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Peer-reviewedCC BYNature Methods

All-optical multimodal mapping of single-cell-type-specific metabolic activities via REDCAT

  • Yajuan Li1
  • Zhaojun Zhang2
  • Archibald Enninful3
  • Negin Farzad3
  • Presha Rajbhandari4,5
  • Hua Tian6,7
  • Jungmin Nam3
  • Xiaoyu Qin3
  • Jorge I. Villazon1
  • Anthony A. Fung3
  • Hongje Jang1
  • Zhiliang Bai3
6 more authors
  • Nancy Ruonan Zhang2
  • Brent R. Stockwell4,5,8,9
  • Rong Fan10,11,12,13,14
  • Mina L. Xu15,16
  • Zongming Ma17
  • Lingyan Shi18
18 affiliations
  1. 1Shu Chien-Gene Lay Department of Bioengineering, University of California, University of California, San Diego, La Jolla, CA, USA
  2. 2Department of Statistics and Data Science, Wharton School, University of Pennsylvania, Philadelphia, PA, USA
  3. 3Department of Biomedical Engineering, Yale University, New Haven, CT, USA
  4. 4Department of Biological Sciences, Columbia University, New York, NY, USA
  5. 5Department of Chemistry, Columbia University, New York, NY, USA
  6. 6Children's Neuroscience Institute, School of Medicine, Pittsburgh, PA, USA
  7. 7Environmental and Occupational Health, Pitt Public Health, Pittsburgh, PA, USA
  8. 8Department of Pathology and Cell Biology, Columbia University Irving Medical Center, New York, NY, USA
  9. 9Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY, USA
  10. 10Department of Biomedical Engineering, Yale University, New Haven, CT, USA. [email protected]
  11. 11Department of Pathology, Yale School of Medicine, New Haven, CT, USA. [email protected]
  12. 12Yale Cancer Center, Yale School of Medicine, New Haven, CT, USA. [email protected]
  13. 13Yale Center for Research on Aging (Y-Age), Yale School of Medicine, New Haven, CT, USA. [email protected]
  14. 14Yale Stem Cell Center, Yale School of Medicine, New Haven, CT, USA. [email protected]
  15. 15Department of Pathology, Yale School of Medicine, New Haven, CT, USA. [email protected]
  16. 16Yale Cancer Center, Yale School of Medicine, New Haven, CT, USA. [email protected]
  17. 17Department of Statistics and Data Science, Yale University, New Haven, CT, USA. [email protected]
  18. 18Shu Chien-Gene Lay Department of Bioengineering, University of California, University of California, San Diego, La Jolla, CA, USA. [email protected]
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Abstract

Metabolism is fundamental to cell function, yet its activities vary across tissue environments. Resolving these processes in situ at single-cell resolution is crucial for understanding physiology in health and disease. However, existing methods lack biochemical specificity or direct linkage to cell identity. Here we report a method, Raman Enhanced Delineation of Cell Atlases in Tissues (REDCAT), an all-optical platform integrating Raman scattering microscopy and high-plex immunofluorescence to co-map metabolism and cell types. REDCAT achieves subcellular profiling of protein, lipid, nuclear metabolites and redox metabolism in human tissues. In lymph nodes, it revealed cell-type-specific metabolic specialization. In lymphoma, REDCAT uncovered profound reprogramming and transitional states during tumor transformation. In the liver, it resolved zonation-dependent metabolic gradients. By linking cell identity to spatial metabolic states, REDCAT provides a framework for studying immunity and cancer, offering a path to deciphering the metabolic basis of disease.

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Cite

BibTeXRIS
BibTeX
@article{li2026alloptical,
  title = {{All-optical multimodal mapping of single-cell-type-specific metabolic activities via REDCAT}},
  author = {Yajuan Li and Zhaojun Zhang and Archibald Enninful and Negin Farzad and Presha Rajbhandari and Hua Tian and Jungmin Nam and Xiaoyu Qin and Jorge I. Villazon and Anthony A. Fung and Hongje Jang and Zhiliang Bai and Nancy Ruonan Zhang and Brent R. Stockwell and Rong Fan and Mina L. Xu and Zongming Ma and Lingyan Shi},
  journal = {Nature Methods},
  year = {2026},
  month = aug,
  doi = {10.1038/s41592-026-03180-0},
  url = {https://doi.org/10.1038/s41592-026-03180-0},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - All-optical multimodal mapping of single-cell-type-specific metabolic activities via REDCAT
AU  - Yajuan Li
AU  - Zhaojun Zhang
AU  - Archibald Enninful
AU  - Negin Farzad
AU  - Presha Rajbhandari
AU  - Hua Tian
AU  - Jungmin Nam
AU  - Xiaoyu Qin
AU  - Jorge I. Villazon
AU  - Anthony A. Fung
AU  - Hongje Jang
AU  - Zhiliang Bai
AU  - Nancy Ruonan Zhang
AU  - Brent R. Stockwell
AU  - Rong Fan
AU  - Mina L. Xu
AU  - Zongming Ma
AU  - Lingyan Shi
PY  - 2026
DA  - 2026/08/27
JO  - Nature Methods
DO  - 10.1038/s41592-026-03180-0
UR  - https://doi.org/10.1038/s41592-026-03180-0
AB  - Metabolism is fundamental to cell function, yet its activities vary across tissue environments. Resolving these processes in situ at single-cell resolution is crucial for understanding physiology in health and disease. However, existing methods lack biochemical specificity or direct linkage to cell identity. Here we report a method, Raman Enhanced Delineation of Cell Atlases in Tissues (REDCAT), an all-optical platform integrating Raman scattering microscopy and high-plex immunofluorescence to co-map metabolism and cell types. REDCAT achieves subcellular profiling of protein, lipid, nuclear metabolites and redox metabolism in human tissues. In lymph nodes, it revealed cell-type-specific metabolic specialization. In lymphoma, REDCAT uncovered profound reprogramming and transitional states during tumor transformation. In the liver, it resolved zonation-dependent metabolic gradients. By linking cell identity to spatial metabolic states, REDCAT provides a framework for studying immunity and cancer, offering a path to deciphering the metabolic basis of disease.
SN  - 1548-7091
SN  - 1548-7105
ER  -