An end-to-end workflow for nondestructive 3D pathology

Recent advances in 3D pathology offer the ability to image orders of magnitude more tissue than conventional pathology methods while also providing a volumetric context that is not achievable with 2D tissue sections, and all without requiring destructive tissue sectioning. Generating high-quality 3D pathology datasets on a consistent basis, however, is not trivial and requires careful attention to a series of details during tissue preparation, imaging and initial data processing, as well as iterative optimization of the entire process. Here, we provide an end-to-end procedure covering all aspects of a 3D pathology workflow (using light-sheet microscopy as an illustrative imaging platform) with sufficient detail to perform well-controlled preclinical and clinical studies. Although 3D pathology is compatible with diverse staining protocols and computationally generated color palettes for visual analysis, this protocol focuses on the use of a fluorescent analog of hematoxylin and eosin, which remains the most common stain used for gold-standard pathological reports. We present our guidelines for a broad range of end users (e.g., biologists, clinical researchers and engineers) in a simple format. The end-to-end workflow requires 3–6 d to complete, bearing in mind that data analysis may take longer. Key points The protocol details tissue preparation and staining using TO-PRO-3, a fluorescent analog of hematoxylin, and eosin. Tissue imaging using light-sheet fluorescence microscopy is described, including strategies for quality control in tissue preparation and 3D microscopy. A fully open-source workflow requiring basic programming skills in Python is explained for initial 3D data processing, such as stitching, intensity leveling and digital staining to mimic the appearance of standard H&E histology. A detailed workflow covering 3D pathology, including tissue preparation, imaging with light-sheet fluorescence microscopy, tools for initial data processing in Python (e.g., stitching, intensity leveling and false coloring) and data quality control.