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Live-dead assay on unlabeled cells using phase imaging with computational specificity.

Chenfei Hu1,2, Shenghua He3, Young Jae Lee2,4

  • 1Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA.

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|February 8, 2022
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Summary

This study introduces a new method for instant cell viability assessment without chemical stains, using label-free imaging and deep learning. The technique accurately distinguishes live from dead cells, offering a nondestructive alternative for various applications.

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Area of Science:

  • Biotechnology
  • Cell Biology
  • Medical Imaging

Background:

  • Traditional cell viability assays rely on chemical staining, which can be time-consuming and interfere with long-term cell studies.
  • Exogenous staining limits the ability to perform rapid, nondestructive, and longitudinal investigations of cell health.

Purpose of the Study:

  • To develop an instantaneous, label-free method for assessing cell viability.
  • To enable rapid and nondestructive evaluation of cell health for various biological and pharmaceutical applications.

Main Methods:

  • Utilized quantitative phase imaging (QPI) for label-free measurement of cellular properties.
  • Applied deep learning algorithms to compute viability markers from QPI data.
  • Validated the method on diverse live cell cultures.

Main Results:

  • Achieved approximately 95% accuracy in distinguishing live and dead cells.
  • Demonstrated that chemical staining reagents can negatively impact cell viability and alter cell metrics like dry mass and nucleus area.
  • Showcased the method's effectiveness in real-time, label-free cell analysis.

Conclusions:

  • The presented computational phase imaging approach offers an accurate and nondestructive alternative to traditional cell viability staining.
  • This label-free technique facilitates rapid, long-term cell monitoring and has potential applications in biopharmaceutical production and cancer treatment assessment.