TY  - JOUR
AU  - Burankova, Yuliya
AU  - Abele, Miriam
AU  - Bakhtiari, Mohammad
AU  - von Toerne, Christine
AU  - Barth, Teresa K
AU  - Schweizer, Lisa
AU  - Giesbertz, Pieter
AU  - Schmidt, Johannes R
AU  - Kalkhof, Stefan
AU  - Müller-Deile, Janina
AU  - van Veelen, Peter A
AU  - Mohammed, Yassene
AU  - Hammer, Elke
AU  - Arend, Lis
AU  - Adamowicz, Klaudia
AU  - Laske, Tanja
AU  - Hartebrodt, Anne
AU  - Frisch, Tobias
AU  - Meng, Chen
AU  - Matschinske, Julian
AU  - Späth, Julian
AU  - Röttger, Richard
AU  - Schwämmle, Veit
AU  - Hauck, Stefanie M
AU  - Lichtenthaler, Stefan F
AU  - Imhof, Axel
AU  - Mann, Matthias
AU  - Ludwig, Christina
AU  - Kuster, Bernhard
AU  - Baumbach, Jan
AU  - Zolotareva, Olga
TI  - Privacy-preserving multicenter differential protein abundance analysis with FedProt.
JO  - Nature computational science
VL  - 5
IS  - 8
SN  - 2662-8457
CY  - London
PB  - Nature Research
M1  - DZNE-2025-00967
SP  - 675 - 688
PY  - 2025
AB  - Quantitative mass spectrometry has revolutionized proteomics by enabling simultaneous quantification of thousands of proteins. Pooling patient-derived data from multiple institutions enhances statistical power but raises serious privacy concerns. Here we introduce FedProt, the first privacy-preserving tool for collaborative differential protein abundance analysis of distributed data, which utilizes federated learning and additive secret sharing. In the absence of a multicenter patient-derived dataset for evaluation, we created two: one at five centers from E. coli experiments and one at three centers from human serum. Evaluations using these datasets confirm that FedProt achieves accuracy equivalent to the DEqMS method applied to pooled data, with completely negligible absolute differences no greater than 4 × 10-12. By contrast, -log10P computed by the most accurate meta-analysis methods diverged from the centralized analysis results by up to 25-26.
KW  - Humans
KW  - Proteomics: methods
KW  - Mass Spectrometry
KW  - Escherichia coli: metabolism
KW  - Privacy
KW  - Algorithms
LB  - PUB:(DE-HGF)16
C6  - pmid:40646319
C2  - pmc:PMC12374843
DO  - DOI:10.1038/s43588-025-00832-7
UR  - https://pub.dzne.de/record/280783
ER  -