Lee Keondo, Kim Seong-Eun, Doh Junsang, Kim Keehoon, Chung Wan Kyun
Department of Mechanical Engineering, Pohang University of Science and Technology, Pohang (POSTECH), 77, Cheongam-ro, Nam-gu, Pohang, Gyeongbuk 37673, South Korea.
Department of Materials Science and Engineering, Seoul National University, 1, Gwanak-ro, Gwanak-gu, Seoul 08826, South Korea.
Lab Chip. 2021 May 4;21(9):1798-1810. doi: 10.1039/d0lc00747a.
Image-activated cell sorting is an essential biomedical research technique for understanding the unique characteristics of single cells. Deep learning algorithms can be used to extract hidden cell features from high-content image information to enable the discrimination of cell-to-cell differences in image-activated cell sorters. However, such systems are challenging to implement from a technical perspective due to the advanced imaging and sorting requirements and the long processing times of deep learning algorithms. Here, we introduce a user-friendly image-activated microfluidic sorting technique based on a fast deep learning model under the TensorRT framework to enable sorting decisions within 3 ms. The proposed sorter employs a significantly simplified operational procedure based on the use of a syringe connected to a piezoelectric actuator. The sorter has a 2.5 ms latency. The utility of the sorter was demonstrated through real-time sorting of fluorescent polystyrene beads and cells. The sorter achieved 98.0%, 95.1%, and 94.2% sorting purities for 15 μm and 10 μm beads, HL-60 and Jurkat cells, and HL-60 and K562 cells, respectively, with a throughput of up to 82.8 events per second (eps).
图像激活细胞分选是理解单细胞独特特征的一项重要生物医学研究技术。深度学习算法可用于从高内涵图像信息中提取隐藏的细胞特征，以便在图像激活细胞分选仪中区分细胞间的差异。然而，由于先进的成像和分选要求以及深度学习算法的处理时间长，从技术角度来看，此类系统的实现具有挑战性。在此，我们基于TensorRT框架下的快速深度学习模型，引入了一种用户友好的图像激活微流控分选技术，能够在3毫秒内做出分选决策。所提出的分选仪基于使用连接到压电致动器的注射器，采用了显著简化的操作程序。该分选仪的延迟为2.5毫秒。通过对荧光聚苯乙烯微珠和细胞进行实时分选，证明了该分选仪的实用性。该分选仪对15微米和10微米的微珠、HL-60和Jurkat细胞以及HL-60和K562细胞的分选纯度分别达到了98.0%、95.1%和94.2%，通量高达每秒82.8个事件（eps）。
