doll Alisa Kleinhempel Lili Niu Johannes B Müller Marie‐Luise Buchholtz Jakob M Bader Daniel Teupser Lesca M Holdt Matthias Mann Plasma Proteome Profiling to detect and avoid sample‐related biases in biomarker studies EMBO Molecular Medicine biomarker discovery mass spectrometry plasma proteomics sample quality study design |
author_facet |
Philipp E Geyer Eugenia Voytik Peter V Treit Sophia Doll Alisa Kleinhempel Lili Niu Johannes B Müller Marie‐Luise Buchholtz Jakob M Bader Daniel Teupser Lesca M Holdt Matthias Mann |
author_sort |
Philipp E Geyer |
title |
Plasma Proteome Profiling to detect and avoid sample‐related biases in biomarker studies |
title_short |
Plasma Proteome Profiling to detect and avoid sample‐related biases in biomarker studies |
title_full |
Plasma Proteome Profiling to detect and avoid sample‐related biases in biomarker studies |
title_fullStr |
Plasma Proteome Profiling to detect and avoid sample‐related biases in biomarker studies |
title_full_unstemmed |
Plasma Proteome Profiling to detect and avoid sample‐related biases in biomarker studies |
title_sort |
plasma proteome profiling to detect and avoid sample‐related biases in biomarker studies |
publisher |
Wiley |
series |
EMBO Molecular Medicine |
issn |
1757-4676 1757-4684 |
publishDate |
2019-11-01 |
description |
Abstract 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 ( www.plasmaproteomeprofiling.org) to assess overall sample‐related bias in clinical studies and to prevent costly miss‐assignment of biomarker candidates. |
topic |
biomarker discovery mass spectrometry plasma proteomics sample quality study design |
url |
https://doi.org/10.15252/emmm.201910427 |
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