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Microfluidic Buffer Exchange for Interference-free Micro/Nanoparticle Cell Engineering
Published on: July 10, 2016
Intelligent label-free droplet microfluidic sorting system for single-cell encapsulation and morphology-guided
Kefan Guo1,2, Qingqing Liu1, Huiling Yuan3,4
1School of Mechanical Engineering, and Jiangsu Key Laboratory for Design and Manufacture of Micro-Nano Biomedical Instruments, Southeast University, Nanjing, China.
Microsystems & Nanoengineering
|June 5, 2026
Summary
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.
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.
