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

Abstract

High-resolution three-dimensional imaging is key to our understanding of biological tissue formation and function. Recent developments in synchrotron-based X-Ray tomography techniques provide unprecedented morphological information on relatively large sample volumes with a spatial resolution better than 50 nm. However, the analysis of the generated data, in particular image segmentation – separation into structure and background – still presents a significant challenge, especially when considering complex biomineralized structures that exhibit hierarchical arrangement of their constituents across many length scales – from millimeters down to nanometers. In the present work, synchrotron-based holographic nano-tomography data are combined with state-of-the-art machine learning methods to image and analyze the nacreous architecture in the bivalve Unio pictorum in 3D. Using kinetic and thermodynamic considerations known from physics of materials, the obtained spatial information is then used to provide a quantitative description of the structural and topological evolution of nacre during shell formation. Ultimately, this study establishes a workflow for high-resolution three-dimensional analysis of fine highly-mineralized biological tissues while providing a detailed analytical view on nacre morphogenesis.