Electrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA.
Bioengineering Department, University of California, Los Angeles, CA, 90095, USA.
Nat Commun. 2024 Sep 12;15(1):7978. doi: 10.1038/s41467-024-52263-z.
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.
系统性淀粉样变涉及错误折叠蛋白在器官/组织中的沉积，导致进行性器官功能障碍和衰竭。刚果红是用于在组织中可视化淀粉样沉积物的金标准化学染色剂，在偏振显微镜下显示双折射。然而，刚果红染色繁琐且昂贵，并且由于淀粉样物质的数量、染色质量和偏振显微镜下组织的手动检查的变化，容易导致误诊。我们报告了无标记人组织的虚拟双折射成像和虚拟刚果红染色，表明单个神经网络可以将无标记组织的自发荧光图像转换为明场和偏振显微镜图像，与它们的组织化学染色版本匹配。在心脏组织上进行的带有定量指标的盲测和病理学家评估表明，我们的虚拟染色偏振和明场图像以一致的方式突出淀粉样蛋白模式，减轻了由于临床工作流程中化学染色质量和手动成像过程的变化而导致的挑战。
