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

Author

Listed:
  • Chenfei Hu

    (University of Illinois at Urbana-Champaign
    University of Illinois at Urbana-Champaign)

  • Shenghua He

    (Washington University in St. Louis)

  • Young Jae Lee

    (University of Illinois at Urbana-Champaign
    University of Illinois at Urbana-Champaign)

  • Yuchen He

    (University of Illinois at Urbana-Champaign
    University of Illinois at Urbana-Champaign)

  • Edward M. Kong

    (University of Illinois at Urbana-Champaign)

  • Hua Li

    (University of Illinois at Urbana-Champaign
    Cancer Center at Illinois
    Washington University in St. Louis)

  • Mark A. Anastasio

    (University of Illinois at Urbana-Champaign
    University of Illinois at Urbana-Champaign)

  • Gabriel Popescu

    (University of Illinois at Urbana-Champaign
    University of Illinois at Urbana-Champaign
    University of Illinois at Urbana-Champaign)

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.

Suggested Citation

  • Chenfei Hu & Shenghua He & Young Jae Lee & Yuchen He & Edward M. Kong & Hua Li & Mark A. Anastasio & Gabriel Popescu, 2022. "Live-dead assay on unlabeled cells using phase imaging with computational specificity," Nature Communications, Nature, vol. 13(1), pages 1-8, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-28214-x
    DOI: 10.1038/s41467-022-28214-x
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    References listed on IDEAS

    as
    1. Mikhail E. Kandel & Yuchen R. He & Young Jae Lee & Taylor Hsuan-Yu Chen & Kathryn Michele Sullivan & Onur Aydin & M. Taher A. Saif & Hyunjoon Kong & Nahil Sobh & Gabriel Popescu, 2020. "Phase imaging with computational specificity (PICS) for measuring dry mass changes in sub-cellular compartments," Nature Communications, Nature, vol. 11(1), pages 1-10, December.
    2. Mikhail E. Kandel & Chenfei Hu & Ghazal Naseri Kouzehgarani & Eunjung Min & Kathryn Michele Sullivan & Hyunjoon Kong & Jennifer M. Li & Drew N. Robson & Martha U. Gillette & Catherine Best-Popescu & G, 2019. "Epi-illumination gradient light interference microscopy for imaging opaque structures," Nature Communications, Nature, vol. 10(1), pages 1-9, December.
    Full references (including those not matched with items on IDEAS)

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