iSort enables automated complex microfluidic droplet sorting in an effort to democratize technology
Jatin Panwar1,2, Ramesh Utharala2, Laura Fennelly2
1Institute of Bioengineering, School of Engineering, École Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland.
Cell Reports Methods
|June 16, 2023
Summary
This study introduces automated fluorescence-activated droplet sorting (FADS) using real-time impedance analysis. This innovation enhances throughput and reproducibility for single-cell analysis, making it more accessible.
Area of Science:
- Microfluidics
- Biotechnology
- Single-cell analysis
Background:
- Fluorescence-activated droplet sorting (FADS) is crucial for high-throughput screening but demands specialized expertise for parameter optimization.
- Current FADS methods face challenges in systematic optimization and tracking individual droplets, leading to compromised sorting accuracy and hidden false positives.
Purpose of the Study:
- To develop an automated system for optimizing FADS parameters in real time.
- To enhance the throughput, reproducibility, and robustness of droplet sorting.
- To make phenotypic single-cell analysis more accessible and user-friendly.
Main Methods:
- Development of a microfluidic setup integrating real-time impedance analysis.
- Monitoring of droplet frequency, spacing, and trajectory at the sorting junction.
- Implementation of automated parameter optimization and perturbation counteraction based on impedance data.
Main Results:
- Achieved continuous, automatic optimization of all sorting parameters.
- Demonstrated higher throughput, improved reproducibility, and increased robustness in droplet sorting.
- Successfully counteracted perturbations in real time, enhancing sorting reliability.
Conclusions:
- The developed impedance-based automated FADS system overcomes key limitations of traditional methods.
- This innovation significantly improves the accessibility and performance of phenotypic single-cell analysis.
- The system represents a significant advancement for the widespread adoption of single-cell analysis platforms.
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