Unlocking the Circulating Proteome: Toward Clinical Translation
- Author(s)
- Ahadi, S; Babačić, H; Cantrell, LS; Hornburg, D; Müller-Reif, JB; Dagley, LF; Guerrera, IC; Albrecht, V; Edfors, F; Yu, X; Sun, Z; Moritz, RL; Schmidt, F; Hauck, SM; Deutsch, EW; Omenn, GS; Schwenk, JM; Geyer, PE;
- Details
- Publication Year 2026-09-04,Volume 25,Issue #9,Page 4367-4379
- Journal Title
- Journal of Proteome Research
- Publication Type
- R
- Abstract
- Blood-based proteomics is approaching a translational inflection point. Driven by advances in measurement technologies, rapid expansion of analytical capabilities, and growing adoption across research and medical communities, there is increasing demand for clinically actionable biomarkers. As the field transitions away from purely large-scale discovery-oriented studies toward more informed, targeted, application-driven analyses, the generation of proteomic data is no longer the bottleneck. Instead, the central challenge is to translate these measurements into robust, reproducible, and clinically meaningful insights. In this Review, we assess recent technological and methodological developments, evaluate persistent preanalytical and interpretative limitations, and outline the key steps required for clinical translation. We focus on three deeply interconnected dimensions: the capabilities and constraints of current measurement platforms, the role of computational and machine learning approaches in extracting biological and clinical signals, and the emergence of large-scale population studies that create new opportunities for validation and generalization. Finally, we discuss a forward-looking vision in which proteomics plays a central role in dynamic, multilayered omics frameworks, where integration with genomics, temporal profiling, and imaging can deepen our understanding of health, disease, and therapeutic response.
- Publisher
- ACS
- Keywords
- Humans; *Proteomics/methods; *Proteome/analysis; Biomarkers/blood; Machine Learning; *Translational Research, Biomedical/methods; Biomarkers; Blood-based proteomics; Causal inference; Clinical proteomics; Early disease detection; Multiomics integration; Patient stratification; Population-scale proteomics; Therapeutic monitoring
- Research Division(s)
- Advanced Technology and Biology
- PubMed ID
- 42695895
- Publisher's Version
- https://doi.org/10.1021/acs.jproteome.6c00277
- Terms of Use/Rights Notice
- Refer to copyright notice on published article.
Creation Date: 2026-10-02 08:18:55
Last Modified: 2026-10-02 08:19:08