Multi‐Object Detector YOLOv4‐Tiny Enables High‐Throughput Combinatorial and Spatially‐Resolved Sorting of Cells in Microdroplets

The encapsulation of cells together with micro-objects in monodispersed water-in-oil microdroplets offers a powerful means to perform quantitative biological studies within large cell populations. In such applications, accurate object detection is crucial ensure control over the content for every compartment. particular, ability rapidly count and localize objects key future applications single-cell -omics, cellular aggregation, cell-to-cell interactions. this paper, authors combine Deep Learning detector YOLOv4-tiny microfluidic Image-Activated Droplet Sorting (DL-IADS), flexible, label-free classification, counting, localization multiple simultaneously at high-throughput. They trained detect SH-SY5Y cells, polyacrylamide beads, aggregates single model, precision 92% 98% 81% aggregates. exploit accuracy counting implement closed-loop feedback that enables controlled loading microbeads via automated adjustment flow rates. subsequently demonstrate combinatorial sorting co-encapsulated beads based on real-time classification up 111 Hz, enrichment factors 145. Finally, they spatially-resolved sorts by evaluating distances isolate doublets high purity.