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Publications

Journal Article Epublish 17 citations

PubMed: 38396004 DOI: 10.1038/s41467-024-46077-2

Virtual histological staining of unlabeled autopsy tissue.

Nat Commun 15(1):1684

Authors: Yuzhu Li, Nir Pillar, Jingxi Li, Tairan Liu, Di Wu, Songyu Sun, Guangdong Ma, Kevin de Haan, Luzhe Huang, Yijie Zhang, Sepehr Hamidi, Anatoly Urisman, Tal Keidar Haran, William Dean Wallace, Jonathan E Zuckerman, Aydogan Ozcan

Affiliations

  • Electrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA. Bioengineering Department, University of California, Los Angeles, CA, 90095, USA. California NanoSystems Institute (CNSI), University of California, Los Angeles, CA, 90095, USA.
  • Electrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA.
  • Bioengineering Department, University of California, Los Angeles, CA, 90095, USA.
  • California NanoSystems Institute (CNSI), University of California, Los Angeles, CA, 90095, USA.
  • Computer Science Department, University of California, Los Angeles, CA, 90095, USA.
  • School of Physics, Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China.
  • Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA.
  • Department of Pathology, University of California, San Francisco, CA, 94143, USA.
  • Department of Pathology, Hadassah Hebrew University Medical Center, Jerusalem, 91120, Israel.
  • Department of Pathology, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90033, USA.
  • Electrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA.
  • Bioengineering Department, University of California, Los Angeles, CA, 90095, USA.
  • California NanoSystems Institute (CNSI), University of California, Los Angeles, CA, 90095, USA.
  • Department of Surgery, University of California, Los Angeles, CA, 90095, USA.

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.

MeSH

Biological Science Disciplines, Cells, Autopsy, Tissues, Autolysis, Covid-19

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