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User-friendly image-activated microfluidic cell sorting technique using an optimized, fast deep learning algorithm.

Keondo Lee1, Seong-Eun Kim1, Junsang Doh2

  • 1Department of Mechanical Engineering, Pohang University of Science and Technology, Pohang (POSTECH), 77, Cheongam-ro, Nam-gu, Pohang, Gyeongbuk 37673, South Korea. wkchung@postech.ac.kr.

Lab on a Chip
|March 18, 2021
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Summary
This summary is machine-generated.

This study introduces a fast, user-friendly image-activated cell sorter using deep learning for rapid cell analysis. The novel microfluidic system achieves high sorting purity and speed, advancing biomedical research capabilities.

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Area of Science:

  • Biomedical Engineering
  • Cell Biology
  • Artificial Intelligence

Background:

  • Image-activated cell sorting (IACS) is crucial for single-cell analysis.
  • Deep learning in IACS faces technical challenges like long processing times and complex requirements.
  • Efficient cell discrimination requires advanced imaging and sorting capabilities.

Purpose of the Study:

  • To develop a user-friendly and rapid image-activated microfluidic cell sorting technique.
  • To integrate a fast deep learning model within the TensorRT framework for real-time sorting decisions.
  • To overcome the technical limitations of existing IACS systems.

Main Methods:

  • Implementation of a simplified microfluidic sorter using a syringe and piezoelectric actuator.
  • Development of a fast deep learning model optimized with the TensorRT framework for <3 ms sorting decisions.
  • Real-time sorting experiments using fluorescent beads and various cell types (HL-60, Jurkat, K562).

Main Results:

  • The developed sorter demonstrated a latency of 2.5 ms.
  • Achieved high sorting purities: 98.0% for 15 μm beads, 95.1% for HL-60/Jurkat cells, and 94.2% for HL-60/K562 cells.
  • Reached a maximum throughput of 82.8 events per second.

Conclusions:

  • The proposed image-activated microfluidic sorter offers a user-friendly and efficient solution for cell sorting.
  • Fast deep learning integration enables rapid, high-purity cell discrimination.
  • This technique significantly advances capabilities in biomedical research and cell analysis.