Raman spectroscopic fingerprinting uncovers a multi-scale structural-mechanical-transcriptomic coupling landscape in osteoporosis.

<h4>Background</h4>Osteoporosis is a systemic skeletal disorder characterized by reduced bone strength and increased fracture risk. Conventional evaluation relies mainly on bone mineral density and microarchitecture, but these measures do not fully capture the tissue-level material properties that contribute to fragility. Here, we integrated Raman-derived compositional information with microarchitectural, local mechanical, and single-cell transcriptomic data to map a multi-scale coupling landscape and identify a conserved compositional fingerprint of osteoporotic trabecular bone.<h4>Methods</h4>Trabecular bone alterations were profiled by Raman spectroscopy in murine models of natural aging and ovariectomy (OVX)-induced osteoporosis. A linear support vector machine (LSVM) classifier was trained for automated phenotyping of Raman spectra. Raman-defined spectral features were then integrated with micro-CT-based microarchitectural measurements, nanoindentation-derived local mechanical properties, and single-cell RNA sequencing (scRNA-seq) of bone marrow mesenchymal stem cells (BMMSCs) to contextualize compositional changes across structure, mechanics, and remodeling programs.<h4>Results</h4>We identified a conserved osteoporotic Raman fingerprint characterized by reduced phosphate and collagen signals and increased lipid-associated bands. These compositional signatures were strongly associated with micro-CT-defined structural deterioration and nanoindentation-derived local mechanical alterations, specifically reduced hardness and increased elastic modulus. Furthermore, scRNA-seq revealed shifts in BMMSC transcriptomic programs related to mineral, extracellular matrix, and lipid metabolism that paralleled the Raman-defined changes. The OVX model further confirmed the etiological robustness of this Raman fingerprint in capturing multi-scale alterations in bone quality.<h4>Conclusions</h4>Raman-based compositional fingerprinting provides a multidimensional readout that can be integrated with structural imaging, mechanical testing, and transcriptomic profiling. This cross-scale framework refines osteoporosis evaluation, supports the development of advanced diagnostic strategies, and offers mechanistic insight into bone fragility beyond conventional structural metrics.