细胞状态多样性决定组织的适应能力、损伤修复以及疾病抵抗能力，但解析该复杂特征一直颇具挑战。现有研究手段多依赖转录组分析，却忽略了细胞器结构 —— 细胞器是反映细胞代谢与应激状态的重要指标。本研究开发*空间细胞器组学（spatial Organellomics, sOrganellomics），这一套成像分析流程将自动分割技术与机器学习相结合，依靠多细胞器特征谱实现细胞状态分类与空间定位。在肝脏与胰腺组织中，利用该特征谱可以区分主要的细胞类别。在肝脏中，空间细胞器组学结果表明：肝小叶分区位置并不能完全解释由细胞器特征定义的肝细胞亚群；肝细胞在经典小叶分区内部形成相互混杂的细胞群落，由此支持经过修正的亚分区多样性模型。营养应激会重塑这种细胞空间组织模式。活体成像结果证实，禁食诱导的细胞器重构与**线粒体膜电位**的体内改变直接相关，证明多细胞器结构可作为组织代谢适应的结构读出指标。
附：英文原文
Title: Multi-organelle signatures map cell-state diversity and metabolic adaptation in tissues
Author: Raghabendra Adhikari, Alexander Hillsley, Alana Dowdell Johnson, Shihong Max Gao, Isabel Espinosa-Medina, Jan Funke, Daniel Feliciano
Issue&Volume: 2026-09-17
Abstract: Cell-state diversity drives tissue adaptability, repair, and disease resilience, but capturing this complexity is a challenge. Current approaches rely on transcriptional profiling and overlook organelle structure, a key indicator of metabolism and stress. We developed spatial Organellomics (sOrganellomics), an imaging workflow that integrates automated segmentation with machine learning to classify and spatially map cell states from multi-organelle signatures. In liver and pancreas, these signatures distinguished broad cellular classes. In liver, sOrganellomics revealed that zonal position did not fully explain organelle-defined hepatocyte categories. Instead, hepatocytes formed intermixed communities within canonical zones, supporting a refined subzonal diversity model. Nutritional stress reshaped this organization. Intravital imaging linked fasting-induced organelle remodeling with altered mitochondrial membrane potential in vivo, supporting multi-organelle architecture as a structural readout of tissue adaptation.
DOI: 10.1126/science.ady6372
Source: https://www.science.org/doi/10.1126/science.ady6372