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MATLABÈ°¿ë Droplet MicrofluidicsÀÇ À̹ÌÁö ±â¹Ý ´ÜÀÏ ¼¿ Á¤·Ä ÀÚµ¿È­
MATLABÈ°¿ë Droplet MicrofluidicsÀÇ À̹ÌÁö ±â¹Ý ´ÜÀÏ ¼¿ Á¤·Ä ÀÚµ¿È­
  • ÀúÀÚMuhsincan Sesen, Graeme Whyte Àú
  • ÃâÆÇ»ç¾ÆÁø
  • ÃâÆÇÀÏ2020-07-13
  • µî·ÏÀÏ2020-12-21
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The recent boom in single-cell omics has brought researchers one step closer to
understanding the biological mechanisms associated with cell heterogeneity. Rare
cells that have historically been obscured by bulk measurement techniques are
being studied by single cell analysis and providing valuable insight into cell
function. To support this progress, novel upstream capabilities are required for
single cell preparation for analysis. Presented here is a droplet microfluidic,
image-based single-cell sorting technique that is flexible and programmable. The
automated system performs real-time dualcamera imaging (brightfield &
fluorescent), processing, decision making and sorting verification. To demonstrate
capabilities, the system was used to overcome the Poisson loading problem by
sorting for droplets containing a single red blood cell with 85% purity.
Furthermore, fluorescent imaging and machine learning was used to load single
K562 cells amongst clusters based on their instantaneous size and circularity. The
presented system aspires to replace manual cell handling techniques by translating
expert knowledge into cell sorting automation via machine learning algorithms.
This powerful technique finds application in the enrichment of single cells based on
their micrographs for further downstream processing and analysis.

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Á¦ 2Æí : ¿¬±¸³í¹®
Image-Based Single Cell Sorting Automation in Droplet Microfluidics

1. System Overview 52
2. Results & Discussion 56
3. Conclusion 59
4. Materials & Methods 60
5. Data availability 61
6. References 62

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