Identifying the Phenotypes of Tumor-Derived Extracellular Vesicles Using Size-Coded Affinity Microbeads.

Area of Science:

• Biotechnology
• Nanotechnology
• Cancer Research

Background:

• Tumor-derived extracellular vesicles (tEVs) are promising biomarkers for cancer diagnosis and treatment.
• Challenges in tEV analysis include high heterogeneity, low biomarker expression, and spectral overlap limiting multiplex detection.
• Existing methods struggle with rapid and sensitive identification of diverse tEV phenotypes.

Purpose of the Study:

• To develop a highly sensitive method for tEV phenotyping.
• To enable rapid and simultaneous detection of multiple tEV biomarkers.
• To overcome limitations of spectral overlap in multiplex biomarker analysis.

Main Methods:

• Utilized size-coded microbeads functionalized with hairpin probes for aptamer binding to tEV biomarkers.
• Designed a microfluidic chip with spacer arrays to segregate microbeads by size, generating location-specific signals.
• Employed in situ rolling cyclic amplification (RCA) for signal amplification and fluorescence spectroscopy for detection.

Main Results:

• Achieved highly sensitive tEV phenotyping with simultaneous detection of six distinct tEV phenotypes.
• Demonstrated multiplex analysis without spectral overlap limitations.
• Showcased the platform's programmability for detecting various tEV phenotype substitutions.

Conclusions:

• The developed method offers a sensitive and rapid approach for tEV phenotyping.
• This strategy provides a new avenue for precise cancer diagnosis and personalized treatment.
• The microfluidic sensing platform is adaptable for diverse biomarker detection in clinical cohorts.