Researchers here provide a proof of concept for the use of Raman spectroscopy to non-invasively measure the burden of senescent cells in an imaged tissue. This works because Raman imaging can provide distinct signatures of different molecules via scattering of photons. Provided the field of imaging is sufficiently constrained, it is possible to produce results that measure differing abundances of specific molecules in different cells. Senescent cells exhibit a range of upregulated and downregulated proteins and other molecules, so in principle creating a system to assess the presence of senescent cells is just a matter of putting in the work to construct and validate a specific implementation.
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 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.
Taken together, RamanOmics provides a tissue-agnostic framework for scalable, multimodal profiling of cellular states.
Link: https://doi.org/10.1038/s43587-026-01219-7
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