Sequential staining technique for acquiring paired images with H&E and microscopy with ultraviolet surface excitation (MUSE)

Some tumor resection procedures, such as Mohs surgery, utilize intraoperative histology for tumor margin assessments. Gold standard rapid histology methods are time-consuming for patients under anesthetic and rapid freezing techniques are prone to artifacts. The recent development of microscopy with ultraviolet surface excitation (MUSE) introduces a new possibility for the rapid imaging of the cut tissue surface using fluorescent dyes. The high attenuation of ultraviolet light limits MUSE signals to thicknesses close to typical histology sections. To generate MUSE images with familiar H&E-like contrast, recent work has explored the transformation of MUSE images to "virtual" H&E-like images using unsupervised deep learning models trained on unpaired images of separate tissues treated with each stain. Here, we present a method for acquiring registered images of the same tissue with MUSE and real H&E imaging using sequential staining and dye removal. Tissue blocks are flash frozen and sectioned for mounting onto slides and staining with MUSE fluorescent dyes. After MUSE imaging, a sequential immersion of the slides in increasing concentrations of ethyl alcohol followed by rehydration, similar to steps in paraffin-based histology processing, is sufficient to remove all fluorescent dyes. Rinsed tissue slides are then subjected to traditional H&E staining and brightfield imaging. Data of registered image fields of skin and pancreas are presented along with initial machine learning-based transformations from MUSE to H&E contrast. This protocol will be useful to obtain paired images for training, testing, and quantitative validation of virtual H&E reconstructions from MUSE images.