Raman spectroscopic fingerprinting uncovers a multi-scale structural–mechanical–transcriptomic coupling landscape in osteoporosis
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By
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Jinyang Wang
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Yongxi Lu
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Xinwei Zhou
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Lei Huang
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Xuanyi Li
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Xiaoxing Kou
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Yang Cao
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Yang Yang
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May 28, 2026
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Clinical Scorecard: Raman Spectroscopy Reveals a Complex Interrelationship Among Structural, Mechanical, and Transcriptomic Factors in Osteoporosis
At a Glance
| Category | Detail |
|---|
| Condition | Osteoporosis |
| Key Mechanisms | Integration of Raman-derived compositional data with structural, mechanical, and transcriptomic datasets. |
| Target Population | Aging populations and murine models of osteoporosis. |
| Care Setting | Research laboratories and clinical diagnostic settings. |
Key Highlights
- Identification of a conserved osteoporotic Raman fingerprint.
- Raman spectroscopy provides a multidimensional readout for osteoporosis evaluation.
- Integration of micro-CT, nanoindentation, and scRNA-seq data enhances understanding of bone fragility.
- Raman-defined changes correlate with structural deterioration and mechanical alterations.
- Ovariectomy model confirms the robustness of Raman fingerprinting in osteoporosis.
Guideline-Based Recommendations
Diagnosis
- Utilize Raman spectroscopy for comprehensive evaluation of bone quality.
Management
- Integrate Raman-derived data with traditional assessments for improved diagnostic strategies.
Monitoring & Follow-up
- Monitor compositional changes in trabecular bone to assess osteoporosis progression.
Risks
- Increased fracture risk associated with reduced bone strength and altered material properties.
Patient & Prescribing Data
Aging individuals at risk for osteoporosis.
Raman spectroscopy may aid in early detection and monitoring of osteoporosis.
Clinical Best Practices
- Combine structural imaging with molecular profiling for a holistic view of bone health.
- Employ machine learning for automated phenotyping of bone composition.
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