Gray-Level Guided Image-Activated Droplet Sorter for Label-Free, High-Accuracy Screening of Single-Cell on Demand.

Area of Science:

• Biotechnology
• Microfluidics
• Cell Sorting

Background:

• Single-cell encapsulation in droplet microfluidics is vital for precision medicine and single-cell analysis.
• Current methods often yield empty or multi-cell droplets and face limitations in sorting accuracy, throughput, and label-free operation.
• Existing microfluidic sorting technologies struggle with fluorescent labeling, multi-cell droplet removal, and low throughput.

Purpose of the Study:

• To develop a label-free, high-accuracy method for screening single-cell droplets.
• To overcome the limitations of existing microfluidic sorting technologies, particularly regarding empty and multi-cell droplet rejection.
• To achieve ultra-high sorting throughput for biological applications.

Main Methods:

• Development of a gray-level guided image-activated droplet sorter (GL-IADS).
• Implementation of a gray-level based recognition method for accurate droplet image classification (empty, single-cell, multi-cell).
• Integration of the recognition method with a detachable acoustofluidic system for sorting.

Main Results:

• The GL-IADS achieves 100% accuracy in differentiating empty and cell-laden droplets.
• Image processing time is reduced to approximately 300 µs, enabling ultra-high sorting throughput (KHz range).
• Sorting purity reached 97.9% for single-cell, 97.4% for multi-cell, and >99% for cell-laden droplets at 43 Hz throughput.

Conclusions:

• The GL-IADS offers a label-free, high-accuracy solution for single-cell droplet screening.
• This technology significantly enhances sorting throughput and purity compared to existing methods.
• The GL-IADS shows great potential for various biological applications, especially those challenging for fluorescence-based techniques.