PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
June 1, 2026Frontiers in Endocrinology0 citationsOpen Access

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

YWYì WángYLYongxi LuXZXinwei Zhou

Key Points

  • This study aims to identify structural, mechanical, and transcriptomic changes in osteoporosis using Raman spectroscopy.
  • Trabecular bone alterations were profiled using Raman spectroscopy in murine models of aging and OVX-induced osteoporosis.
  • An LSVM classifier was trained for automated analysis of Raman spectra integrated with micro-CT and nanoindentation data.
  • Single-cell RNA sequencing provided insights into BMMSC transcriptomic changes related to the observed Raman fingerprints.
  • A conserved osteoporotic Raman fingerprint was identified, showing reduced phosphate and collagen signals, and increased lipid bands.
  • This fingerprint correlated with structural deterioration (micro-CT) and local mechanical changes (lower hardness, higher elastic modulus).
  • scRNA-seq indicated shifts in BMMSC transcriptomic programs associated with mineral and lipid metabolism, paralleling the Raman findings.

Abstract

Background 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. Methods 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. Results 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. Conclusions 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.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Wáng et al. (2026) studied this question.

synapsesocial.com/papers/6a1d208702fbce9130636f88https://doi.org/10.3389/fendo.2026.1860651
Ask AI
Helpful
Bookmark
Share
View Full Paper