Intelligent label-free droplet microfluidic sorting system for single-cell encapsulation and morphology-guided...

The label-free extraction of cellular morphological data from within droplet microenvironments, and its subsequent translation into reliable, high-precision sorting, continues to pose a central challenge in the field of droplet microfluidics. Here, we develop an intelligent label-free droplet sorting (ILFDS) system by integrating droplet microfluidics, real-time image recognition, and dielectrophoresis (DEP) sorting. The developed system operates at low voltages (250-350 V) by using the innovative liquid-metal electrodes, which enable real-time sorting while minimizing droplet deformation and preserving cell integrity. In experiments on sorting single-target encapsulated droplets, the ILFDS system achieves detection accuracies of >98% and sorting efficiencies of >85% for particles, Haematococcus pluvialis, and Scenedesmus quadricauda, with the proportion of single-target droplets increasing by approximately 3.15-fold, 11.37-fold, and 4.59-fold, respectively, after sorting. Furthermore, the ILFDS system demonstrates high-precision sorting of targets from mixed samples based on morphological features, achieving detection accuracies of above 90% and sorting efficiencies of over 89% for mixed Haematococcus pluvialis and Euglena gracilis samples. These results highlight the system's robustness in handling heterogeneous samples and its capability to overcome key limitations associated with conventional droplet encapsulation and detection. By enabling scalable, high-throughput, and label-free sorting based on image recognition, our ILFDS system offers a versatile platform for a wide range of droplet-based analytical and screening applications.