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Updated: Feb 12, 2026

Author Spotlight: Advancing the Analysis of Plasma Extracellular Vesicle Proteome for Cardiovascular Biomarker Studies
Published on: January 31, 2025
Surfactant-Modified Magnetic Nanoparticles Enable Efficient and Cost-Effective Plasma Proteomics for Enhanced
Wei Fang1,2, Hongxian Zhao1, Yuxin Zhang3
1School of Medicine, The Chinese University of Hong Kong, Shenzhen, Guangdong 518172, China.
None:
The application of more blood proteins into health risk prediction and disease diagnostics has long garnered significant interest. However, comprehensive profiling of proteins in blood samples (plasma or serum), particularly low-abundance proteins, remains technically challenging due to the masking effect imposed by high-abundance proteins and the extraordinarily wide dynamic range of protein abundances. Herein, we developed an efficient strategy that leveraged the protein corona formed on surfactant-modified magnetic nanoparticles for the selective enrichment of low-abundance plasma proteins. Coupled with liquid-chromatography tandem mass spectrometry (LC-MS/MS) analysis, this strategy allowed for the detection of over 3500 plasma proteins in a single run, which was approximately three times and five times more than the number of proteins detected by the antibody-dependent depletion method and the direct digestion method, respectively. The application of our method to an acute myocardial infarction (AMI) cohort and corresponding healthy control group resulted in the identification of 5000 more plasma proteins and the discovery of seven potential AMI diagnostic biomarkers, which showed superior accuracy in diagnosis compared to conventionally used cardiac troponins. The magnetic nanoparticles (MNPs) and surfactants are commercially accessible and cost-effective, and moreover, the modification protocol is simple. These features ensure the ready adoption of our method by other laboratories, even those lacking specialized nanotechnology expertise. Additionally, the magnetic properties of MNPs can further facilitate the smooth integration with automated sample processing systems, thereby expediting large-scale clinical studies.
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