A Comprehensive Framework for Unsupervised Deep Analysis of Tissue Bioarchitecture

Despite extensive characterization of cellular components within tissues, the fundamental principles governing their organization remain poorly understood. In this study, we propose a comprehensive framework to bridge this gap by analyzing highresolution 3D images of biological tissues. Our approach employs a semantic segmentation pipeline to achieve digital segmentation of the primary tissue organelles (cells, nuclei, nucleoli, mitochondrial networks, lipid droplet mass, blood capillaries and hemorrhagic areas). From this, we derive five categories of bioarchitectural parameters: size, shape, inter-organelle distance, orientation, and texture, which together encompass a total of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{3 5}$</tex> parameters. We then apply unsupervised machine learning techniques to explore the resulting highdimensional bioarchitectural data, gaining deeper insights into tissue organization. We validate our framework using patient-derived xenograft (PDX) hepatoblastoma tissue samples acquired through serial block face-scanning electron microscopy. This approach elucidates detailed associations among bioarchitectural parameters and their significance, also enabling the identification of key features that differentiate endothelial cells from tumor cells.