Microfluidics-based label-free SERS profiling of exosomes with machine learning for osteosarcoma diagnosis.

Osteosarcoma (OS) calls for early diagnosis to significantly improve patient survival rates. Exosomes hold significant potential as noninvasive biomarkers for the early diagnosis of cancer. Here, we design a microfluidic device to purify and analyze plasma-derived exosomes by label-free surface-enhanced Raman spectroscopy (SERS) profiling for OS diagnosis. Exosomes were isolated, purified, and enriched using a size-dependent microfluidic chip with tangential flow filtration, achieving a high recovery rate of 82 %. The isolated exosomes were then analyzed by label-free SERS using a nanoarray chip with self-assembly monolayers of gold nanoparticles (GNPs). Exosomes originating from different OS cell types were differentiated based on the intrinsic SERS signals. Our approach was further employed to analyze the plasma-derived exosomes from healthy donors and OS patients without the need for specific biomarker labeling. A machine learning-based diagnostic model for OS was constructed, achieving an accuracy of 93 %. The findings indicate that our method is valuable for noninvasive and precise diagnosis of OS and could be generalized to other diseases in the future.