%0 Journal Article
%A Distler, Ute
%A Yoo, Han Byul
%A Kardell, Oliver
%A Hein, Dana
%A Sielaff, Malte
%A Scherer, Marian
%A Jozefowicz, Anna M
%A Leps, Christian
%A Gomez-Zepeda, David
%A von Toerne, Christine
%A Merl-Pham, Juliane
%A Barth, Teresa K
%A Tüshaus, Johanna
%A Giesbertz, Pieter
%A Müller, Torsten
%A Kliewer, Georg
%A Aljakouch, Karim
%A Helm, Barbara
%A Unger, Henry
%A Frey, Dario L
%A Helm, Dominic
%A Schwarzmüller, Luisa
%A Popp, Oliver
%A Qin, Di
%A Wudy, Susanne I
%A Sinn, Ludwig Roman
%A Mergner, Julia
%A Ludwig, Christina
%A Imhof, Axel
%A Kuster, Bernhard
%A Lichtenthaler, Stefan F
%A Krijgsveld, Jeroen
%A Klingmüller, Ursula
%A Mertins, Philipp
%A Coscia, Fabian
%A Ralser, Markus
%A Mülleder, Michael
%A Hauck, Stefanie M
%A Tenzer, Stefan
%T Multicenter evaluation of label-free quantification in human plasma on a high dynamic range benchmark set.
%J Nature Communications
%V 16
%N 1
%@ 2041-1723
%C [London]
%I Springer Nature
%M DZNE-2025-01141
%P 8774
%D 2025
%X Human plasma is routinely collected during clinical care and constitutes a rich source of biomarkers for diagnostics and patient stratification. Liquid chromatography-mass spectrometry (LC-MS)-based proteomics is a key method for plasma biomarker discovery, but the high dynamic range of plasma proteins poses significant challenges for MS analysis and data processing. To benchmark the quantitative performance of neat plasma analysis, we introduce a multispecies sample set based on a human tryptic plasma digest containing varying low level spike-ins of yeast and E. coli tryptic proteome digests, termed PYE. By analysing the sample set on state-of-the-art LC-MS platforms across twelve different sites in data-dependent (DDA) and data-independent acquisition (DIA) modes, we provide a data resource comprising a total of 1116 individual LC-MS runs. Centralized data analysis shows that DIA methods outperform DDA-based approaches regarding identifications, data completeness, accuracy, and precision. DIA achieves excellent technical reproducibility, as demonstrated by coefficients of variation (CVs) between 3.3
%K Humans
%K Proteomics: methods
%K Chromatography, Liquid: methods
%K Blood Proteins: analysis
%K Blood Proteins: metabolism
%K Proteome: analysis
%K Benchmarking
%K Reproducibility of Results
%K Mass Spectrometry: methods
%K Biomarkers: blood
%K Escherichia coli: metabolism
%K Plasma: chemistry
%K Blood Proteins (NLM Chemicals)
%K Proteome (NLM Chemicals)
%K Biomarkers (NLM Chemicals)
%F PUB:(DE-HGF)16
%9 Journal Article
%$ pmid:41038884
%2 pmc:PMC12491457
%R 10.1038/s41467-025-64501-z
%U https://pub.dzne.de/record/281523