User-friendly image-activated microfluidic cell sorting technique using an optimized, fast deep learning algorithm
- User-friendly image-activated microfluidic cell sorting technique using an optimized, fast deep learning algorithm
- Lee, K.; Kim, S.-E.; Doh, J.; Kim, K.; Chung, W.K.
- Date Issued
- Royal Society of Chemistry
- 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 ��m and 10 ��m 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). ? The Royal Society of Chemistry 2021.
- Article Type
- Lab on a Chip, vol. 21, no. 9, page. 1798 - 1810, 2021-05-07
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