Capture and detection of extracellular vesicles derived from human breast cancer cells using a 3D self-assembled nanostructured SiO2 microfluidic chip.

Abstract

Background: Tumor-derived extracellular vesicles offer a minimally invasive approach to evaluate tumor progression and metastasis. However, detecting biomarkers, such as extracellular vesicles in body fluids during the early stages of disease, remains a significant challenge. Conventional methods like ultracentrifugation-based isolation or Western blot protein quantification are time-consuming, require large sample volumes, and offer low yield and sensitivity. Therefore, the development of new biosensors for the specific and efficient analysis of tumor extracellular vesicles is urgently needed.

Methods: Microfluidic devices provide extraordinary benefits for bioanalysis, offering a large surface area for the contact between target molecules and the biosensor, significantly enhancing the specificity, efficiency, and speed. These devices also enable nanoscale and microscale work using reduced sample volumes. In this study, we developed a three-dimensional self-assembled SiO₂-based nanostructured microfluidic chip, bioconjugated with specific antibodies targeting exosomal markers for the selective capture of CD63- and CD81-positive extracellular vesicles from breast cancer-derived conditioned cell culture media.

Results: The three-dimensional SiO₂-based microfluidic chip effectively captured extracellular vesicles expressing CD63 and CD81 antigens from breast cancer cell culture media. This evidence demonstrates the potential of this platform to detect extracellular vesicles as biomarkers for cancer, providing a specific and efficient, non-invasive approach for cancer diagnostics.

Conclusions: This study highlights the potential application of three-dimensional SiO₂-based microfluidic chips for detecting extracellular vesicles as a non-invasive liquid biopsy tool for breast cancer. The findings show a specific and efficient device as an alternative to conventional biomarker detection techniques.