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

Abstract

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