Journal
MICROMACHINES
Volume 13, Issue 12, Pages -Publisher
MDPI
DOI: 10.3390/mi13122105
Keywords
Microfluidic Flow Cytometry; image-based cell sorting; deep learning
Categories
Funding
- Ministry of Trade, Industry and Energy (MOTIE, Korea) [20012378]
- Alchemist Project
- Korea Evaluation Institute of Industrial Technology (KEIT) [20012378] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
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Image-based cell sorting is important in biological research. This study presents an upgraded sorting system with added features to facilitate real-time sorting of cells or particles.
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 mu m and 10 mu m beads with an average throughput of 22.1 events per second (eps).
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