Intelligent image-activated sorting of large cells enabled by elasto-inertial focusing

Area of Science:

• Biotechnology
• Microfluidics
• Cell Sorting

Background:

• Image-activated cell sorting (IACS) is crucial for linking cell morphology and function at the single-cell level.
• Existing IACS methods struggle with sorting large cells and objects due to focusing instability in microfluidic devices.
• Hydrodynamic focusing limits the speed and purity of IACS for larger biological entities.

Purpose of the Study:

• To develop an IACS system capable of sorting large cells and objects at high speeds.
• To overcome the limitations of hydrodynamic focusing in conventional IACS systems.
• To enable high-content, real-time image analysis for sorting large biological entities.

Main Methods:

• Developed an IACS system integrating elasto-inertial focusing for stable particle manipulation.
• Validated the elasto-inertial focuser with large particles (>20 μm) over extended distances (≥35 mm).
• Utilized a convolutional neural network classifier for sorting size-mixed particles and microalgae (Euglena gracilis).

Main Results:

• Demonstrated stable focusing of large particles (>20 μm) using elasto-inertial focusing.
• Achieved 96.0% purity and 80.5% yield for size-mixed particle sorting at 172 events per second.
• Successfully sorted Euglena gracilis based on lipid droplet formation, achieving 4.5-fold enrichment at 128 events per second.

Conclusions:

• Elasto-inertial focusing enables high-speed IACS for large cells and objects, overcoming previous limitations.
• The developed IACS system maintains high sorting purity, yield, and event rates for complex biological samples.
• This technology advances single-cell analysis and sorting capabilities for scientific and industrial applications involving large cellular entities.