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Distler, U.* ; Yoo, H.B.* ; Kardell, O. ; Hein, D.* ; Sielaff, M.* ; Scherer, M.* ; Jozefowicz, A.M.* ; Leps, C.* ; Gomez-Zepeda, D.* ; von Törne, C. ; Merl-Pham, J. ; Barth, T.K.* ; Tüshaus, J.* ; Giesbertz, P.* ; Müller, T.* ; Kliewer, G.* ; Aljakouch, K.* ; Helm, B.* ; Unger, H.* ; Frey, D.L.* ; Helm, D.* ; Schwarzmüller, L.E.* ; Popp, O.* ; Qin, D.* ; Wudy, S.I.* ; Sinn, L.R.* ; Mergner, J.* ; Ludwig, C.* ; Imhof, A.* ; Kuster, B.* ; Lichtenthaler, S.F.* ; Krijgsveld, J.* ; Klingmüller, U.* ; Mertins, P.* ; Coscia, F.* ; Ralser, M.* ; Mülleder, M.* ; Hauck, S. ; Tenzer, S.*

Multicenter evaluation of label-free quantification in human plasma on a high dynamic range benchmark set.

Nat. Commun. 16:8774 (2025)

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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% and 9.8% at protein level. Comparative analysis of different setups clearly shows a high overlap in identified proteins and proves that accurate and precise quantitative measurements are feasible across multiple sites, even in a complex matrix such as plasma, using state-of-the-art instrumentation. The collected dataset, including the PYE sample set and strategy presented, serves as a valuable resource for optimizing the accuracy and reproducibility of LC-MS and bioinformatic workflows for clinical plasma proteome analysis.

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Publication type Article: Journal article

Document type Scientific Article

Keywords Mass-spectrometry; Software Tools; Peptide Identification; Proteome; Depth

ISSN (print) / ISBN 2041-1723

e-ISSN 2041-1723

Journal Nature Communications

Quellenangaben Volume: 16, Issue: 1, Article Number: 8774

Publisher Nature Publishing Group

Publishing Place London

Reviewing status Peer reviewed

Institute(s) CF Metabolomics & Proteomics (CF-MPC)

Grants Research Center for Immunotherapy (FZI) of the Johannes Gutenberg-University Mainz
BMBF LiSyM-Cancer
German Center for Lung Research
DFG Germany's Excellence Strategy
DFG priority program SPP 2225
German Research Foundation
HORIZON EUROPE of the European Research Council
German Ministry of Education and Research (BMBF), National Research Initiative Mass Spectrometry in Systems Medicine (MSCoreSys)