TY  - JOUR
AU  - Distler, Ute
AU  - Yoo, Han Byul
AU  - Kardell, Oliver
AU  - Hein, Dana
AU  - Sielaff, Malte
AU  - Scherer, Marian
AU  - Jozefowicz, Anna M
AU  - Leps, Christian
AU  - Gomez-Zepeda, David
AU  - von Toerne, Christine
AU  - Merl-Pham, Juliane
AU  - Barth, Teresa K
AU  - Tüshaus, Johanna
AU  - Giesbertz, Pieter
AU  - Müller, Torsten
AU  - Kliewer, Georg
AU  - Aljakouch, Karim
AU  - Helm, Barbara
AU  - Unger, Henry
AU  - Frey, Dario L
AU  - Helm, Dominic
AU  - Schwarzmüller, Luisa
AU  - Popp, Oliver
AU  - Qin, Di
AU  - Wudy, Susanne I
AU  - Sinn, Ludwig Roman
AU  - Mergner, Julia
AU  - Ludwig, Christina
AU  - Imhof, Axel
AU  - Kuster, Bernhard
AU  - Lichtenthaler, Stefan F
AU  - Krijgsveld, Jeroen
AU  - Klingmüller, Ursula
AU  - Mertins, Philipp
AU  - Coscia, Fabian
AU  - Ralser, Markus
AU  - Mülleder, Michael
AU  - Hauck, Stefanie M
AU  - Tenzer, Stefan
TI  - Multicenter evaluation of label-free quantification in human plasma on a high dynamic range benchmark set.
JO  - Nature Communications
VL  - 16
IS  - 1
SN  - 2041-1723
CY  - [London]
PB  - Springer Nature
M1  - DZNE-2025-01141
SP  - 8774
PY  - 2025
AB  - 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
KW  - Humans
KW  - Proteomics: methods
KW  - Chromatography, Liquid: methods
KW  - Blood Proteins: analysis
KW  - Blood Proteins: metabolism
KW  - Proteome: analysis
KW  - Benchmarking
KW  - Reproducibility of Results
KW  - Mass Spectrometry: methods
KW  - Biomarkers: blood
KW  - Escherichia coli: metabolism
KW  - Plasma: chemistry
KW  - Blood Proteins (NLM Chemicals)
KW  - Proteome (NLM Chemicals)
KW  - Biomarkers (NLM Chemicals)
LB  - PUB:(DE-HGF)16
C6  - pmid:41038884
C2  - pmc:PMC12491457
DO  - DOI:10.1038/s41467-025-64501-z
UR  - https://pub.dzne.de/record/281523
ER  -