Quantification of sheet nacre morphogenesis using X-ray nanotomography and deep learning

Area of Science:

• Biomineralization research
• Materials science
• Developmental biology

Background:

• High-resolution 3D imaging is crucial for understanding biological tissues.
• Synchrotron X-ray tomography offers nanoscale resolution but data analysis, especially segmentation of complex biomineralized structures, remains challenging.
• Biomineralized structures like nacre exhibit hierarchical organization across multiple length scales.

Purpose of the Study:

• To develop and apply a workflow for high-resolution 3D analysis of complex biomineralized tissues.
• To image and analyze the nacreous architecture in the bivalve Unio pictorum using advanced techniques.
• To quantitatively describe the structural and topological evolution of nacre during shell formation.

Main Methods:

• Combining synchrotron-based holographic nano-tomography with machine learning algorithms.
• Utilizing kinetic and thermodynamic principles from materials physics.
• Analyzing 3D spatial information to understand morphogenesis.

Main Results:

• Successful high-resolution 3D imaging and analysis of the nacreous architecture in Unio pictorum.
• Quantitative description of the structural and topological changes during nacre formation.
• Demonstration of a robust workflow for analyzing complex biomineralized tissues.

Conclusions:

• The study establishes a novel workflow for high-resolution 3D analysis of highly mineralized biological tissues.
• Provides detailed insights into nacre morphogenesis, integrating imaging, machine learning, and materials science principles.
• Advances our understanding of how complex biological structures form at the nanoscale.