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Deep-learning-assisted biophysical imaging cytometry at massive throughput delineates cell population heterogeneity.

Dickson M D Siu1, Kelvin C M Lee, Michelle C K Lo

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This study introduces a novel imaging flow cytometry platform for label-free cell analysis. The technology enables high-throughput profiling of cellular biophysical properties to identify cancer subtypes and rare cell populations.

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Area of Science:

  • Biophysics
  • Cell Biology
  • Optical Imaging

Background:

  • Intrinsic cellular optical and biophysical properties are linked to health and disease.
  • Label-free methods avoid costly, perturbing protocols but lack throughput and sensitivity.
  • Current technologies limit the wide applicability of label-free cell assays.

Purpose of the Study:

  • To develop a high-throughput platform for label-free analysis of single-cell biophysical properties.
  • To enable hierarchical analysis of intrinsic morphological descriptors and mass density.
  • To correlate label-free data with fluorescently labeled biochemical markers.

Main Methods:

  • Developed a large-scale, integrative imaging flow cytometry platform.
  • Utilized optofluidic cytometry for synchronous single-cell acquisition.
  • Applied deep neural networks and transfer learning for data analysis.

Main Results:

  • Demonstrated label-free delineation of cancer subtype biophysical signatures.
  • Successfully detected rare cell populations (10^-5) in heterogeneous samples.
  • Assessed the efficacy of targeted therapeutics using biophysical phenotypes.

Conclusions:

  • The developed platform offers powerful label-free, high-throughput single-cell profiling.
  • This technique can stratify physiological and pathological processes based on biophysical phenotypes.
  • The approach has the potential to advance optofluidic imaging cell-based assays.