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

Chenfei Hu, Shenghua He, Young Jae Lee, Yuchen He, Edward M. Kong, Hua Li, Mark A. Anastasio () and Gabriel Popescu ()
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Chenfei Hu: University of Illinois at Urbana-Champaign
Shenghua He: Washington University in St. Louis
Young Jae Lee: University of Illinois at Urbana-Champaign
Yuchen He: University of Illinois at Urbana-Champaign
Edward M. Kong: University of Illinois at Urbana-Champaign
Hua Li: University of Illinois at Urbana-Champaign
Mark A. Anastasio: University of Illinois at Urbana-Champaign
Gabriel Popescu: University of Illinois at Urbana-Champaign

Nature Communications, 2022, vol. 13, issue 1, 1-8

Abstract: Abstract Existing approaches to evaluate cell viability involve cell staining with chemical reagents. However, the step of exogenous staining makes these methods undesirable for rapid, nondestructive, and long-term investigation. Here, we present an instantaneous viability assessment of unlabeled cells using phase imaging with computation specificity. This concept utilizes deep learning techniques to compute viability markers associated with the specimen measured by label-free quantitative phase imaging. Demonstrated on different live cell cultures, the proposed method reports approximately 95% accuracy in identifying live and dead cells. The evolution of the cell dry mass and nucleus area for the labeled and unlabeled populations reveal that the chemical reagents decrease viability. The nondestructive approach presented here may find a broad range of applications, from monitoring the production of biopharmaceuticals to assessing the effectiveness of cancer treatments.

Date: 2022
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DOI: 10.1038/s41467-022-28214-x

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