Rare cell classification using label-free imaging flow cytometry via motion-sensitive-triggered interferometry.

Area of Science:

• Biomedical Engineering
• Optical Microscopy
• Cell Biology

Background:

• Label-free imaging flow cytometry is crucial for rare cell analysis.
• Traditional methods face challenges with high data volumes and computational demands.
• Detecting and grading circulating tumor cells (CTCs) in liquid biopsies requires sensitive and efficient techniques.

Purpose of the Study:

• To develop and demonstrate a hybrid imaging flow cytometry system for label-free detection and grading of rare cancer cells.
• To reduce data acquisition and processing burdens in rare cell analysis.
• To enable sensitive classification and grading of circulating tumor cells for liquid biopsies.

Main Methods:

• Integration of a microfluidic chip with an event-based camera and an interferometric phase microscopy module.
• Utilizing the event camera for high-speed cell activity detection and triggering a slower, high-sensitivity interferometric camera.
• Employing deep neural networks for classification of rare cells based on raw interferometric data.

Main Results:

• Demonstrated efficient detection and grading of rare cancer cells in blood samples.
• Successfully differentiated between white blood cells and cancer cells using the event camera.
• Achieved grading of cancer cell types (primary/metastatic) with the interferometric camera.
• Significantly reduced data volume and computational load compared to conventional methods.

Conclusions:

• The hybrid imaging flow cytometry system offers efficient, rapid, and sensitive detection and grading of rare cells.
• This approach provides a powerful tool for analyzing circulating tumor cells in liquid biopsies.
• The system has broad potential applications in rare cell detection and processing within imaging flow cytometry.