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Sequential Labeling-Assisted Precise and Multitarget Analysis of Surface Proteins on Extracellular Vesicles.

Xiaomeng Yu1, Ya Cao1, Jianan Xia2

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Summary

This study introduces a new electrochemical method for analyzing multiple surface proteins on extracellular vesicles (EVs). This technique accurately detects breast cancer biomarkers, aiding in early diagnosis and personalized treatment.

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Area of Science:

  • Biotechnology
  • Analytical Chemistry
  • Cancer Research

Background:

  • Extracellular vesicles (EVs) carry surface proteins crucial for understanding cancer biology, especially in heterogeneous breast cancer.
  • Assessing multiple surface proteins on EVs is challenging due to their small size and spatial hindrance.
  • Identifying cancer-specific EV surface proteins can reveal therapeutic targets and diagnostic markers.

Purpose of the Study:

  • To develop a novel sequential labeling-assisted electrochemical method for precise multiprotein analysis on individual EVs.
  • To overcome spatial hindrance limitations in analyzing EV surface proteins.
  • To demonstrate the method's utility in detecting breast cancer biomarkers and its potential for clinical application.

Main Methods:

  • Sequential labeling of EV surface proteins using aptamer probes functionalized with electroactive nanoparticles.
  • Utilizing an oxidative cleavage process mediated by the bleomycin-Fe2+ complex to enable sequential detection.
  • Electrochemical analysis for quantifying target proteins like epidermal growth factor receptor and programmed death ligand-1 on EVs.

Main Results:

  • The sequential labeling method effectively mitigates spatial hindrance, allowing accurate multiprotein detection on EVs.
  • The method achieved precise quantification of target proteins on low concentrations of standard EVs from triple-negative breast cancer (TNBC) cells (as low as 341 particles/mL).
  • Successful application to clinical blood samples from healthy individuals and TNBC patients, demonstrating diagnostic potential.

Conclusions:

  • The developed method provides a feasible tool for precise, multiplexed analysis of surface proteins on individual EVs.
  • This approach offers valuable protein-level information for accurate breast cancer diagnosis and personalized treatment strategies.
  • The technique shows promise for early cancer diagnosis and disease-course monitoring.