Plasma Proteome Profiling to Assess Human Health and Disease

Philipp E. Geyer1,2 , Sophia Doll1,2, Johannes B. Müller1, Lili Niu2, Peter V. Treit1, Eugenia Voytik1,
Florian Meier1, Daniel Teupser3, Lesca M. Holdt3, Matthias Mann1,2

Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Munich
NNF Center for Protein Research, University of Copenhagen
Institute of Laboratory Medicine, LMU Munich

Mass spectrometry (MS)-based proteomics is now starting to live up to its initial promise as a generic technology for the discovery and quantification of proteins that reflect an individual´s health or disease state (1). We developed a ‘Plasma Proteome Profiling’ pipeline that has proven to be very robust and capable of the rapid protein quantification in undepleted plasma and to monitor physiological changes (2-5). This also includes up to 100 FDA approved biomarkers that we quantified in thousands of plasma samples. For example, in a clinical weight loss study, we analyzed about 1300 plasma proteomes, the largest such study to date (3). Due to its high throughput and unbiased nature, plasma proteome profiling allows a paradigm shift to a ´rectangular´ strategy, in which the proteome patterns of large cohorts are correlated with their phenotypes in physiological states. We have measured thousands of plasma proteomes and aim to establish a large knowledge base containing global correlation profiles that facilitate a deeper understanding of human health and disease states.  We report on initial insights from more than 200,000 single correlations of proteins to each other and to clinical parameters, which allows us to find co-regulated protein networks, implicate proteins in new conditions, identify quality marker panels and combine patterns containing multiple proteins to single clinical entities (5, 6). 

1. Geyer et al., Mol Syst Biol, 2017
2. Geyer et al., Cell Syst, 2016
3. Geyer et al., Mol Syst Biol, 2016
4. Meier et al., Nat. Meth., 2018
5. Wewer Albrechtsen et al., Cell Syst, 2018
6. Geyer et al., bioRxiv, 2018