Virtual histological staining of unlabeled autopsy tissue.

Journal: Nature communications
PMID: 38396004

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.

Authors

  • Yuzhu Li
    Electrical and Computer Engineering Department, University of California, Los Angeles, CA, USA.
  • Nir Pillar
    Electrical and Computer Engineering Department, University of California, Los Angeles, CA, USA.
  • Jingxi Li
    Electrical and Computer Engineering Department, University of California, Los Angeles, CA, USA.
  • Tairan Liu
    Department of Mechanical Engineering, Louisiana State University, Baton Rouge, LA, 70803, USA.
  • Di Wu
    University of Melbourne, Melbourne, VIC 3010 Australia.
  • Songyu Sun
    Computer Science Department, University of California, Los Angeles, CA, 90095, USA.
  • Guangdong Ma
    Electrical and Computer Engineering Department, University of California, Los Angeles, CA, USA.
  • Kevin de Haan
    Electrical and Computer Engineering Department, University of California, Los Angeles, Los Angeles, CA, USA.
  • Luzhe Huang
    Electrical and Computer Engineering Department, University of California, Los Angeles, California 90095, USA.
  • Yijie Zhang
    Key Laboratory of Brain, Cognition and Education Sciences (South China Normal University), Ministry of Education, Guangzhou, China; School of Psychology, South China Normal University, Guangzhou, China; Center for Studies of Psychological Application, South China Normal University, Guangzhou, China; Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, China. Electronic address: fanfandez@163.com.
  • Sepehr Hamidi
    Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA.
  • Anatoly Urisman
    Department of Pathology, University of California, San Francisco, CA, 94143, USA.
  • Tal Keidar Haran
    Department of Pathology, Hadassah Hebrew University Medical Center, Jerusalem, 91120, Israel.
  • William Dean Wallace
    Department of Pathology, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90033, USA.
  • Jonathan E Zuckerman
    Department of Pathology and Laboratory Medicine, David Geffen School of Medicine at University of California Los Angeles, Los Angeles, California.
  • Aydogan Ozcan
    Electrical and Computer Engineering Department, University of California, Los Angeles, California 90095, USA.

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