Institute of Biomedicine, University of Eastern Finland, Kuopio, Finland.
Institute of Biomedicine, University of Turku, Turku, Finland.
Lab Invest. 2023 May;103(5):100070. doi: 10.1016/j.labinv.2023.100070. Epub 2023 Jan 25.
Tissue structures, phenotypes, and pathology are routinely investigated based on histology. This includes chemically staining the transparent tissue sections to make them visible to the human eye. Although chemical staining is fast and routine, it permanently alters the tissue and often consumes hazardous reagents. On the other hand, on using adjacent tissue sections for combined measurements, the cell-wise resolution is lost owing to sections representing different parts of the tissue. Hence, techniques providing visual information of the basic tissue structure enabling additional measurements from the exact same tissue section are required. Here we tested unstained tissue imaging for the development of computational hematoxylin and eosin (HE) staining. We used unsupervised deep learning (CycleGAN) and whole slide images of prostate tissue sections to compare the performance of imaging tissue in paraffin, as deparaffinized in air, and as deparaffinized in mounting medium with section thicknesses varying between 3 and 20 μm. We showed that although thicker sections increase the information content of tissue structures in the images, thinner sections generally perform better in providing information that can be reproduced in virtual staining. According to our results, tissue imaged in paraffin and as deparaffinized provides a good overall representation of the tissue for virtually HE-stained images. Further, using a pix2pix model, we showed that the reproduction of overall tissue histology can be clearly improved with image-to-image translation using supervised learning and pixel-wise ground truth. We also showed that virtual HE staining can be used for various tissues and used with both 20× and 40× imaging magnifications. Although the performance and methods of virtual staining need further development, our study provides evidence of the feasibility of whole slide unstained microscopy as a fast, cheap, and feasible approach to producing virtual staining of tissue histology while sparing the exact same tissue section ready for subsequent utilization with follow-up methods at single-cell resolution.
组织学通常基于组织学来研究组织结构、表型和病理学。这包括用化学方法对透明组织切片进行染色，以使肉眼能够看到它们。虽然化学染色快速且常规，但它会永久改变组织，并且经常消耗危险试剂。另一方面，在使用相邻的组织切片进行联合测量时，由于切片代表组织的不同部分，因此会失去细胞分辨率。因此，需要提供基本组织结构的视觉信息并能够从同一组织切片进行额外测量的技术。在这里，我们测试了未染色组织成像在开发计算性苏木精和伊红（HE）染色中的应用。我们使用无监督深度学习（CycleGAN）和前列腺组织切片的全幻灯片图像来比较在石蜡中成像组织、在空气中脱石蜡化以及在安装介质中脱石蜡化的性能，其中切片厚度在 3 至 20 μm 之间变化。我们表明，尽管较厚的切片会增加图像中组织结构的信息量，但较薄的切片通常在提供可在虚拟染色中重现的信息方面表现更好。根据我们的结果，在石蜡中成像和脱石蜡化的组织为虚拟 HE 染色的图像提供了组织的良好整体表现。此外，使用 pix2pix 模型，我们表明使用监督学习和像素级真实值的图像到图像转换可以明显改善整体组织组织学的再现。我们还表明，虚拟 HE 染色可用于各种组织，并且可与 20×和 40×成像放大倍数一起使用。尽管虚拟染色的性能和方法需要进一步发展，但我们的研究提供了证据，证明全幻灯片未染色显微镜作为一种快速、廉价且可行的方法来产生组织组织学的虚拟染色是可行的，同时还可以节省同一组织切片，以备后续以单细胞分辨率使用后续方法。
