多细胞器特征图谱揭示组织中的细胞状态多样性与代谢适应

cell BiOlOGY Multi- organelle signatures map cell- state diversity and metabolic adaptation in tissues Raghabendra Adhikari1†, Alexander Hillsley1,2†, Alana Dowdell Johnson1,3‡, Shihong Max Gao1, Isabel Espinosa- Medina1, Jan Funke1, Daniel Feliciano1* 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. …