实现活体组织中单细胞级3D成像与分割的全流程方法
Single cell resolution 3D imaging and segmentation within intact live tissues
- 结合深度学习与显微成像，实现活组织内荧光标记细胞的3D精准分割
- 针对果蝇翅膀原基成像优化了显微镜参数与样本制备方案
- 提供可复现的计算流程与开源代码，适用于多种复杂3D组织分析
上皮细胞在不同结构中呈现多样形态，从扁平球状类器官到致密伪层组织。对这些结构中细胞属性的量化需依赖高分辨率深层成像与计算技术，以真实还原三维结构特征。本文详细描述了从样本制备、成像到深度学习辅助细胞分割的完整流程，实现了活体组织中荧光标记个体细胞的3D精确量化。基于果蝇翅膀原基的成像经验，我们总结了显微镜模式选择（如物镜、样品固定方式）及可用分割方法的注意事项。同时提供配套计算流程与自定义代码，支持协议复现。尽管聚焦于膜标记细胞轮廓分割，该方法可广泛应用于多种需要复杂3D分析的组织研究。
原文摘要 · Abstract (English)
Epithelial cells form diverse structures from squamous spherical organoids to densely packed pseudostratified tissues. Quantification of cellular properties in these contexts requires high-resolution deep imaging and computational techniques to achieve truthful three-dimensional (3D) structural features. Here, we describe a detailed step-by-step protocol for sample preparation, imaging and deep-learning-assisted cell segmentation to achieve accurate quantification of fluorescently labelled individual cells in 3D within live tissues. We share the lessons learned through troubleshooting 3D imaging of Drosophila wing discs, including considerations on the choice of microscopy modality and settings (objective, sample mounting) and available segmentation methods. In addition, we include a computational pipeline alongside custom code to assist replication of the protocol. While we focus on the segmentation of cell outlines from membrane labelling, this protocol applies to a wide variety of samples, and we believe it be valuable for studying other tissues that demand complex analysis in 3D.
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