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用于无损 3D 病理学的端到端工作流程。

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

机构信息

Department of Mechanical Engineering, University of Washington, Seattle, WA, USA.

Department of Bioengineering, University of Washington, Seattle, WA, USA.

出版信息

Nat Protoc. 2024 Apr;19(4):1122-1148. doi: 10.1038/s41596-023-00934-4. Epub 2024 Jan 23.

Abstract

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.

摘要

近年来,3D 病理学取得了进展,其能够对数量级更多的组织进行成像,而这是传统病理学方法所无法实现的,同时还提供了 2D 组织切片无法获得的体积背景信息,并且所有这些都无需进行破坏性的组织切片。然而,要持续生成高质量的 3D 病理学数据集并非易事,在组织准备、成像和初始数据处理过程中需要仔细注意一系列细节,并且还需要对整个过程进行迭代优化。在这里,我们提供了一个涵盖 3D 病理学工作流程各个方面的端到端程序(以光片显微镜作为说明性成像平台),其详细程度足以进行精心控制的临床前和临床研究。尽管 3D 病理学与各种染色方案和用于视觉分析的计算生成颜色调色板兼容,但本方案侧重于使用苏木精和伊红的荧光类似物,这仍然是用于金标准病理报告的最常见染色剂。我们以简单的格式为广泛的最终用户(例如生物学家、临床研究人员和工程师)提供了指导原则。端到端工作流程需要 3-6 天才能完成,需要注意的是数据分析可能需要更长的时间。

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