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All-Optical Multimodal Mapping of Single Cell-Type-Specific Metabolic Activities via REDCAT.

Yajuan Li, Zhaojun Zhang, Archibald Enninful

    Biorxiv : the Preprint Server for Biology
    |November 24, 2025
    PubMed
    Summary

    Researchers developed REDCAT, a new imaging platform, to map cell metabolism and identity within tissues. This technology reveals cell-specific metabolic changes in diseases like lymphoma and liver conditions, advancing our understanding of tissue physiology.

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    Area of Science:

    • Cellular and Molecular Biology
    • Biomedical Imaging
    • Metabolomics

    Background:

    • Metabolic activities are crucial for cell function but vary significantly across cell types and tissues.
    • Understanding spatial metabolic heterogeneity in situ is vital for deciphering cellular function and tissue physiology in health and disease.
    • Existing spatial metabolomics methods face limitations in resolution, biochemical specificity, and linking metabolism directly to cell identity.

    Purpose of the Study:

    • To introduce REDCAT (Raman Enhanced Delineation of Cell Atlases in Tissues), a multimodal all-optical platform for co-mapping metabolic activities and cell types within the same tissue section.
    • To achieve subcellular resolution profiling of metabolism and extracellular matrix composition in human tissues.
    • To investigate cell-type-specific metabolic specialization in normal tissues and metabolic reprogramming in diseases like lymphoma and liver conditions.

    Main Methods:

    • Integration of stimulated Raman scattering, autofluorescence redox imaging, second harmonic generation, and high-plex immunofluorescence.
    • Application to both FFPE and fresh-frozen human tissues.
    • Analysis of metabolic profiles, including protein, lipid, redox, and nucleic acid metabolism, alongside extracellular matrix composition.

    Main Results:

    • Delineation of distinct redox and lipid remodeling programs in normal lymph nodes, revealing cell-type-specific metabolic specialization.
    • Identification of profound metabolic reprogramming in lymphoma, including lipid accumulation, nuclear metabolic heterogeneity, and transitional states during disease progression.
    • Resolution of cell-type-specific lipid droplet diversity and zonation-dependent nuclear metabolic gradients in human liver, uncovering spatial metabolic organization principles.

    Conclusions:

    • REDCAT enables direct linkage of cell identity with spatial metabolic states at single-cell or subcellular resolution.
    • The platform provides a broadly applicable framework for studying immune function, tumor progression, and tissue physiology.
    • REDCAT offers a new approach to deciphering the metabolic basis of health and disease.