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This study introduces an intelligent label-free droplet sorting (ILFDS) system for high-precision cell sorting. The ILFDS system utilizes microfluidics, image recognition, and dielectrophoresis to achieve efficient and accurate separation of cells based on morphology.

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Area of Science:

  • Biotechnology
  • Microfluidics
  • Cell Sorting

Background:

  • Label-free extraction of cellular morphological data from droplet microenvironments is a significant challenge.
  • Existing droplet microfluidic systems face limitations in achieving high-precision sorting and preserving cell integrity.

Purpose of the Study:

  • To develop an intelligent label-free droplet sorting (ILFDS) system.
  • To enable high-precision, label-free sorting of cells based on morphological features using droplet microfluidics.

Main Methods:

  • Integration of droplet microfluidics, real-time image recognition, and dielectrophoresis (DEP) sorting.
  • Utilization of innovative liquid-metal electrodes for low-voltage operation (250-350 V).
  • Real-time sorting with minimal droplet deformation and preserved cell integrity.

Main Results:

  • Achieved >98% detection accuracy and >85% sorting efficiency for single-target droplets (particles, Haematococcus pluvialis, Scenedesmus quadricauda).
  • Demonstrated >90% detection accuracy and >89% sorting efficiency for mixed samples (Haematococcus pluvialis and Euglena gracilis).
  • Significantly increased the proportion of single-target droplets after sorting.

Conclusions:

  • The ILFDS system offers robust performance for heterogeneous samples, overcoming limitations of conventional methods.
  • Enables scalable, high-throughput, label-free sorting based on image recognition.
  • Provides a versatile platform for droplet-based analytical and screening applications.