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User-friendly image-activated microfluidic cell sorting technique using an optimized, fast deep learning algorithm.
- Source :
- Lab on a Chip; 5/7/2021, Vol. 21 Issue 9, p1798-1810, 13p
- Publication Year :
- 2021
-
Abstract
- 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). [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 14730197
- Volume :
- 21
- Issue :
- 9
- Database :
- Complementary Index
- Journal :
- Lab on a Chip
- Publication Type :
- Academic Journal
- Accession number :
- 150142696
- Full Text :
- https://doi.org/10.1039/d0lc00747a