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Virtual histological staining of unlabeled autopsy tissue

Author

Listed:
  • Yuzhu Li

    (University of California
    University of California
    University of California)

  • Nir Pillar

    (University of California
    University of California
    University of California)

  • Jingxi Li

    (University of California
    University of California
    University of California)

  • Tairan Liu

    (University of California
    University of California
    University of California)

  • Di Wu

    (University of California)

  • Songyu Sun

    (University of California)

  • Guangdong Ma

    (University of California
    Xi’an Jiaotong University)

  • Kevin Haan

    (University of California
    University of California
    University of California)

  • Luzhe Huang

    (University of California
    University of California
    University of California)

  • Yijie Zhang

    (University of California
    University of California
    University of California)

  • Sepehr Hamidi

    (University of California)

  • Anatoly Urisman

    (University of California)

  • Tal Keidar Haran

    (Hadassah Hebrew University Medical Center)

  • William Dean Wallace

    (University of Southern California)

  • Jonathan E. Zuckerman

    (University of California)

  • Aydogan Ozcan

    (University of California
    University of California
    University of California
    University of California)

Abstract

Traditional histochemical staining of post-mortem samples often confronts inferior staining quality due to autolysis caused by delayed fixation of cadaver tissue, and such chemical staining procedures covering large tissue areas demand substantial labor, cost and time. Here, we demonstrate virtual staining of autopsy tissue using a trained neural network to rapidly transform autofluorescence images of label-free autopsy tissue sections into brightfield equivalent images, matching hematoxylin and eosin (H&E) stained versions of the same samples. The trained model can effectively accentuate nuclear, cytoplasmic and extracellular features in new autopsy tissue samples that experienced severe autolysis, such as COVID-19 samples never seen before, where the traditional histochemical staining fails to provide consistent staining quality. This virtual autopsy staining technique provides a rapid and resource-efficient solution to generate artifact-free H&E stains despite severe autolysis and cell death, also reducing labor, cost and infrastructure requirements associated with the standard histochemical staining.

Suggested Citation

  • Yuzhu Li & Nir Pillar & Jingxi Li & Tairan Liu & Di Wu & Songyu Sun & Guangdong Ma & Kevin Haan & Luzhe Huang & Yijie Zhang & Sepehr Hamidi & Anatoly Urisman & Tal Keidar Haran & William Dean Wallace , 2024. "Virtual histological staining of unlabeled autopsy tissue," Nature Communications, Nature, vol. 15(1), pages 1-17, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-46077-2
    DOI: 10.1038/s41467-024-46077-2
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    References listed on IDEAS

    as
    1. Matthew T. Martell & Nathaniel J. M. Haven & Brendyn D. Cikaluk & Brendon S. Restall & Ewan A. McAlister & Rohan Mittal & Benjamin A. Adam & Nadia Giannakopoulos & Lashan Peiris & Sveta Silverman & Je, 2023. "Deep learning-enabled realistic virtual histology with ultraviolet photoacoustic remote sensing microscopy," Nature Communications, Nature, vol. 14(1), pages 1-17, December.
    2. Kevin Haan & Yijie Zhang & Jonathan E. Zuckerman & Tairan Liu & Anthony E. Sisk & Miguel F. P. Diaz & Kuang-Yu Jen & Alexander Nobori & Sofia Liou & Sarah Zhang & Rana Riahi & Yair Rivenson & W. Dean , 2021. "Deep learning-based transformation of H&E stained tissues into special stains," Nature Communications, Nature, vol. 12(1), pages 1-13, December.
    Full references (including those not matched with items on IDEAS)

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