Virtual histological staining of unlabeled autopsy tissue

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, Jonathan E. Zuckerman and Aydogan Ozcan ()
Additional contact information
Yuzhu Li: University of California
Nir Pillar: University of California
Jingxi Li: University of California
Tairan Liu: University of California
Di Wu: University of California
Songyu Sun: University of California
Guangdong Ma: University of California
Kevin Haan: University of California
Luzhe Huang: University of California
Yijie Zhang: 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

Nature Communications, 2024, vol. 15, issue 1, 1-17

Abstract: 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.

Date: 2024
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DOI: 10.1038/s41467-024-46077-2

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