Antigen-independent single-cell phenotyping using biolaser

We introduce a novel, antigen-independent biolaser method to generate distinctive cellular signatures. Suspension of nucleic acid-stained cells is deposited into a Fabry-Perot cavity. The cells are excited by a pump laser at various power densities and the lasing signatures of these cells are collected. A neural network based on ResNet 34 is trained to detect and differentiate lasing patterns of CTCs from WBCs using the collected lasing signatures. This neural network structure is designed to be robust against inter-cavity discrepancies in laser cavities. We tested our system on detecting circulating pancreatic cancer cells from cell lines of T cells (Jurkat) and later spiked patient samples (filtered WBCs), from lasing cavities with uncharacterized Q factors. In both cases, we were able to differentiate the CTCs with an accuracy higher than 90%.