Data from: A versatile pipeline for the multi-scale digital reconstruction and quantitative analysis of 3D tissue architecture

A prerequisite for the systems biology analysis of tissues is an accurate digital three-dimensional reconstruction of tissue structure based on images of markers covering multiple scales. Here, we designed a flexible pipeline for the multi-scale reconstruction and quantitative morphological analysis of tissue architecture from microscopy images. Our pipeline includes newly developed algorithms that address specific challenges of thick dense tissue reconstruction. Our implementation allows for a flexible workflow, scalable to high-throughput analysis and applicable to various mammalian tissues. We applied it to the analysis of liver tissue and extracted quantitative parameters of sinusoids, bile canaliculi and cell shapes, recognizing different liver cell types with high accuracy. Using our platform, we uncovered an unexpected zonation pattern of hepatocytes with different size, nuclei and DNA content, thus revealing new features of liver tissue organization. The pipeline also proved effective to analyse lung and kidney tissue, demonstrating its generality and robustness.

针对组织开展系统生物学（systems biology）分析的前提条件，是基于覆盖多尺度的标记物图像，实现对组织结构的精准数字化三维重建。本研究开发了一款灵活的分析流程，可基于显微镜图像完成组织结构的多尺度重建与定量形态学分析。该流程集成了全新研发的算法，可有效应对致密厚组织重建中的特定挑战。该方案支持灵活的工作流，可扩展至高通量（high-throughput）分析，且适用于多种哺乳动物组织。我们将该流程应用于肝脏组织分析，成功提取了肝血窦（sinusoids）、胆小管（bile canaliculi）与细胞形态的定量参数，并可高精度识别不同类型的肝脏细胞。借助该研究平台，我们发现了肝细胞尺寸、细胞核与DNA含量存在差异的意外分区模式，由此揭示了肝脏组织构建的全新特征。该流程在肺组织与肾组织的分析中同样表现出色，证实了其通用性与鲁棒性（robustness）。