ExtracellularVesicles Separation and Biomedical ApplicationBased on Affinity Recognition and Antifouling Coating BifunctionalMicrosphere

Extracellular vesicles (EVs) are crucial mediators in various physiological and pathological processes, facilitating intercellular communication and offering potential as diagnostic disease markers. However, existing EVs separation methods have limitations that hinder their clinical application. In this study, we present a novel approach using bifunctional silica microspheres (SiO<sub>2</sub>-PTB-PS) for the specific, nondestructive isolation of EVs from complex biological media. The isolated EVs were subsequently used for direct cancer detection in clinical samples. The SiO<sub>2</sub>-PTB-PS microspheres, functionalized with a phosphatidylserine (PS) recognition peptide (PSpep), specifically bound to PS on the EVs surface. Additionally, an anti-adhesion coating on the silica microspheres minimized protein contamination, enhancing purity. This affinity-based recognition and antifouling strategy ensured high-purity EVs separation. Furthermore, we developed a detection system combining SiO<sub>2</sub>-PTB-PS microspheres with surface-enhanced Raman scattering (SERS) nanoprobes to identify protein tyrosine kinase 7 (PTK7) and epithelial cell adhesion (EpCAM) on the EVs membrane, achieving 80% precision in distinguishing cancer patients from healthy donors. The SiO<sub>2</sub>-PTB-PS microsphere system shows significant promise as a biotechnology tool, advancing the clinical application of EVs-based diagnostics.

Publication Details

Journal
Figshare
Published
2025-02-20
DOI
https://doi.org/10.1021/acs.analchem.4c06347.s001
Primary Topic
Rock Mechanics and Modeling
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

ExtracellularVesicles Separation and Biomedical ApplicationBased on Affinity Recognition and Antifouling Coating BifunctionalMicrosphere

Figshare
Rock Mechanics and Modeling
article

ExtracellularVesicles Separation and Biomedical ApplicationBased on Affinity Recognition and Antifouling Coating BifunctionalMicrosphere

article en

Abstract

Extracellular vesicles (EVs) are crucial mediators in various physiological and pathological processes, facilitating intercellular communication and offering potential as diagnostic disease markers. However, existing EVs separation methods have limitations that hinder their clinical application. In this study, we present a novel approach using bifunctional silica microspheres (SiO<sub>2</sub>-PTB-PS) for the specific, nondestructive isolation of EVs from complex biological media. The isolated EVs were subsequently used for direct cancer detection in clinical samples. The SiO<sub>2</sub>-PTB-PS microspheres, functionalized with a phosphatidylserine (PS) recognition peptide (PSpep), specifically bound to PS on the EVs surface. Additionally, an anti-adhesion coating on the silica microspheres minimized protein contamination, enhancing purity. This affinity-based recognition and antifouling strategy ensured high-purity EVs separation. Furthermore, we developed a detection system combining SiO<sub>2</sub>-PTB-PS microspheres with surface-enhanced Raman scattering (SERS) nanoprobes to identify protein tyrosine kinase 7 (PTK7) and epithelial cell adhesion (EpCAM) on the EVs membrane, achieving 80% precision in distinguishing cancer patients from healthy donors. The SiO<sub>2</sub>-PTB-PS microsphere system shows significant promise as a biotechnology tool, advancing the clinical application of EVs-based diagnostics.

Figshare
Openalex Percentile: Top 53%
Rock Mechanics and Modeling
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.