RamanOmics decodes the spatial vibrational–molecular architecture of senescence in aging and repair

Aging and tissue repair involve heterogeneous remodeling across transcriptional, biochemical and cellular dimensions, yet prevailing definitions rely on isolated molecular markers that obscure how these states co-evolve. Here we present RamanOmics, a multimodal framework integrating label-free hyperspectral Raman imaging with single-nucleus RNA sequencing and spatial transcriptomics to link biochemical states with transcriptional programs at single-cell spatial resolution. Applied to young and old mouse lung and skin, RamanOmics reveals tissue-specific programs: lung senescent cells are enriched for extracellular matrix remodeling and transforming growth factor-β signaling, whereas skin senescence is dominated by epidermal differentiation genes (Krt10, Lor and Sbsn). Across tissues, we identified a conserved lipid-linked Raman signature (1,131–1,135 cm−1) marking p21+ senescent cells and developed a machine learning-derived, multimodal barcode enabling nondestructive senescence identification in situ. In a mouse wound-healing model, RamanOmics reveals reactivation of epidermal differentiation genes (Krt10, Lor and Sbsn) in senescent cells, alongside increased lipid-associated Raman signatures. Together, RamanOmics provides a tissue-agnostic framework for scalable, multimodal profiling of cellular states. Zhang, Chen, Monticolo, Sorrentino and colleagues pair label-free Raman imaging with spatial and single-cell transcriptomics to probe biochemical signatures of senescence in aging and repair, identifying a lipid-linked Raman signature of p21+ cells.