Analytical ChemistryBiochemistryPeer-reviewed journal article
High-Efficiency Capture and Proteomic Analysis of Plasma-Derived Extracellular Vesicles through Affinity Purification
Guiyuan ZhangiD, Chengxiao Ma, Le MaiD, Dong Wei, Yanan WuiD, Ying LiiD, Zhen Hua XuiD, Yufeng LiuiD, Yuhan CaiiD, Evan Yi-Wen YuiD, Yefei ZhuiD, Hao ZhangiD
Abstract
Plasma-derived extracellular vesicles (EVs) are promising sources of biomarkers. It is still a challenge to isolate EVs from a small amount of human plasma for downstream proteomic analysis. The separation process is hindered by contamination with high-abundance blood proteins and lipoprotein particles, which adversely impact proteomic analyses. Moreover, although EVs immune-separation via magnetic beads often integrates with flow sorting and western blotting (WB), it lacks compatibility with nanoparticle tracking analysis (NTA) and proteomic analysis. To address these issues, we have developed a functional affinity magnetic bead, EVlent (Extracellular Vesicles isoLated Efficiently, Naturally, and Totally), enabling the rapid and efficient separation of EVs from plasma. By optimizing the quantities of magnetic beads and plasma used, we characterized the isolated EVs through WB, NTA, and transmission electron microscopy (TEM), showing a successfully separation of EVs from plasma. Proteomic analysis of these EVs identified over 2,000 proteins and 15,000 peptides from just 100 L of plasma, and nearly 1,000 proteins from trace samples as small as 5 L. Additionally, this isolation method significantly reduced contaminants, including plasma proteins and lipoproteins, compared to ultracentrifugation. Finally, we applied this strategy to plasma samples of healthy individuals and those with Parkinsons disease, identifying four potential biomarkers that provide a promising guidance for clinical diagnosis.
- organism: human
- assay: proteomics
- research focus: host-pathogen & immune response
- research focus: differential expression
- research focus: cell type annotation
- research focus: benchmarking
- approach: experimental assay
- Citations
- 17
- FWCI
- 4.08
- Field percentile
- top 4%
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