Cell quantification at the osteochondral interface from synchrotron radiation phase contrast micro-computed...

Area of Science:

• Biomedical Engineering
• Orthopedics
• Cell Biology

Background:

• The osteochondral interface, comprising calcified cartilage (CC) and subchondral bone (SCB), is crucial for joint health.
• Chondrocytes and osteocytes within these tissues interact, and their cellular structures are vital for tissue function.
• Understanding cellular changes at this interface is key to investigating joint diseases like osteoarthritis.

Purpose of the Study:

• To develop and validate a novel 3D segmentation method for chondrocyte and osteocyte lacunae.
• To analyze quantitative differences in lacunar parameters between healthy and osteoarthritic human knee tissues.
• To investigate cellular alterations at the osteochondral interface in osteoarthritis.

Main Methods:

• Utilized high-resolution synchrotron radiation phase contrast micro-computed tomography (SR-PC micro-CT) for imaging.
• Developed a hybrid segmentation approach combining marker-controlled watershed (MCW) with deep learning (nnU-Net).
• Quantitatively analyzed lacunar parameters including volume fraction, number density, volume, anisotropy, and structure model index.

Main Results:

• The combined MCW and nnU-Net method significantly improved cell lacunae segmentation.
• Osteoarthritic osteocytes exhibited reduced anisotropy (width/depth ratio) and a trend towards more spherical shapes compared to controls.
• No significant differences were found in chondrocyte lacunae between control and osteoarthritic groups.

Conclusions:

• The novel 3D segmentation technique accurately differentiates CC and SCB, enabling detailed cellular analysis.
• Osteoarthritis is associated with specific morphological changes in osteocytes, suggesting altered bone cell behavior.
• This method provides a valuable tool for advancing research into the cellular mechanisms of osteoarthritis.