To integrate Raman-derived compositional information with microarchitectural, local mechanical, and single-cell transcriptomic data to identify a conserved compositional fingerprint of osteoporotic trabecular bone, highlighting its significance for osteoporosis evaluation.
Key Findings:
A conserved osteoporotic Raman fingerprint was identified, characterized by reduced phosphate and collagen signals and increased lipid-associated bands, with implications for treatment strategies.
These compositional signatures correlated with micro-CT-defined structural deterioration and local mechanical alterations, emphasizing their relevance in clinical settings.
Single-cell RNA sequencing revealed shifts in BMMSC transcriptomic programs related to mineral, extracellular matrix, and lipid metabolism, paralleling the Raman-defined changes.
Interpretation:
Raman-based compositional fingerprinting provides a multidimensional readout that can be integrated with structural imaging, mechanical testing, and transcriptomic profiling, with potential clinical applications.
Limitations:
The study was conducted in murine models, which may not fully replicate human osteoporosis; further validation in larger and more diverse human populations is needed.
Conclusion:
Raman spectroscopy offers a data-rich modality for advanced bone-quality evaluation and diagnostic development, underscoring its importance in future osteoporosis research.
National survey findings suggest many US adults report making health decisions based on social media despite widespread concerns about the accuracy of health information shared to the platforms.
