Single-Cell Analysis and Classification according to Multiplexed Proteins via Microdroplet-Based Self-Driven Magnetic Surface-Enhanced Raman Spectroscopy Platforms Assisted with Machine Learning Algorithms.

A microdroplet-based surface-enhanced Raman spectroscopy (microdroplet SERS) platform was constructed to envelop individual cells in microdroplets, followed by the SERS detection of their extracellular vesicle-proteins (EV-proteins) via the in-drop immunoassays by use of immunomagnetic beads ( i MBs) and immuno-SERS tags ( i SERS tags). A unique phenomenon is found that i MBs can start a spontaneous reorientation on the probed cell surface based on the electrostatic force-driven interfacial aggregation effect, which leads EV-proteins and i SERS tags to be gathered from a liquid phase to a cell membrane interface and significantly improves SERS sensitivity to the single-cell analysis level due to the formation of numbers of SERS hotspots. Three EV-proteins from two breast cancer cell lines were collected and further analyzed by machine learning algorithmic tools, which will be helpful for a deeper understanding of breast cancer subtypes from the view of EV-proteins.