Dual-View Transport of Intensity Phase Imaging-Based Flow Cytometry for Label-Free Cell Analysis and Classification.

Area of Science:

• Biomedical Engineering
• Optical Physics
• Cell Biology

Background:

• Label-free cell analysis is crucial for biological research and clinical diagnostics.
• Traditional flow cytometry often requires cell staining, which can affect cell viability and introduce artifacts.
• Quantitative phase imaging (QPI) provides label-free contrast based on refractive index variations, enabling morphological analysis.

Purpose of the Study:

• To develop and validate a quantitative phase imaging-based flow cytometer for label-free cell analysis and classification.
• To integrate dual-view transport of intensity phase imaging (TPI) with microfluidics on a commercial microscope platform.
• To demonstrate the system's capability in extracting morphological parameters for accurate cell classification.

Main Methods:

• The system utilizes dual-view TPI by capturing simultaneous under-focus and over-focus images of flowing cells.
• Phase distributions are reconstructed from the captured images to extract quantitative morphological parameters.
• Microfluidic channels are integrated with a commercial microscope for continuous cell flow and imaging.
• Cell classification is performed based on the extracted morphological features using machine learning algorithms.

Main Results:

• High-accuracy phase imaging was achieved, validated using a standard phase plate sample.
• The system successfully distinguished and classified different cell types (RAW264.7 and MC3T3-E1) in mixtures.
• Morphological parameters extracted from phase distributions enabled robust cell recognition and classification.
• The system demonstrated label-free analysis capabilities without the need for exogenous stains.

Conclusions:

• The developed quantitative phase imaging flow cytometer provides a simple, precise, and robust platform for label-free cell analysis.
• The integration of dual-view TPI and microfluidics offers significant potential for high-throughput cell classification.
• This technology holds promise for applications in fundamental biological research, drug discovery, and clinical diagnostics.