Blood proteome is a highly informative biological fluid, reflecting physiological and pathological states across the entire body. It contains thousands of proteins spanning a dynamic range of more than 10 orders of magnitude. This makes blood proteome an ideal matrix for disease diagnosis, prognosis, therapeutic selection, and monitoring. However, comprehensive and reproducible analysis of blood proteins remains technically challenging, primarily due to the overwhelming presence of high-abundance proteins that obscure low-abundance targets. Blood proteome analysis offers several advantages over tissue-based diagnostics: it enables real time, longitudinal sampling, captures systemic physiological or pathological changes, and provides access to tissue-derived proteins. Automation offers consistent handling, reduced human error, and better reproducibility, which are essential for translating proteomics into routine biomedical workflows. We designed this platform to integrate Magnetic-COF-based protein capture with scalable robotic handling, enabling blood protein enrichment and LC-MS/MS analysis, resulting in more than 4000 plasma proteins identified with a throughput of 30 samples per day. By combining molecular selectivity, magnetic enrichment, and automation, our strategy addresses key limitations in current blood proteomics workflows and offers a flexible foundation for the research and discovery of proteomics from pancreatic ductal adenocarcinoma patients.
Huang et al. (Thu,) studied this question.
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