Science· 2026Q1
Multi-organelle signatures map cell-state diversity and metabolic adaptation in tissues
- 0citations
- Q1SCImago
- 2026year
Short summary
Spatial Organellomics (sOrganellomics) uses imaging and machine learning to map cell states based on multi-organelle signatures, revealing intermixed hepatocyte communities within liver zones and linking fasting-induced organelle remodeling to altered mitochondrial membrane potential.
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Key points
- Spatial Organellomics (sOrganellomics) maps cell states using multi-organelle signatures, integrating imaging and machine learning.
- In liver, sOrganellomics identified intermixed hepatocyte communities within zones, suggesting a refined subzonal diversity model.
- Nutritional stress (fasting) reshaped this organization and was linked to altered mitochondrial membrane potential in vivo.
- Multi-organelle architecture serves as a structural readout of tissue adaptation and metabolic state.
AI-generated from the title and abstract; the full text is not read.
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.
The authors' abstract, as published at the source. Science, 2026 · DOI ↗
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Field: Hepatology
HepatologyMedicine