Computer-Automated Static, Dynamic and Cellular Bone Histomorphometry.

Area of Science:

• Bone biology and histomorphometry
• Biomedical imaging and analysis
• Osteoporosis and bone disease research

Background:

• Assessing bone health traditionally relies on manual histomorphometry, which is time-consuming, costly, and subjective.
• Dynamic histomorphometry offers biologically relevant insights into bone turnover and health status.
• There is a need for objective, automated methods to improve the efficiency and reliability of bone health assessments.

Purpose of the Study:

• To develop and validate a rapid, automated, and objective method for dynamic histomorphometry.
• To assess cellular activities and bone turnover in mouse models using the developed automated method.
• To compare bone turnover rates between male and female mice using quantitative histomorphometric analysis.

Main Methods:

• A sequential image analysis pipeline was implemented for automated identification of mineralized bone regions and regions of interest.
• Endogenous fluorescent signals (bone mineral, mineralization lines) and specific enzyme stains (TRAP, AP) were captured and aligned.
• The pipeline computed static and dynamic histomorphometric parameters, including osteoclast and osteoblast cellular activities.

Main Results:

• The automated method effectively assessed cellular activities in mouse femur and vertebra.
• Histomorphometric results showed significantly higher bone turnover rates in female mice compared to male mice, irrespective of age.
• The findings validate previously reported outcomes on sex-based differences in bone turnover.

Conclusions:

• The proposed automated dynamic histomorphometry method is effective and objective for assessing bone health.
• This technique provides a more efficient and reliable alternative to traditional manual methods.
• The study confirms significant sex-based differences in bone turnover rates, with females exhibiting higher rates.