Ultrafast automated image cytometry for cancer detection

Area of Science:

• Biomedical Engineering
• Optical Microscopy
• Microfluidics

Background:

• Accurate and rapid detection of rare cells, such as circulating tumor cells (CTCs), is crucial for early cancer diagnosis and monitoring.
• Existing methods for cell analysis often face limitations in speed, throughput, and accuracy, especially when dealing with low cell concentrations.

Purpose of the Study:

• To present a novel method for ultrafast automated single-cell optical microscopy.
• To enable blur-free, real-time image acquisition, recording, and classification of cells in high-speed flow.
• To demonstrate the system's utility for high-throughput screening of rare cancer cells.

Main Methods:

• Integration of ultrafast optical imaging, self-focusing microfluidics, optoelectronics, and information technology.
• Development of a system capable of continuous, real-time image processing and cell classification.
• Application of the system for image-based screening of breast cancer cells in blood samples.

Main Results:

• Achieved ultrafast, blur-free image acquisition and real-time cell classification at a throughput of 100,000 cells/s.
• Demonstrated a record low false positive rate of one in a million for detecting rare breast cancer cells.
• Successfully performed high-throughput image-based screening of rare cells in blood.

Conclusions:

• The presented method offers a significant advancement in automated single-cell optical microscopy.
• This technology holds great promise for early, noninvasive, and low-cost cancer detection.
• The system's high throughput and accuracy make it suitable for clinical applications in cancer diagnostics.