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A deformability-based biochip for precise label-free stratification of metastatic subtypes using deep learning.

Haojun Hua1, Shangjie Zou1,2, Zhiqiang Ma2

  • 1City University of Hong Kong, 83 Tat Chee Avenue, Kowloon, Hong Kong, 999077 China.

Microsystems & Nanoengineering
|October 2, 2023
PubMed
Summary

This study introduces a microfluidic flow cytometry device and computational framework (ATMQcD) for high-throughput cellular deformability analysis. The system accurately assesses cancer cell invasiveness and distinguishes cell types, advancing clinical diagnostics.

Keywords:
BionanoelectronicsMicrofluidics

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Area of Science:

  • Biophysics
  • Cell Biology
  • Microfluidics

Background:

  • Cellular deformability is a key biomarker for cell health and disease.
  • Microfluidic techniques offer precise measurement but often lack scalability due to image analysis limitations.

Purpose of the Study:

  • To develop a scalable microfluidic device and computational framework for high-throughput cellular deformability analysis.
  • To validate the system's accuracy in assessing cancer cell invasiveness and distinguishing between cell types.

Main Methods:

  • Development of a parallel constriction-based microfluidic flow cytometry device.
  • Integration of an automated computational framework (ATMQcD) for object tracking, segmentation, and deformability quantification.
  • Application of a power-law rheology model for stiffness quantification.

Main Results:

  • Achieved 92.4% accuracy in classifying cancer cell invasiveness and stratifying cells based on treatment.
  • Demonstrated 89.5% accuracy in distinguishing cancer cells from leukocytes.
  • Successfully evaluated metastatic potential in various cancer types and mixed populations.

Conclusions:

  • The ATMQcD system provides a robust, transferable platform for microfluidic deformation measurement.
  • This technology has the potential to enable high-throughput clinical applications for cellular deformability assessment.