Multi-Object Detector YOLOv4-Tiny Enables High-Throughput Combinatorial and Spatially-Resolved Sorting of Cells in Microdroplets
Multi-Object Detector YOLOv4-Tiny Enables High-Throughput Combinatorial and Spatially-Resolved Sorting of Cells in Microdroplets
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DOI：
10.1002/admt.202101053
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发表时间：
2021-10-13
影响因子：
6.8
通讯作者：
Gielen, Fabrice
中科院分区：
文献类型：
--
作者：
Howell, Lewis;Anagnostidis, Vasileios;Gielen, Fabrice
将细胞与微物体一起封装在单分散的油包水微滴中提供了在大细胞群体内进行定量生物学研究的有力手段。在这些应用中，准确的物体检测对于确保控制每个隔室的内容至关重要。特别是，快速计数和定位物体的能力是未来在单细胞组学，细胞聚集和细胞间相互作用中应用的关键。在本文中，作者将深度学习对象检测器YOLOv 4-tiny与微流控图像激活液滴分选（DL-IADS）相结合，以高通量同时对多个微对象进行灵活的无标签分类、计数和定位。他们训练YOLOv 4-tiny在单个模型中检测SH-SY 5 Y细胞、聚丙烯酰胺珠和细胞聚集体，细胞的精确度为92%，珠为98%，聚集体为81%。他们利用这种准确性和计数能力来实现闭环反馈，通过自动调节流速来控制微珠的加载。他们随后展示了基于高达111 Hz的实时分类的共封装单细胞和单珠的组合分选，富集因子高达145。最后，他们通过实时评估细胞间距离来展示空间分辨分选，以分离高纯度的细胞双联体。
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.