Upgraded User-Friendly Image-Activated Microfluidic Cell Sorter Using an Optimized and Fast Deep Learning Algorithm
SCIE
SCOPUS
- Title
- Upgraded User-Friendly Image-Activated Microfluidic Cell Sorter Using an Optimized and Fast Deep Learning Algorithm
- Authors
- Lee, Keondo; Kim, Seong-Eun; Nam, Seokho; Doh, Junsang; Chung, Wan Kyun
- Date Issued
- 2022-12
- Publisher
- Multidisciplinary Digital Publishing Institute (MDPI)
- Abstract
- Image-based cell sorting is essential in biological and biomedical research. The sorted cells can be used for downstream analysis to expand our knowledge of cell-to-cell differences. We previously demonstrated a user-friendly image-activated microfluidic cell sorting technique using an optimized and fast deep learning algorithm. Real-time isolation of cells was carried out using this technique with an inverted microscope. In this study, we devised a recently upgraded sorting system. The cell sorting techniques shown on the microscope were implemented as a real system. Several new features were added to make it easier for the users to conduct the real-time sorting of cells or particles. The newly added features are as follows: (1) a high-resolution linear piezo-stage is used to obtain in-focus images of the fast-flowing cells; (2) an LED strobe light was incorporated to minimize the motion blur of fast-flowing cells; and (3) a vertical syringe pump setup was used to prevent the cell sedimentation. The sorting performance of the upgraded system was demonstrated through the real-time sorting of fluorescent polystyrene beads. The sorter achieved a 99.4% sorting purity for 15 μm and 10 μm beads with an average throughput of 22.1 events per second (eps).
- URI
- https://oasis.postech.ac.kr/handle/2014.oak/114856
- DOI
- 10.3390/mi13122105
- ISSN
- 2072-666X
- Article Type
- Article
- Citation
- Micromachines, vol. 13, no. 12, page. 2105, 2022-12
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- There are no files associated with this item.
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