High-throughput label-free flow cytometry based on matched-filter compressive imaging.

Area of Science:

• Biophotonics
• Optical Engineering
• Computational Imaging

Background:

• Conventional flow cytometry often requires labels and can be limited by speed or information capacity.
• Existing methods may struggle with simultaneous multi-object analysis across large fields of view.
• Label-free techniques are desirable for preserving cell viability and simplifying workflows.

Purpose of the Study:

• To introduce a novel label-free computational flow cytometer utilizing compressive imaging.
• To demonstrate enhanced object identification and separation capabilities.
• To achieve high throughput and velocity in flow cytometry without sacrificing data quality.

Main Methods:

• Employs compressive imaging with a deformable mirror to subdivide scattered light into user-defined basis patterns.
• Routes patterned light to dedicated detectors for efficient data acquisition.
• Optimizes patterns for matched-filter detection to align with specific object features.

Main Results:

• Achieved throughputs exceeding 10,000 particles/second.
• Operated at flow velocities greater than 1 meter/second.
• Demonstrated simultaneous probing of multiple objects across large fields of view with extended depth of field.

Conclusions:

• The compressive imaging-based flow cytometer offers a significant advancement in speed and information capacity.
• The label-free, matched-filter approach enables efficient multi-object analysis at high flow rates.
• This technology holds promise for high-throughput biological sample analysis and cell sorting.