According to researchers, a newly developed multimodal platform called RamanOmics integrates single-nucleus RNA sequencing, spatial transcriptomics, and label-free Raman imaging to map the vibrational-biochemical and molecular architecture of aging and senescence directly in intact mouse lung and skin tissues. By combining high-dimensional biochemical profiles with single-cell gene expression, the platform uncovers tissue-specific senescence programs and a conserved lipid-associated Raman signature.
Decoding Spatial Vibrational-Molecular Architecture in Aging Tissues
Aging and tissue repair involve multilayered remodeling across transcriptional, biochemical, and cellular dimensions. While single-nucleus RNA sequencing (snRNA-seq) defines transcriptional profiles, it leaves a major gap regarding the biochemical and metabolic composition of cells, such as lipid distribution, protein conformation, and extracellular matrix (ECM) chemistry.
Did you know? Raman spectroscopy measures the scattering of light as it interacts with molecular vibrations, capturing cellular features like lipid chain branching and nucleic-acid composition that are completely invisible to standard genomic technologies.
To bridge this gap, researchers developed the RamanOmics platform. This multimodal approach integrates snRNA-seq, STARmap in situ sequencing (STARmap-ISS), label-free Raman imaging, and machine learning. By applying this workflow to 2-month-old and 26-month-old mouse lung and skin tissues, the platform successfully maps cellular senescence at single-cell resolution across distinct biological barriers.
Tissue-Specific Senescence Programs in Lung and Skin
According to the published findings, aging manifests differently across protective barrier organs. In the mouse lung, analyses utilizing Earth Mover’s Distance revealed that endothelial cells and T cells undergo pronounced aging-associated transcriptomic remodeling, alongside diminished epithelial renewal. Conversely, mouse skin exhibited metabolic decline and impaired ion homeostasis, with fibroblasts and interfollicular epidermis (IFE) cells showing the most substantial shifts.
When tracking the canonical senescence marker p21 (encoded by Cdkn1a), researchers found that senescent cells populate distinct regions depending on the tissue. In the lung, p21+ senescent cells concentrated in AT2 cells, endothelial cells, and macrophages.
- Lung Senescence: Enriched for extracellular matrix remodeling, TGF-β signaling, and fibrosis-associated genes like Serpine1 and Dab2.
- Skin Senescence: Dominated by keratinization, epidermal differentiation modules, and barrier maintenance programs involving genes such as Sfn and Krt10.
Unifying Molecular and Biochemical Data With Multimodal Barcodes
To connect transcriptomic shifts with physical chemistry, researchers synchronized Raman spectral images with STARmap-ISS spatial data using DNA and DAPI signals for co-registration. According to the data, this integration allowed scientists to extract precise subcellular Raman spectra for both senescent and nonsenescent cells.
Across both lung and skin tissues, a branched-chain fatty-acid-linked biochemical profile and specific Raman signature—particularly peaks at 1,131 to 1,135 cm−1 associated with lipids—robustly marked senescent cells. Building on these features, the team constructed machine-learning-derived “multimodal barcodes.” These quantitative barcodes fuse vibrational-biochemical features (differential Raman peaks) with transcriptional markers (differentially expressed genes), boosting classifier accuracy and precision compared to transcript-only models.
Pro Tip: Integrating label-free biochemical imaging with spatial genomics enhances the classification of cellular states by capturing complementary metabolic data that precede or occur independently of transcriptional changes.
Validating Senescence Signatures During Wound Repair
To confirm that these multimodal signatures extend beyond steady-state aging, researchers tested the framework in a mouse skin wound-healing model. According to the study data, day 3 following injury (D3) showed a marked increase in p21 expression compared to baseline (D0) in old mice. This induction coincided with the reactivation of barrier-repair and epidermal differentiation programs.
Furthermore, STARmap-ISH and Raman intensity mapping confirmed that wounded tissue samples exhibited matching increases in lipid-associated Raman signatures at 1,130 to 1,135 cm−1 alongside genes like Dmkn, Krt10, Lor, Sbsn, and Sfn. This validation establishes that the biometrological framework reliably captures dynamic cellular states during active tissue regeneration and dynamic repair processes.
Frequently Asked Questions
What is RamanOmics?
RamanOmics is a multimodal platform that combines single-nucleus RNA sequencing, spatial transcriptomics, label-free Raman imaging, and machine learning to map the biochemical and molecular architecture of tissues at single-cell resolution.
How does Raman spectroscopy help study cellular aging?
Raman spectroscopy captures the intrinsic vibrational and chemical properties of cells—such as lipid distribution, protein conformation, and nucleic acid composition—without requiring external labels or destructive sample preparation.
Are senescence markers identical in the lung and skin?
No. While both tissues share certain lipid-associated biochemical signatures, lung senescence is primarily characterized by extracellular matrix remodeling and TGF-β signaling, whereas skin senescence is dominated by keratinization and barrier homeostasis.
Can this method be applied to active tissue regeneration?
Yes. Researchers successfully validated the platform using a mouse skin wound-healing model, demonstrating that lipid-associated Raman peaks and barrier-repair genes are co-activated during tissue injury and regeneration.
Explore more articles on our website to discover the latest breakthroughs in spatial genomics, cellular senescence, and advanced biomedical imaging technologies.
Related reading