Virtual birefringence imaging and histological staining of amyloid deposits in label-free tissue using autofluorescence microscopy and deep learning

Xilin Yang, Bijie Bai, Yijie Zhang, Musa Aydin, Yuzhu Li, Sahan Yoruc Selcuk, Paloma Casteleiro Costa, Zhen Guo, Gregory A. Fishbein, Karine Atlan, William Dean Wallace, Nir Pillar () and Aydogan Ozcan ()
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Xilin Yang: University of California
Bijie Bai: University of California
Yijie Zhang: University of California
Musa Aydin: University of California
Yuzhu Li: University of California
Sahan Yoruc Selcuk: University of California
Paloma Casteleiro Costa: University of California
Zhen Guo: University of California
Gregory A. Fishbein: David Geffen School of Medicine at the University of California
Karine Atlan: Hadassah Hebrew University Medical Center
William Dean Wallace: University of Southern California
Nir Pillar: University of California
Aydogan Ozcan: University of California

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

Abstract: Abstract Systemic amyloidosis involves the deposition of misfolded proteins in organs/tissues, leading to progressive organ dysfunction and failure. Congo red is the gold-standard chemical stain for visualizing amyloid deposits in tissue, showing birefringence under polarization microscopy. However, Congo red staining is tedious and costly to perform, and prone to false diagnoses due to variations in amyloid amount, staining quality and manual examination of tissue under a polarization microscope. We report virtual birefringence imaging and virtual Congo red staining of label-free human tissue to show that a single neural network can transform autofluorescence images of label-free tissue into brightfield and polarized microscopy images, matching their histochemically stained versions. Blind testing with quantitative metrics and pathologist evaluations on cardiac tissue showed that our virtually stained polarization and brightfield images highlight amyloid patterns in a consistent manner, mitigating challenges due to variations in chemical staining quality and manual imaging processes in the clinical workflow.

Date: 2024
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DOI: 10.1038/s41467-024-52263-z

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