A microfluidic robot for rare cell sorting based on machine vision identification and multi-step sorting strategy

Abstract

The identification, sorting and analysis of rare target single cells in human blood has always been a clinically meaningful medical challenge. Here, we developed a microfluidic robot platform for sorting specific rare cells from complex clinical blood samples based on machine vision-based image identification, liquid handling robot and droplet-based microfluidic techniques. The robot integrated a cell capture and droplet generation module, a laser-induced fluorescence imaging module, a target cell identification and data analysis module, and a system control module, which could automatically achieve the scanning imaging of cell array, cell identification, capturing, and droplet generation of rare target cells from blood samples containing large numbers of normal cells. Based on the robot platform, a novel “gold panning” multi-step sorting strategy was proposed to achieve the sorting of rare target cells in large-scale cell samples with high operation efficiency and high sorting purity (>90%). The robot platform and the multi-step sorting strategy were applied in the sorting of circulating endothelial progenitor cells (CEPCs) in human blood to demonstrate their feasibility and application potential in the sorting and analysis of rare specific cells. Approximately 1,000 CEPCs were automatically identified from 3,000,000 blood cells at a scanning speed of ca. 4,000 cells/s, and 20 25-nL droplets containing single CEPCs were generated.