Clinical Proteomics: MS from bench to bedside and back
The Clinical Proteomics teams here at the Max Planck Institute, together with our team at NNF-CPR (Centre for Proteomics Research in Copenhagen) develop and apply proteomic methods for the unbiased and system-wide analysis of clinical phenotypes. We aim to systematically profile health and disease states to achieve a holistic understanding of disease mechanisms at the protein level and to uncover novel protein biomarkers for prognostic and predictive purposes. To address clinical needs and translate state-of-the-art MS methods into biomedical research, we have established a network of collaborations with multiple clinics and clinical research groups around the world, including the Medical School of the University of Chicago, Steno Diabetes Center in Denmark, Rigshospitalet Copenhagen and many more.
Robust and reproducible methods are essential pre-requisites for clinical analyses. Using robotic assistance, we developed a blood plasma proteomics pipeline – called ‘Plasma Proteome Profiling’ – to yield a proteomic reflection of an individual’s phenotype from only 1 µl of plasma ( Geyer et al., Cell Syst., 2016). We routinely quantify more than 100 FDA-approved biomarkers and many proteins involved in important biological processes, such as proteins of the inflammation or the lipid homeostasis system. Due to its high throughput and unbiased nature, Plasma Proteome Profiling allows a paradigm shift towards a ‘rectangular’ strategy of biomarker research, in which the proteome patterns of large cohorts are correlated with their phenotypes in physiological states ( Geyer et al., MSB, 2017 ).
Recently, we implemented our expertise on liquid biopsies in the context of neurobiological disorders. Funded by the Michael J. Fox Foundation for Parkinson’s research, we have developed clinical methods for the analysis of Rab phosphorylations to measure LRRK2 activity in patient derived blood-samples ( Karayel et al., MCP, 2020). In addition, we envisioned an alternative strategy of non-invasive profiling and profiled the urinary proteome to classify mutation status and disease manifestation in patients of Parkinson’s Disease ( Virreira Winter and Karayel et al., bioRxiv, 2020 and accepted in EMBO Molecular Medicine). To describe the proteome of Alzheimer's disease (AD) in patient-derived cerebrospinal fluid (CSF), we extended our repertoire by a semi-automated workflow from CSF sample preparation to data‐independent MS acquisition. This strategy allowed us to identify consistently more than 1,000 proteins and a distinct proteomic signature for AD based on independent cohorts ( Bader et al., Mol Syst Biol., 2020).
The Clinical Proteomics Team has also pioneered the proteomic analysis of archived biobank specimens including formalin-fixed and paraffin-embedded (FFPE) tissues ( Ostasiewicz et al., J Proteome Res., 2010, Wiśniewski et al., J Proteome Res., 2011 ). Implementing our advanced data processing and high-throughput methods, we recently optimized the quantitative profiling of FFPE tissue directly from histopathology glass slides simultaneously for large cohorts ( Coscia, Doll et al., J. Pathol., 2020) This has led to the discovery of a novel prognostic biomarker in metastatic ovarian cancer ( Coscia et al., Cell, 2018) as well as identification of a master metabolic regulator fibroblast associated to high-grade serous carcinoma ( Eckert and Coscia et al., Nature, 2019). Furthermore, we have provided proof-of-concept on how a rapid and reproducible proteomic workflow can assist clinical decision-making in end-stage cancer patient ( Doll et al., Mol. Oncol., 2018, Doll, Gnad and Mann, Proteomics Clin. Appl., 2019).
We are currently establishing novel concepts to allow in-depth analysis of biobank tissues with a specific focus on the resolution of cellular complexity in the heterogenous tissue architecture. To this end, we are developing laser microdissection workflows for automated and single-cell based proteomics. Ultimately, our vision is to bring proteomics into the clinic to improve individual health by monitoring, diagnosis and treatment and – even more importantly – into daily life so that individuals stay healthy in the first place.