We compared 3 MS and 1 aptamer proteomics workflows on elite athlete plasma. Platform differences aren't just technical—they reveal distinct biology of exercise adaptation and metabolic health. New preprint just in time for Winter Olympics 🏆 🥇
biorxiv.org/content/10.6...
An adaptive, continuous-learning framework for clinical decision-making from proteome-wide biofluid data www.nature.com/artic...
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#proteomics #prot-paper
Discovery proteomics → clinical diagnostics? ADAPT-MS makes it possible by adapting to each sample's protein coverage on-the-fly. No imputation, no fixed panels, multiple diagnostic questions from one measurement. Out now in @natcomms.nature.com
www.nature.com/articles/s41...
Bead-based plasma #proteomics workflows boost proteome coverage but are vulnerable to cellular contamination, highlighting the need for contamination-aware quality control in #biomarker discovery.
K. Korff, M. Mann, P. Geyer & colleagues @mpibiochem.bsky.social
🗞️ doi.org/10.1038/s443...
Pre-analytical drivers of bias in bead-enriched plasma proteomics | EMBO Molecular Medicine www.embopress.org/do...
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#proteomics #prot-paper
New approach tackles studying rare pediatric diseases! We developed an ontology-guided clustering framework using SNOMED CT to analyze proteomes from 1,140 children across 394 conditions - enabling meaningful insights even with tiny patient numbers!
www.embopress.org/doi/full/10....
Ontology-guided clustering enables #Proteomic analysis of rare #PediatricDisorders
By Ericka CM Itang, Johannes Mueller-Reif and colleagues @mpibiochem.bsky.social & German Center for Child and Adolescent Health
🗞️ doi.org/10.1038/s443...
Our new paper is out in EMBO Mol Med: we combine clinical ontologies with deep proteomics to study rare pediatric diseases. A step toward scalable, systems-level insight into child health.
Grateful to all collaborators & families.
www.embopress.org/doi/full/10....
To bead or not to bead? We evaluate five plasma proteomics workflows, finding bead-based methods enhance detection but show high susceptibility to cellular contamination bias. Strategies to optimize biomarker discovery in plasma proteomics.
www.biorxiv.org/content/10.1...
Check out ADAPT-MS: our flexible, scalable diagnostic framework that transforms discovery plasma proteomics directly into clinical decisions.Dynamic retraining enables single-sample diagnostics for any clinical question.
www.medrxiv.org/content/10.1...
In summary we propose to use discovery proteomics data directly for diagnostic and in future prognostic procedures, bypassing extensive assay development. ADAPT-MS is the framework we developed to enable this vision.
The beauty is that with every measured sample, the prior knowledge growth and enables better and more classification tasks. With growing databases these can be tailored to ever more specific subpopulations fitted to covariates and local specific effects.
This procedure again brought up wider implications: if we can classify one diagnostic question from discovery proteomics data, we can do this for all. The precondition is prior data for this question. With recent population based proteomics efforts this seems to be in reach in not to distant future.
Based on the overlap of this feature list and the available proteins per sample we train sample specific classifiers for each later measured sample where this decision is needed for. This makes the procedure robust and performant.
So we set out to build an ML architecture that can be employed to single sample discovery data without the need to process the sample within a study. The solution for us was a refitting procedure: we preselect features from study data for a specific task, e.g. benign from malignant classification.
After measuring a recent study and building successful classifiers with ML we asked ourselves: could we measure another patient sample now or in half a year and classify with the same performance? Admittedly, the answer is: it is complicated.
A regular question from clinical partners to me is: can you tell me if this patient has disease A or B from the plasma proteome? This sparked a new idea: ADAPT-MS is a concept framework enabling diagnostic classification from single sample discovery proteomics data.
www.medrxiv.org/content/10.1...
An adaptive, continuous-learning framework for clinical decision-making from proteome-wide biofluid data https://www.medrxiv.org/content/10.1101/2025.05.02.25326901v1
Our PCA-N workflow featuring a neutralization step allows direct enzymatic digestion, doubles proteome depth from 5µL neat plasma, and handles 10K+ sample-preps daily. Democratizing plasma proteomics with 50K sample validation. Check it out on bioRxiv!
www.biorxiv.org/content/10.1...
A simplified perchloric acid workflow with neutralization (PCA-N) for democratizing deep plasma proteomics at population scale https://www.biorxiv.org/content/10.1101/2025.03.24.645089v1
A simplified perchloric acid workflow with neutralization (PCA-N) for democratizing deep plasma proteomics at population scale https://www.biorxiv.org/content/10.1101/2025.03.24.645089v1
